Immunologic Signatures of Rejection

Immunologic Signatures of Rejection

Francesco M. Marincola    Ena Wang ●

Editors

Immunologic Signatures of Rejection

Editors Francesco M. Marincola Infectious Disease and Immunogenetics Section (IDIS) Department of Transfusion Medicine Clinical Center and Trans-NIH Center for Human Immunology (CHI) National Institutes of Health Bethesda, 20892 USA [email protected]

Ena Wang Infectious Disease and Immunogenetics Section (IDIS) Department of Transfusion Medicine Clinical Center and Trans-NIH Center for Human Immunology (CHI) National Institutes of Health Bethesda, MD 20892 USA [email protected]

ISBN 978-1-4419-7218-7 e-ISBN 978-1-4419-7219-4 DOI 10.1007/978-1-4419-7219-4 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: PCN Applied for © Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Contents

Part I  Preamble From the “Delayed Allergy Reaction” to the “Immunologic Constant of Rejection”.................................................................................... Ena Wang and Francesco M. Marincola

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Part II  The Immune Biology of Rejection: Basic Principles The Yin Yang of Cancer Related Inflammation............................................ Alberto Mantovani, Cecilia Garlanda, Paola Allavena, Antonio Sica, and Massimo Locati

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The Immune Rejection: Lessons from Experimental Models..................... Anil Shanker

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Turning on and off the Immunological Switch: Immune Response Polarization and Its Control by IL-10 and STAT3....................... C. Andrew Stewart and Giorgio Trinchieri

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The Angiogenic Switch: Role of Immune Cells............................................. Douglas M. Noonan, Agostina Ventura, Antonino Bruno, Arianna Pagani, and Adriana Albini

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Chemokines and Cytotoxic Effector Molecules in Rejection....................... Alan M. Krensky and Carol Clayberger

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Clinical Applications of Activated Immune Cells......................................... Luciano Castiello, Marianna Sabatino, Ping Jin, Francesco M. Marincola, and David Stroncek

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Part III Circulating Patterns Associated with Chronic and Acute Immune Pathology Blood Transcriptional Fingerprints to Assess the Immune Status of Human Subjects............................................................................... 105 Damien Chaussabel, Nicole Baldwin, Derek Blankenship, Charles Quinn, Esperanza Anguiano, Octavio Ramilo, Ganjana Lertmemongkolchai, Virginia Pascual, and Jacques Banchereau Innate Signatures of Immune Mediated Resolution and Persistence of Hepatitis C Virus Infections............................................ 127 Robert E. Lanford Immune Signatures and Systems Biology of Vaccines.................................. 141 F.M. Buonaguro, M.L. Tornesello, and L. Buonaguro Immune Signatures Associated with the Cancer Bearing State.................. 169 Rebecca J. Critchley-Thorne, Hongxiang Yu, and Peter P. Lee Part IV Tissue-Specific Patterns Associated with Chronic Inflammatory Processes HTLV-1 Infected CD4+CD25+CCR4+ T-Cells Disregulate Balance of Inflammation and Tolerance in HTLV-1 Associated Neuroinflammatory Disease......................................................... 189 Yoshihisa Yamano and Steven Jacobson D/2 Predictors of Favorable Outcome in Cancer.......................................... 199 Zoltán Pós and Jérôme Galon The Microenvironment of Ovarian Cancer: Lessons on Immune Mediated Tumor Rejection or Tolerance.................... 211 Lana E. Kandalaft and George Coukos Transcriptional Profiling of Melanoma as a Potential Predictive Biomarker for Response to Immunotherapy.............................. 229 Thomas F. Gajewski Functional Pathway Analysis for Understanding Immunologic Signature of Rejection: Current Approaches and Outstanding Challenges....................................... 239 Purvesh Khatri and Minnie M. Sarwal

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Part V  Signatures Associated with Acute Rejection Chronic Graft Versus Host Disease: Inflammation at the Crossroads of Allo and Auto Immunity.............................................. 259 Frances T. Hakim Immune-Mediated Tumor Rejection.............................................................. 281 Ena Wang and Francesco M. Marincola Signatures Associated with Acute Rejection: Allograft Rejection........................................................................................... 305 Davide Bedognetti Index.................................................................................................................. 347

Part I

Preamble

From the “Delayed Allergy Reaction” to the “Immunologic Constant of Rejection” Ena Wang and Francesco M. Marincola

This book collects salient observational data, derived predominantly from human studies, regarding the mechanism(s) of rejection in various pathologic conditions; the premise is that immune rejection, better defined as “immune-mediated, tissuespecific destruction” (TSD) (Wang et  al. 2008), comprises a broad range of ­phenomena ranging from tumor regression, to clearance of pathogen through destruction of infected cells, autoimmunity, allograft rejection by the host and host vs. graft reactions. Like different hands can turn on or off a switch, distinct ­mechanisms can trigger TSD, however, a convergent pathway is ultimately observed when TSD occurs consisting of a dramatic switch from chronic to acute inflammation (Mantovani et al. 2008).

From the Delayed Allergy Reaction to the Immunologic Constant of Rejection The hypothesis that TSD is a pathophysiological mechanism shared by several diseases is not new; it was suggested long time ago by Jonas Salk, who observed that various phenomena leading to tissue destruction shared common patterns that he defined the “delayed allergy reaction” (Salk 1969). We recently expanded on this concept; comparing our data addressing the transcriptional changes associated with tumor rejection in response to immune manipulation with published observations derived from similar hypothesis-generating approaches applied predominantly (but not exclusively) to the analysis of human tissues during TSD. We concluded that a limited number of functional themes are consistently observed independent of the disease context (Wang et  al. 2008). We reckon, therefore, that the mechanisms E. Wang  (*) Infectious Disease and Immunogenetics Section (IDIS), Department of Transfusion Medicine, Clinical Center and Trans-NIH Center for Human Immunology (CHI), National Institutes of Health, Bethesda, MD 20892, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_1, © Springer Science+Business Media, LLC 2011

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l­eading to TSD are relatively conserved among pathologies and include a relatively small number of genes and pathways predominantly centered on the activation of interferon regulatory factor (IRF)-1. We referred to such convergence of mechanism(s) as the immunologic constant of rejection (ICR). The ICR is, in turn, based on four postulates: 1 . Tissue-specific destruction occurs independently of self-nonself discrimination. 2. The requirements for the induction of a cognate immune response differ from those associated with the development of an effector one. 3. While the mechanisms prompting tissue-specific destruction differ among immune pathologies the effector phase converges into a common activation of adaptive and innate cytotoxic mechanisms. 4. Adaptive immunity triggers a tissue-specific reaction but it is not always sufficient or necessary for tissue-destruction. This book was designed to solicit chapters from outstanding basic and/or observational scientists that have contributed to this area either by defining mechanisms relevant to the understanding of the inflammatory switch or by describing how this occurs in human tissues under different pathological conditions. At the time when this pre-amble is being prepared, Ena and I have not read the individual contributions but we are familiar with the authors’ previous publications. Therefore, we would like to entice our reader(s) to test with us, through, the subsequent reading, whether our hypothesis is correct; we predict that common themes will emerge from the reading particularly if attention will be paid to the general phenomenon of TSD rather than the peculiarities of individual pathologies. We want to emphasize that the ICR does not address “why” rejection occurs but rather “how.” We believe that the pathways leading to the final outcome are quite different in distinct conditions and only at the final effector phase converge into a common one. We also believe that the ICR is not necessarily applicable to all forms of TSD but to most of them; at the moment there is no evidence that either hyperacute rejection or fulminant hepatitis, predominantly complement mediated mechanisms, follow the ICR determinism. We hypothesize that several innate and/or adaptive immune mechanisms can contribute differently to TSD. However, the final mechanisms converge in the end to a relatively simple switch that leads to the activation of acute inflammatory process and, particularly, the simultaneous activation of most immune effectors mechanisms. Although, the ICR hypothesis may not contribute an explanation for the determinism of rejection, it provides, if correct, a simplified road map about the convergent common pathways leading to TSD. This offers the powerful benefit of identifying a common target for immune manipulation with therapeutic purposes. In particular, most ICR-related pathways have been found to be active only in the tissues in which TSD occurs while are shut off in the rest of the organism; this tissue discrimination provides the alluring opportunity of targeting for treatment a disease-specific pathway either with the goal of mitigating (autoimmunity, allograft rejection, graft versus host disease) or activating (clearance of pathogen or ­neoplastic cells) its key components.

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Interferon Regulatory Factor (IRF)-1: Master Switch of Inflammation It has been almost a decade since we first observed that the transcript most upregulated in melanoma metastases undergoing complete regression after immunotherapy was IRF-1 (Wang et al. 2002). This transcription factor has been subsequently recognized to represent a master switch for acute inflammatory process and identified by various groups who studied tissues undergoing TSD as a central modulator of TSD. At the cellular level, IRF-1 is believed to play a purely proinflammatory role activating the expression of cytokines such as IL-12 or IL-15 which have potent effects on immune effector mechanisms (Paun and Pitha 2007; Taniguchi 1997; Taniguchi et  al. 2001; Honda and Taniguchi 2006; Honda et  al. 2006). Furthermore, IRF-1 can also act as a negative regulator of transcription decreasing the expression of proangiogenic factors such as vascular endothelial growth factor (VEGF) which is involved in tissue repair and possess anti-inflammatory properties (Lee et al. 2008). Thus, our working hypothesis is that IRF-1 plays a central role in altering the cellular biology not only by activating proinflammatory stimuli but by simultaneously blocking anti-inflammatory ones. It is presently unclear what pathways regulate the activation of IRF-1 in different conditions although, IRF-1 is believed to be primarily expressed in response to IFN-g stimulation (Honda and Taniguchi 2006). However, we have observed IRF-1 activation in the absence of IFN-g or other IFNs (Worschech et al. 2009) and, on the other hand, we observed IRF-1 to be strongly activated in response to IL-2 stimulation of PBMCs suggesting either a direct or indirect activation of IRF-1 by this cytokine (Jin et al. 2007). Thus, it is likely that other pathways can activate this master regulator or inflammation. Similarly, the complete picture of what pathways are activated or inactivated by IRF-1 in different patho-physiological conditions is missing. It is likely that various pathways can be diversely influenced dependent upon the cellular environment in which IRF-1 interacts with other regulatory factors. Probably, IRF-1 acts differently in relation to the presence of other transcription factors that may be activated in different conditions and/or in different cell types. It is even possible that IRF-1, like other IRFs may affect the function of other master regulators of innate immunity such as the NF-kB complex by enhancing its proinflammatory effects while inhibiting the antiapoptotic ones (Honda and Taniguchi 2006). These questions are not addressed by this book but are salient areas for future investigation and will enhance further understanding of how TSD occurs and how it can be modulated effectively.

The Recurrent Themes Defining the Signatures of Rejection We recently summarized the common functional units associated with TSD. These include overlapping yet distinct themes that are consistently present when TSD occurs:

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1 . The STAT-1/IRF-1/T-bet/IFN-g, IL-15 path 2. The Granzyme A/B, TIA-1 pathway 3. The CXCR3 ligand chemokine pathway 4. The CCR5 ligand chemokine pathway We observed, in different disease models, their presence; studies in humans have identified these signatures to be associated with improved survival of patients with colon, lung and ovarian cancer or melanoma (Benencia et  al. 2005; Pages et  al. 2005; Dieu-Nosjean et al. 2008; Camus et al. 2009; Galon et al. 2006, 2007; Harlin et  al. 2009); the same patterns were observed in neoplastic lesions undergoing rejection during immunotherapy both in humans (Wang et al. 2002; Panelli et al. 2002, 2006) and in experimental models (Shanker et al. 2007). Allo-rejection has been most studied by biopsing organs during the acute phases of rejection and several studies have reported recurrent themes (Sarwal et  al. 2003; Hardstedt et  al. 2005; Karason et al. 2006; Reeve et al. 2009; Saint-Mezard et al. 2009). In particular, Saint-Mezard et al. (2009) analyzed three independent data set of renal biopsies identifying a robust transcriptional signature of acute allograft rejection which well summarizes most of the components of the ICR. Imanguli et al (2009), observed similar patterns by studying biopsies of tissues suffering chronic graft vs. host disease and similar patters where observed in the liver during clearance of HCV infection (Bigger et al. 2001; He et al. 2006; Feld et al. 2007; Nanda 2008; Asselah et al. 2008). Recently similar signatures were observed in the destructive phases of acute cardiovascular events (Zhao et al. 2002; Okamoto et al. 2008) and chronic obstructive pulmonary disease (Costa et al. 2008). With the hope of having contributed with this book something novel and important, we wish our reader a pleasant journey in the wondrous land of immune-mediated, tissue-specific destruction.

References Asselah T, Bieche I, Narguet S et al. Liver gene expression signature to predict response to pegylated interferon plus ribavirin combination therapy in patients with chronic hepatitis C. Gut 2008; 57(4):516–524. Benencia F, Courreges MC, Conejo-Garcia JR et  al. HSV oncolytic therapy upregulates interferon-inducible chemokines and recruits immune effector cells in ovarian cancer. Mol Ther 2005; 12(5):789–802. Bigger CB, Brasky KM, Lanford RE. DNA microarray analysis of chimpanzee liver during acute resolving hepatitis C virus infection. J Virol 2001; 75(15):7059–7066. Camus M, Tosolini M, Mlecnik B et al. Coordination of intratumoral immune reaction and human colorectal cancer recurrence. Cancer Res 2009; 69(6):2685–2693. Costa C, Rufino R, Traves SL, Lapa E Silva JR, Barnes PJ, Donnelly LE. CXCR3 and CCR5 chemokines in induced sputum from patients with COPD. Chest 2008; 133(1):26–33. Dieu-Nosjean MC, Antoine M, Danel C et al. Long-term survival for patients with non-small-cell lung cancer with intratumoral lymphoid structures. J Clin Oncol 2008; 26(27):4410–4417. Feld JJ, Nanda S, Huang Y et al. Hepatic gene expression during treatment with peginterferon and ribavirin: Identifying molecular pathways for treatment response. Hepatology 2007; 46(5):1548–1563.

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Galon J, Costes A, Sanchez-Cabo F et  al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 2006; 313(5795):1960–1964. Galon J, Fridman WH, Pages F. The adaptive immunologic microenvironment in colorectal cancer: a novel perspective. Cancer Res 2007; 67(5):1883–1886. Hardstedt M, Finnegan CP, Kirchhof N et al. Post-transplant upregulation of chemokine messenger RNA in non-human primate recipients of intraportal pig islet xenografts. Xenotransplantation 2005; 12(4):293–302. Harlin H, Meng Y, Peterson AC et al. Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment. Cancer Res 2009; 69(7):3077–3085. He XS, Ji X, Hale MB et al. Global transcriptional response to interferon is a determinant of HCV treatment outcome and is modified by race. Hepatology 2006; 44(2):352–359. Honda K, Taniguchi T. IRFs: master regulators of signalling by Toll-like receptors and cytosolic pattern-recognition receptors. Nat Rev Immunol 2006; 6(9):644–658. Honda K, Takaoka A, Taniguchi T. Type I interferon [corrected] gene induction by the interferon regulatory factor family of transcription factors. Immunity 2006; 25(3):349–360. Imanguli MM, Swaim WD, League SC, Gress RE, Pavletic SZ, Hakim FT. Increased T-bet+ cytotoxic effectors and type I interferon-mediated processes in chronic graft-versus-host disease of the oral mucosa. Blood 2009; 113(15):3620–3630. Jin P, Wang E, Provenzano M, Stroncek D, Marincola FM. Gene expression signatures of interleukin-2 in vivo and in vitro and their relation to anticancer therapy. Crit Rev Immunol 2007; 27(5):437–448. Karason K, Jernas M, Hagg DA, Svensson PA. Evaluation of CXCL9 and CXCL10 as circulating biomarkers of human cardiac allograft rejection. BMC Cardiovasc Disord 2006; 6:29. Lee JH, Chun T, Park SY, Rho SB. Interferon regulatory factor-1 (IRF-1) regulates VEGF-induced angiogenesis in HUVECs. Biochim Biophys Acta 2008; 1783(9):1654–1662. Mantovani A, Romero P, Palucka AK, Marincola FM. Tumor immunity: effector response to tumor and the influence of the microenvironment. Lancet 2008; 371(9614):771–783. Nanda S, Havert MB, Calderon GM et  al. Hepatic transcriptome analysis of hepatitis C virus infection in chimpanzees defines unique gene expression patterns associated with viral clearance. PLoS ONE 2008; 3(10):e3442. Okamoto Y, Folco EJ, Minami M et al. Adiponectin inhibits the production of CXC receptor 3 chemokine ligands in macrophages and reduces T-lymphocyte recruitment in atherogenesis. Circ Res 2008; 102(2):218–225. Pages F, Berger A, Camus M et  al. Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med 2005; 353(25):2654–2666. Panelli MC, Wang E, Phan G et al. Gene-expression profiling of the response of peripheral blood mononuclear cells and melanoma metastases to systemic IL-2 administration. Genome Biol 2002; 3(7):RESEARCH0035. Panelli MC, Stashower M, Slade HB et  al. Sequential gene profiling of basal cell carcinomas treated with Imiquimod in a placebo-controlled study defines the requirements for tissue rejection. Genome Biol 2006; 8(1):R8. Paun A, Pitha PM. The IRF family, revisited. Biochimie 2007; 89(6–7):744–753. Reeve J, Einecke G, Mengel M et al. Diagnosing rejection in renal transplants: a comparison of molecular- and histopathology-based approaches. Am J Transplant 2009; 9(8):1802–1810. Saint-Mezard P, Berthier CC, Zhang H et al. Analysis of independent microarray datasets of renal biopsies identifies a robust transcript signature of acute allograft rejection. Transpl Int 2009; 22(3):293–302. Salk J. Immunological paradoxes: theoretical considerations in the rejection or retention of grafts, tumors, and normal tissue. Ann N Y Acad Sci 1969; 164(2):365–380. Sarwal M, Chua MS, Kambham N et al. Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. N Engl J Med 2003; 349(2):125–138. Shanker A, Verdeil G, Buferne M et al. CD8 T cell help for innate antitumor immunity. J Immunol 2007; 179(10):6651–6662. Taniguchi T. Transcription factors IRF-1 and IRF-2: linking the immune responses and tumor suppression. J Cell Physiol 1997; 173(2):128–130.

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Taniguchi T, Ogasawara K, Takaoka A, Tanaka N. Irf family of transcription factors as regulators of host defense. Annu Rev Immunol 2001; 19:623–655. Wang E, Miller LD, Ohnmacht GA et al. Prospective molecular profiling of subcutaneous melanoma metastases suggests classifiers of immune responsiveness. Cancer Res 2002; 62:3581–3586. Wang E, Worschech A, Marincola FM. The immunologic constant of rejection. Trends Immunol 2008; 29(6):256–262. Worschech A, Chen N, Yu YA et  al. Systemic treatment of xenografts with vaccinia virus GLV-1h68 reveals the immunologic facets of oncolytic therapy. BMC Genomics 2009; 10:301. Zhao DX, Hu Y, Miller GG, Luster AD, Mitchell RN, Libby P. Differential expression of the IFNgamma-inducible CXCR3-binding chemokines, IFN-inducible protein 10, monokine induced by IFN, and IFN-inducible T cell alpha chemoattractant in human cardiac allografts: association with cardiac allograft vasculopathy and acute rejection. J Immunol 2002; 169(3):1556–1560.

Part II

The Immune Biology of Rejection: Basic Principles

The Yin Yang of Cancer Related Inflammation Alberto Mantovani, Cecilia Garlanda, Paola Allavena, Antonio Sica, and Massimo Locati

Introduction Smoldering, nonresolving inflammation is part of the tumor microenvironment (Balkwill and Mantovani 2001; Coussens and Werb 2002; Mantovani et al. 2008a). Inflammatory cells and mediators are present in the microenvironment of cancers epidemiologically related or unrelated to inflammatory or infectious conditions. Leukocyte infiltration and the presence of soluble mediators such as cytokines, and chemokines are key characteristics of CRI. Conditions predisposing to cancer (e.g., inflammatory bowel disease, IBD) or genetic events that cause neoplastic transformation orchestrate the construction of an inflammatory microenvironment. Indeed, alterations of oncogenes drive the production of inflammatory mediators. Thus, an intrinsic pathway of inflammation (driven in tumor cells), as well as an extrinsic pathway driven by chronic inflammatory conditions have been identified, both of which contribute to tumor progression (Mantovani et al. 2008a). CRI has therefore emerged as the seventh hallmark of cancer (Mantovani 2009). Tumorassociated macrophages (TAM) are a major component of leukocytic infiltrate of tumors and have served as a paradigm for cancer-related inflammation (De Palma et  al. 2007; Mantovani et  al. 2008a; Pollard 2009). Macrophages are a double edged sword, with the potential to express pro and antitumor activity (the macrophage balance, (Mantovani et al. 1992, 2002)), the former prevailing in established neoplasia. Here we will focus on selected molecular pathways underlying TAM recruitment and polarization, emphasizing the dual potential of cancerrelated inflammation (Mantovani et al. 2008b) and the diversity of pathways and functions in different tumors.

A. Mantovani (*) Istituto Clinico Humanitas IRCCS, via Manzoni 56, 20089 Rozzano, Italy Department of Translation Medicine, university of Milan, Milan, Italy e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_2, © Springer Science+Business Media, LLC 2011

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Recruitment of TAM Inflammatory chemokines have long been associated with macrophage recruitment in tumors (Bottazzi et al. 1983) and cancer cells have served as a major source for identification of members of these chemoattractants. They act in concert with ­adhesion molecules (Marttila-Ichihara et al. 2009). Yet, only recently has unequivocal genetic evidence been obtained that monocyte attracting inflammatory CC chemokines play a nonredundant role in carcinogenesis (Nibbs et  al. 2007; Popivanova et al. 2009; Vetrano et al. 2010). The “robustness” of the chemokine system in terms of phagocyte recruitment (Mantovani 1999) until recently has prevented an unequivocal demonstration of a nonredundant function of individual inflammatory chemokines or their receptors using gene targeted mice. This missing genetic link has now been obtained taking advantage of the D6 atypical receptor which acts as a decoy and scavenger for most inflammatory CC chemokines (Mantovani et  al. 2006). D6 deficient mice show increased susceptibility to skin carcinogenesis (Nibbs et  al. 2007) and colitis-associated cancer, the latter being representative of a clinical paradigm of the inflammation-cancer connection (Vetrano et al. 2010) Thus, D6 targeted mice provide unequivocal genetic evidence that inflammatory CC chemokines are more than an epiphenomenon in clinically relevant carcinogenesis.

Plasticity of TAM and Promotion of Metastasis In most, but not all (Torroella-Kouri et al. 2009) tumors investigated, TAM have an M2-like phenotype. What signals in the tumor microenvironment are responsible for the protumor function of TAM? Tumor cell products, including extracellular matrix components, IL-10, M-CSF, activate macrophages and set them in an M2, cancer promoting mode (Hagemann et  al. 2006; Kuang et  al. 2007; Erler et  al. 2009; Kim et  al. 2009). Chemokines (CCL17 and CCL22) which interact with CCR4 have been shown to promote M2 polarization (Wallace et al. 2009). CCL2 has been shown to play a role not only in attracting tumor promoting macrophages prostate carcinoma but also in promoting their survival and M2 polarization (Roca et  al. 2009). Anti CCL2 antibodies are undergoing evaluation in humans. Tumor products can instruct dendritic cells to prime IL-13 producing T cells which promote breast cancer (Aspord et al. 2007). Adaptive immune responses can shape the infiltration and function of TAM. In a primary mammary carcinoma model, CD4+ T cells promote metastasis by causing M2 activation via IL-4 (DeNardo et  al. 2009). Interestingly, in multistage epithelial carcinogenesis in the skin driven by transgenic expression of HPV16, a different pathway of orchestration of inflammationmediated tumor promotion by adaptive immune responses is operative. Antibody production against extracellular matrix components orchestrates by remote control cancer-related inflammation (de Visser et al. 2005). Autoantibodies act via FcgR and regulate recruitment and M2-like polarization of myelomonocytic cells which promote tumor progression (Andreu et al. 2010).

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A similarity has emerged in terms of transcriptional profile, functional ­properties and underlying transcription factors between TAM and “tolerant” macrophage (Porta et al. 2009). Tolerant macrophages belong to the wide spectrum and universe of M2-like polarized phagocytes. The finding that PMN have unsuspected plasticity and are subject to a N1-N2 balance in the tumor microenvironment (Fridlender et al. 2009 ) calls for a reappraisal of their role and significance in cancer related inflammation. Macrophages and some of their products (IL-1; TNF; IL-6) have long been known to increase metastasis (Mantovani et al. 2008a) and recent work has provided new evidence (Hagemann et  al. 2005, 2006; Kulbe et  al. 2007; Wyckoff et al. 2007; Kim et al. 2009) In particular, cells of hematopoietic origin and specifically myelomonocytic cells have been shown to home and condition the premetastatic niche. Here they form a secondary niche which favors secondary localization of cancer (Kaplan et al. 2005; Wels et al. 2008; Erler et al. 2009).

Protective Inflammation and Macrophage Activation Appropriate activation of innate immunity has antitumor potential (Duluc et  al. 2009). IFNg has been shown to re-educate TAM (Duluc et al. 2009) and there is proof of principle evidence for antitumor activity of this molecule in minimal residual disease in humans (Colombo et al. 1992; Windbichler et al. 2000; Berek et al. 2003). Given the protumor function of TAM in many cancers, strategies have been directed at blocking recruitment (see above) targeting chemokines or M-CSF or inhibiting TAM directly (Luo et al. 2006; Ritchie and Smyth 2009; Song et al. 2009). Certain forms of inflammation are protective in a preventive or therapeutic setting (Nickoloff et  al. 2005; Mantovani et  al. 2008a; Kryczek et  al. 2009). An immune response has long been known to contribute to the outcome of chemotherapy. It has now been shown that dying tumor cells can be cross presented by dendritic cells and trigger a protective immune response via a TLR4-MyD88 pathway (Apetoh et al. 2007). Inflammasome activation and IL-1b production underlie ultimate activation of protective immunity (Ghiringhelli et al. 2009). Association of TLR4 and PrX7 polymorphisms with clinical outcome are consistent with relevance of these pathways. Thus the challenge rests in blocking cancer promoting inflammation and activating tumor inhibitory innate responses (Mantovani et al. 2008b).

References Andreu, P., Johansson, M., Affara, N. I., Pucci, F., Tan, T., Junankar, S., Korets, L., Lam, J., Tawfik, D., DeNardo, D. G., Naldini, L., de Visser, K., De Palma, M. and Coussens, L. M. (2010). FcRgamma activation regulates inflammation-associated squamous carcinogenesis. Cancer Cell 17: 121–134. Apetoh, L., Ghiringhelli, F., Tesniere, A., Obeid, M., Ortiz, C., Criollo, A., Mignot, G., Maiuri, M. C., Ullrich, E., Saulnier, P., Yang, H., Amigorena, S., Ryffel, B., Barrat, F. J., Saftig, P., Levi, F., Lidereau, R., Nogues, C., Mira, J. P., Chompret, A., Joulin, V., Clavel-Chapelon, F., Bourhis, J.,

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The Immune Rejection: Lessons from Experimental Models Anil Shanker

Abstract  The mechanisms of immune rejection depend on a complex interplay between the elements of innate and adaptive immunity. Several studies in animal models of cancer and other conditions, including obesity and intestinal microbiome, over the past decade have provided evidence for an intricate cross-regulation of innate resistance and adaptive immunity. More recently, it became apparent that adaptive immunity, besides its effector function, also controls innate immune responses beyond its role in immune complex-Fc receptor interaction. This has led to a paradigm shift from the classical unidirectional notion of the innate instruction of adaptive immunity to a bidirectional partnership of the innate and adaptive immune system in controlling various pathological and physiological conditions. This chapter summarizes the advances in our understanding of the mechanisms of immune reactivity in the models of intestinal microbiota, acute infections, obesity, and cancer.

Introduction The immune system has evolved in multicellular organisms as a guard against ­life-threatening infections. Innate immune components provide a first line of defense and function to alert tissue-specific adaptive immunity against persistent tissue insult. Innate immunity is an evolutionarily conserved primitive but robust defense mechanism capable of protecting plants and invertebrate animals from a diverse threat of viral, prokaryotic and eukaryotic parasites and pathogens. Adaptive immunity evolved later at the time of differentiation of vertebrates between the jawless hagfish and lampreys by a gene conversion mechanism giving rise to

A. Shanker  (*) Laboratory of Experimental Immunology, Cancer and Inflammation Program, National Cancer Institute – Frederick, Frederick, MD, USA and Basic Science Program, SAIC-Frederick, Inc, Frederick, MD, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_3, © Springer Science+Business Media, LLC 2011

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v­ ariable lymphocyte receptors (Guo et al. 2009) and subsequent genomic invasion of a retroposon encoding site-specific recombinases (Hiom et al. 1998; Thompson 1995). These receptors achieved remarkable mechanisms of diversification as a result of hypervariability, rearrangement of receptor gene segments and clonal selection to allow the adaptive immune cells to provide specific recognition of unlimited pathogens, and develop long-term immunological memory of insult. Thus, adaptive immunity expanded the capacity of the immune system to recognize the pathogenic proteins, carbohydrates, lipids, and nucleic acids and combat the ever growing pool of diseases and their elaborate immune evasion strategies. Along with the co-evolution of pathogens, commensals and the immune system came stringent regulation of innate and adaptive immunity to avoid overproduction of inflammatory cytokines during an innate immune response or suppress a hyperactivated adaptive response. The innate and adaptive immunity cooperatively acted to maximize host defense while minimizing collateral damage to the host tissues: innate immunity generated help signals in the damaged tissues; adaptive immunity provided specific responses to the pathogenic insult or recruited other powerful innate effector cells that, though not specific by themselves, could act specifically by their guided recruitment. Conventional view suggests a linear progression from innate to adaptive immunity by innate signals following tissue insult triggering adaptive immune cells to respond to the pathogen or disease. However, several recent studies, as discussed in the following sections, demonstrate an equally indispensable adaptive control of the effector mechanisms of innate immunity. In this scheme, adaptive immunity thus assumes an additional role of providing a tighter control of innate immunity not only by regulating innate inflammation but also by activating innate effectors when they are specifically needed.

Rejection of Intestinal Microbiota Mammalian intestinal mucosal surfaces interface with an exceptionally diverse and dynamic microbiota that is ten times as numerous as the body’s cells. Given the enormous numbers of enteric bacteria and the persistent threat of opportunistic infections, immune mechanisms tightly limit the spread of the intestinal microbiota. Intestinal epithelial cells control enteric bacteria through cell-autonomous MyD88dependent toll-like receptor (TLR) activation, triggering expression of multiple microbial factors (Vaishnava et al. 2008). Recently, a flexible continuum between innate and adaptive immunity to maintain host-microbiota mutualism was observed in mice (Slack et  al. 2009). Clean germ-free Myd88−/−Ticam−/− mice deficient in signaling through TLRs and IL-1/IL-18 receptors, or phagocyte oxidative burstdeficient Nos2−/−Cybb−/− mice spontaneously mounted a robust CD4+ T cell-dependent antibody response following intragastric exposure to high doses of Escherichia coli bacteria. Thus, adaptive immunity compensated for the defect in innate immune clearance of bacteria. Implication from these findings is that under ­conditions of

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deficiency in innate immune signaling adaptive immune cells possess the ability to function in antigen-independent innate-type responses. More work is required to dissect the alternate signaling pathways that allow adaptive immune cells to manifest innate-like immune phenomenon. Similar immune compensatory defense mechanisms have been noted in some TLR signaling-deficient IRAK-4−/− children (Ku et al. 2007a, b). The mechanisms of activation of adaptive immune cells to provide innate-type protection and the precise rules of the compensatory innate-adaptive immune collaboration in detecting and controlling bacterial commensals yet remain to be investigated. Nonetheless, it is clear that the innate and adaptive immune mechanisms collaborate and form an interdependent cooperative system.

Rejection of Acute Infections Mortality from acute infections can be caused by the pathogen as well as by a ­disproportionate immune response. T cells play important role in pathogen ­clearance and also in regulating innate-immunity-driven excessive inflammation. This control is in part provided by the activation of T cell effector mechanisms in the relevant tissues. Recently, effector and memory CD4+ T cells and, to a lesser extent, effector CD8+ T cells were demonstrated to suppress potentially damaging inflammation in a murine peritonitis model by blocking macrophage inflammasomemediated cryopyrin and caspase-1 activation, interleukin (IL)-1b release, IL-18 secretion and neutrophil recruitment in an antigen-dependent manner (Guarda et al. 2009). Surprisingly, the T cell-mediated contact-dependent blockade of the caspase-1 axis of inflammasome left the primary inflammatory response and release of inflammatory mediators such as CXCL2, IL-6, IL-12 and tumor necrosis factor (TNF) crucial for tissue healing intact. In this regard, it has also been reported that IFN-g produced during T cell responses to influenza infection in mice inhibits alveolar macrophages (Sun and Metzger 2008). Thus, T cells edit the quality of inflammatory mediators and effectors during excessive innate inflammatory response via TNF family ligands and IFN-g, while maintaining their competence in antigen-specific recognition and stimulation. Lymphocyte-deficient hosts die of acute infection due to not only their lack of an adaptive immune response to clear pathogens but also an uncontrolled innate immune response as seen in a mouse hepatitis viral infection (Kim et al. 2007). In this model, resting T cells were found necessary and sufficient to temper early innate responses by direct contact inhibition, requiring antigen-presenting major histocompatibility complex molecule, but no antigen. Hepatitis viral infection or administration of TLR3 ligand poly-inosinic:polycytidylic acid, which mimics viral double-stranded RNA, led to cytokine storm in lymphocyte-deficient mice. Consequently, infected mice that showed only negligible increases in viral load died from the high amounts of inflammatory cytokines secreted during an

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u­ nrestrained innate immune response due to the lack of control by T cells. The adaptive immune cells thus negatively regulate the unnecessary early innate response. In neonate mice, where the embryonic development of the innate immune ­system precedes that of the adaptive system (Levy 2007), a hypersensitivity to ­various forms of TLR stimulation and high mortality to acute infection is observed most likely due to insufficient numbers of T cells. Dendritic cells are present in larger number in neonatal mice than in adult mice and produce robust TNF-a and IL-6 cytokines (Zhao et  al. 2008). Even in humans, the proportion of T cells is lower in peripheral blood of newborns, especially in infants who are small for ­gestational age (Heldrup et  al. 1992; Kumar et  al. 1994). Expectedly, highly increased levels of inflammatory cytokines such as TNF-a, IL-1 and IL-6 have been detected in some human newborns following acute infections (Atici et  al. 1997; Blackwell et al. 2005; Ozdemir et al. 1994; Vege et al. 1998). B cell-produced IL-10 also is implicated in dampening the neonatal inflammatory response (Zhang et al. 2007). During herpes simplex virus infection in vaginal mucosa of mice, ablation of Treg cells delays the arrival of natural killer (NK) cells, DCs and effector T cells to the site of infection (Lund et al. 2008). Thus, Treg cells, in addition to limiting the extent of an inflammatory response, seem also to coordinate early protective responses to local viral infection by facilitating a timely entry of innate immune cells into the infected tissue. However, the precise signals that activate Treg cells during infection remain unclear. Moreover, rapid non-cognate activation of interferon (IFN)-g-producing CD8+ T cells within 15 h of bacterial infection in response to cytokines secreted by phagocytic cells forms an important component of immune defense against intracellular pathogens (Lertmemongkolchai et al. 2001). Indeed, early antigen nonspecific CD44highCD8+ T cells represent the major population of IFN-g-producers in response to bacterial and viral products (Berg et  al. 2003; Kambayashi et al. 2003). The cooperativity of adaptive immunity and innate immunity thus appears essential in the rejection of acute infections. Identifying the underlying mechanisms responsible for this cooperativity between innate cells and the different populations of resting, effector, memory and regulatory T cells might lead to the discovery of new regulatory networks.

Chronic Inflammation in Obesity Obese adipose tissue shows the hallmarks of chronic inflammation that underlies the development of metabolic diseases. Infiltration of macrophages, T cells and B cells in obese adipose tissue has been described in both mice and humans (Duffaut et  al. 2009; Hotamisligil 2006; Rausch et  al. 2008; Wu et  al. 2007). Lately, the sequence of events that comprises the inflammatory cascade during the development of obesity has become clear. The infiltration of CD8+T cells in the epididymal

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adipose tissue is an early event induced by a high-fat diet in mice, concomitant with a decrease in the numbers of CD4+T helper and regulatory cells (Nishimura et al. 2009). Unknown factors in the obese tissue activate CD8+T cell effectors, which, in turn, promote the recruitment, differentiation and activation of macrophages in this tissue by secreting substantial amounts of humoral factors such as IFN-inducible protein-10, monocyte chemoattractant protein (MCP)-1, MCP-3 and regulation upon activation, normal T cell expressed and secreted protein (RANTES). Thus, CD8+T cells contribute to the initiation and propagation of inflammation by increased expression of the proinflammatory cytokines IL-1, IL-6 and TNF-a, as well as of intercellular adhesion molecule-1 and matrix metalloproteinase-2 in epididymal adipose tissue, leading to systemic insulin resistance and metabolic disorder. Two other studies have also confirmed that obesity alters the balance between Th1 and Th2 stimuli in fat through the depletion of Th2 and adipose tissue Treg cells, and the increase in CD8+T and Th1 cells or both weeks before macrophages infiltrate fat (Feuerer et al. 2009; Winer et al. 2009). It would be interesting to know how other leucocytes such as NK cells, Th17, or B cells influence adipose tissue. These findings have relevance in atherosclerosis that involves the formation of inflammatory arterial lesions. Deposited underneath the endothelium of arteries, the lipid-laden macrophages lead to formation of obstructive atherosclerotic plaques (Erbay et al. 2009). T cells largely via IFN-g-mediated mechanisms regulate the magnitude of the atherogenic proinflammatory response of macrophages and the propensity for thrombus formation as reviewed recently (Andersson et al. 2010).

Tumor Rejection NK cells have been proposed to function as a “fail-safe” mechanism to lyse tumor cells that outgrow following effector T cell rejection and immunoselection (Coulie et al. 1999; Dunn et al. 2002; Shankaran et al. 2001). Recently, tumor ­antigen-specific CD8+T cells, in addition to their direct effector response, were found necessary to provide “help” to otherwise dormant NK cells in eliciting their antitumor function for the rejection of a mastocytoma tumor (Shanker et al. 2007). Surprisingly, antigenescape tumor variants could be lysed by NK cells only if activated CD8+ T cells were present locally in their surroundings. This study suggested a new role for the CD8+T cells to induce dormant NK cells into killer effectors at the site of tumor. A T cell-NK cell cooperative interaction that emerged from this study is illustrated in Fig. 1. The T cells could broaden the effector response by activating NK cells in their vicinity and could eliminate antigen-deficient tumor cells, leading to more effective clearance of tumor. The complete rejection of tumor became possible only when there was a productive interaction of activated T cells and NK cells. The cooperative action of CD8+ T cells and NK cells in tumor rejection was further confirmed when, under conditions of restricted T cell receptor diversity,

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Fig.  1  Broadening of effector response by T cell-NK cell cooperation. Tumor is completely cleared when there is a productive interaction between activated T cells and NK cells

tumor resistance was affected by the variation in precursor frequencies of ­tumor-specific CD8+ T cell and NK cell effectors. NK cells constituted an ­important component contributing to tumor resistance, in part through an NKG2D-mediated mechanism (Shanker et  al. 2009). In another independent study, photodynamic therapy-induced CD8+ T cell-dependent control of distant late stage tumors was also found to require NK cells (Kabingu et al. 2007). Moreover, CD4+T cells have also been found to work in concert with NK cells for maximal antitumor effect in a bladder carcinoma mouse model (Perez-Diez et  al. 2007). The exact T ­cell-dependent recruitment/activation signal for NK cells however remains unclear. CD56bright human NK cells expressing high affinity IL-2 receptor have been shown to use endogenous IL-2 produced by the antigen-activated T cells to ­stimulate IFN-g production (Fehniger et al. 2003). T cell-secreted IL-2, TNF-a or direct T cell-NK cell contact could contribute to NK cell activation. Thus, tumor rejection requires the cooperative mechanisms of innate and ­adaptive immunity. Adaptive immune cells alone, in at least highly immunogenic tumors, contribute to the development of tumor escape variants and the maintenance of the “occult” cancer in equilibrium (Koebel et al. 2007). Innate immune cells alone also fail to bring about complete rejection of tumor, albeit tumor-infiltrating NK cells,

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neutrophils, and macrophages have been shown to cause tumor regression in a spontaneous-regression/complete-resistance mouse model (Hicks et  al. 2006a, b; Yu et al. 2009), and in a xenograft tumor model following oncolytic viral therapy (Worschech et al. 2009).

Concluding Remarks Immune reactivity depends on an intricate cooperativity between both innate and adaptive arms of immunity. Adaptive immune responses are necessary not only to provide effector response but also to mediate tissue specificity in a cognate manner by directing the innate effector response to the site of infection or disease following tissue insult. The concentration of pathogen-associated molecular patterns and/or danger-associated molecular patterns may be a critical parameter for this immune rejection. Although the triggers and magnitude controls that regulate an adaptive immune response during a given infection or disease are not yet precisely understood, it is clear that the immune system has evolved the capacity to generate a powerful immunological “orchestra,” where adaptive T lymphocytes seemingly play a role of “conductor.” The dynamics of this immune orchestra will become clear as more meaningful immune interactions and their regulatory networks in immune rejection are identified. Acknowledgments  This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government. This Research was supported [in part] by the Intramural Research Program of the Center for Cancer Research, National Cancer Institute, National Institutes of Health.

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Lund, J.M., Hsing, L., Pham, T.T., and Rudensky, A.Y. (2008). Coordination of early protective immunity to viral infection by regulatory T cells. Science 320, 1220–1224. Nishimura, S., Manabe, I., Nagasaki, M., Eto, K., Yamashita, H., Ohsugi, M., Otsu, M., Hara, K., Ueki, K., Sugiura, S., et al. (2009). CD8+ effector T cells contribute to macrophage recruitment and adipose tissue inflammation in obesity. Nat Med 15, 914–920. Ozdemir, A., Oygur, N., Gultekin, M., Coskun, M., and Yegin, O. (1994). Neonatal tumor necrosis factor, interleukin-1 alpha, interleukin-1 beta, and interleukin-6 response to infection. Am J Perinatol 11, 282–285. Perez-Diez, A., Joncker, N.T., Choi, K., Chan, W.F., Anderson, C.C., Lantz, O., and Matzinger, P. (2007). CD4 cells can be more efficient at tumor rejection than CD8 cells. Blood 109, 5346–5354. Rausch, M.E., Weisberg, S., Vardhana, P., and Tortoriello, D.V. (2008). Obesity in C57BL/6J mice is characterized by adipose tissue hypoxia and cytotoxic T-cell infiltration. Int J Obes (Lond) 32, 451–463. Shankaran, V., Ikeda, H., Bruce, A.T., White, J.M., Swanson, P.E., Old, L.J., and Schreiber, R.D. (2001). IFNgamma and lymphocytes prevent primary tumour development and shape tumour immunogenicity. Nature 410, 1107–1111. Shanker, A., Buferne, M., and Schmitt-Verhulst, A.-M. (2009). Cooperative action of CD8 T lymphocytes and natural killer cells controls tumour growth under conditions of restricted T-cell receptor diversity. Immunology 129, 41–54. Shanker, A., Verdeil, G., Buferne, M., Inderberg-Suso, E.M., Puthier, D., Joly, F., Nguyen, C., Leserman, L., Auphan-Anezin, N., and Schmitt-Verhulst, A.M. (2007). CD8 T cell help for innate antitumor immunity. J Immunol 179, 6651–6662. Slack, E., Hapfelmeier, S., Stecher, B., Velykoredko, Y., Stoel, M., Lawson, M.A., Geuking, M.B., Beutler, B., Tedder, T.F., Hardt, W.D., et al. (2009). Innate and adaptive immunity cooperate flexibly to maintain host-microbiota mutualism. Science 325, 617–620. Sun, K., and Metzger, D.W. (2008). Inhibition of pulmonary antibacterial defense by interferongamma during recovery from influenza infection. Nat Med 14, 558–564. Thompson, C.B. (1995). New insights into V(D)J recombination and its role in the evolution of the immune system. Immunity 3, 531–539. Vaishnava, S., Behrendt, C.L., Ismail, A.S., Eckmann, L., and Hooper, L.V. (2008). Paneth cells directly sense gut commensals and maintain homeostasis at the intestinal host-microbial interface. Proc Natl Acad Sci U S A 105, 20858–20863. Vege, A., Rognum, T.O., Aasen, A.O., and Saugstad, O.D. (1998). Are elevated cerebrospinal fluid levels of IL-6 in sudden unexplained deaths, infectious deaths and deaths due to heart/ lung disease in infants and children due to hypoxia? Acta Paediatr 87, 819–824. Winer, S., Chan, Y., Paltser, G., Truong, D., Tsui, H., Bahrami, J., Dorfman, R., Wang, Y., Zielenski, J., Mastronardi, F., et al. (2009). Normalization of obesity-associated insulin resistance through immunotherapy. Nat Med 15, 921–929. Worschech, A., Chen, N., Yu, Y.A., Zhang, Q., Pos, Z., Weibel, S., Raab, V., Sabatino, M., Monaco, A., Liu, H., et  al. (2009). Systemic treatment of xenografts with vaccinia virus GLV-1h68 reveals the immunologic facet of oncolytic therapy. BMC Genomics 10, 301. Wu, H., Ghosh, S., Perrard, X.D., Feng, L., Garcia, G.E., Perrard, J.L., Sweeney, J.F., Peterson, L.E., Chan, L., Smith, C.W., and Ballantyne, C.M. (2007). T-cell accumulation and regulated on activation, normal T cell expressed and secreted upregulation in adipose tissue in obesity. Circulation 115, 1029–1038. Yu, Y.A., Galanis, C., Woo, Y., Chen, N., Zhang, Q., Fong, Y., and Szalay, A.A. (2009). Regression of human pancreatic tumor xenografts in mice after a single systemic injection of recombinant vaccinia virus GLV-1h68. Mol Cancer Ther 8, 141–151. Zhang, X., Deriaud, E., Jiao, X., Braun, D., Leclerc, C., and Lo-Man, R. (2007). Type I interferons protect neonates from acute inflammation through interleukin 10-producing B cells. J Exp Med 204, 1107–1118. Zhao, J., Liu, J., Feng, Z., Hu, S., Liu, Y., Sheng, X., Li, S., Wang, X., and Long, C. (2008). Clinical outcomes and experience of 20 pediatric patients treated with extracorporeal membrane oxygenation in Fuwai Hospital. ASAIO J 54, 302–305.

Turning on and off the Immunological Switch: Immune Response Polarization and Its Control by IL-10 and STAT3 C. Andrew Stewart and Giorgio Trinchieri

The innate and immunologic arms of host resistance to pathogenic infections use distinct patterns of cytokine production to direct appropriate tissue responses. This chapter discusses how this “Immunological Switch” is coordinated by distinct T helper cell lineages and how IL-10 and STAT3 signaling controls the inflammatory response. A particular emphasis is given to the role of IL-10 and STAT3 in tumor-associated immunosuppression, where the functional inhibition of either molecule results in acquiescence to proinflammatory and anti-tumor Th1 type immune responses.

T Helper Cell Lineages Adaptive immune responses are defined by their use of distinct lineages of CD4+ T helper (Th) cells (Fig. 1). Th cells are required for generation of antibody responses against thymus-dependent antigens and otherwise shape immune responses through their regulation of cytotoxic CD8+ T cell responses and by directly mediating effector mechanisms including the production of cytokines at inflammatory sites. Historically, the discovery that CD4 T cell clones had different patterns of cytokine gene expression defined the Th1/Th2 paradigm of immunity. This states that Th1 cells, through their production of IFN-g and IL-2, contribute to the activation of macrophages and dendritic cells (DC) to drive cellular-mediated immunity. On the other hand, Th2 cells produce IL-4, IL-5 and IL-13 that contribute to B cell class switching to the IgG1 and IgE isotypes, leading to protective cellular responses against worm infestation but also adverse effects such as allergic reactions and asthma (reviewed in Zhu et  al. (2010)). More recently, additional subsets of Th cells have been identified. Furthermore, evidence has been found that the Th subsets, with the possible exception of well-differentiated Th1 cells, maintain some level of plasticity.

G. Trinchieri  (*) Cancer and Inflammation Program, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_4, © Springer Science+Business Media, LLC 2011

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a

C.A. Stewart and G. Trinchieri Lineage Cell

Inducing Cytokines

Th1

IL-12 IFN-γ

IL-12R T-bet/Eomes STAT1/STAT4 IL-18R

IL-4 IL-2

Th2

IL- 4R

GATA-3 STAT6

IL-33R

Th17

TGF-β IL-6 IL-21

IL-23R

RORγ t STAT3

IL-1R

iTreg

TGF-β IL-2

b

Cytokines Produced

Th2

Th9

Tumors Viruses Intracellular parasites (Mycobacterium)

IFN-γ IL-2

Macrophage/APC activation CD8+ T cell help

IL-4 IL-5 IL-13

B cell activation IgE production

Worms Parasites Allergens

Neutrophil accumulation Tissue repair

Bacteria Inflammation Wound healing (e.g. S. aureus)

IL-17 IL-22

IL-21 B cell activation +TGF-β

Role in Immunity

TGF-β Suppression of T cell responses IL-10

FoxP3 STAT5

Tfh

Mechanism

Prevention of autoimmunity Control of inflammation

TGF-β Oral tolerance

Th3

IL-9 Mast cell recruitment ? Th1

Th1 IL-10 IFN-α/β

Th0

? Tr1

IL-10 Limit Inflammation IFN-γ

? Th2

IL-2 Intermediate IL-4 state?

Fig. 1  CD4 T helper cell lineages. (a) Representation of T helper and inducible T regulatory cell lineages. Each lineage is associated with one or two master transcription factors along with an activating signal transducer and activator of transcription (STAT). For Th1, Th2 and Th17 cells, receptors for STAT-driving cytokines and IL-1 family cytokines are noted. The cytokines driving generation of T helper lineages from naïve T cells, and cytokines produced upon their stimulation are also shown. (b) Other T helper subsets, the cytokines they produce, the cytokines required for their generation, their function and their relationship to other T helper lineages (blue arrows and text)

The identification of the Th17 and inducible T regulatory (iTreg) lineages has rectified some shortcomings of the Th1/Th2 paradigm. Th17 cells produce IL-17A, IL-17F or IL-22, whose receptors are mainly expressed by non-hematopoietic tissue cells such as fibroblasts and epithelial cells and trigger them to produce neutrophilattracting chemokines, thus driving a type of inflammation characterized by abundant infiltration of neutrophils (Weaver et al. 2007a). Treg produce the potent anti-inflammatory cytokine TGF-b and through this and other less well-defined

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cell-mediated and humoral mechanisms suppress proliferation and activation of other CD4 T cells. They may also act directly on antigen presenting cells by modulating their ability to efficiently activate T cells. In addition to the main Th lineages, several other subsets of Th cells have been defined (Fig. 1b). T follicular helper cells produce IL-21 and drive B cell activation and maturation in germinal centers. TGF-b shifts the phenotype of Th2 cells into cells producing IL-9 (also known as Th9 cells) that may contribute to the recruitment of mast cells during a Th2-type response (Veldhoen et al. 2008). Th3 cells are a subset of regulatory cells identified as producers of TGF-b and important mediators of oral tolerance (Weiner 2001). Tr1 cells are induced by IL-10 and/or type I IFN (Roncarolo et al. 2001; Dikopoulos et al. 2005; Levings et al. 2001). They produce high levels of IL-10 and intermediate levels of IFN-g and are possibly related to the IL-10-producing Th1 cells often observed in infected individuals and probably important for limiting Th1-mediated inflammation (Anderson et al. 2007; Jankovic et al. 2007; Nylen et al. 2007; Roers et al. 2004; Trinchieri 2007). Th0 cells produce both IL-2 and IL-4 and are probably a stage preceding commitment to either Th1 or Th2 differentiation (Romagnani 1994).

Differentiation and Activation of T Helper Lineages In addition to their cytokine secretion profile, the Th lineages are defined by the culture conditions that give rise to their differentiation, by the signal transducer and activator of transcription (STAT) transcription factors that are activated in polarizing culture, and by one (or several) master transcription factors involved in their differentiation (Fig. 1). Through positive feedback on a lineage’s driving cytokine and transcription factor and through negative feedback on those of the other lineages, in vitro culture systems produce highly polarized lineage phenotypes. For example, the production of IFN-g by Th1 cells leads to STAT1 activation and transcription of T-bet, the Th1master transcription factor (Lighvani et al. 2001). In addition, IFN-g stimulates DC to produce IL-12, which in Th1 cells drives STAT4 activation and expression of T-bet, of the IL-12 receptor (IL-12Rb2), and of IFN-g (Usui et  al. 2006; Robinson et  al. 1997). Eomes is another T-box transcription factor involved in Th1 differentiation and together with T-bet, they induce IFN-g expression (Pearce et al. 2003). In this way, a positive feedback loop is created in Th1 polarizing cultures. Such loops also exist for Th2 cells involving the GATA3 and STAT6 transcription factors, for Th17 cells involving RORgt and STAT3 and for inducible Treg involving FoxP3 and STAT5 (for detailed discussion, see Zhu et al. (2010)). The exact mechanisms by which lineage commitment is induced by the combination of cytokines and other stimuli still remain controversial and may be qualitatively or quantitatively different in humans and in mice. Human Th1 clones maintain their phenotype when switched to Th2-inducing condition but Th2 clones can be converted into IFN-g producing cells in the presence of IL-12 (Manetti et al. 1994). Similarly, recent epigenetic studies in the Mouse have shown that Th1 cells completely

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shut-off the genes encoding the transcription factor or the cytokine characteristic of other Th subsets (O’Shea and Paul 2010). However, the master regulator genes encoding the Th subset-specific transcription factors remain in a partially open chromatin conformation in Th2 or Th17 cells (O’Shea and Paul 2010), suggesting that the original observations in humans might be also reproduced in the murine system. The receptors for IL-12 and IL-23, two cytokines important for the terminal differentiation of Th1 and Th17 cells respectively, are not expressed in naïve CD4+ T cells and need to be induced by other stimuli (other cytokines, TCR signaling, or co-stimulation), yet they play an essential role in the generation of fully effective Th1 and Th17 cells and, at least in humans, IL-12 signaling is required for the irreversible commitment of high IFN-g-producing Th1 cells (Manetti et al. 1994). The three inflammatory T helper lineages, Th1, Th2 and Th17, are distinctly activated by members of the IL-1 family of cytokines and, when fully committed, differentially express their receptors. The IL-1 family cytokines, including IL-1a, IL-1b, IL-18 and IL-33, are important early mediators of inflammation that are often preformed and become available for cell signaling upon release from cell nuclei associated with cell death or senescence (IL-1a and IL-33) or are converted into an active form by inflammasome-dependent caspase-1-dependent processing prior to release (IL-1b and IL-18). Individual members are able to activate specific lineages of T helper cells. Thus, although maximal activation and cytokine production by the Th cell subsets require antigen-specific stimulation, TCR-independent production of IFN-g by Th1 cells can be induced by stimulation with IL-12 (in combination with IL-2 or IL-18 and co-stimulation), while Th2 cells produce TCRindependent cytokines in response to IL-33 and IL-4 and Th17 cells produce IL-17 when stimulated with IL-1 and IL-23 (Guo et al. 2009). TCR-independent effector functions conferred upon polarized T helper cells by the milieu of inflammationassociated cytokines could be important for the bystander activation of T cells that allows these cells to participate in innate mechanisms of resistance at the side of the typical innate effector cells (NK, NKT, Tg/d cells) but also to contribute to maintain a polarized inflammatory microenvironment in which antigen specific T cells will be reinforced in their commitment.

Function and Plasticity of Th and Treg Lineages In Vivo In vivo, mouse model systems and human diseases have demonstrated requirements for components of each of these lineages in immunoregulation and control of infectious pathogens. For example, patients with genetic defects in Th1 responses are highly susceptible to Mycobacterium infections (Zhang et al. 2008). In mice, host resistance to intestinal helminth infections is critically dependent upon the Th2 cytokines IL-4 and IL-13 (Wynn 2003). Humans with Job’s syndrome due to dominant-negative mutations in STAT3 are defective in Th17 responses and are susceptible to skin and lung infections with Staphylococcus aureus, and Candida

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species (Ma et al. 2008). However, while the T helper lineage functions are critical in host defense, the extent of T cell polarization in  vivo is less clear. Using intracellular cytokine staining, it is possible to identify ex vivo T cells that produce both IL-4 and IFN-g or both IFN-g and IL-17. Conversely, highly polarizing conditions lead to a single prominent T helper cell type, such as the Th1 responses observed during MCMV or Toxoplasma gondii infections (Gaddi and Yap 2007). T cells are also capable of converting from one lineage to another, for example Th17 cells can switch to Treg or to IFN-g producers given the correct mixture of cytokines. This plasticity has been proposed as an important mechanism in CD8+ T cells that can switch from a Tc17 (cytotoxic T cells producing IL-17) to an IFNg-producing response. This switch may facilitate high levels of CD8 T cell accumulation along with optimal cytotoxic function and IFN-g production, thus leading to effective anti-tumor responses (Yen et al. 2009; Hinrichs et al. 2009). The Th response is regulated by immuno-suppressive Treg cells whose function is dependent upon the FoxP3 transcription factor. Mutations in the Foxp3 gene cause immune-dysfunction, polyendocrinopathy, enteropathy X-linked (IPEX) syndrome (Bennett et al. 2001; Wildin et al. 2001). IPEX is a rare and lethal condition characterized by type I diabetes, infections, enteropathy, anemia, endocrinopathy and cachexia. This requirement for functional FoxP3 shows the necessity for Treg function in regulation of the immune system. In several models it has been shown that Treg-mediated control of Th1, Th2 and Th17 responses requires more than a generic Treg program. The transcription factors associated with the Th1, Th2 and Th17 lineages have been found to be expressed by Treg and this expression contributes to Treg-mediated suppression in  vivo. For example, suppression of Th17 cells by Treg requires that the latter express STAT3, a transcription factor central in regulating Th17 cell differentiation. Thus, it appears that Treg at the site of effector immune response activate Th response-specific suppression mechanisms involving the activation of STATs that guide the differentiation of the corresponding class of the immune response (Chaudhry et al. 2009; Wohlfert and Belkaid 2010).

IL-10 Controls Inflammation The cytokines IL-10 and TGF-b are the major mediators of anti-inflammatory communication and immunosuppression (Sanjabi et al. 2009). IL-10 was described 20 years ago by Fiorentino et al. (1989) at DNAX as a cytokine produced by Th2 cells, but it is now known to be produced by other T cell subsets and by other cell types. The apparently conflicting roles for IL-10 as a suppressive cytokine under certain circumstances but a stimulant under others have made it a challenge to understand IL-10s action in  vivo and identify safe clinical applications (Vicari and Trinchieri 2004; Mosser and Zhang 2008). Indeed, in subsets of CD8+ T cells, NK cells and B cells, IL-10 can act to drive proliferation and activation (see Moore et al. (2001)). Also, in monocytes IL-10 can induce receptors involved in immunecomplex recognition and phagocytosis (including CD14, CD16, CD32, CD64)

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(Calzada-Wack et  al. 1996; Rahimi et  al. 2005). In contrast, IL-10 suppresses the proinflammatory activity of APC such as DC, monocytes and macrophages by inhibiting the production of pro-inflammatory cytokines such as TNF, IL-1b, IL-6, IL-8 and IL-12, and impairing APC maturation and upregulation of costimulatory molecules (Moore et al. 2001). As discussed below, in many situations in vivo, the anti-inflammatory effects of IL-10 override stimulatory effects, consistent with a predominant role as a regulator of inflammation through the suppression of APC function. IL-10 limits inflammation during multiple acute and chronic conditions and contributes to limit the pathology associated with unchecked inflammatory responses. IL-10 knockout mice develop chronic enterocolitis when housed under specific pathogen-free conditions that can be limited by administration of antibiotics and prevented in germ-free conditions, illustrating the requirement for commensal microbiota to drive colitis (Kuhn et al. 1993; Sellon et al. 1998). Cell-type specific knockouts of IL-10 have been used to demonstrate that T cells (arguably Tregs) are the critical source of IL-10 in prevention of colitis (Roers et al. 2004; Rubtsov et al. 2008). However, the systemic response to LPS challenge is greatly exacerbated in mice in which IL-10 is specifically suppressed in macrophages/neutrophils. These mice, but not those in which T cells are unable to produce IL-10, succumb to a 20-fold lower dose of LPS than control mice (Berg et al. 1995). Conversely, in the acute response to another TLR ligand, CpG oligonucleotides, protection is conferred by IL-10 from cells other than Tab cells, macrophages or neutrophils (Siewe et al. 2006). The extent of inflammation in many diseases including Th1, Th2 and Th17associated pathologies can be limited by IL-10 (reviewed in Moore et al. (2001)). During T. gondii infection, IL-10-deficient mice die due to immunopathology associated with an overproduction of Th1 cytokines (Gazzinelli et al. 1996). During chronic T. gondii infection it has been shown that the major producer of protective IL-10 is the same IFN-g-producing FoxP3−Tbet+ CD4+ Th1 cell subset that is responsible for the immunopathology and control of the parasite (Jankovic et  al. 2007; Roers et  al. 2004; Trinchieri 2007). Production of IL-10 by Th1 cells in T. gondii infection has been attributed to IL-27 (Stumhofer et  al. 2007) but alternative data have proposed that NK cells producing IL-10 in response to IL-12 stimulation are responsible for the control of inflammation in this infection (Perona-Wright et al. 2009). The conditions that drive IL-10 in T and NK cells, in response to IL-27 and IL-12 respectively during infection, still remain to be determined. Production of IL-10 by Th1 cells also limits inflammation and allows the maintenance of chronic infection by certain Leishmania species, both in humans and mice (Anderson et  al. 2007; Nylen et  al. 2007). During Th2-type responses against Schistosoma mansoni, IL-10 is able to limit IL-4 and IL-13 production along with liver fibrosis and the growth of granulomas (Hoffmann et  al. 2000). Th17 responses during experimental autoimmune encephalomyelitis (EAE) are also controlled by IL-10 and IL-10-deficient mice are unable to recover from CNS inflammation (reviewed in Moore et al. (2001) and O’Garra et al. (2008)). Th17 cells producing both IL-17 and IL-10 can be identified or produced during culture with IL-27 or TGF-b and IL-6 (Stumhofer et  al. 2007; Fitzgerald et  al. 2007;

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Jankovic and Trinchieri 2007; McGeachy et al. 2007). Th17 cells generated in this manner can control EAE caused by autoreactive Th17 cells in an IL-10-sensitive manner (McGeachy et  al. 2007), suggesting that like Th1 cells (Anderson et  al. 2007; Jankovic et al. 2007), Th17 cells may limit their pathogenesis by expressing IL-10 (reviewed in Jankovic and Trinchieri (2007)). Due to the wide-ranging control of immunopathology by T cell IL-10, the therapeutic induction of IL-10 in T cells is likely to provide considerable benefit in various autoimmune and allergic diseases (reviewed in O’Garra et al. (2008)). While IL-10 limits the inflammatory pathogenesis in many diseases, it can also contribute to long term persistence of pathogens. This is seen for the Leishmania major Friedlin strain, which causes self-healing but persistent infection in wild-type mice, but is eliminated in IL-10-deficient or in anti-IL10R antibody-treated mice (Belkaid et  al. 2001). During experimental infections with some species of Mycobacterium (Mycobacterium avium), but not others, administration of anti-IL10R antibody can enhance responses to chemotherapy (Silva et al. 2001). Some viral infections also require IL-10 for persistence. The clone 13 strain of Lymphocytic choriomeningitis virus (LCMV) establishes persistent infection associated with high levels of IL-10 and impaired T cell responses. The treatment of persistently infected mice with anti-IL10R antibody is sufficient to cure the infection (Brooks et al. 2006; Ejrnaes et al. 2006). However, the role of IL-10 in persistence of other viruses and its susceptibility to therapy may be more difficult to define. Murine gammaherpesvirus 68 is a model for gammaherpesvirus infections including human EBV. It induces host production of IL-10 during infection, but IL-10-deficient mice are neither resistant to nor protected from persistent infection, and IL-10 plays a more prominent role in the control of inflammatory pathology associated with the virus (Peacock and Bost 2001; Nelson et al. 2009). During acute viral infections such as influenza, the control of lung inflammation mediated by IL-10 can prevent lethality of the infection (Sun et al. 2009). Therefore it is critical to assess the risk of immunopathology when considering therapies for persistent viral infections, including HCV, EBV and HIV by blockade of IL-10 signaling with or without immunization (Vicari and Trinchieri 2004; Blackburn and Wherry 2007; Brooks et al. 2008; Filippi and von Herrath 2008).

Regulation of IL-10 Production Given its importance in restraining inflammation, the regulation of IL-10s expression by many different cell types is highly complex and it involves a large number of regulatory transcription factors (reviewed in Mosser and Zhang (2008) and Saraiva and O’Garra (2010)). In addition, post-transcriptional regulation occurs at the level of transcript stability due to AU-rich elements (ARE) in the 3¢UTR and due to regulation by micro-RNA (Mosser and Zhang 2008; Sharma et  al. 2009). IL-10 is produced by DC, monocytes/macrophages, neutrophils, eosinophils, mast cells, B cells, NK cells, CD8+ T cells, all CD4+ T-helper cell lineages and perhaps some tissue resident cells such as keratinocytes and endothelial cells. At the

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promoter level, expression is regulated by many factors including Sp1, Sp3, C/EBPb, c-Maf, CREB, IRF, AP-1, NF-kB p50, PBX1, PREP1 and STAT3. Several of the signaling pathways involved in inducing expression of IL-10 are noteworthy. The MAPK (mitogen activated protein kinase) pathway, particularly the ERK pathway, but also p38, is critical for IL-10 induction in many cell types following diverse stimuli including TLR-ligand stimulation and FcR-gamma triggering in macrophages (Mosser and Zhang 2008; Saraiva and O’Garra 2010). During macrophage activation, this ERK signaling is associated with epigenetic changes at the IL-10 promoter and phosphorylation of Histone H3 and increased binding by Sp1 and STAT3 (Mosser and Zhang 2008; Lucas et  al. 2005; Zhang et  al. 2006). A differential requirement for STAT transcription factors and GATA-3 has been shown within the different T helper lineages. Thus during induction of IL-10 expression in T cells by TGF-b and IL-6, STAT3 is required, while IL-27-induced IL-10 production requires both STAT1 and STAT3 (Stumhofer et al. 2007). On the other hand, IL-10 expression by high antigen dose-stimulated Th1 cells requires IL-12 and STAT4 (Gerosa et al. 1996; Saraiva et al. 2009), and NK cells produce IL-10 in response to IL-2/ IL-12 stimulation via a STAT4-dependent pathway (Grant et al. 2008). The precise mechanisms by which various STATs activate IL-10 gene expression have yet to be defined. In Th2 cells, the Th2 lineage “master transcription factor,” GATA-3 is involved in chromatin remodeling at the IL-10 locus, facilitating expression of IL-10 by Th2 cells (reviewed in Mosser and Zhang (2008)). Finally, the potential involvement of suppressive factors including IL-10 itself and TGF-b in control of IL-10 expression suggests that positive feedback mechanisms may act to coordinate the immunosuppressive environment. In human macrophages, IL-10 has been described to induce its own expression via STAT3 (Staples et  al. 2007). TGF-b enhances induction of IL-10 expression by macrophages and by T cells exposed to IL-6 or IL-27 (Stumhofer et al. 2007; Maeda et al. 1995), and a binding site for SMAD4 has been identified in the IL-10 promoter (Kitani et  al. 2003). In addition, the neutralization of TGF-b leads to reduced development of FoxP3+ and FoxP3− IL-10-producing T cells in the gut (Maynard et al. 2007).

IL-10 Signaling and Role of STAT3 in its Regulatory Functions The IL10R is a member of the interferon family of cytokine receptors (Moore et al. 2001). It is a heterodimer composed of a unique high affinity IL-10R1 chain and the IL-10R2 chain that is shared with the receptors for IL-22, IL-26 and the IFN-ls (IL-28A, IL-28B and IL-29) (Uze and Monneron 2007). IL-10 itself is a dimer and brings together two receptor complexes upon binding. This activates the Jak1 and Tyk2 kinases, associated with the IL-10R1 and IL-10R2, respectively, and leads to phosphorylation of tyrosines within two YXXQ sites in IL-10R1. Upon phosphorylation these sites serve as docking sites for STAT3 and to a lesser extent, STAT1, which are themselves phosphorylated by Jak1/Tyk2. Phosphorylated STAT3 homodimers (or STAT1:STAT3 heterodimers or STAT1:STAT1 homodimers) then

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form and translocate into the nucleus to bind STAT-binding sites and modulate transcription (Moore et al. 2001; Weber-Nordt et al. 1996). It is well established that STAT3 plays a central role in the anti-inflammatory effects of IL-10. The Jak1-STAT3 pathway is required for anti-inflammatory function of IL-10 in vitro (Riley et al. 1999). Ectopic expression of constitutively active STAT3 in mouse and human macrophages is sufficient to inhibit cytokine production in response to LPS (Hoentjen et al. 2005; Williams et al. 2007). In the converse situation, Cre-loxP-mediated deletion of STAT3 in macrophages and neutrophils in vivo leads to phenotypes similar to IL-10-deficient mice: these mice are hypersensitive to LPS stimulation and develop chronic enterocolitis (Takeda et al. 1999). Thus, STAT3 is required for the anti-inflammatory action of IL-10. It is not clear whether the canonical scheme of STAT3 tyrosine phosphorylation is sufficient for the anti-inflammatory effect of IL-10 (see discussion on other STAT3-inducing cytokines and stimuli below). One study suggested that a Serinerich region in the C-terminus of the IL-10R was necessary for anti-inflammatory function (Riley et al. 1999), but this has been contradicted by another investigation (Williams et  al. 2007) and the relevance of this region remains uncertain. Transcriptional regulation by STAT3 can occur in the absence of tyrosine phosphorylation, and phosphorylation at Serine 727 within its transcriptional activation domain can modulate its activity. Kinases implicated in this Serine phosphorylation include the MAP-kinases p38, ERK and JNK, mTOR PKCd and PKCe (Schindler et  al. 2007; Aziz et  al. 2007). Germline deletion of STAT3 is embryonic lethal (Takeda et al. 1997), but mice with Serine 727 of STAT3 mutated to Alanine survive and show reduced transcription of STAT3 sensitive genes (Shen et  al. 2004). Recently it has been shown that STAT3 with phosphorylated Serine 727 (STAT3pS) is found in mitochondria and that it is required for upregulation of oxidative phosphorylation by the electron transport chain (Wegrzyn et al. 2009). Finally, unphosphorylated STAT3 plays several other roles in the cell. Unphosphorylated STAT3 binds to stathmin, a tubulin-binding protein that depolymerizes tubulin. Through inhibition of stathmin’s activity, STAT3 maintains the integrity of microtubules and is therefore important for cell motility (Gao et  al. 2006; Ng et  al. 2006). This microtubule-regulating function might be affected during STAT3 signaling, and could hypothetically explain why IL-10 inhibits trafficking of MHC class II from the MHC class II-loading compartment to the plasma membrane (Koppelman et al. 1997). Additionally, unphosphorylated STAT3 can interact with p65 and p50 of NF-kB and can repress or induce gene expression in cytokine and other genes including IL-8 and IFN-b (Yang et al. 2007). IL-10R has also been shown to signal through PI3-kinase (Williams et al. 2004). PI3K activation inhibits cytokine production in DC, as DC deficient in PI3K produce much higher IL-12 and other pro-inflammatory cytokines than wild type DC in response to LPS (Fukao et al. 2002). However, in this case, IL-10 is not required for PI3-kinase mediated suppression. A cytomegalovirus homolog of IL-10 has been reported to suppress TNF production in a PI3K-mediated manner (Spencer 2007), but other studies indicate PI3K is required for IL-10s effect on proliferation rather than for its inhibitory effect on cytokine production (Crawley et al. 1996).

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IL-10 signaling is regulated at a number of levels. While the IL-10R2 chain is widely expressed, the IL-10R1 chain is expressed by most hematopoietic cells at only very low levels. These expression levels are reduced upon T cell activation and conversely are increased in activated monocytes (Moore et al. 2001). Though both STAT3 and STAT1 can be activated in response to IL-10 signaling, it is mainly STAT3 that is activated in myeloid cells (Finbloom and Winestock 1995). Crosstalk from other cytokine signaling systems can also modulate IL-10 signaling. For example, the priming of human macrophages with IFN-a leads to increased STAT1 activation and preformed dimerization, thus conferring a pro-inflammatory gain of function to IL-10 signaling which is then able to induce production of pro-inflammatory factors such as IFN-inducible chemokines (Sharif et al. 2004).

IL-10 has Superior Anti-Inflammatory Activity Compared with Other STAT3-Signaling Cytokines: Role of SOCS3 The anti-inflammatory signaling induced through the IL-10-STAT3 axis contrasts with other STAT3-inducing cytokines that drive more pro-inflammatory signaling. These include IL-6, IL-21, IL-23 and IL-27, some of which also participate in differentiation of T helper cell lineages as discussed above. One component of the difference between anti-inflammatory and pro-inflammatory STAT3-inducing cytokines can be explained by the downstream activity of suppressor of cytokine signaling 3 (SOCS3). SOCS3 is induced following STAT3 activation induced by multiple cytokines and growth factors including IL-10, IL-6 and EGF (Yoshimura et al. 2007; Cassel and Rothman 2009; O’Shea and Murray 2008). In common with other SOCS protein family members, it has a central src homology 2 (SH2) domain that targets it to specific phosphorylated tyrosines and a C-terminal SOCS box that serves as an E3 ubiquitin ligase. In addition, SOCS3 has an N-terminal kinase inhibitory region (KIR) that is required for its inhibition of Jak kinases. The selective effects of SOCS3-mediated suppression are believed to be due to its recognition of a specific phosphotyrosine (Y757) on the gp130 chain common to receptors for the cytokines IL-6, leukemia inhibitory factor (LIF) and IL-27 among others. In contrast, the IL-10 receptor lacks tyrosines capable of forming this interaction and is resistant to SOCS3-mediated suppression. In macrophages in which gp130 was mutated at Y757, the site responsible for binding SOCS3 and SHP-2 upon its phosphorylation, IL-6 acts in a similar manner to IL-10 by inducing prolonged STAT3 tyrosine phosphorylation and suppressing the response to LPS (Yasukawa et al. 2003; El Kasmi et al. 2006). The same conversion of IL-6 signaling from pro-inflammatory to anti-inflammatory is observed in macrophages lacking SOCS3 (Yasukawa et al. 2003; Croker et al. 2003; Lang et al. 2003), demonstrating a highly selective effect of the SOCS3-gp130 interaction. Thus SOCS3 limits the duration of STAT3 activation induced by IL-6 signaling. Indeed, it has been proposed that the sustained STAT3 activation resulting from IL-10 signaling is the sole required feature of an anti-inflammatory response (Yasukawa et al. 2003). However, it is also

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possible that STAT3 activation induced by anti-inflammatory signaling could differ qualitatively from the pro-inflammatory signaling (in terms of protein modification, cooperative partners or other) (O’Shea and Murray 2008).

Molecular Mechanisms of the IL-10-Mediated Anti-Inflammatory Effects The mechanisms by which IL-10 mediates its anti-inflammatory effects are diverse and their details and relative importance remain a matter of debate (Murray 2006; Page et al. 2009; Weaver et al. 2007b; Grutz 2005; Sabat et al. 2007). In the context of pro-inflammatory stimulation by TLR ligands, IL-10s regulatory effects are twofold: it suppresses the expression of proinflammatory genes, in particular cytokines and chemokines including TNF, IL-12, IL-1b, IL-6, KC, IL-8, and it induces the expression of suppressive factors and receptors including the IL-1 receptor antagonist (IL-1ra), TNF-R2, DUSP1, SOCS-3 and Bcl-3 (Moore et al. 2001; Williams et al. 2004). Mechanisms to explain the regulation of these genes must consider the coordinated activity of NF-kB induced during TLR stimulation (see Fig. 2). TLR signaling-induced transcription Pol II

Pro-Inflammatory Cytokine Genes

TLR Signaling NF-κB p65 p50

IL-10 Signaling STAT3pY Y-P YP

TLR and IL-10-induced transcription Chromatin Remodeling Coordinated promoter activaiton YP YP

TNF IL-1 IL-6 IL-12

IL-1ra SOCS3 ETV3 SBNO2

Pol II

Anti-Inflammatory Genes

Bcl-3 IκBNS DUSP1 TTP

1) Termination of Signaling Pol II by interruption of signaling pathways

2) Transcriptional Repression

Pol II

Pro-Inflammatory Cytokine Genes

3) Binding and Inhibition of NF-κB by nuclear IκBlike molecules

Pol II

? (5) Direct IL-10-induced repression ? Chromatin Remodeling Promoter inactivation YP

YP

Pro-Inflammatory Cytokine Genes

Pro-Inflammatory Cytokine Genes

4) Destabilization and degradation of Pro-Inflammatory Cytokine mRNA Pro-Inflammatory Genes

Fig. 2  Coordinated TLR and IL-10 signaling drives a multipart anti-inflammatory response. TLR signaling drives the expression of pro-inflammatory cytokine genes. IL-10 signaling, through STAT3 and in coordination with TLR signaling, induces expression of anti-inflammatory genes. These anti-inflammatory genes block the pro-inflammatory response at multiple levels. For detailed explanations of mechanisms, see text. (1) Extracellular cytokine signaling and intracellular cytokine signaling are inhibited by IL-1ra and SOCS3 respectively. (2) ETV3 and SBNO2 are transcriptional repressors/corepressors (depicted in red) that repress transcription of NF-kBresponsive genes. (3) Nuclear IkB-like molecules (in red) are induced by coordinated TLR + IL-10 signaling and bind, along with NF-kBp50 to promoters that have been inactivated. (4) IL-10 increases the expression of genes involved in destabilizing AU-rich element (ARE) containing mRNAs (including pro-inflammatory cytokine mRNAs). (5) Hypothetically, STAT3 driven by IL-10 signaling might also recruit pre-synthesized nuclear repressors (or repressors activated/ induced by TLR signaling alone) to downregulate expression of some pro-inflammatory genes

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IL-10 can control levels of cytokine expression at the gene transcription and post-transcriptional levels. Earlier studies suggested post-transcriptional regulation for TNF and IL-1 in mouse macrophages, but that expression of genes including TNF, IL-12p35 and IL-12p40 is regulated at the transcriptional level in human monocytes (Aste-Amezaga et al. (1998) and references therein). Using a knockin mouse with the 3¢UTR of TNF replaced by the 3¢UTR of GAPDH, Murray assessed TNF transcription rates that were unaffected by 3¢UTR-mediated degradation. This study showed that, following LPS stimulation, IL-10 reduced generation of the knockin TNF transcript, and therefore that its negative regulation occurs at the transcriptional level (Murray 2005). In many studies the observation that cycloheximide blocked transcriptional repression by IL-10 led to the conclusion that de novo protein synthesis was required and transcriptional repression was due to induced secondary factors (but alternative explanations exist for the effect of cycloheximide – see discussion in next paragraph). Based on this observation, a recent screen for factors co-induced by LPS and IL-10 signaling identified ETV3, a transcriptional repressor, and SBNO2, a transcriptional co-repressor (El Kasmi et al. 2007). The induction of these factors was shown to be STAT3-dependent. Transfection-based studies also showed that they can suppress the activity of a kB- (but not an IRF7) responsive promoter (El Kasmi et  al. 2007) (Fig. 2). It remains to be determined whether these factors are required for the in vivo IL-10-mediated anti-inflammatory response. The mechanisms underlying gene expression that is induced or positively regulated by the combination of TLR and IL-10 signaling can be explained by simpler models of transcription factor cooperation. It was recently observed that the drop in IL-10-mediated suppression caused by cycloheximide treatment coincided with blockade in Jak1 and Tyk2 activity and interruption of IL-10 signaling (Rossato et al. 2007). This finding gives an alternative explanation for the sensitivity of IL-10 suppression to cycloheximide and suggests that additional newly translated positive or negative factors may not be required. The same group recently described studies on how LPS and IL-10 synergize to induce expression of IL-1ra (Tamassia et al. 2010) (Fig. 2). IL-1ra is a powerful natural inhibitor of IL-1 inflammatory reactions whose gene mutation leads to potentially fatal systemic autoinflammatory disease (Reddy et al. 2009; Aksentijevich et al. 2009). Using chromatin immunoprecipitation and analysis of primary transcripts in human monocytes, Tamassia et al. (2010) showed that LPS and IL-10 coordinate to enable RNA polymerase II binding to the promoter and to drive IL-1ra primary transcript production. NF-kB was activated by LPS signaling alone, and STAT3 binding to the promoter required only IL-10 (and not LPS), but NF-kB p65 and p50 binding to the IL-1ra promoter required both IL-10 and LPS. It was not possible to coimmunoprecipitate STAT3 and NF-kB. Thus, the authors proposed that activated STAT3 remodels the chromatin to render it accessible for NF-kB binding (Tamassia et al. 2010). In embryonic stem cells STAT3 binds to transcriptionally active and inactive promoters (Kidder et al. 2008). Therefore, if de novo protein synthesis is affecting only the upstream events of IL-10R signaling, this model of STAT3/NF-kB coordinated binding might equally apply not only to positively regulated genes but also to negatively regulated genes during coordinated signaling with TLR.

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The IL-1ra and other negative regulators that are induced by IL-10 and LPS, including SOCS3, Bcl-3 and IkBNS, complement the inhibition of proinflammatory cytokine production and give IL-10 a broad anti-inflammatory action. SOCS3 may, in addition to its role in suppression of IL-6 and gp130-mediated signaling, play a role in suppression of TLR responses (Yoshimura et al. 2007), through suppression of the TAK1/TRAF6 (Frobose et al. 2006) or through inhibition of type I interferon signaling (Zeng et al. 2008). Bcl-3 is a nuclear IkB-like molecule, whose expression is also induced following LPS and IL-10 treatment of macrophages and may contribute significantly to the anti-inflammatory response (Kuwata et al. 2003; Lang et al. 2002). Using transfection studies, Kuwata et al. showed that Bcl-3 binds to the TNF promoter, but not to the IL-6 promoter. NF-kB p50 subunits share this selective binding to the TNF promoter and also associate with Bcl-3 (Kuwata et al. 2003). Bcl-3 overexpression did not affect the activation of NF-kB p50/p65, but reduced its binding to NF-kB binding sites. Higher levels of Bcl-3 reduced activity of the TNF promoter, while the Bcl-3 knockout displayed increased TNF production, confirming its role as a negative regulator of TNF expression (Kuwata et al. 2003). IkBNS is another nuclear IkB-like molecule that is induced by TLR signaling and can negatively regulate late genes including IL-6 and IL-12p40 (reviewed in Yamamoto and Takeda (2008)). IkBNS was found to be responsive to IL-10 as it is selectively expressed in the lamina propria of wild type but not IL-10-deficient mice. It was also found to regulate cytokine production and bind the IL-6, but not the TNF promoter in association with NF-kB p50 (Hirotani et al. 2005). It is still unclear exactly how different members of the NF-kB family, and particularly p50/ p50 homodimers (Driessler et al. 2004), contribute to positive and negative regulation of target gene expression. However, the role of the nuclear IkB-like molecules in modulation of different NF-kB activities in response to IL-10 is likely to play an important role in its anti-inflammatory activity (Fig. 2). Different cytokines, such as IL-6, IL-1a, KC (or CXCL8) and IL-12 do not share the same regulatory mechanism as TNF, and this contributes an additional level of complexity to IL-10s anti-inflammatory effect. As discussed above, IL-6 is not regulated by Bcl-3 in the same way as TNF (Kuwata et al. 2003). Additionally, comparison of cycloheximide-dependent and -independent effects of IL-10 on primary transcripts of IL-6 suggests that IL-6 transcription is positively regulated by cycloheximidesensitive factors in the absence of IL-10 rather than negatively regulated in its presence (Murray 2005). In other words, de novo protein synthesis induces a protein required for upregulation of IL-6, but only when low levels of IL-10 are present. In the context of LPS signaling, IL-10 increases the post-transcriptional regulation of cytokines including TNF and IL-1a (Fig. 2). mRNA stability and/or translation is repressed through interactions with ARE in the 3¢UTR (Murray 2005; Kontoyiannis et al. 2001; Denys et al. 2002). Tristetraprolin (TTP) is a protein that destabilizes ARE-containing mRNAs. A recent publication (Schaljo et  al. 2009) suggests that IL-10 can influence the TTP/ARE mRNA degradation in two ways: firstly, IL-10 induces an increase in TTP expression. Secondly, IL-10 signaling increases the activity of TTP by reducing p38 MAPK phosphorylation because of increased levels of the phosphatase DUSP1 (also see Hammer et  al. (2005)).

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Thus, post-transcriptional regulation provides a mechanism for IL-10 to rapidly downregulate active cytokine signaling. Therefore at the cellular and molecular levels, IL-10 drives a wide range of transcriptional and post-transcription changes that contribute to its powerful antiinflammatory effects. The mechanism is known to require STAT3, but a detailed understanding of its inhibitory effects on NF-kB-responsive promoters is poorly defined. The recent emergence of the cyclic AMP response element binding protein, ATF3, as a negative regulator of TLR responses (reviewed in Thompson et  al. (2009)) introduces another potential player in IL-10s anti-inflammatory effects (Stearns et al. 2004). Comprehensive chromatin immunoprecipitation studies on NF-kB responsive promoters with multiple factors including STAT3, ETV3, SBNO2, nuclear IkB-like molecules and ATF3 may therefore be required to elucidate the precise mechanisms of IL-10s transcriptional effects.

Roles of STAT3 in Health and Disease As previously discussed, the STAT3 transcription factor is essential for anti-inflammatory signaling by IL-10. Its sustained activation during proinflammatory activation of NF-kB either directly or in concert with secondary factors leads to downregulation of pro-inflammatory gene transcription and translation and upregulation of antiinflammatory genes. However, in addition to IL-10, STAT3 is critical for signaling of many different cytokine receptors, including the gp130-linked receptors for IL-6, IL-11, IL-27, and LIF, as well as other receptors such as those for IL-21, IL-22, leptin, G-CSF and IL-23 (Yu et al. 2009). Furthermore, STAT3 activation can be induced in transformed cells by receptor tyrosine kinases and non-receptor tyrosine kinases such as oncogenic Src. STAT3 is unique among STATs as its deletion results in early embryonic lethality (Takeda et al. 1997). It is also a necessary component of LIF signaling for maintaining the pluripotency of mouse, but not human embryonic stem cells (Raz et  al. 1999; Sumi et  al. 2004). Additional roles for unphosphorylated STAT3 and mitochondrial STAT3 have already been discussed. The recent identification of dominant-negative STAT3 mutations in patients with autosomal dominant hyper IgE syndrome (HIES, or Job’s syndrome) (Minegishi et al. 2007) has provided an important insight into the physiological relevance of different STAT3-dependent pathways in man (reviewed in Heimall et al. (2010)). The major symptoms of these patients are eosinophilia, high serum IgE levels and susceptibility to skin and pulmonary infections. Patients can also have a variety of connective tissue and skeletal abnormalities. A defect in differentiation of Th17 cells may explain the eosinophilia and increased IgE levels and susceptibility to Candida albicans infection. Defective IL-22 function, a cytokine that is produced by Th17 cells but also by other Th subsets and requires STAT3 for signaling, may play a role in impaired resistance to skin and pulmonary infection as, together with IL-17, it induces beta-defensin expression (Wolk et  al. 2004; Liang et  al. 2006). Increased IL-12 production and Th1 skewing in response to LPS stimulation of

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macrophages from these patients is observed possibly due to poor response to IL-10 (Holland et al. 2007). The identification of dominant-negative STAT3 as the etiological agent in Job’s syndrome not only provides a target for treatment of these patients, but also demonstrates the probable side effects that inhibition of STAT3 may cause.

Roles of STAT3 in Cancer STAT3 is a protooncogene that is hyperactivated in many blood and solid tumors including melanoma, head and neck, breast, prostate and lung cancers (reviewed in Buettner et al. (2002)). Its activation leads to improved cell survival, increased cell motility and decreased expression of pro-apoptotic genes. Classical activation of the Jak/STAT pathway leads to STAT3 activation in response to cytokines. However, in transformed cells, STAT3 activation can also be induced by receptor tyrosine kinases such as the EGF receptor and non-receptor tyrosine kinases such as Src, and this activation is required for Src-mediated cell transformation (Bromberg et al. 1998; Yu et al. 1995). Furthermore, stimulation with TLR-ligands LPS and CpG have also been reported to activate STAT3 (Kortylewski et al. 2009a; Samavati et al. 2009). While direct activation of STAT3 by non-cytokine/Jak/STAT pathways may occur under certain experimental conditions, cellular transformation can also lead to autocrine production and signaling through STAT3-inducing cytokines, particularly IL-6 (see Yu and Jove (2004)). Paracrine signaling through STAT3 may be more relevant in vivo during inflammation associated carcinogenesis. This has been demonstrated during colitis and colitis-associated cancer where IL-6, IL-11 and IL-22 from hematopoietic cells and STAT3 in the intestinal epithelial cell are required for optimal proliferation and repair of the intestinal mucosa and elevated carcinogenesis (Grivennikov et al. 2009; Bollrath et al. 2009; Pickert et al. 2009). Recently, Iliopoulos et  al. (2009) identified a role for IL-6 in an epigenetic switch from the immortalized to the transformed cell state. They demonstrated that, by transient activation of the v-Src oncogene, an immortalized cell line (i.e., contactinhibited and unable to form tumors in vivo) could be converted into a transformed state (growing as mammospheres with cancer stem cells and forming tumors in  vivo). Src activation triggered NF-kB signaling and transcription of Lin28b (Iliopoulos et al. 2009). Lin28b is an RNA-binding protein that binds the precursors of Let-7 microRNAs and prevents their processing (Viswanathan and Daley 2010). Let-7 microRNAs were found to block IL-6 mRNA expression and downstream events of STAT3 activation and VEGF production (Iliopoulos et al. 2009). Therefore NF-kB activation due to Src, combined with lower Let-7 microRNA levels, let to robust IL-6 production. This IL-6 was then required for high levels of IL-1 transcription (possibly leading to sustained NF-kB activation) and STAT3 activation, leading to the transformed phenotype. Lin28b and IL-6 were required for growth of transformed MCF10A cells (mammary epithelial line) in nude mice. Similarly, the blockade of IL-6, or Lin28b, or overexpression of Let-7 reverted the transformed

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phenotype of lung, hepatocellular carcinoma, breast, prostate and colon cancer cell lines. Finally, human breast, prostate, and HCC cancer tissue was found to have low Let-7 expression and high IL-6 expression while the inverse was found in normal tissue. Together, their data describe an epigenetic switch involving triggering by NF-kB, maintenance by Lin28b, low Let-7 and high IL-6, and transformation of cells due to activation of STAT3 (Iliopoulos et al. 2009). Whether, in vivo, IL-6 is derived from tumor or inflammatory cells, the existence of such an epigenetic change requiring several factors for its maintenance provides a clear target for therapeutic intervention.

STAT3 and IL-10 in Cancer-Promoting Inflammation Activation of STAT3 leads to inflammatory processes that promote wound repair, but in the context of cancer, can contribute to tumor growth and invasion. Two key events are stimulation of angiogenesis through production of VEGF, whose promoter has binding sites for STAT3 and HIF-a (Niu et al. 2002; Gray et al. 2005), and suppression of antigen presenting cell functions. During chronic inflammatory conditions, a major role for IL-10 in activation of STAT3 is argued by observations that manipulation of either STAT3 or IL-10 leads to highly similar effects. For example, cell-specific deletion of STAT3 in macrophages and neutrophils leads to increased sensitivity to endotoxic shock and chronic colitis in aging mice – a phenotype that matches that of IL-10-deficient mice (Takeda et al. 1999). As discussed below, a prominent role of the IL-10-STAT3 pathway during chronic cancer-associated inflammation may explain why blockade of either molecule is effective in cancer immunotherapy.

IL-10 Inhibition Facilitates Immune Responses to Tumors Antigen presenting cells from tumors respond poorly to stimulation with TLR ligands or other stimuli compared with antigen presenting cells from other locations (O’Garra et al. 2008; Sica et al. 2000; Vicari et al. 2002; Perrot et al. 2007; Bell et al. 1999). In addition to DC, macrophages and myeloid-derived suppressor cells (MDSC) infiltrate the tumor microenvironment and the distinction between these populations based on function or phenotype is not trivial. Tumor infiltrating DC (TIDC) frequently have an immature phenotype with low expression of costimulatory molecules (reviewed in Gottfried et  al. (2008), Rabinovich et  al. (2007), and Zou (2005)). Similarly, tumor associated macrophages have an alternatively activated or “M2” phenotype (including expression of arginase, high IL-10 expression and low IL-12 production) that is linked to an altered NF-kB response (see Chap. “Cancer and Inflammation” by Mantovani) (Anderson and Mosser 2002; Biswas et al. 2006; Hagemann et al. 2009, 2008). A growing body of evidence

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from human and mouse studies shows that IL-10 plays a key role in producing a hyporesponsive phenotype by tumor associated macrophages and DC (Sica et al. 2000; Vicari et al. 2002; Perrot et al. 2007). Based on in vitro observations that tumor associated antigen presenting cells are defective in IL-12 production due to high levels of IL-10 production (Sica et  al. 2000), Vicari et  al. (2002) employed coordinated TLR9 stimulation using CpG oligodeoxyribonucleotide (ODN) and IL-10 signaling blockade with an anti-IL-10R antibody to develop an antitumor immune therapy regime. They observed that TIDC express low levels of MHC class II and costimulatory molecules CD86 and CD40 compared with lymph node DC and do not upregulate these molecules or produce IL-12p70 upon costimulation with LPS, IFN-g and anti-CD40 agonist antibody (Vicari et  al. 2002). Addition of antagonistic anti-IL-10R antibody to cultures revived TIDC cytokine responses and antigen presentation capacity in mixed lymphocyte reaction and cross-presentation assays. The combination of antiIL10R and intra-tumor CpG in vivo, given as a weekly therapy for 3 weeks, led to rejection of established tumor in ~80% of mice, by a mechanism that required NK cells, CD4+ and CD8+ T cells for full effect (Vicari et al. 2002). A follow up study showed that tumor rejection could be attained with CpG alone in IL-10-deficient mice and that Treg have overlapping but separable roles during the immunosuppression (Dercamp et al. 2005). In this way, the removal of IL-10s anti-inflammatory activity permitted potent APC and cytokine responses, along with tumor rejection and immunity, in response to the CpG TLR ligand. The mechanisms behind CpG-mediated rejection in the absence of IL-10 were dissected in detail by Guiducci et al. (2005) using a modified protocol designed to draw antigen presenting cells into tumor tissue. They had previously shown that local administration of an adenoviral vector expressing the human chemokine CCL16 (AdCCL16; CCL16 is a ligand for CCR1, CCR2, CCR5 and CCR8) led to infiltration of T cells, macrophages and DC, retarded growth of TSA mammary tumor nodules and an adaptive immune response that prevented metastatic spread of tumor (Guiducci et al. 2004). A single treatment consisting of intratumoral injection of AdCCL16 at 36 h prior to systemic anti-IL10R antibody and local CpG administration led to rejection of established TSA mammary, MCA38 colon and 4T1 mammary tumors in 60–90% of mice (Guiducci et al. 2005). This effect involved local production of TNF, IL-12 and nitric oxide (NO) by tumor macrophages at ~6 h post-CpG injection, followed by TNF-dependent hemorrhagic necrosis at the tumor site. The complete treatment (AdCCL16 + anti-IL-10R + CpG), but not treatment lacking antiIL10R antibody, led to upregulation of costimulatory molecules and MHC class II on tumor-infiltrating DC along with production of IL-12 and TNF. In vitro, these DC were capable of driving T cells to proliferate and produce high levels of the Th1 cytokine IFN-g in a mixed leukocyte reaction assay. In vivo, DC migration was triggered, followed by activation of the adaptive immune system and tumor rejection that required CCR7 (a chemokine receptor involved in migration to the lymphatics), costimulation by CD40, and both chains of the cytokine IL-12 (Guiducci et  al. 2005). In this way, blockade of IL-10 signaling permits a powerful local hemorrhagic necrosis, which the authors described as comparable to a localized Shwartzman

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reaction, with high levels of TNF and NO and an IL-12-dependent Th1-type immune response. The very rapid necrotic response is fully dependent on the activation of innate resistance mechanisms and even if in a few hours it induces an almost complete destruction of the tumor mass, it is not by itself able to cure the animal: in the absence of an antigen-specific T cell response the tumors eventually progress and kill most of the treated animals. The type of inflammation that characterizes the tumor microenvironment supports tumor growth and dissemination, by favoring e.g., angiogenesis and through the effect of secreted metalloproteases that promote tumor invasion. It is also immunosuppressive, reducing the ability of the antigen-presenting cells in the tumor microenvironment to activate T cells and to migrate in the draining lymph nodes. The treatment of the mice with CpG and anti-IL-10R not only induces an acute type I inflammatory response that is responsible for the rapid necrosis but it also reverts the phenotypes of tumor-resident APC, allowing them to efficiently present tumor antigens to T cells and also to migrate rapidly to the draining lymph node for optimal T cell activation. Thus, effective eradication of the tumors requires the generation of both Th1 and cytotoxic T cell responses against the tumor antigen. This was clearly demonstrated by the fact that anti-IL-10R and CpG treatment induce tumor necrosis but do not cure in mice deficient for CD4 and/or CD8 T cells or in which the T cells and the APC have different MHC antigens than the tumor cells (Guiducci et al. 2005). In addition to the studies on inoculated subcutaneous tumors described above, therapy by TLR ligation and inhibition of IL-10 signaling has been successfully employed in several transgenic carcinogenesis models. Mice expressing SV40 T antigen (Tag) under control of the tyrosinase related protein 1 (TRP-1) promoter develop tumors from the retinal pigmented epithelium of the eye that become macroscopically visible at around 6 weeks of age (Penna et  al. 1998). Treatment of TRP-1/Tag mice with anti-IL-10 antibody in conjunction with CpG ODN i.p. or with the TLR-7/8 ligand R848 delayed eye tumor development for the duration of the treatment in a large proportion of mice (Alain Vicari 2004, personal communication). Similarly, topical treatment of breast tumors in MMTVneu mice with the TLR-7 ligand, imiquimod, limited development of tumors during treatment but mice succumbed to tumor after its cessation. Additional treatment with anti-IL-10 antibodies led to prolonged survival of imiquimod-treated mice (Lu et al. 2010). The success of combination (TLR-L + IL-10-block) therapies in transgenic models probably requires effective adaptive immune responses (against immunogenic Tag or neu, respectively (Lu et al. 2010)). Furthermore, it demonstrates a broad applicability of the combination therapy and strengthens the prospects of its clinical use.

Targeting STAT3 Facilitates Anti-Tumor Immune Responses In 1999, the Yu laboratory described an observation that inhibition of STAT3 in tumors leads to more extensive cell death than could be explained by its immediate effects, suggesting that bystander effect was occurring (Kortylewski and Yu 2007;

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Niu et al. 1999). Using electroinjection of plasmid DNA that achieved no more than ~15% transfection efficiency into B16 melanomas, they observed that half the tumor cells could be driven to apoptosis when a dominant-negative STAT3b isoform was encoded on the plasmid, and that this effect led to tumor regression in ~50% of mice (Niu et al. 1999). This initial observation prompted a series of continuation studies by Hua Yu, in collaboration with Richard Jove and Drew Pardoll, into STAT3s anti-inflammatory and immunological roles in the tumor environment and its accessibility to therapeutic manipulation. Any viable mechanistic explanation of the bystander effect during inhibition of STAT3 should include changes in extracellular levels of pro-apoptotic factors or tumor-survival factors. Soluble factors, including TNF-related apoptosis-inducing ligand (TRAIL) were found to be expressed by STAT3b-transfected B16 cells and contributed to tumor cell apoptosis (Niu et al. 2001). STAT3 was linked to tumor angiogenesis through direct binding and activation of the VEGF promoter and through upregulation of AKT1 and HIF-1 in tumor cells (Niu et al. 2002; Xu et al. 2005). STAT3 was also shown to contribute to tumor cell survival through upregulation of factors including bcl-xl, bcl-2 and survivin and a role in tumor invasion was described through STAT3-mediated upregulation of matrix metalloproteases such as MMP-2 (reviewed in Aggarwal et al. (2009)). It is therefore apparent that STAT3-mediated regulation of extracellular factors could contribute to oncogenesis and complement STAT3s role in oncogene-mediated transformation (Bromberg et  al. 1998; Yu et  al. 1995; Iliopoulos et  al. 2009). However, other functions of STAT3 in the tumor microenvironment, specifically its effects on inflammation, are required to fully explain the bystander effect of STAT3 inhibition. The demonstration that inhibition of STAT3 can lead to proinflammatory cytokine production and can reverse the suppressive effect of tumor culture supernatants on DC maturation provided a mechanistic framework for its anti-inflammatory role in tumors (Wang et al. 2004). Inhibition of STAT3 in a variety of tumor lines by ectopic expression of dominant-negative STAT3b or anti-sense RNA led to increases in expression of proinflammatory cytokines including TNF, IL-6, IFN-b and chemokines including CCL5 (RANTES) and CXCL10 (IP10). Similarly, STAT3 activation by Src-transformation or expression of a constitutively active STAT3 (STAT3C) suppressed expression of proinflammatory cytokines in response to LPS/IFN-g treatment (Wang et al. 2004). By inhibiting STAT3, tumor cells that would normally suppress DC and macrophage activation were capable of promoting DC maturation and macrophage NO production in vitro, and immunization of mice with STAT3-inhibited tumor cells led to enhanced antigen-specific responses. Furthermore, tumor-derived factors also induced STAT3 phosphorylation in DC and their suppression required STAT3. The authors finally demonstrated that IL-10 (and possibly VEGF) could mimic the suppressive effect of tumor cell supernatant in a STAT3-dependent manner, suggesting IL-10 to be a key mediator of DC suppression in the tumor microenvironment (Wang et al. 2004). The relevance of hematopoietic cell STAT3 was demonstrated using inducible deletion of STAT3 in all bone marrow-derived cells using Mx1-Cre;STAT3fl/fl mice (Kortylewski et  al. 2005). STAT3-deficient DC from these mice produced

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higher levels of IL-12 in response to LPS stimulation, and induced higher levels of antigen-specific T cell proliferation in culture. Furthermore, B16 tumor-infiltrating STAT3-deficient DC expressed higher levels of costimulatory CD86 and MHC class II compared with wild-type. Interestingly, STAT3 phosphorylation was triggered in neutrophils and NK cells by IL-10, and both cell types were more cytotoxic following hematopoietic STAT3-ablation. The higher cytotoxic activity of neutrophils may have been related to higher levels of FasL expression. In the tumor-infiltrating populations of STAT3-ablated mice, higher frequencies of IFN-g-producing tumor antigen-specific T cells and lower proportions of CD4+CD25+FoxP3+ Tregs were observed (Kortylewski et  al. 2005). A profound effect of hematopoietic STAT3-ablation was observed in tumor immunity. Using MB49 (bladder carcinoma) and B16 tumors, hematopoietic STAT3-deficient mice were resistant to tumor growth and MB49 tumors were rejected by a mechanism requiring T cells (as shown by CD4 and CD8 depletion). Deletion of hematopoietic STAT3 following establishment of tumor also led to tumor regression (MB49) or slowed growth (B16). A similar T and NK cell-dependent anti-tumor effect could be observed with wild-type mice by administration of the STAT3 inhibitor, CPA7 (Kortylewski et al. 2005). Therefore hematopoietic STAT3 is a critical suppressor of anti-tumor immune responses and its genetic or pharmacological inhibition is sufficient to switch predisposition from tumor-promoting to anti-tumor inflammation. Deletion of STAT3 in hematopoietic cells also rendered tumors highly sensitive to peritumoral injection of CpG (Kortylewski et al. 2009a). Local CpG treatment of STAT3-deficient mice with 10mm diameter B16 tumors led to rapid tumor regression associated with neutrophil infiltration, production of nitric oxide and loss of vascular function as determined by the tracking of a systemic dye using intravital microcopy. The combined STAT3-deficiency and CpG treatment led to augmented costimulatory responses by tumor-draining lymph node DCs and increased frequencies of IFN-g producing tumor antigen-specific T cells. Furthermore, a single treatment of CpG led to tumor rejection in STAT3-deficient, but not STAT3sufficient mice, and long term protection required T cells (Kortylewski et  al. 2009a). In wild type mice, the combination treatment could also be achieved using CPA7 to inhibit STAT3 function combined with peritumoral CpG injection (Kortylewski et al. 2009a). The efficacy of these combination treatments led Kortylewski et al. (2009b) to design a single molecule drug comprised of a CpG stimulatory ODN conjugated to siRNA for STAT3 (CpG-Stat3siRNA). CpG-Stat3siRNA was taken up by DC, B cells and macrophages in  vitro and following peri-tumoral injection in  vivo. Effective uptake and processing by Dicer required the CpG portion of the conjugate and was not achieved with a control GpC ODN conjugate, suggesting that TLR9 recognition and activation were important for processing. B16 tumor growth was blocked by a single peritumoral treatment by a mechanism that required NK cells and partially required T cells. Local injection of CpG-Stat3siRNA led to growth inhibition of multiple tumor types. Furthermore, systemic administration 2 days after i.v. inoculation of B16 tumor cells led to a much lower level of visible metastases. Injection of CpG-Stat3siRNA induced innate and adaptive immune

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responses including the expression of pro-inflammatory cytokines, tumor-infiltration of neutrophils and tumor cell apoptosis, and immunity against tumor-associated antigens (Kortylewski et al. 2009b). The mechanism and effect of combining CpG treatment with STAT3-deficiency in the Kortylewski studies (Kortylewski et al. 2009a, b) are highly reminiscent of the studies by Vicari et  al. using CpG + anti-IL10R treatment to treat tumors (as discussed earlier (Vicari et  al. 2002; Guiducci et  al. 2005)) (Fig.  3). Despite the similarities, Kortylewski et al. (2009a) argue that IL-10 is not required for STAT3 phosphorylation in DCs following CpG treatment and that STAT3 activation can be directly induced by TLR9 triggering. However, this argument is based on studies using splenic DCs and may not hold for tumor-infiltrating populations because lymphoid organ DCs are more readily activated by TLR ligands than tumorinfiltrating APC (Sica et al. 2000; Vicari et al. 2002; Perrot et al. 2007). It is therefore possible that IL-10 plays a predominant role in activating STAT3 within tumor APC in the context of TLR signaling, and that this leads to the potent effects of blockade in IL-10 signaling. Indeed, an incomplete blockade of STAT3 activation by anti-IL10R antibody treatment may account for different therapeutic efficiencies between the Vicari and Kortylewski protocols: three treatments with CpG and anti-IL10R were used for tumor rejection (Vicari et al. 2002) compared with one CpG injection in the context of STAT3-ablation (Kortylewski et al. 2009a) and multiple CpG injections in the context of STAT3-inhibition using CPA7 (Kortylewski et  al. 2009a). Alternatively, the use of poly(I:C) to trigger STAT3 ablation, though performed IL 10

IL-10 IL-10R

STAT3pY

TLR

IL-10R

TLR

STAT3pY

Tumor DC or macrophage Poor inflammatory response

Potent inflammatory response DC migration Th1 Immune Response Protection from tumor regrowth

Fig.  3  Inhibition or blockade of IL-10, IL-10R or STAT3 prevents anti-inflammatory action of IL-10 in the tumor microenvironment. Without IL-10/STAT3, TLR triggering of tumor dendritic cells (DC) and macrophages leads to robust pro-inflammatory cytokine production, DC migration and protective antigen-specific immune responses

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four days prior to B16 tumor inoculation (Kortylewski et  al. 2009a), may have resulted in sufficient type I IFN production to potentiate immunological priming against tumor-associated antigens (Trumpfheller et  al. 2008). In either case, the resultant Th1 type immunity combined with local tumor tissue destruction makes the inhibition of IL-10 or STAT3 important enhancers of TLR-based immunotherapy and provides a foundation for effective clinical protocols.

Conclusions The tumor microenvironment if often characterized by infiltration of inflammatory cells and lymphocytes suggesting that the tumor is interacting with the organism defense mechanisms and not simply escaping them. It has been clearly demonstrated in a subset of cancer patients that an anti-tumor response may be activated and may slow tumor progression. However, in general the tumor microenvironment is characterized by a type of inflammation and possibly immune response that favor tumor growth and diffusion while impeding an effective immune response. This microenvironment is characterized by alternative activation of tumor-infiltrating hematopoietic cells and by production of soluble factors, including chemokines, cytokines, metalloproteases, growth factors, and pro-angiogenic factors that contribute to maintain the pro-tumor and immunosuppressive state. Certain tumorinfiltrating cell types, e.g., Treg and MDSC, play a major role determining the characteristics of the tumor microenvironment. Although many soluble factors and transcription factors also play a major role, the cytokine IL-10 and the transcription factor STAT3 seem to have key functions: blocking either one of them has similar and profound effects by switching the inflammatory response in the tumor to a type I inflammation that is unfavorable for tumor progression and by allowing the generation of an effective anti-tumor Th1/CTL adaptive immune response. Thus, these key factors in the immunological switch represent promising targets for immunotherapy of cancer in order to induce an effective curative anti-tumor immune response.

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The Angiogenic Switch: Role of Immune Cells Douglas M. Noonan, Agostina Ventura, Antonino Bruno, Arianna Pagani, and Adriana Albini

Keywords  Angiogenesis • Endothelial cells • Innate immunity • Specific immunity

Introduction Angiogenesis is a frequent hallmark of inflammation; in acute inflammation angiogenesis occurs to revascularize damaged tissues, with subsequent vascular pruning and maturation, which results in restoration of an adequate blood supply to the tissue involved. Angiogenesis is also a characteristic of chronic inflammation, and may be one of the links between inflammation and cancer (Colotta et al. 2009; Kobayashi and Lin 2009). In addition to excessively proliferating tumor cells, tumors are tissues that contain host components; these “nontransformed” components include stromal cells, an extensive, although poorly organized, vascular network and a characteristic inflammatory infiltrate. In the absence of host support, a tumor cannot grow to clinically relevant dimensions. Experimental studies have shown that tumors without a vasculature are limited to only a few cubic millimeter in dimension, as formation of a new blood supply is required to overcome physical limitations for diffusion of nutrients and oxygen, concepts born out in clinical application (Ferrara and Kerbel 2005; Folkman 2006; Hanahan and Folkman 1996; Hanahan and Weinberg 2000; Kerbel and Folkman 2002). A critical step in tumor progression is acquisition of the capacity to form a new vasculature, a process referred to as the angiogenic switch (Hanahan and Folkman 1996; Hanahan and Weinberg 2000). In the absence of an angiogenic switch, nascent tumors remain as small, clinically indolent hyperplastic foci, a condition often noted in autopsies of several tissues (Bergers and Benjamin 2003). The importance of the angiogenic

D.M. Noonan  (*) Department of Experimental Medicine, Università degli Studi dell’Insubria, Varese, Italy e-mail: [email protected]

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switch, and particularly the concept that this could be prevented clinically, has led to extensive examination of what triggers it. One obvious source of angiogenic factors is the tumor cells themselves. Tumor cells are known to produce a wide range of pro-angiogenic substances, the most crucial is vascular endothelial growth factor: VEGF (Ferrara and Kerbel 2005). VEGF is a prototypic angiogenic factor involved in physiological angiogenesis, as hypoxia induces VEGF production largely through the hypoxia sensing factor HIF1a (Pouyssegur et al. 2006). While tumors may be hypoxic, this does not seem to be the only factor leading to VEGF over-expression, as oncogene activation and metabolic mechanisms appear to also induce tumor VEGF production even in normoxia (Kroemer and Pouyssegur 2008). Thus one mechanism for “flipping” on the angiogenic switch is oncogenesis itself. However, in addition to the role of tumor cells themselves, it is increasingly recognized that host cells also represent key sources of angiogenic agents and are likely prominent modulators of the angiogenic switch (Albini et  al. 2005; Brigati et  al. 2002; Coussens and Werb 2002; De Visser et al. 2006; Noonan et al. 2008; Orimo and Weinberg 2006). Particularly conspicuous is the role of tumor inflammation and immune cells in driving and orchestrating tumor angiogenesis (Albini et al. 2005; Brigati et al. 2002; Coussens and Werb 2002; De Visser et al. 2006; Mantovani et al. 2002; Noonan et al. 2008; Sica and Bronte 2007). Immune cells are also able to produce VEGF, the role of this is controversial, as ablation of VEGF specifically in all myeloid cells a murine model actually resulted in accelerated tumor growth (Stockmann et  al. 2008), apparently due to a more organized and functional vascular network. VEGF from either myeloid or tumor compartments alone could support tumor angiogenesis and growth, loss of both led to a drastic reduction in angiogenesis, while expression in both compartments produced the typical poorly functional tumor vasculature (Stockmann et al. 2008). The observation that most tumors contain numerous inflammatory leukocytes led to the concept of a functional link between chronic inflammation and cancer (Sica and Bronte 2007). Tumor-associated inflammation is largely sustained by a constant influx of myelomonocytic cells that support the tissue remodeling and angiogenesis needed for tumor growth and progression (Sica and Bronte 2007). Clinical studies have provided evidence for a clear association between an increased number/density of tumor-associated macrophages (TAM) and poor prognosis in a variety of murine and human malignancies (Gordon 2003; Martinez et al. 2006; Sica and Bronte 2007). Thus many tumors require a constant influx of inflammatory cells to support the angiogenesis and stromal remodeling needed for their growth (Sica and Bronte 2007). This is mediated by tumorderived factors, which cause enhanced myelopoiesis and the accumulation and functional differentiation of myelomonocytic cells at the tumor site. In addition to innate immune cells, cells of the specific immune system also appear to influence the angiogenic switch (Brigati et al. 2002; De Visser et al. 2006; Noonan et al. 2008). Here we review the roles these diverse cell types may have flipping the angiogenic switch on, and potential approaches towards flipping off the angiogenic switch.

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Innate Immunity Macrophages and the Macrophage Polarization Paradigm Macrophage heterogeneity reflects the plasticity of these cells in response to microenvironmental signals, such as cytokines and microbial products, which in turn produces diverse functional programs and influences polarization of the specific immune system. Macrophages have a key role in promoting the orientation of the adaptive immune response typically towards either a Th1 or Th2 response, as well as by expressing specialized and polarized effector functions themselves (Goerdt and Orfanos 1999; Gordon 2003; Mantovani et al. 2002; Sica and Bronte 2007). Numerous data indicate that macrophages can be phenotypically polarized by the microenvironment to mount specific functional programs: either the classical (or M1) activation to potentially potent effector cells able, for example, to kill tumor cells, or the alternative M2 activation that is closely related to the tumor-associated macrophage (TAM) profile. M1 macrophages are generally characterized by an IL-12high, IL-23high, IL-10low phenotype, with obvious consequences in driving a Th1 response. These M1 cells also can produce copious amounts of reactive oxygen and nitrogen intermediates (Goerdt and Orfanos 1999; Gordon 2003; Mantovani et al. 2002; Sica and Bronte 2007). The M1 macrophages, a terminology mirroring the Th1 nomenclature, are part of the afferent and efferent limbs of Th1 responses; and help mediate resistance against intracellular parasites and tumors. On the other extreme we have alternatively activated, or “M2,” macrophages induced by cytokines such as IL-4 and IL-13. The M2-polarized functions are generally oriented to tissue remodeling and repair, immunosuppression and angiogenesis (Mantovani et  al. 2008). M2 is a generic terminology for various forms of macrophage activation other than the classic M1 activation profile, and includes cells exposed IL-4 or IL-13, immune complexes, IL-10, and glucocorticoid hormones. M2 macrophages generally share an IL-12low, IL-23low, IL-10high phenotype. Substantial data show that an M2 polarized inflammation is a key feature of the tumor microenvironment (Mantovani et al. 2002; Sica and Bronte 2007), influencing angiogenesis, invasion and metastasis, as well as subversion of adaptive immunity. The diverse polarized macrophages differ in terms of receptor expression, cytokine production profiles, chemokine repertoires and effector function. Induction of an M2 phenotype appears to be due to STAT3 activation and/or inhibition of STAT1 phosphorylation (Hu et  al. 2003; Ito et  al. 1999; Mantovani et  al. 2002; Riley et al. 1999), and down-regulation of p65 containing NF-kB along with generation of p50 NF-kB homodimers (Porta et al. 2009). NF-kB is a key axis in tumor angiogenesis, in endothelial cells induction of the angiogenic program involves NF-kB (Karin 2006), and numerous angiogenesis inhibitors block NF-kB activation (see Albini et al. (2007) for review). In addition, the cytotoxic chemotherapy and radiotherapy widely used in treatment of cancer patients also induces NF-kB both in normal and cancer cells (Nakanishi and Toi 2005), with the result of inducing inflammation, angiogenesis and reconstruction of the tumor. Simultaneous use of NSAIDs or cortisone could enhance the efficacy of these treatments.

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Tumor Associated Macrophages (TAMs) Tumor inflammation was historically considered to be part of an abortive rejection process, now the role of tumor associated macrophages (TAMs), has undergone extensive re-evaluation (Fig.  1), TAMs are now considered to express properties that facilitate tumor growth and progression by stimulating tumor-cell proliferation, angiogenesis and favoring invasion and metastasis (Balkwill and Mantovani 2001; Lewis and Murdoch 2005; Mantovani et  al. 2002; Pollard 2004). TAMs derived from circulating monocytic precursors are directed into the tumor by chemokines, largely through hypoxia-dependent mechanisms involving VEGF and the CXCL12/ CXCR4 axis (Mantovani et  al. 2002; Saccani et  al. 2006; Schioppa et  al. 2003).

Tumor “Macro” environment VEGF CXCL12 GM-CSF… B cells

ICs “eDC” Tc, Th1

Treg, Th2 cells

CECs, CEPs…

TEM MDSC

M0 CFUProMonocyte Macrophage monocytic monocyte

ProCFUgranulocyte myelocyte

Dendritic cells

Eosinophilic cells

Neutrophil

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Hematopoietic pluripotent stem cell

mDC

Monocytic MDSC

T cells

Neutrophilic MDSC

Mast cells

iDC M2 TAM

M1 Macrophage

N2 Neutrophil

N1 Neutrophil

“dNK” cells

Tumor Microenvironment Mast cells NK cells

TGFβ, Chemotherapy Radiotherapy…

CD16+ NK cells

Antiangiogenic immunotherapy

Fig. 1  Tumors have a major influence on the immune system, and as a result the immune system becomes a driving force in tumor angiogenesis. Systemic “macroenvironment” effects include mobilization of endothelial precursors (circulating endothelial precursors (CEPs) and endothelial cells (CECs)) that participate in tumor vessel production by vasculogenesis; generation of myeloid derived suppressor cells (MDSCs), T cell skewing and suppressive Treg generation, B cell production of antibodies that form immune complexes (ICs). Local effects include polarization towards M2 Tumor infiltrating macrophages (TAMs), Tie2 expressing macrophages (TEMs), various forms of MDSCs, N2 pro-angiogenic neutrophils, possibly NK cells with potential activities similar to decidual NK cells (dNK), mast cells and immature dendritic cells (iDC) can contribute to angiogenesis as can dendritic transdifferentiation to endothelial cells (eDC). Chemotherapy and radiotherapy may also have similar local effects by activating NF-kB. Therapeutic approaches include angioprevention, inhibiting many activation processes before they start, or forcing an immune “reset” towards a Th1/M1/N1 polarization, eventually restoring a cytotoxic NK and T cell state

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Analyses of TAMs indicate that these express an M2-associated phenotype, with a similar transcriptome profile oriented toward tissue remodeling and repair, immuno-suppression and angiogenesis (Biswas et al. 2006; Mantovani et al. 2002; Murdoch et al. 2008). TAMs recruited into tumors accumulate in hypoxic areas; they are attracted and/or immobilized into avascular (Leek et al. 1996) and necrotic (Leek et al. 1999) areas of hypoxia, apparently by both specific factors produced by hypoxic tumor cells and by a direct immobilizing effect of hypoxia on macrophages (O’Sullivan and Lewis 1994). Higher numbers of TAMs extravasate from the bloodstream into tumors containing high levels of necrosis as compared to those with little or no necrosis (Harmey et  al. 1998). Recent data show that TAMs are stimulated by hypoxia in such sites to cooperate with tumor cells to promote revascularization (Murdoch et al. 2008; Schioppa et al. 2003). The level of hypoxia present in ischemic areas of tumors stimulates the release of VEGF from human macrophages in  vitro (Lewis et  al. 1999). Moreover, TAMs upregulate VEGF in avascular and necrotic tumor areas (Lewis et al. 1999). TAMs respond to hypoxia through upregulation of transcriptional factors including HIFs 1 and 2 that activate a series of glycolytic, angiogenic, and proinvasive genes. TAMs are known to be an important source of proangiogenic factors such as VEGF (Leek et  al. 2000), TNFa (Pusztai et  al. 1994), IL-8 (Fujimoto et al. 2000), and CXCL12 (Schioppa et al. 2003). Angiogenesis is also facilitated by TAM-derived proteases released in tumors. These include the matrix metalloproteinases (MMPs 1, 2, 3, 9, and 12) (Deryugina and Quigley 2010; Egeblad and Werb 2002), urokinase plasminogen activator and its receptor (Pyke et al. 1993), and plasmin. These enzymes release various proangiogenic molecules bound to heparan sulfate on proteoglycans, as well as fragments of fibrin and collagen. Their combined action results in the degradation of the basement membrane and other ECM components, destabilization of the local vasculature and promotion of the migration and proliferation of endothelial cells. This proteolytic activity also contributes to the migration and extravasation of tumor cells during the metastatic process (Deryugina and Quigley 2010; Egeblad and Werb 2002). A series of studies have found that hypoxia itself promotes an invasive program that favors metastatic dissemination, and that VEGF based angiogenesis inhibition can result in a greater incidence in metastasis (reviewed in Schmidt (2009)).

Tie2 Expressing Macrophages A TAM subset that appears to have a particularly intriguing role in angiogenesis are the Tie2 expressing macrophages, or TEMs (De Palma et al. 2007; De Palma and Naldini 2009). These cells are closely associated with the forming vasculature, and are found outside the endothelial cells (Fig.  1). They do not appear, however, to incorporate into the vasculature as can other precursors. Specific deletion of this TAM subset results in inhibition of angiogenesis and repression of tumor growth, indicating that the TEMs are key targets for anti-angiogenic

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therapy (De Palma et al. 2007; De Palma and Naldini 2009; Pulaski et al. 2009). Further, these cells also can be used as “Trojan horses” to vehicle anti-angiogenic and anti-tumor therapies (De Palma and Naldini 2009).

Myeloid Derived Suppressor Cells (MDSCS) Myeloid derived suppressor cells (MDSCs) represent a range of poorly differentiated myeloid precursor that shares markers for both monocyte and granulocytes (Serafini et al. 2006). These cells were originally identified in murine models due to their potent immuno-suppressive activity through a variety of mechanisms, including production of TGFb, high level expression of inducible nitric oxide synthase (NOS2) and arginase (Arg1) which deplete arginine within the tumor microenvironment, as well as mechanims that deplete of cystine and cysteine (Srivatsa et al. 2010), all of which contribute to T cell suppression and apoptosis (Bronte and Zanovello 2005; Kusmartsev and Gabrilovich 2006). There are diverse subsets of murine MDSCs with more monocytic or granulocytic phenotypes (Fig.  1), which apparently have diverse immuno-­suppressive potentials (Bronte 2009; De Santo et al. 2005; Dolcetti et al. 2010). MDSCs are also powerful promoters of angiogenesis as well, by MMP production (Yang et al. 2004) as well as other mechanisms such as VEGF secretion (Sica and Bronte 2007). Stat3 activation in myeloid cells has been linked to production of angiogenic factors such as bFGF and VEGF (Kujawski et al. 2008) and angiogenesis. MDSC tumor infiltrates have been reported to be responsible for the resistance of tumors to anti-angiogenic therapy (Shojaei et al. 2007a, 2009), largely via production of diverse angiogenic factors such as Bv8 (Shojaei et al. 2007b) review: (Shojaei and Ferrara 2008). Myeloid precursors have also been found to be elevated in human cancers (Almand et  al. 2001; Diaz-Montero et  al. 2009; Ostrand-Rosenberg and Sinha 2009; Rodriguez et  al. 2009) and to correlate with disease status (Diaz-Montero et al. 2009) where again represent a variety of myeloid cells blocked at different stages of maturation along the monocytic or granulocytic pathways (OstrandRosenberg and Sinha 2009). The role of human MDSCs in tumor angiogenesis is not fully understood. The question remains as to how to target these cells, approaches to this have largely focused on targeting the immune-suppressive activity, including use of NO donors such as Nitroaspirin (De Santo et al. 2005; Ugel et al. 2009), interfering with recruitment and subsequent angiogenesis by blocking the CSF-1 receptor (Priceman et al. 2010).

Granulocytes Neutrophils are the most abundant circulating leukocyte in humans and the primary granulocytes involved in angiogenesis. These cells play a key role in responses diverse infective agents, based mostly on pattern recognition mechanisms. They are

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quickly and massively recruited into areas producing danger signals, they store in granules several soluble mediators that can be rapidly secreted in both physiological and pathological conditions, as well as respiratory burst production of reactive oxygen species (O2−, H2O2, HOCl). These factors can produce tissue destruction and lysis, which result in significant anti-tumor activity (Di Carlo et  al. 2001). Neutrophils are also sources for several cytokines (including TNFa, IL-1b, IL-1Ra, IL-12 and VEGF), and chemokines (including CXCL1, CXCL-8, CXCL9, CXCL10, CCL3, and CCL4) (Scapini et al. 2000). What neutrophils lack in terms of the levels produced for some secreted products is usually more than made up for by the abundance of these cells in many pathological conditions. These products can influence the immune response and polarization as well as promote tissue reconstruction and angiogenesis. We noted that neutrophils appeared to lead the invasion of vessel sprouts into a matrigel sponge containing tat (Benelli et  al. 2000). The use of corneal pocket assays indicated that angiogenesis induced by IL-8 (CXCL8), a chemokine highly active on neutrophils and a known angiogenic factor acted, through CXCR2 receptors expressed on endothelial cells (Addison et  al. 2000). However, not all endothelial cells express CXCR2 and subsequent studies demonstrated that neutrophils were required for an angiogenic response to CXCL8 and other chemokines active on neutrophils in vivo (Benelli et al. 2002). The link between CXCL8 production and subsequent tumor-associated neutrophil (TAN) recruitment leading to release of angiogenic factors by these cells that induce angiogenesis has been confirmed in tumor models, where this cascade was shown to be critical in linking ras oncogene expression to tumor growth and angiogenesis (Sparmann and BarSagi 2004). TANs have also been shown to be a key source of MMP9, a protease required for the angiogenic switch and tumor growth, in skin carcinogenesis and the Rip-Tag pancreatic cancer models (Coussens et al. 2000; Nozawa et al. 2006). Neutrophils are able to produce a broad range of angiogenic factors (Schruefer et  al. 2005, 2006). The inflammation-associated angiogenic response during wound healing was significantly delayed in animals harboring genetic defects that compromise neutrophil recruitment, in particular deletion of CXCR2 (Devalaraja et al. 2000) or CD18 (Schruefer et al. 2006). Similarly, a dual src kinase knock-out that compromised neutrophil function also blocked the ability to induce angiogenesis in response to CXCL1 (Scapini et al. 2004). In this case the neutrophils were recruited into the site, but were unable to release VEGF, a factor critical for induction of the angiogenic response. Intriguingly, in a major site of physiological angiogenesis in adults, the endometrium, neutrophils closely associate with the growing vessels and appear to be the primary source of VEGF for these vessels (Gargett et al. 2001; Heryanto et al. 2004; Lin et al. 2006; Na et al. 2006), thus intimately nurture angiogenesis in this organ. The divergent roles for neutrophils, on one hand able to produce anti-tumor tissue destruction, on the other to supply angiogenic factors and tissue regeneration signals, lead to the speculation that several subsets of neutrophils may exist (Fig. 1), in particular pro- and anti-angiogenic subsets (Noonan et al. 2008). This speculation based on the observations of multiple neutrophil subsets previously reported

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(Tsuda et al. 2004), as well as the role of neutrophils in pro- and anti-tumor effects on PPARa knock-out models (Kaipainen et al. 2007). Tumors injected into PPARa knock-out mice remained in an essentially dormant state. This was associated with notable neutrophil recruitment and production of the anti-angiogenic matrix molecule thrombospondin (Kaipainen et al. 2007). While neutrophil depletion in wildtype mice slowed tumor growth, consistent with a pro-angiogenic activity and previous observations (Nozawa et al. 2006; Pekarek et al. 1995), neutrophil depletion in the PPARa knock-out mice permitted angiogenesis and tumor growth, indicating an anti-tumor phenotype dictated by the loss of PPARa (Kaipainen et  al. 2007). Additional support for the existence of multiple neutrophil subsets with either pro- or anti-tumor activity comes from analyses of the effects of TGFb blockades. TGFb is an immunosupressive cytokine that itself has a dichotomous activity in cancer; inhibitory at early stages, TGFb is frequently found in the tumor microenvironment at later stages where it enhances tumor progression and creates immuno-suppression (Bierie and Moses 2006). Blockage of TGFb in diverse tumor models can lead to enhanced CD8+ T cell activity and tumor repression (Ge et al. 2006; Nam et al. 2008; Suzuki et al. 2007). Under a TGFb blockade, there is also an accumulation of neutrophils within the tumor, associated with direct neutrophil killing of tumor cells as well as enhanced CD8+ T cell activation (Fridlender et al. 2009). Depletion of neutrophils under the TGFb blockade impaired CD8+ T cell activation and enhanced tumor growth, while in control animals TAN depletion resulted in slower growth and increased CD8+ activation (Fridlender et al. 2009), similar to the observations with PPARa knock-out mice. The authors suggested the existence of two different neutrophil subsets: pro-tumor neutrophils in control animals linked to the presence of TGFb, termed “N2” neutrophils in concordance with the M2/Th2 paradigm, and anti-tumor neutrophils that were recruited into tumors when TGFb was rendered inactive, concordantly termed “N1” neutrophils (Fig. 1). These findings were similar in two different tumor types (NSCLC and mesothelioma) and in three different mice strains, suggesting that the polarization of neutrophils may be a general feature of tumor microenvironment (Fridlender et  al. 2009). The concept of N1–N2 polarization of TAN was supported by a limited transcription analysis with similarities to corresponding macrophage populations, based on eight key genes. The TGFb blockade resulted in significantly lower levels of arginase, CCL2, CCL3 and CCL5, and higher levels of ICAM1 and TNFa (Fridlender et al. 2009). The N2 cells also appeared to have enhanced expression of VEGF, which together with enhanced TNFa could promote vascularization, however the potential role in angiogenesis of these cells was not investigated.

Mast Cells Mast cells were initially suggested to be involved in vascularization during rheumatoid arthritis (Maruotti et al. 2007; Ribatti et al. 1988), and appear to play a key role in angiogenesis in allergic reactions as well (Crivellato et al. 2009). These cells are

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intimately involved in vascularization of hematoligic malignancies, (Ribatti et al. 2009), where they also appear to be able to integrate into the vessel wall by vascular mimicry (Nico et al. 2008). Mast cells play a significant role in angiogenesis in at diverse solid tumor types (Crivellato et al. 2008; Murdoch et al. 2008; Ribatti et al. 2001). In tumors, mast cells contribute to the angiogenic switch by production of diverse angiogenesis-associated cytokines and chemokines (Murdoch et al. 2008; Ribatti et  al. 2004). Further, mast cell derived proteases promote premalignant angiogenesis (Coussens et al. 1999; Ranieri et al. 2009; Soucek et al. 2007), and are becoming a target for anti-angiogenic therapies (Galinsky and Nechushtan 2008; Liu et al. 2009).

Dendritic Cells Dendritic cells (DCs) constitute a heterogeneous population of antigen presenting cells found in virtually every tissue, which through their antigen presentation and cytokine secretion activity link innate and adaptive immunity (Lanzavecchia and Sallusto 2001; Steinman and Banchereau 2007). DC recruitment into tumors has been documented in several studies (Balkwill 2004; Sozzani et al. 2001; Ueno et al. 2007; Vicari et  al. 2004), although their clinical relevance is a matter of debate. Within the tumor microenvironment, DCs generally show an immature phenotype characterized by the low costimulatory molecule expression and low IL-12 production (Vermi et  al. 2003; Vicari et  al. 2004), apparently due to cytokines such as IL-10, IL-6, TGFb, VEGF and M-CSF that block differentiation and maturation (Ratta et al. 2002; Steinman and Banchereau 2007). Tumor associated DCs maintain tolerance to tumor antigens, promote tumor progression and dissemination (Mantovani et al. 2002) as well as angiogenesis and tumor growth (Sozzani et al. 2007) (Fig. 1).

DCs and Angiogenesis DCs express a wide array of pro- and anti-angiogenic mediators that might have a significant role in those pathophysiological settings characterized by DC activation and angiogenesis, including inflammation, would healing, atherosclerosis and tumor growth (Sozzani et  al. 2007). The pro- and anti-angiogenic mediators released by DCs belong to distinct families and can modulate neovascularization by different mechanisms of action (Mantovani 2004; Sozzani 2005). 1. DCs release classical angiogenic growth factors that act on the endothelium by engaging the corresponding signaling receptors on cell surface. Conventional DCs express VEGF-A, FGF2 and ET-1 (Guruli et al. 2004; Riboldi et al. 2005). Alternatively activated DCs can secrete high amounts of VEGF-A and low levels of FGF2 (Bourbie-Vaudaine et al. 2006; Piqueras et al. 2006).

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2. DCs can release cytokines devoid of a direct pro-angiogenic activity but endowed with the capacity to increase the responsiveness of endothelial cells to classic angiogenic growth factors or upregulate the production of angiogenic growth factors by other cell types. These activities include upregulation of TNFa (Caux et  al. 1994; de Graaf et  al. 1996; Verhasselt et  al. 1997), Osteopontin (OPN), (Konno et al. 2006) triggering TEM recruitment and activation (Naldini et  al. 2006), IL-6 and TGFb, (Verhasselt et  al. 1997), and the angiogenic chemokines CXCL1, CXCL2, CXCL3, CXCL5, CXCL8 and CCL2 (Means et  al. 2003; Scimone et  al. 2005; Vermi et  al. 2006). Activated plasmacytoid DCs can express the pro-angiogenic molecules CXCL8 and TNFa (Curiel et al. 2004; Penna et al. 2002; Piqueras et al. 2006). 3. DCs can transdifferentiate into endothelial cells (Coukos et al. 2005), similar to that suggested for diverse hematopoietic precursors, in particular circulating endothelial precursors (CEPs) and endothelial cells (CECs) (Dome et  al. 2009). 4. In contrast, DCs can release cytokines that repress angiogenesis such as IL-12 (Albini et al. 2009; Noonan et al. 2008; Trinchieri 2003), IFNg (Trinchieri 2003) and the angiostatic chemokines CXCL9, CXCL10 and CCL21 (Piqueras et al. 2006). Plasmacytoid DCs are a source of IFNa (Asselin-Paturel and Trinchieri 2005) an anti-angiogenic cytokine (Albini et  al. 2000; Brassard et  al. 2002; Indraccolo et al. 2002). 5. DCs produce anti-angiogenic extracellular matrix (ECM) components including thrombospondin-1 (TSP-1) (Doyen et al. 2003; Rusnati and Presta 2006) and long-pentraxin-3 (PTX3) (Doni et  al. 2003, 2006; Garlanda et  al. 2005).

NK Cells Multiple human NK cells subsets have been found, the major subset of CD56dimCD16+ NK cells constitutes about 90–95% of peripheral blood NK cells, readily kill target cells upon proper recognition, and secrete low cytokine levels (Cooper et al. 2001). In contrast, the CD56brightCD16− NK cells (about 5–10% of peripheral blood NK cells) are poorly cytotoxic but produce large amounts of cytokines, including IFNg, TNFa, and GM-CSF. Only CD56bright NK cells express secondary lymphoid organ homing markers such as CCR7, CD62L, and CXCR3, resulting in an enrichment of this subset in lymphoid organs and sites of inflammation (Campbell et  al. 2001; Fehniger et  al. 2003; Ferlazzo et  al. 2004b). The question of whether or not the development of the human subsets is interconnected has been under investigation for some time. Recently, a number of studies suggested that CD56brightCD16− NK cells are able to differentiate into CD56dimCD16+ NK cells upon prolonged activation (Chan et al. 2007; Romagnani et al. 2007), while the reverse may be possible in the presence of TGFb (Keskin et al. 2007).

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NK Localization NK subset distribution differs between distinct anatomical sites such as secondary lymphoid organs, lung, liver, and skin, suggesting specialization (Ferlazzo and Munz 2004; Ferlazzo et al. 2004a; Gregoire et al. 2007; Trinchieri 1989). A particular CD56superbrightCD16− NK cell subset is found in the deciduas during implanatation (Hanna et al. 2006; Hanna and Mandelboim 2007). Decidual or dNK cells are less cytotoxic (Kopcow et  al. 2005), they guide decidual angiogenesis by producing angiogenic factors (Hanna et  al. 2006) as well as promote decidual cellularity (Ashkar et al. 2000). The CD56brightCD16− NKcell subset is recruited into tumors (Carrega et al. 2008), and we have speculated that tumor infiltrating NK cells may be switched to an angiogenic phenotype (Fig. 1) similar to dNK cells (Noonan et al. 2008). However, NK cells infiltration appears to correlate with a better prognosis in gastric (Ishigami et al. 2000), colorectal (Coca et al. 1997), and lung carcinomas (Takeo et al. 1986; Villegas et al. 2002).

Specific Immunity Much of this volume will be dedicated to specific immunity, here we rapidly focus on the role T and B cells may play in tumor angiogenesis. Early studies indicated that an efficacious CD8+ cytotoxic T cell response against tumors required induction of an IFNg mediated angiogenesis blockade (Qin et al. 2003). However, an altered activation of CD4+ T cells can result in compromised macrophage effector function and enhanced angiogenesis and tumor growth in breast cancer models (DeNardo et al. 2009). The end result is a conditioning of a Th2 and Treg cell response (Fig. 1) that results in generation of a pro-tumor environment (Ruffell et al. 2010). In contrast, in a skin cancer model with induction of chronic inflammation, B cells were found to be necessary for tumor development (de Visser et al. 2005). The involvement of B cells was associated with deposition of immune complexes within the sites of early tumorigenesis (Fig.  1). Since most cells of the immune system have Fc receptors that modulate function, and in particular immune ­complexes play a role in generating M2b macrophages (Martinez et al. 2008), immune complexes could be expected to enhance angiogenesis (Fig.  1). However, if immune complexes are indeed angiogenic, then experimental use of function blocking antibodies to discern the role of specific components in angiogenesis needs to be taken with caution.

Conclusions Tumors induce both local microenvironmental conditioning as well as systemic (Macro-environmental) effects. Local effects include hypoxia, glycolysis and low extracellular pH, necrotic debris signals, TGFb and other cytokines, to skew most

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immune cells into a chronic inflammation, would-healing, pro-tumor phenotype flipping on the angiogenic switch. Systemic effects often target the bone marrow through elevated cytokine and chemokines such as VEGF, CXCL12 and others that mobilized precursor (including endothelial) and immature cells (such as MDSCs) which also exert immuno-suppressive and pro-angiogenic effects when homing to the tumor. Chemotherapy and radiotherapy may well do their share in promoting a chronic inflammatory and pro-angiogenic state. The final question is how to flip off the inflammation associated angiogenic switch. One approach is to jam the switch before it can be turned on, increasing the threshold; this is the concept behind angioprevention (Tosetti et  al. 2002). Another is use of NF-kB blocking agents (Albini et al. 2007; Baud and Karin 2009; Karin 2006; Nakanishi and Toi 2005), ideally targeting p50 homodimer formation (Porta et al. 2009), or the use of pathogen-associated molecular patterns (PAMPs) agonists (Damiano et  al. 2007; Makkouk and Abdelnoor 2009; Smits et  al. 2008) to force Th1/M1/N1 skewing, essentially taking the Coley toxin approach into modern medicine. Acknowledgements  These studies were supported by grants from the AIRC (Associazione Italiana per la Ricerca sul Cancro), the Istituto Superiore della Sanità Alleanza contro il Cancro foundation, the Ministero della Sanità Progetto Finalizzato, the MIUR Progetto Finalizzato and FIRB, the Università degli studi dell’Insubria and the Compagnia di San Paolo. AV is the recipient of a FIRB (Fondazione Italiana per la Ricerca sul Cancro) fellowship, AB and AP are in the Molecular and Cellular Biology PhD program of the University of Insubria.

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Chemokines and Cytotoxic Effector Molecules in Rejection Alan M. Krensky and Carol Clayberger

Abstract  Increased or de  novo expression of chemokines and cytotoxic effector molecules is part of the “immunologic constant of rejection” and are central to the “signature” of broad activation of proinflammatory and cytotoxic cells. Although several chemokines are associated with rejection and provocative results have been obtained in animal models, no real breakthroughs have occurred in translating these findings to clinical transplantation. In contrast, the cytotoxic effector molecules, granulysin, granzymes and perforin have proven to be important biomarkers for rejection. Keywords  Chemokine • Cytotoxic T lymphocyte (CTL) • Granulysin • Granzymes • Natural killer (NK) cell • Perforin In 1984, we set out to identify genes expressed by human T lymphocytes 3–5 days after activation, reasoning that these molecules would be relevant to organ transplant rejection. The molecules identified using subtractive hybridization were RANTES (CCL5) (Schall et al. 1988), a chemokine, and granulysin (Jongstra et al. 1987), a cytolytic and proinflammatory molecule. Twenty-five years later, these molecules and functionally related families have been shown to be important in transplant rejection and are being investigated as potential diagnostics, prognostics (biomarkers), and/or therapeutics. Although a number of interrelated immune responses are involved in transplant rejection, this chapter focuses on the roles of chemokines and cytotoxic effector molecules in this process.

A.M. Krensky  (*) Laboratory of Cellular and Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892-4256, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_6, © Springer Science+Business Media, LLC 2011

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Chemokines Movement of inflammatory cells from the bloodstream to peripheral sites of inflammation is a multistep process involving a variety of cell surface and secreted molecules. Chemokines are chemoattractant cytokines instrumental in regulating leukocyte extravasation from the bloodstream and selective migration into tissues. Most chemokines are small, secreted molecules, although a few are larger and two are cell surface bound. They coordinate leukocyte migration by interacting with two types of receptors: (1) nonsignaling glycosaminoglycans on endothelial cells and extracellular matrix and (2) signaling seven membrane spanning G protein coupled serpentine receptors on inflammatory cells. There are approximately 50 chemokines and 20 G protein coupled chemokine receptors. Chemokines are basic proteins and interact with glycosaminoglycans based upon charge. These chemokine-glycosaminoglycan interactions immobilize and concentrate chemokines in sites of inflammation where they are recognized by the specific G protein coupled chemokine receptors (Fig. 1). This interaction then induces expression of integrins and immunoglobulin gene superfamily members, resulting in firm adhesion by inflammatory

Fig.  1  RANTES (CCL5) chemokine attracts immune cells from peripheral blood to sites of inflammation. Inflammatory leukocytes interact with selectins to “roll” along the vascular endothelium. RANTES, presented by glycosaminoglycans on the endothelium, attracts immune cells by haptotaxis. The leukocytes are activated to express adhesion molecules, adhere firmly and diapedese from the bloodstream into the tissue. The immune cells release metalloproteinases and move through tissues following the RANTES chemokine gradient. T cells, once activated by specific antigen, make more RANTES, amplifying the inflammatory response in time and space. Figure is reprinted with permission from Nature Clinical Practice Nephrology (Krensky and Ahn 2007)

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cells to the endothelium and transmigration of immune cells from the bloodstream into tissues. Chemokines can attract inflammatory cells along a soluble gradient (chemotaxis) or bound to a solid surface (haptotaxis) (Wiedermann et  al. 1993). Haptotaxis occurs on the endothelium. Chemokines freely released into the blood go downstream and would fail to recruit cells to the inflammatory site. Binding to the glycosaminoglycans insures localization to the inflammatory site. Although diverse sets of chemokines and receptors are present in allografts undergoing rejection (Table 1) (Tan and Zhou 2005), only a few chemokine pathways appear to have central roles in allograft rejection (Table 2) (Viola and Luster 2008). Different chemokines and receptors are involved with early changes in expression due to the organ trauma associated with brain death, surgical manipulation and ischemia/reperfusion compared to later changes associated with acute and chronic rejection. Earliest changes are associated with neutrophil and monocyte infiltration. With antigen activation, the adaptive immune response is associated with the presence of monocytes and T lymphocytes. Early events. The earliest cellular infiltrates associated with transplant rejection are composed of neutrophils and monocytes. CXCR1 and CXCR2 are the major neutrophil chemokine receptors in man (Murphy 1997), and CXCL8 (IL-8), the Table 1  Chemokine receptor expression in allograft rejection from Tan and Zhou (2005) Organ/tissue Receptors in rejection Heart CXCR3, CCR5, CCR3, CCR2, CCR1, CXCR1 Kidney CCR1, CCR2, CCR5, CXCR3, CXCR2 Islet CXCR3, CCR5, CCR2, CCR1 Liver CCR5, CCR6, CXCR3, CXCR4 Bone marrow CCR2 Skin CXCR3 Lung CXCR3, CXCR2, CCR5, CCR2 Although most chemokines and receptors have been associated with some stage of transplant rejection, the review by Tan and Zhou (2005) concluded that CXCR3 and CCR5 are the most generally involved in rejection Table 2  Chemokines with central roles in transplant rejection Neutrophils CXCL1, CXCL2, CXCL8 Early Monocytes CCL2 CXCR1 CXCR2 CCL2, CCL4, CCL5, CX3CL1 Monocytes Later CXCL9, CXCL10, CXCL11 Th1 DCCCL3, CCL4, CXCL9, CXCL10 CD8 CCR1 CXCL13 B CCR5 CCR6 CXCR1 CCR1 CCR5 CXCR3 Adapted from Viola and Luster (2008)

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major neutrophil attracting/activating ligand, is induced by inflammatory stimuli, including ischemia-reperfusion injury (De Perrot et al. 2002). There is no murine homolog for CXCR1 or its major ligand CXCL8 (Fan et al. 2007). CXCL1 (KC/ MIP-2 in mouse and MIP-II in rat) are thought to be functional orthologs for the human chemokine (Miura et al. 2003; King et al. 2000). In addition, CCL2, CCL7, CCL8 and CCL13 are involved in early monocyte attraction and activation (Romagnani 2002). Acute rejection. The adaptive immune response to the allograft is induced when naïve antigen specific T lymphocytes encounter dendritic cells presenting foreign alloantigens (MHC/HLA) (Krensky et al. 1990). The T cell-antigen presenting cell interaction occurs in secondary lymphoid organs and involves CCR7 and its ligands, CCL19 and CCL21 (Forster et al. 2008). Dendritic cells present antigen after migrating to the T cell area of the lymph node. Immature dendritic cells express CCR1, CCR5, CCR6 and CXCR1. Expression of these chemokine ­receptors decreases and they are replaced by CCR7, CXCR4 and CCR4 as the dendritic cells mature and enter the afferent lymphatics and lymph nodes (Colvin et al. 2008). T cell entry into the lymph nodes is regulated by CCR7 on the T cells interacting with CCL19 on the dendritic cells and CCL21 on high endothelial venules (Forster et al. 1999). CXCL12 and CCL5 provide additional proinflammatory ­signals (Molon et al. 2005). Before activated T cells leave the lymph node, they downregulate CCR7 and upregulate other chemokine receptors involved in movement through the bloodstream and lymphatics and into tissues and organs. For example, some CD4+ T cells increase CXCR5 expression and localize to the lymph node follicle where they provide help to B cells (Cyster 2005). B cell entry into secondary lymphoid tissues depends on CCR7, CXCR4 and CXCR5 (Cyster 2005). Paradoxically, although CCR7 and CXCR5 are important in generation of alloreactive T cells, they are dispensable for the alloresponse in vivo. Only modest prolongation of heart graft survival occurs in CCR7 (−/−) mice (see below) (Beckmann et al. 2004; Muller et al. 2003). CCR1, CCR5 and CXCR3 recruit monocytes, macrophages and effector T cells during acute and chronic rejection (Fischereder 2007). CCR1 binds CCL3, CCL4, CCL5, CCL7, CCL8 and other chemokines (Anders et  al. 2006). CCR5 binds CCL3, CCL4, CCL5 and CCL8 (Balistreri et al. 2007). In addition to sharing several ligands, CCR1 and CCR5 are expressed on similar cell types, but they act at different stages of leukocyte recruitment and activation. For example, peripheral blood monocytes are high for CCR1 expression but low for CCR5. In contrast, CD45RO+ memory T cells are low for CCR1 and high for CCR5. CCR5 expression is markedly increased in both acute and chronic rejection. T cells upregulate CXCR3 after activation (Panzer et al. 2004). CXCR3 is also expressed by B and NK cells and is commonly increased in expression in transplant rejection along with its ligands CXCL9, CXCL10 and CXCL11 (Hoffmann et al. 2006). Chemokine receptors are differentially expressed by T helper subsets (Mantovani et al. 2004). Th1 cells generally express CCR5 and CXCR3 while Th2 cells express CCR3, CCR4 and CCR8. CCR7 expression has been used to divide memory T cells into two subsets: CCR7+ “central” memory T cells migrate into lymph nodes and

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peripheral organs while CCR7− “effector” memory cells do not re-enter lymph nodes (Sallusto et al. 2000). Although a large variety of chemokines has been associated with transplant rejection, the major chemokines implicated in CD8+ T cells in transplant rejection are CCL3, CCL4 and CCL5, interacting with CCR5 and CXCL9, and CXCL10, interacting with CXCR3. Antigen activated CTL release CCR5 ligands, CCL2 and CCL3 (Selleri et al. 2008), and attract NK cells (Salazar-Mather et al. 1998; Morris and Ley 2004). Activated dendritic cells and B lymphocytes produce the CXCR3 ligand chemokines (Drayton et al. 2006; Deola et al. 2008). As described below, the CCR5 ligands do appear to be clinically relevant in transplant rejection. Even though CXCR3 and its ligands CXCL-9, CXCL10 and CXCL11 are induced in transplant rejection and were thought to be important mediators of CTL infiltration (Lazzeri and Romagnani 2005; Kobayashi et al. 2006), the functional importance of CXCR3 is now controversial based upon results in animal models (see below). Chronic rejection. The chemokines and receptors expressed in chronic rejection are the same as in acute rejection and most preclinical studies implicate similar pathways, including CCR1, CCR5 and CXCR3 (Viola and Luster 2008).

Translation to Clinical Utility The fact that chemokines and their receptors are induced and highly expressed at various stages of transplant rejection suggests that they may prove targets for ­diagnostics, prognostics (biomarkers) and/or therapeutics. Although animal models have implicated a number of chemokines for these uses, results to date moving preclinical observations to the clinics have been disappointing. This is likely due to the considerable differences between mouse and man, the redundancy of the chemokine-receptor interaction network, the pleiotropic effects of many ­chemokines, and kinetic/microenvironment considerations. Preclinical evaluation. The major approaches to studying chemokines in ­transplant rejection involve studies with targeted gene deletion animals, blocking antibodies, small molecules, and gene therapy. Gene deletion mice. Preclinical studies using chemokine receptor deficient mice implicated CCR1, CXCR2 and CCR5 in acute allograft rejection (Gao et al. 2000, 2001; El-Sawy et al. 2005). Based upon results in experimental animals, CCR7 and CXCR3 now appear less involved. Only modest prolongation of heart allografts was found with CCR7(−/−) mice (Beckmann et al. 2004), and, although mice deficient for CXCR3 were initially described as highly resistant to acute allograft rejection (Hancock et  al. 2000), subsequent studies failed to confirm this observation (Halloran and Fairchild 2008). Of note, a chemoattractant lipid, sphingosine-1-phosphate (S1P), interacts with a G protein coupled receptor to control lymphocyte egress from lymphoid organs (Cyster 2005). Remarkably, S1P1-deficient lymphocytes fail to exit secondary ­tissues (Matloubian et al. 2004). Based on these observations, an inhibitor of S1P,

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FTY720, was developed and advanced to clinical trials but proved no better than existing therapies for transplantation (Mansoor and Melendez 2008). Neutralizing antibodies. Blocking antibodies have proven useful in inhibiting cell surface interactions for integrins and immunoglobulin superfamily genes. Examples include anti-LFA-1 and anti-VLA4 treatments for multiple sclerosis and Crohn’s disease (Ulbrich et al. 2003; Ghosh et al. 2003; Miller et al. 2003). G protein coupled receptors would appear to be excellent potential therapeutic targets. CXCR1 and CXCR2 antibodies block the neutrophil infiltrate associated with ischemia-reperfusion injury (Sbrana et al. 2005; Cugini et al. 2005; Bertini et al. 2004). Neutralizing antibodies against CXCR2 and its ligands CXCL1, CXCL2 and CXCL3, prolong graft survival in a variety of mouse and rat allograft rejection models (El-Sawy et al. 2005). Anti-CXCL2 and CXCL3 antibodies prolong kidney graft survival in mice (Cugini et al. 2005; Bertini et al. 2004). Anti-CXCR1 and CXCR2 blocks rejection in the rat (Cugini et al. 2005; Bertini et al. 2004). CXCR2 antibodies block heart rejection and reduce infiltrate and graft injury in lung transplants (El-Sawy et al. 2005; Belperio et al. 2005). Small molecules and modified chemokines. Small molecules, including modified chemokines, have also been used to inhibit transplant rejection in animal models. Since CCR5 was shown to be a receptor for HIV entry into cells, many small molecules and modified RANTES (CCL5) have been developed and tested as potential HIV therapeutics (Tan and Zhou 2005). In agreement with data from CCR5 deficient mice (Abdi et al. 2002a), TAK-779, a small molecule antagonist for CCR5 and CXCR3, prolonged allograft survival in both acute and chronic cardiac and islet models in mice (Akashi et al. 2005). After the amino terminus was shown to be important for the chemokine specificity of CCL5, several modifications, including Met-RANTES, AOP-RANTES and other amino terminal modifications were developed (Grone et al. 1999; Proudfoot et al. 1996; Simmons et al. 1997; Lederman et  al. 2004). Met-RANTES, a functional antagonist for both CCR1 and CCR5 (Grone et al. 1999), reduces damage and prolongs graft survival in both acute and chronic rejection models (Yun et al. 2004). On the negative side, MET-RANTES causes glomerular damage and proteinuria, undercutting its clinical potential (Anders et al. 2003). BX471, a CCR1 receptor antagonist, is effective in rat heterotopic heart, rabbit orthotopic kidney and a rat chronic kidney allograft rejection models (Horuk et al. 2001a, b). Gene transfer. There is much less data involving gene transfer of chemokines or chemokine inhibitors in transplantation but a few studies are notable. Vassalli and colleagues used gene transfer of the chemokine antagonist RANTES (CCL5) 9-68 (Vassalli et al. 2006) and terminal deletion mutants of CCL5 (RANTES) and CCL2 (MCP-1) to prolong cardiac allograft survival in rats (Fleury et  al. 2006). AntiCCL2 (MCP-1) gene therapy also attenuated graft vasculopathy (Saiura et al. 2004) and transduction-mediated overexpression of CXCR4 significantly improved marrow engraftment of cultured peripheral blood stem cells (Brenner et al. 2004). Clinical data. Inhibitors of CCR1 and CCR5 are being tested in human diseases and may eventually be available in transplantation (Fischereder 2007). Early results

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with some CCR1 antagonists for multiple sclerosis (Zipp et al. 2006) and rheumatoid arthritis (Pease and Horuk 2005) and CCR5 antagonists for HIV have failed (Kuritzkes 2009), but several additional compounds and studies continue to move forward (Pease and Horuk 2009a; Pease and Horuk 2009b). Perhaps the single most relevant clinical observations to date involve the CCR5∆32 null allele. Approximately 1% of people of Northern European descent have a 32 base pair deletion in the CCR5 coding region resulting in a null allele. A study of a large cohort of renal transplant patients showed a significant prolongation in graft survival for patients with this homozygous deletion (Fischereder et al. 2001). There are additional chemokine polymorphisms of clinical relevance in transplantation (Abdi et  al. 2002b). These include decreased transplant rejection associated with the CCR2-64I allele (Kruger et al. 2002) or CCR5 59029-A/G allele (Yigit et al. 2007) and decreased transplant survival for the G allele of CCL2/-2518 promoter (increased CCL2 expression) (Kruger et al. 2002). Summary. In general, exciting results from animal studies have been poorly translated to humans for a variety of reasons. Knock out animals are not the same as small molecule or neutralizing antibody inhibitors in localization or kinetics. Zlotnik points out the major differences between mouse and man in chemokine expression and function (Fig.  2) (Zlotnik et  al. 2006). He documents that some chemokine genes are present in one species but not the other. CXCL8, noted above is an example

Fig. 2  Human and mouse chemokine genes differ. The lack of correlation is evident as genes that exist in one species but not the other, as functional equivalents but not identical, and/or as chemokines with different functions between the species. Adapted from Zlotnik et al. (2006) and Viola and Luster (2008)

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of a chemokine gene present in man but not mouse. Alternatively, a given chemokine may be related to more than one ortholog in the other species or the match may not be exact. These differences are due to independent gene duplication events. The complexity of multiple ligands for many receptors, multiple receptors on any particular cell and the hierarchy of potential combinatorial interactions (and potential for redundancies) functionally multiply and complicate differences between individual chemokine molecules. An additional level of complexity is due to the pleiotropic effects of any given chemokine. For example, CCL5 is produced and released by a wide variety of cell types, including lymphocytes, macrophages, fibroblasts, platelets, smooth muscle, endothelial and epithelial cells. Its can bind CCR1, CCR3 and CCR5. Furthermore, it can produce other activating and proinflammatory events in addition to chemoattraction. Lastly, differences in microenvironments (local effects) and kinetics provide ultimate complexity.

Cytotoxic Effector Molecules Transplant rejection usually includes infiltration of CTL and NK cells and broad activation of expression of cytotoxicity associated gene products (Krensky 2000). The other late expressed T cell molecule we first identified by subtractive hybridization was granulysin, originally named 519 (Jongstra et al. 1987). Granulysin is expressed constitutively by natural killer (NK) cells and after antigen stimulation in cytotoxic T lymphocytes (CTL) (Krensky and Clayberger 2009). It is present in cytolytic granules of these cell types, overlapping with other cytolytic effectors, granzymes and perforin. Much of the immune effector function in the transplant literature focuses on granzymes because mice and rats do not express granulysin. Granulysin is expressed in man, cows, pigs and horses. Cell-mediated cytotoxicity occurs by two pathways (Clayberger 2009). A nonsecretory pathway involves the interaction of fas with fas ligand, giving rise to apoptosis. The secretory pathway involves directional release of cytolytic granules onto target cells, also resulting in caspase activation and cell death by apoptosis. Granulysin and perforin and granzymes and perforin collaborate in cell death while granulysin and granzymes function in entirely separate mechanistic pathways. Like most chemokines, these cytolytic effector molecules are not expressed by resting cells but are induced by T cell activation. They have proven to be potent biomarkers for rejection as activated T lymphocytes are intimately associated with this process. Granulysin. Granulysin was first identified by subtractive hybridization in a search for genes expressed “late” (3–5 days) after T cell activation (Jongstra et al. 1987). It is present in CTL and NK cell cytotoxic granules with perforin and granzymes and is directionally released at target cells upon antigen activation (Krensky and Clayberger 2009). Recombinant granulysin kills tumor targets by causing ion fluxes that increase intracellular calcium and decrease intracellular potassium.

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Mitochondria are damaged, giving rise to cell death via apoptosis. Recombinant granulysin has broad antimicrobial and tumorcidal activity. In addition to its cytotoxic activity, it is a chemoattractant and proinflammatory, activating expression of a number of cytokines and chemokines, including RANTES, MCP-1, MCP-3, MIP1a, IL-1, IL-6, IL-10 and IFNa. There is no mouse homolog for granulysin, although cows, pigs and horses express homologs. Granulysin is highly expressed in a wide variety of diseases (Krensky and Clayberger 2009). Increased granulysin expression is associated with good outcomes in a number of cancers. Granulysin is highly expressed in cell-mediated transplant rejection and may be a biomarker for steroid resistance (Sarwal et  al. 2001). Urinary mRNA for granulysin is an excellent noninvasive biomarker for transplant rejection (Kotsch et al. 2004). Perforin. Perforin is also a cytolytic molecule present in granules of CTL and NK cells (Chavez-Galan et al. 2009). It is homologous to complement proteins C8 and C9 involved in the membrane attack complex (MAC) of the complement cascade. Upon degranulation, perforin inserts itself into the target cell membrane, forming pores. Perforin works in concert with molecules like granulysin or granzyme B to induce apoptosis in target cells. Studies with perforin knockout mice led to the discovery of the Fas-Fas ligand pathway for cell death and underscored the importance of perforin in the immune response to tumors and viruses (Kagi et al. 1996). Remarkably, although perforin expression correlates strongly with allograft rejection in man and experimental models, perforin knockout mice reject allografts with the same kinetics as wild type recipients (Bose et al. 2003). There is a perforin deficiency state in man, first described in 1999 (Stepp et al. 1999). Hemophagocytic Lymphohistiocytosis (HLH) is a complex syndrome including high fevers, hepatosplenomegaly, cytopenias and hemophagocytosis, due to many entities, including perforin deficiency. Skin rashes, hepatic and central nervous system dysfunction often accompany the other symptoms. There is a breakdown in normal immunoregulation due to defects in both innate and adaptive immunity. These patients show uniform deficiency in NK function but variable CTL responses, suggesting some redundancy in CTL-mediated cytotoxic mechanisms (Janka 2007). Granzymes. Granzymes are caspase-like serine proteases that are released by CTL to kill viruses and tumors (Trapani and Smyth 2002). There are several more granzymes in mouse than man. Man expresses granzymes A, B, H and M while mice express A, B, C, D, E, F, G and M. While it appears that both granzymes A and B are important in killing target cells in collaboration with perforin, granzyme B appears to be the most important in inducing apoptosis and has been highly associated with allograft rejection in animal models and man (Trapani and Sutton 2003). Cell death is mediated by granzyme B via Bid cleavage and subsequent caspase activation and mitochondrial damage. Granzyme A functions by inducing DNA damage through noncaspase pathways. Compared to Granzyme B, Granzyme A is less important in target cell lysis and a less specific marker of organ transplant rejection (Li et al. 2001).

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Clinical utility. Although direct inhibition of these cytotoxic effector molecules have yet to be effective targets for inhibition of transplant (or tumor) rejection, they do appear to be useful biomarkers for rejection in tissues, urinary mRNA and perhaps peripheral blood (Clayberger 2009). They may also have prognostic significance in discrimination of steroid resistance (Sarwal et al. 2001). These CTL associated genes are expressed more highly in steroid free patients with stable graft function than patients treated with steroids. Therefore, these markers should be applied differently in patients who are taking steroids vs. those that are not (Satterwhite et al. 2003). There are recent studies that suggest these cytolytic effector molecules may also be expressed in regulatory T cells (Treg) (Grossman et  al. 2004). Gondek et  al. (2008) reported in a mouse skin transplant model that granzyme B expression by FoxP3+ cells is required for transplant tolerance. As noted above, a variety of molecular profiling studies have identified perforin, granzyme B, granulysin and FasL in allograft rejection (Nickel et al. 2001; Sharma et al. 1996). Hidalgo and colleagues examined gene expression in CTL and NK cells and in transplant rejection. They first evaluated CD8+ CTL, CD4+ CTL and NK cell transcripts after subtracting out B cell and monocyte mRNA (Hidalgo et al. 2008a). Both CD4+ and CD8+ CTL have similar profiles and no transcripts were identified that differentiated them. NK cells were different from CTL, but more like CD8+ CTL than CD4+ CTL. High levels of the cytotoxic molecule transcripts were identified in all three groups, with granulysin the highest expressed transcript by CTL. Of particular note, they found that the CD4+ CTL expressed high levels of granzymes and granulysin and that CD8+ CTL expressed high levels of FasL. This is in contrast to the commonly held notion that CD8+ CTL primarily function via granule exocytosis while CD4+ CTL generally kill via the nonsecretory pathway. It must be stressed that these studies were examining mRNA, not protein, expression and that, although the populations were more than 90% pure, small contaminating populations could impact these results (Hidalgo et al. 2008a). In a second report, this group found that the same hierarchy of CTL-associated transcripts was present in renal transplant biopsies undergoing rejection (Hidalgo et al. 2008b). These CTL associated transcripts was strongly correlated with T cell infiltration of the graft. Early studies of cytotoxic gene expression in blood and urine underscored the utility of noninvasive transcriptional profiling for diagnosis of transplant rejection (Kotsch et al. 2004; Li et al. 2001; Vasconcellos et al. 1998). Recent studies have not always reproduced these findings and have suggested other markers, including lower expression of ICOS and CD154 (Alakulppi et al. 2007) or increased expression of FoxP3 (Aquino-Dias et al. 2008) may prove more useful as biomarkers.

Conclusion Genes that are markedly increased in expression after T cell activation are associated with transplant rejection and other T cell-mediated diseases. When we undertook to identify such genes 25 years ago, we identified RANTES (CCL5) (Schall et al. 1988)

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and granulysin (Jongstra et  al. 1987). We now understand that these are important molecules in a large number of human diseases and that are members of large families of molecules of mechanistic relevance (Krensky and Clayberger 2009; Krensky and Ahn 2007; Nelson and Krensky 2001). Chemokines and cytotoxic effector molecules are important diagnostics and biomarkers and may prove useful in development of new therapies for transplant rejection and/or the development of transplant tolerance. Acknowledgment  This work was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute Laboratory of Cellular and Molecular Biology.

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Clinical Applications of Activated Immune Cells Luciano Castiello, Marianna Sabatino, Ping Jin, Francesco M. Marincola, and David Stroncek

Introduction Adoptive immune therapy has been used for many years to treat viral diseases, hematologic malignancies and cancer. While some diseased have been effectively treated with adoptive T cell immune therapy, many have not. Most successful T cell therapies have required ex vivo T cell expansion and some have required ex vivo T cell sensitization. NK cells and genetically engineered T cells have also be used for adoptive immune therapy. Dendrtic cells have been extensively used and studied to enhance immune therapies and dendritic cell polarized toward Th1 cells appears to be essential to their effectiveness.

Immune Therapy for Viral Infections Cytomegalovirus Cytomegalovirus (CMV) remains a serious opportunist pathogen in hematopoietic stem cell transplant recipients. Antiviral drugs can be used to treat CMV infection, but they are associated with serious toxicities including suppression of hematopoiesis and renal toxicity. CMV-specific T cells are essential for the control of CMV infections (Peggs 2009). Typically, in healthy subjects who have been infected with CMV a very large proportions of all their CD4+ and CD8+ T cells, 3–5%, are CMV-reactive (Sylwester et al. 2005). Furthermore, following hematopoietic transplantation the risk of CMV-infection is inversely correlated with the reconstitution of CMV-specific T cell immunity. The reconstitution of CD8+ T cells specific for the immune dominant

D. Stroncek (*) Department of Transfusion Medicine, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892-1184, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_7, © Springer Science+Business Media, LLC 2011

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CMV protein pp65 provides protective immunity (Cwynarski et  al. 2001; Gratama et al. 2001), but long term control of CMV infection is associated with reconstitution of CMV-specific CD4+ cells responses (Hebart et al. 2002). T cell adoptive immune therapy has been successful in preventing CMV-infection and in treating CMV disease. The adoptive transfer of ex vivo expanded CMV-specific donor derived T cells is effective in preventing and treating CMV disease. Walter et al. (1995) have shown that CMV-specific cytotoxic CD8+ T cell clones from CMVseropostive hematopoietic transplant donors expanded ex vivo with CMV-infected fibroblasts and infused following the transplant prevent CMV infection. Another successful CMV adoptive immune therapy strategy makes use of oligo or polyclonal CD8+ T cells, CD4+ T cells or a mixed population. These CMVspecific T cells have been generated by ex vivo expansion of peripheral blood T cells from CMV-positive donors using lyzed CMV infected cells as a source of antigens or by expanding T cells with monocyte-derived dendritic cells (DCs) loaded with CMV-lysate or peptides derived from CMV pp65. These ex vivo expanded CMV-specific T cells have been used to prevent and treat CMV-infections (Einsele et al. 2002; Peggs et al. 2003; Micklethwaite et al. 2007). CMV-specific T cells have also been produced ex vivo using EBV-transformed B cell lines (EBVLCL) transduced with an adenovirus vector containing CMV pp65. Cytotoxic T cells produced with these EBV-LCL are specific for EBV, CMV and adenovirus (Leen et al. 2006). The infusion of these cells to 11 hematopoietic stem cell transplant patients resulted in the in vivo expansion of multispecific cytotoxic lymphocytes and was associated with a reduction in the levels of all three viruses with the resolution of virus-associated symptoms and signs (Leen et al. 2006). CMV-specific CTLs can also be selected directly from peripheral blood ­mononuclear cells (Peggs 2009). HLA-Class I multimers loaded with CMV immune dominant ­peptides have been used to select the CTLs. This strategy is effective due to the large proportion of CMV-specific T cells in the peripheral blood of CMV-seropositive subjects. Direct selection of CTLs avoids lengthy ex vivo expansion and allows for the rapid preparation of CMV-specific cytotoxic T cells. HLA class I CMV peptide tetramers coupled to paramagnetic beads have been used to isolate CMV-specific T cells from the peripheral blood mononuclear cells collected from hematopoietic stem cell transplant donors by apheresis (Cobbold et al. 2005). When used clinically in hematopoietic stem cell transplant patients CMV viremia was reduced in all nine patients treated and was cleared completely in eight of the patients (Cobbold et al. 2005).

Immune Therapy for Viral Induce Malignancies Posttransplant EBV-Associated Lymphoproliferative Disease Patients receiving transplants from HLA-matched unrelated donors or mismatched unrelated related donors are at risk for Epstein–Barr virus (EBV)-positive lympho­ proliferative disease (EBV-LPD). These EBV-associated lymphomas are highly immunogenic tumors that express immune dominant viral antigens such as

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EBNA3 protein. Immune therapy has been effective for treating EBV-associated lymphomas. Since most people have been infected with EBV and have circulating cytotoxic T cells specific for EBV, peripheral blood leukocytes from hematopoietic stem cell transplant donors have been given to transplant recipients to treat EBV-LPD. The infusion of donor leukocytes isolated from the blood has been found to be effective in 70% of patients. However, donor leukocyte infusions can induce or worsen acute GVHD. To avoid or reduce the risk of developing acute GVHD, many centers are using donor leukocytes that have been highly enriched for EBV-specific cytotoxic T lymphocytes (CTLs) to treat EBV-LPD. These adoptively transferred EBVspecific CTLs are produced by culturing the transplant donor’s T cells with EBV-LCLs. A multicenter trial has found that EBV-specific CTLs produced with EBV-LCLs were 100% effective in preventing EBV-associated lymphomas in 101 at risk patients and were effective in treating EBV-associated lymphomas in 11 of 13 patients treated (Heslop et  al. 2010). Gene marking studies showed that the EBV-specific CTLs could be detected in the circulation for up to 9 years (Heslop et al. 2010).

Immune Therapy for Leukemia Donor Leukocyte Infusions Peripheral blood mononuclear cells (PBMCs) collected by apheresis from allogeneic hematopoietic stem cell (HSC) transplant donors are often given to the transplant recipient as posttransplant adoptive immune therapy (Tomblyn and Lazarus 2008; Loren and Porter 2008). Unmanipulated or minimally manipulated PBMCs are given to speed immune recovery and to prevent leukemia relapse following transplantation with T cell depleted grafts. They are also used to treat leukemia relapse following transplantation. They are most effective treating relapse of CML. They are effective in treating CML relapse in more than 60% of patients and in treating molecular relapse of CML in 90% of cases (Tomblyn and Lazarus 2008). Donor leukocyte infusions (DLIs) are also effective in treating relapsed multiple myeloma with a 40–50% response rate. The major complication of DLI therapy is the development of GVHD. Approximately one-third of patients treated with DLIs develop acute or chronic GVHD. Some of these donor leukocyte products are processed extensively. For example, some hematopoietic stem cell transplant recipients are given donor T cells treated to polarized them to a TH2 phenotype to maintain antiviral and antitumor immunity while avoiding GVHD (Fowler et al. 2006).

NK Cells NK cells have potent antitumor activity and play an important role in the treatment of leukemia by allogeneic transplantation. The mismatching of transplant donor

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NK cell inhibitor killer-like receptors (KIR) with its HLA Class I antigen ligands expressed in the recipient has been associated with less leukemia relapse in recipients of unrelated donor transplants and in mismatched related donors transplants. Recently, some groups have been using NK cells as primary immune therapy to treat cancer and hematologic malignancies. One challenge of NK cell therapy is delivering an adequate dose of NK cells. Typically NK cells are isolated from PBMC concentrates collected by apheresis. To increase the number of effecter NK cells for clinically therapy NK cells are expanded either in vivo or in vitro. In vivo expansion has been accomplished by the administration of IL-2 and lymphocyte depleting chemotherapy with high dose cyclophosphamide and fludaribine. Lymphocyte depletion results in a transient increase in circulating IL-15 levels. The elevated levels of IL-2 and IL-15 induce in  vivo expansion in most patients. This approach has been used with NK cells isolated from HLA haplotype identical donors to treat patients with hematogic malignancies and was associated with hematological remission in 5 of 19 poor prognosis patients with acute myelogenous leukemia (Miller et al. 2005). Two methods have been reported for in vitro expansion. One protocol involves stimulating NK cells with IL-5 and K562 cells that have been transduced with 4-1BB ligand (Imai et  al. 2005). Another protocol uses IL-2 and EBV-LCLs to stimulate NK cells (Berg et al. 2009). This approach results in more than 100-fold expansion of NK cells and is being used to treat patients with cancer. Adoptive immune cancer clinical trials with NK cells expanded in vitro with EBV-LCLs are underway, but the results are not yet available.

Immune Therapy for Cancer Tumor Infiltrating Leukocytes for Melanoma Adoptive immune therapy with cytotoxic T cells has been effective in treating cancer. Rosenberg and colleagues have found that tumor infiltrating leukocytes (TIL) from metastatic melanoma lesions expanded ex vivo and administered along with intravenous IL-2 therapy induces objective clinical responses in approximately 31–35% of patients with metastatic melanoma (Rosenberg and Dudley 2009). Clinical responses were associated with the in vitro lysis of autologous tumor by the administered TIL. Gene marking studies, however, found that these cells were short lived with less than 0.1% circulating for more than 1 week. Even better clinical responses have been obtained when TIL are given to patients with metastatic melanoma who have been given nonmyeloablative chemotherapy with cyclophosphamide and fludarabine prior to the administration of TIL and IL-2 therapy. They have also treated melanoma patients with the same nonmyeloablative chemo­ therapy followed by low dose whole body irradiation and autologous CD34+ cell rescue before TIL infusion. With these modifications the object response rate of

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TIL therapy increases to 49–72%. Favorable response rate were associated with persistence of the infused cells (Rosenberg and Dudley 2009).

Dendritic Cells Dendritic cells (DCs) are being used for adoptive immune cancer therapy. DCs are key players in both innate and adaptive immune responses. The use of DC-based cellular therapies to enhance innate and adoptive immune mediated tumor rejection is a promising regimen which has shown evidence of improving patient survival and objectively enhancing tumor elimination. DCs are considered essential for the initiation, programming and regulation of antigen-specific immune responses (Steinman and Banchereau 2007). They play a fundamental role in supporting the survival and the effector function of primed T cells while coordinating communication among cells of the immune system. Since DCs were first generated ex vivo for immunotherapy (Mayordomo et al. 1995) and used in clinical trials (Hsu et al. 1996; Nestle et al. 1998), they have been found to be effective in treating patients with lymphoma, melanoma and renal cell carcinoma (Kalinski et al. 2009), although their potential utilization is wider and goes beyond cancer therapy. However, the clinic response rates of DC therapies are only 10–15% (Kalinski et  al. 2009). Various reasons for the limited efficacy of DC-based vaccines have been hypothesized, with inefficient activation of Th1polarized responses due to incomplete DC maturation being the most sited (Kalinski et al. 2009; Lee et al. 2008; Giermasz et al. 2009). Most DC adoptive immune therapy protocols produce DCs from peripheral blood monocytes. The frequency of DCs in the peripheral blood is naturally low and they are difficult to separate from other peripheral blood leukocytes (Nicolette et al. 2007). Large quantities of mononuclear cells can easily be collected from the peripheral blood by leukapheresis and monocytes can be isolated from the other leukocytes with high purity by adherence, elutriation, or selection using immunomagnetic beads (Felzmann et al. 2003; Wong et al. 2001; Berger et al. 2005). In order to produce immature DCs (iDCs), monocytes are usually incubated with GM-CSF and IL-4. Because mature DCs (mDCs) are superior to iDCs for the stimulation of cytotoxic T-cells, iDCs derived from monocytes are often treated with various exogenous stimuli known to induce DCs maturation (Gilboa 2007; Albert et al. 2001). The DC maturation process is complicated: DCs can secrete different cytokines, growth factors and chemokines that can, in turn, attract different immune cells and differentially influence their activation in situ (Muthuswamy et al. 2008; Messmer et al. 2003; Moller et al. 2008). Moreover, the methods that are currently used to produce DCs for adoptive immune therapy may not be optimized: immune responses against tumors, pathogens and autoimmunities need different Th specific polarization (Kalinski and Moser 2005a). For these reasons Th-specific immune responses should be obtained by the stimulation of DCs to produce cells with a

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specific Th polarizing ability that has still not well understood, characterized and re-produced ex vivo (Kalinski and Moser 2005b). Several strategies have been used to produce mature DCs, which are characterized by high immune cell activation potential. Factors used to mature immature DCs included lipopolysaccharide (LPS), CD40 ligand (CD40L), tumor necrosis factor-a (TNF-a), IFN-g, IFN-g and cocktails combining several factors, to better re-create the inflammation environment, such as prostaglandin E2 (PGE2), interleukin-1b (IL-1b, IL-6, polyinosinic:polycytidylic acid (poly (I:C)). Global gene expression micro arrays have been used to compare DCs matured with various maturation protocols. DC maturation with LPS plus IFN-g was associated with waves of gene expression changes and the genes upregulated during DC maturation included many involved with immune function, cell differentiation, and migration (Jin et al. 2010). Some genes were upregulated or downregulated throughout maturation and others were upregulated or downregulated after 4 and 8  h of maturation but then returned to baseline levels after 24  h. The upregulated genes were most likely to be in pathways involved with the cellular immune response, cytokine signaling, transcriptional regulation and the inflammatory response, while downregulated genes were most likely to belong to metabolic pathways. The expression of inflammatory chemokine ligands including CCL2/MCP-1, CCL3/MIP1a, CCL4/MIP1b, CXCL1/GROa, CXCL9/MIG and CXCL11/ITAC reached a peak at hour 4 h of LPS plus IFN-g maturation, but then returned to baseline levels (Jin et al. 2010). The expression of chemokines CCL5/RANTES, CCL8/ MCP-2, and CXCL10/IP-10 peaked after 8 h and sustained high expression levels through hour 24. Chemokine ligands that were part of Toll-like receptor signaling pathways such as CCL3, CCL4, CCL5, CXCL9, CXCL10, and CXCL11 were all upregulated more than sevenfold during maturation. The levels of most of these genes peaked at hour 4 except for CCL5 and CXCL10 which peaked at hour 8 and sustained high levels of expression through hour 24. Chemokine ligands that preferentially attract Th1 T cells such as CXCL9, CXCL10, and CXCL11 were also markedly increased after 4 h. However, two chemokine ligands for CCR4, which are important attractants of Th2 cells CCL17/TARC and CCL22/MDC, were only slightly upregulated or showed no significant change after 24 h of maturation (Jin et al. 2010). Although after 24  h of LPS and IFN-g maturation DCs were well-armed to induce Th1 responses as exemplified by significant elevations in the expression of the Th1 cell attractants CXCL9, CXCL10, CXCL11 and CCL5 (Jin et al. 2010), another study has found that prostaglandin E2 (PGE2),induces mature DCs which produced high levels of the regulator T cell (Treg) attracting cytokines CCL22 and CXCL12 (Muthuswamy et  al. 2008). These Treg cells can counter the effects of Th1 responses by cytotoxic T cells, Th1 cells, and NK cells. In contrast, LPS and IFN-g maturated DCs did not increase the levels of CCL22 and CXCL12 expression (Jin et al. 2010). We have also reviewed the results of DNA microarray data on the maturation of DCs in order to compare different conditions of maturation (Castiello et al. 2010).

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Fig. 1  Immune pathways activated during the maturation of DCs. When iDCs are matured with LPS, the interaction of LPS with Toll-like receptors leads to a cascade of events leading to the activation of NF-kb, p38-MAPK, ERK1/2 and JNK. Although IL-1, CD40L and TNF-a bind different receptors, when they activate iDCs their downstream signals activate the NF-kb RelA/B complex. In contrast, IFNs stimulate a different pathway that includes the activation of JAKs, TYK2 and STAT1/2

We categorized the various maturation protocols based on the level of ­overlap of canonical pathways identified by the gene expression data that were believed to be primarily activated by each compound (Fig. 1) (Castiello et al. 2010). We clustered various maturation protocols into three major categories: LPS, CD40L/TNF-a, and IFN-g dependent maturation strategies. In addition, a fourth category was needed for protocols that included cocktails of factors that represented a combination of the other categories. For instance, LPS interacts with Toll-like receptors (TLR)-4 leading to the activation of different transcription factors including nuclear factor -kB (NF-kB), p38 mitogen-activated protein kinase (p38 MAPK), c-Jun N-terminal kinase (JNK), and extracellular signal-regulated protein kinase (ERK1/2). On the other hand, although both CD40L and TNF-a activate NF-kB, this activation occurs through different and only partially overlapping signaling cascades. Quite differently, IFN- and IFN-g signal through the Janus kinase (JNK) and tyrosine kinase (TYK) cascade leading to activation of signal transducer and activator of transcription proteins (STATs), which lead to down-stream effects where the action of NF-kB is also complemented by the action of several interferon regulatory factors (IRFs). LPS consistently induced CCL5, CCL4, CCL18, CCL19, CCL20, CCL23 and IMCAM1, SOD2 and MAPK inhibitors (Table 1) (Castiello et  al. 2010). Interferons induced the Th1-polarizing factors CXCL9, CXCL10, CXCL11, MXa, and ISG-15. TNFa and CD40L induced the Th2-polarizing factors CCL17, CXCR4, and IL-10R.

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Table  1  Genes consistently induced by each maturation agent and their immune related functions Maturation compound Induced genes Effect Recruitment of a variety of LPS Chemokines: CCL5, CCL4, immune cells; activation CCL20, CCL18, CCL19, of a primary immune CCL23 response against a Inflammatory-related genes: pathogen ICAM1, SOD2 MAPK inhibitors: DUSP1, DUSP5, TNFAIP3 Activation of the Th2-polarized TNF-a/CD40L Chemokines: CCL17 immune response Surface proteins: CXCR4, IL-10R, IL-13R, GM-CSFR Activation of the Th1-polarized IFNs IFN stimulated genes: CXCL-9, immune response CXCL-10, CXCL-11, MXa, ISG-15 Immune-mediated tissue rejection genes: STAT-1, IRF-1, IL-15

New Directions While TIL cell therapy is effective for treating melanoma, TIL cells cannot be produced from all patients since metastatic lesions cannot be biopsied from all patients and lymphocytes cannot be cultured from all biopsies. In addition, T cells from some patients have low affinity T cell receptors. To overcome this limitation several groups are investigating the use of genetically engineered T cells to treat cancer patients. Autologous peripheral blood lymphocytes are genetically modified with genes encoding high affinity TCR a and b chains. High affinity T cell receptor a and b chains from class I HLA-restricted T cell receptors from tumor reactive cytotoxic T cells have been cloned and transferred to T cells from cancer patients. These high affinity T cell receptors give the recipient cells the same antigen specificity as the donor T cells. Melanoma patients have been treated with a genetically modified T cells with MART-1-specific T cells. Synthetic chimeric antigen receptors are also being used to treat patients with cancer. T cells are being genetically modified with chimeric receptors using signaling chains from CD28, 41BB, and CD3 zeta plus antibodies directed to Her2/neu or CD19.

References Albert ML, Jegathesan M, Darnell RB. Dendritic cell maturation is required for the crosstolerization of CD8+ T cells. Nat. Immunol. 2001;2(11):1010–7. Berg M, Lundqvist A, McCoy P, Jr., Samsel L, Fan Y, Tawab A, Childs R. Clinical-grade ex vivoexpanded human natural killer cells up-regulate activating receptors and death receptor ligands and have enhanced cytolytic activity against tumor cells. Cytotherapy. 2009;11(3):341–55.

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Part III

Circulating Patterns Associated with Chronic and Acute Immune Pathology

Blood Transcriptional Fingerprints to Assess the Immune Status of Human Subjects Damien Chaussabel, Nicole Baldwin, Derek Blankenship, Charles Quinn, Esperanza Anguiano, Octavio Ramilo, Ganjana Lertmemongkolchai, Virginia Pascual, and Jacques Banchereau

Abstract  The blood transcriptome affords a comprehensive view of the status of the human immune system. Global changes in transcript abundance have been measured in the blood of patients with a wide range of diseases. This chapter presents an overview of the advances that have led to the identification of therapeutic targets and biomarker signatures in the field of autoimmunity and infectious disease. It also provides technology and data analysis primers as means of introducing blood transcriptome research to a broad readership. Specifically, we compare microarrays with some of the most recent digital gene expression profiling technologies available to date, including RNA sequencing. Furthermore, in addition to the basic steps involved in the analysis of microarray data we also present more advanced data mining approaches for blood transcriptional fingerprinting.

Blood Transcript Profiling A wide range of molecular and cellular profiling assays are now available to study the human immune system (Fig. 1). Among the systems-wide molecular profiling technologies genomics approaches are the most mature and scalable for high throughput use. The human genome can be investigated from two different angles. Sequence variations, which can be detected using for instance Single Nucleotide Polymorphisms (SNP) chips, permits the identification of common polymorphisms or rare mutations associated with diseases. Hundreds of thousands of SNPs can be typed using these platforms, yielding a genome-wide, hypothesis-free, scan of genetic associations for a given phenotype of interest. The second genome-wide profiling approach employed for the study of immune-mediated diseases consists in the measurement of transcript abundance. Generating transcriptional profiles on

D. Chaussabel (*) Baylor Institute for Immunology Research, Baylor Research Institute, 3434 Live Oak, Dallas, TX 75204, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_8, © Springer Science+Business Media, LLC 2011

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Fig.  1  The immune profiling armamentarium. The number of high throughput molecular and cellular profiling tools available for the assessment of the human immune system from blood is increasing rapidly

a genome-wide scale is both straightforward and cost-effective, affording the most comprehensive view of the status of the human immune system to date. Indeed, such studies can inform us on mechanisms of pathogenesis and thereby create opportunities for discovering potential therapeutic targets and novel clinically relevant biomarkers (Alizadeh et al. 2000; Baechler et al. 2003; Bennett et al. 2003; Sarwal et al. 2003; Wright et al. 2003; Achiron et al. 2004; Batliwalla et al. 2005a; Griffiths et  al. 2005; Pascual et  al. 2005). Transcriptional profiles have been obtained from many human tissues; including for instance the skin (Deonarine et  al. 2007; Panelli et  al. 2007; Greco et  al. 2010; Cole et  al. 2001), muscle (Berchtold et  al. 2009), liver (Frueh et  al. 2001; Flanagan et  al. 2009), kidney (Flechner et al. 2004; Bunnag et al. 2009) or brain (Glatt et al. 2005). Specifically, this review will focus on the use of blood transcript profiling. Blood is the pipeline of the immune system, with immune cells exposed to factors released in the bloodstream or present in peripheral tissues from which they re-circulate. It is an accessible tissue for which sampling can easily be standardized, with robust blood collection and RNA stabilization systems becoming widely available in recent years (Debey et  al. 2006; Asare et  al. 2008). In contrast with

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many other tissues blood can also be sampled repeatedly over time, which constitutes a key property for monitoring the immunological status of human subjects.

Profiling Human Subjects in Health and Disease Profiling Autoimmune Diseases The field of autoimmunity has proved a fertile ground for blood transcriptional studies. The work has initially focused on diseases with clear systemic involvement such as SLE (Baechler et al. 2003; Bennett et al. 2003). This has contributed to the identification of a type 1 interferon signature in the blood of a majority of lupus patients, prompting the development of therapies targeting this pathway (Yao et al. 2009). Furthermore, the potential value of this and other blood transcriptional signatures for the assessment of disease activity has been examined (Sandrin-Garcia et al. 2009; Petri et al. 2009; Nikpour et al. 2008; Nakou et al. 2008; Bauer et al. 2009; Chaussabel et al. 2008). Systemic onset Juvenile Arthritis (SoJIA) is another disease with systemic involvement that greatly benefited from the study of blood transcriptional profiles, with this proof of principle work leading to the development of both therapeutic and diagnostic modalities (Pascual et al. 2005, 2008; Allantaz et al. 2007a, b). Diseases with specific organ involvement have also been the subjects of significant, yet not always extensive, blood profiling efforts. Thus blood signatures have been obtained from patients with multiple sclerosis (Achiron et  al. 2004; Bomprezzi et al. 2003). Given the inaccessibility of the brain, blood constitutes a particularly attractive source of surrogate molecular markers for this disease. These efforts have yielded a systemic signature and identified potential predictive markers of clinical relapse and response to treatment (van Baarsen et al. 2008; Gurevich et al. 2009; Achiron et al. 2007a). Transcriptional signatures have also been generated in the context of dermatologic diseases. In this case the target organ being readily accessible, efforts have been focusing on profiling transcript abundance in skin tissues (Nomura et  al. 2003; de Jongh et  al. 2005). However, systemic involvement has been recognized in recent years to be an important component of autoimmune skin diseases and unique blood transcriptional profiles have also been identified for example in patients with Psoriasis (Batliwalla et al. 2005a; Stoeckman et al. 2006; Koczan et al. 2005). Blood transcriptional profiles have been generated in the context of many other autoimmune diseases. Indeed, the range of autoimmune/autoinflammatory diseases that have been investigated encompasses: SLE (Baechler et al. 2003; Bennett et al. 2003; Crow and Wohlgemuth 2003; Han et al. 2003), juvenile idiopathic arthritis (Pascual et  al. 2005; Allantaz et  al. 2007a; Ogilvie et  al. 2007; Fall et  al. 2007; Barnes et  al. 2009), multiple sclerosis (Achiron et  al. 2007b; Singh et  al. 2007), rheumatoid arthritis (Edwards et al. 2007; van der Pouw Kraan et al. 2007; Lequerre et al. 2006; Batliwalla et al. 2005b), Sjogren’s syndrome (Emamian et al. 2009), diabetes (Kaizer et  al. 2007; Takamura et  al. 2007), inflammatory bowel disease

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(Burczynski et al. 2006), psoriasis and psoriatic arthritis (Batliwalla et al. 2005a; Stoeckman et al. 2006), inflammatory myopathies (Greenberg et al. 2005; Baechler et  al. 2007), scleroderma (Tan et  al. 2006; York et  al. 2007), vasculitis (Alcorta et al. 2007) and antiphospholipid syndrome (Potti et al. 2006). The body of work produced that focuses on blood transcript profiling in the context of autoimmune diseases has been covered at length in a recent review (Pascual et al. 2010).

Profiling Infectious Diseases Global changes in transcript abundance have also been measured in the blood of patients with infectious diseases. In this context, alterations of blood transcriptional profiles are a reflection of the immunological response mounted by the host against pathogens. This response is mediated by specialized receptors expressed at the surface of host cells recognizing pathogen-associated molecular patterns (Janeway and Medzhitov 2002). Different classes of pathogens signal through different combinations of receptors, eliciting in turn different types of immune responses (Aderem and Ulevitch 2000). This translates experimentally in distinct transcriptional programs being induced upon exposure of immune cells in vitro to distinct classes of infectious agents (Nau et al. 2002; Huang et al. 2001; Chaussabel et al. 2003). Similarly, patterns of transcript abundance measured in the blood of patients with infections caused by different etiological agents were found to be distinct (Ramilo et al. 2007). Predictably, dramatic changes were observed in the blood of patients with systemic infections (e.g., sepsis) (Pankla et al. 2009; Tang et al. 2009). However, profound alterations in patterns of transcript abundance were also found in patients with localized infections (e.g., upper respiratory tract infection, urinary tract infections, pulmonary tuberculosis, skin abscesses) (Allantaz et al. 2007a; Ramilo et al. 2007; Jacobsen et al. 2007). An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Measuring changes in host transcriptional profiles may therefore prove of diagnostic value even in situations where the causative pathogenic agent is not present in the test sample. Importantly, it may also help ascertain the severity of the infection and monitor its course. Infections often present as acute clinical events, thus it is important to capture dynamic changes in transcript abundance that occur during the course of the infection from the time of initial exposure. Blood signatures have been described in the context of acute infections caused by a wide range of pathogenic parasites, viruses and bacteria, including: Plasmodium (Griffiths et  al. 2005; Franklin et  al. 2009), respiratory viruses (Influenza, Rhinovirus, Respiratory Syncytial Virus) (Ramilo et al. 2007; Reghunathan et al. 2005; Popper et al. 2009; Zaas et al. 2009; Thach et al. 2005), Dengue virus (Ubol et al. 2008; Nascimento et al. 2009), Adenovirus (Popper et al. 2009), as well as Salmonella (Thompson et al. 2009), Mycobacterium tuberculosis (Jacobsen et  al. 2007), Staphylococcus aureus (Ardura et  al. 2009),

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Burkholderia pseudomallei (Pankla et al. 2009) and in the general context of bacterial sepsis (Tang et al. 2009; Wong et al. 2009; Payen et al. 2008; Johnson et al. 2007). Some of those pathogens will persist and establish chronic infections (e.g., Human Immunodeficiency Virus, Plasmodium) that may lead to a state of latency (e.g., Tuberculosis), and transcript profiling may in those situations be used as a surveillance tool for the monitoring of disease progression or reactivation. Blood profiling of infectious diseases remains also limited in scale. In particular, additional studies will be necessary to ascertain dynamic changes occurring over time.

Profiling other Diseases Blood transcript profiling studies have been carried out in the cancer research field. While hematological malignancies have led the way (reviewed in (Staratschek-Jox et al. 2009)), blood profiles have also been obtained more recently from patients with solid tumors (Aaroe et  al. 2010). Notably, these signatures can reflect the immunological or physiological changes effected by cancers but also by the presence of rare tumor cells in the circulation (Findeisen et al. 2008; Hayes et al. 2006; Martin et al. 2001). Blood signatures have also been obtained from solid organ transplant recipients in the context of both tolerance (Martinez-Llordella et  al. 2008; Kawasaki et  al. 2007; Brouard et al. 2007) and graft rejection (Flechner et al. 2004; Lin et al. 2009; Alakulppi et al. 2008). While such signatures can also be detected in biopsy material (Sarwal et al. 2003; Mueller et al. 2007; Scherer et al. 2003) blood offers the distinct advantage of being accessible for safely monitoring molecular changes on a routine basis. Some work has also been done in the context of cardiovascular diseases where inflammation is known to play an important role. Hence, profiles have been identified in a wide range of conditions, including stroke, chronic heart failure or acute coronary syndrome (Tang et al. 2001; Nakayama et al. 2008; Moore et al. 2005; Cappuzzello et al. 2009). Other efforts have yielded blood transcriptional signature in patients with neurodegenerative diseases (Maes et al. 2007; Lovrecic et al. 2009; Borovecki et al. 2005), in response to stress, environmental exposure (Peretz et al. 2007; McHale et al. 2009; Bushel et al. 2007), exercise (Kawai et al. 2007; Connolly et al. 2004) or even laughter (Hayashi et al. 2007). The body of published work would be too large to be cited in this review – and it is likely to be only the tip of the iceberg, with a lot more unpublished data scattered throughout the public and private space. However, the vast majorities of these studies are underpowered and sometimes lack even the most rudimentary validation steps. All too often primary data are not available for reanalysis either, reflecting a lack of enforcement of editorial policies, or the absence thereof in some journals. Hence one of the main challenges for this field is to move beyond the proof of principle stage and consolidate the wealth of data being generated.

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Collectively, studies published thus far demonstrate that alterations in transcript abundance can be detected on a genome-wide scale in the blood of patients with a wide range of diseases. We have also learned that: (1) multiple diseases can share components of the blood transcriptional profile. This is for instance the case for inflammation or interferon signatures; (2) while no single element of the profile may be specific to any given disease it is the combination of those elements that makes a signature unique; (3) finally, the work that has been accomplished to date highlights the importance of carrying out analyses aiming at directly comparing transcriptional profiles across diseases. Indeed, much for instance can be learned about autoimmunity from studying responses to infection, and vice and versa. Furthermore, such efforts may eventually lead us closer towards a molecular classification of diseases.

Technology Primer (Fig. 1) Microarray technologies are limited by several factors, such as hybridization noise (background signal, nonspecific binding) and lack of sensitivity for transcripts expressed at very low or very high levels (dynamic range). Additional limitations derive from the fact that they rely on existing sequence knowledge and lack the capacity to quantify alternative messages, such as splice variants of a given gene. When considering human studies with potential clinical applications, perhaps the main limitation, however, is that direct comparability of data across batches and platforms is sometimes impossible. Real-time PCR technology is currently considered the gold standard for measuring transcript abundance. However, the number of transcripts that can be detected using this technique is limited. Products have been introduced recently that partially address this shortcoming. Alternative technology platforms have also become recently available, such as one developed by Nanostring, which can detect transcripts abundance for up to 500 transcripts with high sensitivity (Geiss et al. 2008). The approach is “digital” since it consists in counting individual RNA molecules. But a distinct advantage of this technology, which like microarrays is hybridizationbased, is that sample preparation needs are reduced to a minimum – for instance none of the steps involving enzymatic reactions. Also, given its high sensitivity, fast turnaround time, sample throughput and intermediate multiplexing ability this approach seems particularly promising for bedside applications. Methods relying on high-throughput sequencing for the genome-wide measurement of RNA abundance are also becoming available (Wold and Myers 2008). RNA-seq (RNA sequencing) (Sultan et al. 2008) starts with a population of RNA (total or fractionated, such as poly(A)+) that is converted to a library of cDNA fragments. High thoughput sequencing of such fragments yields short sequences or reads which are typically 30–400 bp in length, depending on the DNA-sequencing technology used. For a given sample, tens of millions of such sequences will then be uniquely mapped against a reference genome. The higher the level of expression

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Fig. 2  Cutting-edge RNA profiling technologies. Several technology platforms are available for measuring RNA abundance on large scales. Microarray and Nanostring technologies rely on oligonucleotide probes to capture complementary target sequences. Nanostring and RNA-seq technologies measure abundance at the single molecule level, with results respectively expressed as molecule counts and sequence coverage. Microarray and RNA-seq technologies require extensive sample processing, which include amplification steps

of a given gene the higher the number of reads that will be aligned against it (Fig. 2). Thus, this approach does not rely on probe design and provides information on not only transcript abundance but also transcriptome structure (splice variants), noncoding RNA species such as microRNAs (miRNA), and genetic polymorphisms. The use of RNA-seq has not reached the mainstream. Indeed, challenges ahead are multiple, including sample preparation, storage of massive amounts of data and sequence alignment (Wang et al. 2009). In time, RNA-seq is expected to become sufficiently cost-effective and practical to eventually supersede microarray technologies.

Microarray Data Analysis For years the scale of blood transcriptional studies has been constrained by the cost of the technology. With the price tag on a commercial whole genome microarray decidedly below the $100 USD mark, it is not the case anymore. Also data analysis and exploitation, that has from the start been one of the challenges for transcriptome research, has now clearly become the main rate-limiting step.

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Analysis Primer This section covers some of the basic steps and considerations involved in microarray data analysis. Per-Chip Normalization: This step controls for array-wide variations in intensity across multiple samples that form a given dataset. After background subtraction, a normalization algorithm is used to rescale the difference in overall intensity to a fixed intensity level for all samples across multiple arrays. Data filtering: Typically more than half of the probes present on a microarray do not detect a signal for any of the samples in a given analysis. Thus, a detection filter is applied to remove such probes. This step avoids the introduction of unnecessary noise in downstream analyses. Unsupervised analysis: The aim of this analysis is to group samples on the basis of their molecular profiles without a priori knowledge of their phenotypic classification. The first step consists in selecting transcripts that are expressed in the dataset (detection filter), and display some degree of variability (which will facilitate sample clustering). For instance, this filter could select transcripts with expression levels that deviate by at least twofold from the median intensity calculated across all samples. Importantly this additional filter is applied independently of any knowledge of sample grouping or phenotype (which makes this type of analysis “unsupervised”). Next, pattern discovery algorithms are often applied to identify molecular phenotypes or trends in the data. Clustering: Clustering is commonly used for the discovery of expression patterns in large datasets. Hierarchical clustering is an iterative agglomerative clustering method that can be used to produce gene trees and condition trees. Condition tree clustering groups samples based on the similarity of their expression profiles across a specified gene list. Other commonly employed clustering algorithms include k-means clustering and self-organizing maps. Class Comparison: Class comparison analyses identify genes differentially expressed among groups and/or time points. The methods for analysis are chosen based on the study design. For studies with independent observations and two or more groups, t-tests, ANOVA, Mann-Whitney U tests, or Kruskal-Wallis tests are used. For more complex studies (e.g., longitudinal) appropriate linear mixed model analyses are chosen. Multiple Testing Correction: Multiple testing correction (MTC) methods provide a means to mitigate the level of noise in sets of transcripts identified by class comparison (in order to lower permissiveness of false positives). While it reduces noise, MTC promotes a higher false negative rate as a result of dampening the signal. The methods available are characterized by varying degrees of stringency, and therefore they produce gene lists with different levels of robustness. • Bonferroni correction is the most stringent method used to control the familywise error rate (probability of making one or more type I errors) and can drastically reduce false positive rates. Conversely, it increases the probability of having false negatives.

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• Benjamini and Hochberg false discovery rate (Benjamini and Hochberg 1995) is a less stringent MTC method and provides a good balance between discovery of statistically significant genes while limiting false positives. By using this procedure with a value of 0.01, 1% of the statistically significant transcripts might be identified as significant by chance alone (false positives). Class Prediction: Class prediction analyses assess the classification capability of gene expression data for a study subject or sample. K-nearest neighbors is a commonly used technique for this task. Using Euclidian or other measures of distance, this method identifies the user defined “k” number of closest observations for an unclassified sample. Class prediction is then determined by the lowest p-value, which is calculated for each group. The p-values are based on the likelihood of obtaining the observed number of neighbors for a specific class given the overall class proportion in the data set. Other available class prediction procedures include, but are not limited to, Discriminant Analysis, General Linear Model Selection, Logistic Regression, Distance Scoring, Partial Least Squares, Partition Trees, and Radial Basis Machine. Sample Size: The number of samples necessary for the identification of a robust signature is variable. Indeed, sample size requirements will depend on the amplitude of the difference between and the variability within study groups. A number of approaches have been devised for the calculation of sample size for microarray experiments, but to date little consensus exists (Dobbin et  al. 2008; Jorstad et al. 2008; Pawitan et al. 2005; Yang et al. 2003). Hence, best practices in the field consist in the utilization of independent sets of samples for the purpose of validating candidate signatures. Thus, the robustness of the signature identified will rely on a statistically significant association between the predicted and true phenotypic class in the first and the second test sets.

Analysis of Significance Patterns The diagnosis of SoJIA takes weeks to months, as it is based on clinical criteria which lack specificity (Cassidy and Ross 2001). Indeed, initial symptoms mimic infections or malignancies and it is only when arthritis appears that the disease can be recognized. We surmised that the blood transcriptome of these children could be a source of diagnostic biomarkers. The profiles that differentiate SoJIA patients from healthy controls, however, were highly similar to those of children with febrile infectious diseases of both bacterial and viral origin (Allantaz et  al. 2007a). Additionally, because SoJIA can present at any age during childhood, matching the control groups for this disease and for infections that predominate at earlier or later times during childhood was a challenge. Thus, we devised a custom meta-analysis strategy for biomarker selection relying on the analysis of patterns of significance (Chaussabel et  al. 2005). This approach can be used to compare diseases across multiple datasets, each being analyzed in relation to its own set of healthy controls. First, statistical comparisons were performed between each group of patients

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(SoJIA, S. aureus, S. pneumoniae, E. coli, influenza A, and SLE) and their respective control groups composed of age- and gender-matched healthy donors. The p-values obtained from each comparison were then subjected to selection criteria. This permitted us to identify genes significantly changed in SoJIA patients vs. their control group, and not in any of the other disease vs. their own control groups. The SoJIA-specific signature that we obtained using this algorithm was composed of 88 transcripts. Treatment with IL1 antagonists was able to extinguish this signature in the majority of patients. Using a more stringent analysis, 12/88 transcripts were used to correctly classify an independent group of patients during the systemic phase of the disease against healthy and febrile disease controls. These 12 genes were not dysregulated however in SoJIA patients who had resolved the systemic phase and were left with chronic arthritis, suggesting that they are specifically dysregulated in the initial phase of the disease, probably the time of greatest sensitivity to IL1 blockade. The specificity of 7/12 genes has been recently validated in an independent study of PBMC transcriptional profiles including different types of JIA patients (Barnes et al. 2009). The same type of analysis has allowed us to identify blood disease-specific transcriptional markers differentiating SLE patients from patients with diseases that also display a Type I IFN signature such as Influenza infection (Chaussabel et al. 2005).

A Modular Analysis Framework A myriad of approaches have been developed for the analysis of genome-wide transcriptional profiling data (Mootha et al. 2003; Segal et al. 2003a; Allison et al. 2006; Horvath & Dong 2008). The main challenges encountered while mining such data are several fold: (a) dimensionality, or how to cope with the fact that the number of parameters measured exceeds by several order of magnitude the number of conditions included in any given experiment; (b) noise; a direct consequence of the first point is that results from microarray analyses are particularly permissive to noise (false discovery); (c) data visualization is critical as it helps promote insight and supports data interpretation. Dimension reduction techniques can help address some of those issues. Several groups have developed approaches that consist in grouping genes into distinct units or “modules”. Those genes may be grouped together based on similarities in transcriptional patterns or function. Multiple approaches have been used for the construction of such modules (Chaussabel et al. 2008; Horvath and Dong 2008; Ruan et al. 2010; Segal et al. 2003b; Suthram et al. 2010; Ulitsky and Shamir 2009). We have developed a modular data mining strategy for the specific purpose of analyzing and interpreting blood transcriptional profiles (Chaussabel et  al. 2008). This approach consists in a priori grouping sets of genes with similar transcriptional patterns. This is repeated for different datasets and subsequently when comparing the cluster membership of all the genes across those datasets, the genes with similar membership are grouped together to form a transcriptional module. Structuring the

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data permits to focus downstream statistical testing on these sets of transcripts that form coherent transcriptional modular units. This is in contrast with more traditional approaches applying statistical tests iteratively for thousands of individual transcripts that are treated as independent variables. The modular transcriptional framework that we have developed constitutes a dimension reduction technique and as such can: a) facilitate functional interpretation; b) enable comparative analyses across multiple datasets and diseases; c) minimize noise and improve robustness of biomarker signatures; and d) yield multivariate metrics that can be used at the bedside. Data visualization is also of critical importance for the interpretation of large-scale datasets. We have developed a straightforward approach for mapping global transcriptional changes for individual diseases on a modular basis (Fig. 3 and interactive web version: www.biir.net/modules). Briefly, differences in expression levels between study groups are displayed for each module on a grid. Each position on the grid is assigned to a given module; a red spot indicates an increase and a blue spot a decrease in transcript abundance. The spot intensity is determined by the proportion of transcripts reaching significance for a given module. A posteriori, biological interpretation has linked several modules to immune cells or pathways (see legend of Fig. 3).

Interpretation Important technical and biological constraints must be taken into account when interpreting blood transcriptome data. For one, reproducibility issues remains a legitimate concern (Shi et al. 2006, 2008; Tan et al. 2003). It is important to avoid confounding the analysis with technical variables. For example, reuse of pre-existing data for direct group comparison should be avoided. Samples should be run if possible in one single batch. If this is not possible, case and control samples should be randomized across the different runs. Overall, the availability of cost-effective commercial platforms has reduced the number of formats used for analysis and contributes to enhance data quality and reproducibility when compared to early cDNA arrays. Meta-analysis of data obtained using different platforms and from different laboratories is possible, but one must proceed with caution. A common strategy consists in the use of a control group that is common to all datasets under study (e.g., non stimulated or healthy controls) (Allantaz et al. 2007a; Chaussabel et al. 2005; Butte and Kohane 2006; Rhodes et al. 2004). Disease heterogeneity is also an important initial limiting step. Thus, patient clinical characteristics and disease stages should be taken into account and carefully recorded at the time of sample collection. Furthermore, drug treatments and comorbidities may impact blood transcriptional signatures and those variables cannot always be isolated, as patients cannot be taken off treatments. These factors also pose significant challenges in terms of study design and downstream data analysis. We have found that including samples from recently diagnosed and untreated patients is useful to select biomarkers related to disease pathogenesis. Selective inclusion criteria can be subsequently relaxed to broaden the scope and potential clinical impact of a study.

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Fig. 3  Modular analysis of blood leukocyte transcriptional profiles. (a) Gene expression levels from patients with acute S. pneumoniae infection and respective healthy volunteer PBMCs were compared (p < 0.05, Mann-Whitney U test). Graphs represent transcriptional profiles of genes that were significantly changed in modules M1.3, M1.5, M1.8 and M3.2. The results obtained for each of the 28 modules constituting this analysis framework are represented on the grid. Blue dots indicate modules for which transcript abundance is significantly lower in patients vs healthy (e.g., M1.3 and M1.8), red dots indicate modules for which transcript abundance is higher in patients (e.g., M1.5 and M3.2). Results obtained for the 28 PBMC transcriptional modules are displayed on a grid. Coordinates indicate module IDs (e.g., M2.8 is row M2, column 8). (b) Disease fingerprints: three additional datasets were similarly processed. Profiles were obtained from patients with Systemic Lupus Erythematosus, Liver transplant recipients under pharmacological immunosuppression infection and patients with metastatic melanoma. Functional interpretation is indicated on a grid by a color code

Working with pediatric populations is attractive because it is easier to accrue patients in the initial phases of disease and significantly less comorbidities and modalities of treatments will be encountered. The importance of implementation of common methods and standards for the collection of this type of information, samples and measurements cannot be stressed enough (Snyder et al. 2009; Whitney et al. 2003).

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Finally, when interpreting results from blood transcriptome analyses it is important to consider that changes in transcript abundance might reflect two phenomena: (1) transcriptional regulation; (2) relative changes in cell composition. The later can also provide reliable and valid clinical correlate/biomarker signatures, as shown in patients with SLE who display plasma cell precursor and/or immature neutrophils signatures (Bennett et al. 2003). The analysis of purified cell subpopulations might represent a useful way to identify differences that would otherwise be masked when analyzing whole blood. Subpopulation frequencies (i.e., shifts in naïve, memory and/or effector populations of T and/or B cells) may still be responsible however for transcriptional differences between patients and controls. Cell purification techniques might also be a source of biased results. Positive selection might for example alter the steady state transcriptome by delivering signals through surface receptors, while enrichment purification methods tend to be more prone to contamination with other cell types. Another approach consists in deconvoluting whole blood transcriptional profiles “in silico”. This type of analysis attempts to deduce cellular composition or cell-specific levels of gene expression using statistical methodologies (Wang et  al. 2006; Lu et  al. 2003; Lahdesmaki et al. 2005; Abbas et al. 2009; Repsilber et al. 2010).

Conclusions Blood transcript profiling has earned its place in the molecular and cellular profiling armamentarium used to study the human immune system. Changes in transcript abundance in blood recapitulate both cellular and molecular events, thus providing a comprehensive snapshot of the immunological status of an individual. The interpretation of transcript profiling data generated from complex tissues such as blood is a challenge, but it is not an insurmountable one. Indeed, powerful analysis strategies are now available that can be used to streamline the exploitation of the massive amounts of data generated in the context of such studies. Whole genome transcript profiling has become both robust and cost-effective. Furthermore, in contrast with other immunological assays the collection and preservation of blood samples for microarray analysis can be easily standardized. Altogether these aspects makes this an assay of choice for large-scale immunomonitoring. Acknowledgements  The work of the author is supported by the Baylor Health Care System Foundation and the National Institutes of Health (U19 AIO57234-02, U01 AI082110, P01 CA084512).

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Innate Signatures of Immune Mediated Resolution and Persistence of Hepatitis C Virus Infections Robert E. Lanford

Abstract  Hepatitis C Virus infections have a high propensity to induce lifelong persistent infections. A number of host and environmental variables have been shown to impact the rate of persistent infection and the outcome of interferon (IFN)-based therapies. This review briefly highlights a few of the relevant observations that have been made with regard to viral clearance. HCV infection induces a highly elevated interferon stimulated gene (ISG) response in the liver during both acute and chronic infections. The role of this response in containing the spread of the virus in the liver and in mounting an effective adaptive immune response is poorly understood. The fact that an elevated hepatic ISG response during chronic infection negatively correlates with the outcome of IFN therapy is counter-intuitive and remains to be explained satisfactorily. The failure of the T cell response to clear virus appears to reside in a deficiency in the CD4 T cell help leading to anergic CD8 T cells, which may also be related to whether the innate response properly orchestrates the adaptive T cell response early in infection. The importance of the innate response in the outcome of infection is illustrated by recent genome wide association studies revealing a remarkable correlation between genetic polymorphisms in IL28B (IFNl3) and clearance of infected cells whether during acute infection or IFN therapy, yet the mechanisms by which IFNl3 and other innate effectors influence HCV infection are currently poorly understood. Keywords  Chimpanzee • Chronic infection • HCV • Hepatitis C virus • Hepatocyte • IL28 • Innate immune response • Interferon • Interferon lambda • ISG • Liver • Microarray • Persistent infection

R.E. Lanford (*) Department of Virology and Immunology, Southwest Foundation for Biomedical Research and Southwest National Primate Research Center, 7620 NW Loop 410, San Antonio, TX 78227, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_9, © Springer Science+Business Media, LLC 2011

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HCV Disease and Therapy Three percent of the worldwide population is persistently infected with hepatitis C virus (HCV) including 4% of the U.S. adult population. Chronic infections develop in 50–85% of exposed individuals depending on a number of variables. The disease is typically asymptomatic or mild for several decades; however, cirrhosis develops in 10–20% of the chronic infections with an increased risk of end-stage liver disease and hepatocellular carcinoma. HCV infection is the leading cause of liver transplantation in the U.S., and hepatocellular carcinoma is now the most rapidly increasing cause of cancer death in the U.S. The current therapy for HCV infection is pegylated interferon-alpha (peg-IFNa) in combination with ribavirin for 48 weeks. This is a harsh therapy with significant side effects that many people are unable to complete. For those that complete therapy, the sustained virological response rates are 40–50% for genotype 1 and 80–90% for genotypes 2 and 3. The virus clearly plays are role in response to therapy, since specific genotypes have different response rates and sequence variation within a genotype influence outcome as well. The development of direct acting antivirals is a major effort at pharmaceutical companies, and many promising antivirals are in clinical trials at this time. However, these new drugs will be administered with peg-IFNa and ribavirin until an effective IFN-free cocktail can be developed. The most advanced trials are for the protease inhibitors, but nucleoside analogs, polymerase inhibitors and NS5A inhibitors are progressing through trials (Shimakami et al. 2009) as are novel therapies inhibiting cellular factors required by the virus such as miR-122 (Lanford et al. 2010) and cyclophilin (Flisiak et al. 2009) inhibitors.

Viral Clearance HCV infections have been studied intensely using a broad array of technologies in the hopes that a better understanding of the factors that influence progression to chronic infection will advance efforts to develop a vaccine and more efficacious therapies. The rate of persistent infection is difficult to determine in humans, since most acute infections go undetected. Common source exposures have provide valuable information on the rate of viral clearance, since all individuals have similar exposure (Kenny-Walsh and Irish 1999). The rate of viral clearance can be as high as 50% in young healthy adults with a single exposure. This compares favorably to a rate of 66% clearance for chimpanzees (Lanford 2006). In contrast, individuals repeatedly exposed or exposed to large doses of virus, such as intravenous drug users, hemophiliacs, and transfusion recipients, have persistent infection rates of 80–90% (Thomas et al. 2000). The importance of the dose of virus is particularly apparent in studies on genetic polymorphisms in HLA-C and NK cell KIR receptors which affect viral clearance only in low dose recipients, IV drug users, but not in transfusion recipients and hemophiliacs (Khakoo et  al. 2004). Recently, genome

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wide association studies revealed that variants in the IL28B (also known as IFNl3) region have a remarkable association with viral clearance during acute HCV infection (Thomas et  al. 2009) and treatment with IFN-based therapies (Ge et  al. 2009; Suppiah et al. 2009; Tanaka et al. 2009). However, most studies on viral clearance have focused on direct analysis of the innate and adaptive immune responses.

HCV Viral Proteins Modulate the Innate Response HCV is a member of the Flaviviridae family. The genome is single-stranded RNA of positive sense. There are six major geneotypes with up to 30% divergence at the amino acid level. The 5’ RNA terminus is uncapped and contains an internal ribosome entry site for initiation of translation. Ten viral proteins are derived by proteolytic cleavage of a polyprotein encoded by a single open reading frame (Fig. 1). The structural proteins are organized at the amino terminus followed by the nonstructural (NS) proteins and components of the viral RNA replicase. The viral proteins include a capsid protein, two envelope proteins E1 and E2, a porin p7, an autoprotease NS2, a RNA helicase-serine protease NS3, a protease cofactor NS4A, a hydrophobic membrane protein NS4B, an RNA binding, phosphoprotein NS5A, and a viral RNA polymerase NS5B. HCV has developed a number of mechanisms for subverting the innate immune response. NS3/4a blocks both TLR3 and RIGI pathways and prevents the activation of IRF3 (Foy et al. 2003, 2005; Li et al. 2005) and NFkB, transcription factors involved in activation of the IFNb promoter.

Fig.  1  HCV viral genome and polyprotein. The HCV genome is depicted with the 5’ NCR containing an internal ribosome entry site (IRES) and the 3’ NCR including a conserved terminal 98 nucleotide stem loop structure involved in RNA replication. The open reading frame of the polyprotein is depicted as a cylinder with demarcation of the individual viral protein domains (C, e1, e2, p7 and NS2-NS5B), and the positions of some of the viral functions are indicated above. The NS3 and NS5A proteins are involved in the subversion of the host innate immune response that recognizes dsRNA and results in the induction of the type I IFN response. NS3 protease cleaves TRIF and IPS-1 blocking both the TLR3 and RIG-I pathways of IRF3 activation and type I IFN induction. NS5A binds PKR but is also known to have PKR independent mechanisms involved in blocking IFN production

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The TLR3 pathway is blocked by cleavage of TRIF (Li et al. 2005; Foy et al. 2003), while the RIGI/MDA5 pathway is blocked by cleavage of the adapter protein IPS-1 (aka CARDIF, MAVS, VISA) (Xu et al. 2005; Seth et al. 2005; Kawai et al. 2005; Meylan et al. 2005). NS5A is a phosphoprotein that contains a sequence known as the ISDR, or interferon sensitivity determining region. Sequence diversity in this region has been shown to correlate with the outcome of IFN therapy (Enomoto et al. 1996). This region interacts with PKR (Gale et al. 1998), but NS5A appears to be involved in several PKR-independent mechanisms of blocking the IFN response (Polyak et al. 2001). It is important to note that NS5A may play a role in blocking the response to exogenous IFNa, while NS3/4a prevents synthesis of IFNb and expression of IRF3-dependent genes. The core protein interacts with STAT1 and blocking the IFN-induced JAK-STAT pathway (Lin et al. 2006). Gene expression studies demonstrated the induction of TLR3 and RIG-I by IFNa in the liver of chimpanzees and in primary human hepatocytes (Lanford et al. 2006). Thus, TLR3 and RIGI may play a role in maintaining the high ISG expression levels and IFN production in hepatocytes during HCV infection, especially in newly infected cells prior to suppression by NS3. However, in other viral systems plasmacytoid dendritic cells (pDC) produce most of the type 1 IFN via interaction of viral RNA with TLR7. Conclusive studies on the source of the IFN inducing ISG expression in the liver during HCV infection have not been performed.

Acute HCV Infection and Intrahepatic Induction of ISGs Chimpanzees are an ideal animal model for probing the factors influencing clearance and persistence of HCV due to the high rate of resolution during acute infection. Bigger et al. (2001) and Su et al. (2002) initiated studies on viral–host interactions using microarray analysis in the chimpanzee model over 10 years ago. The initial studies examined changes in liver gene expression during acute-resolving infection. These were the first studies to utilize microarray technology in an animal model of an infectious disease. Remarkably consistent trends in gene expression were observed with two major waves occurring temporally across the acute infection, representing the innate and adaptive immune responses. During acute HCV infection, viremia rapidly increases within days of infection reaching near peak values within 1–2 weeks followed by a plateau phase with little change until detection of the T cell response 10–15 weeks after infection (Fig.  2). Also within days of infection, a rapid and pronounced increase in ISG expression occurs in the liver in parallel to viremia. The increase and decrease in hundreds of ISG transcripts during acute resolving infection parallels the rise and fall of viremia. A few genes are upregulated by as much as 100fold and many are up by 20-fold or more. Although HCV proteins are known to block aspects of the IFN response, in vivo there is a dramatic elevation of ISG expression in the liver. Among the most prominent ISGs are the chemokines IP-10 (CXCL10) and ITAC (CXCL11) which play important roles in recruiting T lymphocytes, dendritic cells

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Fig. 2  Hepatic ISG response during acute HCV infection. The three panels illustrate the events during acute resolving HCV infection in chimpanzees. HCV viral RNA increases rapidly during the first week after infection and plateaus until the adaptive immune response emerges 8–12 weeks after infection often resulting in a very rapid decline in viremia and loss of infected cells. The viremia profile is paralleled by rapid increases and decreases in hepatic expression of hundreds of interferon stimulated genes (ISGs) some of which are directly involved in the IFN signal transduction pathway

and NK cells to the liver (Helbig et al. 2009). The role of hepatocytes, rather than other intrahepatic cell types, in the expression of critical chemokines in response to viral infection was confirmed in studies treating primary human and chimpanzee hepatocytes with IFNa and demonstrating the induction of ITAC (CXCL11), IP-10 (CXCL10), MIG (CXCL9) and MCP2 (CCL8) (Lanford et al. 2006). One aspect of the infection that must be taken into context of the events in the liver is the percentage of hepatocytes that become infected. Unlike HBV infection where 100% of the hepatocytes become infected, in HCV, a minor fraction of hepatocytes are infected at any one time. Based on the level of viral RNA present in the liver during the plateau phase less than 10% of hepatocytes are infected, thus something limits the spread of the virus in the liver (Bigger et al. 2004; Lanford et al. 2006, 2007), and the most likely candidate is the innate immune response potentially involving both the ISG response and the NK cells recruited to the liver by the induced chemokines. Although noncytolytic inhibition of viral replication by the ISG response probably plays a major role in the halt of viral spread, considerable cell death can be detected in the liver in association with a mild elevation in ALT prior to the appearance of the T cell response. Two sources of data other than viral RNA levels support the estimated low level of infected cells during HCV infection. Total virus production is estimated at 1 × 1012 particles per day (Neumann et  al. 1998) or approximately one particle per hepatocyte, and recent studies using

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enhanced fluorescence microscopy methods have detected clusters of infected cells representing no more than 10% of the liver (Liang et al. 2009). Collectively, the data suggest that initially a low percentage of hepatocytes are infected, less than 10% in most infections. The dsRNA response in infected hepatocytes and the interaction of viral RNA with TLR7 on dendritic cells results in the production of type I IFN. Secreted IFN induces zones of resistant cells surrounding IFN producing cells resulting in a loss available replication space in the liver. The plateau phase ensues with a decrease in viral spread and no further increase in viremia. This may continue as a dynamic equilibrium of cell loss due to NK cell killing balanced by newly infected cells. Eventually a T cell response emerges and aids in the clearance of infected cells. T cell induced cytokines such as IFNg may help mediate clearance by suppressing viral replication. This model is consistent with inhibition of the IFN response by viral proteins if one assumes that newly infected cells respond to dsRNA and secrete IFN until the level of viral protein becomes sufficient to inhibit this pathway or that the IFN is primarily produced by pDCs which are not infected by HCV and not subject to the inhibition of IFN production by viral proteins. The link between the innate immune response and the adaptive immune response is well established. In addition to aspects of the innate immune response in priming the adaptive response, the innate response contains the virus and permits the adaptive response time to eliminate the infection. For HCV, the duration of this time is significant, since the T cell response is delayed for 8–12 weeks after infection for reasons that are not understood.

Chronic HCV Infection and Hepatic ISG Expression The changes in hepatic gene expression in chimpanzees with chronic HCV infection are remarkably similar to those observed during acute infection with a highly upregulated ISG response (Fig. 3). Selection of ten chronically infected animals with serum viral loads that varied by 1,000-fold revealed that ISG expression in the liver was highly elevated and that the degree of induction was independent of viral RNA level (Bigger et  al. 2004). The data suggest that the ISG response is maximally upregulated in response to the virus even at relatively low levels of viral RNA. The highly elevated IFN response persists in the livers of these animals for decades with little evidence of pathology. The ISG response involves over 1,000 genes, and the response is largely tissue specific in comparing PBMC and liver (Lanford et  al. 2006) with most of the commonly induced genes representing ISGs previously characterized in tissue culture. The functions of most the ISGs are unknown, but it is suspected that many have antiviral or immunomodulatory properties. In contrast to chimpanzees, humans can be classified as having high or low hepatic ISG during chronic infection prior to IFN therapy, and the high ISG phenotype ­negatively correlates with outcome of IFNa therapy (Chen et  al. 2005; Feld et  al. 2007). A similar observation has been made for serum levels of IP-10 (CXCL10) with

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Fig. 3  Hepatic ISG response during chronic HCV infection. Microarray analysis was performed on liver samples from 15 HCV chronically infected animals and 6 uninfected animals using Affymetrix total genome human microarrays. Genes were filtered based on fold change in expression of >2.0 fold change with a p value of <0.02. The data are presented as a heat map with upregulated genes in red, downregulated genes in green and unaltered genes in black. Most genes upregulated in the HCV chronic liver are interferon stimulated genes (ISGs). Some genes are decreased in expression during HCV infection, as shown at the bottom of the heat map

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low pretreatment levels being predictive of successful therapy outcome (Lagging et al. 2006; Diago et al. 2006; Butera et al. 2005; Romero et al. 2006). Serum levels of IP-10 can be considered a surrogate marker for hepatic ISG levels during HCV infection since the bulk of IP-10 is being secreted by the liver. These observation are partially explained by the finding that chimpanzees have no increase in hepatic ISG expression following treatment with IFNa, presumably because the response is already maximally induced, and have no decrease in viral RNA in response to pegIFNa treatment (Lanford et al. 2007; Huang et al. 2007). Thus, chimpanzees represent the extreme in the nonresponsive, high ISG phenotype and are a model for the human null IFN response phenotype. Confirmation of this relationship came from studies with paired biopsies in humans demonstrating that ISG expression is induced from a low baseline in rapid responders to IFN, but the high ISG expression phenotype was unresponsive to exogenous IFN (Sarasin-Filipowicz et  al. 2008). Two of these studies identified signature gene sets whose expression levels at baseline could accurately predict SVR. Only five genes were found in common between the two signature sets of 29 genes (Sarasin-Filipowicz et  al. 2008) and 18 genes (Chen et al. 2005 6521 /id) and many of the genes in both sets were not ISGs. This may reflect that different parameters were used for patient selection, sustained viral response (SVR) vs. rapid response, but may also reflect the complexity of this phenotype. IFN nonresponders are not a uniform group and represent individuals with viral rebound after therapy, relapse during therapy and null response with no decrease in viral load. Patients with the highest probability for SVR can be identified during therapy based on the rate of decrease in viral RNA, with rapid responders (RR) having undetectable viremia at 4 weeks of therapy and a very high probability for SVR, in contrast to patients without an early viral response (EVR) of greater than 2 log decline in the first 12 weeks having almost no chance of a SVR. The factors contributing to the relationship between hepatic ISG levels and response to therapy may precede the development of a chronic infection and maybe reflective of genetic polymorphisms in the innate immune response (see below). However, uninfected chimpanzees destined to become null IFNa responders have a robust response to IFNa in both PBMC and liver, while chronically infected animals have a robust response only in PBMC with the liver having a maximally induced ISG response that is unresponsive to further stimulation (Lanford et al. 2007). Clearly, the null response to IFN therapy is not due to an obvious defect in the ISG response, on the contrary, it is the individuals with a lack of response to the virus (low ISG phenotype) that have a favorable outcome to IFNa therapy for reasons that are poorly understood.

IL28B (IFNl3) in HCV Viral Clearance and Response to Therapy In addition to the known viral factors (genotype, viral load, sequence variation) and host phenotype (sex, age, body mass index, fibrosis, alcohol consumption) influencing responsiveness, degrees of IFN responsiveness are likely due to multiple genetic variations in the IFN regulatory pathways that may be reflected by the levels of

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hepatic ISG expression. Recent data from genome wide association studies indicate that IL28B (IFNl3) directly influences the outcome of infection (Thomas et al. 2009) and IFNa therapy (Ge et al. 2009; Suppiah et al. 2009; Tanaka et al. 2009), but the mechanisms are not understood. The distribution of the protective genotype in different races seems to account at least in part for the racial differences in response to IFNa therapy and in outcome of acute infection. . Remarkably the protective genotype appears to be recessive, since only homozygous individuals seem to benefit. Associations were detected with SNPs in upstream, downstream, intron and exon regions of the IL28B gene. The responsive genotype for IFN therapy is associated with small increases in the levels of IL28 transcripts in PBMC (Suppiah et al. 2009; Tanaka et al. 2009), and a missense mutation involving a conserved lysine to arginine change at residue 70 of IL28B is in linkage disequilibrium with the upstream SNP with the highest association with outcome of infection (Thomas et al. 2009). Further studies will be required to determine the relevance of the increased expression or the missense mutation and the protective genotype. The lack of association with any polymorphisms in IL28A (IFNl2) is puzzling considering the 96% homology between IL28B and IL28A, but the two cytokines may not be expressed by the same cell types or under the same conditions. IFNls are members of the IL10 superfamily and utilize a heterodimeric receptor comprised of IL28RA and IL10R2 that has a more restricted tissue distribution than the receptor for type I IFNs. IL28RA is an ISG that is induced in the liver in response to IFNa in chimpanzees (Lanford et al. 2006) suggesting that the activity of IFNls would be enhanced during HCV infection and IFNa therapy. IFNl1 (IL29) and IFNl2 have potent HCV antiviral activity in vitro (Marcello et al. 2006; Robek et al. 2005; Zhu et al. 2005), and pegIFNl1 is currently in clinical trials for HCV (Miller et al. 2009). The innate immune response represents a major determinant of clearance in HCV infection due in part to its role in orchestrating the adaptive immune response. However, as described above, data from a number of studies suggest a more direct role in clearance. The genetic variations in IL28B that have a major impact on both the outcome of therapy and acute infection may be functionally related to the phenotypes of high and low hepatic ISG expression in chronic carriers that are associated with outcome of HCV therapy. One possible conclusion from these data is that predetermined characteristics of the innate response influence the adaptive response and together they determine resolution of infection. There are a number of critical steps involving the innate response that aid in priming and maturation of the T cell response including upregulation of MHC class I expression, maturation of dendritic cells, and stimulation of NK cell activity, but the full extent of the immunomodulatory properties of type I and III IFNs is unknown.

T Cell Response and Viral Clearance The correlates of a successful adaptive immune response to HCV have been reviewed in depth elsewhere and will only be briefly discussed here (Bowen and Walker 2005; Rehermann and Nascimbeni 2005). The adaptive immune response

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to HCV including the antibody and T cell response is highly delayed for reasons that are not understood and normally becomes detectable 8–12 weeks after infection coincident with the appearance of liver disease or a rise in serum ALT levels. One of the main predictors of HCV clearance during acute infection is an early and broad T cell response to multiple HCV epitopes (Diepolder et al. 1995; Cooper et al. 1999; Lechner et al. 2000; Thimme et al. 2001), while persistent infection is characterized by a delayed and narrow CTL response that becomes highly anergic as the infection progresses. Immune escape of class I epitopes is common (Weiner et  al. 1995), while escape of class II epitopes in uncommon (Fuller et al. 2010). Mutations occurring outside of T cell epitopes that are not within the envelope region may represent conversion to a more fit consensus sequence (Cox et al. 2005b). Escape variants appear during acute infection with few new epitope specificities appearing during the chronic phase which is consistent with an anergic T cell response (Cox et al. 2005a). A number of markers have been shown to correlate with the exhausted phenotype including PD1, CTLA-4 and TIM-3, but the actual events that precede the failed T cell response are largely unknown. The correlation of PD-1 with T cell exhaustion or anergy is well established. Upregulation of programmed death-1 (PD-1) and downregulation of CD127 have been linked to functional exhaustion of T cells in chronic HCV and other infections (Day et al. 2006; Grakoui et al. 2006; Nakamoto et al. 2008; Radziewicz et  al. 2007; Urbani et  al. 2006) but not all anergic T cells display this phenotype. In chimpanzees that have cleared HCV and are rechallenged, the magnitude and duration of viremia are dramatically reduced or undetectable (Bassett et  al. 2001; Lanford et  al. 2004; Major et  al. 2002; Nascimbeni et  al. 2003; Bukh et  al. 2008; Prince et  al. 2005). These studies alone suggest that the adaptive immune response plays an essential role in viral clearance; however, a strong innate response is detected in the liver during rechallenge including the chemokines that recruit nonspecific macrophages, NK cells and dendritic cells that are involved in the secretion of proinflammatory cytokines. Thus, it is not possible to entirely discount the role of the innate response in the elimination of infected cells or clearance of viral RNA by noncytolytic mechanisms. As discussed above, genetic studies have confirmed the role of the NK response in controlling low dose exposures (Khakoo et al. 2004), and studies in humans have also demonstrated that prior exposure and clearance provide protection from chronicity during subsequent exposures (Mehta et  al. 2002; Osburn et al. 2010). The most compelling evidence of the essential nature of the T cell response comes from antibody depletion of T cell subsets in the setting of rechallenge studies in the chimpanzee. Antibody mediated depletion of CD4 T cells prior to rechallenge in an animal that has previously cleared virus results in persistent infection, thus there is a continuous requirement for CD4 help of the CD8 CTLs (Grakoui et  al. 2003). Depletion of CD8 T cells in the same setting prolongs the viremic phase until HCV-specific CD8 T cells return to the liver (Shoukry et al. 2003).

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Conclusions The innate immune response performs several essential roles in the elimination or control of HCV infections. A rapid and prominent ISG response is detected in the liver involving hundreds of genes, many of which may exhibit direct antiviral activity. The source of the stimulus for the ISG response remains to be determined, but it is likely responsible for the containment of the infection, since only a low percentage of hepatocytes are infected during both acute and chronic infections. In addition, antigen nonspecific immune cells are recruited to the liver and activated by the chemokines and cytokines that are part of the robust ISG response. These innate effector cells may be responsible for killing infected cells prior to the appearance of the antigen specific T cell response. The ISG response that maintains the recruitment and activation of antigen nonspecific cells continues throughout the chronic phase, thus this same response may be responsible for a significant percentage of the cell killing occurring in the chronic phase as well, when the CD8 T cell response has become ineffective and anergic. Finally, the innate response plays an essential role in orchestrating the adaptive T cell response through the induction of HLA for antigen presentation, activation and maturation of dendritic cells and macrophages, and production of the chemokines required to home activated T cells to the liver. Genetic variations in the innate response such as the IL28B and KIR/ HLA-C polymorphisms suggest that the innate response may predetermine whether viral clearance or persistence occurs in HCV exposed individuals. The essential role of the T cell response is well documented, but it is not known whether the critical events determining persistence have occurred before it emerges and help define its effectiveness.

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Immune Signatures and Systems Biology of Vaccines F.M. Buonaguro, M.L. Tornesello, and L. Buonaguro

Abstract  Vaccines represent a strategic successful tool to prevent or contain diseases with high morbidity or mortality. However, despite the extensive and wide use, we still have a limited knowledge on mechanisms underlying the effective elicitation of protective immune responses by vaccines, which represents the final outcome of a effective cooperation between the innate and adaptive arms of the immunity. Immunity is made of a multifaceted set of integrated responses involving a dynamic interaction of thousands of molecules, whose list is constantly updated to fill the several empty spaces of this puzzle. The recent development of new technologies and computational tools allows to perform a comprehensive and quantitative analysis of the interactions between all of the components of immunity over time. Here we review the role of the innate immunity in the host response to vaccine antigens and the potential of systems biology in providing relevant and novel insights in the mechanisms of action of vaccines in order to improve their design and effectiveness. Keywords  Innate immunity • PRRs • PAMPs • TLRs • APCs • Adaptive immunity • Vaccine • Adjuvants • Immune memory • Systems biology • Immunogenomics • Proteomics

 L. Buonaguro (*) Lab. Mol Biology and Viral Oncogenesis and AIDS Reference Center, Istituto Nazionale Tumori “Fond. G. Pascale”, Via Mariano Semmola, 1, 80131 Naples, Italy e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_10, © Springer Science+Business Media, LLC 2011

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Vaccine Development Immune Response The mammalian immune system is based on two arms, innate and adaptive immunity. The innate immune system recognizes invading pathogens through receptors defined pathogen recognition receptors (PRRs), is relatively nonspecific and does not induce immune memory. The innate immune system acts at the first early phase of the immune response, involves cellular (macrophages, dendritic cells [DCs], neutophils, natural killers) and humoral components (cytokines and chemokines) and is crucial for triggering and amplifying the adaptive immune response. The second late phase of the immune response is carried out by the adaptive immunity, involving cellular (T and B lymphocytes) and humoral components (antibodies), which plays a major role in eliminating pathogens. A hallmark of the adaptive immunity is its ability to generate a pathogen-specific immunological memory, based on clonal lymphocytes which are readily activated at a later encounter with the same foreign antigen in the form of vaccine or pathogen (Kaech et al. 2002). The fundamental property of the immune system as whole “to generate an antigenspecific immunological memory” is the basis for vaccination, which represents the most effective measure in preventing and treating infectious and noninfectious diseases. To this aim, the goal of a successful vaccine is to induce long-term protective immunity. An effective vaccine needs to mimic as close as possible the “real” biological entity from which it is derived (i.e., pathogen, cancer cell); therefore, it is recognized by the host immune system as real “danger” and initiates the cascade of molecular and cellular events involving the innate immune system and, downstream, the adaptive immunity to establish the immunological memory. However, most successful vaccines have been empirically derived and the immunological mechanisms underlying the effective induction of long-term protective immunity remain largely unknown (Plotkin 2005; Rappuoli 2004). Several levels of cross-talks between the innate and adaptive immune systems are required for an effective immune response. In particular, it is a recent acquisition that induction of co-stimulatory molecules and secretion of cytokines and chemokines by the cells of the innate immune system significantly affect the quality and quantities of T and B cells of the adaptive immune system. In this chapter, we will address the current understanding of the molecular and cellular events in immune response to vaccine antigens and the most advanced experimental system levels strategies involved in these studies.

Vaccines Most of the current successful vaccines are based on live attenuated or inactivated pathogen “particles” carrying their own unique and specific antigens, with definite main characteristics distinguishing the two strategies.

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The live attenuated vaccines are a weakened form of the “wild” viruses with a limited replication of the virus in the host upon injection. Live attenuated viral vaccines, indeed, carry the native pathogen-associated molecular signals “PAMS” (i.e., viral genetic material) which trigger the activation of the innate immune system binding to the PRRs. These particles, closely mimic a natural infection and spread to multiple host immune organs or tissues where are taken up by DCs or other antigen presenting cells (APCs) which migrate towards lymphoid organs to present the antigens to T and B lymphocytes and initiate adaptive immune response . They elicit immune responses similar to those from natural infections and often are effective after a single administration (Bachmann et al. 1998). However, such vaccines may cause mild-to-severe adverse effects in patients, often as consequence of the limited replication in the host. On the contrary, the inactivated vaccines cannot replicate due to irreversible damage of genetic material induced by heat or chemical treatment. They are safer than live vaccines, lacking of any replication in the host, but generally less effective, requiring multiple administrations to boost the immune response antibody titer over time. The inactivated vaccines are made as whole cell or as subunit vaccines, the latter based on the delivery of the individual viral proteins relevant for conferring protective immunity. Recent advances in genomics and proteomics have provided essential tools to develop alternative nonreplicating vaccine strategy, including recombinant proteins, synthetic peptides, DNA, particulate structures (i.e., Virus-Like Particles). Nonreplicating vaccines activate innate responses only at their site of injection and, considering the high number of DCs in the skin dermis (Banchereau and Steinman 1998), intradermal skin immunization may result in a more effective induction of protective immune responses reducing the vaccine dose (Mikszta et al. 2006; Pancharoen et al. 2005; Van et al. 2009). However, while the selected antigens, free of any potentially infectious material, offer important safety advantages, they are not always effectively processed and presented by the immune system. In particular, it has been shown that subunit split influenza vaccine is much less efficient than live or heat-inactivated whole viruses in activating expression of co-stimulatory molecules (CD80, CD86, CD40) and human leukocyte antigen-DR (HLA-DR) as well as production of IFN-g upon in vitro stimulation of PBMC. Therefore, most formulations of nonliving vaccines must include an adjuvant as “danger” signal to trigger a sufficient activation of the innate system and, downstream, of the adaptive immune response (Pashine et  al. 2005).

Innate Immunity and Vaccine Recognition Pathogen recognition receptors (PRRs) are expressed constitutively in the host on cells of the innate immune system (i.e., Antigen-presenting cells, APCs) and detect foreign antigens in the form of pathogen or vaccine (Akira et al. 2006; Pulendran and Ahmed 2006), activating specific signaling pathways to drive biological and

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immunological responses. Among the PRRs, a key role is played by Toll-like receptors (TLRs) which are widely expressed on innate immune cells (including DCs, macrophages, mast cells, neutrophils), endothelial cells, and fibroblasts. Recent findings show that functional TLRs are expressed also on epithelial as well as cancer cells (Sato et al. 2009). Their expression is regulated rapidly in response to foreign antigens (including vaccines), a variety of cytokines, and environmental stresses (Beutler 2004; Germain 2004; Janeway and Medzhitov 2002; Takeda et al. 2003). TLRs are type I integral membrane glycoproteins with extracellular domains containing varying numbers of leucine-rich-repeat motifs and a cytoplasmic signaling domain homologous to that of the interleukin-1 receptor (IL-1R), termed the Toll/IL-1R homology domain (Bowie and O’Neill 2000). To date, 12 members of the TLR family have been identified in mammals and different TLRs are expressed extra or intracellularly. In particular, TLRs 1, 2, 4, 5, and 6 are expressed on the cell surface, whereas TLR3, 7, 8, and 9 are found almost exclusively in intracellular compartments such as endosomes (Akira et  al. 2006; Pulendran and Ahmed 2006). TLRs recognize structural components shared by many bacteria, viruses and fungi (Takeda et al. 2003). Examples of such components include lipopolysaccharides (LPS; recognized by TLR4) (Poltorak et al. 1998; Shimazu et al. 1999), lipopeptides (by cooperation of TLR2 with TLR1 or TLR6) (Alexopoulou et al. 2002; Ozinsky et al. 2000; Takeuchi et al. 2001, 2002), viral single- or double-stranded RNA (by TLR7 with TLR8 and by TLR3, respectively) (Alexopoulou et al. 2001; Diebold et al. 2004; Heil et al. 2004; Hemmi et al. 2002), bacterial or viral DNA containing CpG motifs (by TLR9) (Ahmad-Nejad et al. 2002; Latz et al. 2004b) and flagellin (by TLR5) (Hayashi et al. 2001). Although much research has focused on the TLR family as innate sensing receptors, other important families of PRRs are plasma-membrane and cytoplasmic receptors, including the C type lectins and NOD proteins (Akira et  al. 2006; Geijtenbeek et al. 2004; Inohara et al. 2005). The C type lectins such as DC-specific ICAM-3 grabbing nonintegrin (DC-SIGN) and DC-associated C-type lectin-1 (Dentin-1) recognize a range of microbial stimuli from pathogens such as HIV, HCV, Helicobacter pylori, and Mycobacterium tuberculosis (den Dunnen et  al. 2009). NOD proteins recognize components of intracellular bacteria (Inohara et al. 2005). Furthermore, viral nucleic acids can also signal through TLR-independent mechanisms. For example, RIG-I and melanoma differentiation-associated gene 5 (Mda5; also called helicard), both intracellular RNA helicases, can sense dsRNA (Yoneyama et al. 2004, 2005; Yoneyama and Fujita 2009) (Fig. 1).

Innate Signaling and Translation to the Adaptive Immune Response The interaction between TLRs expressed on innate immune cells and the foreign antigen expressed by the vaccine triggers a downstream signaling cascade leading

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Fig. 1  Schematic representation of the structure and main signaling pathways of the PRR families

to several cellular processes, including production of proinflammatory cytokines and chemokines (Takeda and Akira 2007). TLR activation results in the engagement of signaling intermediates, including myeloid differentiation factor-88 (MyD88), Toll–interleukin (IL)-1 receptor (TIR)-associated-protein (TIRAP, also known as MAL); Toll receptor-associated activator of interferon (TRIF), Toll receptor-associated molecule (TRAM), IL-1 receptor-associated kinases (IRAK) and tumor necrosis factor (TNF) receptor-associated factor 6 (TRAF6) (Akira et  al. 2006; Takeda and Akira 2007). The endpoint of this signaling cascade is the activation of transcription factors (IRF3, IRF7, AP-1, NF-kB) inducing the activation of inflammatory cytokine genes, such as TNF-b, IL-6, IL-1b, and IL-12. Consequent to the TLR-ligand interaction, APCs uptake and process vaccine antigens to be exposed on cell membrane surface in association to major histocompatibility complex (MHC) molecules for efficient presentation to adaptive immune cells (Villadangos and Young 2008). In order to be effective, this process requires the full activation and maturation of APCs characterized by the increased expression of co-stimulatory molecules (CD40, CD80, CD86), production of chemokines (TNF-alpha, RANTES, MIP1a, MIP1b) and their migration from the infection site to the regional lymph node, where the induction of T and B cell responses occurs. Among APCs, DCs are known to have the unique and most potent capacity to provide antigen-specific activation as well as co-stimulatory signals to naïve T cells and possibly B cells, representing the bridging cell between the innate and adaptive immune system (Alvarez et al. 2008; Pasare and Medzhitov 2005; von Andrian and Mempel 2003).

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Hitting Innate Immune System by Vaccines The antigen screening by DCs is mediated by TLRs and the engagement of TLR by the vaccine antigen allows the APCs to identify nonself foreign antigens, resulting in enhanced immunogenicity and presentation of the antigen by MHC molecules. Consequently, vaccine formulations should be able to activate the innate PRRs, in order to be successful in eliciting an effective adaptive immune response. For most currently licensed vaccines, the degree of engagement of TLRs has not been studied, with only few exceptions. The Bacillus Calmette–Guerin (BCG) has been shown to engage the TLR-2 and 4 (Tsuji et al. 2000; Uehori et al. 2003); the Haemophilus influenzae type b (HiB) vaccine, conjugated with neisserial outermembrane protein complex (OMPC), interacts with the TLR-2 (Latz et al. 2004a); the live attenuated yellow fever vaccine has been demonstrated to activate multiple DC subsets via TLRs 2, 7, 8 and 9 (Querec et  al. 2006). For many other licensed vaccines the engagement of TLRs has not been documented but it is possible to guess based on studies performed on the original virus from which the vaccine is derived. In general, TLR activation is very likely to play a key role in the protective immunity induced by live attenuated vaccines. Single-stranded RNA of live attenuated (cold adapted) influenza vaccines, indeed, are likely to activate TLR 3 and 7 during intra-cellular replication leading to the up-regulation of inflammatory cytokines (Diebold 2008; Lund et al. 2004). Similarly, TLR9 has been demonstrated to be activated by bacterial and viral DNA containing unmethylated CpG motifs (Hemmi et al. 2000, 2003). On the contrary, there is no evidence to date that the immunogenicity of vaccines composed of killed pathogens or subunit elements is mediated by TLR engagement and may explain the generally observed lower and short-lasting immune response. In these cases, the vaccine effectiveness may greatly benefit from the addition of an adjuvant in the formulation. Very few adjuvants are approved for human use, including Alum, MF59 (Ott et al. 1995), MPL (Baldridge and Crane 1999), AS04 (consisting of MPL adsorbed on alum), (Keam and Harper 2008; Tong et al. 2005), immunopotentiating reconstituted influenza virosomes (IRIVs) (Gluck et al. 2004; Holzer et  al. 1996) and Cholera toxin B subunit (CTB) (Ryan and Calderwood 2000). Of these, only MPL is known to engage a TLR (TLR-4), being a nontoxic derivative of the lipopolysaccharide (LPS) of Salmonella Minnesota. Many new vaccine adjuvants are under development and, since the recognition of the TLRs as key molecules for the effective activation of adaptive immune response, most of them are selected because of their properties to activate TLRs and evaluated in preclinical and human clinical trials (Fraser et  al. 2007; Kang and Compans 2009; Kwissa et al. 2007b; Lahiri et al. 2008; Reed et al. 2009). Recent evidence suggests that several adjuvants might function in a TLRindependent pathway. Intracellular NOD-like receptors (NLRs) and retinoic acid inducible gene (RIG)-like receptors might play an important role in this regard. Activation of these pathways might play a significant role in adjuvant activity of some bacterial-product-based vaccine adjuvants (e.g., cell wall, flagellin or RNA based) (Ishii and Akira 2007; Monie et al. 2009).

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TLRs Signaling for a Potent and Prolonged Adaptive Immune Responses The ultimate goal of vaccination is to induce potent as well as long-term protective immunity, which is a hallmark of adaptive immunity. The long-term protective immunity is provided by the vaccine antigen-specific immune effectors and the induction of immune memory cells that can be efficiently and rapidly reactivated upon pathogen exposure. Adaptive immune responses are initiated in the T cell-rich areas of the secondary lymphoid organs, where antigen-bearing DCs migrate from the site of vaccination to encounter naïve T cells which undergo to clonal expansion and differentiation into effector CD4+ T helper cells or CD8+ cytotoxic T lymphocytes (CTL). In particular, antigen-specific activated CD4+ T-helper cells support the generation and maintenance of both humoral antibody-based B cell and cellular CD8+ CTL responses. CD4+ T-helper cells, indeed, can be directed into a Th1, Th2 or T-Reg polarization upon direct contact with antigen-bearing APCs and induction by specific cytokines (Groux et al. 1997; McGuirk and Mills 2002; Moser and Murphy 2000; Mowen and Glimcher 2004; O’Garra and Robinson 2004; O’Garra and Vieira 2004). The polarized cells can antagonize each other’s actions, by blocking either the polarized maturation of the opposite cell type or its receptor functions (Lafaille 1998). Consequently, the T-helper cells polarization will ultimately lead the adaptive immune system toward either a cellular T-cell, sustained by CD8+ CTL (Th1), or a humoral antibody (Th2) or a tolerance (T-Reg) response. After removal of the antigen, a subset of polarized activated effector T cells survive and further differentiate into long-lasting memory T cells whose numbers are maintained over time, in order to readily induce an immune response at subsequent encounters with the same antigen (Kaech et al. 2002). Needless to say that generation of a potent memory response after vaccination is a crucial point that has to be addressed during an effective vaccine design. The considerable amount of available information on the role of the TLRs to link innate with adaptive immunity allows to exploit these biological properties on a knowledge-based approach, to optimize both magnitude and type of immune as well as memory response induced by vaccine candidates.

Vaccines Inducing Humoral or Cellular Immune Response Most vaccines licensed so far induce a long-lasting protective humoral adaptive immune response and have been successful because the targeted pathogens can be entirely prevented by appropriate amounts of immunoglobulin G antibodies in the serum (Rappuoli 2007; Robbins et al. 1995, 1996). Nevertheless, although not experimentally proven, a common belief is that for some vaccines, the induced protection seems to go beyond the effect mediated by the antibodies alone (Belshe et al. 2007; Kimberlin and Whitley 2007).

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In the last years, the development of vaccines able to elicit also an effective cellular adaptive immune response is considered of high priority. In fact, if antibodybased vaccines provide prevention and protection from infection, T-cell–based vaccines may be relevant in controlling an established chronic infection, such as HCV and HIV viruses (Houghton and Abrignani 2005; Korber et al. 2009; RerksNgarm et  al. 2009; Sallberg et  al. 2009), or a cancer (Banchereau et  al. 2009; Draper and Heeney 2010; Sioud 2009). In this respect, the engagement of TLRs by the vaccine antigen, besides the upregulation of co-stimulatory molecules in the APCs, leads mainly to production of Th-1 cytokines like IL-1, IL-6, TNF and IL-12 (Brightbill et al. 1999). In particular, the TLR-3 ligand poly I:C, a synthetic analog of dsRNA, has been shown to have a potent adjuvanting effect in several experimental systems (Asahi-Ozaki et al. 2006; Longhi et al. 2009; Navabi et al. 2009). A modified imidazoquinoline ligand for the TLR7 (3M-019) has also been shown to be a potent adjuvant for protein vaccines (Johnston et  al. 2007) and TLR7/8 agonists enhanced the generation of Th1 response and CD8 T cell proliferation of HIV Gag protein (Wille-Reece et  al. 2006). Bacterial unmethylated CpG DNA is recognized by TLR9 and the therapeutic potential has been well documented in different pathophysiological conditions like cancer, infectious disease and asthma (Krieg 2006, 2008). A combination of CpG DNA and the TLR8 synthetic agonist, R-848, induced an enhanced production of antibody and memory T cell against the HBS-Ag vaccination (Ma et al. 2007). CpG DNA induced a significant enhancement of the magnitude of the cytokine production, antigen-specific CD8+ T cell response and control of viral loads after challenge with SIV (Kwissa et  al. 2007a). Finally, CpG DNA induced rapid and strong antigen-specific T cell responses to melanoma antigen A (Melan-A; identical to MART-1) in a phase I clinical trial (Speiser et al. 2005). Likewise, TLR ligands have been identified to further stimulate Th2 responses (Agrawal et al. 2003; Dillon et al. 2006; Dillon et al. 2004; Redecke et al. 2004). Bacterial lipoproteins, like outer-surface lipoprotein (OspA), have been found to exert adjuvant function in Lyme disease vaccination engaging the TLR1 (Alexopoulou et al. 2002; Thomas and Fikrig 2002). Flagellin, which is present in the flagellar structure of many bacteria, is recognized by TLR5 and has been shown to greatly enhance immune response to influenza vaccine candidate (Huleatt et al. 2008). Only a subset of the TLRs (TLRs 3, 4, 7, and 9) expressed on plasmacytoid DCs can induce type 1 interferons, which are important for antiviral defense (Gilliet et al. 2008; Liu 2005). Furthermore, aiming to improve the immunological memory of the antigen delivered with the vaccine, TLR ligands like poly I:C and CpG DNA have been shown to enhance the survival of activated CD4+ T helper cells, through NF-kB activation, both in vitro and in vivo without any help from the APCs (Gelman et al. 2004). In a subsequent study using poly I:C or CpG DNA, it was observed that fully functional CD8 memory cells were generated without any CD4 help in the presence of TLR ligands (Assudani et  al. 2008; Hervas-Stubbs et  al. 2007). Furthermore, TLR7/8 agonist has been shown to enhance both effector and memory T cell response to HIV gag antigen(Wille-Reece et al. 2006).

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Platform of System Levels Analyses Systems Biology in Vaccine Studies The innate immune system is at the interface between the vaccine antigen and the host’s adaptive immune response, therefore, the evaluation of the molecular effects induced by vaccines on PRRs biology is of high relevance. Studying molecular signatures that are induced rapidly after antigen presentation will predict, indeed, the subsequent development of protective immune responses, enabling the evaluation of the efficacy or immunogenicity of untested vaccines in the general population or the identification of unresponsive individuals to vaccination. Furthermore, the predictive signatures would uncover new correlates of protection and further decipher the biological mechanisms by which such molecular signatures modulate vaccine-induced immunity and protection. In order to have new insights into the complex nature of the interactions within the ménage à trois among the three player of this game (vaccine, innate and adaptive immunity), it is necessary to employ the detailed level of investigation provided by systems biology approaches. The advancement of high-throughput technologies, together with the extensive identification of new genes, proteins and other biomolecules in the “omics” era, has facilitated large-scale biological measurements. The new experimental paradigm of systems biology aims to consider a biological system as not just a set of distinct elements, but rather as a complex product of the inter­ actions among these elements and their relationship with the surrounding environment. Despite the increasing use of such approaches in prognosis and therapy response prediction in oncology (Alizadeh and Staudt 2000; Potti et  al. 2006; Sorlie et  al. 2001) as well as in autoimmunity and infections (Chaussabel et  al. 2008; Ramilo et al. 2007), they have only recently been applied to vaccinology (Fig. 2).

Role of Transcriptomics Transcriptomics represents the most widely used methods for studying the immune response on a genome-wide scale, enabling to identify specific set of genes and pathways differentially regulated upon encounter with a foreign antigen. DNA microarray technology has led to several new insights into interactions between pathogens and innate immunity, which represent the background information for understanding and predicting the host response to vaccines (Elkon et  al. 2007; Jenner and Young 2005; Leber et  al. 2008; McCaffrey et  al. 2004; RicciardiCastagnoli and Granucci 2002). The near future of transcriptomics is represented by new technologies, including next-generation sequencing (Wang et al. 2009) and exon and microRNA arrays. However, in order to reduce the number of meaningless observations, the results generated by transcriptomics studies need to be integrated by meta-analysis

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The technologies of Systems Biology Genomics • High throughput DNA sequencing

Transcriptomics • Gene transcript measurement • SAGE • gene chips • microarrays

Proteomics • • • • • •

Mass spectometry 2D – PAGE Protein chips Yeast-2-hybrid X-ray NMR

Metabolomics • NMR • X-ray • Capillary electrophoresis

Genetics • SNPs • miRNA profiling • SiRNA screens • ENU random mutagenesis

Computational Biology and mathematical modeling

Data integration leveraging multi-disciplinary collaboration

Biomarker discovery Rationale vaccine design Biomarkers for responsiveness

Fig.  2  Systems biology approaches for vaccine studies interactions and the implications on translational research

performed on multiple independent datasets, in order to select only genes behaving similarly across several independent experiments. Meta-analysis requires access to several accessible data and in the last years several publicly available databases of innate (and adaptive) immunology-related transcriptomics datasets have been created, including the reference database of immune cells (RefDIC) (Hijikata et al. 2007), the immune response in silico database (IRIS) (Abbas et al. 2005) and the innate immune database (IIDB) (Korb et  al. 2008) (for a more complete list of databases, please refer to Ref. (Gardy et  al. 2009)). Furthermore, in order to improve integration of immunology datasets of these different databases, the Immunological Genome Project initiative has been recently established with the ambitious goal to combine immunology and computational biology laboratories in a systems-level approach (Heng and Painter 2008). Meta-analysis combining data from 32 in vitro human studies enabled the identification of an expression signature made of a cluster of 511 genes, which are induced in many different cell types in response to exposure to several different pathogen species and designated as the “common host response” (Jenner and Young 2005). In particular, these genes can be divided in few discrete clusters including cytokine genes, IFN-stimulated genes (ISGs), transcriptional factors and components of signal transduction pathways, genes that limit the immune response and allow the cell to return to its unactivated state as well as genes that have not previously been associated with the immune response. The identification of this shared transcriptional program to foreign antigens among different host cells, even outside of the immune system, suggest the evolution of a multicellular and multicompartmental line of host defense to infection.

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Proteomics Proteomics gives a comprehensive picture of the immune interactome, representing the interactions involving the genes and gene products known to participate in the immune response. Although much of these data was present in the biomedical literature, they were not organized in databases crucial for systems-level analyses. The first attempt to build an innate immunity-specific interaction database was a map of the mammalian TLR signaling network (Oda and Kitano 2006). More recently, the InnateDB project has been established and more than 7,000 innate immunity-relevant interactions involving 2,000 human and mouse genes have collated, reviewed, annotated and made available, up to now (Lynn et al. 2008). The relevance of proteomics data in the vaccinology field is represented by new insights in the host–pathogen innate immunity interactome which can be readily and valuably transferred to vaccine development. Several viral proteins, indeed, are involved in viral evasion of the innate immune system through interactions with key proteins in innate immunity pathways (Bowie and Unterholzner 2008) and, in this respect, a proteome-wide map of the interactions between hepatitis C virus-encoded proteins and human proteins has been recently described (de Chassey et  al. 2008). Furthermore, few databases have been recently established to provide online resources describing known host-pathogen protein interactions (Ceol et al. 2010; Chatr-aryamontri et al. 2009; Driscoll et al. 2009; Navratil et al. 2009). In addition to studies on protein-to-protein interactions, a field of active research is focused on how innate immune response is influenced by post-translational modifications and protein dynamics. The so-called “phosphoproteomics” will help understanding the dynamics of the innate immune response signaling cascades, providing a global picture of protein phosphorylation as marker of the cellular signal transduction, to identify the complete “kinome” of a cell. Proof of concept of such approach have been recently reported (Bakal et al. 2008; Stahl et al. 2009).

RNAi and miRNA to Study Innate Immunity Functional annotation of several genes in a single experimental series is now possible by large-scale RNAi screens, in which genes are progressively knocked down using appropriate inhibitory RNA molecules. This technology has the potential to rapidly identify innate immune signaling genes involved in host-pathogen interactions and allowed the identification of the host proteins involved in HIV and West Nile virus infections (Brass et al. 2008; Krishnan et al. 2008). Similarly, the profiling of miRNAs expression can provide valuable information on how these post-transcriptional regulators of gene expression can modulate genes relevant to innate immunity and, therefore, the immune response itself.

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Microarray-based platforms that enable the profiling of miRNA expression on a large or even global scale are also now available (Yin et al. 2008) and recently the leukocyte miRNA response to LPS stimulation has been investigated (Schmidt et al. 2009; Vasilescu et al. 2009; Zhou et al. 2009).

Genetic Polymorphisms in Innate Immunity Genes Genetic polymorphisms can adversely affect expression of genes as well as proteins of the innate immune system and, consequently, host-pathogen interactions and molecular signaling. This represents an additional level of analysis to be included in the global evaluation of factors involved in the host response to foreign antigens (Bochud et al. 2007a; Dickinson and Holler 2008; Georgel et al. 2009; Misch and Hawn 2008). The association between polymorphisms in TLR9 gene and clinical course of HIV-1 infection (Bochud et al. 2007b) as well as susceptibility to tuberculosis and specific polymorphisms in the TLR2 gene have been described (Ogus et al. 2004; Yim et al. 2006; Yoshida et al. 2009). Two nonsynonymous SNPs in the extracellular domain of TLR4 found to be in linkage disequilibrium and polymorphisms in the TLR4 gene have been associated with an increased susceptibility to several infections (Ferwerda et al. 2007, 2008; Lorenz et al. 2002; Miyairi and DeVincenzo 2008; Nguyen et al. 2009; Rezazadeh et al. 2006), although these findings are controversial and not consistently confirmed (Allen et  al. 2003; Read et  al. 2001; Smirnova et al. 2003). Implications on infectious diseases progression of polymorphisms in other TLRs genes have been reported (Pine et al. 2009; Velez et al. 2010; Wurfel et al. 2008).

Systems Biology for Prediction of Vaccine Immunogenicity Systems level studies have been recently performed to identify gene “signatures” in humans predicting immune responses to yellow fever vaccine (YF-17D) (Gaucher et  al. 2008; Querec et  al. 2009) and VLP-based HIV vaccine (Aricò et  al. 2005; Buonaguro et  al. 2006, 2009a; Monaco et  al. 2009). In parallel, a role for TLRs polymorphisms in immune response to vaccines has been demonstrated for measle (Dhiman et  al. 2008), rubella (Ovsyannikova et  al. 2010) and pertussis (PT) (Kimman et al. 2008). Gene Signatures of Yellow Fever Vaccine YF-17D Subjects vaccinated with YF-17D showed a significant variable response over time in terms of both magnitude of the antigen specific CD8+ T cell responses

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and neutralizing antibody titers. Gene transcriptional profile of early innate immune genes in PBMCs from vaccinated individuals showed that vaccination with YF-17D induced in most of the vaccinees a molecular signature including several genes involved in innate sensing of viruses and antiviral immunity. It was also recently reported that the YF17D virus activates DCs by triggering their TLR2, 7, 8, and 9 (Querec et  al. 2006). Among these, indeed, were observed genes encoding innate sensing receptors (i.e., TLR7, RIG-I), transcription factors that regulate the expression of type I IFNs, IFN regulatory factor 7 (IRF7) and signal transducer and activator of transcription 1 (STAT1). Furthermore, genes encoding proteins in the complement pathway (i.e., C1QB) and the inflammasome were induced. In particular, a group of transcription factors, including IRF7, STAT1 and ETS2, were identified as key regulators of the early innate immune response to the YF-17D vaccine (Gaucher et  al. 2008; Querec et  al. 2009). In particular, ETS2 is involved in immune response since its expression is up-regulated in activated and proliferating T cells (Bhat et al. 1990; Gallant and Gilkeson 2006) and induces in IL-12 p40 (Th1) and IL-5 (Th2) gene expression (Blumenthal et al. 1999; Sun et al. 2006). Furthermore, the enhanced transcription of several downstream genes that play critical roles in the maturation and differentiation of T cells, B cells, NK cells, and macrophages was observed (Gaucher et al. 2008). The upregulation of this antiviral molecular signature for more than 2 weeks post vaccination is possibly reflecting the ongoing stimulation of innate immune cells in response to viral replication of the live attenuated YF-17D vaccine, which peaks at 7 days (Monath 2005). However, there was no correlation between the induction of these genes and the magnitude of the CD8+ T cell or neutralizing antibody response, possibly implying that the observed molecular signature is only due to the virus replication. Further bioinformatics approaches, applied in a second YF-17D vaccine trial, identified two genes — solute carrier family 2, member 6 (SLC2A6) and eukaryotic translation initiation factor 2 alpha kinase 4 (EIF2AK4) – that did correlate (at 90% accuracy) with the magnitude of antigen specific CD8+ T cell responses and antibody titers. In particular, EIF2AK4 regulates protein synthesis in response to environmental stresses by phosphorylating elongation initiation factor 2a (eIF2a) (Richter and Sonenberg 2005; Ron and Walter 2007). Indeed, YF-17D vaccination induced the phosphorylation of eIF2a as well as the formation of stress granules, and other genes involved in the stress response pathway correlated with the CD8+ T cell response (Querec et al. 2009). The TnF receptor superfamily, receptor 17 (TnFRSF17), which is a receptor for B cell-activating factor (BAFF), was shown to be a key gene in the predictive signatures. BAFF, indeed, is thought to optimize B cell responses to B cell receptorand TlR-dependent signaling (Hoek et al. 2009; Khan 2009). Furthermore, PBMCs isolated from YF17D-vaccinated volunteers display a mixed T helper cell phenotype with the induction of a mixed Th1/Th2 profile (Gaucher et al. 2008; Santos et al. 2008).

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Gene Signatures of HIV-VLPs Vaccine Similar studies have been performed using a baculovirus-expressed HIV-VLPs developed in our laboratory. These HIV-VLPs induce HIV-1-specific CD4+ and CD8+ T-cell responses and cross-clade neutralizing antibodies in immunized BALB/c mice (Buonaguro et al. 2002). Moreover, the intraperitoneal and intranasal administration of HIV-VLPAs in mice induces antibody responses at systemic and mucosal (vaginal and intestinal) levels (Buonaguro et al. 2005, 2007). Baculovirus-expressed HIV-VLPs induced maturation and activation of monocytederived DCs (MDDCs) from HIV-1 seronegative subjects and this effect was partially mediated by the internal Toll-like receptors 3 and 9. The HIV-VLP-activated MDDCs produced a pattern of cytokines indicative of both Th1 and Th2 pathways and induced primary and secondary responses in autologous human CD4+ T cells in an ex vivo immunization assay (Buonaguro et  al. 2006). Similar results were observed on whole PBMCs from both HIV-1 seronegative and seropositive subjects and, in the latter group, different levels of circulating HIV viremia did not correlate with different activation pattern (Buonaguro et  al. 2008, 2009a; Monaco et  al. 2009). Some HIV-1-seropositive subjects, however, showed a complete lack of maturation induced by HIV-VLPs in CD14+ circulating cells, which does not consistently correlate with an advanced status of HIV-1 infection, indicating the relevance of this multiparametric approach to identify possible nonresponders. In this experimental setting, HIV-VLPs induced a significantly increased production of Th2 cytokines only, strongly suggesting that specific Th1 adjuvants would be required for therapeutic effectiveness in HIV-1-infected subjects (Buonaguro et  al. 2009a, 2009b). Moreover, the baculovirus-expressed HIV-VLPs induced specific transcriptional profiles of genes involved in the morphological and functional changes characterizing innate and early adaptive immune response. This immune signature was observed in MDDCs (Aricò et al. 2005) as well as in PBMCs from HIV-1 seronegative and seropositive subjects (Buonaguro et al. 2008; Monaco et al. 2009). In particular, as described for the yellow fever live attenuated YF-17D vaccine, HIV-VLPs induced a molecular signature including several genes involved in innate sensing of viruses and antiviral immunity. Expression of proinflammatory mediators CXC-chemokine ligand 10 (CXCL-10) and interleukin-1a (IL-1a) genes were found upregulated. Similarly, several genes were identified encoding innate sensing receptors (i.e., TLR2), transcription factors that regulate the expression of type I IFNs, IFN regulatory factor 1 (IRF1) and signal transducer and activator of transcription 2 (STAT2). The gene signature predictive of both humoral and cellular adaptive immune response included several genes. The CD83 and CD28 genes indicate a strong activation of the Th2 development and B lymphocytes (Andres et al. 2004; Kozlow et al. 1993; Magistrelli et al. 1999). The TnF receptor superfamily, receptor 1B and 6B (TnFRSF1B and TnFRSF6B) are a marker for T and B cell activation (TnFRSF1B) (Beltinger et al. 1996) and for blocking the proapoptotic activity of the FAS-ligand (TnFRSF6B) (Pitti et al. 1998). The TNFSF9 is a T-cell activation marker (Alderson et  al. 1994; Stephan et  al. 2007) and the

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TLR2, TLR7, TLR8, TLR9

PKR, OAS1, OAS2, TRIM5

Innate Immune response

mTOR, IRF1, IRF7

Th2 cells

EIF2AK4, EIF2 2a phosphorilation, JUN, TNFSF9

Th1 cells

CD8+ T cell

TNFRSF17, TNFRSF1B CD83, CD28

B--cell Yellow = YF17D = HIV-VLP Blue Green = shared

Fig.  3  Innate correlates of YF-17D and HIV-VLP immunogenicity identified by systems biological approaches

CD40 is one of the key players in activation of both humoral and cell-mediated immune responses (Kawabe et al. 1994; Schonbeck and Libby 2001). These studies provide a global description of the innate and adaptive immune responses induced by two vaccines based on two different strategies, live attenuated (YF-17D) and nonreplicating Virus-Like Particles (HIV-VLPs), showing the networking of the innate immune response that is required for the induction of effective longlasting immune protection. Of note is the observed commonalities between the signatures induced by the two vaccines, suggesting the possible identification of specific shared predictive gene expression meta-signatures with a broad application in vaccinology (Fig. 3). Polymorphisms and Response to Measle Vaccine Associations between SNPs in TLRs 3, 4, 5 and 6 and the downstream intracellular signaling molecules, MyD88 and MD2, with variations in both antibody and cellular responses following measles vaccination have been recently described (Dhiman et  al. 2008). In particular, the associations between TLR3 and measles vaccine immunity are particularly intriguing given that TLR3 has been previously identified as a prime target for laboratory adapted measles virus strains in the generation of host immunity (Tanabe et al. 2003). A SNP in the 3’UTR of TLR3 (rs5743305 at −976 bp of TLR promoter) has been identified demonstrating an association between heterozygous variant AT and low antibody as well as low lymphoproliferative responses in vaccinees. Similarly, the GA variant of a nonsynonymous SNP also in the TLR3 gene was associated with lower antibody production.

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Moreover, heterozygous variants for two nonsynonymous SNPs (Gly299Asp and Ile399Thr) have been identified in the TLR4 gene and associated with higher IL-4 secretion to the measles vaccine strain. Of note, the same two SNPs have been studied extensively in association with septic shock after infection with gram-nega­tive bacteria, premature birth, myocardial infarction and allograft rejection (Schroder and Schumann 2005). However, given that vaccine strain of measles viruses is known not to signal through TLR4, the biological relevance of the associations between the SNPs in TLR4 and measles vaccine-induced immunity needs further validation. Similarly, associations between SNPs in TLR5 and TLR6 genes and variations in IFN-g secretion in response to measles virus stimulation have been identified, whose significance is still unclear. Associations between SNPs in genes of intracellular signaling molecules associated with TLRs and the immune response to measles have been also investigated and a minor allele variant for a SNP in the 3’UTR of MyD88, the intracellular adaptor molecule that signals for most of the TLRs, was found to be associated with a lower antibody response to measles vaccine. Furthermore, several intronic SNPs in TLR and their associated intracellular molecule genes were significantly associated with variations in cellular immune responses to measles vaccine (Dhiman et al. 2008). Polymorphisms and Response to Rubella Vaccine Associations between SNPs in TLR and the downstream intracellular signaling molecules with variations in both antibody and cellular responses following rubella vaccination have been recently described (Ovsyannikova et al. 2010). Polymorphisms in promoter and intronic regions of TLR3 and TLR4 genes have been found associated with rubella virus specific cytokine immune responses, such as IFN-g, IL-2, TNF-a, and GM-CSF. In particular, two SNPs in the TLR3 gene appear to be significantly associated with lower rubella IFN-g secretion in an allele dose-related manner. Of note, the promoter polymorphism (rs5743305, −8,441 A > T) in the TLR3 gene, associated with rubella virus induced GM-CSF secretion, is the same SNP suggested to be a risk factor for lower antibody and low lympho­ proliferative responses to measles vaccine (Dhiman et  al. 2008). This finding strongly suggests that rs5743305 in the TLR3 gene may play a role in viral immunity and may be a key control point for humoral and cellular immune responses to both measles and rubella vaccines. In the same study have been identified 22 associations of polymorphisms in promoter and intronic regions of vitamin A and vitamin D receptor genes and their downstream mediators of signaling with different immune response to rubellaspecific cytokine. In particular, considering that SNPs in the vitamin D receptor (VDR) genes have been associated also with protection from HIV-1 infection (de la Torre et al. 2008), it can be postulated that proinflammatory immune responses to viral infection or live viral vaccination are influenced by functional polymorphisms in the VDR gene.

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Furthermore, a correlation between GM-CSF, IL-6, TNF-a response and SNPs in the non-TLRs PRRS CARD domain and RIG-I gene has been observed, in agreement with recent observations, suggesting that the IFN-g response to Newcastle disease and Influenza viruses infection in human DCs is strongly dependent on the level of RIG-I (DDX58) regulated by a specific polymorphism (Hu et al. 2007). Finally, associations of polymorphisms in the TRIM5 gene with variations in rubella virus-specific immune responses (TNF-a, GM-CSF and IL-2) have been observed, in concordance with recent findings on the role of the same TRIM5-gene SNPs in the immune response to retroviral (HIV-1) infection (van Manen et  al. 2008). Polymorphisms and Response to Pertussis Vaccine The involvement of TLR4 in immunity to B. pertussis vaccine has been extensively shown and specific SNPs in the promoter region of the TLR4 gene influencing the antibody response to the PT vaccine have been identified (Banus et  al. 2007). Several haplotype-tagging SNPs in genes of the TLR signaling pathway have been further associated with the PT-specific antibody response following vaccination (Kimman et al. 2008). The evidence of association was most consistent and strong for the SNPs in the TOLLIP gene, which showed association in three independent analyses. TOLLIP is a small protein that binds the activated IL-1 receptor type I (IL-1RI) complex, as well as TLR2 and TLR4 complexes, coordinating optimal signaling through IL-1RI and TLR4 (Bulut et al. 2001; Didierlaurent et al. 2006). Furthermore, associations of SNPs in TIRAP and TICAM1 genes and immune response to PT vaccine can be explained by the knowledge that these two factors belong to the Toll/Interleukin-1 receptor (TIR) domain-containing adaptors, also including MyD88, that modulate TLR signaling pathways. Furthermore, the signal transduction mediators of the Toll and IL-1 receptor (IL-1R) families, namely IRAK3 and IRAK4, showed evidence for association with immune response to PT vaccine. The summarized three studies, aiming to identify associations between SNPs in the promoter or intronic regions of PRRs genes and downstream mediators of signaling with different immune response, give strong indications for the involvement of the TLR signaling pathway in the response to vaccination, as well as for the cooperation of its genes in a functional interacting network. Furthermore, they provide evidences that genetic variants are involved in the mechanisms underlying heterogeneous immune responses to vaccines. Of note is the identification of the same SNPs involved in the modulation of immune response to vaccines, suggesting the possible identification of specific shared predictive polymorphisms with a broad application in vaccinology. All these studies, nevertheless, need to be replicated in independent and larger cohorts to validate the findings increasing the statistical association between SNPs and host immune response to vaccines. Overall, these results indicate that a comprehensive analysis at system levels would greatly facilitate screening for responsiveness to vaccines and an understanding of eventual failures in individuals enrolled in clinical trials. On the other hand, it will

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guide the identification of optimal antigens, and antigen formulations (i.e., adjuvanted antigens), inducing the sought cluster of genes and immune pathways leading to the required adaptive immune response.

Conclusions The comprehension of the key role of the innate immunity and in particular of the pathogen recognition receptors (PRRs) in the host responses upon exposure to a foreign antigen, in the form of vaccine or pathogen, is continuously growing. In particular, how the molecular recognition of antigens by cells of the innate immune system plays a significant role in determining the nature as well as the duration (immune memory) of the adaptive T and B cell immune responses. This knowledge acquires even more relevance nowadays when traditional vaccine strategies (i.e., inactivated or killed vaccines) are progressively substituted with safer recombinant vaccines which, however, need to integrate the immunogenic features responsible for effectively inducing neutralizing antibodies or protective immunity. In this regards, the molecular and cellular components of innate and adaptive immunity that are responsible for inducing protective immunity by nonreplicating vaccines mostly remain undefined. The use of TLR signaling to boost vaccine efficacy thus provides a major avenue of further research and the use of several TLR agonists have been documented in several established vaccines. Detailed understanding of the exact mode of action of the synthetic ligands should help to develop more potent vaccine for applicable diseases. In this context, the precise roles played by individual TLRs, specific combinations of TLRs or non-TLR PRRs in the induction of long-term T and B cell responses and memory is an area for fertile exploration. The understanding of the molecular mechanisms involved in the induction of an effective immune response can take a great advantage from a system-levels analysis. Systems biology, indeed, holds considerable promises for discovery and new insights into processes as complex as innate immunity and the downstream adaptive immune response. Systems biology not only has the potential to accelerate the discovery of new regulators of innate immunity but will also provide more comprehensive insights into the kinetics of regulation at the transcriptional, protein–protein interaction and post-transcriptional levels. In this way we might, for example, begin to understand how innate immunity can distinguish between different pathogens and danger signals to mount an appropriate response, despite having a much smaller repertoire of receptors and diversity than is utilized in the adaptive response. All these information are of high impact on vaccine development, providing molecular prediction markers of the immunogenicity of a vaccine, uncovering new correlates of vaccine efficacy as well as guiding the design of new vaccine antigens or formulations. Moreover, such system level approaches could permit the identification of vaccine responders vs. nonresponders, allowing a better immunological coverage of the licensed vaccines.

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In conclusion, this represents the real switch from the “empirical” to the “knowledge-based” age of the vaccinology which should enable the development of even more successful vaccines for preventive as well as therapeutic intervention strategies for human diseases.

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Immune Signatures Associated with the Cancer Bearing State Rebecca J. Critchley-Thorne, Hongxiang Yu, and Peter P. Lee

Abstract  Dysfunction of the host immune system can be detected in many patients with different cancer types, and contributes to tumor escape and failure of therapies that aim to elicit or boost anti-tumor immunity. Yet, immune-mediated tumor rejection and long term cancer control have been documented in a minority of patients. A better understanding of immune dysfunction in the cancer bearing state and the immune mechanisms that lead to tumor rejection will enable design of novel therapeutic approaches to overcome cancer-induced immune dysfunction and augment effective anti-tumor immunity. This chapter discusses the signatures associated with immune dysfunction vs. immune-mediated rejection in cancer patients, and highlights the applications of such signatures in cancer diagnostic testing and as therapeutic opportunities. Keywords  Immune dysfunction • Anergy • Tumor rejection • Immunotherapy • Immune biomarkers

Background Immune dysfunction is an early event in cancer development that continues to develop throughout progression to metastatic disease (Staveley-O’Carroll et  al. 1998). Modulation of the host immune response by cancer is both local and systemic; global immune dysfunction eventually develops in patients with many cancer types (Critchley-Thorne et  al. 2009a). Quantity, phenotype, and functional status of leukocytes can be altered by direct interaction with tumor cells (at the tumor site or tumor-draining lymph nodes) and also via tumor-derived soluble factors, membrane fragments and microvesicles. Endogenous CD8+ T cells specific for tumor-associated antigens (TAAs) are present in patients with many types of

P.P. Lee (*) Division of Hematology, Department of Medicine, Stanford University, Stanford, CA 94305, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_11, © Springer Science+Business Media, LLC 2011

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cancer; such cells can also be elicited by vaccines and immunotherapies in cancer patients. However, in the majority of cancer patients, TAA-specific CD8+ T cells do not appear to control tumor progression or lead to tumor rejection. Multiple aspects of tumor-immune interactions and immune dysfunction have been identified, however, the mechanisms by which the immune system is impaired in the cancer state are still poorly understood. Immune dysfunction contributes to tumor progression and also poses a significant barrier to cancer immunotherapies, which have yielded disappointing results in clinical trials. A clear understanding of the mechanisms underlying immune dysfunction in the cancer state may enable development of therapies to overcome immune dysfunction and enable effective immune-mediated control of tumors. It is equally important to understand the mechanisms of immune-mediated tumor rejection. While immune dysfunction is evident in the majority of patients and in many cancer types, there are limited numbers of cases of spontaneous or therapy-induced immune-mediated tumor control and rejection. An understanding of the processes involved in tumor rejection will aid in the design of novel immunotherapy approaches to potentiate this process in cancer patients. Immune signatures associated with the cancer bearing state have multiple applications as targets for immunotherapies and as biomarkers in cancer diagnostic testing.

Signatures of Immune Dysfunction in Cancer Patients Multiple signatures of immune modulation and dysfunction have been described in many types of cancer. Tumors evolve mechanisms to escape immune surveillances by progressively disrupting different immune cells in multiple respects.

Increased Apoptosis of Anti-Tumor Effector Cells The viability of leukocytes is affected at the tumor site, draining lymph nodes and in peripheral blood due to “tumor counterattack” mediated directly by interaction with tumor cells, or indirectly via tumor-derived soluble factors and membrane fragments/microvesicles shed from tumors. Increased apoptosis of T cells, natural killer (NK) cells, and dendritic cells (DCs), due in part to interaction with tumorexpressed death ligands, has been observed in the blood of cancer patients and is associated with aberrant homeostasis of these leukocyte subsets (Gastman et  al. 1999; Hoffmann et al. 2002a; Bauernhofer et al. 2003a; Pinzon-Charry et al. 2006). Higher percentages of peripheral blood T cells, and also NK cells and monocytes, are Fas+ in cancer patients (Yuen et al. 2001; Hoffmann et al. 2002a; Bauernhofer et al. 2003a; Demir et al. 2007). Fas expression renders these cells susceptible to apoptosis mediated via FasL expressed on the surface of tumor cells, secreted by tumors and on tumor-shed microvesicles (Reichert et al. 1998; Taylor et al. 2003; Kim et al. 2005). The majority of apoptotic T cells in the blood of cancer patients

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are CD25-negative, whereas T cells expressing high levels of CD25, which are mostly regulatory T cells (Tregs), are protected from FasL-induced apoptosis (Bauernhofer et al. 2003b) and accumulate in cancer patients.

Impaired Effector Cell Activation and Function The activation and effector function of lymphocytes are often impaired in cancer patients and this contributes to anergy/non-responsiveness and escape of tumors from immunity. T cells specific for TAAs are present in the blood of patients with cancer and at the tumor site, however, in most patients these cells fail to control tumors and are anergic (Lee et al. 1999; Zippelius et al. 2004). Impaired signaling via the T cell receptor has been described in peripheral blood T cells from cancer patients. Several components of this signaling pathway are defective in T cells from cancer patients, including p56 lck, CD3z, and translocation of the NF-kB p65 subunit (Finke et  al. 1993). CD3z expression is downregulated at the protein level in T cells and NK cells within tumors, tumor-draining lymph nodes, and in the peripheral blood in many types of cancer (Kono et al. 1996; Reichert et al. 1998; Dworacki et al. 2001). Downregulation of CD3z expression is mediated via various tumor-related mechanisms, including soluble factors secreted by tumors that activate peptidases within T cells (Taylor et al. 2001), tumor-induced apoptosis of T cells, and products of other leukocytes including suppressor cells (Ezernitchi et  al. 2006). Low CD3z is associated with functional defects in T cells, such as low proliferation in response to mitogens (Reichert et al. 2002) and contributes to general anergy of T cells in cancer patients. Lymphocytes from cancer patients also exhibit impaired responses to activating stimuli, including cytokines such as interferons (IFNs). The type-I IFN signaling pathway is impaired in T cells and B cells and type-II IFN signaling is impaired in B cells in the peripheral blood of breast cancer, melanoma and gastrointestinal cancer patients (Critchley-Thorne et al. 2007, 2009a). Interferons act as an antiapoptotic and proliferative “third signal” required in addition to the first (antigen) and second (co-stimulation) signals for full activation, clonal expansion and development of memory vs. tolerance (Dondi et  al. 2004; Curtsinger et  al. 2005; Kolumam et  al. 2005; Le Bon et al. 2006). Thus, defects in interferon signaling in lymphocytes in cancer patients are associated with downstream functional defects in cellular activation (Critchley-Thorne et al. 2009a). Responses to other cytokines such as IL-2 are also altered in lymphocytes in cancer patients (Mortarini et al. 2009). The anti-tumor functions of B cells are limited in cancer patients. Oligoclonal antibody responses specific to TAAs can be detected in patients with various types of cancer (Disis et  al. 1997, 2000; Coronella et  al. 2002; Pavoni et  al. 2007). However, the TAA-specific antibodies do not attain sufficient systemic titers to result in tumor rejection, while responses to non-TAA remain intact (Brydak et al. 2001; Schiffman et al. 2002). In addition to antibody production, B cells have antigen presenting functions and can secrete cytokines to regulate immune responses (Mosmann 2000; Duddy et  al. 2004; Rodriguez-Pinto and Moreno 2005).

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Emerging evidence suggests these functions are also impaired by the cancer bearing state.The relative and absolute count of CD27+ memory B cells in the blood are decreased in advanced melanoma and other cancers. This is accompanied by aberrant systemic plasmacytosis. B cells from these patients also show defective cytokine production and fail to provide sufficient costimulation signal to amplify CD4 T cell responses (Carpenter et al. 2009). There are also signatures of immune dysfunction in the innate immune system of cancer patients. Patients with breast cancer have impaired NK cell activity that limits the potential anti-tumor cytolytic responses of these cells (Sabbioni et  al. 2000; Varker et  al. 2007). The expression of NKG2D on NK cells, and also on CD8 T cells and gd T cells, is reduced in cancer patients due to overexpression of NKG2D ligands, such as MICA/B and ULBP, on the surface of tumors and secretion of soluble NKG2D ligands by tumors (Groh et al. 2002; Madjd et al. 2007; Osaki et  al. 2007), and also by TGF-b produced by tumors and/or Treg cells (Castriconi et  al. 2003; Lee et  al. 2004; Ghiringhelli et  al. 2005a). Excessive NKG2D signaling by these ligands leads to endocytosis and degradation of NKG2D on NK cells and T cells, which disrupts interaction with target tumor cells. Reduced expression of natural cytotoxicity receptors on NK cells has also been observed in cancer patients and is associated with reduced cytotoxic function of these cells (Costello et al. 2002; Le Maux Chansac et al. 2005; Fauriat et al. 2007). Absolute counts of total NK cells are not significantly reduced in cancer patients, however there is a reduction in the CD56bright subtype of NK cells, which are cytokine-producing, and a corresponding increase in the CD56dim subtype, which are cytolytic (Bauernhofer et al. 2003a). Although the cytolytic NK cells are increased in number, their activity is impaired and these cells have high rates of spontaneous apoptosis and rapid turnover in cancer patients (Bauernhofer et al. 2003a). Dendritic cells, particularly myeloid-derived DC1 cells, are reduced in number in the blood of cancer patients. The reduction in DC1 cells alters the DC1:DC2 ratio, which affects the balance between humoral and cell-mediated immunity in cancer patients (Hoffmann et al. 2002b; Della Bella et al. 2003; Ferrari et al. 2005; PinzonCharry et  al. 2007). The loss of DC1 cells is accompanied by accumulation of immature CD11c− CD123− DCs that have low expression of HLA-DR and costimulatory molecules (Pinzon-Charry et al. 2005; Sakakura et al. 2006). These cells are resistant to tumor-induced apoptosis and are dysregulated in antigen presentation (Pinzon-Charry et  al. 2005). Such cells likely prime the differentiation of CD4 T cells to Treg phenotype (Ghiringhelli et al. 2005b; Pinzon-Charry et al. 2005) and cause imbalances between lymphocyte subsets and suppressed T cell activation responses (Gerosa et al. 2002; Sakakura et al. 2006; Chikamatsu et al. 2007).

Polarizing Immune Balance in Favor of Tumor Progression There is a delicate balance between immune mediators that promote tumor progression vs. mediators that promote tumor rejection. This balance is often shifted in

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favor of tumor progression by the cancer bearing state (Ostrand-Rosenberg 2008). Specific immune cell types are polarized or subverted by tumors or tumor-derived products to form immunosuppressive networks and enable malignant progression. The cytokine profiles in cancer patients are frequently polarized towards Th2 rather than Th1. Th1 phenotype produces IL2 and IFNg, which promote the activation and expansion of cytotoxic T cells to kill tumor cells. Th2 phenotype produces IL-4 and IL-13 which help B cells to produce antibodies, thereby directing immunity away from a tumor-rejecting Type 1 response (Lee et al. 1997; Botella-Estrada et al. 2005). Many types of cancers have taken advantage of this regulatory role of cytokines to down-regulate appropriate immune responses targeted to destroy cancer cells. Cancer patients also showed elevated level of immunosuppressive cytokines such as transforming growth factor-beta (TGF-b), IL10 and vascular endothelial growth factor (VEGF) to further dampen anti-tumor responses (Nemunaitis et al. 2001; Santin et al. 2001; Kim et al. 2003). Peripheral blood T cells, and T cells in tumors and draining lymph nodes, are enriched in cells of regulatory or suppressor phenotype (Liyanage et  al. 2002; Ichihara et al. 2003; Okita et al. 2005; Chikamatsu et al. 2007). In addition to natural Tregs, conventional CD4+ T cells are induced to differentiate to CD4+ Treg cells in cancer patients, which alter the balance between effector cells and regulatory cells in cancer patients. This imbalance contributes to immune tolerance to tumors (Curiel et al. 2004; Jarnicki et al. 2006). The differentiation of CD4 T cells to Tregs is mediated by immunosuppressive factors such as TGF-b and IL-10 (Seo et  al. 2001; Chen et al. 2003; Fantini et al. 2004), which are produced by both tumor cells and by immune cells polarized towards a suppressor phenotype. Suppressor/regulatory subsets of CD8+ T cells are also increased in the blood of cancer patients and within tumors and play a role in maintaining tolerance to tumors (Ebihara et  al. 1991; Yacyshyn et  al. 1995; Nakamura et  al. 2002; Chaput et  al. 2009). Tumorinfiltrating Th17 cells have been reported in ovarian cancer, melanoma and prostate cancer but their function in cancers remains controversial (Miyahara et  al. 2008; Muranski et al. 2008; Sfanos et al. 2008). NKT cells are another subtype of T cells with important immune regulatory functions and serve as a bridge between the innate and adaptive immune system (Cerundolo et  al. 2009). Type I NKT cells show defective IFNg production in many cancer patients (Tahir et al. 2001; Molling et al. 2005) and low levels of circulating type I NKT cells indicates poor survival in head and neck squamous cell carcinoma patients (Molling et al. 2007). Tumor cells can also hijack myeloid cells to mediate a protective acute inflammatory process to further tumor progression. Macrophages that infiltrate tumors are converted to M2-type tumor-associated macrophages (TAMs) that promote tumor progression and facilitate metastasis (DeNardo et  al. 2008; Sica et  al. 2008). Tumor-associated neutrophils (TANs) are polarized toward pro-tumor N2-type TANs that promote tumor angiogenesis (Fridlender et al. 2009; Tazzyman et  al. 2009). DCs can be polarized to an immunosuppressive phenotype that induce both CD4+ and CD8+ regulatory T cells and inhibit the function of cytotoxic T lymphocytes in cancer patients (Ghiringhelli et al. 2005b; Wei et al. 2005; Norian et al. 2009). These tumor-promoting subsets of monocytes and dendritic

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cells accumulate in the blood of cancer patients and are recruited into tumors by various tumor-derived factors including CSF-1, IL-6 and IL-10, VEGF and GM-CSF (Gabrilovich et al. 1998; Bronte et al. 1999; Lin et al. 2002; Steinbrink et  al. 2002; Park et  al. 2004). Such cells can be considered part of a heterogeneous population of cells known as myeloid-derived suppressor cells (MDSCs), which have immature phenotype and regulate adaptive immunity by multiple molecular and cellular mechanisms, including production of arginase I and activation of indoleamine 2,3-dioxygenase (IDO) at the tumor site, in draining lymph nodes and in the blood of cancer patients. Arginase I depletes arginine, which is required for T cell effector functions and its depletion by MDSCs is in part responsible for T cell anergy in cancer patients (Rodriguez et  al. 2004; Ochoa et  al. 2007). IDO production by suppressive dendritic cells is involved in the production of immunosuppressive tryptophan metabolites and contributes to immune dysfunction in cancer (von Bergwelt-Baildon et al. 2006). MDSCs also produce immunosuppressive factors such as IL-10 and TGF-b that inhibit the cytolytic activity of NK cells (Li et  al. 2009) and promote the development of regulatory T cells, which in turn prevent anti-tumor immunity (Frey 2006; Huang et al. 2006). In summary, the prominent immune signature in cancer is that of immune dysfunction (summarized in Table  1), wherein tumors directly inhibit anti-tumor responses by inducing apoptosis and a tolerant, anergic phenotype of both TAAspecific adaptive cells and innate cells and indirectly by subverting multiple subsets of immune cells to a tumor-promoting phenotype that induce and maintain tolerance to tumors.

Signatures of Tumor Rejection in Cancer Patients Substantial efforts have been made to establish predictors of clinical outcome in many cancers by gene expression profiling. The presence of signatures associated with immune effector functions often indicates good prognosis and better clinical responses. These signatures can be considered as signatures of tumor rejection. Melanoma is a cancer characterized by predisposition to natural and induced immune rejection. Analysis of melanoma-associated genes has revealed immunological signature resulting from immune cell infiltration of the tumor tissue (Wang et  al. 2004). These immunological signatures of tumor rejection may explain the immune responsiveness in melanoma and can also be used to guide therapy for these patients since the active profile of immune genes related to T cell regulation in melanoma metastases corresponds to clinical responses to therapies (Wang et al. 2002). In breast cancer, an IFN-regulated cluster was identified in an effort to classify breast cancer molecular types (Hu et al. 2006). Interestingly, Teschendorff et al. showed that this subset of patients defined by altered IFN-regulated gene expression had better prognosis compared with the basal and HER2+ groups (Teschendorff et  al. 2007). Additionally, they also observed an immune response gene expression module in ER− breast cancer patients. This immune module consists of complement and

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Table 1  Summary of immune signatures in cancer: immune dysfunction vs. tumor rejection Feature Affected cell types/processes T cells, natural killer (NK) Signatures of immune Apoptosis induced directly cells, dendritic cells dysfunction in cancer by contact with tumor (DCs) cells and indirectly by tumor-secreted factors and shed membrane fragments T cells, B cells, NK cells, DCs, Impaired anti-tumor effector B cells cell activation and functions Anergy/non-responsiveness T cells, B cells, NK cells CD4+ Tregs, CD8+ Tregs, Shift towards regulatory and suppressor Th2 phenotype, tumorphenotype associated macrophages, tumor-associated neutrophils, tumorinfiltrating DCs, myeloidderived suppressor cells Th2-polarized cytokine T cells, B cells profiles Interferon-related genes, T cell Immunological signatures Signatures of tumor regulation genes associated with anti-tumor rejection in cancer effector functions and good prognosis TAA-specific humoral B cells responses TAA-specific cytotoxic T cell CD8+ T cells responses that adapt to escape variants in long term cancer survivors Autoimmunity Vitiligo, thyroid dysfunction, autoantibodies, myalgias

immune response pathways and is associated with good prognosis independent of lymphocytic infiltration and lymph node status (Teschendorff et al. 2007). A recent meta analysis of breast cancer gene expression profiles identified a large cluster of 600 genes with immune functions, which can be classified into seven immune functional clusters (IgG, HCK, MHC-II, LCK, MHC-I, STAT1, interferon) (Rody et al. 2009). Of these, the T cell signature (LCK) indicates a favorable prognosis of ER− and HER2+ patients and the IgG and LCK signatures correspond to better responses to neoadjuvant chemotherapy (Rody et  al. 2009). An immune response enriched 72-gene prognostic predictor has also been established for early-stage non-small-cell lung cancer (NSCLC) (Roepman et  al. 2009). The immune signatures of tumor rejection are not only limited to tumor tissues. By gene expression profiling of nonneoplastic muscosa, a prognostic signature was established for stage II colon cancer that included genes related to immune responses (Barrier et al. 2007). Gene expression signatures from peripheral blood have also been used to distinguish individuals with early-stage NSCLC from individuals with non-malignant lung disease (Showe et al. 2009). In brief, gene expression profiling studies of cancers frequently reveal

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immune response gene signatures that have prognostic significance. These tumor rejection-associated signatures may be of great importance in understanding the basis of anti-tumor immune responses. Spontaneous rejection of tumors by the immune system is rare; however, such rejections have been documented (Ross et  al. 1982; Nakano et  al. 2000) and the associated immune signatures may provide valuable insight into how the immune system can be manipulated to successfully control and reject tumors. Spontaneous tumor regression or delayed tumor progression can result from endogenous anti-tumor responses or therapy-induced responses. Objective responses to immunotherapies and biological therapies that target the immune system such as IFNs, IL2 and antibodies have been documented in cancer patients. Since TAAs are self antigens, effective anti-tumor responses are often associated with autoimmunity, which can be considered an immune signature of tumor rejection. Tumor regression resulting from immunotherapies including IFNs, IL-2 and anti-CTLA-4-based therapies has been associated with autoimmune responses such as autoantibodies, thyroid dysfunction, vitiligo, myalgias and arthralgias (Nordlund et al. 1983; Vallisa et al. 1995; Phan et al. 2001; Attia et al. 2005; Gogas et al. 2006; Moschos et al. 2008). Endogenous and immunotherapy-induced antitumor humoral responses have been described in cancer patients and are associated with improved prognosis (DiFronzo et  al. 2002; Pavoni et  al. 2007). Such humoral responses are thought to play a role in spontaneous regression of tumors (Nakano et al. 2000), although this is a rare occurrence since production of TAAspecific antibodies is limited in cancer patients and thus has been difficult to characterize. Immune adaptation has been shown to occur in cancer patients, where the immune system adapts over time to counteract tumor escape mechanisms. As immunodominant peptides are lost from tumors in the process of “immune editing” by tumor cells (Dunn et  al. 2002), the adaptive immune system must develop cytolytic T cell responses to remaining epitopes and new epitope variants. Endogenous T cell responses have been shown to adapt in response to new epitope variants in sequential metastases, shifting to development of responses specific to new immunodominant peptides at each round of selection (Yamshchikov et al. 2005). Successful anti-tumor cytolytic T cell responses can persist for many years in long term cancer survivors (Yamshchikov et al. 2001; Ueda et al. 2007) and may provide important insight into effective strategies for tumor control. In summary, although the dominant immune signature in cancer is that of immune dysfunction, immunological signatures of tumor rejection are observed in some cancers (summarized in Table 1). Gene expression profiling of tumors often display signatures related to immune effector functions, suggesting immune-mediated tumor rejection occurs in the cancer bearing state. Other tumor-rejection associated signatures include autoimmune responses, TAA-specific T cells responses that are longlived and adapt to new epitope variants and TAA-specific humoral responses that do not reach sufficient titers in most cancer patients but may be involved in tumor rejection in subsets of patients experiencing responses to immunotherapies or spontaneous tumor rejection. It is important to understand the mechanisms by which

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successful anti-tumor immune responses occur to aid the design of novel immunotherapies that can effectively overcome immune dysfunction.

Applications of Cancer-Associated Immune Signatures in Therapeutics and Diagnostics Therapeutics Research on immune signatures in cancer has identified many immune biomarkers that present attractive opportunities for novel therapies that target the immune system to control and eradicate cancer. Immune targets of therapies under investigation include Treg cells that are elevated in cancer patients and function to inhibit anti-tumor T cell responses. Both natural and induced Treg cells represent significant barriers to immunotherapy strategies. Therapies that target molecules overexpressed in Tregs, including CTLA-4 and GITR, are being clinically evaluated (Ko et al. 2005) with the aim of depleting Tregs in cancer patients to enable effective anti-tumor immunity. This approach is showing promise, however, the limitations include lack of specificity since TAAs are self-antigens and thus depletion of Tregs induces significant autoimmunity, which is a signature of tumor rejection but can also cause severe toxicity. Tumor-associated macrophages are critical symbiotic partners of tumor cells at the tumor site and as such TAMs are the target of therapeutic strategies that aim to breakdown the complex structural networks within tumors to prevent tumor progression and elicit tumor rejection (Sica et al. 2008). Myeloid-derived suppressor cells (MDSCs) accumulate in cancer patients and potently inhibit anti-tumor immunity and promote tumor progression; therefore, these cells are also the focus of novel therapeutic approaches to overcome immune dysfunction in cancer patients (Ko et al. 2009a, b). Many aspects of immune dysfunction in cancer have been described; however, the underlying mechanisms remain largely unknown. A greater understanding of the mechanisms by which the immune system is impaired in the cancer state will enable the development of novel therapeutic approaches that aim to normalize immune function in cancer patients and enable effective anti-tumor immune responses. Such therapies may target specific functions of malignant cells that are responsible for immune dysfunction and/or aim to normalize the levels and functions of specific subsets of immune cells, such as Tregs or MDSCs, to enable effective anti-tumor immunity. The complex interplay between tumor cells and immune cells in the cancer bearing state is a dynamic process, which involves multiple mechanisms and influences multiple cell types. The next generation of therapies should focus on normalizing multiple aspects of this dynamic system rather than aiming at correcting individual immune dysfunction. The significant challenges with these approaches will be to target the appropriate immune processes to effectively overcome immune dysfunction while avoiding significant toxicities related to autoimmunity and to maintain ­effective anti-tumor immunity in the long term to prevent tumor recurrence.

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Prognostic and Predictive Cancer Testing Immune signatures that have been associated with the cancer state have applications in prognostic, therapeutic sensitivity, and therapeutic efficacy testing for cancer. The cellular and molecular immune signatures within tumors, tumor draining lymph nodes, and in peripheral blood correlate with tumor progression and have been found to be independently associated with patient survival and responses to therapies. These signatures include altered frequencies, phenotypes and functions of leukocyte subsets as described in Sect. “Signatures of Immune Dysfunction in Cancer.” Specific immune biomarkers with prognostic significance include CD3z chain expression in peripheral blood T cells and at the tumor site. CD3z is an independent marker of aggressive disease and poor survival in several cancer types (Zea et  al. 1995; Reichert et al. 1998; Whiteside 2004; Gruber et al. 2008). CD3z can be used to predict response to IL-2 therapy in cancer patients (Kuss et al. 2002) and its intensity in NK cells has been correlated with antibody-dependent cellular cytotoxicity (ADCC) associated with the response to trastuzumab in cancer patients (Varchetta et al. 2007). Additional immune processes with prognostic significance include the density and phenotype of macrophages and dendritic cells in tumor tissues (Tsutsui et al. 2005; Ladanyi et al. 2007), the frequency of Treg cells (Bates et al. 2006) and the tissue expression of inhibitory molecules such as programmed cell death 1 ligand 1 (PD-L1) (Ghebeh et al. 2006; Hamanishi et al. 2007). Profiles of immune cells in tumor-draining lymph nodes have also been demonstrated to have prognostic value and may have applications in the analysis of dissected nodes to accurately determine tumor stage and predict survival (Kohrt et al. 2005; Elliott et al. 2007). Immune signatures in tumors have been shown to have even higher prognostic significance than signatures associated with malignant cells, highlighting the importance of the immune status in influencing tumor progression, and the promising role of immune biomarkers in cancer testing (Budhu et  al. 2006; Seike et  al. 2007; Teschendorff et al. 2007; Finak et al. 2008). For example, Finak et al. (2008) identified a stromal gene signature in breast cancer that includes genes involved in immune responses, angiogenesis and hypoxia. This signature has improved prognostic significance over standard clinical variables and over whole-tumor signatures in predicting clinical outcome in breast cancer. These studies demonstrate the importance of considering cancers not as structures of purely malignant cells but as a tumor system in which immune and stromal processes together determine tumor progression and tumor rejection, and have significance in prognostic and predictive testing. The complex structural network of the tumor system and the critical interactions of tumor cells with multiple types of immune cells and stromal cells highlight the need for a systems biology approach to cancer prognostic and predictive testing, which combines multiplexed panels of key tumor, immune and stromal biomarkers with informatics tools to produce a readout of the tumor system relevant to diagnosis, prognosis or therapeutic sensitivity to improve cancer testing (Critchley-Thorne et al. 2009b). Biological therapies, such as antibodies and cytokines, require intact immune function for efficacy. Therefore, immune signatures in cancer patients may have

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applications in identifying patients likely to benefit from specific therapies and in monitoring response to therapies in follow-up care. Immune signatures have been identified that correlate with survival following treatment of cancer patients with immunotherapies. The leukocyte infiltrate, including macrophages and memory leukocytes, has been shown to correlate with response to trastuzumab (Herceptin, Genentech, CA) in breast cancer (Gennari et al. 2004). The number and function of CD16+CD56+ NK cells have also been correlated with the antibody-dependent cellular cytotoxicity response to antibodies such as trastuzumab (Varchetta et al. 2007; Beano et al. 2008), and this response forms a key part of the mechanism by which trastuzumab induces apoptosis of tumor cells. T cell signal transduction molecules, such as CD3z, and activation markers, such as CD69 and HLA-DR, expressed by lymphocyte subsets in peripheral blood have also been shown to have significance in predicting clinical outcome following immunotherapy in cancer patients (Yacyshyn et  al. 1995; Kuss et  al. 2002). Immune signatures correlated with response to therapies or possibly even single components of signatures that are identified as key predictive biomarkers may have applications in predicting therapeutic sensitivity prior to initiating therapy and in monitoring the response of patients to immunotherapies during the course of treatment.

Summary Signatures of the immune system in cancer patients reveal immune dysfunction and tumor escape. Immune dysfunction has thus far limited the success of immunotherapies for cancer. For immunotherapy to be successful, the goal in designing such therapies must expand from inducing the maximum possible anti-tumor immune response to include overcoming immune dysfunction to enable endogenous or therapy-induced tumor rejection. Understanding signatures of tumor rejection is equally important; these signatures can be used to measure responses to therapies, and to aid in the design of novel immunotherapeutic approaches. Immune signatures in cancer also hold prognostic and predictive significance in cancer patients, at the tumor site, in tumor-draining lymph nodes, and in the blood. Systems biology approaches that incorporate immune signatures into cancer diagnostic testing have the potential to improve testing to predict survival and response to therapies in cancer patients.

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Part IV

Tissue-Specific Patterns Associated with Chronic Inflammatory Processes

HTLV-1 Infected CD4+CD25+CCR4+ T-Cells Disregulate Balance of Inflammation and Tolerance in HTLV-1 Associated Neuroinflammatory Disease Yoshihisa Yamano and Steven Jacobson

Abstract  HTLV-1 is a T lymphotropic human retrovirus that is the causative agent of adult T cell leukemia/lymphoma and is associated with multiorgan immunological disorders such as chronic myelitis, uveitis, Sjögren syndrome, bronchoalveolitis, arthritis, and polymyositis. HTLV-1 infection of T-cells induces a variety of abnormalities such as cellular activation, proinflammatory changes, and proliferation. Here, we discuss the mechanism of HTLV-1 infection-induced immune dysregulation. Since the majority of autoimmune diseases are of unknown etiology, the discovery of HTLV-1 and its association with these immunological disorders has greatly enhanced our understanding of the pathogenic mechanisms of organspecific immune abnormalities. Keyword  ATL • CD4+CD25+CCR4+ T cell • exFoxp3+ cell • HAM/TSP • HTLV-1 • Regulatory T cell

Human T-Lymphotropic Virus Type 1 (HTLV-1) and Associated Disorders Human T-lymphotropic virus type 1 (HTLV-1) is a human retrovirus that is associated with chronic, persistent infection of T-cells. Although HTLV-1 has been reported to infect a number of cell types in vitro and in vivo (Koyanagi et al. 1993; Nagai et al. 2001a, b, c; Yamano et  al. 2004; Jones et  al. 2008; Enose-Akahata et  al. 2008; Azakami et al. 2009), CD4+ T-cells, especially CD4+CD25+CCR4+ T-cells, are the predominant viral reservoir in the peripheral blood (Yamano et al. 2009). HTLV-1 provirus genome encodes structural genes (gag, pro, pol, env), and nonstructural genes (tax, rex). A regulatory gene, tax, the protein product of which, Tax, acts in

S. Jacobson (*) Viral Immunology Section, Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_12, © Springer Science+Business Media, LLC 2011

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trans both to up-regulate the transcription of viral genes itself and also to regulate the transcription of host genes (Matsuoka and Jeang 2007), which consequently induce the functional abnormality in the infected cells. HTLV-1 infection is associated with a variety of human diseases, including an aggressive mature T-cell malignancy termed adult T-cell leukemia (ATL) (Uchiyama et al. 1977), which is defined as a neoplastic growth of HTLV-1-infected T-cells with severe organ infiltration. HTLV-1 is also associated with non-neoplastic immunological disorders characterized by multiorgan lymphocytic infiltrates, including HTLV1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) (Gessain et al. 1985; Osame et al. 1986), uveitis (Mochizuki et al. 1992), Sjögren syndrome (Eguchi et al. 1992), bronchoalveolitis (Maruyama et al. 1992), arthritis (Nishioka et al. 1989), and polymyositis (Morgan et al. 1989). Importantly, some patients have more than one of these HTLV-1-associated immunological disorders (Nakagawa et al. 1995). A progressive course and persistent inflammation affecting various organs are common features of idiopathic autoimmune disorders of unknown etiology. Therefore, these HTLV-1associated multiorgan immunological disorders are extremely important models for understanding the pathogenic mechanisms of organ-specific immune disease.

Immunopathogenesis of HAM/TSP Although HTLV-1-associated disorders have been extensively studied, the exact mechanism by which HTLV-1 induces these immunological disorders is not completely understood. The viral load of HTLV-1 could contribute to the development of these HTLV-1-associated immunological disorders, since the circulating number of HTLV-1 infected T-cells in the peripheral blood is higher in patients with HAM/ TSP than in asymptomatic HTLV-1-infected individuals (Nagai et al. 1998; Yamano et al. 2002), and even higher in the cerebrospinal fluid of patients with HAM/TSP (Nagai et  al. 2001b). In HAM/TSP patients, the proviral load correlates with the percentage of activated CD4+ T-cells as well as HTLV-1-specific CD8+ CTLs (Nagai et al. 2001c; Yamano et al. 2002; Yakova et al. 2005). These HTLV-1 specific CTLs produce various factors including IFN-g and TNF-a that may suppress viral replication and kill infected cells or promote bystander activation and killing of nearby resident cells in the central nervous system (CNS) (Jacobson et al. 1990; Kubota et al. 2000a, b; Hanon et al. 2001; Vine et al. 2004). In addition, increased viral expression particularly of the transactivating viral gene encoding HTLV-1 Tax has been suggested to play a role in HTLV-1 disease progression (Nagai et al. 2001b; Yamano et al. 2002). HTLV-1 Tax induces the expression of various cellular genes, including IL-2 (Siekevitz et  al. 1987), the a-chain of IL-2 receptor (IL-2Ra) (Cross et  al. 1987), IL-15 (Azimi et  al. 1998), and IL-15Ra (Mariner et  al. 2001). Increased expressions of these critical immune mediators directly contributes to CD8+ T-cell activation and the ex vivo T-cell proliferation observed in patients with HAM/TSP (Waldmann 2006). These evidences suggest that invasion by HTLV-1-infected T-cells triggers, together with viral gene expression and cellular-signaling mechanisms, a strong virus-specific immune response and increased proinflammatory cytokine production, leading to CNS inflammation and autologous tissue damage (Fig.  1).

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Fig. 1  Model of immunopathogenesis in HAM/TSP. HTLV-1-infected and uninfected T-cells are attracted to the CNS where they are activated and proliferate. They secrete proinflammatory cytokines that activate bystander cells such as HTLV-1 specific CD8+ CTLs, macrophages and glial cells and recruit more cells to the CNS. These activated cells secrete cytokines such as interferon-g and tumor necrosis factor-a, which may contribute to tissue destruction

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However, the precise mechanisms underlying the induction of uncontrolled immune reactions by HTLV-1-infected T cells, and the induction of autologous tissue damages by the accelerated immune responses are not well understood.

HTLV-1 and Regulatory T-Cells The recent discovery of regulatory T-cells (Treg cells) has provided new opportunities and generated increased interest in elucidating mechanisms associated with HTLV-1 immunopathogenesis. In healthy individuals, a subset of CD4+CD25+ T-cells, namely, Treg cells has been demonstrated to suppress the proliferation of and cytokine production by pathogenic T-cells, and thereby plays a key role in the maintenance of immune system homeostasis (Sakaguchi et  al. 1995). Although Treg cells are phenotypically similar to activated T cells, they can be identified ex vivo by the intracellular expression of the transcriptional regulator Foxp3 (Hori et al. 2003). Foxp3 is critical for the development and function of Treg cells in both mice and humans. Significant reductions in Foxp3 expression and/or Treg cell function have been observed in several human autoimmune diseases, including multiple sclerosis (MS), myasthenia gravis, and type I diabetes (Sakaguchi et al. 2008), suggesting that defects in Foxp3 expression and/or Treg function may precipitate the loss of immunologic tolerance. CD4+CD25+ T cells are also the predominant viral reservoir in the peripheral blood of HTLV-1-infected individuals (Yamano et  al. 2004). Recently, significant reductions in Foxp3 expression and Treg cell function have also been observed in CD4+CD25+ T cells from patients with HAM/TSP (Yamano et al. 2005; Oh et al. 2006; Michaëlsson et al. 2008; Hayashi et al. 2008). Notably, overexpression of HTLV-1 tax can reduce Foxp3 expression and inhibit the suppressive function of Treg cells (Yamano et al. 2005). Furthermore, we have demonstrated that because of a Tax induced defect in TGF-b signaling, Foxp3 expression is decreased, and Treg functions are impaired in HAM/TSP patients (Grant et  al. 2008). HTLV-1 induced dysfunction of CD4+CD25+ Treg cells may therefore be one of the mechanisms underlying the induction of uncontrolled immune reactions by HTLV-1-infected T cells.

HTLV-1 Infected Foxp3−CD4+CD25+CCR4+ T-Cells Are Proinflammatory and Increased in HAM/TSP Patients Recently, the chemokine receptor CCR4 has been shown to be expressed on HTLV1-infected leukemia cells in ATL patients (Yoshie et  al. 2002). Since CCR4 is known to be selectively expressed on Th2, Th17, and Treg cells (Yoshie et al. 2001) and since ATL cells have been shown to express high levels of Foxp3, a specific marker of Treg (Karube et  al. 2004), it was hypothesized that ATL cells may be derived from Treg (Kohno et  al. 2005). However, in HAM/TSP patients, we and

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others have demonstrated that CD4+CD25+ T cells exhibit reduced Foxp3 expression and Treg suppression (Yamano et al. 2005; Oh et al. 2006; Michaëlsson et al. 2008; Hayashi et al. 2008). Furthermore, it has been demonstrated that HTLV-1infected CD4+ T cells in HAM/TSP patients produce Th1 cytokines (IFN-g) (Hanon et  al. 2001; Yamano et  al. 2005). Therefore, it was expected that CD4+CD25+ T cells might exhibit reduced CCR4 expression in HAM/TSP patients. However, we clearly demonstrated that CCR4 was selectively overexpressed on HTLV-1-infected T cells (Yamano et  al. 2009). We also demonstrated that the majority of CD4+CD25+CCR4+ T cells were infected with HTLV-1 and that this T cell subset was increased in HAM/TSP patients (Yamano et al. 2009). Thus, CD4+CD25+CCR4+ T-cells are a major reservoir of HTLV-1 infected T-cells whose numbers are increased both in HAM/TSP and ATL patients. More detailed flow cytometric analysis of Foxp3 expression in the CD4+CD25+ CCR4+ T-cells demonstrated that the frequency of “Foxp3− population” was increased in CD4+CD25+CCR4+ T cells of HAM/TSP patients (Yamano et al. 2009). Moreover, analysis of the proinflammatory cytokine expression in this Foxp3−CD4+CD25+CCR4+ T cell subset demonstrated that these cells were unique because they produced multiple proinflammatory cytokines such as IL-2, IL-17, and few IFN-g in healthy individuals while Foxp3+CD4+CD25+CCR4+ T cells (Treg cells) did not. Furthermore, it was demonstrated that HAM/TSP patients exhibited only few Foxp3+CD4+CD25+CCR4+ T cells that do not produce such cytokines. Rather, the Foxp3−CD4+CD25+CCR4+ T cells in HAM/TSP were increased and found to overproduce IFN-g. Further, the frequency of these IFN-g-producing Foxp3−CD4+CD25+CCR4+ T cells may have a functional consequence since this population was associated with increased clinical disease activity and severity in HAM/TSP (Yamano et  al. 2009). These results suggest that IFN-g-producing Foxp3−CD4+CD25+CCR4+ T cells (tentatively designated THAM cells) that are rarely encountered in healthy individuals, may play an important pathogenic role in HAM/ TSP by augmenting inflammation in the CNS. Since some HAM/TSP patients are known to experience complications with other autoimmune disorders (Nakagawa et al. 1995), it would be of interest to determine if this newly defined T cell subset (THAM cells: IFN-g+ Foxp3−CD4+CD25+CCR4+ T cells) may also be abnormally increased and functionally deregulated in other immunological diseases. Notably, it has recently been reported that Foxp3lowCD4+CD25+ memory T cells are increased in patients with active systemic lupus erythematosus (SLE) (Miyara et al. 2009). Although CD4+CD25+CCR4+ T cells are predominantly infected with HTLV-1 in both HAM/TSP and ATL (Yoshie 2005; Yamano et  al. 2009), it was demonstrated that the ratio of THAM cells (CCR4+Foxp3− with IFN-g production) to Treg cells (CCR4+Foxp3+ with no cytokine production) in the CD4+CD25+CCR4+ T cell subset were high in HAM/TSP and low in ATL (Yamano et al. 2009). This differential THAM/Treg ratio in HTLV-1-infected T cells may be associated with the differential immune responses observed between HAM/TSP and ATL (Fig.  2). ATL patients have very low frequencies of Tax-specific CD8+ T cells in PBMCs and tend to develop opportunistic infections (Kannagi 2007; Matsuoka 2005), while HAM/TSP is characterized by extraordinarily high levels of Tax-specific CD8+ CTL (Jacobson et  al. 1990; Kubota et  al. 2000b; Nagai et  al. 2001b;

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Differential Immune Responses between HAM/TSP and ATL HAM/TSP

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Fig. 2  Differential immune responses between HAM/TSP and ATL. An imbalance in the THAM/ Treg ratio in HTLV-1-infected CD4+CD25+CCR4+ T cells may be an important factor that contri­ butes to immunological differences of the host immune response between HAM/TSP and ATL

Yamano et al. 2002). It has been reported that the immunosuppressive function of CD4+CD25+ T cells with high expression of Foxp3 in ATL patients is intact (Chen et al. 2006). Thus, the increased CD4+CD25+CCR4+ leukemia T cells with Treg function may also contribute to the clinically observed cellular immunodeficiency in ATL patients. However, HAM/TSP patients show extremely high cellular and humoral immune responses such as high frequencies of Tax-specific CD8+ T cells as well as cytomegalovirus (CMV)-specific CD8+ T cells in PBMCs (Jacobson et al. 1990; Hayashi et al. 2008); high antibody titer to HTLV-1 (Nakagawa et al. 1995); and increased production of proinflammatory cytokines such as IL-12 and IFN-g (Furuya et  al. 1999). It has been reported that HAM CD4+CD25+ T cells with low expression of Foxp3 (Yamano et  al. 2005) or HTLV-1 Tax-expressing Foxp3+ Treg cells (Toulza et al. 2008) are defective in their immunosuppressive function. Moreover, it was demonstrate that HTLV-1-infected IFN-g overproducing CD4+CD25+CCR4+Foxp3− T cells (THAM cells) are increased in HAM/TSP patients, and these levels can be correlated with disease severity (Yamano et  al. 2009). Thus, CD4+CD25+CCR4+ T cells with increased proinflammatory function together with a defective Treg compartment (Yamano et al. 2005; Oh et al. 2006; Michaëlsson et al. 2008; Hayashi et al. 2008; Yamano et al. 2009) may overcome the regulatory effect of HTLV-1-uninfected Treg cells (Toulza et al. 2008) and at least partly account for the heightened immune response observed in HAM/TSP patients. Collectively, these observations support the hypothesis that imbalance of the THAM/Treg ratio in HTLV-1-infected CD4+CD25+CCR4+ T cells is an important factor that contributes to immunological differences of the host immune response between HAM/TSP and ATL (Fig. 2).

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Do THAM Cells Include exFoxp3+ Cells? Recently, Hieshima et al. demonstrated the molecular mechanism of HTLV-1 tropism to CCR4+CD4+ T cells. Although HTLV-1 Tax cannot induce the expression of CCR4, Tax induces the expression of CCL22, which is the ligand for CCR4. Therefore, HTLV-1-infected T cells produce CCL22 through Tax and selectively interact with CCR4+CD4+ T cells, resulting in preferential transmission of HTLV-1 to CCR4+CD4+ T cells (Fig.  3). In HTLV-1-seronegative healthy individuals, CD4+CD25+CCR4+ T cells mainly include suppressive T cell subsets such as Th2 and Treg (Yoshie et  al. 2001). However, as mentioned above, this T-cell subset becomes Th1-like cells with overproduction of IFN-g in patients with HAM/TSP, while in ATL patients leukemogenesis develops and maintains the Foxp3+ Treg phenotype (Fig.  3). To determine whether HTLV-1 expression contribute to this different fate of HTLV-1 infected CD4+CD25+CCR4+ T cells between HAM/TSP and ATL patients, the differences in the HTLV-1 provirus DNA load and the HTLV-1 tax mRNA expression of this population were analyzed. HTLV-1 tax Differential fate of HTLV-1 infected CD4+CD25+CCR4+T cells between HAM/TSP and ATL

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Fig. 3  Differential fate of HTLV-1 infected CD4+CD25+CCR4+ T cells between HAM/TSP and ATL. After HTLV-1 infection of CD4+CD25+CCR4+ T-cells that are mainly Th2 and Treg cells in healthy condition, they become IFN-g+Foxp3− T cells (THAM cells) with high intracellular HTLV-1 tax expression in patients with HAM/TSP. In ATL patients, leukemogenesis develops and maintains the Foxp3+ Treg phenotype

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mRNA expression in CD4+CD25+CCR4+ T cells was significantly increased in HAM/TSP patients than in the ATL patients, while the HTLV-1 provirus DNA loads between the two groups were equivalent (Yamano et  al. 2009). This high HTLV-1 tax expression in HAM/TSP CD4+CD25+CCR4+ T cells (Foxp3−) and low HTLV-1 tax expression in ATL CD4+CD25+CCR4+ T cells (Foxp3+) suggests that intracellular HTLV-1 expression may act as a “switch” that directs T cell plasticity from Foxp3+ Treg cells to IFN-g+Foxp3− T cells. Indeed, a recent report highlighted that loss of Foxp3 in Treg cells and acquisition of IFN-g may result in the conversion of suppressor T cells into highly autoaggressive lymphocytes (exFoxp3+ cells), which can favor the development of autoimmune conditions (Zhou et  al. 2009; Tsuji et al. 2009). These findings support the hypothesis that HTLV-1 tax may be one of the exogenous retrovirus genes responsible for immune dysregulation through the induction of failure in maintaining a tight control of the equilibrium between inflammation and tolerance. This hypothesis is currently under investigation to elucidate the precise molecular mechanisms by which HTLV-1 influences the fate and function of CD4+CD25+CCR4+ T cells, especially Foxp3+ Treg cells. Since the majority of autoimmune diseases are of unknown etiology, the discovery of HTLV-1 and its association with multiorgan immunological disorders has greatly enhanced our understanding of the pathogenic mechanisms underlying organ-specific immune disorders. Further investigations on the mechanism of HTLV-1 action in immunological disorders may result in the identification of new molecular pathways that will further elucidate the basic mechanisms underlying immune-mediated disorders.

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Nagai M, Kubota R, Greten TF et  al. (2001c) Increased activated human T cell lymphotropic virus type I (HTLV-I) Tax11-19-specific memory and effector CD8+ cells in patients with HTLV-I-associated myelopathy/tropical spastic paraparesis: correlation with HTLV-I provirus load. J Infect Dis 183:197–205 Nakagawa M, Izumo S, Ijichi S et  al. (1995) HTLV-I-associated myelopathy: analysis of 213 patients based on clinical features and laboratory findings. J Neurovirol 1:50–61 Nishioka K, Maruyama I, Sato K et al. (1989) Chronic inflammatory arthropathy associated with HTLV-I. Lancet 1:441. Oh U, Grant C, Griffith C et  al. (2006) Reduced Foxp3 protein expression is associated with inflammatory disease during human t lymphotropic virus type 1 Infection. J Infect Dis 193:1557–1566 Osame M, Usuku K, Izumo SI et al. (1986) HTLV-I associated myelopathy, a new clinical entity. Lancet 1:1031–1032 Sakaguchi S, Sakaguchi N, Asano M et  al. (1995) Immunologic self-tolerance maintained by activated T cells expressing IL-2 receptor alpha-chains (CD25). Breakdown of a single mechanism of self-tolerance causes various autoimmune diseases. J Immunol 155:1151–1164 Sakaguchi S, Yamaguchi T, Nomura T et al. (2008) Regulatory T cells and immune tolerance. Cell 133:775–787 Siekevitz M, Feinberg MB, Holbrook N et al. (1987) Activation of interleukin 2 and interleukin 2 receptor (Tac) promoter expression by the trans-activator (tat) gene product of human T-cell leukemia virus, type I. Proc Natl Acad Sci USA 84:5389–5393 Toulza F, Heaps A, Tanaka Y et al. (2008) High frequency of CD4+Foxp3+ cells in HTLV-1 infection: inverse correlation with HTLV-1 specific CTL response. Blood 111:5047–5053 Tsuji M, Komatsu N, Kawamoto S et al. (2009) Preferential generation of follicular B helper T cells from Foxp3+ T cells in gut Peyer’s patches. Science 323:1488–1492 Uchiyama T, Yodoi J, Sagawa K et  al. (1977) Adult T-cell leukemia: clinical and hematologic features of 16 cases. Blood 50:481–492 Vine AM, Heaps AG, Kaffantzi L et  al. (2004) The role of CTLs in persistent viral infection: cytolytic gene expression in CD8+ lymphocytes distinguishes between individuals with a high or low proviral load of human T cell lymphotropic virus type 1. J Immunol 173(8):5121–5129 Waldmann TA (2006) The biology of interleukin-2 and interleukin-15: implications for cancer therapy and vaccine design. Nat Rev Immunol 6:595–601 Yakova M, Lezin A, Dantin F et al. (2005) Increased proviral load in HTLV-1-infected patients with rheumatoid arthritis or connective tissue disease. Retrovirology 1:2–4 Yamano Y, Nagai M, Brennan MB et al. (2002) Correlation of human T-cell lymphotropic virus type 1 (HTLV-1) mRNA with proviral DNA load, virus-specific CD8(+) T cells, and disease severity in HTLV-1-associated myelopathy (HAM/TSP). Blood 99:88–94 Yamano Y, Cohen CJ, Takenouchi N et al. (2004) Increased expression of human T lymphocyte virus type I (HTLV-I) Tax11-19 peptide-human histocompatibility leukocyte antigen A*201 complexes on CD4+ CD25+ T cells detected by peptide-specific, major histocompatibility complex-restricted antibodies in patients with HTLV-I-associated neurologic disease. J Exp Med 199(10):1367–1377 Yamano Y, Takenouchi N, Li HC et al. (2005) Virus-induced dysfunction of CD4+CD25+ T cells in patients with HTLV-I-associated neuroimmunological disease. J Clin Invest 115:1361–1368 Yamano Y, Araya N, Sato T et  al. (2009) Abnormally high levels of virus- infected IFNg+CCR4+CD4+CD25+ T cells in a retrovirus-associated neuroinflammatory disorder. PLoS One 4(8):1–14 Yoshie O (2005) Expression of CCR4 in adult T-cell leukemia. Leuk Lymphoma 46:185–190 Yoshie O, Imai T, Nomiyama H (2001) Chemokines in immunity. Adv Immunol 78:57–110 Yoshie O, Fujisawa R, Nakayama T et  al. (2002) Frequent expression of CCR4 in adult T-cell leukemia and human T-cell leukemia virus type 1-transformed T cells. Blood 99:1505–1511 Zhou X, Bailey-Bucktrout SL, Jeker LT et al. (2009) Instability of the transcription factor Foxp3 leads to the generation of pathogenic memory T cells in vivo. Nat Immunol 10:1000–1007

D/2 Predictors of Favorable Outcome in Cancer Zoltán Pós and Jérôme Galon

Prediction of disease outcome in cancer is usually achieved by histological evaluation of tissue samples obtained during surgical extirpation of the primary tumor, mostly focusing on histological characteristics of cancer cells in the tumor mass, such as the extent of atypical cell morphology, of tissue integrity, aberrant expression of protein markers or malignant transformation, senescence and proliferation, various characteristics of the invasive margin and surrounding tumor stroma, depth of invasion, and the extent of vascularization. In addition, histological or radiological analysis of both tumor draining- and distant lymph nodes and remote organs can be carried out looking for evidence of metastases. Based on these data, evaluation of cancer progression can be performed and serve as an estimate of patient prognosis. This is done on the basis of statistical data available of patients exhibiting similar progression characteristics and their actual outcome parameters, such as average disease-free (DFS), disease-specific (DSS) and overall survival (OS). To this end, several dozens of tumor-type specific staging and grading systems have been developed such as Clark’s and Breslow’s indexes for melanoma, Gleason’s score for prostate cancer, Duke’s for colorectal cancer, Boden-Gibb’s staging for testicular cancer, the Evans staging system for neuroblastoma, etc., and also more universal ones such as the TNM system, which summarizes data on tumor burden (T), presence of cancer cells in draining and distant lymph nodes (N) and evidence for metastases (M). With the large body of statistical data available on cancer patients’ survival with a given progression stage, such approaches have been shown to be valuable and in many cases of acceptable accuracy in estimating disease outcome in cancer.

Z. Pós (*) Infectious Disease and Immunogenetics Section, Department of Transfusion Medicine, Clinical Center, and Center for Human Immunology, National Institutes of Health, Bethesda, MD 20892, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_13, © Springer Science+Business Media, LLC 2011

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Still, it is well known that cancer outcome can significantly vary between patients within the same histological progression stage. Progression of advanced stage cancer can remain stable for years, and partial or full regression of large ­metastatic lesions can also occur spontaneously. On the other hand, rapid relapse and death of patients diagnosed with early stage cancer, even after an apparently complete surgical removal of the tumor, with no signs of metastasis and with undetectable levels of residual tumor burden also frequently happens. It has been argued that one reason for the apparently limited accuracy of traditional tumor staging techniques in predicting disease outcome could result from the fact that they usually estimate cancer progression as a largely autonomous procedure, focusing mostly on cancer cells, without considering that cancer progression is actually determined by a balance of various external and internal factors which can either support or suppress the tumor. As the sporadic cases of rejection of even advanced cancer demonstrate, the popular assumption that clinically manifested tumors are already advanced enough to repel any antitumor mechanisms maintaining tissue integrity and immune homeostasis, is probably not always true. Similarly, seemingly well-contained, less advanced lesions can evolve into rapidly progressing tumors. In fact, a large body of evidence supports the hypothesis that cancer development is aggressively controlled by the host’s immune system, suggesting that even at the level of clinically apparent tumors, systemic and local immunological biomarkers must be evaluated when predicting disease outcome. Moreover, evidence is available that under circumstances, such markers can be even superior to conventional histologic criteria in estimating DFS or OS (Galon et al. 2006). It has been well documented that developing cancer cells are exposed to and usually eliminated by an intact immune system (cancer immunosurveillance). Growth of established cancers can be effectively suppressed and contained over large periods of time (immunological equilibrium). Cancer progression at the molecular level is strongly influenced by this immunological pressure (cancer immunoediting) (Dunn et al. 2002, 2004) and even tumor cells escaping this control and developing large tumor masses can be rejected by the host’s immune system upon proper re-stimulation (Rosenberg et al. 2008; Schon and Schon 2008).

Predictors of Favorable Outcome in Colorectal Cancer In a large series of studies conducted on several hundreds of colorectal cancer samples with well documented histological and molecular data, it was convincingly demonstrated that absence of a set of histological markers of ongoing metastatic invasion, i.e., vascular emboli, lymphatic invasion, and perineural invasion (collectively termed as VELIPI), closely associated with both longer disease-free survival (DFS) and overall survival (OS), and infiltration of the tumor with CD3+ T cells, CD3+CD4+ T helper and CD3+CD8+ cytotoxic T cells (Pages et al. 2005). Expression of genes involved in cytotoxic antitumor responses, such as granzyme B (GZMB) and granulysin (GLNY), genes of Th1-differentiation, such as the

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transcription factors T-box protein 21 (T-BET), interferon regulatory factor 1 (IRF-1), and the characteristic Th1 cytokine interferon g (IFNG) were increased in patients with VELIPI-negative tumors without relapse, compared to VELIPI-positive patients with relapse. Interestingly, it was demonstrated that striking differences between VELIPI-positive and VELIPI-negative tumors can be found in the number of CD45RO+ memory T cell compartment, including both early memory T cells (CD45RO+CCR7-CD28+CD27+) and effector memory T cells (CD45RO+CCR7CD28-CD27-) suggesting that properly stimulated memory T cells are involved in the suppressed progression of colorectal cancers and in general, they are associated with a favorable outcome (Pages et al. 2005). Extending the results of this study, it was demonstrated that in a selected group of gene expression markers, Th1 response related genes CD3-z, CD8b, GZMB, GLNY, T-BET, IRF-1, IFNG actually create an extremely tightly regulated gene cluster (Galon et  al. 2006), the expression of which was in an almost linear inverse correlation with probability of relapse. Although frequency of immune cells staining positive for these markers were sometimes highly variable in different tumor areas, (such as the tumor core compared to the margin of the tumor), assessing the density of CD3+, CD8+, GZMB+, and CD45RO+ cells in both areas by immunostaining, the results could be confirmed at the protein level too. Strikingly, it was very convincingly shown that combination stain for CD3 and CD45RO of colorectal cancer can predict disease outcome more reliably than conventional TNM histopathology-based evaluation (Galon et al. 2006). These results suggest that proper spatial organization and high density of effector T cells associated with optimal gene expression-level activation correlated with a good prognosis in patients with colorectal cancer. Interestingly, similar conclusions can be made by comparing colorectal cancer patient cohorts categorized by high or low density of immune infiltrate (Hi and Lo) and presence or absence of metastases (META+ and META−, respectively) (Camus et al. 2009). Although there are no striking differences in the composition of the infiltrate of Lo META− and Lo META+ patients, however, brisk infiltration of the primary tumor, along with increased numbers of CD3+CD8+ cytotoxic T cells, NKT cells and tumor-associated macrophages (TAMs) is associated with absence of metastases (typical for the Hi META− patient group). Conversely, decreased frequency of cells involved in cellular immune responses, and a shift towards B-cell infiltration was typical for patients developing metastasis in spite of immune infiltration (Hi META+ group). Furthermore, a massive immune infiltrate predicted absence of metastases, provided that the late stage memory T cell arm (CD45RA-CD27-) was well presented, while there was no major difference between Hi META− and Hi META+ patients in earlier phases of memory T cell differentiation. Correlation analysis of various T cell markers suggested that it was mainly CD8+ cytotoxic T cells, the memory cell development of which is compromised in Hi META+ patients. Not surprisingly, in Hi META- patients, markers of early T cell activation (CD45RA+CD27+/CD25+/CD28+/CD69+) showed strong correlation with intense macrophage and DC immigration, while in contrast, in Hi META+ patients, all such clusters of coexpression were dramatically disrupted (Camus et al. 2009).

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Taken together, these data suggest that sufficient immune infiltration, successful priming of CD8 T cells and their differentiation is vital for successful suppression of metastasis development. In case of weak immune cell infiltration (Lo), however, practically all the above correlations were completely absent, regardless of the presence or absence of metastases (Lo META+ vs. Lo META−), implying that either immune suppression and escape can be locally initiated before actual metastatic spread, and/or factors other than immune activation are also frequently involved in suppression of metastasis development in colorectal cancer. Finally, the apparent inefficiency of the immune infiltrate to reach a critical level of organization and significantly influence metastatic spread in Lo META+ and Lo META− patients can also indicate that a given level of immune infiltration is a prerequisite of successful organization of an antitumor immune response. When, instead of metastasis formation, the impact of an organized immune response was measured on the primary tumor mass by Ki67 as a marker of proliferation, and M30 as a marker of apoptosis, it was also demonstrated that the primary tumor mass is largely resistant to immune-mediated attacks, which is in striking contrast with the apparent vulnerability of developing metastases, ultimately determining disease outcome. Interestingly, mRNA level markers of efficient cytotoxic response, such as coexpression of GLNY and IRF-1, predicted more than nine times longer DFS than DFS observed in their absence (Camus et  al. 2009), while many classic tumor markers of apoptotic resistance, cancer spread, vascularization and general progression, e.g., survivin, carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1), CD97, angiotensin converting enzyme (ACE), estrogen receptor-binding fragment-associated gene 9 (EBAG9), matrix metallopeptidase 7 (MMP7), and vascular endothelial growth factor (VEGF) had little impact on actual clinical disease outcome (Mlecnik et al. 2010). Markers of TH2 commitment, e.g., gata binding protein 3 (GATA3) and immunesuppression, such as interleukin 10 (IL-10) and forkhead box P3 (FoxP3) were not found to be important in affecting DFS (Mlecnik et al. 2010). Still, high levels of EGFR expression predisposed patients to a short DFS (Mlecnik et al. 2010) and although VEGF did not have a clear impact on DFS when analyzed in all colorectal cancer patients, it decreased DFS in patients with an otherwise favorable GLNY+IRF-1+ infiltrate, probably suggesting that enhanced capillary formation is an escape option from immune-mediated destruction of early metastases (Camus et al. 2009). It seems that in general, effective immune infiltration, response and long DFS in colorectal cancer is orchestrated by a well defined set of chemokines, known as capable of attracting T cells, macrophages, and to some extent dendritic and NK cells, consisting of CX3CL1, CXCL10, CXCL9, that support infiltration via upregulation of some adhesion molecules on both activated endothelial cells in capillaries surrounding the tumor, and immune cells infiltrating it, e.g., mucosal vascular addressin cell adhesion molecule 1 (MADCAM1), vascular cell adhesion molecule 1 (VCAM1) and intercellular adhesion molecule 1 (ICAM1). Specific subsets of T cells could be identified as more or less sensitive to these stimuli, as the CD8+ cytotoxic T cell compartment as such was sensitive to all three, CD45RO+ memory

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T cells were attracted by CXCL9 and CXCL10, while CX3CL1 seemed to attract rather effector T cells and activated Th1-commited T cells in colorectal cancer (Mlecnik et al. 2010).

Predictors of Favorable Outcome in Other Cancers Data obtained from prognostic immune markers in other cancer types at this depth are rather rare, but similar to the case of colorectal carcinoma, observations made in various other cancers support the conclusion that immune phenotyping of cancer can be of value for prediction of disease outcome. Such data typically underline similarities between various cancers in several areas, such as, T cell infiltration and T cell activation markers, but, on the other hand, also point out the fact that immunity against distinct cancer types is probably highly different, as illustrated for example by differences in the relevance of regulatory T cells in the prediction of disease outcome. One example for common patterns of cancer rejection is ovarian cancer, where, in a study analyzing both the primary tumor and metastases by tissue microarrays, similar to colorectal carcinoma, strong tumor infiltration by T cells, predominantly CD8+ T cells and CD45RO+ memory T cells, and only limited presence of Foxp3+ regulatory T cells was associated with a favorable outcome and increased DSS, independently of other predictors of disease outcome (Leffers et al. 2009). In another study, also performing tissue microarrays on a large cohort of patients with anal squamous cell carcinoma, it was shown that patient survival assessed by local, nodal, or distant recurrence (NED survival) was significantly improved in patients with increased numbers of CD3+ T cells, CD8+ cytotoxic T cells in the tumor infiltrate, elevated levels of GZMB, while density of CD68+ macrophages and FoxP3+ T regs was considered to be irrelevant (Grabenbauer et al. 2006). On the other hand, this study also demonstrated that although these markers showed good correlation with NED survival, they were not sufficient to predict overall survival. Renal cell carcinoma (RCC) is a highly immunogenic cancer, which is exploited in IL-2-based immunotherapy. In RCC, it was shown that CXCR3, receptor of CXCL9, CXCL10 and CXCL11, is an independent predictor of disease outcome, as high levels of CXCR3 predict longer DFS in RCC (Klatte et al. 2008). This is highly interesting because, as discussed previously, these interferon inducible chemokines are also involved in the immunity against colorectal cancer, and it was convincingly shown that CXCR3 is capable of promoting Th1-oriented and cytotoxic T cell recruitment, cellular cytotoxicity, and also suppressing neoplastic capillary formation. In metastatic melanoma, it was shown that massive presence of tumor infiltrating lymphocytes predicts decreased potential for metastatic spread to draining lymph nodes, and also a slightly improved DFS, on the other hand, it is not associated with any significant benefit in OS (Taylor et  al. 2007). In a large scale study performed on laser microdissected breast cancer samples and publicly available breast cancer gene expression data, it was shown that stromal gene

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expression profiles can predict disease outcome independently from tumor subtype. This is in contrast with intratumoral gene expression patterns that stratify breast cancer samples according to tumor subtypes, such as human epidermal growth factor receptor 2 (HER2) positive, estrogen receptor or progesterone receptor (ER/ PR+) positive, or triple negative breast cancer, which are all well-characterized independent predictors of disease outcome, however, do not provide much information about immune infiltration and activation (Finak et  al. 2008). In this study, a stroma-derived classifier of 26 genes, highly predictive of disease outcome independently from disease subtype, was created based on more than 50 microdissected stroma samples, and successfully tested on several hundred publicly available gene expression data. Interestingly, out of the 26 genes, many were involved in immigration, activation and extended survival of T cells and NK cells, such as CXCL14, CD3-z, CD8A, T cell receptor b variable 5-4 (TRBV5-4), granzyme A (GZMA), CD48, CD52, and GTPase IMAP family member 5 (GIMAP5) (Finak et al. 2008). Finally, studies conducted on nonsmall cell lung cancer (NSCLC) also suggest that a specific T cell response provides an advantage in DFS, DSS and OS also for NSCLC patients (Dieu-Nosjean et al. 2008; Kawai et al. 2008). In one such study, formation of tertiary lymphatic structures in the tumor microenvironment, designated as tumor-induced bronchus-associated lymphoid tissues (Ti-BALT) was found in a subset of NSCLC patients, and identified as a predictor of favorable disease outcome. Ti-BALT, like other “tertiary” (sometimes also referred to as “ectopic”) lymphoid tissues, are structurally and functionally identical to follicles of canonical secondary lymphoid organs (e.g., lymph nodes), containing fully active and interacting dendritic cells, T and B cells. In contrast to secondary lymph follicles however, tertiary follicles are rather uncommon, arise temporarily, usually at sites of massive inflammation and ongoing immunological activity, and are suspected to provide additional temporary support for local adaptive immune responses. Analysis of Ti-BALT structures observed around NSCLC lesions disclosed that high numbers of immigrating mature DC-Lamp+ dendritic cells, suspected organizers of such structures, is a predictor of favorable disease outcome. Interestingly, it was shown that Ti-BALT contain large numbers of B cells, CD8+ and CD4+ T cells, and that in Ti-BALT, a sufficient number of DC-Lamp+ dendritic cell is particularly critical for the maintenance of CD4+ T helper cells, and stable expression of the T-BET, the transcription factor serving as master inducer of type 1 T helper cell polarization (Dieu-Nosjean et al. 2008). In addition to markers of immune infiltration associated with good prognosis and extended survival, markers of immune cell immigration and activation can also predict rapid prognosis or relapse in cancer. It is well documented that in contrast to acute inflammation that is generally thought to be protective against cancer, smoldering chronic inflammation, immunocomplexes, some leukocytes, such as, regulatory T cells, B cells, mast cells, neutrophils and various forms of myeloid suppressor cells, can be attracted, exploited by the tumor and forced to compromise antitumor activity, or enhance progression via various mechanisms, for example by masquerading tumor invasion as a reparative/wound healing process (Aggarwal et al. 2006; Balkwill and Mantovani 2001; Ben Baruch 2006; Bui and Schreiber 2007;

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Coussens and Werb 2002; Denardo et al. 2008; Dhodapkar et al. 2008; Mager 2006; Strieter et al. 2006; Talmadge et al. 2007). Hence, depending on tumor type, stage of progression, composition of the infiltrate, immune activity can be detrimental and a predictor of a bad prognosis. Discrimination between the two sides of this “immune coin” can be a daunting task for clinicians and pathologists. The list of immune markers predicting poor outcome is long; in peritoneal and pleural effusions of patients diagnosed with ovarian carcinoma, NK cells and B cells were found to be associated with more advanced stage of cancer and in general, shorter OS (Dong et  al. 2006). In a study looking at myeloid cells in localized clear cell renal cancer, it was also shown that brisk infiltration of the primary tumor by mature activated CD66b+ neutrophils is an independent predictor of poor outcome, and similar conclusions could be made in patients with metastases (Jensen et al. 2009; Donskov 2006). In addition, it has been argued that data available on blood neutrophil counts, and neutrophil infiltration in other cancers suggest that elevated numbers of neutrophils ether in the tumor or in the blood are generally predictors of shorter DFS and OS, and that this phenomenon is not limited to renal cell cancer (Jensen et  al. 2009). In hepatocellular carcinoma, several groups have demonstrated that increased numbers of CD4+CD25+Foxp3+ regulatory T lymphocytes are associated with both shorter DFS and OS (Gao et  al. 2007; Unitt et  al. 2006; Fu et  al. 2007). A unified interpretation of these findings is not yet available, but it was suggested that T regs probably interfere with cytotoxic T cell activity, as various markers of accumulation and appropriate activation of CD8+ activated T cells (perforin, GZMA, GZMB) are absent or deregulated in their presence, or that T regs might directly contribute to tumor progression and enhanced tumor vascularization (Gao et  al. 2007; Unitt et  al. 2006; Fu et al. 2007). In renal cell cancer, there is substantial evidence that elevated levels of B7-H4 (also known as B7x) expression, a negative regulator of T cell activation, member of the B7 costimulatory molecule family, and independently, also a putative enhancer of cancer cell survival, is associated with poor disease outcome. B7-H4 is mainly expressed by T cells and APCs interacting with T cells, but also frequently upregulated by many tumors. In RCC, it is strongly displayed by both tumor cells and cells of the tumor vasculature, and in soluble form, it is also present in the blood; markers the elevated levels of which are associated with decreased DSS and/or RCC stage, respectively (Krambeck et al. 2006; Thompson et al. 2008). Similar to RCC, compromised activity of the B7 costimulatory axis was also described in prostate cancer. Enhanced expression of B7-H3 is a marker associated with several markers of prostate cancer progression, e.g., tumor size, Gleason score and progression-free survival (Roth et  al. 2007). Finally, there is a large body of evidence available on the immunosuppressive properties of indoleamine 2,3-dioxygenase (IDO), an enzyme involved in Trp catabolism and, if aberrantly expressed, exhaustion of Trp reserves in lymphocytes. It was shown that in some cancers, for example endometrial cancer, high IDO expression in tumor cells interferes with infiltration of the tumor by CD8+ T cells and NK cells, and predicts faster tumor invasion and metastasis formation. In line with this, it was possible to confirm that IDO was an independent

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prognostic marker of progression-free survival, however, it did not significantly affect OS (Ino et al. 2008). In summary, although many immune markers predict clinical outcome, the adaptive immune reaction within the tumor appeared to be the most important parameter predicting the outcome after surgical treatment with curative intent, and may change the paradigm of cancer prognosis (Galon et al. 2006). These adaptive immune parameters include the nature (CD3, CD8, CD45RO), the density, the functional orientation (Th1) and the location of immune cells within the tumor. Indeed, natural adaptive immune reaction influenced clinical outcome at all stages of the disease. The immune reaction at tumor site determined cancer evolution and clinical outcome regardless of the local extent and spread of the tumor. A weak adaptive immune reaction correlated with a very poor prognosis even in patients with a minimal tumor invasion. Conversely, a high density of adaptive immune cells correlated with a highly favorable prognosis whatever the local extent of the tumor and the invasion of regional lymph nodes. Beneficial in situ adaptive immune reaction was not restricted to patients with minimal tumor invasion, indicating that in situ immunological forces may persist along with tumor progression. Multivariate Cox analysis revealed that the immune criteria remained the unique parameter significantly associated with prognosis. The histopathogical staging system (T-stage, N-stage and tumor differentiation) did not present a significant predictive value anymore. Compared to the immune contexture, no immune marker and no tumor parameter associated with survival has been reported to achieve a similarly high level of significance in colorectal cancer. The strength of the immune reaction identified in our studies could advance our understanding of cancer evolution and have important consequences in clinical practice (Galon et al. 2006).

Immunotherapy in Cancer Besides prognostic markers of disease outcome discussed so far, a new generation of biomarkers is required to assess disease outcome in cancer immunotherapy. Unfortunately, at the moment, only a limited number of FDA-approved or promising intensively developed therapies (e.g., therapies with ongoing or finished Phase III trials) are available. Examples of such therapeutic strategies include, but are not limited to administration of immune activating cytokines (e.g., IFNa, IL-2), vaccination with cancer antigens (e.g., gp100 melanoma antigen, new york esophageal squamous cell carcinoma 1/NY-ESO1, melanoma-associated antigen 3/MAGEA3), administration of ex vivo selected, activated, expanded and/or TCR-engineered autologous or allogeneic T cells with or without selective immunodepletion (adoptive cell therapy), blockade of inhibitory signaling (e.g., cytotoxic T lymphocyte antigen 4/CTLA-4 blockade), viral introduction of tumor antigens (e.g., TroVax expressing the tumor antigen 5T4) with or without further potentiating the immune response (e.g., PANVAC-VF expressing CEA and MUC-1 tumor antigen and B7.1, ICAM-1, and leukocyte function-­ associated antigen 3/LFA-3) (Rosenberg et al. 2008; Kirkwood et al. 2008; Tahara

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et  al. 2009; Brichard and Lejeune 2008). In general, such therapies are frequently limited in their effectiveness, associated with significant toxicity and many sideeffects, such as autoimmune responses, can require hospitalization or even intensive care for some patients. Still, it is clear that they can induce objective response, either partial or complete, in a reproducible number of patients. Hence, it is imperative to identify biomarkers predictive of treatment outcome in immunotherapy, thereby decreasing the numbers of patients that have to cope with side effects without any significant chance of a benefit. So far, however, only a few studies addressed the question of predictive markers for response in immunotherapy; data about mechanistic or end-point markers, assessing direct treatment efficacy rather than overall disease outcome, are more frequently reported. In case of IL-2 therapy for metastatic melanoma, it was recently shown that high levels of serum VEGF and fibronectin can predict nonresponse to the therapy with relatively high accuracy. These results are in line with observations suggesting that besides its proangiogenic activities, VEGF can induce immunosuppression in cancer per se, and that fibronectin acts by further potentiating this effect. On the other hand, the same data also suggest that low levels VEGF and fibronectin in metastatic melanoma do not necessarily predict a favorable treatment outcome in IL-2 therapy, and it is not yet clear to what extent these markers are independent predictors of disease outcome, or rather markers of disease burden which is known to be inversely correlated with response to IL-2 (Sabatino et  al. 2009; Kirkwood and Tarhini 2009; Guminski and Thompson 2009). As for IL-2 therapy in renal cell cancer, it was demonstrated that classic histological analysis extended by staining for carbonic anhydrase IX (CAIX) can be of additional value for outcome prediction. CAIX is regulator of pH that is usually not expressed in normal tissues but is frequently elevated under hypoctic conditions, acidosis and in many types of cancer. In renal cell cancer, evidence is available that CAIX expression is downmodulated as cancer progresses to the metastatic phase, and in line with this, high levels of CAIX in the primary tumor before IL-2 therapy predict relatively low risk of relapse (Atkins et al. 2005). For adjuvant IFNa therapy in metastatic melanoma, although no widely accepted predictor of disease outcome is available, it seems that intact signaling via the IFNAR pathway, as measured by analyzing levels of STAT1 phosphorylation (STAT1-p) in T cells of the host, is frequently disrupted in patients. This is observed at frequencies comparable to that of the percentage of nonresponse to therapy. Therefore, it is suspected that this step, along with other treatment end-point markers, such as treatment-induced CD69 expression on the surface of affected T cells could be exploited as a marker for response (Critchley-Thorne et al. 2007). Also, ratio of baseline STAT1-p/STAT3-p signals in the affected tumor, two STAT proteins in intense cross-talk in melanoma cells, the balance of which has critical impact of cell survival, has been suggested as a predictive marker for therapy success (Wang et al. 2007), and there is data available that high pre-treatment inflammatory activity, as assessed by measuring serum levels of IL-1b, IL-1a, IL-6, tumor necrosis factor-a (TNFa) and the chemokines CCL3 and CCL4, is also associated with longer DFS in the context of IFNa therapy (Yurkovetsky et al. 2007).

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For adoptive cell therapy combined with lymphodepletion, it seems that one of the most useful predictive markers of disease outcome is telomere length of the antigen-specific T lymphocytes utilized for ACT (Shen et  al. 2007; Zhou et  al. 2005). Availability of tumor-specific T cells with shorter telomeres, that is, the number of T cells with broad replicative potential and no sign of early activationinduced senescence, is a predictive marker of positive therapeutic response. It seems that in ACT, persistence of adoptively transferred T cell clones is critical for successful eradication of the tumor, because even if a large number of T cells is prepared capable of tumor recognition, and availability of T cell supporting growth factors is secured by eliminating competitive T cells by lymphodepletion prior to ACT, number of transferred T cells can rapidly diminish unless sufficient telomere length is maintained. Finally, in MAGEA3-based vaccination for metastatic melanoma, unpublished data from initial studies utilizing high throughput gene expression profiling suggest that a combination of genes with immune-related function including CCL5, CCL11, IFNG, inducible T-cell costimulator (ICOS) and CD20 expression could predict response to therapy, and a similar approach has been used to identify immunologic signature predicting response to MAGEA3 in nonsmall cell lung cancer (Louahed et al. 2008; Vansteenkiste et al. 2008).

Summary In summary, there is strong evidence to suggest that favorable disease outcome in cancer can be predicted more accurately if appropriate immune markers of cancer rejection are added to classic histological analysis. Selection of such markers is a challenging research task but is of obvious importance in predicting patient survival. Data available indicate that (1), antitumor immunity has a relevant impact on disease outcome in cancer, although in advanced stage cancer this is frequently shifted towards an impact more on DFS than OS (2) significant differences can be observed between predictive markers identified in different cancers, possibly not only because of actual biological differences between the cancers, but also because validation of the same marker in various cancer types is a goal difficult to achieve and therefore not frequently aimed at in basic research (3) similarities suggest that appropriate activation of T and NK cell-mediated immunity, markers of Th1-commitment, memory T cell development, and efficient cytotoxic response, are all predictors of favorable outcome in cancer.

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The Microenvironment of Ovarian Cancer: Lessons on Immune Mediated Tumor Rejection or Tolerance Lana E. Kandalaft and George Coukos

Introduction Epithelial ovarian carcinoma (EOC) is the fourth most common cancer in women, and the most lethal gynecologic malignancy in the United States, accounting for approximately 22,000 new cases and 15,000 deaths per year. Due to incremental improvements in surgery and chemotherapy, the 5-year survival rate has increased from 37% in the 1970s to 45% in the 1990s (Bukowski et al. 2007). Based on large cooperative randomized clinical trials, the combination of carboplatin and paclitaxel still remains the best performing chemotherapy regimen. Yet, no substantial decrease has been seen in death rates, as the majority of patients relapse and die from their disease despite response to first-line therapy (Ozols 2006). Thus novel therapeutic approaches are direly needed. Although traditionally considered unresponsive to immune therapy, increasing evidence indicates that ovarian cancers are, in fact, immunogenic tumors. This evidence comes from diverse epidemiologic and clinical data comprising evidence of spontaneous antitumor immune response and its association with longer survival in a proportion of ovarian cancer patients; evidence of tumor immune evasion mechanisms and their association with short survival in some ovarian cancer patients; and finally pilot data supporting the efficacy of immune therapy. In EOC, antigen characterization has not been systematic, but evidence exists that tumor-associated antigens are present. The best differentiation tumor rejection antigen identified to date is the onconeuronal protein cdr2, shared by ovarian cancer cells and cerebellar Purkinje cells. Its recognition by cytotoxic lymphocytes (CTLs) is associated with paraneoplastic cerebellar degeneration and occult ovarian cancer (Albert et  al. 1998). Other antigens identified in ovarian cancer include HER-2 protein, the product of c-erBb-2 oncogene; p53 tumor suppressor gene protein product; topoisomerase-IIa; folate binding protein; amino enhancer of split protein; sialylated TN (sTN), a mucin antigen; MUC-1; NY-ESO-1, a testis differentiation antigen; and mesothelin (reviewed in Coukos et al. (2005)). In addition, universal G. Coukos (*) Ovarian Cancer Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_14, © Springer Science+Business Media, LLC 2011

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tumor antigens such as the human telomerase reverse transcriptase (hTERT), cytochrome P450 CYP1B1 and survivin (Gordan and Vonderheide 2002) are expressed by EOC (Vonderheide et al. 1999; Counter et al. 1994). Tumor-specific T cells secreting interferon-gamma (IFN-g) were also reported in peripheral blood of patients with advanced stage ovarian carcinoma, indicating that tumor antigens are in fact recognized spontaneously in vivo (Schlienger et al. 2003). Below we will discuss the biology underlying ovarian cancer immune rejection or tolerance and we will discuss its association with clinical outcome. We believe that understanding these pathways at the tumor microenvironment will lead to novel strategies for enhancing ovarian cancer immunotherapy.

T Cells in Ovarian Cancer and Their Correlation with Outcome T cells bearing the ab T cell receptor are the dominant adaptive immune effector cells that target human ovarian tumors. T cells have been detected in both solid tumor nodules and in ascites. In ovarian tumors, T cells have been observed within tumor cell islets (called intratumoral or intraepithelial T cells) and/or the stroma surrounding tumor islets (Zhang et al. 2003). Although most ovarian tumors harbor T cells in the stroma surrounding tumor islets, approximately 50% of ovarian tumors lack intraepithelial T cells. The presence of intraepithelial CD3+ T cells within tumor islets was evaluated as a prognostic factor for overall survival (Zhang et al. 2003). The presence of intraepithelial tumor-infiltrating lymphocytes (TILs) predicted significantly longer survival in ovarian cancer. We first reported this finding in an Italian cohort of patients. Patients whose tumors had intraepithelial T cells experienced longer progression-free and overall survival as compared to patients whose tumors lacked intraepithelial T cells. Survival at 5 years was substantial (38%) in patients whose tumors had intraepithelial T cells (n = 102) and was negligible (4.5%) in patients lacking them (n = 72), even after complete response to chemotherapy (Zhang et al. 2003). Another subsequent study showed a strong predictive value of CD8+ T cells, the subset of T cells that comprises mainly CTL. Patients with higher frequencies of intraepithelial CD8+ T cells demonstrated improved survival compared with patients with lower CD8+ T cell frequencies (median survival 55 vs. 26 months; hazard ratio = 0.33; P < 0.001) (Sato et al. 2005). Interestingly, in the Zhang study CD4+ and CD8+ T-cell infiltrates correlated (R2 = 0.66, P < 0.001; n = 30) and intraepithelial CD4+ and CD8+ cells were either present or absent by immunohistochemistry, similarly to total CD3+ cells. The impact of intraepithelial CD3+ and/ or CD8+ T cells has since been confirmed by multiple independent studies on ethnically and geographically diverse populations (Sato et al. 2005; Adams et al. 2009; Clarke et al. 2009; Hamanishi et al. 2007; Shah et al. 2008; Tomsova et al. 2008; Stumpf et al. 2009; Milne et al. 2009). There is substantial evidence that T cells infiltrating ovarian cancer recognize and react to tumor antigens ex vivo (Santin et al. 2001; Kooi et al. 1993; Peoples et al. 1995;

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Dadmarz et al. 1996; Santin et al. 2000; Freedman and Platsoucas 1996). However, it remains unclear to date whether the observed association between intraepithelial T cells and improved clinical outcome can be attributed to the direct function of tumor-infiltrating T cells or merely to an association of T cells with indolent tumors with low proliferation. For example, it could be hypothesized that intraepithelial T cells are more able to accumulate in slowly growing tumors, which are associated with improved survival, while they are outpaced and excluded by aggressive tumors with elevated rates of proliferation, which are associated with poor survival. Interestingly, intraepithelial T cells were found to be more prevalent in tumors with increased tumor cell proliferation index, indicating that improved outcome is not due to indolent tumor cell behavior (Adams et  al. 2009). We tested this hypothesis by surveying a panel of 134 patients with advanced stage III and IV ovarian cancer for the presence of intraepithelial T cells and expression of Ki67 (Adams et al. 2009), a nuclear protein, which is expressed during all active phases of the cell cycle. We found that patients with highly proliferative tumors were more likely to possess a higher number of CD8+ intraepithelial T cells (odds ratio = 2.4, P = 0.041). Patients with aggressively growing Ki67hi tumors did not benefit clinically from the elevated presence of CD8+ intraepithelial T cells. However, high levels of CD8+ intraepithelial T cells were linked to an increased 5-year survival rate (73.3 vs. 15%) in patients with less aggressively growing Ki67lo tumors. Indeed, high frequency of intraepithelial CD8+ T cells and/or low proliferation (Ki67 expression) defined subsets of patients with more indolent tumors who exhibited the best outcome even following suboptimal cytoreductive surgery (Zhang et al. 2003; Tomsova et al. 2008). The data described above identified a positive association between high Ki67 expression tumor cells and CD8+ T cell infiltrate. Mitotically active ovarian cancers are more likely to exhibit genetic instability (Blegen et  al. 2000) and possibly express a more diverse antigenic repertoire, including neoantigens that elicit a cellular immune response. Similar observations have been made in breast carcinomas with mutations in BRCA1, a gene involved in maintaining genomic stability (Gudmundsdottir and Ashworth 2006) which are higher grade but are also characterized by T cell infiltration and improved survival (Lakhani et al. 1998; Kuroda et al. 2005). Similarly, colorectal cancers with microsatellite genetic instability are characterized by T cell infiltration and better prognosis in spite of being higher grade (Buckowitz et al. 2005). Interestingly, a recent study found that ovarian cancers with p53 mutations were more likely to have intraepithelial T cells (Shah et  al. 2008). This data, together with previous epidemiologic evidence (Zhang et  al. 2003; Sato et al. 2005; Hamanishi et al. 2007; Curiel et al. 2004) and molecular and functional studies (Zhang et al. 2003; Santin et al. 2001; Kooi et al. 1993; Peoples et al. 1995; Dadmarz et al. 1996; Santin et al. 2000; Freedman and Platsoucas 1996) suggest that T cells may indeed react to tumor antigens and directly contribute to reduced tumor growth. If this were the case, it is possible that tumor biomarkers could help individualize therapy and guide the selection of chemotherapy combinations that synergize with spontaneous antitumor mechanisms and immune or immunomodulatory therapy (Coukos et al. 2005; Sabbatini and Odunsi 2007; Chu et al. 2008; Kandalaft et al. 2009).

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Polyfunctional T Cells Are Present in the Ovarian Cancer Microenvironment An important aspect revealed by our data is that an improved clinical outcome depends on the infiltration of T cells specifically in tumor islets rather than stroma, as the mere presence of T cells in stroma alone did not predict better outcome. Indirect evidence of T cell activation selectively in tumors with intraepithelial T cells was provided by the expression of Ki-67 and CD45R0 by intraepithelial T cells (Zhang et al. 2003). Furthermore, mRNA levels of IFN-g and interleukin-2 (IL-2), two cytokines associated with T cell activation, were 10 (P = 0.019) and 26-fold (P = 0.091) higher in tumors with intraepithelial T cells as compared with tumors lacking intraepithelial T cells, and were undetectable in 7/10 (70%) and 9/10 (90%), respectively, of tumors lacking intraepithelial T cells. IL-2 and IFN-g have been shown to induce T-cell-mediated rejection of tumors in experimental models of syngeneic tumors transplanted into immunocompetent animals (Freedman and Platsoucas 1996; Pertl et  al. 2001). IL-2 promotes T cell expansion and enhances the cytotoxicity of effector immune cells (Pertl et al. 2001). In addition, IL-2 was shown to restore T cell function following suppressive signaling by inhibitory receptors such as PD-1 (Pertl et  al. 2001). Thus, secretion of IL-2 by activated effector cells in ovarian cancer can further enhance antitumor immune response and counter tumor-induced anergy in TILs through PD-1 signaling. IFN-g is secreted by activated effector T cells and NK cells in response to target recognition and has been shown to have direct anti-proliferative activity on ovarian cancer cells in  vitro, which proved to be synergistic with chemotherapy (Nehme et al. 1994; Melichar et al. 2003; Wall et al. 2003). Tumor-specific T cells secreting IFN-g were recently reported in peripheral blood of patients with advanced stage ovarian carcinoma, indicating that tumor antigens are in fact recognized spontaneously in vivo (Schlienger et al. 2003). IFN-g up regulates HLA class I and class II molecules and antigen presentation in ovarian tumor cells in  vitro and in  vivo (Freedman et al. 2000), a requisite for recognition by T cells. In fact, HLA class I expression by the tumor correlates with the intensity of T cell infiltration (Kooi et al. 1996), a predictor of longer survival. Importantly, besides immune modulatory functions, IFN-g has been reported to effectively inhibit angiogenesis in tumors (Coughlin et al. 1998; Maheshwari et al. 1991; Sidky and Borden 1987). It has been demonstrated that IFN-g causes a decrease in vascular endothelial proliferation. Furthermore, IFN-g was shown to enhance the release of anti-angiogenic chemokines, such as CXC chemokines CXCL9 and CXCL10 (Sgadari et al. 1997; Strieter et al. 1995) and down-regulate platelet endothelial cell-adhesion molecule 1 (PECAM-1) (Stewart et al. 1996) a molecule constitutively expressed at vascular endothelial cell junctions. In addition, IFN-g has been reported to down-regulate aVb3 integrin, an adhesion receptor that plays a key role in tumor angiogenesis (Ruegg et al. 2002). In multiple mouse models, tumor rejection by CD8+ effector T cells was dependent on the ability of IFN-g to inhibit angiogenesis (Qin et al. 2003). It was proposed that inhibition of angiogenesis by CD4+ T cell-derived IFN-g was preventing rapid tumor

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progression, thereby allowing other, perhaps direct killing, mechanisms to eliminate residual tumor (Qin et al. 2003). Thus, Th1 cells releasing IFN-g could contribute to tumor growth suppression by modifying the tumor microenvironment, facilitating further tumor immune recognition by effector T cells and by suppressing angiogenesis, both resulting in tumor growth suppression. The above observations suggest that the presence of type 1 polarized intraepithelial T cells secreting IL-2 and IFN-g is evidence of an antitumor immune response that is spontaneously established in patients with ovarian cancer. Its association with prolonged progression-free and overall survival suggests that these mechanisms control tumor growth and thus raise expectations that immune therapy will benefit patients with ovarian cancers. Indeed, pilot clinical data indicate that ovarian cancer patients can respond to the same immunotherapy approaches as patients with other immunogenic tumors such as melanoma, including interleukin-2 (IL-2), IFN-g, CTLA-4 antibody and adoptive transfer of ex vivo expanded TIL (Rosenberg and Dudley 2009). Notably, all of these therapies capitalize on preexisting antitumor immune response. Importantly, weekly intraperitoneal IL-2 infusion produced a ~17% complete pathologic response rate in patients with platinum-resistant ovarian cancer (Edwards et  al. 1997; Vlad et  al. 2009), while objective responses and/or prolonged survival have been seen with adoptive transfer of ex vivo expanded TIL (Sato et  al. 2005; Hamanishi et  al. 2007) or anecdotally with CTLA-4 antibody (Hodi et al. 2003, 2008). Encouraging results have been reported with recombinant human (rh)IFN-g either as intra-peritoneal monotherapy or in combinations in early phase trials (Pujade-Lauraine et al. 1996; Chen et al. 1992; Colombo et al. 1992; Schmeler et al. 2009). Theoretically, the effects are likely to be greatest in women who are also receiving chemotherapy because of IFN-g’s nonspecific immune modulatory effects (Berek 2000). Confirming expectations, a threefold prolongation of progression-free survival was observed in a phase-III multi-center study from Europe with subcutaneous administration of rhIFN-g combined with MTD cisplatin and cyclophosphamide chemotherapy, with minimal added toxicity (Windbichler et  al. 2000). However, in a subsequent randomized phase-III trial conducted in the U.S., addition of subcutaneous rhIFN-g to carboplatin and paclitaxel did not improve survival (Alberts et al. 2008). Although one cannot exclude that racial and other demographic differences may account for opposite results, this data may indicate that the choice of chemotherapy drugs in combination with rhIFN-g is critical in the outcome of immune therapy. Besides Th1 cells, the function of which has been long established in tumors, T helper 17 (Th17) cells were recently identified in ovarian cancer. Th17 cells produce interleukin-17 (IL-17), an important inflammatory cytokine, and have been shown to promote inflammation in a number of autoimmune diseases (Weaver et al. 2006; Dong 2006; Wynn 2005; Harrington et  al. 2005; Tato and O’Shea 2006; Ouyang et al. 2008). Th17 cells are formed by the differentiation of naïve T cells in the presence of interleukin-6 (IL-6) and transforming growth factor-b (TGF-b) and are expanded by IL-1 and IL-2. They are considered the sole cellular source for IL-17 in the human tumor microenvironment. The function of Th17 cells has been controversial in tumors. Earlier studies have shown that exogenous IL-17 either

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enhanced antitumor immunity (Benchetrit et  al. 2002; Hirahara et  al. 2001) or promoted tumor growth by the induction of tumor vascularization in tumor-bearing mice (Numasaki et al. 2003, 2005). It is worthwhile to point out that the potential role of endogenous IL-17 (or Th17 cells) has not been examined in tumor initiation in spontaneous mouse tumor models, including those induced by infectious pathogens or chemical carcinogens or in humans with preclinical diseases. It is possible that endogenous IL-17 (or Th17 cells) may play distinct roles in tumor initiation vs. established tumor growth. Further, the roles of exogenous and endogenous IL-17 may be potentially distinct, depending on local biologic levels of IL-17 and the microenvironment. Although Th17 cells have been implicated in tumor growth in mouse tumor models, recently Th17 cells have been associated with improved outcome in human tumors. Most studies have investigated the presence of Th17 cells in peripheral blood rather than the tumor itself (reviewed (Zou and Restifo 2010) however, an extensive study of tissue distribution of Th17 cells in patients with ovarian cancer was performed by Kryczek and colleagues. In these patients, the prevalence of Th17 cells in tumor-draining lymph nodes and peripheral blood was similar to that found in the peripheral blood of healthy donors; however, higher proportions of Th17 cells were found in tumors. This suggested that Th17 cells may be recruited and/or induced by the tumor microenvironment (Kryczek et al. 2009a). Kryczek and colleagues found that tumor-infiltrating Th17 cells expressed several effector cytokines, but no molecules associated with immune suppression. This phenotype was universally found in six different human cancer types. This cytokine profile revealed a polyfunctional effector T cell phenotype similar to that observed in patients with infectious diseases (Almeida et al. 2007; Precopio et al. 2007) and suggested that tumor-associated Th17 cells are polyfunctional effector T cells, which are an important component of antitumor immune response. Consistent with this hypothesis, IL-17 was positively associated with tumor-infiltrating IFN-g secreting effector T cells (Tato and O’Shea 2006). The collaborative effects among these cytokines, including IL-17 and IFN-g, may be decisive in determining the biologic activities of Th17 cells in human tumors as demonstrated in human studies (Kryczek et al. 2008), which may not be seen in mice. Th17 cells may also mediate antitumoral activity indirectly by facilitating the recruitment of other effector cells (Zhang et al. 2003; Sato et al. 2005; Galon et al. 2006). In line with this possibility, the authors found that Th17 cells were negatively associated with the presence of regulatory T cells (Curiel et  al. 2004) and were positively associated with effector immune cells, including IFN-g secreting effector T cells, CD8+ T cells, and NK cells in the same tumor microenvironment. Furthermore, IL-17 expression in tumor-associated ascites positively predicted patient survival. This data was consistent with the fact that transgenic T cells polarized with TGF-b and IL-6 can induce tumor eradication in mice (Muranski et al. 2008), and that forced expression of IL-17 ectopically in tumor cells can suppress tumor progression through enhanced antitumor immunity in immune-competent mice (Benchetrit et al. 2002; Hirahara et al. 2001) and finally that IL-17-deficient mice exhibited accelerated tumor growth and lung metastasis (Kryczek et  al. 2009b).

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Furthermore, in a separate study, it was demonstrated that adjuvant IL-7 treatment (Pellegrini et  al. 2009) resulted in improved antitumor immunity, which was ­associated with marked CD8+ T-cell activation and Th17 cell enhancement.

Chemokines Recruiting Antitumor T Cells to the Tumor Microenvironment An analysis of factors implicated in T cell trafficking revealed a strongly positive association of select chemokines with the detection of intraepithelial T-cells. Interferoninducible chemokines such as CXCL9, also known as monokine induced by IFN-g (MIG), and CXCL10, also known as IFN-inducible protein-10 (ICP10), attract primarily activated T cells expressing the cognate CXCR3 receptor (Loetscher et al. 1996; Sallusto et al. 1999). We found that tumors with intraepithelial T-cells exhibited 50 times higher levels of CXCL9/MIG mRNA than tumors lacking intraepithelial T-cells (P = 0.049) (Zhang et al. 2003). Strong expression of CXCL9/MIG protein was confirmed in tumors with intraepithelial T-cells by immunohistochemistry (Zhang et al. 2003). CCL21, also known as secondary lymphoid organ chemokine (SLC) or exodus-2, and CCL22, or macrophage derived chemokine (MDC), attract naïve or memory/noneffector T cells (Cyster 1999; Tang and Cyster 1999). Tumors with intra-tumoral T-cells displayed 43-fold higher CCL21/SLC/exodus-2 (P = 0.050) and 14-fold higher CCL22/MDC mRNA levels (P = 0.034), as compared with tumors lacking intratumoral T-cells. Neither chemokine mRNA was detectable in 5/10 tumors lacking intra-tumoral T-cells. Expression of SLC/exodus-2 and MDC protein in tumor islets of tumors with intra-tumoral T-cells was confirmed by immunohistochemistry (Zhang et al. 2003). These chemokines induce T-cell-mediated tumor rejection in experimental models of syngeneic tumors transplanted to immune competent animals (Pertl et al. 2001; Sun et al. 2001). Our data indicate that they may be implicated in mechanisms affecting clinical outcome. Given the role of MIG in attracting activated T-cells (Sallusto et al. 1998, 1999) and inhibiting angiogenesis (Strieter et al. 1995), and the ability of SLC/exodus-2 and MDC to attract memory T-cells as well as mature ­dendritic cells, and promote antigen presentation (Cyster 1999; Tang and Cyster 1999), these chemokines may be involved in antitumor mechanisms. We tested the ability of molecular markers to predict T-cells or outcome in the 26 patients whose tumors were analyzed for chemokines. A logistic regression analysis and associated receiver-operating curve showed that MDC was strongly associated with late recurrence (>40 months, odds ratio = 1.568; area under the curve = 0.732; P = 0.082). Interestingly, chemokines implicated also in chemo-attraction of other immune cell types, such as stroma-derived factor-1 (SDF-1), monocyte chemo-attractant protein-1 (MCP-1) or I-309 were similarly expressed in tumors with or without intra-tumoral T cells (Zhang et al. 2003). These data collectively indicate that specific chemokines are implicated in the recruitment of antitumor T cells and can help orchestrate the engraftment of an inflammatory infiltrate with type 1 and 17 polarization. It is possible that pharmacologic manipulation of the tumor microenvironment facilitating the expression of these chemokines will enhance the effects of immunotherapy.

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Regulatory T Cells, Presence and Outcome CD4+FoxP3+CD25+ regulatory T cells (Treg), a subset of T cells endowed with powerful suppressor activity, are an important mediator of peripheral immune tolerance. These cells prevent T cell-specific immunity by suppressing CD8+ T cell activation and secretion of IL-2 and IFN-g; inhibit specific cytotoxicity in a contactdependent fashion and/or through contact-independent, paracrine mechanisms (Bluestone and Abbas 2003; Sakaguchi 2004; Read and Powrie 2001); and affect the function of other immunosuppressive populations like tolerogenic antigenpresenting cells. Consistent with this concept, experimental depletion of Treg cells in mice with tumors improves immune-mediated tumor clearance (Shimizu et al. 1999) and enhances the response to immune-based therapy (Steitz et al. 2001). The first evidence for the contribution of Treg to immune dysfunction in cancer in the human was presented in patients with ovarian cancer and lung tumors, where increased frequency of TGF-b-secreting Treg with potent immunosuppressive functions were identified in tumors, ascites and peripheral blood (Woo et al. 2001). In human ovarian cancer, Tregs were demonstrated to play an important role in inhibiting effector T cell function and paralyzing antitumor immune response in vivo, which was associated with progressive tumor growth in a transplantation mouse tumor model (Curiel et al. 2004). There were significantly fewer Treg cells in tumor-draining lymph nodes than in control lymph nodes and tonsils. Treg cell numbers were also significantly lower in tumor-draining lymph nodes in late stage (III and IV) as compared with early stage (I and II) ovarian cancers. By contrast, there was a significant trend towards a higher accumulation of Treg cells in ascites and the solid tumor mass in later tumor stages (Curiel et al. 2004). These findings were recently confirmed by two other groups; quantification of FoxP3 mRNA was found to correlate with the frequency of Treg cells by immunohistochemistry in ovarian cancer patients from Austria, while increased FoxP3 mRNA expression correlated with significantly worse overall survival (27.8 vs. 77.3 months, P = 0.0034) and progression-free survival (18 vs. 57.5 months; P = 0.0041) (Wolf et  al. 2005). Furthermore, in a parallel study conducted on patients undergoing debulking surgery at Roswell Park Cancer Institute, high ratio of CD8+/Treg cells (FoxP3+CD25+) was a strong positive prognosticator. The median survival for patients with high CD8+/Treg ratios was 58 months, whereas patients with low ratios had a median survival of 23 months (hazard ratio = 0.31; P = 0.0002) (Sato et al. 2005). Interestingly, another study found that increased FoxP3+ cells (as well as CD8+ cells) were associated with increased disease-specific survival (Leffers et al. 2009). Although this study appears to contradict earlier studies, these results may be interpreted by the observation that increased CD8+ cells are accompanied by increased FoxP3+ cells and ultimately, the ratio of CD8+ to FoxP3+ cells is the most reproducible predicting factor of clinical outcome, which was confirmed in our recent study (Adams et al. 2009). The above studies demonstrated that Treg cells seem to migrate preferentially and predominantly to the tumor mass and the associated malignant ascites. The lack

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of apparent migration to lymph nodes suggests that tumor Treg cells may primarily work by inhibiting extra-nodal effector T cell function rather than by suppressing naive T cell priming in lymph nodes, at least in later stages of tumors. The recruitment of Tregs to the tumor microenvironment could be due to multiple factors. Tumor Treg cells were shown to express functional CCR4, the receptor for CCL22, and can therefore migrate to CCL22 present in the tumor microenvironment. Curiel et  al. suggested that the source of CCL22 is the ovarian tumor and associated macrophages. These authors have demonstrated that blocking CCL22 in  vivo reduces human Treg cell tumor trafficking suggesting that, in addition to depleting Treg cells, blocking Treg cell tumor trafficking represents a potential strategy for treating human cancers. Also, recent work from our laboratory demonstrated that hypoxic tumor cells up regulate CCL28 chemokine and recruit preferentially CD4+CD25+FoxP3+ regulatory T cells (Treg) from peripheral blood mononuclear cells (PBMC) than non-hypoxic cells. This was mediated by the CCL28 receptor CCR10, indicating a link between hypoxia, CCL28 and Treg accumulation in human tumors. Indeed, murine ovarian tumors over expressing CCL28 were found to progress more rapidly, which was associated with increased Treg accumulation in tumors and was specifically abrogated by Treg depletion. Finally, Treg accumulation was associated with tolerogenic and pro-angiogenic tumor milieu (Facciabene and Coukos 2010, personal communication). These data collectively indicate that Treg cells are implicated in immune suppression in ovarian cancer and their neutralization should augment antitumor immune response in the context of conventional therapies or bona fide immunotherapy.

The Tumor Endothelial Barrier: Another Layer of Immune Regulation The tumor vascular endothelium presents a significant challenge to the success of immune therapy, as it provides a physical barrier through which tumor-reactive T cells must extravasate before they get to recognize tumor cell epitopes and exert their cytotoxic effects. It has been noted in many T cell based immune therapy studies that while activated T cells could be found in the periphery, they often failed to produce tumor responses (Dudley et al. 2002; Boon and van Baren 2003; Lurquin et  al. 2005). Successful migration through the tumor endothelial barrier by activated effector lymphocytes may be impaired in some of these cases, resulting in tumor evasion. Precisely how the tumor vasculature establishes immune privilege is not well known, but the ongoing process of tumor angiogenesis may facilitate immune escape. Specifically, tumor-derived VEGF and the endothelin system may play a pivotal role in reducing leukocyte homing to and extravasion through the vascular endothelium. Members of the endothelin system have been identified in broad array of tissue types, including neuronal, renal and vascular tissues, and regulate a number of

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critical physiological processes including reproduction, embryonic development, and cardiovascular homeostasis (Grant et  al. 2003; Kedzierski and Yanagisawa 2001; Yanagisawa et al. 1998; Meidan and Levy 2007). The endothelin system has well-known roles in regulating vasoconstriction, and mediates both cardiovascular and renal disorders (Nelson et al. 2003; Bagnato and Rosano 2008). Particularly, the endothelin system is an important regulator of physiologic and pathogenic angiogenesis, and VEGF signaling is intimately involved in dynamic crosstalk with the endothelin system (Nelson et  al. 2003; Bagnato and Rosano 2008). The endothelin system is comprised of four endothelin (ET) peptide ligands, ET-1, 2, 3, and 4 (Yanagisawa and Masaki 1989; Saida et al. 1989) that signal through their two G protein-coupled receptors, ETAR and ETBR (Meidan and Levy 2007; Frommer et al. 2008). Biologically active ETs are derived from precursor proteins following cleavage by membrane-bound metalloproteinases termed endothelinconverting enzymes (ECE) (Valdenaire et al. 1995). Amongst the four endothelin ligands, ET-1 is the most potent ligand and is widely expressed in multiple cells types, notably endothelial cells (Luscher and Barton 2000) as well as tumor cells including ovarian, breast, renal, colon, and prostate cancer (Nelson et  al. 2003; Bagnato and Rosano 2008). Binding of the ETAR and ETBR by ET peptides triggers downstream signal transduction pathways, including, but not limited to, the RAF/ MEK/MAPK and the PI3K/AKT pathway (Nelson et al. 2003). ET-1 has been shown to directly promote tumor angiogenesis by inducing endothelial cell survival, proliferation and invasion in an ETBR-dependent manner (Salani et  al. 2000). ETBR may promote angiogenesis indirectly by upregulating VEGF production in the vasculature (Jesmin et al. 2006). Furthermore, there is a strong correlation between ETBR and VEGF expression in a number of different tumor specimens (Kato et al. 2001). ET-1 induces the expression of VEGF in cancer cell lines in vitro (Salani et al. 2000; Spinella et al. 2007; Rosano et al. 2003a, b; Spinella et al. 2002), which is mediated by HIF-1a (Salani et al. 2000) via ETAR activation (Spinella et al. 2004a). Ovarian tumor growth was inhibited in nude mice by the ETAR-selective antagonist ABT-627, an effect associated with reduced VEGF expression (Spinella et al. 2004b). In addition, the endothelin axis is believed to activate autocrine/paracrine loops that promote proliferation, protection from apoptosis, invasion and metastatic dissemination of tumors (Nelson et  al. 2003; Bagnato and Rosano 2008). We recently demonstrated a novel role of ETBR in tumor immune regulation (Buckanovich et  al. 2008). We showed that the endothelial barrier exists in tumors, where it prevents T cell homing, establishing an immune privileged status. This is partly mediated by the ETBR. This mechanism was uncovered in human ovarian cancer, where endothelial cells were micro-dissected from tumors with brisk tumor-infiltrating lymphocytes (TILs) and tumors lacking TILs, to examine differences in their gene expression profile. ETBR was one of the few genes over expressed in tumor endothelial cells dissected from tumors lacking TILs by Affymetrix array analysis (Buckanovich et al. 2008). We confirmed that ETBR mRNA as well as protein over expression is associated with absence or

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paucity of TILs, especially of intraepithelial T cells (Buckanovich et al. 2008), and similarly to the association of intraepithelial T cells with longer survival in ovarian cancer (Arnett et al. 2003), lower expression of ETBR mRNA in tumors was associated with longer survival (Buckanovich et al. 2008). By immunostaining, ETBR was mostly localized to the endothelium and some stroma cells in human ovarian cancers. On the other hand, the cognate ligand ET-1 is over expressed in ovarian cancer cells (Bagnato and Rosano 2008) and we found that ET-1 mRNA was significantly higher in micro-dissected tumor cells from tumors lacking TILs relative to tumors with brisk TILs. Thus, there is a tumor-to-endothelium ET-1/ ETBR paracrine axis, which seems up regulated in ovarian cancers lacking TILs. Importantly, we showed that recombinant human ET-1 can block the adhesion of activated T cells to endothelial cells in  vitro. These results establish a vascular mechanism of tumor immune evasion mediated by the endothelin system, whereby the tumor cells over express the ligand, while the associate endothelium over expresses the receptor, resulting in suppression of T cell adhesion and extravasation (see Kandalaft et al. (2009)). TNF-a is a major inflammatory cytokine implicated in carcinogenesis, tumor angiogenesis and progression, and it is upregulated in ovarian cancer (Merogi et al. 1997). We have previously reported that the overall TNF-a mRNA levels are similar in ovarian tumors with or without intraepithelial T cells (Zhang et  al. 2003). This was counterintuitive, as TNF-a is a major factor activating endothelium and promoting adhesion of T cells. We have also found that ET-1 efficiently blocks adhesion of T cells to endothelial cells even when endothelial cells are activated with TNF-a (Buckanovich et al. 2008). This observation explains the paradox of how tumors may exhibit inflammation yet be prohibitive to T cell infiltration, thus establishing immune privilege even in the face of inflammation. ET-1 was found to abrogate T cell adhesion to endothelium via ETBR and through suppression of endothelial ICAM-1 expression at base line as well as following endothelial activation with TNF-a. Furthermore, it was found that ETBRinduced suppression of ICAM-1 expression and surface clustering was mediated by nitric oxide (NO). ETBR blockade with the selective antagonist BQ-788 up regulated endothelial ICAM-1 expression, promoted ICAM-1 clustering at the cell surface, and restored adhesion of T cells to ET-1-treated endothelial cells. ICAM-1 neutralizing antibody abrogated the effect of ETBR blockade to promote T cell adhesion to endothelium in  vitro (Buckanovich et  al. 2008). These observations indicate that the endothelin system is crucial for controlling lymphocyte homing in tumors and that endothelial ETBR over expression, which can sway the vascular ETAR/ETBR balance towards ETBR hyperactivity, results in suppression of T cell homing. This evidence is substantiated by complementary data in lung inflammation; ETAR activation is required for endotoxin-induced inflammation (Wanecek et al. 2000), while T cell homing to lungs in response to an inflammatory stimulus is abrogated by ETAR blockade (Sampaio et al. 2000, 2004). Thus, vascular ETAR activation results in increased T cell homing, while increased ETBR signaling facilitates immune privileged status.

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To test the activity of ETBR in controlling T cell homing to tumors and the effects of its blockade in vivo in the context of immunotherapy, we used vaccine approaches that have no efficacy in delaying tumor growth. It was found that vaccine failure was associated with poor accumulation of T cells at the tumor site, in spite of detectable systemic antitumor immune response. ETBR blockade with specific antagonist BQ-788 greatly enhanced the efficacy of prevention and therapeutic vaccines. BQ-788 did not increase systemic immune response to the vaccine in vivo, but rather greatly enhanced T-cell infiltration in tumors following vaccine (Buckanovich et al. 2008). This was attenuated by ICAM-1 neutralizing antibody, confirming the requirement for adhesive interactions mediated by ICAM-1 following ETBR blockade in vivo. Furthermore, BQ-788 markedly increased homing of T-cells to tumors after adoptive transfer in mice. Thus, in many tumors there is hyperactivation of a paracrine ET-1/ETBR axis established between tumor cells and endothelium, whereby tumor cells over express and release ET-1 while the tumor endothelium over expresses ETBR. This axis tonically suppresses T cell homing (even in the presence of tumor inflammation), and can be disrupted by ETBR blockade, which in  vivo markedly enhances tumor immune therapy (Buckanovich et al. 2008). This mechanism may not be unique to ovarian cancer. For example, ETBR is also over expressed in breast cancer vasculature (Rajeshkumar et al. 2005). Interestingly, ETBR upregulation predicts poor outcome both in breast and ovarian cancer (Wulfing et al. 2003; Grimshaw et al. 2004; Balint et al. 2008). The mechanisms underlying ETBR over expression in tumor endothelium are not fully understood, but VEGF may be implicated (Salani et al. 2000; Spinella et al. 2002). Our results argue that ETBR antagonists warrant testing in combination with passive or adoptive immunotherapy. There are unique features that render ETBR blockade an attractive strategy in cancer immunotherapy. First, as outlined above, the axis ET-1/ETBR appears to be selectively up regulated in the tumor compartment but not in normal tissues. Indeed, in mouse experiments, ETBR blockade by BQ-788 did not result in systemic inflammation or illness, and frequency of CD45+ lymphocytes or CD3+ T cells in liver, spleen, lungs or kidneys after vaccine or adoptive T cell transfer was not affected by BQ-788 (Buckanovich et al. 2008). This is in contrast to current immune modulatory approaches, which achieve systemic activation of effector cells by attenuating peripheral tolerance or other homeostatic checkpoint mechanisms and can result in significant autoimmune toxicity (Hodi and Dranoff 2006). Second, ETBR-selective antagonists, including BQ-788, have been tested in humans and are well tolerated even in patients with cardiovascular disease (Halcox et  al. 2007; Cardillo et  al. 1999; Cowburn et  al. 2005). Thus, ETBR can be pharmacologically perturbed with existing drugs to enhance the efficacy of immune therapy. Third, ETBR blockade is likely to have also direct anti-angiogenic effects through suppression of endothelial nitric oxide. Unlike in patients with sepsis (Lopez et al. 2004), NO inhibition is safe and has been well tolerated in cancer patients (Ng et  al. 2007). Although the anticancer effect of ETBR (or NO) blockade as monotherapy may be modest, the concomitant administration of immunotherapy may act synergistically against angiogenesis (Rajeshkumar et al. 2005; Cemazar et al. 2005).

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Conclusions Investigation of tumor-host interactions has enhanced our understanding of immune mechanisms underlying tumor progression and outcome. This investigation promises to yield important therapeutic approaches. At the same time, it is evident that immunologic investigation has already detected important signatures of rejection in ovarian cancer. The clinical application of these signatures is not yet clear, but potential applications include disease prognosis and perhaps classification for selection of different therapy. This chapter highlighted our understanding of these signatures and focused on their association with the tumor microenvironment. Targeting both the crucial microenvironmental players and those immunogenic markers will fortify and augment spontaneous anti-tumor immune responses and immunotherapy in general.

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Transcriptional Profiling of Melanoma as a Potential Predictive Biomarker for Response to Immunotherapy Thomas F. Gajewski

That melanoma is a tumor type that can respond to immunotherapies has been well documented. However, it has been equally clear that only a subset of patients is responsive to such immune-based interventions. The response rate to high-dose interleukin-2 (IL-2) is around 15% with around 5% of patients achieving a durable complete response (Atkins et al. 1999). While it might be considered that the majority of patients with melanoma simply have tumors that are generally resistant to all forms of therapy, this is probably not the case, as it has been shown that there is not cross-resistance to IL-2 and treatment with chemotherapy (Richards et  al. 1992). This simple observation suggests that there might be subsets of melanomas having biologic characteristics that make them responsive to certain modes of therapy; specifically, there may be a molecular subtype of melanoma that is more amenable to treatment with immunotherapeutic approaches. This notion has begun to be investigated, beginning with gene expression profiling of tumors from individual patients undergoing treatment with experimental melanoma vaccines. Similar studies have been initiated in patients treated with IL-2 and with those receiving anti-CTLA-4 monoclonal antibodies (mAbs). A pattern has emerged suggesting that a minor subset of melanomas has an inflammatory microenvironment that includes expression of chemokines and other markers indicating the ability to recruit activated T cells into the tumor site. The remaining major subset of tumors may have characteristics that render resistance to a productive interaction with the host immune system, but which do not preclude sensitivity to other treatment modalities. Collectively, these observations have suggested a potential predictive biomarker for melanoma responsiveness to immunotherapies, and additionally indicate possible new opportunities for therapeutic intervention to target these barriers.

T.F. Gajewski (*) Department of Pathology and Department of Medicine, Section of Hematology/Oncology, University of Chicago, 5841 S. Maryland Avenue MC2115, Chicago, IL 60637, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_15, © Springer Science+Business Media, LLC 2011

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Analysis of T Cell Responses in the Peripheral Blood with Melanoma Vaccines The molecular characterization of antigens preferentially expressed on melanoma cells but not on most normal cells of the host has led to the pursuit of antigenspecific vaccination of patients against melanoma-associated targets. The underlying principle is that vaccination will increase the frequency of tumor-reactive CD8+ cytotoxic T lymphocytes (CTL) that will in turn identify and kill tumor cells in vivo. In general, most vaccine approaches utilize a source of antigen (e.g., antigen-derived protein or peptides restricted by specific HLA molecules) combined with a vaccine adjuvant. Adjuvants generally provoke nonspecific inflammation that is thought to facilitate transition to a productive adaptive immune response. The most frequently used antigens in melanoma vaccine studies have been the differentiation antigens Melan-A/MART-1, gp100, and tyrosinase (Restifo and Rosenberg 1999). Peptides or proteins corresponding to MAGE antigens (MAGE-1, -3, and -10), NY-ESO-1, and the alternatively spliced molecule NA17, also have been explored (Guilloux et al. 1996; Jager et al. 1998; van der Bruggen et al. 1994), among others. In terms of adjuvants, Montanide ISA51 has arguably been used most frequently (Cormier et  al. 1997), in addition to peptide-loaded antigen-presenting cells (APCS) (Banchereau et al. 2001; Peterson et al. 2003), recombinant viral vectors (Rosenberg et  al. 1998), and designer adjuvants containing Toll-like receptor (TLR) ligands (Appay et al. 2006). Numerous such vaccine trials have been conducted in patients with measurable stage IV melanoma or in the postsurgical adjuvant setting. In many cases, substantial increases in frequencies of antigen-specific CD8+ T cell responses have been detected following vaccination. In fact, some individual patients have been observed to have preexisting T cell responses against defined epitopes, from the antigens Melan-A/MART-1, MAGE-10, or NY-Eso-1 (Baumgaertner et al. 2006; Peterson et al. 2003). Despite these biologic effects, clinical responses to melanoma vaccines have been uncommon, with overall response rates being less than 10% overall. Arguably an additional subset of patients experiences prolonged disease stabilization for 6 months or longer, although it is difficult to know for certain if that outcome is caused by the therapy in the absence randomized clinical trial data. Taken together, the observation of increased tumor antigen-specific T cell frequencies in most patients with infrequent tumor regression has suggested the likelihood that additional barriers downstream from T cell priming exist that render tumors resistant to the immune response induced. Much of this resistance might theoretically be dictated at the level of the tumor microenvironment.

Interrogation of the Melanoma Tumor Microenvironment In order to probe potential mechanisms of resistance to antitumor immunity at the level of the tumor microenvironment, it became necessary to integrate tumor sampling into vaccine clinical trials. In order to capture maximal information from small amounts of tissue, our group has utilized transcriptional profiling of the entire

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cellular composition of fresh tumor biopsies obtained prior to tumor antigen vaccination. To help ensure that a balanced representation of the tumor site was obtained, either excisional biopsies or multiple core biopsies were utilized. This was performed in a pilot trial of 19 patients vaccinated with a combination of four tumor antigen peptides plus the cytokine IL-12 (Gajewski et al., unpublished data). Patients were categorized as having a favorable (CR + PR + SD) or unfavorable (PD) clinical outcome. Supervised hierarchical clustering revealed a small subset of genes differentially expressed in the tumors between these two patient populations. In particular, patients who responded clinically showed evidence of an inflammatory tumor microenvironment that existed prior to the initiation of vaccination. These results suggested that patients in whom a dialog had already been established between the tumor and the host immune response might have been predisposed towards clinical benefit from active immunization against melanoma antigens. In order to broaden this analysis to a larger sample size, a bank of melanoma metastases was analyzed similarly, to determine the frequency at which this inflammatory tumor microenvironment was detected, and to characterize it further. A set of 44 melanoma biopsies, 5 melanoma cell lines, and 3 primary melanocyte cell lines were studied. Nonsupervised hierarchical clustering identified a defined subset of 18 tumors (40%) that showed evidence of an inflamed phenotype. Of the 58 genes that uniquely characterized this group, 41 of them (71%) were clearly immune-related, and 20 of these (34%) are thought to be uniquely expressed in lymphocytes (Harlin et  al. 2009). The presence of CD8+ T cells and B cells in those tumors was confirmed by immunohistochemistry. Together, these results suggest that a subset of melanoma patients have metastatic tumors that contain an ­inflammatory infiltrate that includes T cells, and that such individuals are more likely to respond clinically to melanoma vaccines (Fig. 1).

Fig. 1  Categorization of two major subtypes of melanoma metastasis based on gene expression profiling and confirmatory assays. We have designated Type I melanoma metastases those that show lack of a CD8+ T cell infiltrate, low expression of chemokines, and minimal evidence of an inflammatory gene expression signature. Type II metastases show evidence for CD8+ T cell infiltration, and expression of chemokines and other inflammatory mediators. However, they also express the highest levels of the negative regulatory factors IDO, PD-L1, and FoxP3, and show poor expression of B7-1/B7-2, consistent with a pro-anergy environment. Positive clinical outcome to immunotherapies appears to be associated with a type II microenvironment

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Chemokines and an Argument for Control at the Level of T Cell Trafficking into Tumor Site The rich presence of activated T cells in a subset of tumors raises the question of how they accumulate in a subset of tumors but not in others. Trafficking of activated T cells into target tissue sites is driven, in part, by the presence of specific chemokines along a concentration gradient. In fact, analysis of the gene expression patterns revealed that a subset of ten chemokines was selectively expressed in the melanoma metastases that contained T cell markers (Harlin et al. 2009). In order to focus on those most relevant for migration of activated T cells, naïve vs. effector CD8+ T cells from normal donors were analyzed for the expression of chemokine receptors. Effector T cells were found to upregulate expression of CCR1, CCR2, CCR5, and CXCR3. Based on known receptor/ligand interactions, this restricted attention to a subset of six chemokines: CCL2, CCL3, CCL4, CCL5, CXCL9, and CXCL10. Each of these chemokines in recombinant form was shown to be sufficient to recruit CD8+ effector T cells in a transwell assay in vitro. Therefore, there may be some redundancy among these factors, at least from the perspective of homing of activated CD8+ T cells, although it is likely that these chemokines differ with respect to their ability to recruit other cell populations. It is not completely certain which cell subsets within the tumor microenvironment produce each individual chemokine in vivo. However, analysis of a series of human melanoma cell lines revealed that at least some melanoma cell lines are indeed capable of spontaneously secreting each of these factors. The presence of relevant chemokines was confirmed functionally, as those melanoma cells successfully recruited activated CD8+ T cells both in a transwell assay in  vitro and in a reconstituted mouse xenograft setting in vivo (Harlin et al. 2009). Together, these results suggest that a subset of melanomas may have a distinct set of signaling pathways and/or transcription factors engaged that could regulate the expression (or repression) of immunologically relevant genes such as chemokines. The ability of tumors to support recruitment of activated T cells into the tumor microenvironment may influence whether a given patient has an opportunity to experience a clinical response following an immunotherapeutic intervention such as a cancer vaccine.

Innate Immune Signals that May Drive “Sterile” Adaptive Immunity to Tumors The observation that a subset of patients with metastatic melanoma appears to be able to prime a spontaneous antitumor CD8+ T cell response, all the way through to the point of achieving migration of effector T cells into the tumor microenvironment, raised two new paradoxical questions. The first of these challenges has been to understand how it is possible for sterile tumors to induce a productive adaptive immune response at all. Generation of a specific T cell response against defined

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antigens is though to require engagement of cells of the innate immune system. For immune responses during infection, this typically involves engagement of Toll-like receptors (TLRs) by conserved ligands derived from the pathogenic organism, including bacterial wall products (such as LPS) or hypomethylated CpG DNA sequences. However, it is not immediately evident which innate immune activating ligands might be involved in the context of cancers, most of which are not caused by infectious organisms. It was reasoned that clues might be extracted through interrogation of our melanoma metastasis gene expression profiling data for innate immune genes associated with the presence of T cell transcripts. One observation made was that tumors having a lymphocyte signature also showed a transcriptional profile characteristic of signaling by type I IFNs. We therefore have pursued mechanistic studies in the laboratory to determine whether host type I IFNs are required for spontaneous CD8+ T cell priming against tumor antigens in mouse models. In fact, using a series of well-defined transplantable tumor systems, we found that early T cell priming in the draining lymph node typically was detected within around 1 week following tumor implantation (see (Kline et al. 2008) for example with B16 melanoma). This was preceded by detectable production of IFN-b in the tumor-draining lymph node at around day 3. Studies utilizing host mice deficient in either the type I IFN-a/bR or Stat1 showed defective spontaneous T cell priming against tumors, arguing that the host response to type I IFNs is necessary as an innate immune bridge to the adaptive immune response to tumor antigens. The defect in T cell priming mapped to the level of the dendritic cell compartment. Mechanistic studies revealed that most dendritic cell properties examined in the absence of type I IFN signaling were intact; however, an isolated defect in the accumulation of CD8a+ dendritic cell subset in the tumor site was noted. The recent publication on the necessity of CD8a+ dendritic cells for cross-priming of antiviral CD8+ T cell responses supports a critical role for this DC subset in vivo (Hildner et al. 2008). Thus, our working conclusion is that host type I IFN signals are required for the priming of antitumor CD8+ T cells via CD8a+ dendritic cells in vivo (Fuertes and Gajewski, manuscript in preparation). This model system is being utilized to identify the tumor-derived factors that induce type I IFN production from host dendritic cells, and which receptor system is responsive to those tumor-derived factors.

Immune Suppressive Mechanisms in the Tumor Microenvironment If some melanoma metastases do indeed display evidence of spontaneous inflammation, expression of appropriate chemokines, and recruitment of CD8+ T cells into the tumor microenvironment, then a second conundrum to solve is why those tumors are not rejected spontaneously in patients. The results of several laboratories have indicated that CD8+ T cells isolated from melanoma metastases show evidence of dysfunction (Harlin et al. 2006; Mortarini et al. 2003), arguing that even if they

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are initially activated properly they become suppressed in the context of the tumor microenvironment. Multiple immune suppressive mechanisms that may inhibit T cell function in the cancer context have been described in the past several years (Gajewski 2007; Gajewski et al. 2006). Analysis of our melanoma metastasis gene array data revealed that tumors which contained T cell transcripts also contained high expression of the mRNA encoding indoleamine-2,3-dioexygenase (IDO). IDO has been shown to be critically involved in the induction of peripheral immunologic tolerance, for example in the context of the maternal/fetal interface (Mellor et al. 2001). Directed interrogation for other putative immune suppressive mechanisms revealed that those inflamed tumors also contained high levels of transcripts for PD-L1 (a ligand for the inhibitory receptor on activated T cells PD-1, (Dong and Chen 2003)) and FoxP3 (a transcription factor characteristic of regulatory T cells (Tregs), (Nomura and Sakaguchi 2005)). In addition, analysis for expression of the costimulatory ligands B7-1 and B7-2 revealed absence of expression in the majority of tumors, suggesting that the melanoma microenvironment may also support induction of classical T cell anergy (Schwartz 1997). These results argue that once activated T cells do indeed become successfully recruited into the tumor microenvironment, that at least four immune regulatory mechanisms come into play to restrict the effector function of those T cells, thus allowing tumor escape. Mechanistic experiments in mouse models have indicated that each of the four negative regulatory pathways described above can be relevant for specific tumor models in vivo (Blank et al. 2004; Brown et al. 2006; Kline et al. 2008; Uyttenhove et  al. 2003; Zhang et  al. 2009). Importantly, because multiple inhibitory mechanisms appear to operate in concert, it may be necessary to block the activity of two or more of them in order to achieve maximal therapeutic efficacy. Indeed, our group has found that strategies to eliminate Tregs combined with a strategy to uncouple T cell anergy can be potently synergistic in vivo (Kline et al. 2008). Such combinatorial approaches will be attractive to apply clinically. An additional characteristic worth noting is that, because these multiple immune inhibitory mechanisms are present at higher levels in tumors that contain activated T cells, and those tumors which have activated T cells may respond more favorably to cancer vaccines, one could make the observation that expression of inhibitory molecules like IDO could paradoxically have positive prognostic value. It is important, therefore, to consider a more global analysis of tumor microenviroment features before assigning mechanistic importance to single immunoregulatory molecules in the tumor site.

Analysis of the Tumor Microenvironment with Other Immunotherapy Approaches: IL-2 and Anti-CTLA-4 mAb Based on our single institution experience studying gene expression profiling in a relatively limited number of melanoma patients, a follow up study has been carried out analyzing pretreatment tumor biopsies from another vaccine trial, in collaboration

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with Gerold Schuler’s group. In that study, metastatic melanoma patients were vaccinated with a dendritic cell-based vaccine that included multiple class I and class II MHC-binding peptides. Similarly to our study, a transcriptional profile including T cell markers and specific chemokines was associated with clinical benefit to this vaccine (Gajewski et al. 2009). In parallel, the group at GSK-Biologics has performed gene expression profiling of pretreatment tumor biopsies from patients undergoing vaccination with MAGE-3 protein emulsified in a designer adjuvant. That study also revealed a transcriptional profile that predicted a positive outcome from vaccination, which also included a signature for chemokines and T cell markers (Louahed et al. 2008). Thus, although these have been relatively small studies individually, together they make a compelling argument that a gene expression profile indicative of an inflammatory melanoma microenvironment may have general value as a predictive biomarker for melanoma vaccine clinical benefit. The question arises as to whether a similar phenomenon might also be true for other immunotherapeutic interventions, such as treatment with the T cell growth factor IL-2, or with antibodies against the inhibitory receptor CTLA-4. Pilot studies of tumor microenvironment analysis in the context of these two treatment modalities have recently been presented. Interestingly, a set of transcripts suggestive of an inflammatory tumor phenotype was found to be positively associated with clinical response with high-dose IL-2 (Sullivan et al. 2009). In addition, study of pretreatment tumor samples from patients treated with anti-CTLA-4 mAb revealed that expression of IDO and FoxP3 in the tumor site was positively associated with clinical benefit (Hamid et al. 2009). Since, as mentioned above, those inhibitory mechanisms are themselves indicative of an inflammatory tumor microenvironment, the presence of those factors could be identifying the same tumor phenotype as identified in the vaccine studies. Together, these early data suggest that the presence of an inflammatory tumor microenvironment may emerge as a general predictor of possible clinical benefit from a range of cancer immunotherapeutic approaches.

Conclusions and Future Directions Based on these collective results, it is logical to incorporate molecular profiling of the tumor microenvironment into large-scale prospective immunotherapy trials, to determine if these observations can be validated on a large multicenter basis. If confirmed, then one could envision in the future using specific molecular markers to preselect appropriate patients to be considered for immunotherapies, as an enrichment strategy for the subpopulation of patients most likely to respond. As signal transduction inhibitors that target specific mutated tyrosine kinases continue to be developed in parallel as an alternative treatment approach for melanoma (Jiang et al. 2008; Smalley and Flaherty 2009), it is not difficult to envision simultaneous analysis of mutant B-Raf, mutant c-Kit, and an inflammatory tumor microenvironment to aid in selecting the overall best treatment approach for each individual patient.

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While most of these studies have been carried out in the context of melanoma, it will be important to determine if similar rules apply to other cancers. The presence of activated CD8+ T cells in the tumor microenvironment has been associated with a positive clinical outcome in ovarian cancer (Zhang et al. 2003) and colorectal cancer (Galon et al. 2006), among others, suggesting value as a prognostic factor. Whether these or related markers might also serve as a predictive biomarker for response to immunotherapies, which have been less explored in these diseases, remains to be determined. In addition to these clinical studies, mechanistic experiments should continue in the laboratory to understand the functional relevance of the array of both positive and negative immunoregulatory mechanisms identified to occur in human melanoma metastases. These continued studies should shape the development of new clinical strategies to overcome resistance barriers to the effector phase of the antitumor immune response at the level of the tumor microenvironment.

References Appay, V., Jandus, C., Voelter, V., Reynard, S., Coupland, S. E., Rimoldi, D., Lienard, D., Guillaume, P., Krieg, A. M., Cerottini, J. C., et  al. (2006). New generation vaccine induces effective melanoma-specific CD8+ T cells in the circulation but not in the tumor site. J Immunol 177, 1670–1678. Atkins, M. B., Lotze, M. T., Dutcher, J. P., Fisher, R. I., Weiss, G., Margolin, K., Abrams, J., Sznol, M., Parkinson, D., Hawkins, M., et al. (1999). High-dose recombinant interleukin 2 therapy for patients with metastatic melanoma: analysis of 270 patients treated between 1985 and 1993. J Clin Oncol 17, 2105–2116. Banchereau, J., Palucka, A. K., Dhodapkar, M., Burkeholder, S., Taquet, N., Rolland, A., Taquet, S., Coquery, S., Wittkowski, K. M., Bhardwaj, N., et al. (2001). Immune and clinical responses in patients with metastatic melanoma to CD34(+) progenitor-derived dendritic cell vaccine. Cancer Res 61, 6451–6458. Baumgaertner, P., Rufer, N., Devevre, E., Derre, L., Rimoldi, D., Geldhof, C., Voelter, V., Lienard, D., Romero, P., and Speiser, D. E. (2006). Ex vivo detectable human CD8 T-cell responses to cancer-testis antigens. Cancer Res 66, 1912–1916. Blank, C., Brown, I., Peterson, A. C., Spiotto, M., Iwai, Y., Honjo, T., and Gajewski, T. F. (2004). PD-L1/B7H-1 inhibits the effector phase of tumor rejection by T cell receptor (TCR) transgenic CD8+ T cells. Cancer Res 64, 1140–1145. Brown, I. E., Blank, C., Kline, J., Kacha, A. K., and Gajewski, T. F. (2006). Homeostatic proliferation as an isolated variable reverses CD8+ T cell anergy and promotes tumor rejection. J Immunol 177, 4521–4529. Cormier, J. N., Salgaller, M. L., Prevette, T., Barracchini, K. C., Rivoltini, L., Restifo, N. P., Rosenberg, S. A., and Marincola, F. M. (1997). Enhancement of cellular immunity in melanoma patients immunized with a peptide from MART-1/Melan A [see comments]. Cancer J Sci Am 3, 37–44. Dong, H., and Chen, L. (2003). B7-H1 pathway and its role in the evasion of tumor immunity. J Mol Med 81, 281–287. Gajewski, T. F. (2007). Failure at the effector phase: immune barriers at the level of the melanoma tumor microenvironment. Clin Cancer Res 13, 5256–5261. Gajewski, T. F., Meng, Y., Blank, C., Brown, I., Kacha, A., Kline, J., and Harlin, H. (2006). Immune resistance orchestrated by the tumor microenvironment. Immunol Rev 213, 131–145. Gajewski, T. F., Zha, Y., Thurner, B., and Schuler, G. (2009). Association of gene expression profile in melanoma and survival to a dendritic cell-based vaccine. J Clin Oncol 27, 9002.

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Functional Pathway Analysis for Understanding Immunologic Signature of Rejection: Current Approaches and Outstanding Challenges Purvesh Khatri and Minnie M. Sarwal

Introduction Diagnosis of graft acute rejection, chronic rejection and tolerance are important issues in solid organ transplantation. Rejection is usually associated with a loss of function, atrophy, scarring and graft loss (Meier-Kriesche et al. 2004), which may be manifested as either an episode of functional deterioration or as subtle deterioration. The current standard in graft dysfunction diagnosis is histopathology (Racusen et  al. 2003; Solez et  al. 2007), which is subjective, nonquantitative and does not offer any insights into rejection mechanisms. Furthermore, histopathologic grading of rejection has been shown to be difficult to reproduce despite persistent feedback (Furness et al. 2003). On the other hand, application of high throughput technologies can provide objective, quantitative molecular measurements. Advent of high throughput technologies such as DNA microarrays, protein microarrays, and next-gen sequencing have revolutionized biological research. For instance, since the introduction of microarrays in 1995 (Schena et  al. 1995), the number of publications describing results obtained from microarray experiments have increased continuously (Fig. 1). In 2008, there were 6,030 publications related to microarray experiments in PubMed. Oncology has been by far the largest benefactor of this technology (Weintraub and Sarwal 2006). As a consequence of this large body of research, Pathwork Diagnostics was awarded the approval by the US Food and Drug Administration (FDA) for its microarray-based test to determine which type of cancer cells are present in a malignant tumor. The test is based on Affymetrix platform and considers 15 common malignant tumor types including bladder, breast and colorectal tumors. Since 2001, microarrays are being used increasingly in various organ transplants (Jun et al. 2001) in a number of different conditions to identify specific patterns of gene expression that can predict and characterize acute and chronic M.M. Sarwal (*) Department of Pediatrics, Stanford University, G360, 300 Pasteur Drive, Stanford, CA 94305, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_16, © Springer Science+Business Media, LLC 2011

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Fig.  1  Number of papers in PubMed published every year using microarrays since their i­ ntroduction. The number of papers using microarray for each year was extracted using custom queries in PubMed. For instance, the query for all microarray papers published in 2004 was “microarray” [All Fields] AND “2004” [Publication Date]. Similarly, the query for all microarray papers published in 2004 related to transplant was microarray [All Fields] AND 2004 [Publication Date] AND transplant [All Fields]. Although these queries are not very specific, their results clearly represent the low number of papers published in solid organ transplant using microarrays

rejection as well as tolerance, and to improve our understanding of the pathways driving graft dysfunction (Akalin et al. 2001; Xu et al. 2001; Sotiriou et al. 2002; Sarwal et al. 2003; Scherer et al. 2003; Flechner et al. 2004; Fujino et al. 2004; Inkinen et  al. 2005; Lu et  al. 2006). Furthermore, the number of publications using microarrays in transplantation is also increasing, albeit very slowly (Fig.  1). Out of the 6,030 microarray-related publications in 2008 in PubMed, there were only 37 papers related to organ transplant, which included studies on liver, heart, pancreas, kidney, and lung transplants that addressed problems related to acute rejection, operational tolerance, chronic allograft rejection, and identification of biomarkers. (Brouard et al. 2007; Kawasaki et al. 2007; Lande et al. 2007; Martinez-Llordella et al. 2007; Mas et al. 2007; Schena et al. 2007). Following these encouraging studies, there have been proposals to include microarray gene expression profiles in transplant diagnosis (Flechner et  al. 2004; Mueller et al. 2007). In this chapter, first we briefly discuss some of the recent results in identifying immunologic signatures of injury in solid organ transplant. We then discuss the limitation of current biomarkers in terms of explaining the injury mechanism at pathway level and to help better understand the graft rejection mechanism. We then discuss some of the existing pathway analysis approaches that can be helpful in this regard. We then discuss some of the outstanding challenges that must be addressed to improve understanding of graft rejection mechanisms.

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Insights from Application of Microarrays in Solid Organ Transplant Insights gained from microarrays in organ transplantation have been the focus of a number of recent reviews, which we refer the readers to for more details (Weintraub and Sarwal 2006; Zarkhin and Sarwal 2008; Khatri and Sarwal 2009; Ying and Sarwal 2009). Table 1 lists some of the recent solid organ transplant studies using microarrays. Here we briefly discuss the major insights obtained in transplantation using microarrays. Acute Rejection: Expression profiles of solid organ grafts in the past decade have identified genes involved in a number of cellular functions including immune response, cell proliferation, Wnt signaling and cell cycle (Gimino et  al. 2003; Sarwal et  al. 2003; Flechner et  al. 2004; Jovanovic et  al. 2008). Furthermore, a landmark study using microarray expression profiles has identified molecular heterogeneity in renal allografts (Sarwal et al. 2003). This study also found that the expression profiles of grafts at higher risk of loss included genes involved in infiltration and activation of lymphocytes as well genes expressed in cytotoxic T cells, natural killer cells, macrophages and B cells (Sarwal et  al. 2003). On the other hand, the grafts with good outcome had genes involved in cell cycle and cell proliferation up-regulated, and genes involved in lymphocytes infiltration and activation down-regulated. Chronic Rejection: Despite general consensus on presumed triggers for chronic graft injury such as alloimmune responses, donor age, brain death, preservation/reperfusion injury, and posttransplantation and systemic stresses in the recipient environment (Gourishankar and Halloran 2002), early injury biomarkers for controlling progression of injury have not yet been identified. Due to the difficulty in recipient consents for multiple posttransplantation biopsies, CAN-specific biomarker discovery has been challenging (Ying and Sarwal 2009). Another challenge in studying chronic rejection has been that most of the published studies have been conducted at a single time-point (Eikmans et  al. 2005; Kurian et  al. 2005; Hotchkiss et  al. 2006; Park and Stegall 2007). Tolerance: Achieving long-term, drug-free graft acceptance is an unsolved critical problem in clinical transplantation. Strategies to understand, monitor for, and support the development of transplantation tolerance will be a great advance for the management of transplant patients and for the prevention and treatment of autoimmune diseases. Tolerance in transplantation has been associated with diverse mechanisms including anergy (Lechler et al. 2001; Macian et al. 2004; Merrell et al. 2006; Najafian et al. 2006), suppression (Jiang and Lechler 2003; Trani et al. 2003), clonal deletion (Kurtz et al. 2004), co-stimulation blockade (Markees et al. 1998), and chimerism (Thomson et al. 1995). We refer the readers to recent review for more details of current status of applying microarrays to understand transplant tolerance and underlying mechanisms (Zarkhin and Sarwal 2008).

Transplant Proc 2008

Heart

Kidney

BMC Immunology 2008

BMC Genomics 2008

Doki et al. (2008)

Edemir et al. (2008)

Kidney

Molecular Medicine 2008

Liver, spleen, peripheral blood Kidney, heart

Maluf et al. (2008)

Ashton-Chess et al. J Am Soc (2008) Nephrology 2008

Lu et al. (2006)

Table 1  Microarray-based studies in solid organ transplant Author Journal Organ Kidney Vitalone et al. J Am Soc (2008) Nephrol 2008 Allanach et al. Am J Transplant Kidney (2008) 2008

Rat

Mouse

Human

Human, rat

Compared gene sets from four different published microarray studies of late graft injury; Found TRIB1 to be a potential blood and tissue biomarker of chronic AMR. More importantly, TRIB1 was also found to be up-regulated in rat heart transplant, suggesting that there may be a common mechanism for chronic AMR in different organs in different species Compared IF/TA samples with normal allografts and normal kidney; found chemokines, chemokine receptor, interleukin and interleukin receptor genes to be up-regulated and were involved in apoptosis and matrix production-deposition; angiogenesis related genes were down-regulated Genes involved in stress and immunity and signal transduction are significantly up-regulated in early tolerant grafts compared with syngeneic control grafts Analyzed genome wide changed in gene expression 4 days after syngeneic and allogeneic transplantation; Identified simultaneous activation of pathways for counter regulatory and protective mechanisms that would balance and limit the ongoing inflammatory/immune responses

RT-PCR and microarray probes had good correlation with each other and histology classifier. RT-PCR had higher dynamic range and identified two genes that were not identified on microarrays Used pig-to-monkey transplant to investigate pig-to-human xenotransplantation

Human

Pig, monkey, human

Summary/significance Epithelial-to-mesenchymal transition does not play a significant role in the development of early fibrosis

Species Human

242 P. Khatri and M.M. Sarwal

Am J Transplant 2008

Diabetes 2008

Physiological Genomics 2007 Am J Transplant 2007

International Surgery 2007

Streblow et al. (2008)

Brands et al. (2008)

Li et al. (2008)

Kawasaki et al. (2007)

Mueller et al. (2007)

J Immunology 2008

Jovanovic et al. (2008)

Human, mouse

Kidney

Human

Human

Kidney

Liver, PBMC

Mouse

Rat

Rat

Fetal pancreas

Heart

Kidney

Analyzed intragraft expression profiles in long-term accepted kidneys; Gene MMP7 expression was restricted to vascular smooth muscle cells and was specific to anticlass II Ab-induced tolerance; MMP7 was undetectable in other models of renal and heart transplant tolerance and chronic rejection induced across the same strain combination. Interestingly, this study for the first time implicated WNT signaling pathway in transplantation Cytomelanovirus up-regulates the genes involved in WR and AG, and was the highest during the critical time of transplant vascular sclerosis acceleration Compared first-trimester fetal pancreas with second-trimester pancreas. Although it is generally accepted that a fetal tissue is more immune privileged than an adult tissue, this study further distinguishes between immunogenicity of first- and second-trimester tissues Demonstrated that current method of globin reduction is not sufficient to reduce the dominant expression of globin genes in whole blood and obtain gene signature for acute rejection Annotated differentially expressed genes based on major biologic event in allograft rejection; compared expression of the annotated transcripts in each group with histopathologic lesion and clinical diagnoses. This is the first study to meticulously investigate relationships between transcript sets, which represent major biologic event, and histopathologic lesions and clinical diagnoses on a global scale not done before. As a result of this study, a very rich renal biopsy dataset is available for further analysis Compared peripheral blood mononuclear cells from recipients of living-donor liver transplants (LDLTs) with nontransplanted normal healthy volunteers; Identified 717 up- and down-regulated genes specific to tolerant LDLT recipients

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Need for Functional Pathway Analysis Although a number of differentially expressed genes and markers for different conditions in solid organ transplants have been identified, a crucial question is yet unanswered: none of the studies, to the best of our knowledge, describe whether the differentially expressed genes are the causes of the injury to the graft or the effects of the injury to the graft (Fig. 2). Although a biomarker that is an effect of injury to the graft can potentially be used to monitor graft injury progression in clinics, it cannot prevent the injury. However, a biomarker that is also a cause for the graft injury is a better therapeutic target as it can be potentially used for both prevention and monitoring of graft injury. A better identification of “causal” biomarkers requires better understanding of the injury mechanisms. Although general mechanisms of rejection have been proposed (e.g., infiltration and activation of lymphocytes, innate immune response, apoptosis), no clear injury mechanisms have been described in the literature. It has been hypothesized that although the tissue-specific prompts may be different, there may be a common underlying rejection mechanism across all solid organ transplants (Wang et  al. 2008). This hypothesis is supported in the literature as a number of common genes have been found to be differentially expressed across solid organ transplants (Zhang and Reed 2006). One way to identify the common rejection mechanism is to analyze expression data across solid organ transplants at functional pathway level. With the increasing application of the high throughput molecular profiling techniques, the challenge in transplant is no longer a lack of data or identification of the genes involved in the condition, but to understand the data in the context of the condition under study (e.g., acute rejection, chronic rejection, tolerance). Today the challenges are to understand their roles as well as how they interact with other genes in order to bring about overall changes in the biological systems. Analysis of high-throughput molecular measurements at the functional pathway level is very appealing for two reasons. First, grouping thousands of genes, proteins and other biological molecules by the functions they are involved in reduces the dimensionality to a few hundred pathways. Second, identification of a functional group that is different between two phenotypes has more explanatory power than a set of different genes or proteins (Glazko and Emmert-Streib 2009). Host Immune Response

Graft Injury

Sampling “Effect” marker?

“Causal” marker? Biomarker

Experiment

Fig. 2  Schematic of a typical high-throughput experiment. The host immune response is initiated as soon as a graft is transplanted. Consequently, a typical transplant study uses samples where graft injury has already occurred. Therefore, it is not clear whether a biomarker identified from the experiment represents the cause of the injury or an effect of the injury

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In order to help the researchers address these challenges, a large number of knowledgebases, which annotate different aspects of a biological system, are being made available in public domain (BioCarta; Ogata et  al. 1999; Ashburner et  al. 2000; Maglott et al. 2000; Karp et al. 2002; Joshi-Tope et al. 2005; Maglott et al. 2005). Arguably, one of the most successful annotation database is the gene ontology (GO) annotation database that has been very useful in interpretation of high throughput gene expression data, as demonstrated by the large number of publications using results obtained using GO, and over 2,750 citations to the original paper describing GO. Analysis of high throughput gene expression data using GO and other knowledgebases has been used in a wide spectrum of applications including annotation of sequenced genomes, text mining, network modeling and clinical applications.

Current Functional Pathway Analysis Approaches and Existing Tools Current pathway analysis approaches can be categorized on multiple axes such as type of functional annotations supported (e.g., GO, KEGG, BioCarta, Reactome), type of analysis employed (e.g., over-representation, functional class scoring [FCS]), and type of statistics used (e.g., hypergeometric, binomial, t-test, correlation). In this chapter, we discuss the tools by categorizing them based on the type of analysis they employ by dividing them into the following three categories: (1) overrepresentation analysis (ORA), (2) FCS and (3) pathway-topology based analysis.

Over-Representation Analysis (ORA) Approaches Approach: Out of more than 50 tools listed on the GO web site as Tools for Analysis of Data Sets (http://www.geneontology.org/GO.tools.shtml#micro), virtually all of these tools employ one or more variations of the same ORA strategy (CastilloDavis and Hartl 2002; Khatri et al. 2002; Berriz et al. 2003; Doniger et al. 2003; Draghici et al. 2003; Beissbarth and Speed 2004; Boyle et al. 2004; Martin et al. 2004). First, for each gene in the input list, its pathway annotations are retrieved. Then, for each pathway, input genes that are known to be in the pathway are counted. This process is repeated for an appropriate background list of genes (e.g., all genes measured on a microarray). Next, pathways are identified that are over- or under-represented in the list of differentially expressed genes compared to the reference list of genes by computing statistical significance for each pathway (e.g., using Fisher’s exact test, hypergeometric distribution or binomial distribution). For more details about ORA, we refer the readers to a recent comparison of GO-based ORA tools (Khatri and Draghici 2005a, b) (Table 2).

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Table 2  ORA pathway analysis tools

Name Onto-express

Scope of analysis GO

GenMAPP GO, KEGG, /MAPPFinder MAPP GoMiner GO

p-value Hypergeometric, binomial, chi-square Percentage/z-score

FatiGO GOstat GOTree machine FuncAssociate GOToolBox

GO, KEGG GO GO GO GO

Relative enrichment, Fisher’s exact test Fisher’s exact test Chi-square Hypergeometric Fisher’s exact test Hypergeometric

GeneMerge

GO

Hypergeometric

Correction for multiple hypotheses FDR, Bonferroni, Sidak, Holm None None

Availability Web

Standalone Standalone, Web Web Web Web Web Web

None FDR None Bootstrap Bonferroni, Holm, FDR, Hommel, Hochberg Bonferroni Web

Limitations: Despite the availability of a large number of tools and their widespread usage, the ORA approaches have a number of limitations. First, different statistics used by ORA (e.g., Fisher’s exact test, hypergeometric distribution, binomial distribution, chi-square distribution, etc.) are independent of the measured changes in expression. By discarding the measured expression changes, ORA treats each gene equally. However, genes are expressed to different extents in any given condition. The data providing information about the extent of gene regulation (e.g., fold-changes, significance of a change in gene expression, etc.) can be useful in assigning different weights to input genes as well as to the pathways they are involved in, which in turn can provide more information than the current ORA approaches. Second, by treating each gene equally, ORA assumes that each gene is independent of the other genes. However, biology is a manifestation of interactions between gene products that constitute different pathways, to achieve a common biological objective. One goal of gene expression analysis might be to gain insights into how the interactions between gene products are manifested as changes in gene expression. A strategy that assumes the genes are independent is significantly limited in its ability to provide insights into interactions between gene products and the underlying biology. Third, ORA typically uses only the most significant genes and discards other genes (Pavlidis et al. 2002). For instance, the input list of genes is usually obtained from a microarray experiment using an arbitrary threshold (e.g., genes with foldchange ³2 and/or p-values £0.05). When using an arbitrary threshold, marginally less significant genes (e.g., fold-change = 1.999 or p-value = 0.051) are missed, resulting in information loss. Pavlidis et al. showed that the use of predetermined threshold results in inconsistent results (Pavlidis et  al. 2004). Breitling et  al. proposed an iterative ORA approach that adds one gene at a time, to find a set of genes for which a pathway is most significant (i.e., a pathway has minimum p-value) to avoid choosing threshold (Breitling et al. 2004).

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Functional Class Scoring (FCS) Approaches The ad hoc division of expression data into significant and nonsignificant genes using threshold, the decoupling of expression data from functional analysis, and the assumption of independence between genes motivated the development of FCSbased approaches. The FCS-based approaches aim to analyze genes from a given pathway in the context of the entire list of genes, while taking into consideration their coexpression. The hypothesis used by FCS approaches is that although large changes in individual genes can have significant effect on pathways, weaker but coordinated changes in sets of functionally related genes can also have significant effects (Table 3). Approach: Virtually all FCS-based analysis approaches use a variation of the following general framework (Ackermann and Strimmer 2009). An FCS approach generally consists of the following steps: (1) compute a gene-level statistic (local statistic), (2) compute a pathway-level statistic using gene-level statistic (global statistic) and (3) assess the significance of the pathway-level statistic. Currently, a large number of methods have been proposed that apply these steps in different combinations. For instance, the gene-level statistics used to rank the genes in the first step include correlation of gene expression with phenotype (Pavlidis et  al. 2004; Al-Shahrour et  al. 2005), signal-to-noise ratio (Mootha et  al. 2003; Subramanian et al. 2005), t-test (Al-Shahrour et al. 2005; Tian et al. 2005), Q-statistic (Goeman et al. 2004), ANOVA (Al-Shahrour et al. 2005), Z-score (Kim and Volsky 2005) and Hotelling’s T2 (Kong et  al. 2006). The pathway-level statistic used by the current approaches include the sum, mean or median of gene-level statistic (Jiang and Gentleman 2007), Kolmogorov-Smirnov statistic (Mootha et al. 2003; Subramanian et al. 2005), Wilcoxon rank sum (Barry et al. 2005), and maxmean statistic (Efron and Tibshirani 2007). The final step in FCS is assessing the statistical significance of pathway statistic, which can be computed by either permuting class labels (phenotypes) for each sample or by permuting gene labels for each pathway. Both of these correspond to related but different null hypotheses. Permuting phenotype labels is equivalent to testing a null hypothesis that expression levels of none of the genes in the pathway are associated with the classes, whereas permuting the gene labels is equivalent to testing a null hypothesis that the expression levels of the genes in the pathways are at most as differentially expressed as the genes not in the pathway (Tian et al. 2005; Efron and Tibshirani 2007). Limitations: By preserving and accounting for the coexpression profiles among the genes, FCS-based approaches provide a clear improvement over the ORAbased approaches. However, they also have several limitations. First, similar to ORA, FCS analyzes each pathway independently. Note that the pathways can overlap as a gene can be in more than one pathways. Consequently, in an experiment, while a pathway may be truly affected, we may observe other pathways as being significantly affected due to the set of overlapping genes. Such a phenomenon is very common when using the GO terms for creating pathways due to the hierarchical nature of the GO. Jiang et al. have proposed dividing the genes into three distinct

PCOT2

GlobalTest

Category SAFE

sigPathway

Name GSEA

User specified

Hotelling’s T2

Scope of analysis Gene-level statistic Signal-to-noise ratio, GO, KEGG, t-test, cosine, BioCarta, MAPP, eucledian and transcription manhattan distance, factors, miRNA Pearson correlation, (log2) fold-change, log difference t-statistic GO, KEGG, BioCarta, human paths GO, KEGG t-statistic GO, KEGG, Student’s t-test, Welch’s PFAM t-test, SAM t-test, f-statistic, Cox proportional hazards model, linear regression GO, KEGG

Table 3  FCS pathway analysis tools

simple and multinomial logistic regression, Q-statistics mean

Wilcoxon rank sum, Fisher’s exact test statistic, Pearson’s test, t-test of average difference

NA R package FWER (Bonferroni, R package Holm’s step-up), FDR (BenjaminiHochberg, YekutieliBenjamini) NA R package

R package

Phenotype permutation, asymptotic distribution, gamma distribution R package Phenotype permutation, FDR (Benjaminigene set permutation Hochberg, YekutieliBenjamini), FWER (Bonferroni, Holm, Hochberg, Hommel)

Phenotype permutation Phenotype permutation

Phenotype permutation, FDR (NPMLE) gene set permutation

Wilcoxon rank sum

Correction for multiple hypotheses Availability FDR Standalone, R package

p-value Phenotype permutation, gene set permutation

Gene set statistic Kolmogorov-Smirnov

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sets and analyzing each set independently. The three sets are (1) genes common to both gene sets A and B, (2) genes only in gene set A and (3) genes only in gene set B (Jiang and Gentleman 2007). Second, most of the approaches propose computing a sum of the statistic used to rank genes for calculating a score for a pathway. Because the pathways are of different sizes, it is important to normalize the score for the pathway size. Otherwise, larger pathways will have higher scores. However, some FCS approaches do not perform this normalization (Pavlidis et al. 2002; Pavlidis et al. 2004). Third, although FCS approaches consider coexpression between genes in a given pathway, similar to ORA, they give equal weight to each gene. The change in gene expression is only used to rank the genes in a given pathway, and discarded from the further analysis. For instance, let us assume that two genes in a pathway, gene A and gene B, have measured fold change of 2 and 20, respectively. As long as these genes have the same coexpression profiles with other genes in the pathway, FCS will treat gene A and gene B equally although a gene with higher fold-change should probably get more weight.

Pathway Topology (PT)-Based Approaches Neither ORA nor FCS methods exploit the full knowledge embedded into pathways. A large number of pathway knowledgebases with more embedded knowledge are available in public domain such as KEGG (Kanehisa and Goto 2000), MetaCyc (Karp et al. 2002), BioCarta, STKE, Reactome (Joshi-Tope et al. 2003), RegulonDB (Huerta et al. 1998), PantherDB (Thomas et al. 2003), etc. In addition to describing which genes are involved in which pathways, these knowledgebases also describe which gene products interact with each other in a given pathway, how they interact (e.g., activation, inhibition, etc.) and where they interact (e.g., cytoplasm, nucleus, etc.). It is expected that these pathways will change as more information about interactions between gene products become available. Consequently, many interactions will be removed, added or redrawn. Both ORA and FCS methods only consider the number of genes on a pathway or correlation between them to identify significant pathways. Hence, even if the pathways are completely redrawn with new links between the genes, as long as they contain the same set of genes, ORA and FCS will continue to produce the same results (Table 4). Recently a number of pathway analysis approaches have been proposed that consider topology when analyzing pathways. However, unlike ORA and FCS approaches, no common themes have yet emerged among the existing pathways topology (PT)-based approaches. Rahnenfurer et al. proposed integrating PT as an extension to FCS to score a gene set (Rahnenfurer et al. 2004). Instead of giving equal weights to all pairwise correlations, they propose weighing it by the distance between them such that the correlation is divided by the distance between the two genes. Hence, farther two genes are in a pathway, lesser the weight to the correlation between them. The distance between two genes is defined as the number of reactions

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Table 4  PT pathway analysis tools

Name ScorePAGE

Pathwayexpress/ SPIA

Scope of analysis

Gene-level statistic

p-value

Gene set KEGG (correlation, permutation (metabolic) covariance, cosine, dot product) + number of reactions HypergeoKEGG Number and type metric, (signaling) of interactions, binomial fold-change

Correction for multiple hypotheses

Availability

NA FDR (BenjaminiHochberg)

FDR

R package, web

needed to connect the two genes in a pathway. Although this approach is proposed to only analyze metabolic pathways, in theory, it may also be applicable to signaling pathways by using the number of interactions between genes as a distance instead of reactions. A potential shortcoming of this approach is the implicit assumption of hierarchical topology in the definition of a pathway. Consequently, it is not clear how the distance between two genes is measured when they are part of a loop. A recent impact factor (IF) analysis approach analyzes signaling pathways from a systems biology perspective by incorporating a number of important biological factors in addition to the number of differentially expressed genes in a pathway (Draghici et  al. 2007; Khatri et  al. 2008). These factors include change in gene expression, the type of interaction and the positions of genes in a pathway. Briefly, IF analysis models a signaling pathway as a graph where nodes represent genes and edges represent interactions between genes. Further, it defines a perturbation factor (PF) of a gene as a sum of its measured change in expression and a linear function of the PFs of all genes in a pathway. Because PF of each gene is defined by a linear equation, the entire pathway is defined as a collection of linear equations, i.e., as a linear system. Representing a pathway as a linear system also addresses loops in the pathways (see (Khatri et al. 2008) for details). IF of a pathway is defined as a sum of PF of all genes in a pathway. IF analysis was recently improved to address the dominating effect of change in expression on PF and high false positive rate for a small list of input genes (Tarca et al. 2009). FCS methods, when considering correlations between genes, and PT-based methods Score-PAGE and IF analysis assume that the underlying network does not change as the experimental conditions change. However, it has been shown that correlation structure between ARG2 and other genes in urea-cycle pathway changes with change in expression of ARG2 (Li 2002). Change in the correlation structure suggests change in the underlying PT. Shojaie et al. proposed a method that takes into account both the change in correlation as well as the change in network structure as experimental conditions change (Shojaie and Michailidis 2009). Their proposed approach models expression of a gene as a linear function of other genes in the network similar to IF analysis. It differs from IF analysis in two aspects. First, it accounts for the baseline expression of a gene by representing it as a latent variable

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in the proposed model. Second, it requires that the pathways are represented as directed acyclic graphs (DAG). If a pathway contains cycles than it requires using additional latent variables affecting the nodes in the cycle. Note that IF analysis does not impose any constraint on the structure of a pathway (Khatri et al. 2007).

Outstanding Challenges Need for deconvolution: Graft injury is a very complex immunologic state that results from continuous interaction between the graft and the host’s immune system. Numerous interrelated immune pathways are responsible for the delicate balance between graft rejection and acceptance, and are further complicated by manipulation by immunosuppressive medications. These precise molecular mechanisms remain poorly understood, specifically the interplay between co-regulated immune pathways and gene families that may as yet be “unlinked.” Furthermore, when studying graft injury in clinical environments, the heterogeneity of graft specimens (e.g., gender, age, etc.), co-morbidity, co-medications and the challenges in sample collection and preparation complicate conclusions regarding the underlying mechanisms of graft injury, rejection and immune regulation. The significant influence of these factors on gene expression further compromises the results obtained from microarrays. However, the advances made in functional pathway analysis can be very helpful in discovering these intricate complex regulation events, which in turn can offer new insights in to the pathogenesis of posttransplant clinical events. Deconvolution of gene signatures is also necessary for identifying better noninvasive markers in blood. The excessive expression of globin mRNA in red blood cells hampers accurate assessment of other genes. Li et  al. compared gene expression profiles of peripheral blood of stable renal allograft recipients with acute rejection, using four different protocols of preparation, amplification and synthesis of cRNA or cDNA (Li et  al. 2008). Their results demonstrated that, despite application of additional mathematical depletion, the existing globin reduction method is not sufficient to discover rejection-specific expression changes in whole blood. This study demonstrates the immediate need for development of a better globin reduction method and novel data analysis techniques to analyze gene expression profiles in whole blood that can identify cell-type specific gene expression profiles such as lymphocytes, neutrophils, monocytes, etc. Recently, a statistical approach was described to estimate cell-type specific expression profiles from complex tissues (e.g., whole blood) (Shen-Orr et al. 2010). Applying this approach to whole blood expression profiles of renal transplant patients, 318 differentially expressed genes in monocytes were identified although no differentially expressed genes between the two groups in whole-blood analyses were identified as sample heterogeneity may have masked biological differences (Shen-Orr et al. 2010). These cell-type specific expression profiles, in conjunction with functional pathway level analysis, can potentially better identify and explain humoral rejection mechanisms.

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Need for integrative meta-analysis: In order to find the underlying common rejection mechanism proposed in the literature (Wang et al. 2008), it is imperative to integrate data from multiple experiments from multiple organs in multiple conditions. One way to accomplish this goal is to utilize the large amount of expression data available in public repositories such as NCBI Gene Expression Omnibus. Recently, there have been reports of meta-analysis of multiple datasets in transplant. Kong et al. (Kong et al. 2008) analyzed two independent CAN studies using nonparametric approach and identified six KEGG pathways that are significantly over-expressed in CAN. In another study, Morgan et  al. (Morgan et  al. 2010), combined the data from five independent experiments to study acute rejection in solid organs including kidney, heart and lung. Their results showed that there is consistent expression of “immune response” related genes across different organs. Need for integrating multiple ‘omics technologies: In order to address the challenge deconvoluting expression signature that would consequently lead to better biomarker discovery is to integrate data from multiple high throughput technologies such as genomics, proteomics, peptidomics and metabolomics. In a recent study Li et  al. combined genomic datasets generated from seven different microdissected kidney regions (Higgins et  al. 2004) with antibody response generated using protein-arrays. Integration of these two data types allowed them to define differential immunogenicity of the human kidney, and determine the specificity of de-novo posttransplant antibody responses after renal transplantation to specific regions of the kidney. Functional pathway analysis of have the potential to identify shared critical pathways and validate common noninvasive cause-specific biomarkers for transplant injury across different solid organs.

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Schena, M., D. Shalon, et al. (1995). “Quantitative monitoring of gene expression patterns with a complementary DNA microarray.” Science 270(5235): 467–70. Scherer, A., A. Krause, et  al. (2003). “Optimized protocol for linear RNA amplification and application to gene expression profiling of human renal biopsies.” Biotechniques 34(3): 546–50; 552–4; 556. Shai Shen-Orr, Robert Tibshirani, Purvesh Khatri, Dale Bodian, Frank Staedtler, Nicholas M. Perry, Trevor Hastie, Lihua Ying, Minnie M. Sarwal, Mark M. Davis, and Atul J. Butte (2010). A Method to systematically identify Cell-Type-Specific Differential Gene Expression from Complex Tissues. Nature Methods 7: 287–289. Shojaie, A. and G. Michailidis (2009). “Analysis of gene sets based on the underlying regulatory network.” J Comput Biol 16(3): 407–26. Solez, K., R. B. Colvin, et al. (2007). “Banff ‘05 Meeting Report: differential diagnosis of chronic allograft injury and elimination of chronic allograft nephropathy (‘CAN’).” Am J Transplant 7(3): 518–26. Sotiriou, C., C. Khanna, et  al. (2002). “Core biopsies can be used to distinguish differences in expression profiling by cDNA microarrays.” J Mol Diagn 4(1): 30–6. Streblow, D. N., C. N. Kreklywich, et al. (2008). “The role of angiogenic and wound repair factors during CMV-accelerated transplant vascular sclerosis in rat cardiac transplants.” Am J Transplant 8(2): 277–87. Subramanian, A., P. Tamayo, et  al. (2005). “Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.” Proc Natl Acad Sci U S A 102(43): 15545–50. Tarca, A. L., S. Draghici, et  al. (2009). “A novel signaling pathway impact analysis (SPIA).” Bioinformatics 25(1): 75–82. Thomas, P. D., M. J. Campbell, et al. (2003). “PANTHER: a library of protein families and subfamilies indexed by function.” Genome Res 13(9): 2129–41. Thomson, A. W., L. Lu, et al. (1995). “Microchimerism, dendritic cell progenitors and transplantation tolerance.” Stem Cells 13(6): 622–39. Tian, L., S. A. Greenberg, et al. (2005). “Discovering statistically significant pathways in expression profiling studies.” Proc Natl Acad Sci U S A 102(38): 13544–9. Trani, J., D. J. Moore, et al. (2003). “CD25+ immunoregulatory CD4 T cells mediate acquired central transplantation tolerance.” J Immunol 170(1): 279–86. Vitalone, M. J., P. J. O’Connell, et  al. (2008). “Epithelial-to-mesenchymal transition in early transplant tubulointerstitial damage.” Clin Res 19(8): 1571–83. Wang, E., A. Worschech, et al. (2008). “The immunologic constant of rejection.” Trends Immunol 29(6): 256–62. Weintraub, L. A. and M. M. Sarwal (2006). “Microarrays: a monitoring tool for transplant patients?” Transpl Int 19(10): 775–88. Xu, B., L. I. Sakkas, et al. (2001). “Apoptosis in chronic rejection of human cardiac allografts.” Transplantation 71(8): 1137–46. Ying, L. and M. Sarwal (2009). “In praise of arrays.” Pediatr Nephrol 24(9): 1643–59; quiz 1655; 1659. Zarkhin, V. and M. Sarwal (2008). “Microarrays: monitoring for transplant tolerance and mechanistic insights.” Clin Lab Med 28(3): 385–410. Zhang, Q. and E. F. Reed (2006). “Array-based methods for diagnosis and prevention of transplant rejection.” Expert Rev Mol Diagn 6(2): 165–78.

Part V

Signatures Associated with Acute Rejection

Chronic Graft Versus Host Disease: Inflammation at the Crossroads of Allo and Auto Immunity Frances T. Hakim

Abstract  The mechanisms underlying the pathogenesis of chronic graft versus host disease (CGVHD) remain obscure, despite the clinical importance of this disorder in transplant recipients. In this chapter we review current evidence and theories concerning the two main aspects of CGVHD: the development of inflammatory infiltrates that target epithelia and the initiation of extensive fibrosis. We further address the effects of transplantation on Treg cells and on central tolerance. Keywords  Chronic graft versus host disease • Hematopoietic stem cell transplantation • Type I interferon • Plasmacytoid dendritic cells • Tumor derived growth factor beta • Th1, Th2, Th17, regulatory T cells Abbreviations CGVHD HSCT IFN pDC TGFb Treg

Chronic graft versus host disease Hematopoietic stem cell transplantation Type I interferon Plasmacytoid dendritic cells Tumor derived growth factor beta Th1, Th2, Th17, regulatory T cells

Chronic graft versus host disease (CGVHD) is a disorder of dysregulated immunity, whose symptoms typically become evident at 3–24 months following allogeneic hematopoietic stem cell transplantation (AHSCT) (Filipovich et al. 2005). Individual presentation is highly varied, but commonly affected tissues include lacrimal and salivary glands, skin, oral mucosa, gastrointestinal tract and lungs (Filipovich et al. 2005; Shulman et al. 2006; Bolanos-Meade and Vogelsang 2008). Developing in

F.T. Hakim (*) Experimental Transplantation and Immunology Branch, National Cancer Institute, NIH, Bethesda, MD, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_17, © Springer Science+Business Media, LLC 2011

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40–70% of transplant recipients, CGVHD is the most common long-term complication of AHSCT. It is a major factor adversely affecting health and quality of life after transplantation, and in severe forms can result in long-term disability and early death (Carpenter 2008). Therapeutic options for CGVHD remain limited. Front line therapies, such as high dose systemic steroids or long-term use of immunosuppressive drugs, result in severe immune deficiency and susceptibility to infection (Arora 2008; Hossain et  al. 2007). Moreover, many of the recent shifts in transplant practices – use of mobilized peripheral blood stem cells not marrow, use of matched unrelated donors, transplant of older recipients, treatment with delayed lymphocyte infusions – have all contributed to an increase in the frequency of CGVHD (Bolanos-Meade and Vogelsang 2008; Vogelsang 2003). Indeed, improvements in supportive care at transplant and in the treatment of acute GVHD (AGVHD) and infections have increased the pool of long-term transplant survivors, without having any impact on the frequency of CGHVD. For all these reasons, identification of the dysfunctional immune pathways contributing to the initiation and development of CGVHD is a critical step in the development of new, more targeted therapies. Many of the diagnostic clinical signs of CGVHD resemble those of inflammatory and fibrotic autoimmune disorders, such as lichen planus, systemic sclerosis and Sjogren’s syndrome. Indeed at the tissue level, CGVHD shares many common histopathologic features with these autoimmune disorders. Other symptoms associated with CGVHD – such as diarrhea, rashes, elevated liver function – overlap with those of AGVHD. The mechanisms of CGVHD pathogenesis, however, remain unclear. Several factors have contributed to this. Studies have generally reported cross-sectional patient cohorts, rather than longitudinal assessments, so that the progression of changes in tissues has not been adequately studied at a cellular or molecular level. Studies of immune function have utilized peripheral blood lymphocytes, which may not accurately reflect the populations that have migrated into tissues. Animal models have investigated the effects of blocking specific immunologic pathways, but have never completely replicated the smoldering timecourse or the diverse symptoms of CGVHD. Recently, however, immunohistochemical and molecular analyses have focused on affected tissues, providing new insights into the effector cells and molecules involved in the immediate pathologic process. Furthermore new animal models have provided insights into the role of immune regulators in the development of CGVHD. Several key questions need to be addressed in CGVHD. What lymphocyte populations are critical to CGVHD and what cytokines and chemokines initiate and drive the CGVHD process and direct the movement of effectors into tissue? Both inflammatory and fibrotic processes appear involved in the tissues; are these linked as sequential stages in a single process or are they the consequence of separate paths? Finally, is CGVHD an alloimmune response directed against minor antigenic disparities between donor and host (like AGVHD) or is it an autoimmune disorder, a failure of the post-transplant immune system to successfully regulate immunity to self tissue antigens? Clearer answers to these questions could support the development of new treatment modalities.

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Acute GVHD: Initiation of Inflammation, Recruitment of Effectors and T Lineage Determined Tissue Damage The NIH Consensus Conference on CGVHD has recently developed new guidelines for the diagnosis of acute and chronic GVHD. Previously the main determinant was the time that symptoms presented post transplant, with 100 days used as a demarcation. The NIH Consensus guidelines now recognize that AGVHD may occur in the first months after transplant (classic acute) or may be persistent, recurrent or have late onset, often following delayed lymphocyte infusions (Filipovich et  al. 2005; Bolanos-Meade and Vogelsang 2008). Independent of the time of onset, AGVHD is defined by the presence of its characteristic maculopapular erythematous rash, gastrointestinal symptoms or cholestatic hepatitis. CGVHD may develop subsequent to AGVHD, or may arise de novo, without any prior evidence of clinical AGVHD. Like AGVHD, the new guidelines have divorced CGVHD from the earlier rigid time frame and linked the diagnosis to the presence of one or more of the characteristic CGVHD symptoms, such as lichen planus-like changes in the skin or oral mucosa, or sclerotic changes in skin (Filipovich et al. 2005; Bolanos-Meade and Vogelsang 2008). Many symptoms, such as inflammatory changes in skin, are held in common between acute and chronic GVHD. The mechanisms that contribute to the development of AGVHD may therefore also contribute to CGVHD, particularly those aspects characterized by an inflammatory infiltrate into tissues. The pathophysiology of AGVHD is well understood, primarily through the use of animal models that have used gene knockouts and antibody blockades to identify critical pathways. As has been described in recent reviews (Socie and Blazar 2009; Ferrara et al. 2009), AGVHD results from an interplay of host-reactive donor T cells, antigen presenting cells (APC), and environmental inflammatory triggers that accompany transplant. This self-reinforcing cycle of inflammation has been termed the cytokine storm of AGVHD (Socie and Blazar 2009; Ferrara et al. 2009). The process is initiated when radiation and chemotherapy-based conditioning regimens that precede transplantation cause damage to host tissues. This damage triggers activation of antigen presenting cells and release of proinflammatory cytokines (such as tumor necrosis factor [TNFa] and interleukin-1 [IL-1]), initiating the cytokine storm. Damage to the gastrointestinal tract is particularly important because the destruction of the epithelial barrier permits an influx of bacterial lipopolysaccharide, which enhances inflammatory mediator release and APC activation. Activated APC then upregulate expression of costimulatory and Class I and Class II major histocompatibility complex (MHC) molecules and become more effective in initiating T cell responses. Donor T cells become activated by genetic disparities in major and minor histocompatibility antigens presented by these APC. Residual host APC, that persist after transplantation conditioning regimens, are critical for this initial T cell activation (Haniffa et al. 2009; Shlomchik et al. 1999; Zhang et al. 2002a), although donor APC may support later T cell expansion (Matte et  al. 2004). Donor T cells interact with these APC in lymph nodes and spleen (Beilhack et al. 2005, 2008). As these T cells expand and differentiate into effectors,

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the cytokines they release further sustain activation of APC. After this expansion phase, effector T cells traffic into the gut, liver and skin following signals of locally produced chemokines and adhesion factors (Wysocki et al. 2005). Finally effector T cells attack target tissues, producing damage by direct cytotoxicity and by induced apoptosis via Fas/FasL and TNF/TNFR interactions. The key effectors are IFN-producing Th1/Tc1 T cells, but recent evidence also suggests roles for Th17 and Th2 cells in organ specific attacks (Yi et al. 2009). Deletion of any of these subsets can ameliorate severe lethal disease or shift the main target organs (Yi et al. 2009; Liu et al. 2001). Additional effectors, such as NK cells and Treg cells, may then be recruited to target organ sites and may regulate the cytotoxic attack on host tissues (Olson et al. 2009, 2010; Wu et al. 2009). The earliest changes in target organ pathology in AGVHD involve a sparse perivascular infiltrate of lymphocytes and myeloid cells. The initial lymphocytes are mainly perivascular. Evidence of target organ attack, such as the appearance of apoptotic basal keratinocytes in the skin, can occur while the infiltrate is still sparse. Th1/Tc1 T cells play a dominant role in the development of ACGHVD. Other T cell lineages may also contribute to the inflammatory AGVHD process. Th17 cells may increase the severity of AGVHD. Supplementing naive donor T cells with in vitro differentiated Th17 cells resulted in an exacerbation of skin and pulmonary lesions (Carlson et al. 2009). Mice developed severe hair loss, epidermal hyperplasia, and skin ulcerations. On the other hand, injecting IL-17−/− donor cells reduced Th1 activity and AGVHD severity (Kappel et al. 2009). The balance of Th1, Th2 and Th17 cells elicited in AGVHD may also determine the distribution of affected tissues (Yi et al. 2009). Lack of Th1 cells in the donor inoculum (IFNg−/− donors) reduced GVHD tissue damage in gut and liver, but exacerbated damage to lung and skin; Th2 and Th17 cells were increased in these mice (Yi et al. 2009). Lack of both Th1 and Th2 (IFNg−/− and anti-IL-4 blockade) resulted in augmented Th17 differentiation and increased damage to skin, whereas lack of both IFNg and IL-17 led to increased Th2 differentiation and idiopathic pneumonia (Yi et al. 2009). Movement of T effectors into specific target organs is driven by locally produced chemokines acting on chemokine receptors expressed by specific T cell subsets, as well as by interactions of lymphocyte integrins and their tissue specific ligands (reviewed in Wysocki et al. 2005). Mast cells were found to be present in the tissue early and may be important sources of mediators that recruit other leukocyte populations into the tissue (Murphy et al. 1994; Wu et al. 2008). During AGVHD, the interferon (IFN)-induced chemokine CXCL10 (IP-10) was upregulated in the skin; cells expressing the receptor for this chemokine, CXCR3, were in low frequency in the blood, but were present at higher frequency in the infiltrate in skin (Piper et al. 2007; Bouazzaoui et  al. 2009). Organ-specific chemokine production may direct lymphocytes into different targets, as demonstrated by the use of donor mice lacking particular chemokine receptors or by antibody blockade of receptor-ligand interactions (Arora 2008). Blockading CCR5 or its ligand CCL3 (MIP-1a) with antibodies prevented infiltration of CD8 T cells into liver and reduced liver damage (Murai et al. 1999). Similarly, blockade of MadCAM, the gut associated ligand for lymphocyte-expressed a4b7 integrin, blocked lymphocyte migration into the gut

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and reduced AGVHD (Dutt et al. 2005). Specific Th lineages may home to different tissues based on differential expression of chemokine receptors. When Th1 cells and associated gut and liver damage predominated, T cells expressed higher levels of a4b7 and CCR5, and gut and liver expressed mRNA for the ligands for these receptors (Yi et al. 2009; Palmer et al. 2010; Ma et al. 2010). In contrast, movement of Th2 cells to lungs was associated with high levels of CCR3 and CCR4 expression on the Th2 cells and high levels of the corresponding chemokine ligands (CCL11 and CCL17/CCL22, respectively) in the lungs. When Th17 movement to the skin dominated, increased CCR6 was found on T cells and CCL20 (MIP3a) was found in skin (Yi et al. 2009).

CGVHD: Consequences of Chronic Inflammation in Tissues, Humoral Immunity and Fibrosis Questions persist as to whether acute and chronic GVHD are independent disorders initiated by allogeneic transplant, or are phases of the same process. Certainly there are basic commonalities in the cellular constituents of infiltrates involved in inflammatory attack on tissues. The infiltrate of lymphocytes, macrophages, and myeloid and plasmacytoid dendritic cells may be even more extensive in lichen planus-like CGVHD than in AGVHD (Gilliam and Murphy 1997). The clinical presentation of oral lichen planus-like CGVHD is characterized by patchy or lacy white areas of hypertrophic keratinization on the buccal mucosa and tongue. In severe cases the buccal mucosa may appear red and inflamed, with areas of ulceration. The clinical severity of oral lesions correlated with the extent of submucosal infiltrates of hematopoietic (CD45+) cells and the presence of apoptotic basal keratinocytes (expressing the active cleaved form of caspase 3) (Imanguli et  al. 2009). These apoptotic epithelial cells were often found adjacent to infiltrating T cells that formed a major component in the infiltrate. The CD3+ T cells were identified as primarily of the Type I cytokine pattern (Th1/Tc1) based upon expression of T-bet, a transcription factor essential for this lineage. The predominant subset in the infiltrating T cell population was CD8+ memory/effector (CD45RO+) cells that expressed cytotoxicity-associated proteins, including granzyme B and TIA-1 (Imanguli et al. 2009). Myeloid cells (CD68) formed a major component of the infiltrate, while NK and B cells were rare. Based on immunohistochemistry and real time PCR analyses of buccal mucosa, we have proposed that interferon (IFN), in particular Type I IFN (IFNa/b), plays a critical role in the development of this infiltrate (Imanguli et al. 2009). In severely affected oral mucosa, we have demonstrated the presence of plasmacytoid dendritic cells (pDC), known to be major producers of IFNa, as well as large numbers of T-Bet+ T cells that can produce IFNg. Myeloid cells in the dermal infiltrate and epidermal keratinocytes showed evidence of phosphorylation and nuclear translocation of STAT1, consistent with the intracellular signaling cascade initiated by IFN. Moreover, elevated levels of IFN-inducible factors (MxA, IL-15, MIG) were found in

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the myeloid cells of the infiltrate and in the epithelial keratinocytes. Whereas both T-cell produced Type II IFN and Type I IFN use the STAT1 signal transduction pathway and can stimulate production of IL-15 and MIG, production of MxA is primarily dependent on Type I IFN. The expression of MxA in the same locations as MIG and IL-15 is further evidence of a role of IFNa/b in the induction of these factors. These locally produced factors may be critical for the inflammatory process. Most T cells in the infiltrate co-expressed the chemokine receptor, CXCR3 (Imanguli et  al. 2009), which is directly induced by T-Bet (Lord et  al. 2005; Taqueti et al. 2006). CXCR3 is the receptor for the IFN-inducible chemokine MIG (CXCL9) that was found in the mucosal epithelium. Comparatively few CD4 or CD8 T cells in the peripheral blood were found to express CXCR3. This disparity is consistent with selective trafficking of CXCR3+ T cells to the inflammatory site. As in AGVHD, IFN-induced chemokines may have recruited Th1/Tc1 effectors to the tissue in oral mucosal CGVHD. IFN and IFN-induced factors may also have contributed to the development of cytotoxic effectors in the tissue. Type I IFN can directly support the differentiation of Th1/Tc1 cytotoxic effectors (Kolumam et al. 2005). IL-15, recognized for its role in supporting expansion and survival of CD8 effector/memory cells, was notably expressed in both myeloid cells and in the keratinocyte layer. Both myeloid cells and keratinocytes have been previously demonstrated to produce both IL-15 and IL-15R-alpha (Ruckert et al. 2000; Liu et al. 2002; Han et al. 1999), a receptor chain necessary for effective trans-presentation of IL-15 to T cells. Consistent with the presence of IL-15, the CD8+ T cells in the buccal mucosal infiltrate were predominantly CD45RO+ memory/effector cells and many co-expressed Ki-67, an indicator of ongoing cellular proliferation (Imanguli et al. 2009). Hence local production of IFNa and IFN-induced factors like IL-15 and MIG, could contribute to the emigration, expansion and differentiation of T effectors in this tissue. Continued production of IFNg by T-Bet+ CD4 and CD8 T cells would then contribute to a cycle of inflammation, recruitment and expansion of effectors, and tissue attack. We have observed similar expression of these IFN-induced factors in the skin in patients with erythematous CGVHD (Imanguli et  al, unpublished data). Both MxA (specific for type I IFN) and MIG were expressed in the epidermal keratinocytes and in infiltrating myeloid cells in the dermis in affected skin, but not in unaffected skin in the same patients. As in oral mucosa, the predominant T cell was a T-Bet+ CD8 cell. Wenzel similarly noted a bandlike subepidermal inflammatory infiltrate in lichenoid cutaneous cGVHD (Wenzel et al. 2008). CD3 cells predominated, with TIA-1+ CD8 cells being the main cells invading the basal epidermal layer and CD4 cells found more commonly near the dermal vasculature. As was noted in the buccal mucosa, 60–90% of the infiltrating lymphoid cells expressed the chemokine receptor CXCR3. Furthermore the epidermal keratinocytes and infiltrating cells in the dermis expressed the IFN-inducible chemokines MIG (CXCL9) and IP-10 (CXCL10). The Type I IFN-inducible factor MxA followed the same distribution as these chemokines, just as in the oral mucosa. Finally, in situ hybridization detected IFNa in large pDC infiltrating the dermis (Wenzel et al. 2008; Wenzel and Tuting 2008).

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Infiltration of cytotoxic effectors is a common feature of early development of CGVHD in multiple tissues. In minor salivary glands and in lacrimal glands, a predominantly CD8+ T cell infiltrate was observed in the periductal areas and was associated with upregulation of HLA-DR on the epithelia (Hiroki et al. 1996; Levy et  al. 2000; Nagler et  al. 1996; Nagler and Nagler 2004; Ogawa et  al. 2003). Atrophy or destruction of acini and fibrosis followed. Similar pathways of IFNinduced chemokine production have also been detected. In the conjunctiva of patients with dry eye symptoms of CGVHD, real time PCR detected significantly elevated levels of CXCL9 and CXCL10 and their receptor CXCR3, as compared to healthy controls. Furthermore, chemokine production was detected by immunohistochemistry in the conjunctival stroma and CXCR3 on infiltrating cells (Westekemper et al. 2010). The cell populations and the cytokines/chemokines present in lichenoid CGVHD of the skin or oral mucosa are characteristic of interface dermatitis (Wenzel and Tuting 2008). The same combination of IFN-associated chemokines and cytotoxic T cell infiltrate is the central feature in the skin in response to herpes simplex viral lesions and viral warts, lichenoid actinic keratosis and invasive squamous cell carcinoma (Wenzel and Tuting 2008). In all cases, the presence of apoptosis in the basal epidermal layer is associated with an infiltrate of cytotoxic CD8+ T cells. The infiltrating T cells express CXCR3 and the keratinocytes and myeloid cells express IFN-inducible chemokines that bind to that receptor. This immunohistochemical evidence of IFN-induced processes has been buttressed by the upregulation of multiple IFN-induced genes (including MxA, IFI27, IFITM1, IFITM2, IRF-1, and G1P3) in tissue microarrays in lichen planus, malignant keratinocyte carcinoma, and cutaneous lupus (Wenzel and Tuting 2008). Gene expression in murine models of GVHD has similarly identified a pattern of upregulation of cytotoxic effector molecules and IFN-inducible factors (Bouazzaoui et al. 2009; Sugerman et  al. 2004; Zhou et  al. 2007). These studies have been performed in three quite distinct models in mice. In (B6 ® B6D2F1) mice, there are both Class I and Class II MHC disparities and a rapid and often lethal AGVHD proceeds affecting liver, lung, tongue and gut (Bouazzaoui et al. 2009). In (B10.BR → CBA/J), only minor histocompatibility antigen disparities exist, the process of GVHD is slower and, over several weeks, skin develops a more lichenoid involvement including a dense sub-epidermal infiltrate, hypertrophic epidermis and thickened dermis (Sugerman et al. 2004). Finally in (B10.D2 → BALB/c) mice an early lymphocytic infiltrate into the skin is followed by extensive development of severe fibrosis in the dermis; this strain combination serves as a model for sclerodermatous CGHVD (Zhou et al. 2007). In the early weeks after transplant, genes indicative of cytotoxic T effectors (granzyme B, lymphotoxin, IFNg, and TNF) were elevated in the first two mouse models. In addition IRF-7 (a transcription factor specific for IFNa/b, Honda et al. 2005), and the IFN-induced chemokines CXCL9, CXCL10, and CXCL11 were increased. Over the first months in the sclerodermatous model, the expression of genes for Th1 effectors (IFN, IL-2) and IFN-induced cytokines/ chemokines predominated; later these declined while TGFb1 remained elevated (Zhou et al. 2007).

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AGVHD and lichenoid forms of CGVHD therefore form a continuum. The infiltrating donor T cell populations are dominated by Th1/Tc1 cytotoxic effectors, while Th17 and Th2 cells may play a role in specific tissues. T cell migration is coordinated by locally produced chemokines and T lineage-specific receptors. Finally the expansion, cytotoxic differentiation and maintenance of T effectors are driven by cytokines produced by myeloid tissues and the epidermal keratinocyte targets. IFN plays a critical coordinating role throughout this process in driving the production of factors that control T cell recruitment and function.

Systemic Involvement of IFN in CGVHD Thus IFN-induced processes may be broadly involved in inflammatory sites of CGVHD. But IFN-induced factors may contribute to humoral autoimmunity in CGVHD as well. The cytokine BAFF (B cell activating factor of the TNF family; TNFSF13B) has previously been identified as a potential marker of CGVHD (Sarantopoulos et al. 2009; Fujii et al. 2008). We have found elevated levels of BAFF (TNFSF13B) in the plasma of patients with either inflammatory or sclerotic forms of cutaneous CGVHD in the CGHVD patients in the NCI natural history cohort. BAFF plays many roles in humoral immunity. It acts as a homeostatic cytokine, supporting B cell survival, and also as a cofactor that facilitates B cell activation and chain switching. Elevated BAFF levels have been correlated with increased frequencies of activated and memory B cells in CGVHD as compared to transplant patients without CGVHD (Sarantopoulos et  al. 2007, 2009). BAFF is constitutively produced by many cell populations and its levels in the body are inversely correlated with B cell numbers; BAFF levels increase on depletion of B cells during transplantation regimens and decline toward normal circulating levels when B cell populations are regenerated. But BAFF is also an inducible cytokine. Monocytes and dendritic cells produce BAFF in response to stimulation with IFN (Kim et al. 2008). BAFF transcripts are upregulated, like those of many IFN-inducible genes, in autoimmune disorders (York et  al. 2007; Yao et  al. 2009). We observed that elevated plasma levels of BAFF correlated with circulating levels of MIG (CXCL9) and IP-10 (CXCL10), IFN-inducible chemokines upregulated in inflamed tissues in oral and cutaneous CGVHD (Hakim et al., in preparation). Elevated expression of BAFF may contribute to the development of humoral autoimmunity. Mice with sustained elevations in BAFF levels (either by injection or by creation of BAFF overexpressing transgenics) developed autoantibodies such as anti dsDNA and autoimmune dysfunction (Stadanlick and Cancro 2008; Thorn et al. 2009; Zekavat et al. 2008). In these mice, elevated BAFF was found to short-circuit negative selection in B cell maturation, permitting an increased frequency of auto-reactive B cells to enter the peripheral pool. Although CGVHD is considered to be a disease primarily produced by dysfunction in cellular immunity, many patients do have autoantibodies (Fujii et al. 2008; Kapur et al. 2008). Some of these autoantibodies have been proposed to result in activation of the PDGF receptors on fibroblasts and contribute to skin

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sclerosis (Svegliati et  al. 2007). Furthermore, depletion of B cells has produced significant improvements in cutaneous and oral CGVHD (Kapur et al. 2008). Thus, IFN-induced factors may contribute not only to differentiation and trafficking of T effectors, but also to the generation of humoral immunity in CGVHD.

CGVHD: Chronic Fibrosis Sclerotic changes in skin and fibrosis in exocrine glands and lungs are a serious problem in CGVHD. Increased fibrosis in skin may affect only superficial dermal areas or, as in fascitis, may produce severe restrictions on mobility. Although biopsies collected early after CGVHD diagnosis demonstrate inflammatory periductal lymphocytic infiltrates in lacrimal and salivary glands, the later stages of loss of gland secretion are mainly characterized by extensive replacement of secretory acini and ducts by fibrosis. Bronchiolitis obliterans, a syndrome involving progressive circumferential fibrosis and ultimately obstruction of small terminal airways, is a major factor in early mortality in CGVHD patients. There is continuing argument about the mechanism of fibrosis in CGVHD. Three main mechanisms have been proposed. The first is that the primary event in sclerosis is a lymphocytic attack on blood vessels. Biederman and Gratwohl have proposed that sclerodermatous changes begin with an attack on endothelial cells in the upper epidermis (Biedermann et al. 2002; Hausermann et al. 2008). They noted the early presence of perivascular infiltrates primarily composed CD4 T cells, associated with damage and loss of small capillaries. Von Willibrand factor was increased in the plasma of CGVHD patients, consistent with capillary damage (Biedermann et  al. 2002). According to this hypothesis, and to similar mechanisms proposed in systemic sclerosis (SSc – scleroderma), capillary loss would then result in tissue hypoxia, which in turn would trigger fibroblast activation and fibrosis. In contrast, long term transplant recipients of who did not develop sclerotic CGVHD continued to maintain normal capillary density in the skin (Hausermann et al. 2008). Fleming recently has countered this hypothesis with parallel morphometric measurements of skin and observed very little capillary loss in CGVHD compared to that occurring in SSc patients (Fleming et al. 2009). Although initially using the classic endothelial marker CD31, they repeated their observations using binding of Ulex reagent, as was used by Biederman, but continued to find no evidence of extensive capillary loss. Fleming noted, however, the increased frequency of myofibroblasts in the dermis. The increased presence of myofibroblasts in the densely fibrotic dermal layer is consistent with an alternative hypothesis developed in SSc. The dispute concerning the role of early capillary destruction and resultant hypoxia in development of fibrosis is also an issue in studies of SSc. While some uphold the capillary loss hypothesis, others propose that capillary rarefaction (which all agree is found in SSc) is a secondary phenomenon, associated with massive increases in collagen bundles and fibrosis. The proposed alternative hypothesis in SSc is that fibrosis is triggered by cytokine-induced increases in myofibroblasts. Myofibroblasts are the

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main producers of collagen and extracellular matrix in fibrosis. Myofibroblasts are identified primarily by the cytoplasmic presence of alpha smooth muscle actin (SMA), but also express a distinguishing ED-A splice variant form of fibronectin (ED-A FN) and the surface marker CD90 (Rajkumar et  al. 2005). In SSc and in CGVHD, spindle shaped SMA+ myofibroblasts are found in the deep fascial layers where dense fibrosis begins (Fleming et  al. 2009; Rajkumar et  al. 2005). Myofibroblasts can arise from multiple sources. Pericytes are SMA-expressing mesenchymal cells located around blood vessels in normal tissues. Upon activation, these can migrate into the dermis as myofibroblasts. Activated fibroblasts in the dermis can also convert into myofibroblasts. Circulating blood borne mesenchymal progenitors also have a myofibroblast-like phenotype and are termed fibrocytes (Abe et al. 2001); these can contribute to the presence of donor-derived fibroblasts in fibrotic lesions in CGVHD (Ogawa et al. 2005). Finally myofibroblasts can be derived from epithelial cells in a process termed epithelial-mesenchymal transition. Electron microscopic evidence supports this route for duct epithelia in CGVHD in lacrimal glands (Ogawa et al. 2009). Two main cytokines have been demonstrated to drive the activation, proliferative expansion and increased collagen synthesis of myofibroblasts: platelet derived growth factor BB (PDGF BB) and tumor derived growth factor beta (TGFb). Both of these are produced by multiple cell types but can be significantly produced by activated macrophages. TGFb can also be synthesized by activated fibroblasts, producing an autocrine feedback loop (Wynn 2008). In addition to actual secretion of the cytokines, Svegliati has identified the presence of agonist autoantibodies in both SSc and CGVHD serum that bind to the PDGF receptor on normal fibroblasts and stimulate proliferation and collagen production. Such an autoantibody is not found in normal plasma, but autoantibodies may result from failure of negative selection of B cells in CGVHD. TGFb as long been associated with fibrosis and with the sclerotic forms of CGVHD. Elevated expression of TGFb genes in skin and lungs in the (B10.D2 → BALB/C) mice was present prior to the development of fibrosis (Zhang et al. 2002b). Blockade of TGFb with antibodies prevented the development of fibrosis (McCormick et al. 1999). Drugs such as halofuginone, which prevents TGFb signaling through phosphorylation of SMAD3 in fibroblasts, have been effective in topical treatment of fibrotic skin in CGVHD (Pines et al. 2003), as well as in a murine sclerotic CGVHD model (Levi-Schaffer et  al. 1996). Imatinib (Gleevec), which blocks the tyrosine kinase pathways used by PDGF or TGFb, has similarly been found to be effective not only at stopping the progression of scleroderma-like CGVHD, but in reversing fibrosis (Rajkumar et al. 2006; Olivieri et al. 2009; Magro et al. 2009). The role of T cells and inflammation in sclerodermatous CGVHD (the third hypothesis) remains unclear. T cell infiltrates have observed in affected skin by histopathology, albeit at levels much lower than in lichenoid CGVHD (Wenzel et al. 2008). In the (B10.D2 → BALB/C) sclerodermatous CGVHD model, CD4 T cells were found to be required to induce the disorder; CD8 cells were not needed. CD4 T cell products could potentially stimulate fibrosis. The Th2 cytokines IL-4 and IL-13 have been demonstrated to stimulate fibrosis and direct TGFb

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release by M2 macrophages. IL-6, which is produced by Th2 and by Th17 T cells, is a known fibroblast growth factor (Wynn 2008). Yet the early infiltrates in the skin in these mice primarily included Th1 CD4 cells, producing IFNg and IL-2, and not Th2 cells producing IL-4 (Zhou et al. 2007; Askew et al. 2007). IL-6 gene expression was elevated (Zhou et al. 2007), but Th17 cells were not observed in the skin (Radojcic et al. 2010). IL-13 message was elevated only in the first week and not during the fibrosis that progressively developed over the subsequent 4 months. Highly elevated production of IFN-induced chemokines (CXCL9, CXCL10 and CXCL11) and their receptor CXCR3 was prominent in skin in the early weeks after transplant in (B10.D2 → BALB/C) sclerodermatous CGVHD (Zhou et al. 2007). The Th2 receptor CCR4 in contrast, was not evident (Zhou et al. 2007), unlike the AGVHD model of Th2 movement into lungs (Yi et al. 2009). The lack of strong evidence of the presence of Th2 cells in the skin in these mice suggests that the Th2 pathway is not the critical one in this model. Despite the presence of IFNg and IL-2, TNFa was not produced by the CD4+ T cells infiltrating the skin in this CGVHD model, although it was produced by splenic CD4+ T cells (Zhou et al. 2007; Askew et al. 2007). Gilliam has correlated this difference in spleen and skin with different populations of DC in the two locations. She has further proposed that this lack of cutaneous TNF could contribute to the shift to fibrosis because of this factor’s antagonist effects on TGFb (Askew et al. 2007). By this hypothesis, there would be a shift toward cytotoxic attack (TNF skewed) or toward fibrosis (TGFb skewed) depending upon the dominant cytokine. This hypothesis provides an interesting parallel to current work on the role of Type I IFN in the development of autoimmune disorders, including ones like SSc, which include a significant component of fibrosis and not cytotoxic attack. Banchereau has proposed that the development of autoimmunity is affected by a TNF – IFNa/b axis (Banchereau and Pascual 2006). Elevated levels of IFN-inducible factors – including chemokines and MxA – have been found in affected tissues and in blood in several autoimmune disorders including SSc, Sjogren’s and systemic lupus erythematosus (Milano et al. 2008; Chaussabel et al. 2008; Duan et al. 2008; Emamian et al. 2009; Wildenberg et al. 2008). Banchereau has suggested that elevated Type I IFN may play a critical role in elevating these chemokines and in countering TNF (Banchereau and Pascual 2006). Although evidence of Type I IFN in CGVHD has focused upon the cytotoxic attack on epithelia in lichenoid skin and buccal mucosa, the role of Type I IFN in sclerotic CGVHD remains to be explored.

CGVHD as an Autoimmune Disorder of Dysregulated Immunity There is no dispute that CGVHD is initiated by and dependent upon donor T cells. T cell depletion of the donor graft significantly reduces the incidence of CGVHD. Beginning in the early stages of CGVHD, lymphocytic infiltrates are evident in the tissues. T effectors derived from the donor play a key role in producing direct

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cytotoxic damage and may be implicated in triggering fibrosis. But the issue here is whether the broad range of CGVHD pathology arises not from the donor T cell response to minor tissue-specific antigenic disparities between donor and host, but rather from the inability of the immune system after transplant to appropriately regulate autoimmune reactivity. Certainly, the similarities between CGVHD and many autoimmune disorders, such as systemic sclerosis, Sjogren’s syndrome and Lichen Planus have long been noted. These parallels extend from histopathology to molecular characterization of tissue infiltrates. Many details differ, but the similarities may represent the final common paths of immune activation. The strongest evidence for considering CGVHD an autoimmune rather than an alloimmune disorder arises, however, from a series of murine studies that suggest that the conditions of transplantation can disrupt many of the mechanisms of self-tolerance. The first line of evidence is that the allo-activation of donor T cells in the transplant setting may break tolerance to self antigens. In the setting of a full MHCmismatched allogeneic transplant (C57Bl/6 → BALB/c), Drobyski has found that adoptive transfer of allo-activated donor T cells into secondary unirradiated B6 Rag−/− hosts (syngeneic to the donor) brought on symptoms of CGVHD colitis within weeks (Tivol et al. 2005). In contrast, transfer of allo-activated donor T cells from GVHD hosts into secondary allogeneic hosts failed to transfer GVHD (Tivol et al. 2005; Anderson et al. 2003). Although both CD4 and CD8 T cells contributed to the initial GVHD in the BALB/c host, the disorder was transferred by CD4 T cells and, indeed, could be serially passaged through further hosts. Furthermore, the development of autoimmune colitis was dependent upon cognate interactions of the donor CD4+ T cells and syngeneic APC. Chimeric (BALB/c → C57Bl/6 Rag−/−) hosts were created by transplant of T depleted BALB/c bone marrow into irradiated hosts; in such chimeras the somatic cells were B6, but all APC were BALB/c derived. Transplant of the B6 effectors isolated from GVHD mice into these chimeric hosts did not produce colitis; transplant into the reciprocal (C57BL/6 → BALC/c) hosts did (Tivol et al. 2005). Thus the CGVHD colitis develops based on the response of lymphocyte effectors to syngeneic APC. Finally the model was not limited to the MHC disparities in the (C57Bl/6 (H-2b) → BALB/c (H-2d)) combination, but could be replicated in other primary transplant combinations such as (C57BL/6 (H-2b) → B10.BR (H-2k)) hosts. This murine model suggests that the T cell reactivity underlying the attack on tissues in CGVHD may be based on dysregulated responses to self, not to allo antigens. In the initial acute GVHD generated by the primary transplant in this model, donor T cells differentiated as Th1 and Th17 effectors, in an environment of elevated serum levels of pro-inflammatory cytokines including IFNg, TNFa, IL-6, IL-17, G-CSF, and IL-1b (Chen et al. 2007). Increasing evidence has suggested that Th17 may play an important role in AGVHD and in active CGVHD (Dander et al. 2009). Th17 cells produce IL-17 and IL-6 and induce macrophages and endothelial cells to generate G-CSF, IL-1b, and TNF (Fossiez et  al. 1996; Jovanovic et  al. 1998). In Drobyski’s model, Th17 cells were not necessary in order to induce the loss of self-tolerance and development of autoimmune colitis in the secondary host (Chen et al. 2010). Neither blockade of IL-17 by antibody nor use of IL-17−/− donor

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cells blocked the development of autoimmune pathology. The contribution of Th17 seemed to be the reduction in regulatory T cell (Treg) frequency in the primary GVHD host, and consequently in the adoptively transferred T cells in the secondary B6 Rag−/− recipient (Chen et al. 2007). Addition of donor CD4+CD25+ Treg to the adoptively transferred cells markedly reduced the serum levels of proinflammatory cytokines and the frequency of IFNg and IL-17 producing splenic T cells in the secondary host. This decline prevented the development of autoimmune pathology in colon and liver (Chen et al. 2007). Furthermore the inflammatory cytokine milieu in the primary host may have played a critical role in the lack of regulatory T cells. Whereas TGFb supports the induction of Treg from memory CD4, the presence of IL-6 in addition to TGFb supports the differentiation of CD4+ Th17 effectors. Anti IL-6R blockade in the primary host increased the frequency of CD4+CD25+ Treg and reduced the frequency of Th17 cells (Chen et al. 2009). By comparison, when generation of donor Th17 cells was prevented, the colitis proceeded via Th1 IFNproducing effectors (Chen et  al. 2010). This work supports the concept that CGVHD proceeds not from specific recognition of minor host alloantigens, but rather from the loss of regulation of the cytotoxic immune response. A similar conclusion was reached in a study of induction of chronic sclerodermatous GVHD in the (B10.D2 → BALB/c) model (Radojcic et al. 2010). In this model the donor inoculum initially upregulated inflammatory cytokines and chemokines (Zhou et al. 2007; Radojcic et al. 2010) and supported the development and expansion of Th1 and Th17 CD4+ T cells prior to the development of extensive dermal fibrosis. When STAT3KO B10.D2 was used as donor, the host mice developed the symptoms of systemic GVHD, but not the cutaneous fibrotic CGVHD symptoms. STAT3 is a critical step in signal transduction in the response to IL-6, IL-21, and IL-23 in Th17 cells. In recipients of STAT3KO donor T cells, the levels of early inflammatory cytokines and chemokines were lower in the skin, Th17 cells did not develop in the spleen and liver of the host, and the overall expansion of donor CD4+ T cells was reduced. In contrast to this decline in Th17 cells, the frequency of FoxP3+ Treg cells steadily increased (Radojcic et al. 2010). Therefore, reducing the frequency of Th17 cells, whether by blocking IL-6 receptors (Chen et al. 2009), or by blocking signal transduction (Radojcic et al. 2010), resulted in an increase in Treg and a corresponding decline in CGVHD pathology. Treg cells can mature in the thymus (producing “natural” or nTreg), arise by conversion from existing CD4+CD25-FoxP3-T cells (termed induced or iTreg), or result from activation and proliferative expansion of either of these populations. The adoptive transfer model producing colitis in syngeneic hosts was fully effective when the primary allogeneic hosts had been thymectomized (Chen et al. 2007). Hence the origin of the regulatory T cells critical to this model was expansion from existing donor Treg in the original inoculum or conversion from CD4+CD25-T cells during the primary GVHD. Similarly expansion of CD4+CD25+ Treg present in the initial donor inoculum was affected in the sclerodermatous CGVHD model (Radojcic et  al. 2010). In addition, later thymic-dependent production of new Treg was found to increase in the STAT3KO injected mice as compared to those receiving normal donor cells (Radojcic et al. 2010). This evidence

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suggests that an early AGVHD attack on the thymus can have consequences on later maturation of Treg cells. A final adoptive transfer model converting AGVHD to CGVHD focuses more directly on the effect of acute GVHD and the transplant milieu on the thymic maturation of T cells. In this model Emerson used T cell-depleted marrow and isolated CD8+ T cells from donor C3H.SW (H-2b) to produce an AGVHD in irradiated MHC-matched B6.SJL (H-2b). Subsequent collection of CD4+ T cells from the spleen of the primary host and adoptive transfer into a second irradiated B6.SJL host resulted in donor infiltrates in liver and gut, and the development of epidermal hyperplasia and dermal fibrosis, consistent with chronic GVHD. The key element here is that the donor-derived CD4 T cells that produced the CGVHD were not derived from mature T cells in the original donor inoculum. Rather, these CD4 T cells had been generated by maturation from donor stem cells in the thymus of the primary host during the course of AGVHD. Thymectomy of the primary host prevented the maturation of new T cells and the appearance of donor CD4 cells in the spleen and thymus of the primary host (Zhang et al. 2007). Furthermore, adoptive transfer into either B6.SJL (matching the primary host) or into C3H.SW (matching the donor) resulted in the development of CGVHD pathology in gut, liver and skin (Zhang et al. 2007). A defect in the frequency of Treg cells emerging from the thymus during GVHD may be part of the problem, consistent with data from the previous models. But adoptive transfer of CD4-depleted splenocytes from the primary host did not result in CGVHD pathology in the second, despite an even greater relative reduction in Treg. This evidence suggests that the transferred CD4 T cells included autoreactive cells and is consistent with a GVHD-induced defect in the central tolerance process and not a focused donor-anti-host alloantigen response. These studies provide evidence that transplant-induced damage to the thymus may impact upon the development of CGVHD in several ways. First of all, the thymus is the main source of immune reconstitution in the post transplant period. Renewal of thymopoiesis in adults after transplant is delayed due to prior agedependent thymic involution and decline of thymic epithelial cells (TEC) critical to T cell maturation (Lynch et al. 2009; Hakim and Gress 2007). Transplant preparative regimens damage the capacity of thymic stroma to produce IL-7, a critical survival factor in early thymopoiesis (Chung et  al. 2001). Transplant recipients have low CD4 T cell numbers and disproportionately low naïve CD4 frequencies long after autologous transplant (Hakim et al. 2005). This process is further exacerbated by allogeneic transplant and GVHD (Weinberg et  al. 2001). The consequence of reduced thymic productivity is prolonged immune deficiency, diminished TCR repertoire and a skewing of the population toward peripherally expanded memory and effector cells (Hakim and Gress 2007). Second, in AGVHD the thymus is a target organ for cytotoxic effectors (Zhang et  al. 2007; Hauri-Hohl et  al. 2007). Donor lymphocytes infiltrate the thymus, attack TEC and DC and disrupt thymic structures such as Hassall’s corpuscles (Zhang et al. 2007; Hauri-Hohl et al. 2007; Ghayur et al. 1988; Min et al. 2007; Rossi et al. 2007). The medullary TEC that express the AIRE gene not only support thymocyte maturation but also are the basis for negative selection of auto-reactive

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thymocytes (Guerau-de-Arellano et  al. 2009). Damage to TEC during AGVHD might permit self-reactive cells to pass into the periphery (Desbarats and Lapp 1993; van den Brink et al. 2000). Finally, the thymic DC located in Hassall’s corpuscles are essential for the maturation of Treg cells (Hanabuchi et al. 2010; Watanabe et al. 2005). The donor antihost cells that develop during AGVHD attack DC in vitro and deplete host-derived DC in the thymus during AGVHD (Zhang et al. 2007). After induction of AGVHD, Hassall’s corpuscles are the last structures to be reconstituted (Ghayur et al. 1988). Hence AGVHD may produce a protracted deficit in thymically generated “natural” Treg. Constraints or delays in thymic maturation of Treg would shift the Treg population toward iTreg cells that have expanded or arisen from conventional CD4+ T cells. Expansion of a small population of Treg or conversion of memory CD4 cells into Treg would result in a regulatory T cell population of limited TCR repertoire diversity, which may limit its ability to control new responses. Indeed, Drobyski’s work suggests that the elevated levels of IL-6 in the inflammatory cytokine milieu during AGVHD would further constrain Treg conversions (Chen et al. 2007, 2009; Hakim and Gress 2007). Furthermore, Treg populations normally include a higher population of cells in cycle than non-Treg and consequently are more prone to shorter telomeres and development of clonal exhaustion, anergy and senescence (Vukmanovic-Stejic et al. 2006). Expansion from a quantitatively limited population of mature donor cells would exacerbate this problem. Studies of regulatory T cells in CGVHD patients (as opposed to murine models) remain inconclusive. Different studies have found that Treg cells are more common in frequency and number than in transplant patients without CGVHD, that they are less common, or that there are no differences (Clark et  al. 2004; Meignin et  al. 2005; Miura et  al. 2004; Sanchez et  al. 2004; Zorn et  al. 2005). These studies have generally involved small numbers of patients and have been cross-sectional rather than longitudinal. Most have relied on CD4+CD25bright to identify Treg or have looked at FoxP3 mRNA levels. These studies need to be repeated in the context of a more complete characterization of Treg levels studied longitudinally in patients with and without CGVHD. In an alternative approach, the transcriptome of donor cells was tested for correlations with GVHD. Elevated levels of TGFb1 and TGFb pathway signal transduction genes like SMAD3 correlated with a reduced frequency of CGVHD post transplant; the same patterns were evident in the recipient at a year after transplant (Baron et al. 2007; Busque et al. 2009). These studies would suggest that an elevated level of circulating Treg could reduce CGVHD, but the studies did not examine actual frequencies of Treg in the administered donor population. Similar studies have correlated elevated donor Treg with reduced AGVHD (Rezvani et al. 2006). Beyond issues of Treg frequency, the contributions of thymic generation vs. peripheral conversion and expansion of Treg need to be examined. These alternative paths have consequences in terms of TCR repertoire diversity and telomere length that may impact upon function. Finally, the key issue may be the frequency of Treg in affected tissues, not that in the circulation. It is unclear at this point whether higher circulating Treg levels may prevent the development of

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CGVHD in patients, or whether Treg movement into the tissues is necessary to ameliorate CGVHD pathology. Murine models have demonstrated that FoxP3+ Treg stimulated by IFNg can upregulate expression T-Bet and CXCR3 and migrate to inflammatory sites, including sites of autoimmune activation (Koch et al. 2009). In man, Treg cells formed a significant component of T cells infiltrating chronically inflamed livers; these Treg expressed CXCR3 and used this receptor to migration to sinusoidal endothelia (Oo et  al. 2010). In both AGVHD and inflammatory sites of CGVHD, infiltrating myeloid elements as well as host keratinocytes express the IFN-induced chemokines recruiting CXCR3+ cells (Piper et al. 2007; Imanguli et al. 2009; Wenzel et al. 2008; Westekemper et al. 2010). The question is whether Treg, if present in adequate numbers, would similarly migrate to these sites. In a murine AGVHD study, FoxP3+ Treg cells transfected to express CXCR3 were better able than control Treg cells to migrate into affected liver, lung and intestines and to ameliorate AGVHD (Hasegawa et  al. 2008). Conversely, in CXCR3−/− mice, experimental autoimmune encephalopathy was more diffuse and less well controlled, in part because fewer Treg cells are found at inflammatory sites (Muller et al. 2007). These issues remain unexplored in CGVHD models or patient populations.

Conclusion Chronic GVHD remains a major source of morbidity and mortality following hematopoietic stem cell transplantation. Allogeneic transplantation sets in motion several processes that may contribute to the final pathogenesis of the disorder. The inflammatory conditions engendered by transplant regimens may establish the setting for activation of APC in the tissues. Allogeneic disparities in minor antigens trigger the initial production of donor-anti-host effectors. IFN secreted by pDC could initiate production of factors supporting the recruitment, expansion and differentiation of T effectors. Depending upon the degree of continued pDC and myeloid support in the target tissues, these effectors may generate the transient attacks of AGVHD or the chronic interstitial dermatitis of lichenoid CGVHD. The continuing production of IFNg by Th1/Tc1 T cells could then contribute to a self-reinforcing cycle of T cell emigration and cytotoxic differentiation. Meanwhile, under as yet poorly understood conditions, the presence of Type I IFN could downregulate TNF and shift cytotoxic conditions into fibrosis through production of factors triggering myofibroblast activation, proliferation and elaboration of collagen and matrix. Finally, the conditions producing CGVHD would also limit the role of Treg cells. Early production of IL-6 by APC could short-circuit production of Treg from memory T cells. Direct cytotoxic attacks on thymic medullary TEC by donor cells could interfere with central tolerance by negative selection and delay recovery of adequate Treg to enforce peripheral tolerance. The culmination of these processes would be a complex interplay of effector and regulatory T cells, and of factors stimulating cytotoxicity and fibrosis.

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Immune-Mediated Tumor Rejection Ena Wang and Francesco M. Marincola

Abstract  Fundamental strides in the understanding of the molecular basis of tumor rejection were made in the last decade thanks to observational studies performed at relevant time points in human cancerous tissues. The following concepts emerged: immune surveillance against tumors is a likely occurrence. When cancer cells evolve to escape the ongoing immune defense, the neoplastic process reaches a clinically observable phase. By necessity, at this stage, escape mechanisms override anti-cancer mechanisms for tumors to be observable. When cancers become established, two molecular phenotypes can usually be observed: one is characterized by a tumor microenvironment infiltrated by immune cells bearing transcriptional signatures consistent with a status of partial activation. Although incapable of dramatically affecting tumor growth, immune infiltration bears a favorable prognostic and/or predictive connotation on the natural history of the disease or its responsiveness to therapy. In this chapter, we will discuss the significance of transcriptional signatures observed in pre-treatment biopsies as predictive of responsiveness to biological therapy. Moreover, we will discuss the transcriptional signatures observable during and after therapy documenting the switch from chronic to acute inflammation that leads to tumor rejection. We will further discuss how chemotherapy and viral oncolytic therapy, both believed to eliminate tumors exclusively through direct cytotoxicity may play an adjuvant role in stimulating this inflammatory switch. Finally, we will discuss how mechanisms leading to tumor rejection, largely overlap those associated with other aspects of immune-mediated tissue-specific destruction (TSD) such as allograft rejection, graft vs. host disease, acute clearance of pathogen and autoimmunity.

E. Wang () Infectious Disease and Immunogenetics Section (IDIS), Department of Transfusion Medicine, Clinical Center and Trans-NIH Center for Human Immunology (CHI), National institutes of Health, Bethesda, MD 20892, USA e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_18, © Springer Science+Business Media, LLC 2011

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The Current Understanding of Immune Surveillance and Its Molecular Basis Burnet’s suggestion (1970) that the host’s immune system could detect and eliminate transformed cells before the onset of clinically detectable tumors has experienced ups and downs since its conception. Experimental work has, however, provided mounting evidence that Burnet was correct confirming that innate and adaptive immune responses can limit the incidence of syngeneic tumor growth (Shankaran et al. 2001; Dunn et al. 2002, 2004). Furthermore, clinical evidence suggests that immune suppressed individuals are more prone to develop cancer than normal individuals (Mantovani et  al. 2008; Wang et  al. 2008a; Andres 2005; Ippoliti et  al. 2005; Vallejo et  al. 2005; Ghelani et  al. 2005; Taylor et  al. 2005). A striking example of immune surveillance is the insurgence of Epstein Barr Virus-induced lymphoproliferative disorders during immune suppression (Taylor et al. 2005). This is a ubiquitous virus that asymptomatically infects the majority of people within the first years of life. Although armed with oncogenic properties, the virus does not cause neoplastic transformation in the immune competent host. However, in immune compromised individuals the virus induces lymphomas. Reversal of immune suppression or adoptive transfer of virus-specific cytotoxic T cells can induce their complete regression (Rooney et al. 1998; Green 2001; Heslop and Rooney 1997; Khanna et al. 1999; Haque et al. 2001; Gottschalk et al. 2002). Transcriptional profiling and immuno histochemical analyses of established human tumors provide a molecular and cellular basis for immune surveillance. Independent observations point to an association between the presence of immune infiltrates and improved prognosis (Mandruzzato et al. 2006; Zhang et al. 2003; Galon et al. 2006, 2007; Pages et al. 2005, 2009, 2010). It appears that melanoma (Marincola et al. 2000), prostate (Wallace et al. 2008), breast (Martin et al. 2009), ovarian (Zhang et  al. 2003) and pancreatic cancer (Monsurro’ et  al. 2009) arise into two distinct phenotypes of which one is enriched in immune-related signatures and, correspondently, immune cells. These may reflect a response to inert properties of cancer cells perceived as “non-self” or, more generally, abnormal by immune cells. However, cancer cells are not readily recognized as “dangerous” by the host’s immune cells without a conducive pro-inflammatory microenvironment (Fuchs and Matzinger 1996); it is possible that apoptotic and necrotic processes associated with cancer may release danger associated molecular patterns (DAMPs) such as the high mobility group box  1 protein capable of triggering innate and, indirectly, adaptive immune responses (Kepp et al. 2009; Bianchi 2009; Srikrishna and Freeze 2009). We observed that melanoma metastases come in two distinct phenotypes: either immunologically active or dormant (Marincola et  al. 2003). Immunologically active metastases express a broad array of interferon-stimulated genes (ISGs) and various cytokines, chemokines, growth factors, angio-regulatory molecules and proteins associated with tissue repair resembling tissues in the early phases of wound repair (Deonarine et  al. 2007). Immune signatures within the melanoma microenvironment have been associated with improved natural history (Mandruzzato et al. 2006) or increased responsiveness to immune therapy (Wang

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et al. 2002). Experimental evidence suggests that the immune activation of cancer cells is, at least in part, due to intrinsic biologic characteristics of cancer cells since an “anti-viral” state can be observed not only in biopsied specimens but also in cancer cell lines cultured in vitro (Monsurro’ et al. 2009; Harlin et al. 2009). This observation suggests that the immune response mounted by the host is dependent upon a constitutive activation of immune pathways within cancer cells. Thus, it is possible that cancer cells orchestrate immune responses through the endogenous production of growth factors and cytokines. Their expression may serve the useful purpose of recruiting host’s cells that can activate tissue repair mechanisms, which in turn provide a tissue network for the formation of a self-sustaining cancerous organ (Mantovani et al. 2000, 2008; Balkwill and Mantovani 2001; Mantovani 2005). Whatever the reason, some cancers are engulfed with an active immune population which, although incapable of eradicating the tumor may slow its progression (prognostic significance) (Galon et al. 2006; Pages et al. 2005, 2010) or enhance the chances of rejection upon exogenous immune activation (predictive significance) (Wang et al. 2002; Harlin et al. 2009; Brichard and Lejeune 2007). Although intra-tumoral activation of immune cells does not represent direct evidence of immune surveillance since at this stage tumors are growing undisturbed, the observed immune responses may represent a vestige of partially failing immune surveillance. Indeed, these indolent immune responses can sometimes be rekindled resulting in spontaneous tumor regression. This rare though natural phenomenon has been well-documented (Burnet 1970; Barry and Rosenberg 1992). Waxing and waning of subcutaneous melanoma metastases well exemplifies the continuous battle between the host and its cancer. Similarly, pulmonary metastases from renal cancer occasionally decrease in size or completely disappear upon removal of the primary tumor (Davis et al. 1989; Lokich 1997; Sanchez-Ortiz et al. 2003). These rare but well-documented observations substantiate the tenuous balance governing tumor growth in the presence of anti-tumor immune effector mechanisms. Recently, independent groups observed that transcriptional signatures similar to those associated with better prognosis are predictive of responsiveness to immunotherapy (Tahara et al. 2009). Brichard and Lejeune (2007) reported that IFN and IFN-dependent chemokine expression predict response to active-specific immunotherapy of melanoma with a MAGE-3-based vaccine. A follow up phase III trial aimed against other cancers identified the same signatures as predictive of response and prognostic of increased survival. A similar pattern was observed experimentally in a melanoma xenograft mouse model in which endogenously produced chemokines of the CXCR3 and CCR5 ligand families induced cancer regression by recruitment of CD8-expressing T cells in the xenografts (Harlin et al. 2009). The latter and its clinical implications are discussed in the chapter by Thomas Gajewski. As these signatures are predictive of responsiveness to therapy but are observed in a growing tumor mass, they may describe the requirements for a biology conducive to tumor rejection upon immune stimulation while the activation of these genes may be insufficient by itself to carry it out. As we will discuss later, the requirements for tumor rejection can be best described by studying tissues during therapy when rejection is more likely to occur (Wang and Marincola 2000; Wang et al. 2008b).

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Strategies to Understand the Mechanism of Tumor Rejection As previously discussed, evidence based on the study of human tumors demonstrates that cancer is a dynamically interactive tissue and that the inflammatory process that favors its growth has simultaneously the potential to hamper it. Thus, inflammation plays a contrasting role in cancer development through complex interactions among a large number of variables. With the exception of rare spontaneous regression (Wang et al. 2008a), in the large majority of cases, the balance favors cancer growth (Table 1). Thus, the study of established cancers inevitably fosters the biased conclusion that chronic inflammation produces a microenvironment favorable to cancer growth. However, the ongoing inflammatory response makes cancer intrinsically different from normal tissues, which are usually not inflamed. Immunotherapy with immune-stimulatory agents such as interleukin (IL)-2, IFN or antibodies against cytotoxic T cell antigen-4 may act through activation of immune cells already infiltrating tumors, thus, providing partial specificity to an otherwise non cancer-specific treatment. Understanding the mechanisms leading to the inflammatory switch responsible for this activation has been the focus of our work. Studying the cancer microenvironment in pre-treatment biopsies provides a static view of factors that may or may not predict response. Teasing out the differences between lesions that will respond from those that will not may provide important insights about the causality determining the phenomenon (“why” is the rejection occurring). Additionally, the analysis of tissues biopsied during therapy when the immunologic switch is most likely to occur (Wang et al. 2004, 2005, 2006, 2008a;

Table 1  Examples of putative factors that may govern tumor growth underlying the concept that the sum of all of them is responsible for disease outcome naturally or in response to therapy in humans rather than each factor’s individual contribution; in animal models, the exaggeration of each factor’s contribution by causing over-expression of complete knock-down, determines, in the experimental settings, the uniqueness of causation missing in human reality The immune algorithm governing tumor growth In favor of tumor growth   Unlimited growth potential (UGP) (oncogene 1 + 2 +…n) (Tumor suppressor 1 + 2+…n)   Loss of antigenic potential (LAP) (loss of antigen HLA LMP … etc)   Autocrine/paracrine secretion of growth factors, angiogenic factors and tolerogenic factors (GAT) (cytokine 1 + 2 +…n) (Angiogenic factor 1 + 2 +…n) (etc…) Against tumor growth   Innate immune responses (IIR) = Killer vs. Repair Mechanisms (NK 1 + 2 +…n) (F1 + F2 +…n) (pDC1 + pDC2 +…n) etcn   Adaptive immune responses (AIR) = Effector vs. Tolerogenic Mechanisms (T helper h1 +h2 + h17 +…hn) (T reg 1 + 2 +…n) (CTL 1 +2 +…n) etcn UGP + LAP + GAT > IIR (KvRM) + AIR (EvTM)

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Wang and Marincola 2000) may provide insights about the mechanisms of cancer rejection (“how” is rejection occurring) and guide future therapeutic strategies. Experimental animal tumor models have been useful in testing distinct principles of tumor immunology but are unlikely to shed useful information about the actual mechanisms regulating the relationship between the human hosts and their cancer. The reason is a quantitative one. Animal experiments have shown that, given enough manipulation of the system, almost anything can be achieved in tumor immunology. A recent good example is IL-23 that according to animal model can either foster or hamper tumor growth (Shan et  al. 2006; Hao and Shan 2006; Overwijk et  al. 2006; Langowski et  al. 2006; Oniki et  al. 2006; Hu et  al. 2006). Similar discordant and surprising results could be observed with IL-10 (Mocellin et  al. 2002). Human cancers, however, differ from experimental models because each one is the result of a different evolutionary process that has reached a final favorable balance against the host’s immune response by adopting its own biological solution. Considering the enormous redundancy of immune functions, it may not surprise that a monothematic approach to the understanding of such balance has been so far incapable of shedding information about the actual mechanism(s) regulating cancer growth in the host in spite of the innumerable hypotheses that very well apply to the individual experimentally-controlled homogeneity of each animal tumor model (Wang et al. 2009). It is our hypothesis that the human tumor microenvironment does not differ substantially in its biological qualities from spontaneous or induced animal tumor models. The salient differences are quantitative and relate to the different proportion of various immune regulatory vs. pro-inflammatory processes that act in each individual human tumor. This variability makes challenging the identification of a common pattern that may be targeted for therapy. On the contrary, animal models can be manipulated by exaggerating each individual components one at the time by drastically enhancing or eliminating its function. This exaggeration of individual factors can give the false impression that each one is pivotal in determining the individual history of each human cancer. However, it is likely that in humans, none of the potential mechanisms plays individually a role of the weight that can be created by over-expressing or eliminating the expression of a protein in animals. Thus in humans, it is the sum of all potential vectors that determines the natural history of each tumor and its responsiveness to therapy. This fine balance is dictated by a large variety of immunological variables. These properties favor the survival of cancer cells and at the same time affect the microenvironment through the production of factors that may hamper the function of immune cells; these factors can be the result of a direct secretion of soluble molecules or act through cell-to-cell contact (Mocellin and Nitti 2007); moreover, tumors may produce substances that recruit within their microenvironment immune cells such as macrophages which foster tumor growth while hampering immune effector functions (Mantovani et al. 2002). In addition tumor cells may lose functions that are not necessary for tumor cell survival but may be important for their recognition by a competent immune system such as those related to TAA processing and presentation (Marincola et al. 2000, 2003). These formidable adaptations are unique for each individual tumor and are

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continuously evolving (Dunn et al. 2004). Thus, attempts to correlate one or a few of these variables with clinical parameters with the ultimate goal of understanding the principles of tumor/host interactions is unlikely to provide a comprehensive picture (Wang and Marincola 2008). Thus, we concluded that the understanding of the mechanisms of tumor rejection in humans required a non-linear approach (Cucuianu 1998; Coffey 1998; Callard et  al. 1999; Dalgleish 1999; Rew 1999) applied to the study of the tumor microenvironment at appropriate time points (Marincola et al. 2003). This strategy allows the detection of dynamic changes such as the switch from a chronic inflammatory process (i.e., pre-treatment biopsies) to an acute one capable of causing cancer rejection (i.e., biopsies during treatment or after treatment during tumor regression). Because, even synchronous tumor metastases are quite heterogeneous and rapidly evolving (Wang et al. 2002; Cormier et al. 1998; Ohnmacht et al. 2001), we suggested that minimally invasive biopsies that allow retention of biopsied tissues in vivo, constitute a more suitable approach to the understanding of the phenomenon because they allow serial sampling of the same lesion (comparison of pre-treatment vs. treatment-induced transcriptional patterns) and direct comparison of the transcriptional profile identified in one lesion with its own history. The latter is important because the response of individual lesions to immunotherapy can widely vary and in extreme cases “mixed responses” can be observed whereby some lesions completely regress while others continue undisturbed their growth in response to the same treatment thus, excluding the host’s genetic background and environmental conditions as independent determinants of responsiveness (Aptsiauri et al. 2008; Carretero et al. 2008). Thus, we propose the following strategy for the understanding the mechanisms of tumor rejection: samples should be studied obtaining minimally invasive biopsies to allow in vivo retention of the tumor: 1 . At a time when immune rejection is likely to occur (Panelli et al. 2002, 2006) 2. Comparing serial time points to delineate the dynamic changes associated with the inflammatory switch (Mantovani et al. 2008; Wang et al. 2008b) 3. Applying high-throughput discovery-driven approaches that can inform about the global status of a given lesion (Wang et al. 2005) We have previously shown that fine needle aspirated (FNAs), tru-cut or punch biopsies minimally perturb the tumor microenvironment (Deonarine et al. 2007; Wang et  al. 2002; Panelli et  al. 2002, 2006) without affecting the outcome of therapy (Ohnmacht et al. 2001). By analyzing a lesion left in place, it is possible to directly link biological information to treatment outcome (Wang and Marincola 2000; Wang et al. 2005). This is important since we have previously shown at the histological and transcriptional level that synchronously biopsied metastatic lesions are quite heterogeneous (Wang et  al. 2002; Cormier et  al. 1998). This heterogeneity questions the accuracy of utilizing biological information obtained from one lesion to predict the behavior of another. We have successfully applied this strategy to monitor the effects of anti-cancer vaccination (Wang et al. 2002), characterize the mechanism of action of IL-2 within the tumor microenvironment (Panelli et  al. 2002) in melanoma, and the effects of Toll-like receptor (TLR)-7 agonists for the

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treatment of basal cell cancer (Panelli et al. 2006). Biopsies were performed in an easily accessible lesion under local anesthesia obtaining two consecutive FNA (or other non-invasive biopsies) passages according to a previously described four quadrant aspiration technique (Wang et  al. 2002; Wang and Marincola 2000). For FNAs, a first aspiration is used for cytospin preparation and assessment of cellular content and a second is immediately stored at the bedside in RNAlater in ice for total RNA preparation. Messenger RNA can be amplified with high-fidelity for transcriptional analysis (Wang et al. 2000; Wang and Marincola 2002; Wang 2005). The same lesion is biopsied again for assessment of treatment effects. The sequential approach causes some alterations in the transcriptional profiling of the lesion at the time of the second biopsy, however, such changes are limited and mostly irrelevant to the biological phenomenon studied (Deonarine et al. 2007). We have extensively studied the transcriptional changes caused by the procedure itself and a data base is available for interpretation of the specificity of experimental results (Deonarine et al. 2007; Panelli et al. 2002, 2006).

Immunological Signatures from the Tumor Microenvironment The inflammation that promotes cancer growth provides a fertile ground for immune manipulation of cancer-infiltrating immune cells. In the following paragraphs we will summarize our understanding of the mechanisms that trigger a switch from a chronic inflammatory process that tolerates tumor growth to an acute one capable of causing cancer rejection. It is fair to assume that although qualitatively similar, the biology of human cancers may differ significantly among them quantitatively due to the diverse contribution of biological vectors potentially affecting cancer growth (Table 1). Thus, mono or oligothematic approaches to the study of human tumor rejection is unlikely to provide a broad enough view of each lesion’s biology and the dynamics of its transformation during therapy. However, it may be possible to identify converging themes that are required for the determinism of tumor rejection and may, therefore, consistently occur independently of tumor heterogeneity as clouds inevitably populate the skies in a rainy day (Wang and Marincola 2000; Wang et al. 2004, 2005, 2008b). Although innumerable reasons could be envisioned to explain why tumor cells could escape or succumb to immune rejection by the host, we observed, by studying directly human tumors undergoing immune therapy, that common patterns seem necessary for the occurrence of cancer rejection. Here we will focus primarily on the mechanism leading to rejection of metastatic melanoma following systemic administration of human recombinant IL-2 (Wang et al. 2002; Panelli et al. 2002) and the reproducible immune-mediated dissolution of basal cell carcinomas in response to local treatment with TLR-7 agonists (Panelli et al. 2006). We observed that the occurrence of tumor rejection is associated with extensive changes in the transcriptional profile of responding lesions while no changes are observed when lesions to do respond to therapy (Wang et al. 2002; Ohnmacht et al. 2001).

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Thus, we hypothesized that while tumor rejection is associated with significant enhancement of immune cell activation, lack of rejection is associated with minimal effects of therapy at the tumor side; this distinction is important because it downplays the role that the insurgence of escape variants cells could play in this situation (Marincola et  al. 2003). We concluded that tumor cell escape variants are more likely to appear after successful immune therapy if eradication of tumor cells is incomplete (Lee et  al. 1998). Supporting the hypothesis that lesions undergoing regression upon immune therapy are subjected to a powerful acute inflammatory process is the clinical observation that during IL-2 therapy these lesions become tender and swell before disappearing. Similarly, basal cell carcinomas become extremely inflamed and ulcerated during treatment with TLR agonists while the surrounding skin remains unaffected. In our first study in which the tumor microenvironment of lesions ultimately undergoing complete remission was monitored with serial biopsies, we identified interferon regulatory factor (IRF)-1 as the most significantly and consistently upregulated transcript compared to its level of expression in paired pre-treatment biopsies (Wang et al. 2002). This finding, subsequently supported by larger studies by our group strongly emphasizes the central role that this transcription factor associated with the activation of acute inflammatory processes plays in determining the switch from chronic to acute inflammation (Hochhaus et al. 1997; Taniguchi 1997; Ogasawara et al. 1998; Girdlestone et al. 1993; Paun and Pitha 2007). The study was designed to compare pre-treatment biopsies with biopsies of the same lesions performed about 3 weeks after the end of treatment when patients would return to the clinics for follow up. At that time, the remnant of transcriptional changes caused by treatment could be observed as a contracting yet still active immune response continued to attack cancerous cells. However, the broader effects of therapy could not be observed at such a late time point. To better understand the inflammatory switch, we have been interested in following the mechanism of action of immune stimulators by comparing the transcriptional profile of lesions before and during therapy (Panelli et  al. 2002, 2006). With this approach we could clearly observe that, contrary to what previously believed, systemic administration of recombinant human IL-2 does not promote migration of cytotoxic T cells to the tumor microenvironment nor their activation or proliferation but rather induces a cytokine storm (Panelli et  al. 2003, 2004) responsible for the release of a broad array of immune stimulatory cytokines by circulating immune cells. These cytokines reach secondarily the tumor microenvironment and activate several innate immune mechanisms which in turn are responsible for the killing of tumor cells, uptake and presentation of antigen and release of chemo-attractants that can within a few days promote the localization of adaptive immune cells at the tumor site. These studies were performed through serial biopsies of the same lesions using FNAs. Using this strategy the response of each lesion could be followed accurately and transcriptional signatures could be directly linked to clinical parameters demonstrating that several alterations due to the effects of IL-2 therapy occurred independently of treatment outcome such as the up-regulation of classical ISGs induced by type I IFNs. Conversely, clinical response was more closely

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related to the expression of ISGs induced preferentially by IFN-g/STAT-1/IRF-1 pathways including the expression of human leukocyte antigen (HLA) class I and II molecules (Panelli et al. 2002; Girdlestone et al. 1993) and genes associated with effector function (TIA, NK-4/IL-32) (Panelli et al. 2002) (Figs. 1 and 2). A more comprehensive analysis of signatures associated with immune responsiveness could be performed by serially following the response of BCC to local applications of the TLR-7 agonist Imiquimod because this treatment consistently causes tumor rejection particularly at the dosing schedule evaluated by the study (Panelli et al. 2006). Although this local treatment does not represent a potential cure for a systemic disease like cancer, it provides in principle an outstanding model for the study of the mechanism of immune-mediated tumor rejection. This model emphasizes the quantitative aspects of immunotherapy suggesting that the high concentrations of immune stimulator that could be achieved with a topical treatment could shift the balance between host and cancer cell interactions in favor of the host by local manipulations of the microenvironment that cannot easily and specifically achieved through systemic routes. The study provided the first comprehensive glance of transcriptional signatures associated with tumor rejection in the earliest

Fig. 1  Partial description of genes involved in the immunologic constant of rejection (ICR). This image focuses on genes in regressing melanoma metastases following immunotherapy that are included in the first network according to Ingenuity Pathway analysis (in red, up-regulated genes)

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Fig. 2  Overlay of genes associated with the ICR presenting their expression in response to IFN-a (a) or IFN-g (b). In red, genes up-regulated and in green, genes down regulated. The IFN-specific expression was obtained stimulating peripheral monocytes with either IFN-a2b or IFN-g in parallel culture conditions; gene expression is presented in comparison with unstimulated monocytes

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phases when causative pathways rather than downstream effects take the lead. TRL agonists could clearly induce activation of specific signatures that could be differentiated from artifacts related to the biopsies themselves or the eccipients used for the delivery of the active component since this was a double blinded placebo controlled randomized study. The result of this analysis demonstrated that the eradication of BCC is a complex multi factorial phenomenon. Of 637 genes specifically induced by Imiquimod, only a minority (98 genes) were canonical type I IFNinduced ISGs (Panelli et al. 2006) while the rest portrayed activation of immune functions involving innate and adaptive immune effector mechanisms. Thus, also in this model, ISGs appeared to be necessary but not uniquely responsible for tissue specific immune rejection. More recently, we observed strikingly identical signatures in two melanoma metastases undergoing tumor regression after immunotherapy compared with three synchronous lesions that continued to progress in their growth (Aptsiauri et al. 2008; Carretero et al. 2008). Transcriptional comparisons between responding and non-responding lesions identified IRF-1 as the central modulator of the top functional network activated in responding lesions (Carretero et al. manuscript in preparation). Analysis of the genes over expressed in the predominant transcriptional network activated in the responding lesions strongly pointed to the activation of genes induced by IFN-g as the central modulator of rejection while IFN-a induced ISGs only minimally influenced the transcriptional profile associated with rejection. Importantly, however, the expression of several genes observed to be over expressed in regressing lesions compared to the autologous synchronous non responding ones could not be explained by either IFN-a or IFN-g signatures, suggesting that other pathways may be involved which we have not been able as yet to characterize. Analysis of the genes associated with tumor rejection in this model clearly suggested that some functional families were predominantly included among the signatures of rejection (Table 2). As we will later discuss, these functional categories are not only associated constantly with various models of tumor rejection but represent the core transcriptional changes associated with several other aspects of immune-mediated tissuespecific destruction (TSD).

Immune Aspects of Chemotherapy Conventional anti-cancer therapy with chemotherapy or radiotherapy is generally believed to induce cancer cell elimination through direct cytostatic or cytotoxic mechanisms; this concept has been recently questioned (Apetoh et  al. 2007a, b, 2008; Tesniere et  al. 2008, 2010; Zitvogel et  al. 2008). It is now clear that DNA damage caused by conventional therapies that disrupt cell cycle has direct immune stimulatory effects (Zitvogel et al. 2008). Several agents induce, through DNA damage, the expression of NKG2D ligands by cancer cells which are target of natural killer cells and activated T cells (Gasser and Raulet 2006). Interestingly, it has also been

Table  2  Summary of transcriptional studies in which signatures of rejection were identified by studying the affected tissues directly in humans or in experimental model STAT-1/1RF-1 T-bet+/IFN-g// GNLY/GZM CXCL-9 to -11 CCL2, 3, 5 IL-15 TIA CXCR3 CCR5 References Cancer Prognosis   Colon hu CA ↑ ↑ Camus et al. (2009); Pages et al. (2005)   Lung hu CA ↑ ↑ Dieu-Nosjean et al. (2008)   Melanoma hu rexo n.t. n.t. ↑ ↑ Harlin et al. (2009)   Ovarain hu CA xeno ↑ ↑ ↑ ↑ Benencia et al. (2005) Rejection   Masto cytoma mus ↑ ↑ Shanker et al. (2007)   Breast hu CA xeno ↑ ↑ ↑ ↑ Worschech et al. (2009a, b)   BCC hu CA ↑ ↑ ↑ ↑ Panelli et al. (2006) Allo Tx Rejection   Kideney hu ↑ ↑ ↑ ↑ Reeve et al. (2009); SaintMezard et al. (2009); Sarwal et al. (2003);   Heart hu n.t. n.t. ↑ n.t. Karason et al. (2006)   Islet pig n.t. ↑ ↑ ↑ Hardstedt et al. (2005)   Liver rat ↑ ↑ ↑ Hama et al. (2009) GVHD ↑ ↑ ↑ n.t. Imanguli et al. (2009) HCV Viral clearance   Chimp ↑ ↑ ↑ Bigger et al. (2001); Nanda et al. (2008)

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Human

↑

↑

Asselah et al. (2008); Feld et al. (2007); He et al. (2006) Acute cardiovascular ↑ ↑ Okamoto et al. (2008); Zhao events (human) et al. (2002) COPD (human) ↑ Costa et al. (2008) ↑ ↑ ↑ Kim et al. (2009) Villitis (human) Four functional categories often observed to be activated during immune-mediated tissue-specific destruction (TSD) are shown

 

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shown that irradiation of a local single metastasis may have generalized effects on distant ones, which are mediated by the immune system (Reits et  al. 2006). Experimental evidence suggests that the immune system involvement is critical to induce cancer regression in response to chemotherapy; tumors do not regress in immune depleted animals treated with doxorubicin given in combination with IL-12. Several other agents have been shown to induce powerful immune responses against cancer when given with cytostatic and cytotoxic purposes (Zitvogel et  al. 2008). Indirect evidence of the involvement of innate immunity in modulating the effect of chemotherapy is suggested by the prognostic significance of a functional polymorphism in the extra-cellular domain of TLR-4, which affects binding of HMGB1 and is negatively correlated with survival and time to recurrence in patients undergoing chemotherapy for breast cancer (Apetoh et al. 2007b). Although the effects of most chemotherapeutic agents are due to the induction of cellular damage and subsequent activation of innate immune responses through the release of DAMPs, no information is available about the transcriptional signatures induced by chemotherapy or radiotherapy within the tumor microenvironment. However, a few experimental models suggest that at least some anti-vascular flavonoids act by inducing the release of cytokines and chemokines such as CXCL-9, CLCX-10, CCL2, CCL3, CCL5, IL-6, IFN-g and TNF (Jassar et al. 2005), a signature shared with immune-mediated tumor rejection (Roberts et  al. 2007) The relevance of IFN-g involvement in chemotherapy-induced tumor regression is also supported by the immune stimulatory effects of imatinib mesylate that induces activation of IFN-g secreting natural killer cells and, in the presence of IL-2, the activation of interferon-producing killer DCs (Taieb et al. 2006).

Immune Aspects of Viral Oncolytic Therapy In the last few years an increasing number of pre-clinical and clinical trials have been carried out using tumor-tropic, oncolytic viruses. As for chemotherapy, this approach is believed to work by a direct virally-induced oncolytic process. However, viral oncolytic therapy might operate with the “assistance” of the host’s immune system (Worschech et al. 2009a, b). The oncolytic properties of certain viruses were first noticed in the early twentieth century when some cancer patients were observed to experience tumor regression after systemic viral infections (Sinkovics and Horvath 1993; Dock 1904). The realization that some viruses with lytic properties selectively colonize tumors (Parato et al. 2005) steered interest in the concept of oncolytic therapy (Vaha-Koskela et al. 2007). Vaccinia virus (VACV) has been a promising candidate for oncolytic therapy due the extensive experience gathered in humans because of its use as an anti smallpox vaccine. Immune reactions against VACV infection have been studied and appear to differ according to the route of administration (Reading and Smith 2003; Jacobs et al. 2006; Selin et al. 2001). However, IFN-g appears to be critical for the clearance of acute VACV infection (Karupiah et  al. 1990; Huang et  al. 1993).

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Whereas a type II IFN-mediated immune response is responsible for the activation of the acute inflammatory response associated with oncolytic therapy and is central to the activation of cell-mediated immunity (Farrar and Schreiber 1993), both type I and type II IFNs are equally important in the containment of poxvirus infections (Huang et al. 1993; Schellekens et al. 1981; Deonarain et al. 2000). In addition to its direct antiviral effects, IFN-a orchestrates a wide array of immune regulatory effects (Garcia-Sastre and Biron 2006) including the regulation of NK cell activation (Biron et al. 1999; Biron and Brossay 2001). Direct signaling of IFN-a on NK cells and not on dendritic cells (DCs) through IL-15 secretion (Lucas et al. 2007) seems to be required for activation and exhibition of their effector function which leads to VACV clearance both in vitro and in vivo (Martinez et al. 2008). Transcriptional analysis of mouse xenografts using a mouse-specific platform to identify the host’s response genes revealed the activation of innate immune mechanisms in regressing GI-101A tumors compared to non-infected control tumors (Zhang et al. 2007). Up-regulation of pro-inflammatory chemokine ligands such as CXCL9, CXCL10, CXCL12, CCL2, CCL9, and CCL12 was seen together with an increase in interleukin (IL), and chemokine receptors (IL-13r, IL-18, and CCR2) transcripts. Additionally, a significant activation of ISGs was observed in association with increased STAT-1. This strongly suggested that type I and/or type II IFNs are critically involved in the process. Immunohistochemistry of VACV-infected, regressing xenografts showed an intense peri- and intra-tumoral infiltration of mononuclear cells, which confirmed the up-regulation of CD69, CD48, CD52, and Cd53 seen on the host’s gene expression arrays. These markers are expressed on activated T-cells, NK cells, macrophages, granulocytes and DCs, and are associated with leukocyte activation and NK cytolytic function (Zhang et al. 2007). To better dissect the mechanisms associated with VACV-driven tumor destruction, we compared VACV-infected GI-101A xenografts sensitive to oncolytic therapy to GI-101A xenografts from non-infected animals and HT-29 colon cancer xenografts that do not respond to oncolytic therapy in spite of VACV colonization. Moreover, we evaluated gene expression profiles of the oncolytic interaction by adopting organism-specific microarray platforms. We applied 36k whole genome human arrays to test for alterations in the human cancer cells; 36k whole genome mouse arrays to examine the host’s infiltrating stromal cells and lastly; custommade 1K VACV arrays to characterize changes in viral transcription patterns. Human transcript analysis revealed no differences in non-responding, infected HT-29 tumors compared to control tumors and only a limited set of genes which was altered after GLV-1h68 inoculation in regressing GI-101A xenografts; most transcriptional changes were observed in the infected responding tumors at a time when cell death had not occurred yet and revealed profound down-regulation of genes associated with cellular metabolic processes reflecting the shutdown of cancer cell metabolism due to VACV infection. Analysis of mouse expression arrays representing the host’s infiltrating cells demonstrated that infected, non-responsive HT-29 tumor were not affected by the viral presence in cancer cells similarly to HT-29 tumors from non-infected control animals. On the contrary, a large number of genes were up-regulated in GI-101A tumors after VACV delivery compared to non-infected

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GI-101 xenografts. Further analysis discovered a significant enrichment of immunerelated genes; among those, ISGs and other IFN signaling genes represented the most up-regulated canonical pathways at an early time point where tumors were still continuing to grow (21 days post infection) and later on (42 days post infection) at the time when tumor rejection started to occur. These signatures strictly resembled those previously observed in human basal cell cancers treated with TLR-7 agonists (Panelli et al. 2006). Up-regulation of IL-18 and IL-18 binding protein played a dominant role early in the infection, whereas IL-15 became the predominant cytokine expressed at later stages at a time when tumors growth reached a plateau and started to revert. Among chemokines, CXCL 9, 10, 11 and 12 were strongly expressed in regressing GI-101A xenografts together with CCL5 (Table 2). Based on these findings, we concluded that in this immune deficient mouse model, the activation of innate immune responses may be sufficient to lead to tumor regression in cooperation with the viral oncolytic process.

Immune-Mediated Tumor Rejection as a Mechanism Shared by Other Pathologies: The Immunologic Constant of Rejection In 1969, Jonas Salk proposed that chronic infections, allograft rejection, autoimmune disorders and cancers belong to a common phenomenon that he termed the “delayed allergy reaction” (Salk 1969). The underlying mechanisms of these pathologies are variable and distinct from each other. Infectious diseases like hepatitis C virus (HCV) infections become chronic if the pathogen is not cleared through acute hepatitis (Rehermann and Nascimbeni 2005). Allograft rejection can be controlled only through immune suppression because broad antigenic differences between allograft and host can trigger a strong immune response. Whereas both allograft rejection and pathogen clearance represents an immune reaction against non-self structures, the immune system can also attack “self” tissues as observed in autoimmunity. Immune responses against tumors fit self or non-self discrimination, as tumor cells derive from normal progenitor cells and mostly express non-mutated TAAs (Kawakami et  al. 1998). However, some cancers display mutated antigens (Robbins et al. 1996) unique for the tumor cells that can be recognized as non-self by the immune system. Even though the underlying triggering mechanisms differ among distinct immune pathologies, we postulated that TSD follows a common pathway which we called the “immunologic constant of rejection (ICR)” (Wang et al. 2008b). We formulated four axioms that summarize the phenomenon: (1) TSD does not necessarily occur because of non-self recognition but also occurs against self or quasi-self; (2) the requirements for the induction of a cognate immune response differ from those necessary for the activation of an effector one; (3) although the prompts leading to TSD vary in distinct pathologic states, the effector immune response converges into a single mechanism; and (4) adaptive immunity participates as a tissue-specific trigger, but it is not always sufficient or necessary (Wang et al. 2008b).

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The limited work so far performed by our group to study in real-time the events occurring before and during therapy in the tumor microenvironment suggests that immune rejection is associated with the activation of ISGs accompanied by the activation of genes that are expressed naturally by NK cells and by CTLs upon activation. Among them we observed that NK cell and activated CTL function seems to predominate during the phases of acute rejection. Interestingly, comparison with other human system of immune-mediated tissue rejection suggests that this final effector pathway is common to other pathologies. Over expression of perforin, granzymes, IL-2 receptor a and b chain, IL-15 receptor, IL-15 itself seem to be part of a general pathway that occurs also during acute rejection of renal allografts (Sarwal et  al. 2003). We recently summarized the common functional units associated with TSD. These include overlapping yet distinct themes that are consistently present when TSD occurs: 1 . The STAT-1/IRF-1/T-bet/IFN-g, IL-15 path 2. The Granzyme A/B, TIA-1 pathway 3. The CXCR3 ligand chemokine pathway 4. The CCR5 ligand chemokine pathway We observed, in different disease models, their presence; studies in humans have identified these signatures to be associated with improved survival of patients with colon, lung and ovarian cancer or melanoma (Galon et al. 2006, 2007; Pages et al. 2005; Harlin et al. 2009; Benencia et al. 2005; Dieu-Nosjean et al. 2008; Camus et al. 2009); the same patterns were observed in neoplastic lesions responsive to immunotherapy both in humans (Wang et al. 2002; Panelli et al. 2002, 2006) and in experimental models (Shanker et  al. 2007). Allografts have been studied by biopsying organs during the acute phases of rejection and several studies have reported recurrent themes (Sarwal et al. 2003; Hardstedt et al. 2005; Karason et al. 2006; Reeve et al. 2009; Saint-Mezard et al. 2009; Hama et al. 2009). In particular, Saint-Mezard et al. (2009) analyzed three independent data set of renal biopsies identifying a robust transcriptional signature of acute allograft rejection which well summarizes most of the components of the ICR. Imanguli et  al. (2009), observed similar patterns by studying biopsies of tissues suffering chronic graft vs. host disease and similar patters where observed in the liver during clearance of HCV infection (Bigger et al. 2001; He et al. 2006; Feld et al. 2007; Nanda et al. 2008; Asselah et al. 2008). Recently similar signatures were observed in the destructive phases of acute cardiovascular events (Zhao et al. 2002; Okamoto et al. 2008), chronic obstructive pulmonary disease (Costa et al. 2008) and placental villitis (Kim et al. 2009) (Table 2).

Conclusions Immune-mediated rejection of human cancer is a reality that occurs reproducibly in specific model systems such as the regression of melanoma and RCC in response to systemic IL-2 therapy, BCC treated locally with TLR-7 agonists or the adoptive transfer of EBV-specific CTL in lymphoproliferative disorders. It is likely that each

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model system has its own idiosyncrasies but, at the same time, commonalities dictate the final outcome of rejection. Similarly, other tissue destructive immune pathologies seem to share common effector pathways. Understanding the basic mechanisms that can switch a chronic inflammatory process incapable of eradicating its cause into an acute reaction with the power of destroying completely the triggering cause, may shed insights that may guide the development of novel therapeutic strategies. Even more importantly, identifying the mechanisms that lead to this final common pathway in individual tumors may define a better rational for targeted therapies that may take advantage of each individual cancer’s biology.

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Signatures Associated with Acute Rejection: Allograft Rejection Davide Bedognetti

Abstract  The physiopathology of immune-mediated tissue injury of acute allograft rejection is not completely understood. Although several mechanicistic experiments have been conducted in  vitro and in animal models, only few hypotheses have been confirmed in humans and, paradoxically, findings in humans often lack mechanistic explanations. Before high throughput gene expression analysis advent (microarrays), the use of optic microscopy, immunohistochemistry and reverse transcriptase protein chain reaction (RT-PCR), allowed the “in situ” evaluation of only a few variables simultaneously. Conversely, the integration of the aforementioned methodologies with microarrays has cast new lights on unrecognized mechanisms that are now deemed as central for the development of the alloresponse. Pari passu with underlining the molecular heterogeneity between apparently similar lesions, this approach has also unveiled the activation of common mechanisms among clinically and histopathologically different lesions. Universal standardization procedures were slowly and incompletely developed while microarray methodology was at its dawn and in continuing evolution. Moreover, the lack of uniformity among different investigating groups (in terms of sample collection, microarray platforms, genes coverage, bioinformatic analysis and study design) has probably been one of the reasons accounting for the relatively small overlap in the relevant genes detected by individual studies. Nevertheless, in spite of the aforementioned limitations, it is difficult to ignore the relevance of those gene/gene pathways consistently detected as simultaneously upregulated among independent studies even in the presence of such technical and analytic limitations. The aim of this chapter is to shed some light on the physiopathology of allorejection, pointing at the activation of genes and molecular pathways thought to play

D. Bedognetti (*) Infectious Disease and Immunogenetics Section (IDIS), Department of Transfusion Medicine, Clinical Center, and Trans-NIH Center for Human Immunology (CHI), National Institutes of Health, Bethesda, MD, USA and Department of Oncology, Biology and Genetics, and Department of Internal Medicine, University of Genoa, Genoa, Italy e-mail: [email protected] F.M. Marincola and E. Wang (eds.), Immunologic Signatures of Rejection, DOI 10.1007/978-1-4419-7219-4_19, © Springer Science+Business Media, LLC 2011

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a leading role in the development and/or maintenance of the tissue destructive process, which characterizes acute rejection. Rather than trying to explain the cause of the activation of this acute response, we will try to depict a molecular fresco of the battlefield where tissue destruction is fought. To this purpose, we will focus on human microarray studies performed on tissue biopsies taken during episodes of acute allograft rejection, providing also a brief description of the results obtained with other approaches (RT-PCR or immunohistochemistry). We will apply the knowledge derived from mechanicistic studies and from inductive and deductive reasoning to explain the possible roles and relations of the described pathways.

A Brief Historical Perspective The first observations about the role that genetic differences among individuals may play in the development of allograft rejection were made by Emile Holman, a young surgeon working in Boston, in the 1920s (Tilney 2000; Starzl 1995). He noted, studying skin-grafted burnt childrens, that the healing process occurred when autologous but not maternal skin grafts were transplanted and that the intensity of the destruction increased after a second grafting from the initial donor but not from a third party (Tilney 2000). This clinical observation was placed in a scientific context by a young Oxford zoologist, Peter Medawar, who would be sharing in 1960 the Nobel Prize for his work on acquired immunological tolerance (Medawar 1944; Billingham et al. 1953). He showed, initially in human studies involving World War II burn victims, and later in elegant experiments in rabbits, that the skin rejection was an immune-mediated mechanism (Starzl 1995; Medawar 1944). In the mid ’50s, this phenomenon was shown to be analogous to the process that confers immunity to infectious diseases such as tuberculosis, a cell mediated tissue destruction process, also named cellmediated delayed hypersensitivity (Mitchison 1954; Billingham et al. 1954). In the same years, while Peter Medawar proposed the cellular immunity theory, another influent scientist, Peter Gorer advocated the role of humoral response in the development of acute rejection (Gorer 1942). We now know that both humoral and cellular mechanisms are involved in allograft rejection, and the international pathologic classification divides it in T-cell mediated and antibody mediated rejection (Solez et al. 2008). In spite of distinct morphologic features of the two conditions, highthroughput gene expression analyses suggested that both the humoral and cellular rejection converge in to identical molecular mechanisms (Reeve et  al. 2009; Mueller et  al. 2007). These molecular pathways observed while acute allograft rejection occurs are similar to the pathways activated in other forms of immunemediated rejection, such as tumor rejection, autoimmune disease, or pathogens clearance (Wang et al. 2008; Bedognetti et al. 2010). Thus, the analogies between allograft rejection and cell-mediated delayed hypersensitivity, cunningly observed more than 50 years ago, seem to have found today their molecular explanation.

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Introduction Allorecognition, acute allograft rejection, and allo-response – The adjective “acute” could be deceiving. In fact, this process can occur not only days or weeks after transplantation but even months or years following an apparently successful engraftment (Alausa et al. 2005; Bagnasco et al. 2007; Doria et al. 2006; Hippen et al. 2005; Kayler et al. 2007; Krukemeyer et al. 2004; Tsai et al. 2006; Sarwal et  al. 2003). The term allorecognition refers to the ability of the host immune system to detect nonself antigens expressed by organs received from a donor belonging to the same species (Larosa et al. 2007; Le Moine et al. 2002). The activation of the alloreactive T cells can occur via a direct pathway, driven by donor antigen presenting cells (APCs), which are “passengers” in the transplanted tissue, or via indirect pathway, mediated by recipients APCs (Larosa et al. 2007; Le Moine et al. 2002). CD8+ alloreactive T cell cells can also be partially activated directly in the graft, independently of professional APCs (Kreisel et al. 2002). This allo-recognition per se is, however, not sufficient to induce rejection: there must be in addition an appropriate environment of cell surface molecules and cytokines to fully activate the effector function of T cells (Larosa et al. 2007; Le Moine et al. 2002; Matzinger 2002). However, B cells, by virtue of their antigen specific B cell receptor (BCR), can, directly bind and process surface antigens, such as nonself major histocompatibility complexes in the transplanted organ. This direct binding leads to their in situ activation, where they function as APCs (Balin et  al. 2009; Noorchashm et  al. 2006). Activated alloreactive B cells can participate to the alloresponse trough the production of antibodies (Balin et  al. 2009; Noorchashm et  al. 2006; Terasaki 2003). Thus, the rejection is due to cell-mediated injury (acute cellular rejection) but it can also be mediated by antibodies (acute humoral rejection). Topics of the chapter – It is not the purpose of this chapter to describe the physiopathological process of allorecognition, nor debate the humoral or cellular theories of the allorejection (Terasaki 2003). Accepting that both humoral and cellular responses are involved in alloresponse, we will describe the final steps (the tissue destruction) that occur in the graft during rejection, summarizing molecular events observed through gene expression profiling studies conducted directly in the grafted tissues. Other microarray studies have been conducted analyzing gene expression profiles of peripheral blood mononuclear cells (Weintraub and Sarwal 2006; Li et al. 2008; Deng et al. 2006; Horwitz et al. 2003; Flechner et al. 2004). They also offer an interesting and non invasive diagnostic/predictive tool, and contribute the explanation of the physiopathology of allograft rejection (Weintraub and Sarwal 2006; Li et  al. 2008; Deng et al. 2006; Horwitz et al. 2003; Flechner et al. 2004). However, our aim is to describe what happens inside the graft during this specific tissue destruction process. For these reasons the latter studies will not be analyzed here. We will focus on pathways that have been most consistently observed by different studies. Since microarray studies greatly differ in terms of the organ analyzed, the platform used, the number of genes investigated and the sample size, we briefly summarized in Table  1 the main features of acute allograft rejection microarray

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studies conducted in humans, in order to provide the reader with a useful tool to weight the conclusions we reached in interpreting their findings. If one dataset was used by the same group to generate different analyses, we will summarize in this table only one pubblication. However, if useful, we will describe in the text additional results obtained by subsequent integrative analyses. We will also provide the reader with a table summarizing relevant genes detected in such studies (Table 2). A list of 50 key genes consistently observed by the studies will also be used to generate networks, in the attempt to provide an overview of the complex relations occurring between them (Fig.  1a, b). We will describe the microarray studies respecting, approximately, the chronologic order in which they were published. However, when a relevant biological pathway is encountered, we will give a partial explanation of it, and we will also briefly illustrate confirmatory or contradicting results obtained by other investigators.

Early Studies in Kidney and Liver Acute Allograft Rejection and the Detection of Recurrent Themes After being used to study gene expression in multiple sclerosis lesions (Whitney et al. 1999), melanoma (Bittner et al. 2000), colon (Alon et al. 1999), breast (Nacht et  al. 1999) at the beginning of the century, microarrays were employed to study allograft rejection. One of the first studies utilizing such technology in the field of solid organ transplantation was published by Akalin et  al. (2001), which analyzed ten kidney recipients. The authors simply compared seven acute rejection samples with the three control samples. This study, in spite of the small sample size, identified several genes that would be confirmed in subsequent microarray studies (see Table 2). This achievement was probably due to the restrictive criteria chosen for the selection of the annotated transcripts, which had to be highly upregulated (> fourfold) in at least six out seven biopsies. Among the ten transcripts associated with acute rejection, most genes coded for proteins associated with immunity and inflammation. In particular, the overexpression of CXCL9/HuMig (human monokine induced by interferon-g – IFN-g (Zhao et al. 2002; Murphy et al. 2000; Fahmy et al. 2003; Qin et al. 1998)), STAT-1 (signal transducer and activation of transcription 1, a transcription factor activated by IFNs (Platanias 2005; Reich and Liu 2006; Pos et al. 2010)), and TAP-1 (transporter 1, ATP-binding cassette, sub-family B, a member of MHC peptide transporters, regulated by IFN-g (Hidalgo and Halloran 2002)), suggested the activation of IFN-g pathway during the acute reaction. Interestingly, although transcripts associated with B cell presence in allograft rejection biopsies would be described later (Sarwal et al. 2003; Morgun et al. 2006; Gimino et al. 2003), the authors detected the over-expression of CCL19/ Mip-3b (macrophage inflammatory protein-3beta), a chemokine that is critical not only for T and dendritic cell trafficking, but also plays a role in B cell chemotaxis (Qin et  al. 1998; Sanchez-Sanchez et  al. 2004). However, the data from this

Atlas human cDNAb ~600 Genes

Affymetrix HU6800a ~6,400 Genes

Tannapfel et al. (2001) Liver (biopsies)

Sreekumar et al. (2002) Liver (biopsies)

Results This was the first study analyzing gene expression during kidney allograft using microarrays. Comparison analysis shows that between 32 and 219 gene transcripts are up-regulated (>fourfold) during acute rejection. Of these transcripts, ten were expressed in acute rejection in the majority of acute rejection biopsy samples: HuMig(CXCL9), TCR-b, IL-2-stimulated phosphoprotein , RING4/TAP-1, SGFG3/ STAT-1, C3, NNMT, MCL1, MIP3-b (CCL19), CD18 95 Genes were aberrantly expressed among the Aim. To investigate the expression of multiple inflammatory and subgroups. These included genes involved apoptosis related genes in allograft rejection in stress response, cytokine production, Methods. 97 biopsies, 62 patients. Acute allograft rejection (n = 32), cell cycle regulation, signal transduction, HCV reinfection (n = 18), CMV infection (n = 5), acute rejection apoptosis and growth-factor and hormoneand HCV infection (n = 3), stable graft function (n = 30) and after receptor production. IL-3, MMP-9, TIMP-1, treatment of acute rejection (n = 9) TNF-a, IL-10, clusterin and TGF-b1-3 were expressed differentially in allograft rejection specimens as compared to those with viral hepatitis or stable graft Acute cellular rejection was associated with Aim. To study intragraft gene expression patterns in acute cellular overexpression of MHC class I and II, IGF-1, rejection and during recurrence of HCV in HCV infected recipients apoptosis induction (TNF-a) and T/NK-cell Methods. Four patients with HCV infection and ACR and 4 patients activation: granzyme B, IL-2, IRF9. C1q and C3 with HCV infection without ACR genes were also overexpressed (continued)

Table 1  Microarray studies evaluating gene expression profile in acute allograft rejection in humans References (dataset) Organ (samples) Array Aim and methods Akalin et al. (2001) Affymetrix Aim. To analyze gene expression profile using microarrays in acute GeneChip HU6800a Kidney the allograft rejection biopsies) ~6,400 Genes Methods. Seven human renal allograft biopsies with histological evidence of acute cellular rejection and three renal allograft biopsies without evidence of rejection. All biopsies were performed within 8 weeks after transplantation. Each acute rejection sample was compared with each control sample, leading 21 comparison analyses

Array

c

Gimino et al. (2003) (Minneapolis) Lung (BAL)

Affymetrix Human Genome U133Ad ~18,000 Genes

Sarwal et al. (2003) Lymphochip   > 12,000 (Stanford) Genes Kidney (biopsies)

Table 1  (continued) References (dataset) Organ (samples) Results

Aim. To investigate the possibility that variations in gene-expression Biopsy samples from patients with similar clinical diagnoses clustered together on patterns in allograft-biopsy samples from patients with acute the basis of corresponding similarities rejection and related disorders would permit the identification of in gene expression, irrespective of the molecularly distinct subtypes of acute rejection that may be related immunosuppressive administered regimen. to differences in clinical behavior Biopsy samples from patients with acute Methods. 50 pediatric recipients. 67 biopsies: 52 during acute or rejection that are indistinguishable on chronic allograft dysfunctions (between 1 month and 10 years conventional histological analysis reveal after transplantation). The others were obtained at the time of the extensive differences in gene expression, engraftment or when graft function was stable which are associated with differences in immunologic and cellular features and clinical course. Surprisingly, the authors detected a B-cell infiltration in a subgroup of patients with acute rejection. However, the presence of dense B cell clusters was strongly associated with severe graft rejection. Finally, such B cell infiltrate was not correlated with diagnosis of antibody mediated rejection, suggesting a pivotal role of infiltrating B cells also in acute cellular rejection Two-dimensional hierarchical clustering grouped Aim. To determine markers of acute rejection in lung recipients all acute rejection samples into one cluster Methods. 26 patients. 7 samples with diagnosis of acute rejection and the majority of non rejection samples and 27 without diagnosis of rejection were analyzed into a second cluster. T cell rereceptors, granzymes, perforin, Lectin-like NK receptor, C4B, CD3, CD8, CD28, STAT4, IL-2 related genes and IFN-g were among genes upregulated during acute rejection

Aim and methods

Affymetrix   GeneChip HG-U95Av2e ~10,000 Genes

Karason et al. (2006) Affymetrix Human Heart (biopsies) Genome U133Ad ~18,000 Genes

Flechner et al. (2004) (Cleveland) Kidney (biopsies and PBLs)

 

The authors developed a data analysis schema Aim. To determine gene expression profiling in transplants patients based on expression signal determination, including normal donor kidneys, well functioning transplants class comparison and prediction, hierarchical without rejection, kidneys undergoing acute rejection, and clustering, statistical power analysis and qRTtransplants with renal dysfunction without rejection PCR. However the Investigators identified Methods. Patients. Biopsies were obtained from 9 living donor gene expression signatures for both biopsies controls, 7 recipients with diagnosis of acute rejection, 5 recipient with renal dysfunction without rejection on biopsy, and 10 biopsies and PBLs that correlated significantly with each of the different classes of transplants carried out more than 1 year post transplant in patient with patients. CCR5, IL10RA, IL10RB, CD64, good transplant function and normal histology. PBLs were also CD16, Granzyme A, immunoglobulins, collected. qRT-PCR was also performed and IFN-g stimulated genes were among genes upregulated in acute rejection samples compared to samples with stable functions The authors identified genes that were Aim. To utilize DNA microarray analysis to search for upregulated during the rejection episode and potential biomarkers of cardiac allograft rejection. Routine returned to baseline levels with its resolution. endomyocardial biopsies were performed according to the Among these were CXCL9, CXCL10 protocol and many IFN-g stimulated genes (e.g., Methods. Twenty patients were enrolled, episode rejection was HLAs, STAT-1, IGFBP4, PSME2, GBP1, observed in 14 patients. The Authors matched myocardial tissue RARRES3). C3, C4A and NPPA were also and serum samples at 3 points: biopsy with normal histology, upregulated. qRT-PCR confirmed the trend biopsy with rejection episode, biopsy with normal histology after observed for CXCL9. CXCL10 displayed a the rejection episode. qRT-PCR was performed for selected genes similar pattern without reaching statistical (CXCL9, CXCL10, NNPA) significance. No change in serum level before, after and during the rejection episode was observed for both the chemokines (continued)

Table 1  (continued)

Array

Morgun et al. (2006) In-house oligonucleotide San Paulo Heart arrayf (biopsies) ~14.000 Genes

References (dataset) Organ (samples)

Results

The most significant category among the Aim. To analyze gene expression differences between rejection, no upregulated genes was related to the rejection and Trypanosoma cruzi infection immune response and included groups Methods. 76 cardiac biopsies from 40 hearth recipients undergoing rejection, no rejection and Trypanosoma cruzi infection recurrence. of genes involved in innate and adaptive Biopsies were routinely performed during follow-up and additional immunity, antigen presentation, cytokine binding and chemotaxis and upregulated biopsies were taken where rejection was suspected. The authors both in rejection and in infection samples. reanalyzed the data from Cleveland (Kidney) Stanford (Kidney) Immunoglobulin genes were among those and Minneapolis (Lung) datasets. Expression of selected genes most up-regulated suggesting that B cells may were confirmed by RT-PCR have a local effect at the site of inflammation. Energy metabolism pathways were also downregulated in both pathologies. However the Authors found a set of genes that discriminated between rejection and infection. The authors found high agreement , using their predictors set, with the histology of the other three studies in kidneys

Aim and methods

(continued)

Affymetrix Human Saint-Mezard et al. Aim. To identify a robust and reliable molecular signature for Acute This work identifies a consistent molecular signature for renal acute allograft rejection Genome U133 Plus (2009) (Paris) Rejection in human in biopsies. Between the three data-sets, 2.0g > 38,000 Genes Methods. 16 patients (adults), 18 biopsies. 47 renal biopsies for Kidney (biopsies) (Paris, Stanford, Cleveland, NHP, including clinical indications from 45 patients: patients with AR or CAN 36 AR, 3 borderline, 32 non rejecting (chronic allograft nephropathy or borderline) were analyzed. and 34 nontransplanted control samples) Mean time of biopsy after transplantations differed between a common transcriptional profile of 70 groups (3–53 months) represented genes was characterized. This As controls, normal kidney tissue was obtained from transcriptional profile, defined as acute histopathologically unaffected areas of the cortex of native rejection transcripts set (ARTS) strongly nephrectomies performed for renal carcinoma. This dataset correlates with the severity of Banff AR (Paris) was compared with two public human datasets: (1) types. However the ARTS scores correlate Stanford° dataset and (2) Cleveland dataset and with one Non with the lesion intensity of interstitial Human Primate (NHP) model of acute renal allograft. However, inflammation, tubulitis and vasculitis in a the authors used 143 biopsy microarray data from University of very similar way to the PBTs defined by the Alberta (Edmont dataset) as in independent confirmation set Edmont’s group. Indeed, most of the genes constituting the ARTS are associated to antigen presenting cells (e.g., CD52, CD163, MHCs), CTLs (e.g., CD 8, Granzyme A), or IFN-g responses (e.g., CCL5, CXCL9, STAT-1). The relationship between the ARTS and IFN-g signaling is further supported by a functional transcriptional analysis using MetaCore, which identifies the most significant regulatory networks centered on STAT-1, IRF-1. PU.1 and NF-kB were other transcription factors that regulate most significantly the genes represented in the ARTS

 

Array

Aim and methods Results

a

BOS bronchiolitis obliterans syndrome; PBLs peripheral blood lymphocyte; qRT-PCR quantitative real time protein chain reaction Affymetrix GeneChip HU6800 Array containing >7,000 oligonucleotide probe sets representing ~6,400 human genes (Affymetrix, Santa Clara, CA) b Atlas human cDNA microarrays ~588 gene analyzed c In-house microarrays containing >28,000 cDNA probes representing >12,000 genes (Lymphochip, Stanford University) d Affymetrix Human Genome U133A Array containing >22,000 oligonucleotide probe sets representing >18,000 transcripts (~14,500 human genes) (Affymetrix) e Affymetrix GeneChip HG-U95Av2 Array containing ~12,000 oligonucleotide probes representing ~10,000 human genes f In-house oligonucleotide array platform designed by Qiangen/Operon (Alameda, CA) and printed at NIAID Microarray Facility, representing ~14,000 human genes g Affymetrix Human Genome U133 Plus 2.0 Array containing >54,000 oligonucleotide probe sets representing >47,000 transcripts (~38,500 human genes) (Affymetrix)

This study meticulously investigates the Mueller et al. (2007) Affymetrix Human Aim. To analyze pathogenesis based transcripts (PBT, previously relationship between transcript sets, Genome U133 Plus (Edmont) defined in mouse kidney transplants and confirmed in cell which represent major biologic event, 2.0g > 38,000 Genes Kidney (biopsies) cultures by the same group) in human kidney transplants biopsies and histopathological lesions and clinical for clinical indications diagnoses on a global scale not done before. Methods. PBTs representing three biologic processes were PBTs correlated with histopathological previously developed: (1) CATs (cytotoxic lymphocyte associated lesions and were the highest in biopsies transcripts); (2) GRIT (IFN-g transcripts); (3) KT (parenchymal with clinically apparent rejection episodes. deterioration causing loss of kidney transcript). 109 patients. 143 Surprisingly, antibody mediated rejection had biopsies for clinical indications obtained between 1 week and 20 changes similar to T-cell mediated rejection. years posttransplant (median 19 months). Results were validated Biopsies lacking PBT disturbances did not in 51 additional biopsies. As controls, normal kidney tissue was have rejection. Expression of selected genes obtained from histopathologically unaffected areas of the cortex (CD8A, GZMB, CXCL9, CXCL11 and of native nephrectomies performed for renal carcinoma FAB1) was confirmed by qRT-PCR

References (dataset) Organ (samples)

Table 1  (continued)

Table 2  Key Genes associated with acute allograft rejection in human microarray studies

Akalin et al. (2001) Organ: kidney CXCL9a C3a CD18 MIP-3b ISGF-3 MCL1a a a (ITGB2 ) (CCL19a) (STAT1 ) Tannapfel et al. (2001) Organ: liver IL3 MMP9 TGFB1-3 TIMP1 TNF-2 CLUSTERIN Sreekumar et al. (2002) Organ: liver C1QB C3 LIPC GZMB HSPA1A IGF1 HLA-I HLA-II PCK1 SELPG TGFB1 TNF CTPS UBE2N ADAM17 GYS2 Sarwal et al. (2003) (STANFORD dataset) Organ: kidney HLA-A HLA-B HLA-C HLA-E HLA-DR HLA-DQ TCR DARC C4B CXCL9 SCYA3 (CCL3) SCYA5 (CCL5) CD53 NFKB1 NK4 CX3CR1 GZMA STK17B STAT1 CASP10 IFNGR1 IGHG3 IGKC IGL Gimino et al. (2003); Lande et al. (2007) (MINNEAPOLIS dataset) Organ: lung C4B CCR7 CD28 CD3E CD84 CTLA4 IGKC ITK KIR PRF1 STAT4 IL2RA Flechner et al. 2004 (CLEVELAND dataset) Organ: kidney C1QB CCL5 CD14 CD163 TRB@ CD16 CD53 CD64 CD8 CDW52 CXCR4 GZMA IL4R ISG20 PKR RAGE4 TNFRSF1b Karason et al. 2006  Organ: heart C3 C4A CXCL9 CXCL10 GBP1 HLA-C PSME2 RARRES3 STAT1 (IRF9a) TNFSF10

HLA-DMB4 SCYA2 (CCL2) IL2RB LENG-4 GZMK Zap-70 CD27 IFI30

HLA-J

HLA-DMA CCR5 IL6R IGHM CXCR3 IL2RB CD2 HLA-F

HLA-F

IGFBP4

CD3D IL10RA

GZMA LCK-kinase

TGFBR2 CD20 IL15R CD59

STAT1 UBB

RING4 (TAP1a) TCR-Bb

IL2 TNFAIP3

IL-10

NNMTa

(continued)

NPPA

CD48 IL10RB

IFNG

TGFR1 PERFORIN IL16 VCAM1

ACADM UBA1

IL2-SFc (LCP1a)

Table 2  (continued)

CG012 FLJ11151 HSPC043 KIAA1348 PAK4 SMG1

CD48 GBP2 HLA-DQB1 IL10RA NMI SERPING1 WARS CXCL11 LILRB1 XCL2

CD74 FLJ11106 HSD17B7 KIAA1257 NT5C2 SLC14A2 WSX1

CD44 GBP1 HLA-DPA1 IGHM MMP7 RUNX3 UBE2L6 CXL10 LCP2 XCL1

FAM26F NLRC5

CD52 GMFG HLA-DQB2 ISG20 PLEK SLA WFDC2

CHD3 FLJ11467 HSPC129 LIAS PCDHGA8 SORL1

GBP1 PSMB9

CD53 GZMA HLA-DRA ITGB2 PLSCR1 STAT1 T3JAM

CORO1A FY IGKC LOC90586 PSCD4 STAT1

List of 50 key genes often described as upregulated in human microarrays allograft rejection studies (highlighted in bright green): C1QB, C3, C4B, CASP1, CCL4, CCL5, CD14, CD3D, CD3E, CD53, CD8A, CXCL9, CXCL10, GBP1, GBP2, GZMA, GZMB, HLA-B, HLA-C, HLA-DMA, HLA-DMB, HLADPA1, HLA-DQB1, HLADQB2, HLA-DRA, HLA-DRB3, HLA-DRB4, HLA-E, HLA-F, HLA-G, IFI30, IFITM1, IGKC, IL10RA, IL2RB, ITGB2, LCP2, MMP7, PRF1, PSMB8, PSMB9, RARRES3, STAT1, TAP1, TIMP1, TNF, TRB@, UBD, UBE2LS, WARS This table reports genes associated with acute allograft rejection detected by microarrays technology analyzing human graft samples (bronchoalveolar lavage for lung samples biopsies for the others). Comprehensive lists of relevant upregulated genes, in according with the original publication, are reported for the following studies: Akalin et al., Tannapfel et al., Sreekumar et al., Karason et al. (genes most frequently upregulated during the rejection episode and returned to baseline levels with its resolution, Reeve et  al. (genes most frequently represented in the predictive analysis for microarrays classifier),

Morgun et al. 2006 (SAN PAULO dataset) Organ: heart + lung + kidney Prediction analysis (SAN PAULO+ MINNEAPOLIS+STANFORD+CLEVELAND datasets) ABCA7 BTN3A3 C1QA CCL18 CCL5 CD14 CTSS D21S2056E ZNRD1 F2R FKBP14 FLJ10244 GMFG HA-1 HLA-DMA HLA-DRB3 HLA-E HLA-F ITGB2 KCNJ5 KCNK6 KIAA0924 KIAA1030 KIAA1170 LOC92033 LTB MAFF MSH3 NM23-H6 NPHP1 RAB7L1 RARRES3 RASGRP2 RBL1 RIMS1 RU2 SULT1A3 TAPBP TRB@ UBE2B UBE2L6 UCP2 Saint-Mezard et al. (2008); Saint-Mezard et al. (2009) (PARIS dataset) Organ: kidney Comparative analysis (PARIS+STANFORD+CLEVELAND+non humanprimates datasets) ARHGDIB ARPC2 CASP1 CASP4 CCL5 CD163 CD8A CSPG2 CXCL10 CXCL9 FCER1G FER1L3 HCK HCLS1 HLA-B HLA-C HLA-DMA HLA-DMB HLA-DRB3 HLA-E HLA-F HLA-G IFI30 IFITM1 LAPTM5 LCK LCP1 LCP2 LTF LYZ PRG1 PRKCB1 PSMB10 PSMB8 PSMB9 RAC2 TAP1 TCIRG1 TIMP1 TNC TNFRSF7 UBD Reeve et al. 2009 (EDMONT dataset) Organ: kidney APOBEC3G CCL4 CCL5 CD8A CRTAM CXCL9 GBP2 GBP4 GBP5 GZMA GZMB INDO PTPRC SLAMF7 TAP1 TLR8 UBD WARS

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Saint-Mezard et al.; Morgun et al.: we reported the upregulated genes selected from the list of 98 genes belonged to the first predictor set that discriminate acute cardiac, renal and lung rejection from non rejection. CCL5 belonged to the second prediction set. Sarwal et al.: we selected key genes from the list of genes upregulated in three different subtypes of acute allograft rejection see also Monsfield et al. 2004 and Weintraub et al. 2006. Gimino et al. and Lande et al.: we selected key genes from a list of genes reported as upregulated during acute rejection according to the first (Gimino et al.) and second (Lande et  al.) analyses. Flechner et  al.: we selected key genes a list of genes upregulated in acute rejection samples compared to samples without diagnosis of rejection Others upregulated genes included in the original list were: Morgun et al.: Homo sapiens cDNA FLJ10266 fis, clone HEMBB1001024; Homo sapiens cDNA FLJ10580 fis, clone NT2RP2003533, mRNA sequence; Homo sapiens cDNA FLJ10981 fis, clone PLACE1001610; Homo sapiens mRNA, cDNA DKFZp434P1019; Homo sapiens mRNA; cDNA DKFZp564P073; Homo sapiens mRNA; cDNA DKFZp586H0718; Homo sapiens mRNA; cDNA DKFZp761G0924; Homo sapiens mRNA; cDNA DKFZp761P221; DKFZP434B033; Unknown (protein for IMAGE:4251653) [Homo sapiens], mRNA sequence; Unnamed protein product [Homo sapiens]. Karason et al.: Homo sapiens Alu repeat (LNXI) mRNA sequence. Reeve et al.: affymetrix id 235529_at and 238725_at a Gene symbols, according with Gene Bank or Affymetrix id reported in the original publications are provided in the brackets b The original name reported in the publication was: TCR Active b-chain related gene (MI2886: unsnopped) c The original name reported in the publication was: IL-2-stimulated phosphoprotein d The original name reported in the publication was: ubiquitin

Signatures Associated with Acute Rejection: Allograft Rejection 317

Fig. 1  First two Networks according to Ingenuity Pathways Analysis software (http://wwwingenuity. com), representing schematic relations among some genes upregulated in acute allograft rejection. The gene list uploaded was represented by 50 key transcripts commonly described in human microarrays studies (see Table 2). The genes from the aforementioned list are represented with brightgreen background. Red border: IFN-g stimulated genes (for this purpose genes upregulated in peripheral monocytes after IFN-g stimulation were selected). (a) The first Network focuses on HLAs, TNF-a, and IFN-g. (b) The second Network focuses on STAT-1, NF-kB, CXCR3 ligands (CXCL9 and CXCL10) and CCR5 ligands (CCL4 and CCL5). The bold line indicate direct interaction, the dotted one indicates indirect interaction

Signatures Associated with Acute Rejection: Allograft Rejection

319

study also suggested that a tight communication between cells of innate and adaptive immunity might play a central role in the acute rejection process. This was underlined, among others, by the over-expression of two pathways associated with the switch from innate to adaptive immunity such as IFN-g and CXCR3/CXCL9 pathways (Wang et al. 2008). Several other studies would be needed before these initial genomic hints about the acute rejection process, could find further confirmation and, eventually, be explained. Interferon-g (IFN-g) pathway – IFN-g is a pleiotropic cytokine and plays a complex role in the modulation of many aspects of the immune response. Studies conducted involving IFN-g−/− mice suggested that this cytokine, in addition to its proinflammatory functions, might be important for the graft acceptance, preventing early graft necrosis and maintaining microvascular viability (Hidalgo et al. 2002, 2005). However, the molecular mechanisms through which it exerts these antiinflammatory actions during the early phases of the engraftment are unclear. IFN-g is produced predominantly by NK cells as part of the innate immune response, and by CD4 T helper 1 cells (Th1) and CD8 cytotoxic T cells (CTLs) once antigen-specific immunity develops (Hidalgo and Halloran 2002; Schoenborn and Wilson 2007). Its over expression has been also observed in acute allograft rejection in several human studies where RT-PCR was applied (Gimino et al. 2003; Kirk et al. 1995; Moudgil et al. 1999; Nast et al. 1994). Microarray studies allowed to detect not only the expression of IFN-g gene but also the consequence of its downstream effects, e.g., by detecting genes over expressed following IFN-g stimulation (IFN-g stimulated genes). Nevertheless, the simple detection of IFN-g stimulated genes is not enough to discriminate its effect from the effect of other molecules, due to the induction of many IFN-g stimulated genes even by other cytokines, such as IFN-a (de Veer et al. 2001), which also could be involved in the mechanism of acute rejection. However, the detection of IFN-g transcripts (Kirk et al. 1995; Moudgil et al. 1999; Nast et al. 1994; Lande et  al. 2002), the flurry of IFN-g stimulated genes described in several microarrays studies (Reeve et al. 2009; Sarwal et al. 2003; Flechner et al. 2004; Akalin et al. 2001; Gimino et al. 2003; Lande et al. 2002; Sreekumar et al. 2002), and the display of genes/signaling pathways that, directly or indirectly, enhance the INF-g loop (e.g., TNF-a, CCR5 pathway and CXCR3 pathway as described later), rank the IFN-g as a driving pathway sustaining acute allograft rejection (Reeve et  al. 2008, 2009; Mueller et al. 2007; Sarwal et al. 2003; Lande et al. 2002; Saint-Mezard et al. 2009). Although for each individual IFN-g stimulated genes, some functions have been described; their overall orchestration is not completely understood. A partial description of the relations among frequently described IFN-g stimulated genes detected in microarrays studies is illustrated in Fig. 1a, b. IFN-g, primarily through interferon regulatory factor 1 (IRF-1, see also Sect. “Comparative Analyses and Role of STAT-1/IRF-1 and NF-kB”), upregulates both MHC class I and II, by increasing, for example, the expression of antigen peptide transporters TAP1-2, or class I transactivator CIITA, respectively (Hidalgo and Halloran 2002; Ramassar et al. 1996; Goes et al. 1996). Indeed, IFN-g promotes the differentiation of naïve T CD4 cells into Th1 cells, which are, among the lineage of

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CD4 T cells (Th1, Th2, Th17 and T Reg) the only ones that produce a consistent amount of IFN-g (Schoenborn and Wilson 2007; Zhu and Paul 2008). This cytokine in turn, and usually in synergy with tumor necrosis factor-a (TNF-a), induces the expression of CXCR3 ligands (Hidalgo and Halloran 2002; Ramsey et  al. 2008; Yeruva et al. 2008) and CCR5 ligands (Liu et al. 2005). CXCR3/CXCR3 ligands (CXCL9, -10, and 11) and CCR5/CCR5 ligands (CCL3, -4, and -5) – CXCR3 ligands and CCR5 ligands are the chemokine most frequently described as upregulated during acute allograft rejection in human studies performed by microarrays (Reeve et al. 2009; Sarwal et al. 2003; Flechner et al. 2004; Akalin et al. 2001; Karason et al. 2006) or RT-PCR (Zhao et al. 2002; Fahmy et al. 2003; Belperio et al. 2000, 2002, 2003; Melter et al. 2001) (see also Table 2). Accordingly, the upregulation of CXCR3 and CCR5, has been also often described (Reeve et  al. 2009; Sarwal et  al. 2003; Zhao et  al. 2002; Fahmy et  al. 2003; Gimino et al. 2003; Belperio et al. 2003; Melter et al. 2001). Of interest, in clinical trials evaluating the presence of the proteins encoded by the above mentioned genes, high urinary CXCR3 ligands levels were found elevated in patients that experienced acute kidney rejection urinary (Schaub et al. 2009; Hauser et al. 2005; Hu et al. 2004; Tatapudi et al. 2004). Indeed, CCR5 delta 32 polymorphism, that encodes for a non functional CCR5 receptor, resulted in highly reduced risk of acute rejection in kidney (Fischereder et al. 2001) and liver transplantation (Heidenhain et al. 2009). The driving role of these two pathways in allograft rejection was suggested by in vivo models one decade ago (Hancock et al. 2000, 2001, 2003; Gao et al. 2001). The lack of host CCR5 caused a threefold increase in allograft survival rate, but the targeting of any of the three main ligands using knockout mice or monoclonal antibodies had no effect on allograft survival (Gao et al. 2003). Conversely, a significant improvement in survival was observed in CXCL10−/− recipients (Hancock et al. 2001), and the lack of CXCR3 led to accept the graft indefinitely (Hancock et al. 2000). However, this supposed non redundant effect of CXCR3 that has been assumed as correct for almost 10 years, is today object of debate (Halloran and Fairchild 2008). Recently, in fact, three independent studies reported that disruption or blockade of recipient CXCR3 had relatively little effect on rejection (Haskova et al. 2007; Kwun et al. 2008; Zerwes et al. 2008). Here, we will try to illustrate the hypothetic role of these two pathways during the alloresponse. Upon antigen stimulation, the co-expression of CXCR3 and CCR5 is a marker of Th1 cell polarization, whereas CCR3, CCR4, CCR8, and CRTh2 are expressed in Th2 cells (Zhu and Paul 2008). The genes/gene pathways frequently over-expressed during acute allograft rejection are consistent with the predominance of Th1 cell polarization. Among CXCR3 and CCR5 ligands, CXCL9-10 and CCL4-5 were the most frequently reported chemokines associated with acute rejection in microarray studies (Reeve et al. 2009; Mueller et al. 2007; Sarwal et al. 2003; Flechner et al. 2004; Akalin et al. 2001; Karason et al. 2006) (see also Table 2). Although CXCL9-10 (through CXCR3) and CCL4 (through CCR5) are specific chemo-attractant factors for Th1 cells, CCL5 (RANTES) is a ligand for both CCR5 and CCR3, therefore involved also in Th2 cell trafficking (Murphy et  al. 2000; Moser et  al. 2004).

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However, this latter positive stimulus on Th2 cells evoked by CCL5, is probably nullified by the action of CXCL9-10, which also have a potent antagonistic activity on CCR3, thereby blocking the migration of CCR3+ cells (Moser et al. 2004). This contributes further to Th1 type immune-response polarization. These chemokines expressed by dendritic cells, activated macrophages and endothelial cells (Zhao et al. 2002; Murphy et al. 2000; Fahmy et al. 2003; Qin et al. 1998; Moser et al. 2004) attract, in addition to the Th1 cells, CTLs and NK cells, since all of them have both CXCR3 and CCR5 receptors (Murphy et al. 2000). The recruitment and activation of these cells leads to an increased production of IFN-g, with consequent amplification of the Th1 cascade. Thus, these molecules contribute to the switch from innate to adaptive immunity, while strengthening the innate cytotoxic mechanisms by sustaining NK responses. Even though up to 25% of circulating B cells express CXCR3 (Qin et al. 1998; Jones et al. 2000), and these cells can also produce CXCR3 ligands (Deola et al. 2008), the recruitment of B cells through this mechanism during acute allograft rejection has not been described yet. Furthermore, this complex cascade of cytokines and the coordinate activation of specific pathways so far described, leads, when tissue destruction occurs, to the activation of immune effector function (IEF) genes (perforin, granzymes A/B, Fas/ Fas ligand and caspases). Immune-effector function genes (IEF) genes – The release of granzymes, perforines and the activation of Fas/Fas ligand and caspases by CTLs and NK cells, represent the major effector mechanism of cell-mediated immunity (Rosado et al. 2007). Before microarray studies, IEF genes have been found upregulated during acute (Sharma et  al. 1996; Strehlau et  al. 1997; Li et  al. 2001), but not chronic (Sharma et al. 1996) kidney rejection by RT-PCR. Transcripts associated with IEFs (i.e., granzyme B) were first detected with the microarray technology (in a broader contest of immune-mediate stimuli) in a study conducted by Sreekumar et al. (2002) who compared the gene expression profiles of eight liver transplants with or without acute cellular rejection during hepatitis C virus infections reactivation. These transcripts would constantly be described as associated with allograft acute rejection in subsequent microarray studies (Sarwal et al. 2003; Flechner et al. 2004; Gimino et al. 2003; Saint-Mezard et al. 2009). Tumor necrosis factor-a (TNF-a), and innate immunity as mechanisms of sustaining or triggering the allograft rejection – In the Sreekumer study (2002), many of the 24 genes overexpressed in the rejection group were associated with innate immunity: TNF-a, ubiquitin, C3, Heat shock protein 70 (HSPA1A, which is the endogenous ligand of Toll-like receptor 4, TLR4 (Matzinger 2002; Vabulas et al. 2002)) and IRF9 (a protein that interacts with STAT-1-2 to form ISGF3, transcription factor of IFN-a (Platanias 2005; Reich and Liu 2006)). These genes suggest also the activation of innate immunity mechanisms, possibly upstream of those previously described as responsible, like CXCR3-ligand chemokines and the activation of IEFs, for the switch from innate to adaptive immunity with production of IFN-g (Wang et al. 2008; Larosa et al. 2007; Akira et al. 2006). Most transcripts upregulated in this study, and several genes described in other acute allograft rejection studies, are either Interferon stimulated genes (ISGs) canonically involved in the IFN-a pathway

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but most also IFN-g dependent (Fig.  1a, b). Although the activation of ISGs is a mechanism often described in immune-mediated tissue destruction (and does not necessarily require a de novo synthesis of IFNs, (Worschech et al. 2009; Navarro et  al. 1998)), the individual role of IFN-a in the pathogenesis of the allograft rejection is unknown, and mechanicistic studies are lacking. The upregulation of the TNF-a pathway, detected by the Sreekumar study, is another signature often associated with acute allograft rejection (Sreekumar et al. 2002; Tannapfel et al. 2001). This signature is not indicative of acute inflammation, and is usually seen also in chronically inflamed tissues (Wang et al. 2008; MullerLadner et al. 2009; Connolly et al. 2009). Although the process of transformation from an indolent process to an acute one is unknown, it seems plausible that innate stimuli, that lead to increase TNF-a, could contribute to elicit a cascade of events associated with acute response (Wang et  al. 2008). Rather than the increase of TNF-a per se, these stimuli could produce a series of interconnected events, of which TNF-a upregulation might be one of the consequences. For example, the engagement of toll-like-receptors (TLRs), by the endogenous danger-associated molecules (the rise of which can be caused by the intervention itself or by the ischemia-reperfusion injury (Larosa et  al. 2007; Matzinger 2002)), may lead to NF-kB (nuclear factor kappa B) activation and transcription of NF-kB induced genes, among which TNF-a (Bonizzi and Karin 2004). This latter cytokine is a potent activator of NF-kB, thus amplifying a positive loop. Moreover, NF-kB, through the induction of transcription of CXCR3- and CCR5-ligands (Bonizzi and Karin 2004), could trigger and then sustain the IFN-g cascade by promoting the migration of IFN-g-producing Th1 T cells, cytotoxic T cells and NK cells. At the same time, the activation of TLRs on antigen presenting cells (APCs) could also enhance antigen presentation and induce up-regulation of costimulatory molecules, prompting adaptive responses and recruiting CTLs (Akira et al. 2006; Iwasaki and Medzhitov 2004), whit further amplification of the immune response. The important role of innate immunity in the pathogenesis of allograft rejection has been observed in vivo using RAG−/− mice, which lack both T and B cells. He et al. showed that the expression of chemokine receptors and proinflammatory cytokines (e.g., IL-1, CCL3 and CCL4) occurred soon after transplantation and was similar in immunodeficent RAG−/− mice and wild type allogeneic recipients, thereby demonstrating the existence of innate rejection mechanisms independent of adaptive immunity (He et  al. 2002). However, comparing the immune-response between RAG−/− recipients, syngeneic recipients (which lack adaptive immunity) and allogeneic wild type recipients, the same authors showed that expression of markers induced by innate stimuli (e.g., IL-1, IL1-RA, CCR5), were present in RAG−/− mice, and further amplified in the wild type allogeneic but not in syngeneic recipients (He et al. 2003). Thus, this model represented an in vivo demonstration of a positive feedback on innate immunity by the adoptive one (He et  al. 2003). Nevertheless, while the innate (antigen-independent) stimuli can be important in mouse models, the relative weight of this process in humans, compared with the adaptive (antigen-dependent) mechanism, is not completely defined. Intriguingly,

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the fact that kidneys from living donors are accepted more easily than those from cadavers (D’Alessandro et al. 1998) could be explained by the stronger activation of “danger signals” in this latter condition supporting the hypothesis of innateimmunity triggering (Matzinger 2002). In allograft, the continuous and abundant availability of antigens from the surface of donor cells, and the interaction with T and possibly with B cells (directly through interaction of B cell receptor and MHCs), cause a labile condition particularly prone to be easily switched into a destructive acute response. Thus, whether this condition is per se sufficient to determine an acute response (according to the self-non self model) or needs to be prompted (according with the danger model), is object of an ongoing debate (Matzinger 2002; Brent 2003). In conclusion, we could hypothesize that both innate and adaptive mechanisms synergize in generating/ sustaining the immune-response. As a matter of fact, the common presence of such strong stimuli leads almost inevitably to a progressive destructive response, thereby requiring (except for rare cases of spontaneous tolerance than are an interesting object of study (Brouard et al. 2007)) prolonged and deep immunosuppression. Innate mechanisms as the final effectors of allograft rejection – The upregulation of common effector molecular pathways (e.g., CXCR3 and CCR5 pathways and IFN-g pathways) that are able to lead the activation of adaptive and innate immunity suggests that both of them could play a role not only in initiating or sustaining, but also to completing the tissue destruction process. In humans, however, studies analyzing the individual contribution of innate immune cells in mediating the final step of the alloresponse are lacking. NK cells, for example, are present in allograft rejection infiltrates (Andersen et  al. 1994; Meehan et  al. 1997; Kitchens et  al. 2006), but their evaluation through a molecular-gene expression approach has some intrinsic difficulty. A study conducted by Hidalgo et al. (2008) showed that cytolytic NK cells, alloactivated CD4 effector T cells and CTLs have a strictly similar gene expression profile. IEF genes (perforin 1, granzyme B, Fas-ligand), evaluated by microarray and confirmed by quantitative RT-PCR (qRT-PCR), were highly expressed among the three cell populations, and did not allow to discriminate from one another. In addition, similar transcripts were also found in T effector memory cells. These data can also be interpreted as evidence that different cell phenotypes share common cytotoxic “NK-like” functions. Therefore, the frequent observation of IEF gene upregulation during allograft rejection could mirror the activation of such “NK like” effector function. This also appears to be a convergent molecular mechanism of other immuno-mediated tissue destruction processes (Wang et al. 2008). In animal models, NK cells do not seem to be sufficient to reject solid organs directly since mice that have intact NK cell function but absence of adaptive immunity (RAG−/− or SCID) accept indefinitely skin and cardiac graft transplants (Larosa et al. 2007; Kitchens et al. 2006; Bingaman et al. 2000). However, these findings should not be misunderstood. The inability to reject the graft does not prove that innate cells (in this case NK cells) are unable to mediate tissue destruction. A possible explanation could be that, in these models, the lack of stimuli derived from a reciprocal

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feed-back between innate and adaptive cells (as previously illustrated), does not allow to trigger or sustain a strong enough “NK like” cytotoxic effector function. Conversely, it has been recently observed that nude mice treated with oncolytic viruses can reject tumor xenografts (Worschech et  al. 2009). This rejection was associated with the activation of ISGs (both IFN-g and IFN-a stimulated genes), upregulation of CXCR3 and CCR5 pathways and activation of IEF genes (granzyme B, caspase 8). Since these mice lack both T and B cells, this immunemediate tissue destruction is supposed to be induced by innate immune effectors such as NK cells and activated macrophages. This study suggests that, at least in this model, innate immunity can be an independent effector of tissue-specific destruction not requiring the adaptive immunity. Complement and allograft – C3 was described as upregulated in the aforementioned Akalin et Sreekumar studies (Akalin et al. 2001; Sreekumar et al. 2002). However, C3 and other complement components (C1 and C4) have been often associated with acute allograft rejection in several other microarray studies, conducted in renal (Flechner et al. 2004), heart (Morgun et al. 2006; Karason et al. 2006) and lung (Gimino et al. 2003) transplants. Recently, interest in the role of the complement as a regulator of the alloresponse is rising (Lin et al. 2007; Sacks et al. 2009; Naesens et al. 2009). In a recent study conducted by Naesens et al. (Stanford group (2009)) the authors observed upregulation, before transplant, of complement genes in deceased donor kidney biopsies, compared to living donor ones. In the same publication, the authors reported a significant overexpression of complement cascade genes (including C1 and C3) comparing 32 acute rejection samples and 20 nonrejection samples obtained from pediatric kidney recipients (Naesens et al. 2009). Complement is the archetypal innate defense mechanism, and provides a vital link between innate and adaptive functions (Lin et al. 2007; Sacks et al. 2003, 2009). Briefly, the central event in complement activation is the proteolysis of C3 (activated by antibodies or microbial cell surfaces) to generate biologically active products that lead to the formation of membrane attack complexes and result in the activation of granulocytes and cell lysis (Lin et al. 2007; Sacks et al. 2003, 2009). Most of the circulating complement is produced by hepatic synthesis; however, the detection of C3 transcription during acute allograft rejection by gene expression profile indicates that local production also occurs. Local sources of complement include endothelial cells, macrophages and neuthrophils, as well as epithelial cells (particularly renal tubular epithelial cells). The molecular pathways that lead, during the alloresponse, to the activation of complement transcription are not completely clear but the activation of NF-kB pathway could play a role in the synthesis of C3 (Lee and Burckart 1998; Schreiber et al. 2006). C3 could also be responsible of the Th1 response observed during the allorejection, directly, sustaining Th1 development (Peng et al. 2006), or by inhibiting Th2 polarization (Kawamoto et  al. 2004). Priming mice with C3 deficient dendritic cells led to delayed skin allograft rejection. In addition, complement can activate B cells and initiate humoral responses (Carroll 2004). Even in kidney transplantation, local renal C3 production leads to faster allograft rejection in animal models (Pratt et  al. 2002; Li et  al. 2004). An opposite results to what has been described so far was reached in three independent studies which analyzed liver

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transplant models. These works found an association between overexpression of C3 and tolerance (Cordoba et al. 2006; Fujino et al. 2004; Pan et al. 2004). Thus, at least in animal models, it is possible to hypothesize the existence of diverse regulatory mechanisms in different organs.

Studies in Heart and Lung Acute Allograft Rejection, and Continuum from Acute to Chronic Rejection Kidney and liver transplants protocols usually do not schedule bioptic assessment in the absence of clinical indication; however, the determination of the health of transplanted hearth needs intense surveillance in the absence of symptoms of organ failure through frequent endo-myocardial biopsies. Biopsies may be obtained at least monthly for the first year after transplantation (Morgun et al. 2006; Karason et al. 2006). A similar situation occurs in lung transplantation, where the follow up is usually performed by serial sampling, consisting of bronchoalveolar lavage and transbronchial biopsies (Gimino et al. 2003; Belperio et al. 2002; 2003). The need for repeated biopsies facilitates the search for biological markers and yields a unique in-depth longitudinal examination of gene expression variations related to the clinical condition. These studies allow not only to compare a cross section of phenotypes (e.g., rejecting vs. tolerant patients) but also to determine intra-individual variations, employing the patients as their own controls. In this context, a microarray study conducted by Karason et al. (2006), involving twenty heart transplant recipients, identified 16 genes that were upregulated during the rejection and returned to baseline after its resolution. Among them, CXCL9 and CXCL10 (CXCR3 ligands) were found. Most other transcripts were IFN-g stimulated genes (HLAs, STAT-1, IGFBP4, PSME2, GBP1), suggesting the activation of these pathways specifically in course of acute rejection (see also Table  2). CXCL9, IFN-g stimulated genes together with CCL5 (CCR5 ligand), were independently described as upregulated during cardiac acute rejection in another microarray study conducted by Morgun et al. (2006) (see also Sect. “Comparative Analyses and Role of STAT-1/IRF-1 and NF-kB”). In the lung microarray study conducted by Gimino et al. (Minneapolis dataset) INF-g, CXCR3 and IEF genes (granzyme A and perforin) were among the genes overexpressed during acute rejection (Gimino et al. 2003; Lande et al. 2002). Thus, this perturbation in gene expression profiling, induced by a complex cascade of cytokine and coordinate activation of specific pathways, shows inter-organ similarities. These data are consistent with the results of other four longitudinal large clinical trials (analyzing altogether more than 400 samples) which investigated, by quantitative RT-PCR (qRT-PCR) and/or Immunohistochemistry, the role of CXCR3/CXCR3 ligands (Zhao et al. 2002; Belperio et al. 2002, 2003; Melter et al. 2001) and CCR5/CCR5 ligands (Fahmy et al. 2003; Melter et al. 2001; Belperio et al. 2000) in heart (Zhao et al. 2002; Fahmy et al. 2003; Melter et  al. 2001) and lung transplants (Belperio et  al. 2000, 2002, 2003). Although such approaches do not allow to obtain a comprehensive overview of molecular processes, the combined immunohistochemistry and quantitative RT-PCR

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(qRT-PCR) analysis are important to confirm microarray data and to establish the individual contribution of different cells to gene expression perturbations. In such way, Zhao et al. (2002) found that, despite the expression by macrophages of all three CXCR ligands (CXCL9, -10 and -11), CXCL10 was primarily expressed by donor vascular cells. This reveals a complex synergistic intertwining between donors and recipients in the recruitment of lymphocytes in to transplanted organ. The detection of CXCR3 even on vascular cells and the association with persistently elevated expression of CXCL9 and CXCL10 in cardiac allograft vasculopathy (CAV), purport a dual role of these chemokines, in either sustaining inflammation, or directly generating morphologic vascular alterations (Zhao et al. 2002). Accordingly, Belperio et al. (2002), examining lung transplants, detected the upregulation of CXCL9, -10 and -11 (evaluated by qRTPCR) in association with acute rejection and bronchiolitis obliterans, underlying a continuum from acute to chronic rejection. Similarly, in renal transplantation, glomerular infiltration by CXCR3+ T cells was associated with the development of chronic allograft nephropathy (Akalin et  al. 2003). Moreover, in a recent microarray study conducted in kidney transplants, CCR5 pathways, IFN-g pathways, and IEFs (granzyme B), were upregulated during tubular atrophy and interstitial fibrosis (Maluf et al. 2008). These molecular findings fit with previously clinical observation that acute rejection, particularly the low grade one, predisposes to chronic process (Zhao et al. 1995; Kobashigawa et  al. 1995; Arcasoy and Kotloff 1999). Basadonna et  al. (1993), for example, observed that in kidney allograft recipients, patients that experienced acute allograft rejection had less than 1% chances of developing chronic disease as compared with up to 35% in patients who sustained such episode in the first 2 months. It is therefore conceivable that the same pathways that are activated during the acute tissue destruction process, could even mediate chronic processes, directly or through the activation of other pathways, such, for example the tumor grow factor-b (TGF-b) pathway, which is a potent inductor of collagen synthesis and fibroblast proliferation (Demirci et al. 1996; Shihab et al. 1995; Aziz et al. 2000). In allograft, differently from other immuno-mediated rejections (e.g., tumor rejection), the transition from an acute to an indolent process is not spontaneous; since immunosuppression is required to prevent the otherwise unavoidable organ destruction. Nonetheless, according to the above mentioned findings in chronic conditions, even after the clinical resolution of the acute episode, the same molecular pathways, that are responsible for the acute process, can somehow remain active though to a lower degree (Maluf et al. 2008). It is presently unclear if the benefit of pharmacological intervention (or spontaneous tolerance mechanism) is derived by keeping these pathways below a critical activation threshold or by avoiding their coordinated interactions necessary for full activation of the effector arm. Ruster et al. (2004), observed that CCL5 and CCR5 were highly upregulated during acute rejection but lower detectable levels were expressed during chronic allograft nephropathy, supporting the hypothesis that some of the changes leading to rejection may quantitative and not only qualitative. Nevertheless, neither the threshold that these pro inflammatory effect need to reach to cause rejection, nor the triggers that cause their rapid unbalanced activation (required for the re-exacerbation of the acute rejection) are known. Understanding such key questions is one of the remaining big challenges of transplantation immunology.

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Acute Allograft Rejection in Heterogeneous Condition and the Emerging Role of B Cells Acute rejection is a heterogeneous condition, with different features in terms of clinical presentation, response to therapy, and pathologic classification. In the previous study on kidney transplantation, the small sample size (Akalin et al. 2001) did not allow the detection of subtle differences. Sarwal et  al. (Stanford group) (2003) studied 50 pediatric transplant patients and analyzed 67 biopsies obtained during an overt episode of acute allograft dysfunction, chronic dysfunction, at the time of the engraftment, or when graft function was stable. The sample size allowed meaningful stratification of cases according to clinical and pathological diagnosis (Tusher et al. 2001). Unsupervised analysis clustered all the acute rejection samples separately from normal kidneys. However, biopsies with diagnosis of acute rejection were molecularly heterogeneous: with some grouping together with histopathologically classified chronic nephropathy, infection or drug toxicity samples. The acute rejection samples that clustered with chronic rejection biopsies over expressed genes involved in cellular proliferation and cell cycling suggesting that active repair and regeneration was occurring despite the histopathology classification of acute rejection. Another group of specimens, with mild form of acute rejection, showed a genomic profile close to that of samples with diagnosis of drug toxicity or infection. Thus, the similar patterns of expression of immune-related genes, including CCL5, CXCL9, CCR5, and other cytokines/cytokine receptors, suggested the activation of common mechanism during the alloresponse against pathogens and allo-antigens. The most populated cluster was represented by samples with an aggressive infiltration of lymphocytes, macrophages and NK-cells. The signature of this severe form of acute rejection was characterized by the activation of the INF-g and NF-kB pathways (e.g., HLAs, STAT-1, IL-2RB, IL-15R), CXCR3/ CCR5 pathways (e.g., CXCL9, CCR5 and CCL5) and the overexpression of IEF genes (granzyme A) (Sarwal et al. 2003; Mansfield and Sarwal 2004). Surprisingly, the authors found, in this cluster also a robust signature related to immunoglobulin production and B-cell presence. B cells – The unexpected of CD20+ B-cell infiltration (suggested by a strong presence of B Cells transcripts and confirmed by immunohistochemistry) in a consistent proportion of biopsy samples deserves discussion (Sarwal et al. 2003). Although ectopic germinal center formation with B-cell activation and plasma cell activation can occur locally in chronically inflamed tissue (Cassese et  al. 2001; Magalhaes et al. 2002), the in situ presence of B cells was not historically reported in acute allograft rejection (Sarwal et al. 2003; Platt et al. 1982; Zarkhin and Sarwal 2008). This CD20+ B cell infiltration, was not associated with intra-graft C4d deposition or with the detection of donor-specific antibodies, indicating that it was not necessarily related with the presence of humoral reaction. The presence of such CD20+ dense clusters in a significant proportion of samples from patients diagnosed with acute allograft rejection would, thereafter, be confirmed by several independent studies (Alausa et al. 2005; Bagnasco et al. 2007; Doria et al. 2006;

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Hippen et al. 2005; Kayler et al. 2007; Krukemeyer et al. 2004; Tsai et al. 2006; Mengel et al. 2005), with an incidence varying from 15 (Mengel et al. 2005) to 60% (Kayler et  al. 2007). However, the correlation with poor outcome suggested by Sarwal et  al. (2003), was confirmed by some studies (Hippen et  al. 2005; Krukemeyer et al. 2004; Tsai et al. 2006; Martins et al. 2007) but not others (Alausa et al. 2005; Bagnasco et al. 2007; Doria et al. 2006; Kayler et al. 2007; Einecke et al. 2008). B cell transcripts (immunoglobulins) have been detected as up regulated during acute rejection, even in lung and heart transplants (Morgun et  al. 2006; Gimino et al. 2003). The subsequent lineage analysis revealed that CD20+ cells cluster expressed MHC class II antigens and are surrounded by CD4+ T cells, suggesting a putative role of these cells in antigen presentation, driving a T-cell dependent cellular rejection (Zarkhin et  al. 2008). Another cluster of B cells was represented by CD138+ mature plasma cells (Zarkhin et al. 2008). Recently, studies conducted in heart transplantation models, showed that a deficiency in B cell mediated antigen presentation leads to lack in CD4 T cell activation and alloantibody production (Noorchashm et al. 2006). Other in vivo observations pointed to the possible pilot role of B cells in the context of the pathogens (Schultz et  al. 1990; Yang and Brunham 1998) or auto-immune (Cross et  al. 2006) induced T cell response. However, in addition to functioning as antigen presenting cells, B cells may promote T cell mediated rejection. A novel T/B cell interaction, independent of Ag presentation by B cells, has recently been described. Here, B cells play a helper role recruiting T cells in areas of inflammation. As activated, T cells enter into contact with B cells, they are stimulated to proliferate. This interaction between activated T and B cells leads to the release of chemo-attractant cytokines such as CXCR3 ligands by B cells, which may act on the corresponding receptor expressed by activated T cells (Deola et al. 2008). These chemokines, as described before, play an important role trafficking CTLs and NK cells into the graft. Finally, targeting B cells, is now emerging as possible new therapeutic perspective, and the anti-CD20 monoclonal antibody Rituximab, seems to be a promising drug for the treatment of antibody-mediated and cell-mediated acute allograft rejection (Sarwal et al. 2007; Becker et al. 2006).

Other Studies in Kidneys Acute Allograft Rejection, Role of IL-10, and Molecular Analogies Among Cellular and Humoral Rejection Before Sarwal’s study (2003), allograft microarray study analyses were based on the dichotomy separating samples with or without diagnosis of rejection. Since spontaneous and long term graft acceptance is rarely observed in solid organ transplantation (Brouard et  al. 2007), patients are routinely treated with immunosuppressive drugs and, therefore, the independent effects of the suppressive regimen

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cannot be easily discerned. Furthermore, no normal autologous or heterologous kidney biopsies are available (Akalin et al. 2001; Sreekumar et al. 2002). Aware of these limitations, Flechner et al. (Cleveland dataset) (2004), conducted a study in adults kidney recipients following a study design similar to that implemented in pediatric patients by Sarwal et  al. The authors analyzed 31 allograft-samples including a control group of normal kidney specimens collected from living donors. Unsupervised analysis clustered in separate groups donor samples, biopsies from transplant patients with normal kidney function, and those from patients with acute rejection. However, it was not possible to distinguish acute rejection samples from samples with acute dysfunctions without such a diagnosis, underlining similarities among different mechanisms of acute inflammation. Supervised analysis compared samples from transplant recipients with stable organ function (treated with immunosuppressive drugs) to normal donor kidneys. The largest group within the immune/inflammatory genes upregulated in the well-functioning transplants was histocompatibility antigens, consistently with the hypothesis of an ongoing but low-grade immune response. Nevertheless, the concurrent presence of TNF-a, IFN-g pathway, chemokines (e.g., CCL5) and IEFs (granzyme A) was detectable only in the acute rejection group, in accordance with the hypothesis that, when a strong destructive response occurs, it is characterized by a coordinate activation of these pathways. Interestingly, IL-10 receptor alpha (IL-10RA) upregulation would be later confirmed by a comparative analysis of three independent human acute allograft rejection microarray-datasets (Saint-Mezard et al. 2009). Interleukin 10 (IL-10) – Although the canonical effects of IL-10 are regulatory limiting and ultimately terminating the inflammatory process (Moore et al. 2001), this cytokine cannot be merely classified as anti-inflammatory due to its pleiotropic ability of influencing positively and negatively the function of innate and adaptive immunity in preclinical models (Mocellin et  al. 2003, 2004, 2005). In humans, intravenous administration of recombinant IL-10 produces proinflammatory effects by enhancing the release of IFN-g, TNF-a and IL-1 and appears to induce the activation of CTLs and NK cells, as reflected by increased plasma levels of granzyme-B (Tao et al. 2001; Lauw et al. 2000). In human monocyte lineage cells, IL-10 increases the expression of TLR, which might sensitize these cells to “danger signal” mediators. This suggests an important role of this cytokine in the early phase of the acute immune response. Systemic administration of IL-10 exacerbates allo-reactions in murine models (Qian et al. 1996; Li et al. 1997), and, accordingly, the administration of anti-IL-10 monoclonal antibody prolongs graft survival (Li et al. 1998). In addition, by inhibiting APCs maturation and postponing their migration to lymphnodes, this cytokine might lead to a more efficient antigen loading and might activate locally adaptive effectors (Mocellin et al. 2003, 2004, 2005). In humans, posttransplant levels of IL-10 were associated with risk of kidney acute rejection (Karczewski et al. 2009) and IL-10 transcript was described as upregulated during acute rejection in liver transplant recipients (Tannapfel et  al. 2001). Finally, an association between acute rejection and homozygosity for specific IL-10 polymorphisms has been reported in kidney transplants (Grinyo et al. 2008).

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Molecular analogies between cellular and humoral rejection – A different analysis approach was attempted by the Edmont’s group researchers (Reeve et al. 2009; Mueller et al. 2007; Einecke et al. 2005; Famulski et al. 2006). First, they defined a priori three major biological processes in allograft rejection: cytotoxic T cell infiltration (CAT), interferon-g effects (GRIT) and parenchymal deterioration causing loss of kidney transcripts (KT) (Einecke et al. 2005; Famulski et al. 2006). Thereafter, they used mouse transplants and cultured cells to annotate the corresponding three pathogenesis-based transcripts and correlated their relationships with histopathological lesions and clinical diagnosis in humans (Mueller et  al. 2007; Einecke et al. 2005; Famulski et al. 2006). The investigators assigned to each sample a score (disturbance) based on the presence and upregulation (compared to normal control) of genes associated with the three processes. Analyzing 143 samples, the authors found that the score they created had a strong quantitative but not qualitative correlation with lesion phenotype (Banff histopathological score) (Mueller et al. 2007). In fact, although biopsies with histopathological diagnosis of acute rejection had elevated scores, whether categorized as antibody-mediated and cellular-mediated rejection, displayed similar expression patterns demonstrating cytotoxic T cell infiltration and Interferon-g affect transcripts. Activation of IEF genes such perforin, granzyme B, and Fas ligand, occurred in T cell-mediated rejection but also in antibody-mediated rejection (Reeve et al. 2009; Mueller et al. 2007). This observation suggests that effector T cells and antibodies lead to activation of a common final pathway of tissue destruction. Moreover, cells of innate immunity such as NK cells, which produce IFN-g and release granzyme and perforin, can also be recruited by complement and Fc receptor, representing another commonality between cellular and antibody mediated tissue destruction. In a subsequent study, the same authors analyzed in detail the gene expression profile associated with allograft rejection (Reeve et al. 2009). Among the top 100 genes significantly upregulated in acute rejection samples (compared to nonrejected samples) most (93%) were cytotoxic T cell infiltration transcripts and interferon-g transcripts. Furthermore, they built a classifier to distinguish rejection from non rejecting samples using predictive analysis for microarray (Tibshirani et al. 2002). As showed in Table 2, most of the genes selected by this analysis can be incorporated in the pathways previously described, namely IFN-g pathway (GBP2, PSMB9 TAP1), CXCR3 pathway (CXCL 9, -10 and 11), CCR5 pathway (CCL4 and CCL5), and IEFs pathway (granzyme A and granzyme B) (Reeve et al. 2009).

Comparative Analyses and Role of STAT-1/IRF-1 and NF-kB The comparative analysis between different microarray datasets is difficult for several reasons. (i) Microarrays used in the different studies cover a different range of genes, according to the platform utilized. (ii) The procedures employed for data collection, as well as the tools for data processing and statistical analysis differ greatly among laboratories. (iii) The methods of sample collection vary strongly

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among pathologies: whereas, for example, heart and kidney studies are performed directly using biopsies of the target tissue, lung gene expression analyses are often carried out with specimens derived from bronchoalveolar lavage (Gimino et  al. 2003; Lande et  al. 2002). (iv) The design of the studies, as well as the samples selected as a control are frequently different (e.g., healthy organ rather than transplanted grafts without histopathological alterations). (v) Uniform database designs are absent with consequent frequent difficulties in retrieving patients clinical data. In spite of these technical differences, cross comparison of data sets has been remarkably revealing (Morgun et  al. 2006; Saint-Mezard et  al. 2009), probably because of the highly conserved molecular patterns associated with immunemediated tissue destruction. The first comparative analysis was performed by Morgun et al. (Morgun et al. 2006) who, after identifying a gene sets predictive of acute-rejection in a series of hearth allograft recipients, analyzed the data from two published studies on kidney (Stanford (Sarwal et al. 2003) and Cleveland (Flechner et  al. 2004) databases) and lung (Minneapolis database (Gimino et  al. 2003)) transplants. The authors observed a striking agreement with the histological diagnosis of the three studies. The predictive accuracy of the gene set obtained from studying hearts was close to 95% in kidney and lung acute rejection underlining the similarity among pathways activated in different damaged organ transplants. Similarly to what observed in kidney transplants (Sarwal et  al. 2003), B cell transcripts (immunoglobulins) were among those most up regulated, suggesting that B cells may have a local effect also in heart rejection. Another interesting finding was the similar pattern of immuno-response gene expression (antigen presentation, innate immunity, chemotaxis, immunoglobulins and cytokines) among samples with diagnosis of acute rejection vs. infection. Here, the gene expression of rejection transplant recipients was similar to that of patients with Trypanosoma cruzi infection (which represents a frequent cause of chronic heart failure and consequently of heart transplant in Latin America, where the study was conducted). Again, the immuno-mediated process at the root of the two types of tissue destruction, namely allograft rejection and infection, pin pointed the activation of common pathways, as previously suggested by Sarwal et al. (2003). A more systematic comparative analysis has been recently conducted by SaintMezard et al. This group reanalyzed the data from the two aforementioned Stanford (Sarwal et al. 2003) and Cleveland (Flechner et al. 2004) kidney datasets, comparing those with their own, which consisted of human and nonhuman primate kidney acute rejection biopsies. By doing so the authors analyzed 36 acute rejection samples, identifying 70 genes that systematically upregulated during acute allograft rejection. Importantly, the authors validate successfully their findings using 143 microarrays from Edmont dataset (Mueller et al. 2007). Most of these genes were associated with antigen presenting cells (e.g., CD52, CD163, HLAs), IEFs activation (granzyme A, caspases), CCR5 and CXCR3 pathways (CCL5 and CXCL9-10, respectively), and IFN-g response (e.g., STAT-1, IFI30, PSMB 8-9-10, GBP1). These genes recapitulated a scenario often seen in other immune-mediate rejection processes (e.g., tumor rejection, pathogen clearance, graft vs. host disease) (Wang et  al. 2008). This strengthens the concept of the immunologic-constant of rejection.

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Utilizing GeneGo Meta Core algorithms (a web-based suite for functional analysis of experimental data (https://portal.genego.com/ez.html)) STAT-1, Interferon Regulatory Factor (IRF-1), Nuclear Factor Kappa B (NF-kB), and PU.1 (a transcription factor involved in the in the development of myeloid and lymphoid cells (Bonadies et al. 2010)) were identified as the main transcription factors that regulated the 70 genes consistently represented in the three aforementioned kidney studies. Similarly, we identified the same transcription factors regulating a set of 50 key-genes represented in most of the microarray studies so far performed, including also other genes detected in liver, lung and heart transplants (Fig. 2a, b). We must however underline that the analysis we conducted is of a strictly explorative nature, aimed mainly at understanding the possible interconnections of the genes so far discovered, and far from being a complete and exhaustive metanalysis of the existing literature. With these limitations, the first two networks generated by such analyses were centered on STAT-1 (p value = 8.50 × 10−58) and IRF-1 (p value = 2.34 × 10−47) followed by a network centered on RELA (NF-kB p 65 subunit, p value = 2.46 × 10−32). The relationship among different protein–protein interactions, activation of transcription factors, and functional response are often difficult to establish because there are complex and based on incompletely understood relationship among signaling pathways. In simplistic terms, in alloresponse, NF-kB reflects a constitutive activation of innate immunity (e.g., TNF-a pathway), and IRF-1 super imposes a switch toward the adaptive one (trough IFN-g pathway). As described in Sect. “Early Studies in Kidney and Liver Acute Allograft Rejection and the Detection of Recurrent Themes,” these two pathways can amplify each other, and can also collaborate in inducing the transcription of common genes. For example, IFN-g through IRF-1 and TNF-a (through NF-kB) can synergize in promoting the overexpression of common genes such as CXR3 ligands (Yeruva et al. 2008) and CCR5 ligands (Bonizzi and Karin 2004). Figure 3 summarizes a likely reciprocal enhancement of function between the NF-kB and the STAT-1/IRF-1 pathway during allograft rejection. Beyond the function of master regulator of innate immunity, NF-kB is also important in driving the adaptive response. In fact, it plays a key role in IL-2 and TCR signaling, and in the regulation of immunoglobulin production (Lee and Burckart 1998). It is noteworthy that most of the drugs effective in the treatment and/or prevention of acute allograft rejection (e.g., glucocorticoids, cyclosporine and tacrolymus), interact with NF-kB pathway, resulting in reduced production of several cytokines such as IL-2 and TNF-a (Lee and Burckart 1998). Accordingly, NF-kB activity impairment leads to an attenuation of acute rejection in heart (Finn et al. 2001; Basil et al. 2006; Tiao et al. 2005), lung (Ohmori et al. 2005) and skin (Zhou et al. 2003) animal models. The in depth description of the NF-kB pathway, is beyond our purposes and will therefore not be covered here. We will conclude our chapter providing a brief description of the other master of inflammation: IRF-1. Interferon regulatory factor 1 (IRF-1) – IRF-1 is an inducible second generation IFN-g transcription factor and is it transcribed in response to IFN-g via STAT-1 (Hidalgo and Halloran 2002; Honda and Taniguchi 2006). This transcription factor

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Fig.  2  Transcription regulatory network analysis according to Meta Core algorithms (https:// portal.genego.com/ez.html). The gene list uploaded was represented by 50 key transcripts often described as upregulated in human microarray studies (see Table 2). This analysis generates subnetworks centered on transcription factors. These sub-networks are ranked by a p-value. The first two networks (a) were centered on STAT-1 (p = 8.50 × 10−58) and IRF-1 (p = 2.34 × 10−47) The third network (b) was centered on RELA (NF-kB p65 subunit) (p = 2.46 × 10−32). Line colors indicate inhibition (red), activation (green) and no clear link (gray)

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Fig.  3  Possible mechanism of reciprocal enhancing between innate and adaptive immunity, through NF-kB system and STAT-1/IRF-1-pathway. This sketch is built according to genes often described as upregulated during acute allograft rejection in human studies. NF-kB can be activated by a variety of inflammatory stimuli. For example, the engagement of toll-like-receptors (TLRs) by the endogenous danger-associated molecules may lead to NF-kB activation and transcription of NF-kB induced genes, among which also TNF-a. This latter chemokine is itself a potent activator of NF-kB, thus forming an amplifying feed forward loop. Indeed, NF-kB, through the induction of transcription of CXCR3- and CCR5-ligands (e.g., CXCL9, -10 and CCL5 respectively), engages Th1 cells, CTLs and NK cells, since all of them express CXCR3 and CCR5. These cells in turn produce IFN-g with consequent activation of the STAT-1/IRF-1 pathway, that leads to further production of chemoattractant CCR5 and CXCR3 ligands with amplification of the IFN-g response. Furthermore, IRF-1 can also induce the TNF-a production, with further amplification of the loop

could mediate the upregulation of several genes/gene pathways during acute allograft rejection, as showed in Fig. 2a. Genes upregulated by IRF-1 include proinflammatory cytokines (e.g., TNF-a (Vila-del Sol et  al. 2008)), chemokines (e.g., CXCL10 (Ramsey et al. 2008; Yeruva et al. 2008), CCL5 (Liu et al. 2005)), and MHC class I and class II molecules (Hidalgo and Halloran 2002; Penninger et al. 1997). It could also drive the synthesis of IL-10 RA (Ramsey et al. 2008). Another important proinflammatory function of this gene consists in the induction of IL-12 (Taki et al. 1997) and IL-15 (Ogasawara et al. 1998) with consequent enhancement of the IFN-g cascade. IRF-1 has been better described in relationship to tumor rejection (Wang et al. 2002). In a study conducted in melanoma patients by Wang et al. (2002), IRF-1 was the most significantly and consistently upregulated transcript in metastatic melanoma lesions undergoing clinical regression after the systemic administration of

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high-dose interleukin-2. IRF-1 appeared to play a central role in orchestrating the immune-concert, generating the switch from chronic to acute inflammation in that and several subsequent studies (see Chap. “Immune Mediated Tumor Rejection” by Wang and Marincola). Regarding the allograft, although statistical algorithms recognize IRF-1 as one of the main transcription factors that regulate genes involved in acute allograft rejection, it should be noted that its overexpression per se has not been yet identified in human microarray studies. Thus, these data must be interpreted with care. Nevertheless, STAT-1 has been nearly always described as upregulated during acute allograft rejection (see Table 2), suggesting the regulation of IRF-1 through the IFN-g/STAT-1 pathway as a plausible phenomenon. In a recent microarray study in mice liver transplant models, IRF-1 was one of the two genes overexpressed both in leukocytes and in graft during acute cellular rejection (together with GBP2, an other IFN-g inducible gene) (Hama et al. 2009). Accordingly, other groups have reported an association between IRF-1 and acute cellular rejection in heart transplant models (Stegall et al. 2002; Erickson et al. 2003). On the other hand, other authors have reported STAT-1/IRF-1 pathway to be upregulated in tolerant models (Cordoba et al. 2006; Fujino et al. 2004). In order to explain these findings, the investigators proposed the induction of T cells apoptosis by IFN-g signaling (Cordoba et al. 2006): STAT-1/IRF1 are transcripts involved also in the induction of apoptosis via a caspase-mediated mechanism (Kroger et al. 2002) (see also Fig. 2a). Thus, it is likely that IRF-1 plays a different role according to the independent coactivation of different pathways, which can greatly differ from cells to cells but can also vary with changes in the surrounding environment (Kroger et al. 2002). Although IRF-1 seems to be primarily regulated by IFN-g signaling (Honda and Taniguchi 2006), in vivo and in vitro observations suggested that IRF-1 regulation does not necessarily require the intervention of such cytokine. Indeed, IRF-1 has been observed in response to IL-2 stimulation in vitro (Jin et al. 2007) and in the absence of IFNs upregulation in animal models (Worschech et al. 2009). In addition, IRF-1−/− mice have defects not seen in IFN-g or IFN-g- receptor−/− animals (such as alterations in CD8+ T cells and thymocyte development), supporting the existence of an IRF-1, IFN-g-independent activation pathway (Hidalgo and Halloran 2002; Matsuyama et al. 1993). Viceversa, even supposing a central role for this protein in the induction of proinflammatory mediators, a recent microarray study in heart transplanted mice suggested that IRF-1 functions could be bypassed by other mediators (Erickson et al. 2004). In this latter cited study the authors showed that the expression profile of the allograft from IRF-1−/− mice and wild type mice were nearly identical to each other and very different from the profile of isograft control.

Conclusions Some of the pathways thought to be central during acute allograft rejection have been described in this chapter. Most of the pathways analyzed (IFN-g/STAT-1/IRF-1 path, CXCR3/CXCR3 ligands path, CCCR5/CCR5 ligands path, and IEFs path) have also been associated with other immune-mediated processes, underlining the

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existence of common molecular convergent mechanisms (Wang et  al. 2008; Bedognetti et al. 2010), but it is clear that many other signaling molecules are also involved in generating the allo-response. Even if the pathways analyzed are consistently observed in humans, experiments in animal models failed to demonstrate them as necessary or sufficient for the development of rejection, in concordance with the high redundancy of mammalian immune system (Hidalgo and Halloran 2002; Kwun et al. 2008; Zerwes et al. 2008; Zhou et al. 2003). Moreover, some of the genes associated with acute rejection seem also to play a role in tolerance models (e.g., STAT-1/IRF-1 (Cordoba et  al. 2006; Fujino et  al. 2004)), stressing the pleiotropism of such molecules, as well as reminding us of the complexity of their networks. Moreover, although their current presence is often associated with acute rejection, these genes are also overexpressed, to a various degree, in chronic inflammation (Zhao et al. 2002; Belperio et al. 2002; Maluf et al. 2008; Ruster et al. 2004). Thus, the codes that govern the balance between tolerance and rejection, as well as the events than can suddenly induce the switch from an indolent process into an acute one are still far from being decrypted. Nevertheless, we can conclude that, when an immune-mediated tissue destruction occurs, common pathways are activated in a coordinate fashion (Wang et al. 2008; Bedognetti et al. 2010). In conclusion, we hope we contributed a description of how tissue specific destruction occurs. The understanding of the why is one of the more intriguing and big challenges of modern human immunology. Acknowledgments  I would like to say thanks to Dr. Gabriele Zoppoli, MD and Prof. Alessandro Materietti not only for their useful suggestions and comments, but, especially, for their outstanding friendship. A special thanks to my Mentor (and friend!), Prof. Mario Roberto Sertoli, MD.

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Index

A Activated immune cells. See Adoptive immune therapy Acute graft versus host disease (AGVHD), 261–263 Acute infections rejection, 19–20 Adaptive immune systems, 142 Adjuvants, 146, 154 Adoptive cell therapy, cancer prognosis, 208 Adoptive immune therapy cancer affinity, T cell receptors, 100 dendritic cells (DCs), 97–100 induced genes and maturation compound, 100 TIL, melanoma, 96–97 leukemia donor leukocyte infusions, 95 NK cells, 95–96 viral induce malignancies, 94–95 viral infections, 93–94 Adult T-cell leukemia (ATL) Foxp3 expression, 194 THAM/Treg ratio, 193–194 Allograft rejection. See also Graft dysfunction analogy with, 306 B cells role, 327–328 comparative analyses of B cell transcripts, 331 difficulties, 330–331 interferon regulatory factor 1 (IRF-1), 332–335 STAT-1, 332 definition, 307 heart and lung transplantation, 325–326 kidney and liver transplantation acquired immunological tolerance, 306 chemokines role, 308 complement system, 324–325

CXCR3/CXCL9 pathway, 320–321 immune effector function (IEF) genes, 321 innate immunity role, 322–324 interferon-g (IFN-g) pathway, 319–320 interleukin 10 (IL-10) role, 329 molecular analogies, 330 tumor necrosis factor-a (TNF-a), 321–322 microarray gene expression profile, 307–314 ingenuity pathways analysis, 318 key genes, 315–317 Meta Core algorithms, 333 Anergy, 171, 174 Angiogenesis. See Angiogenic switch Angiogenic switch dendritic cells (DCs) angiogenesis, 65–66 tumor microenvironment, 65 granulocytes neutrophils, 62–64 TGFb blockade, 64 innate immunity macrophage polarization paradigm, 59 Tie2 expressing macrophages, 61–62 tumor associated macrophages (TAMs), 60–61 mast cells, 64–65 myeloid derived suppressor cells (MDSCS), 62 NK cells localization, 67 subsets, 66 specific immunity, 67 Annotation database, graft dysfunction, 245 Anti-CTLA-4 mAbs melanoma, 234–235

347

348 B Blood transcriptional fingerprints. See Peripheral lymphocytes Bronchiolitis obliterans, 267 C Cancer bearing state applications prognostic and predictive testing, 178–179 therapeutic, 177 immune dysfunction apoptotic T cells, 170–171 immune mediators balance, 172–174 impaired effector cell activation and function, 171–172 vs. tumor rejection, 170, 175 tumor rejection autoimmune responses, TAAs, 176 breast cancer gene expression, 174–175 immune adaptation, T cells, 176 vs. immune dysfunction, 170, 175 non-small-cell lung cancer (NSCLC) gene expression, 175 Cancer prognosis colorectal, 200–203 immune activity mechanisms, 204–205 immunotherapy adoptive cell therapy, 208 IFNa therapy, 207 IL-2 therapy, 207 therapeutic strategies, 206–207 NED survival, 203 non-small cell lung cancer (NSCLC), 204 poor outcome markers, 205 renal cell carcinoma (RCC), 203–204 staging and grading systems, 199 Cancer related inflammation. See Tumor associated macrophages Chemokines acute rejection, 80–81 allograft rejection, 79 chronic rejection, 81 clinical data, 82–83 clinical utility translation gene deletion mice, 81–82 gene transfer, 82 human vs. mouse, 83 neutralizing antibodies, 82 preclinical evaluation, 81 small molecules, 82 early events, 79–80 T cell trafficking, 232

Index Chemotherapy, tumor rejection, 291–294 Chimpanzee, 128, 131, 132, 135, 136 Chronic fibrosis cytokines, 268 mechanism, 267 myofibroblasts, 267–268 T cells role, 268–269 Chronic graft versus host disease (CGVHD) autoimmune disorder allo-activated donor T cells, 270 CD4 T cells, 272 dentritic cells, 273 T effectors cells, 269–270 Th17 cells, 270–271 thymic epithelial cells (TEC), 272–273 Treg cells, 271–274 clinical presentation of, 263 cytotoxic effectors infiltration, 265 fibrosis, 267–269 cytokines, 268 mechanism, 267 myofibroblasts, 267–268 T cells role, 268–269 interferon (IFN) role, 263–264, 266–267 lichenoid forms, 265–266 Chronic HCV infection, 132–134 Chronic inflammation, humoral immunity and fibrosis Colorectal cancer prognosis chemokines, 202–203 effector T cells density, 201 histological markers, 200 metastasis development, 201–202 Cytomegalovirus (CMV) infection, 93–94 Cytotoxic effector molecules clinical utility, 86 cytotoxic T lymphocyte (CTL), 84–86 granulysin, 84–85 granzymes, 85 molecular profiling studies, 86 perforin, 85 D Delayed allergy reaction, 3 Dendritic cells (DCs) angiogenesis, 65–66 tumor microenvironment, 65 E Epithelial ovarian carcinoma (EOC) antigen characterization, 211–212

Index endothelial barrier endothelin (ET) peptide ligands, 220 ETBR activity, 220–221 TNF-a, 221 tumor-infiltrating lymphocytes (TILs), 220–221 VEGF, 219 epidemiology, 211 T cells CD8+ intraepithelial cells, 212–213 chemokines role, 217 IFN-g, 214–215 regulatory (Treg) cells role, 218–219 T helper 17 (Th17) cells, 215–217 Experimental models of rejection acute infections, 19–20 chronic inflammation, obesity, 20–21 intestinal microbiota, 18–19 T cell–NK cell cooperation, 22 tumor rejection, natural killer (NK) cells, 21–23 F Fibrosis. See Chronic fibrosis Functional class scoring (FCS) method, graft rejection, 247–249 Functional pathway analysis, graft dysfunction, 244–246 G Gene ontology (GO) annotation database Genetic polymorphisms, 152 Graft dysfunction. See also Allograft rejection; Graft versus host disease (GVHD) deconvolution, 251 functional pathway analysis annotation database, 245 common rejection mechanism, 244 functional class scoring (FCS) method, 247–249 necessity, 244–245 over-representation analysis (ORA), 245–246 pathway topology (PT)-based method, 249–251 integrative meta-analysis, 252 microarray applications, 241–243 multiple ‘omics technology, 252 Graft versus host disease (GVHD). See also Graft dysfunction acute GVHD (AGVHD), 261–263

349 chronic GVHD (CGVHD) autoimmune disorder, 269–274 clinical presentation of, 263 cytotoxic effectors infiltration, 265 fibrosis, 267–269 interferon (IFN) role, 263–264, 266–267 lichenoid forms, 265–266 clinical signs, 260 definition, 259 Granulocytes neutrophils, 62–64 TGFb blockade, 64 Granulysin, 84–85 Granzymes, 85 H HCV infections. See Hepatitis C virus infections Heart transplantation, allograft rejection, 325–326 Hepatic ISG response acute HCV infection, 130–132 chronic HCV infection, 132–134 IFN therapy IP-10 (CXCL10), 130–131 relationship factors, 134 Hepatitis C virus infections acute infection, 130–132 chronic infection, 132–134 hepatic gene expression, 132–134 innate response modulation, 129–130 intrahepatic induction, ISGs, 130–132 polyprotein, 129 T cell response, 135–136 therapy, 128 tumor rejection, 296 viral clearance IL28B (IFNl3), IFNa therapy, 134–135 T cell response, 135–136 Hepatocyte, 130–132 HIV-VLPs vaccine, 154–155 HTLV-1. See Human T-lymphotropic virus type 1 HTLV-1-associated myelopathy (HAM) immunopathogenesis, 190–191 T-cells proinflammation flow cytometric analysis, 193 Foxp3 expression and Treg suppression, 192 THAM/Treg cell ratio, 193–194

350 Human T-lymphotropic virus type 1 (HTLV-1) autologous tissue damage, 190–191 genome, 189–190 myelopathy/tropical spastic paraparesis (HAM/TSP) immunopathogenesis, 190–191 tax mRNA expression, 195–196 T-cells proinflammation, 192–194 and regulatory T-cells, 192 I ICR. See Immunologic constant of rejection Immune biology adoptive immune therapy cancer, 96–100 leukemia, 95–96 viral induce malignancies, 94–95 viral infections, 93–94 angiogenic switch dendritic cells (DCs), 65–66 granulocytes, 62–64 innate immunity, 59–62 mast cells, 64–65 myeloid derived suppressor cells (MDSCS), 62 NK cells, 66–67 cancer and inflammation, 11–13 chemokines acute rejection, 80–81 allograft rejection, 79 chronic rejection, 81 clinical data, 82–83 clinical utility translation, 81–84 early events, 79–80 cytotoxic effector molecules clinical utility, 86 granulysin, 84–85 granzymes, 85 molecular profiling studies, 86 perforin, 85 experimental models of rejection acute infections, 19–20 chronic inflammation, obesity, 20–21 intestinal microbiota, 18–19 tumor rejection, 21–23 immunological switch IL-10, 31–34, 37–40 STAT3 transcription factor, 40–42, 44–47 T helper (Th) cells, 27–31 Immune biomarkers, 170, 177–179 Immune dysfunction, cancer bearing state apoptotic T cells, 170–171

Index immune mediators balance cytokine profiles, 173 myeloid-derived suppressor cells (MDSCs), 174 T cells, 173 impaired lymphocytes B cells, 171–172 CD3z expression, 171 dendritic cells, 172 NK cells, 172 T cells, 171 vs. tumor rejection, 170, 175 Immune-mediated tumor rejection chemotherapy, 291–294 hepatitis C virus (HCV) infections, 296 immunological signatures, 287–291 MAGE-3-based vaccine, 283 mechanism, 284–287 melanoma metastases, 282–283 tissue-specific destruction (TSD), 296–297 viral oncolytic therapy, 294–296 Immune reactivity. See Experimental models of rejection Immune signatures cancer bearing state applications, 177–179 immune dysfunction, 169–174 tumor rejection, 170, 174–177 peripheral lymphocytes blood transcript profiling, 105–107 human subjects profiling, 107–110 microarray data analysis, 111–117 technologies, 110–111 vaccines, systems biology development, PRRs, 142–148 system levels analyses, 149–158 Immunogenomics, vaccines HIV-VLPs, 154–155 measle vaccine, 155–156 pertussis vaccine, 157–158 rubella vaccine, 156–157 YF-17D, 152–153, 155 Immunological signatures, tumor rejection IL-2 and TLR-7 agonists, 287 immunologic constant of rejection (ICR), 289–290 IRF-1, 288 TLR-7 agonist, 289 TRL agonists, 291 Immunological switch IL-10 anti-inflammatory effects, mechanisms, 37–40

Index blockade of, pro-inflammatory cytokine production, 47 inflammation controls, 31–33 regulation of, production, 33–34 TLR signaling, 37 STAT3 transcription factor in cancer, 41–42 cancer-associated inflammation, 42 CpG treatment, 46–47 DC maturation, 45–46 in health and disease, 40–41 hematopoietic cell ablation, 45–46 inhibition of, 44–45 T helper (Th) cells CD4 cell lineages, 27–29 differentiation and activation, 29–30 function and plasticity, in vivo, 30–31 Immunologic constant of rejection (ICR), 4 Immunotherapy, cancer prognosis adoptive cell therapy, 208 IFNa therapy, 207 IL-2 therapy, 207 therapeutic strategies, 206–207 Inactivated vaccines, 143 Innate immune response, 129, 132, 134, 135 Innate immune systems, 142 Innate immunity macrophage polarization paradigm, 59 Tie2 expressing macrophages, 61–62 tumor associated macrophages (TAMs), 60–61 Innate signatures Hepatitis C virus infections acute infection, 130–132 chronic infection, 132–134 hepatic gene expression, 132–134 innate response modulation, 129–130 intrahepatic induction, ISGs, 130–132 polyprotein, 129 T cell response, 135–136 therapy, 128 viral clearance, 128–129, 134–136 Integrative meta-analysis, graft rejection, 252 Interferon regulatory factor-1 (IRF-1) allograft rejection tumor rejection, 334–335 upregulated genes, 334 inflammatory process, 5 Interferon (IFN) role CGVHD, 263–264, 266–267 IFN-a cancer prognosis, 207 Interferon stimulated genes (ISGs) response, HCV infections hepatic gene expression, 132–134

351 intrahepatic induction innate vs. adaptive immune response, 131 microarray analysis, 130 RNA levels and hepatocytes, 131–132 Interleukin 10 (IL-10) anti-inflammatory effects, mechanisms, 37–40 blockade of, pro-inflammatory cytokine production, 47 inflammation controls, 31–33 kidney and liver transplantation, 329 regulation of, production, 33–34 TLR signaling, 37 Interleukin 28 (IL28), 134–135 Interleukin 2 (IL-2) therapy, cancer prognosis, 207 Intestinal microbiota rejection, 18–19 IRF-1. See Interferon regulatory factor-1 ISGs. See Interferon stimulated genes (ISGs) K Kidney transplantation, allograft rejection, 320–325 L Live attenuated vaccines, 143 Liver transplantation, allograft rejection acquired immunological tolerance, 306 chemokines role, 308 complement system, 324–325 CXCR3/CXCL9 pathway, 320–321 immune effector function (IEF) genes, 321 innate immunity role, 322–324 interleukin 10 (IL-10) role, 329 molecular analogies, 330 tumor necrosis factor-a (TNF-a), 321–322 Lung transplantation, allograft rejection, 325–326 M MAGE-3-based vaccine, tumor rejection, 283 Mast cells, angiogenic switch, 64–65 Master switch of inflammation. See Interferon regulatory factor-1 Measle vaccine, 155–156 Melanoma immunotherapy, 207 transcriptional profiling adaptive immunity induction, 232–233 chemokines and T cell trafficking, 232 Il-2 and anti-CTLA-4 mAbs, 234–235

352 Melanoma (cont.) immune suppressive mechanisms, 233–234 subtypes of, 231 T cell response analysis, 230 tumor microenvironment, 230–231 Meta Core algorithms, allograft rejection, 333 Microarrays allograft rejection gene expression profile, 307–314 ingenuity pathways analysis, 318 key genes, 315–317 Meta Core algorithms, 333 chimpanzee model analysis, HCV infections, 130, 133 solid organ transplant, 241–243 Myeloid derived suppressor cells (MDSCS), 62 N Nanostring technology, 111 Natural killer (NK) cells chemokines, 80, 81 cytotoxic effector molecules, 84–86 localization, 67 subsets, 66 Non-small cell lung cancer (NSCLC) prognosis, 204 tumor rejection, cancer bearing state, 175 O Ovarian carcinoma. See Epithelial ovarian carcinoma (EOC) Over-representation analysis (ORA), graft rejection, 245–246 P Pathogen-associated molecular patterns (PAMPs), 68 Pathogen recognition receptors (PRRs). See Vaccines, systems biology Perforin, 85 Peripheral lymphocytes blood transcript profiling, 105–107 cutting-edge RNA profiling technologies, 111 human subjects profiling autoimmune diseases, 107–108 infectious diseases, 108–109 multiple diseases, 109–110

Index microarray data analysis analysis primer steps and considerations, 112–113 interpretation, 115–117 modular analysis framework, 114–116 significance patterns analysis, 113–114 Persistent infection, 128, 136 Pertussis vaccine, 157–158 Plasmacytoid dendritic cells (pDC), 263, 264 Post transplant Epstein-Barr virus (EBV)associated lymphoproliferative disease, 94–95 Proteomics, 151 R Renal cell carcinoma (RCC), 203–204 RNA-seq technology, 111 Rubella vaccine, 156–157 S Solid organ transplantation, microarrays, 241–243 Specific immunity, angiogenesis, 67 Staging and grading systems, cancer, 199 STAT3 transcription factor, immunological switch in cancer, 41–42 cancer-associated inflammation, 42 CpG treatment, 46–47 DC maturation, 45–46 in health and disease, 40–41 hematopoietic cell ablation, 45–46 inhibition of, 44–45 Systemic lupus erythematosus (SLE) Foxp3lowCD4+CD25+ memory T cells, 193 T TAM. See Tumor associated macrophages T helper (Th) cells, immunological switch CD4 cell lineages, 27–29 differentiation and activation, 29–30 function and plasticity, in vivo, 30–31 Thymic epithelial cells (TEC), 272–273 TIL. See Tumor infiltrating leukocytes Tissue-specific destruction (TSD), 296–297 ICR, 4 IRF-1, 5 recurrent themes, 5–6 Toll-like receptors (TLRs). See Vaccines, systems biology

Index Transcriptional profiling, melanoma adaptive immunity induction, 232–233 chemokines and T cell trafficking, 232 IL-2 and anti-CTLA-4 mAbs, 234–235 immune suppressive mechanisms, 233–234 subtypes of, 231 T cell response analysis, 230 tumor microenvironment clinical trial, 230–231 IL-2 and anti-CTLA-4 mAb effects, 234–235 Transcriptomics, 149–150 Transplantation allograft rejection heart and lung, 325–326 kidney and liver transplantation, 321–325 microarrays applications, 241–243 Tumor-associated antigens (TAAs), 176 Tumor associated macrophages (TAMs) innate immunity, 60–61 plasticity and metastasis promotion, 12–13 protective inflammation and activation, 13 recruitment, 12 Tumor infiltrating leukocytes (TIL), 96–97, 100 Tumor necrosis factor-a (TNF-a) kidney and liver transplantation, 321–322 Tumor rejection cancer bearing state autoimmune responses, TAAs, 176 breast cancer gene expression, 174–175 immune adaptation, T cells, 176 vs. immune dysfunction, 170, 175 non-small-cell lung cancer (NSCLC) gene expression, 175 host immune system chemotherapy, 291–294 hepatitis C virus (HCV) infections, 296

353 immunological signatures, 287–291 MAGE-3-based vaccine, 283 mechanism, 284–287 melanoma metastases, 282–283 tissue-specific destruction (TSD), 296–297 viral oncolytic therapy, 294–296 V Vaccines, systems biology development, PRRs adaptive and innate immune systems, 142 humoral or cellular immune response, 147–148 inactivated vaccines, 143 innate immune system engagement, 146 innate immunity, 143–144 innate signaling and translation, 144–145 live attenuated vaccines, 143 long-term protective immunity, 147 main signaling pathways, 145 system levels analyses genetic polymorphisms, 152 immunogenicity prediction, 152–158 proteomics, 151 RNAi and miRNA, 151–152 systems biology, 149 transcriptomics, 149–150 Viral oncolytic therapy, tumor rejection, 294–296 Y Yellow fever (YF-17D) vaccine, 152–153, 155