Single topic volume
Cancer Genomics
Editors
L.A. Cannizzaro, Bronx, N.Y. K.H. Ramesh, Bronx, N.Y.
88 figures, 48 in color, and 35 tables, 2007
Basel • Freiburg • Paris • London • New York • Bangalore • Bangkok • Singapore • Tokyo • Sydney
Cover illustration Partial ideograms showing the abnormal chromosomes (only) which are involved in what is known as the ‘classic translocations’ that are diagnostic of some hematologic and soft tissue malignancies. 1st Row The t(9;22)(q34;q11.2) diagnostic of chronic myelogenous leukemia (CML). The t(8;21)(q22;q22) diagnostic of acute myeloid leukemia (AML). The t(15;17)(q22;q12) diagnostic of acute promyelocytic leukemia (APL). 2nd Row The t(2;5)(p23;q25) diagnostic of anaplastic large cell lymphoma (ALCL). The t(11;14)(q13;q32) diagnostic of mantle cell lymphoma (MCL). The t(8;14)(q24;q32) diagnostic of Burkitt lymphoma (BL). 3rd Row The t(1;13)(p36;q14) diagnostic of alveolar rhabdomyosarcoma. The t(X;18)(p11.2;q11.2) diagnostic of synovial sarcoma. The t(11;22)(q24;q12) diagnostic of Ewing sarcoma. Cover illustration from K.H. Ramesh and Linda A. Cannizzaro, Cytogenetics Laboratory, Montefiore Medical Center, Bronx, NY.
S. Karger Medical and Scientific Publishers Basel • Freiburg • Paris • London New York • Bangalore • Bangkok Singapore • Tokyo • Sydney
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Vol. 118, No. 2–4, 2007
Contents
91 Preface Cannizzaro, L.A.; Ramesh, K.H. (Bronx, NY) 92 Head and neck cancer: reduce and integrate for
optimal outcome Belbin, T.J.; Bergman, A.; Brandwein-Gensler, M.; Chen, Q.; Childs, G.; Garg, M.; Haigentz, M.; Hogue-Angeletti, R.; Moadel, R.; Negassa, A.; Owen, R.; Prystowsky, M.B.; Schiff, B.; Schlecht, N.F.; Shifteh, K.; Smith, R.V.; Zheng, X. (Bronx, NY) 110 Origin and functional significance of large-scale
chromosomal imbalances in neuroblastoma Stallings, R.L. (San Antonio, TX) 116 Genomic signatures of breast cancer metastasis Urquidi, V.; Goodison, S. (Jacksonville, FL) 130 High-resolution array comparative genomic
hybridization of chromosome 8q: evaluation of putative progression markers for gastroesophageal junction adenocarcinomas van Duin, M.; van Marion, R.; Vissers, K.J.; Hop, W.C.J.; Dinjens, W.N.M.; Tilanus, H.W.; Siersema, P.D.; van Dekken, H. (Rotterdam) 138 Pathology and genetics of adipocytic tumors Hameed, M. (Newark, NJ) 148 Molecular cytogenetic characterization of
pancreas cancer cell lines reveals high complexity chromosomal alterations Griffin, C.A.; Morsberger, L.; Hawkins, A.L.; Haddadin, M.; Patel, A. (Baltimore, MD); Ried, T.; Schrock, E. (Bethesda, MD); Perlman, E.J.; Jaffee, E. (Baltimore, MD) 157 Molecular mechanisms underlying the MiT
translocation subgroup of renal cell carcinomas Medendorp, K.; van Groningen, J.J.M.; Schepens, M.; Vreede, L.; Thijssen, J.; Schoenmakers, E.F.P.M.; van den Hurk, W.H.; Geurts van Kessel, A.; Kuiper, R.P. (Nijmegen)
Contents
166 Disruption of the FA/BRCA pathway in bladder cancer Neveling, K.; Kalb, R. (Würzburg); Florl, A.R. (Düsseldorf); Herterich, S.; Friedl, R.; Hoehn, H. (Würzburg); Hader, C.; Hartmann, F.H. (Düsseldorf); Nanda, I.; Steinlein, C.; Schmid, M. (Würzburg); Tönnies, H. (Berlin); Hurst, C.D.; Knowles, M.A. (Leeds); Hanenberg, H. (Düsseldorf); Schulz, W.A.; Schindler, D. (Würzburg) 177 The genetics of bladder cancer: a cytogeneticist’s
perspective Wolff, D.J. (Charleston, SC) 182 The cytogenetics and molecular biology of
endometrial stromal sarcoma Sandberg, A.A. (Phoenix, AZ) 190 Pathogenetic mechanisms in endometrial stromal
sarcoma Micci, F.; Heim, S. (Oslo) 196 Influence of a nonfragile FHIT transgene on murine
tumor susceptibility McCorkell, K.A. (Philadelphia, PA); Mancini, R. (Rome); Siprashvili, Z. (Stanford, CA); Barnoski, B.L. (Philadelphia, PA); Iliopoulos, D. (Columbus, OH); Siracusa, L.D. (Philadelphia, PA); Zanesi, N.; Croce, C.M.; Fong, L.Y.Y.; Druck, T.; Huebner, K. (Columbus, OH) 204 Exogenous mycoplasmal p37 protein alters gene
expression, growth and morphology of prostate cancer cells Goodison, S. (Jacksonville, FL); Nakamura, K.; Iczkowski, K.A.; Anai, S.; Boehlein, S.K.; Rosser, C.J. (Gainesville, FL) 214 SNP-Array genotyping and spectral karyotyping
reveal uniparental disomy as early mutational event in MSS- and MSI-colorectal cancer cell lines Melcher, R. (Würzburg); Al-Taie, O. (Aschaffenburg); Kudlich, T.; Hartmann, E.; Maisch, S.; Steinlein, C.; Schmid, M.; Rosenwald, A.; Menzel, T.; Scheppach, W.; Lührs, H. (Würzburg)
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310 Genetic alteration associated with chronic
222 Familial cancer syndromes: catalog with comments Hecht, F. (Scottsdale, AZ)
lymphocytic leukemia Cotter, F.E.; Auer, R.L. (London)
229 Interphase FISH as a new tool in tumor pathology Tibiletti, M.G. (Varese)
320 Genetics and epigenetics of 1q rearrangements in
hematological malignancies
237 The dynamics of cancer chromosomes and genomes Ye, C.J.; Liu, G.; Bremer, S.W.; Heng, H.H.Q. (Detroit, MI)
Fournier, A.; Florin, A.; Lefebvre, C.; Solly, F.; Leroux, D.; Callanan, M.B. (Grenoble)
247 The mystery of chromosomal translocations in cancer Koss, L.G. (Bronx, NY)
328 Cell migration patterns and ongoing somatic
mutations in the progression of follicular lymphoma Adam, P.; Schoof, J.; Hartmann, M. (Würzburg); Schwarz, S. (Regensburg); Puppe, B.; Ott, M.; Rosenwald, A.; Ott, G.; Müller-Hermelink, H.K. (Würzburg)
252 MicroRNAs in carcinogenesis Hagan, J.P.; Croce, C.M. (Columbus, OH) 260 Non-random inactivation of large common fragile site
genes in different cancers
337 Molecular cytogenetic analysis of follicular
lymphoma (FL) provides detailed characterization of chromosomal instability associated with the t(14;18)(q32;q21) positive and negative subsets and histologic progression
McAvoy, S.; Ganapathiraju, S.C.; Ducharme-Smith, A.L.; Pritchett, J.R.; Kosari, F.; Perez, D.S.; Zhu, Y. (Rochester, MN); James, C.D. (San Francisco, CA); Smith, D.I. (Rochester, MN) 270 Telomere dysfunction and telomerase activation in
Nanjangud, G. (New York, NY); Rao, P.H. (Houston, TX); Teruya-Feldstein, J.; Donnelly, G.; Qin, J.; Mehra, S.; Jhanwar, S.C.; Zelenetz, A.D.; Chaganti, R.S.K. (New York, NY)
cancer – a pathological paradox? Calcagnile, O.; Gisselsson, D. (Lund)
345 Molecular cytogenetics of IGH rearrangements in
277 Etiology of specific molecular alterations in human
malignancies
non-Hodgkin B-cell lymphoma
Vauhkonen, H.; Heino, S.; Myllykangas, S.; Lindholm, P.M.; Savola, S.; Knuutila, S. (Helsinki)
Bernicot, I.; Douet-Guilbert, N.; Le Bris, M.-J.; Herry, A.; Morel, F.; De Braekeleer, M. (Brest) 353 Molecular cytogenetics in the study of cutaneous
284 FISH panels for hematologic malignancies Sreekantaiah, C. (Stratford, CT)
T-cell lymphomas (CTCL)
297 Genetic and epigenetic alterations in myelodysplastic
Karenko, L.; Hahtola, S.; Ranki, A. (Helsinki)
syndrome Mihara, K.; Takihara, Y.; Kimura, A. (Hiroshima) 304 DNA profiling by arrayCGH in acute myeloid
leukemia and myelodysplastic syndromes
362 Author Index Vol. 118, No. 2–4, 2007 363 Author Index Vol. 118, 2007 after 364 Contents Vol. 118, 2007
Suela, J.; Alvarez, S.; Cigudosa, J.C. (Madrid)
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Cytogenet Genome Res Vol. 118, 2007
Contents
Cytogenet Genome Res 118:91 (2007) DOI: 10.1159/000108289
Preface
This special themed issue is dedicated to the founder of this Journal, Dr. Harold P. Klinger (1929– 2004). Dr. Klinger devoted most of his research career to the study of the genetics of cancer. He was instrumental to the creation of this Journal and rounded out his very interesting and illustrious life by being an avid salmon fisherman. Many of us who were privileged to know him benefitted from both of his lifes’ passions. His faithful and tireless devotion to his research and this Journal along with the multitude of professional contributions have been enumerated in another issue of this Journal (Vol. 109(4), 2005). The efforts and contributions within our present volume are an added way to cherish and honor Dr. Klinger’s many achievements in the area of genetic research. We would like to acknowledge and thank Dr. Michael Schmid for his sage advice and constant support for the duration of the preparation of this issue. We also wish to thank the many investigators and clinicians who participated in this endeavor for their inspiring ideas and innovative contributions. Advances in the area of cancer genomics have blossomed in the last decade as a result of an enormous diversity of technological advances. Momentous strides made in genome sequencing efforts have transformed this area of investigation in a significant fashion. The papers contained in this volume represent the enormous progress made in recent years in the study of leukemias, lymphomas and solid tumors. We consider ourselves privileged to consolidate such a pivotal volume in cancer genomics as we feel it contains a compendium of information and knowledge that will impact the study of cancer for many years to come. We have organized the papers to elicit the advances made in solid tumor exploration by placing these investigations at the beginning of the volume. In the past, solid tumor genomics has been a difficult area for investigations to yield much in the way of new information due to the difficulty in obtaining premalignant and early stage tumor samples from patients. Now with the availability of more sensitive diagnostic expertise and state of the art techniques, the number of samples obtainable at earlier and hence, more treatable stages, has significantly increased the success rate of tumor diagnoses. This needs to be emphasized in our approach to presenting this information to our readers. The middle of the volume is devoted to investigations which have utilized some of the more refined and sensitive technologies now available to study cancer. And the latter part of the volume represents advances specifically in the investigation of leukemias and lymphomas. All of the work presented in this volume represents just a small smattering of the continually expanding area of cancer genomic research, i.e., the collaboration of physicians, scientists, and everyone who is responsible for detecting, diagnosing and treating malignancies in patients with cancer. We also celebrate this work represented by a much improved quality of life and survival for patients with cancers of all kinds. Linda A. Cannizzaro K. H. Ramesh Bronx, NY, July 2007
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Accessible online at: www.karger.com/cgr
Etiology Environmental factors Infectious agents
Cancer
Clinical visit
Anatomic site LN+
LN–
Mutations
Diagnostic evaluation Imaging
Surgery Radiotherapy Chemotherapy Combination therapy Palliative therapy
History (exposure, risk factors, etc.) Physical exam
Susceptibility Individual’s genome
Treatment selection
Pathology Programmed cell death
Fig. 1. Factors affecting development and behavior of HNSCC. Etiologies include exposure to environmental factors that can vary geographically and by individual choice and to infectious agents. Genetic determinants of risk will influence whether cells accumulate DNA mutations in response to carcinogens or undergo programmed cell death. Once a cancer has developed in an individual, genetics will determine the range of responses to the cancer and therapy. For small (T1 and T2) tumors, there is a small chance of lymph node (LN) metastasis at initial presentation when the tumor is located in the oral cavity (⬃10%) as compared to a relatively frequent rate of metastasis for the same sized tumor in the oropharynx (⬃70%).
have a predilection for developing cancers in particular locations, e.g., nasopharynx in Southern China and oral cavity in India. The vast majority of head and neck cancers are squamous cell carcinomas arising from the surface epithelium of the upper aerodigestive tract mucosa. Although tobacco exposure and alcohol consumption are the most common environmental exposures related to this disease, other factors associated with this disease are HPV or EBV infection, chronic irritation, Betel quid exposure, chewing tobacco and a variety of other agents. Thus, the complexity of the anatomy, the variety of etiologies and the heterogeneity of the populations affected create a significant challenge in treatment selection and management of patients with HNSCC. Despite the global importance of this disease, the fiveyear survival rate for HNSCC (ca. 50%) has remained relatively stable over the past three to four decades. Meta-analyses report a marginal improvement in survival of approximately 8% over the past decade when using combination and concurrent therapies (Pignon et al., 2000; Khuri and Jain, 2004; Pignon and Burdett, 2006). Some improvements in survival are observed for certain head and neck sites when compared to others, notably a decline in survival for oral cavity and laryngeal tumors compared to pharyngeal cancers (Carvalho et al., 2005). As a result, it is estimated that 45,641 cases and 11,210 deaths will occur in 2007 in the United States from HNSCC (Jemal et al., 2007). HNSCC, as noted, constitute an anatomically heterogeneous group arising most often from the oral cavity, oropharynx, hypopharynx, larynx and nasopharynx. Certain anatomic sites have a predilection for early metastasis (Lindberg, 1972; Schwartz et al., 1998, 2000; Barnes et al., 2001) and treatment planning depends entirely upon anatomical staging of the disease at presentation. At this point, staging and treatment decisions rely solely upon the anatomic location of the primary cancer and the presence, or absence, of metastatic disease. Physical examination plays a pivotal role in local-
Biomarkers?
Fig. 2. Diagnosis and treatment decision for HNSCC. The transition from a patient’s complaint to the development of a treatment plan currently consists of an assessment of an individual’s risk factors, a determination of the anatomic location of the tumor (physical exam and imaging) and a description of the tumor type (histology). An appropriate treatment plan is then developed based upon a clinician’s cumulative understanding of the disease from their experience and medical knowledge. A better way to develop an appropriate treatment plan would be to incorporate clinically meaningful biomarker analyses which would assist in predicting response or resistance to particular therapies and tumor aggressiveness for early stage (I and II) tumors. Such biomarkers could potentially enhance survival or minimize toxicities and improve quality of life in these patients.
izing the tumor and regional spread to the lymph nodes. Imaging techniques, such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are all used to augment the physical exam and identify occult tumor extension into non-visualized, adjacent structures or lymph nodes. It is also important to assess the possibility of distant metastasis to the lungs for advanced stage disease. Pathologic features may also have an important impact in staging particular tumors of the head and neck and are critical in the most accurate assessment of the lymph nodes in patients who undergo surgical management of the neck (Fig. 2). Imaging techniques have improved our ability to diagnose occult disease and more accurately stage tumors prior to therapy. Standard imaging is composed predominantly of CT scanning, though MRI may be superior for some tumor sites such as the tongue base. These anatomic imaging techniques rely upon certain characteristics to predict involvement of structures with a tumor. Thus far, however, with the exception of the anatomical structures involved, a function of the standard clinical TNM (Tumor, Node, Metastasis) stage, imaging has not been predictive of outcome or response. Functional imaging with PET scanning has become commonplace in the initial evaluation of HNSCC patients, particularly those with advanced disease, as well as in the follow-up of patients after completion of therapy. This functional technique relies upon the transport of interstitial glucose into cells, mediated by a family of homologous glucose transport proteins (GLUTs), which differ in their tissue distribution and physiological properties (Joost and Thorens, 2001). Of these isoforms, GLUT1, an insulin independent transporter, in particular, is over-expressed in a variety of
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cancers including HNSCC (Mellanen et al., 1994; Reisser et al., 1999), and is associated with poorer prognosis (Mineta et al., 2002; Kunkel et al., 2003; Oliver et al., 2004; De Schutter et al., 2005). High 18F-fluordeoxyglucose (FDG) uptake is seen in PET scans of HNSCC patients with overexpression of GLUT1. Unfortunately, to date, the relative uptake of PET isotopes has not shown a consistent relationship with prognosis. Efforts to correlate nuclear imaging with response have been attempted in the past without significant impact. Multi-drug resistance P-glycoprotein (MDR) is a significant factor in HNSCC resistance to chemotherapy (Rabkin et al., 1995) and expression is induced by external beam radiotherapy (Ng et al., 1998). Imaging and uptake of 99mTc-SetaMIBI (MIBI), a substrate for MDR, is a surrogate for MDR activity in many cancers. However, there was no correlation between MIBI uptake and MDR expression pathologically in patients with head and neck cancer (Leitha et al., 1998) and modulation of MDR expression in a head and neck cancer xenograft model was not accurately assessed with MIBI scanning (Mubashar et al., 2004). Additional functional imaging techniques are emerging which may hold promise in helping predict treatment response or other tumor characteristics. Diffusion weighted MRI (DWI) is a new, emerging functional technique which provides information about tissue cellularity and integrity of cellular membranes (Koh and Padhani, 2006). Diffusion imaging detects movement of water molecules. While in a freely diffusible solute, diffusion is random (Brownian motion), in biological tissues, diffusion is restricted by interaction of water molecules with hydrophobic cellular membranes and macromolecules. The ‘apparent diffusion coefficient’ (ADC) quantifies the magnitude of diffusion in tissues. Different tissues have different apparent diffusion coefficients, producing contrast between the tissues. Wang et al. (2001) found that malignant tumors had lower ADC compared to benign lesions. They attributed the relatively restricted diffusion in malignant tumors to their greater cellular density as shown by histology. Vandecaveye et al. (2006) found diffusion measurements could differentiate between recurrent tumor and necrosis after radiation therapy. Loss of cellular structure due to necrosis is thought to lead to the increase in diffusion following radiotherapy. Ultimately, DWI MRI may be used in conjunction with conventional imaging techniques to differentiate between normal and abnormal tissues as well as to ascertain the response to therapy. Treatment for any given tumor depends upon the disease site, the extent of the primary tumor and the presence of regional or distant metastases. Conventional treatment may employ surgery, radiation therapy and chemotherapy either alone or in combination with any, or all, of the other modalities. Any of these therapies can, and do, produce morbidities affecting speech, swallowing and overall quality of life. These morbidities are often increased by the routine use of combined therapy in patients with advanced disease (Calais et al., 1999; Brizel et al., 2000; Epstein et al., 2001; Gal et al., 2003; Cooper et al., 2004). This is done in an effort to improve survival and locoregional control. Despite these
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interventions, recurrence of the disease is observed in about 50% of patients, locally, regionally or at a distant site with high rates of associated mortality (Takes et al., 1997). In fact, with the modest improvement in overall survival over the past decades, one must question whether a large percentage of patients are overtreated, incurring additional morbidity. Strategies must be developed to improve the selection of the most appropriate treatment for any given patient, and clinicians may need to reconsider the strategy of treatment escalation in all patients. Clinical experience has shown us, as cited above, that such escalation increases the morbidity of the therapy but does not necessarily translate into improved survival. In contrast, it may significantly reduce the quality of life in these patients and impact their ability to socialize and interact with family and friends (List and Bilir, 2004). While difficult to predict, treatment failures for HNSCC can be attributed to multiple factors. Some of the factors causing treatment failure are unknown and addition of isolated biomarkers has not improved our ability to predict outcome. Currently, the best predictors are the clinical findings of the TNM staging system (Tumor, Node, Metastasis). These factors, in conjunction with newer data supporting the use of aggressive chemotherapy and radiotherapy regimens (Forastiere et al., 2003; Bernier et al., 2004; Cooper et al., 2004; Bonner et al., 2006), argue for the need to develop new treatment paradigms. Concurrently, it would be useful to identify those patients who would benefit from multimodality therapies versus those patients who may receive less aggressive and less toxic therapy without impacting survival expectations (Smith RV et al., 2004; Beitler et al., 2007); the latter group might receive single modality therapy, or, more likely, the introduction of biologic response modifiers with potentially less morbidity. This paradigm has been recently employed through the use of the EGFR antagonist cetuximab in combination with radiation therapy and shown to improve locoregional control, median duration of overall survival and progression-free survival when compared to radiation therapy alone (Bonner et al., 2006). Efforts to identify biomarkers for HNSCC have been numerous and none have proven to be useful clinically; i.e., no biomarker is used to guide treatment selection at initial diagnosis. Studies on p53, EGFR, BCL2, MMPs, cyclins and molecular markers have demonstrated inconsistent, and at times contradictory, results (Kyzas et al., 2005; McShane et al., 2005a; Lothaire et al., 2006). Overall, identification of a single genetic or protein abnormality is likely to have a limited clinical benefit due to the inherent heterogeneity of these tumors and patients. In squamous cell carcinomas, the multistep carcinogenesis pathway, as proposed by Califano et al. (1996), accounts for many events in the transition from normal to cancer cells. These events are likely to vary with different etiologic agents in individual patients. One might expect a spectrum of genetic changes to occur, with some common themes, throughout the progression from normal tissue to carcinoma. In fact, early gene expression changes can be seen in oral mucosa after relatively limited exposure to tobacco smoke (Smith RV et al., 2006). Recent data support such differences, particularly with respect to
HPV exposure. Therefore, global changes in gene expression, DNA methylation status, proteomic expression or other measures are more likely to provide us with prognostic information rather than individual mutations. In such a model, each specific change is not as important as the overall pattern of gene expression. In fact, expression changes predictive of tumor behavior may represent changes in the host’s ability to control and respond to a tumor, i.e. immune response, angiogenesis induction, apoptosis induction, rather than tumor specific factors such as alterations in EGFR expression or p53 expression. The complexity of the tumor-host interaction and the tumor microenvironment, almost by necessity, limits the potential utility of single biomarkers (Condeelis and Pollard, 2006). This is also demonstrated by the experience of predictive gene expression arrays in breast cancer, where patterns of expression have been demonstrated to be useful in outcome prediction and treatment determination (Perou et al., 2000; van de Vijver et al., 2002; van ‘t Veer et al., 2002; Potti et al., 2006). Molecular characterization of this genetically complex disease has provided some insight into individual genetic abnormalities that contribute to tumor progression (Forastiere et al., 2001). Gene profiling can predict differences in tumor behavior not discernable by histopathology (Belbin et al., 2002). Our ability to provide effective treatment will depend upon our ability to determine that surgical margins are adequate, and our ability to predict tumor potential for metastasis and local recurrence and response to therapy. Thus, profiling of primary tumors using global RNA expression and global proteomic analyses coupled with sophisticated analyses identifying combinatorial and modifier effects will likely yield new diagnostics with high clinical impact that will predict aggressive growth, metastatic potential and responsiveness to several types of therapy. Selection, collection and management of complex data
Because of the challenge we face in understanding HNSCC and in improving the treatment for this cancer, we designed our program to collect as much information as possible on each patient and piece of tissue. We considered the platforms available with the idea that each platform should provide unique data that could be analyzed in the context of the clinical data and could be integrated with data from other platforms to complement our understanding of the pathophysiology and the responses of individual patients. For example, results from RNA expression profiling are commonly used to classify tumors; however, integration with results from CpG island methylation data will provide better insight into gene silencing and potential new therapeutic targets. Alternatively, expression of RNA may not lead to protein expression if miRNA is present. In addition, the activity of certain proteins is regulated posttranslationally without major changes in RNA expression. Thus having genomic and proteomic data for each cancer will enable us to identify aberrant pathways in the cancer and, in the context of the genome of each patient (e.g., prev-
alence of single nucleotide polymorphisms), will allow us to integrate data across platforms to identify key factors in disease progression and patient response. The sections below give our considerations and strategy in setting up infrastructure and our experience in data collection with respect to the field. Design considerations for data and sample collection Data management challenges. Two areas where epidemiological and biostatistical expertise are indispensable are in experimental design and data analysis requiring interaction with the investigators in all stages of the research, beginning with the formulation of the research question, through the experimental design stage and data collection stage and quality control and data management (including regular reports to monitor missing data, out-of-range values, inconsistent answers, etc.), to the generation of analytical datasets and data analysis and interpretation and dissemination of results. This also extends to the collection and integration of clinical, laboratory and epidemiological data necessary to conduct comprehensive statistical analyses on microarray platform data and account for the heterogeneity inherent in head and neck cancer risk data. In order to handle such complex and multi-source data, existing epidemiological studies now often develop and depend on extensive web-based data collection systems to recruit and collect participant study data. The advantage is that all data collected through this systems-based approach can be tracked, queried and even analyzed through automated processes, often in real time, while also allowing interactive and flexible interfaces online for investigators (Fig. 3). The head and neck cancer program website at Albert Einstein College of Medicine (AECOM) was developed for this purpose (http://www.aecom.yu.edu/headandneck/). The development of such a system, however, requires a truly multi-disciplinary team including data managers, network administrators and database programmers, in addition to the requisite clinical and scientific investigators. Further incorporating microarray platform data like that being generated at AECOM, demands the additional expertise of bioinformatics professionals experienced in manipulating and tracking multiple data forms including microarray, proteomic and genomic data. Finally, the success of an integrated research program, such as ours, necessitates the establishment of quality control procedures and standardized protocols by experts in all fields and effective communication between all participants. Quality control and validity analysis. In population studies involving microarray hybridization techniques, a second assay is routinely employed to verify the results obtained from this high-throughput approach. For example, methylation-specific PCR (MS-PCR) is one of the most trusted means of determining the methylation status of a given CpG island region. The choice of MS-PCR is also an alternative to the use of methylation-specific restriction enzymes, as used in our MSRE microarray assay, that is known to be less prone to false-positive results due to the inclusion of primer sets that serve as a control for the efficiency of bisulfite mod-
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ORL-HNS
Pathology Clinical database De-identification Hospital Firewall
a
b
H&N information system Relational database and web application c
e
High throughput Genomic data Proteomic data Systems biologist
e
d
Project specific data e
Biomedical scientist
Epidemiologist Biostatistician
f Integrated data analysis and model development
Fig. 3. Head and Neck Cancer Project Information System. a, b: Clinical data collected by Otorhinolaryngology-Head and Neck Surgery (ORL-HNS) including tumor staging, treatment, pathology, tissue inventory and follow-up visits are collected and entered into the project database behind the hospital firewall. These data are available to healthcare providers caring for the patients. Relevant data is de-identified and downloaded onto the relational database; c: raw datasets from genomics, proteomics and tissue microarray platforms are uploaded into the database and linked by a unique (de-identified) number to the clinical data; d: project specific data is uploaded into the database and linked by a unique (de-identified) number to the clinical data; e: data entry, queries and reports can be accessed in real time through a web portal that is password protected and customized for the specific project, also annotated and integrated datasets are generated for correlative and prognostic biostatistical analyses; f: integrated data analysis and model development in collaboration with experts covering various biomedical disciplines. Blue arrows indicate data flow and green arrows indicate communication.
ification (Liu and Maekawa, 2003). The sensitivity of MSPCR would allow the detection of hypermethylated CpG islands even if they contribute relatively little to the overall DNA sample. Furthermore, as it is also known that hypermethylation of CpG island promoter elements is often associated with the transcriptional silencing of associated genes, the examination of the effects of specific CpG island methylation on expression of neighboring genes offers an opportunity to add to the list of genes transcriptionally silenced by DNA methylation, and to identify new tumor suppressor genes in head and neck cancer. Using online software at NCBI, such as Map Viewer, island boundaries can be positioned by aligning partial sequences from these islands against the genomic sequence (Dombrowski and Maglott, 2003). Assessing the reliability of microarray data. Additional quality control measures employed in systemic approaches to population studies like ours include: (i) the collection of paired samples from the same patient (i.e., biopsy and resections or tumor and normal), (ii) the collection of tissue through multiple sources (i.e., fresh frozen and paraffinembedded tissue) and (iii) multiple samples from the same tissue (i.e., collecting multiple cores from the same tumor for constructing tissue arrays) or running multiple microarray assays on the same tumor specimen.
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For example, when validating our MSRE microarray assay for DNA methylation, where the quantity of amplified PCR product depends upon complete digestion of DNA by the restriction enzyme, we estimated the reproducibility of the MSRE assay by intraclass correlation (ICC) for three tumor samples, with four replicate experiments each, using normalized log-transformed ratio values (Adrien et al., 2006). Samples represented stage IV squamous cell carcinomas derived from the hypopharynx (tumor 1), larynx (tumor 2) and oropharynx (tumor 3). The ICC assesses reproducibility in a given assay by comparing the variability of different ratings of the same clone to the total variation across all ratings and all clones (Shrout and Fleiss, 1979). As such, the ICC decreases in response to both lower correlation between repeated assays and larger mean differences between assays. We also evaluated the gain in reliability achieved by filtering the array results based on fluorescence intensity and used these results to select optimal cutoff values for filtering. The overall reproducibility of the MSRE assay was high, with baseline (unfiltered) ICCs across the three tumor specimens of 0.68 to 0.85. ICC values close to 1.0 indicate that a larger proportion of the variance observed in fluorescence ratios is due to variability between rather than within CpG islands. We also assessed the change in ICC under different filtering conditions based on fluorescence intensity. Interestingly, the largest change in ICC occurred after restricting observations to results with a fluorescence intensity of 200 or more. We also evaluated whether the reliability of the fluorescence ratios was a function of their location on the microarray slide. Sites were restricted to individual grid blocks, each containing 525 clones, which can be identified on the microarray grid. Overall, differences in ICC between blocks were small (!0.05) and showed no pattern by location on the grid. We attempted to estimate the number of repeated assays that would be needed to achieve a given level of reliability without filtering. Using a variant of the Spearman-Browne prophecy formula (Shrout and Fleiss, 1979), we calculated that we would need to repeat our assay twice at most to obtain an average reliability of 0.8 and five times to achieve a reliability of 0.9. Pathology and tissue procurement Tissue procurement. Optimal procurement, processing and distribution of solid human tissue requires close coordination among surgeons, pathologists and clinical and laboratory investigators. Once IRB approval has been obtained, the pathologist works with investigators and technical staff to develop a project-specific protocol for tissue procurement and distribution (Fig. 4). The coordinated process insures that sufficient specimen is available for diagnosis and that tissue delivered to the investigator has a histopathologic diagnosis (Neiderhuber, 2006). In addition, appropriate staff familiar with each protocol are aware of special requirements for handling, processing and distribution of tissue. The research specimen is snap frozen in liquid nitrogen and stored in a –80 ° C freezer. In selected situations where there is clearly ample tissue for a diagnostic speci-
men, fresh tissue may also be taken for cell culture or transplantation into immunodeficient mice. A separate portion of the selected research specimen is prepared for standard histology. This is a quality assurance measure that documents and insures the nature of the specimen procured. No tissue is released to a research laboratory until a final pathologic diagnosis is rendered. In the event of a discrepancy (e.g., there is a clinical suspicion of cancer, and the biopsies designated for diagnosis are negative), the snap frozen tissue is retrieved and processed for histopathology. The full integration of clinical and translational research operations yields the highest quality of tissue data. Tissue microarray. Tissue microarray (TMA) is the process of utilizing needle cores of paraffin embedded pathology specimens to construct paraffin microarray blocks (Rimm et al., 2001). These blocks are then used for immunohistochemical (IHC) analysis. The advantage of this technique is that one block can be designed to hold multiple cores from scores of patients, thus compressing the informative potential to a single TMA slide, facilitating high throughput analyses while preserving the limited resource of patient samples. The microarrayer holds the pathology specimen block (donor block), the TMA block in preparation (recipient block) and is coupled to a microscope holding the corresponding pathology slide. Selecting the exact areas of interest on the pathology slide (tumor versus non-tumor areas) allows donor cores to be harvested from the corresponding area in the paraffin block. Three or more cores are harvested from the areas of interest. TMA IHC analysis is an important validation arm of molecular profiling. It allows us to (i) correlate RNA expression and gene silencing results with resultant proteomes, (ii) identify the cells with significant expression alterations (tumor versus stromal cells), and (iii) localize subcellular expression (nuclear vs. cytoplasmic vs. membranous staining patterns). The latter two issues can only be addressed at the in-situ tissue level. Protein expression may be discordant with respect to RNA expression due to reasons stated above. Alternatively, intracellular localization may affect function and tumor behavior as has been shown for ERM proteins (Kobayashi et al., 2004; Madan et al., 2006). An ultimate goal of biomarker discovery is to apply a panel as a routine diagnostic test. IHC analysis is a simple analytic tool for biomarker detection that can be used daily in surgical pathology reporting. Thus biomarker discovery, and its validation by TMA IHC, can lead back to its application as a routine IHC diagnostic test. Histopathology. Histopathology has a crucial role in initiating treatment decisions as it confirms the diagnosis of malignancy. There are many elements of the histopathologic analysis that predict tumor behavior (described below); however, since the initial analysis is performed on a small biopsy specimen, there is limited potential for gleaning further significant information (e.g., perineural invasion which would foretell the need for adjuvant therapy). If the decision is made to treat the patient surgically, then the histopathologic findings from the resection have greater informative potential. The pathology report establishes the
Hypothesis
Study design
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PRC and IRB approval
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Pathology 1-Routine pathology 2-Tissue distribution 3-Tissue microarray
Tissue processing 1-Separation of macromolecules 2-Storage of samples
Viable tissue 1-Implantation 2-Cell culture
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Genomics 1-RNA expression 2-DNA hypermethylation analysis Proteomics 1-Global proteomics 2-Targeted proteomics
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Fig. 4. Tissue procurement and distribution for head and neck cancer program. The highly interactive, multidisciplinary group of investigators works together to develop study design and documents for regulatory approval. Tissue procurement protocols are developed and coordinated between Surgery, Pathology and individual investigators. Tissue processing and banking are performed in pathology laboratories. High throughput genomic and proteomic analyses are performed in shared resource laboratories at Einstein Medical School. Data collection and analysis is carried out as described in Fig. 3.
AJCC tumor stage with respect to primary site and nodal status. The reporting of inadequate resection margins or perineural invasion will lead to adjuvant therapy. The finding of extranodal tumor extension will lead to increased radiation dosing to the neck. But what further insight into tumor biology can be derived from studying a resection specimen? Single histological variables such as tumor grade, mitotic rate and degree of keratinization have been disappointing with respect to providing additional prognostic information. Tumor thickness for T1 tongue SCC has strongly associated with propensity for lymph node metastasis, and survival (Martinez-Gimeno et al., 1995), but is not a predictor for local recurrence (Kim et al., 1993; Po Wing et al., 2002). Multiparameter prognostic histological assessments have been developed and refined over the last two decades, based on variables that include pattern of invasion, degree of keratinization, nuclear pleomorphism, lymphocytic response and mitotic rate (Anneroth et al., 1987; Bryne et al., 1989, 1992, 1995; Kim et al., 1993; Spiro et al., 1999; Bundgaard et al., 2002). Pattern of tumor invasion (POI) refers to the manner in which cancer infiltrates tissue at the tumor/ host interface. It is intuitive that neoplasia infiltrating in a widely dispersed manner is more aggressive than those growing in a bulky pushing fashion. POI alone, and as part of weighted scoring systems, has been demonstrated to predict local recurrence and overall survival. We have further refined the histological definitions of POI to recognize a subgroup with greater aggressive potential and we devel-
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oped a risk score model that is highly predictive of local disease-free and overall survival when corrected for resection margin status and adjuvant treatment (BrandweinGensler et al., 2005). This risk model can be assessed by examining the resection specimen and quantifying three significant histological variables: 1) tumor pattern of invasion (POI) at the advancing tumor edge, 2) perineural invasion, and 3) lymphocytic host response at the patient interface. Importantly, this risk assessment identified a subset of patients who benefited from post-surgery adjuvant therapy (‘high-risk’ patients) despite the absence of traditional indications; these were patients with Stage I or II cancers, negative resection margins and no perineural invasion. Patients with high-risk scores including perineural spread would have received adjuvant therapy based on the finding of perineural tumor invasion by usual treatment paradigms. But patients with an intermediate degree of lymphocytic host response at the interface and either a type 4 or 5 POI represent an example of a newly defined high-risk category. We are prospectively validating this ‘risk assessment’ model on a new patient cohort. Our histological risk assessment is derived from reading a patient’s primary resection specimen; it cannot be reliably determined from preoperative biopsies. Is this valuable and simple predictive tool limited to patients who are treated with primary surgery? A future direction will be to use the risk scoring to identify surrogate biomarkers. Filtering RNA expression data by the risk grouping of the corresponding resection specimens will make possible the identification of biomarkers that associate with low-, intermediate- and high-risk groups. We believe that a panel of biomarkers derived in this manner may predict treatment response with respect to the initial decision bifurcation of primary surgery versus organ-sparing protocol. This panel of surrogate biomarkers will reflect risk score but can be determined at initial patient workup. Thus we return to the issue of limited informative potential gleaned from the initial diagnostic biopsy. Our goal is to push this envelope and identify and validate a prognostic biomarker panel that has informative added value to traditional TNM prognostication. Epigenetic alterations and head and neck carcinogenesis Whole genome approaches using high-throughput methodologies such as DNA microarrays have permitted the classification of many tumor types, including HNSCC, based on parameters such as the patterns of global gene expression or chromosomal aberrations. The results of these studies have revealed many new molecular ‘signatures’ for previously indistinguishable subtypes of these diseases. These and other technologies have now been extended to include so-called ‘epigenetic’ changes, as it is now well known that such alterations also contribute significantly to the onset of human malignancies. By far, the most studied epigenetic event has been the addition of a methyl group to the carbon-5 position of cytosine nucleotides. Of particular interest are the aberrant DNA methylation events associated with CpG-rich sequences, known as ‘CpG islands’, which
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range in length from several hundred to several thousand nucleotides and are frequently associated with gene promoters or first exons. In certain diseases, including cancer, aberrant hypermethylation of these CpG islands is associated with the inappropriate transcriptional silencing of critical genes. Such transcriptional repression can therefore act as one of the hits in the Knudsen two-hit model for tumor generation by abrogating gene function in a manner similar to a mutation or gene deletion but without any change in the nucleotide sequence (Robertson, 2001). As for other cancers, epigenetic markers are of prognostic value to HNSCC. Numerous studies have identified promoter methylation of CDKN2A (p16), DAP kinase (DAPK), and DNA repair genes MGMT and MLH1 (Rosas et al., 2001; Maruya et al., 2004). In HNSCC patients, methylation of the promoter region of MGMT was associated with decreased expression of MGMT, as well as increased tumor recurrence and decreased patient survival independent of other factors (Zuo et al., 2004). In studies by Ogi et al. (2002) with oral SCC, methylation of the DCC gene was significantly associated with bone invasion by gingival tumors, aggressive invasiveness of tumors of the tongue and reduced survival. Methylation of two CpG islands (MINT1 and MINT31) also correlated with poor prognosis in these patients, whereas methylation of p14ARF actually correlated with a good prognosis. Invasion and metastasis of oral SCC cells have recently been shown to be dependent on methylation of the E-cadherin promoter with associated reduction of E-cadherin expression (Kudo et al., 2004). In addition to the value of individual epigenetic events, there is also the potential that overall patterns of genomic DNA methylation may play a critical role in the molecular characteristics of HNSCC. For example, it is known that a subset of colorectal tumors with widespread DNA hypermethylation (the so-called ‘CpG island methylator phenotype’, CIMP) have distinctive clinicopathological and molecular characteristics (Bariol et al., 2003). Recent studies have now shown that this phenotype is also an independent predictor of response to 5-fluorouracil (5FU) and survival for patients with stage III colorectal cancer (van Rijnsoever et al., 2003). It has been suggested that a CIMP may also exist for HNSCC (Rosas et al., 2001) but more studies will be required to confirm its existence for HNSCC and any clinicopathological consequences for the patient. On a global ‘epigenomic’ scale, new technologies to study global promoter hypermethylation events in human malignancies have also revealed a great deal of information. For example, technologies such as restriction landmark genome scanning (RLGS) have demonstrated that overall patterns of aberrant CpG island methylation are tumor-type specific (Costello et al., 2000). Similarly, microarrays containing thousands of CpG island clones have been utilized to generate profiles based not on gene expression, but on the DNA methylation status of each CpG island clone. Studies by our group using primary HNSCC tumor DNA have revealed that such aberrant DNA methylation events affect hundreds of CpG island clones. Furthermore, these DNA methylation profiles of primary HNSCC tumors are tumor-specific and can be uti-
lized to distinguish individual tumors from one another in a reproducible manner (Adrien et al., 2006). And finally, classification of HNSCC tumors based on patterns of DNA methylation appeared to reveal three distinct epigenomic subtypes of the disease, with clustering predominantly influenced by the number (high, medium, low) of methylated CpG island clones observed for each cluster of tumors. Additional statistical approaches are now underway to evaluate the clinical implications of these DNA methylation profiles for HNSCC. These and other findings have laid the groundwork for population-based studies to examine DNA methylation patterns in human malignancies and helped to identify associations between specific epigenetic signatures and clinical parameters. Already, epigenomic profiling techniques have revealed that CpG island methylation is associated with the histological grades of breast tumors (Yan et al., 2000). A similar study of late-stage ovarian carcinomas with CpG island microarrays revealed that a higher degree of CpG island methylation was significantly associated with early disease recurrence following chemotherapy (Wei et al., 2002). Most importantly, this study also identified a select group of CpG island loci that could be used as epigenetic markers for predicting outcome in ovarian cancer patients. The integration of these events with other systematic data (chromosomal deletion maps, microRNA) is a key to understanding DNA methylation profiles and elucidating transcriptional silencing mechanisms as a convergence of epigenetic-silencing, RNA interference and chromosomal events. Aberrant DNA methylation events remain an exciting molecular marker for future investigations, due to the fact that it represents a stable tumor-specific marker that often occurs early in tumor progression and can be easily detected by PCR-based methods. Such PCR-based detection methods for diagnostic and prognostic biomarkers would be minimally invasive to the patient and would require trace amounts of genetic material. Such approaches have already been utilized to detect aberrant DNA methylation in the saliva of HNSCC patients (Rosas et al., 2001). Gene expression profiling and the evolution of HNSCC tumor classification The vast majority of genomic profiling and molecular classification of all tumor types, including HNSCC, has involved high throughput methodologies to measure differential gene expression. One of the best known of these technologies is the DNA microarray, known also as the ‘DNA chip’ or ‘gene chip.’ Many of the early investigations in HNSCC research used microarrays for a comparison of two biological states. In the case of cDNA microarrays, RNA was extracted to compare tissues of two different states (e.g., a pair of normal and tumor tissues) and labeled with distinct fluorescent dyes. By combining and simultaneously hybridizing these labeled products to their corresponding cDNAs on the microarray, one could assess the relative abundance of thousands of genes in parallel simply by measuring the fluorescence ratio of the two dyes.
Many of the studies involving DNA microarrays have focused on the identification of alterations in gene expression that are associated with HNSCC carcinogenesis, specifically the identification of genes whose expression has changed in HNSCC tissue samples compared to normal tissue. For example, in an initial study Villaret et al. (2000) using microarrays containing 985 clones, examined sixteen HNSCC cases and identified nine over-expressed genes. A later study, utilizing laser capture microdissection of HNSCC cells to measure the expression of 588 known cancer-related genes, demonstrated increased expression of genes related to the Wnt and Notch signaling pathways, as well as a decrease in expression of differentiation markers such as cytokeratins (Leethanakul et al., 2000). Using a similar study design with cDNA expression microarrays (Belbin et al., 2005), our group examined gene expression changes in primary HNSCC versus adjacent mucosa, as well as primary HNSCC versus metastatic carcinoma. One of the genes identified as consistently increasing in expression during tumor progression was moesin, a member of the ERM (ezrin; radixin; moesin) family of closely related membrane cytoskeletal linkers known to regulate cell adhesion (reviewed in Mangeat et al., 1999). In addition to gene discovery and mechanistic studies elucidating the roles of altered gene expression in carcinogenesis, the use of microarrays permits the identification of distinct ‘molecular signatures’ of tumor cells that are of prognostic value. This approach has been applied successfully for HNSCC, demonstrating that tumors originating from different anatomic sites within the head and neck can exhibit varying behavior that is not predictable by histopathology of the primary tumor but is discernable by gene profiling (Belbin et al., 2002). In oral cancer, 23 differentially expressed genes were identified that correlated with tumor stage (III–IV) and metastasis (Warner et al., 2004). In one of the early studies correlating gene expression profiling and treatment response, Hanna et al. (2001) identified 60 tumor-related genes from a cDNA microarray containing 1,187 genes that could successfully predict tumor response to radiation. Gene expression signatures have now been identified that are associated with recurrence of HNSCC disease (Ginos et al., 2004). While these initial findings are promising, they have not been accepted widely for use in managing patients with HNSCC. Microarray experiments designed to examine differential gene expression or molecular tumor classification each face unique issues, both with experimental design and with measurement quality (Cheung et al., 1999; Novoradovskaya et al., 2004). Overall, extensive data collection and analysis by our group confirm that cDNA microarrays represent a reproducible and accurate high throughput measurement tool for gene expression profiling. Nevertheless, in the case of direct differential gene expression profiling, validation using technologies such as real time PCR, Northern blots and immunohistochemistry have become common. It is noteworthy that differential gene expression profiling is complicated by the inclusion of non-tumor cells in tumor specimens and precancerous changes in so-called ‘normal’
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mucosa. Differential expression profiling also does not reveal information related to post-translational modification or subcellular localization that may be critical to the specific functioning of the translated protein. In fact, the combination of elevations in expression of certain genes in cells, with their translation into proteins and the localization of these gene products, may confer additional information that reflects pathogenesis. A systems approach is therefore critical to integrating diverse forms of collected data and understanding the mechanisms at work. For example, in the identification of upregulation of moesin described previously, it was the cytoplasmic localization of moesin that was subsequently shown to be significantly associated with metastasis and poor survival in HNSCC patients (Kobayashi et al., 2004). Similarly, our group has shown that cytoplasmic ezrin, another member of the ERM family of proteins, may have prognostic significance in this disease (Madan et al., 2006). Overall, the identification of ERM family members provides an example of the rapid appraisal of molecular markers that is possible with direct comparisons using new genomics-based technologies such as DNA microarrays coupled with validation studies using tissue microarrays. For molecular classification studies with a population of tumor samples, it is becoming increasingly clear that analysis of tumor gene expression patterns has a valuable predictive and prognostic capacity. In fact, one of its most promising aspects is the possibility of isolating smaller subsets of genes whose expression (or lack thereof) correlate significantly with clinical parameters, and the ability to construct a disease-specific microarray chip that can be used to predict clinical outcome in the future. However, in addition to issues mentioned above, care must be taken in experimental design to maximize the ability to discriminate between members of the tumor population. While direct comparisons are commonly used in simple microarray hybridization experiments, this approach quickly becomes impractical when trying to compare differential gene expression in a larger population of samples. Under these circumstances, the most popular approach is to co-hybridize each sample with a common ‘reference’ sample and then infer differences in gene expression among the sample population via an ‘indirect’ ratio measurement (Yang and Speed, 2002). In some studies, such as the comparison of gene expression profiles in a population of tumor samples, the choice of a reference RNA to use is not immediately obvious. One of the more common approaches in use today is a reference pool of RNA produced from a collection of tissues or established cell lines representing a variety of cell types (Puskas et al., 2002). Studies by our group indicated a strong overall correlation between direct and indirect fluorescence ratio measurements with cDNA microarrays, but also a high degree of false positives in our indirect measurements (Belbin et al., 2004). These results indicated that the applications of more stringent ratio filters are required when assessing differential gene expression utilizing a common RNA reference in classification studies. The choice of a reference sample close in overall gene expression profile to those samples within the tumor population also maximizes the probability of dis-
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criminating subtle gene expression differences among the sample population that may have potential as a prognostic predictive gene expression signature (Cao et al., 2004). Relating gene expression to HPV: an example of impact of etiology Epidemiological and laboratory evidence now warrant the conclusion that, in addition to tobacco and alcohol, human papillomaviruses (HPV) play an etiologic role in some HNSCC (Gillison and Shah, 2001; Schlecht, 2005). To characterize the molecular profiles of HPV-positive head and neck cancer, we compared differences in gene expression patterns between HPV-negative and -positive HNSCC tumors using cDNA microarrays. Tumor samples were collected from histological confirmed HNSCC patients undergoing treatment at Montefiore Medical Center and AECOM affiliated medical center in the Bronx. HPV detection and genotyping was performed by PCR using several primer protocols to specifically detect multiple types of HPV. Total RNA was extracted and purified from frozen tumor samples and gene expression levels were assessed using our 27,323 cDNA microarray chip compared to a universal human reference library of RNA (http://microarray1k.aecom.yu.edu) (Cheung et al., 1999; Novoradovskaya et al., 2004). To identify genes that were differentially expressed between HPV+ and HPV– tumors, statistical analysis of tumor samples based on gene expression profiles was performed (Eisen et al., 1998; Tusher et al., 2001; Tibshirani et al., 2002; Slebos et al., 2006). Genes with differing expression profiles between HPV+ and HPV– tumors were then identified. While some authors have reported a higher prevalence of HPV in tumors of non-smokers (Lindel et al., 2001; Herrero et al., 2003), as we observed in our population, others have reported that individuals who are positive for HPV and who smoke are at greater risk of HNSCC than individuals presenting with either exposure alone (Schwartz et al., 1998; Smith EM et al., 2006). However, both additive and competing relationships have been reported between tobacco smoking and HPV in HNSCC (Smith EM et al., 2004; Strati et al., 2006). Therefore, given the putative association between smoking and anatomic site, and the reported disparate etiologic pathways for tobacco carcinogens and HPV (Califano et al., 1996; Gillison and Shah, 2001; Ragin et al., 2006), we assessed whether the observed gene expression profiles also differed between head and neck cancer tumors as a result of either tobacco exposure or HPV infection. To separate the tobacco- and HPV-induced effects on HNSCC pathogenesis, we segregated patients on their smoking history, reducing the analyses to smokers and never-smokers, and therefore removing potential bias from our analysis of HPV expression signatures. Among the most prominently up-regulated genes in HPV16+ tumors of nonsmokers were cyclin-dependent kinase inhibitor 2C (CDKN2C) and the retinoblastoma (RB1) gene. CDKN2C encodes the p18 tumor suppressor protein, a cyclin-dependent kinase inhibitor of the retinoblastoma tumor suppressor protein (pRb). Others have also found CDKN2C to be
up-regulated in HPV16+ HNSCC derived cell lines when compared to normal oral tissue (Martinez et al., 2006). In addition, we observed differential expression of several genes in our analysis of HPV16+ tumors of smokers and nonsmokers combined that are known to be regulated by p53 proteins or E2F transcription factors including replication factor C4 (RFC4), cell division cycle (CDC7), and transcription factor E2F dimerization partner 2 (TFDP2). RFC4 encodes an ATP-binding domain of the heteropentamer RFC (also called activator 1), an accessory protein required for the elongation of primed DNA templates by DNA polymerase (Lee et al., 1991) that is not responsive to E2F signaling (Angus et al., 2004). It was particularly interesting that restricting analyses to never-smokers revealed the most overlap with previously identified HPV-induced gene profiles from cervical SCC. A role for microRNAs in head and neck squamous cell carcinoma There has been a great deal of evidence to indicate that microRNAs have an important role in the regulation of cell division and differentiation (Lee et al., 1993; Lagos-Quintana et al., 2001; Lau et al., 2001; Lee and Ambros, 2001) and through these key processes play a part in numerous cancers (Lu et al., 2005; Calin and Croce, 2006a; Cummins and Velculescu, 2006; Zhang et al., 2006). MicroRNAs are a recently discovered group of non-protein coding short 21–23 nucleotide RNA molecules that are expressed in relative abundant quantities in a tissue specific and stage specific manner. They are highly conserved throughout the animal and plant kingdoms and there are several hundred to possibly thousands of miRNA genes predicted from the human genomic sequence. Individual miRNAs regulate the stability and translation of target genes that have homologous sequences within their transcribed mRNAs using a cellular mechanism similar or identical to siRNA inhibition (Couzin, 2002). Several groups have looked at the global patterns of expression of miRNAs in a number of different cancer models (Lu et al., 2005; Calin and Croce, 2006a; Cummins and Velculescu, 2006; Pallante et al., 2006; Yanaihara et al., 2006; Zhang et al., 2006). Malignant cells have been shown to express altered patterns of miRNAs relative to their normal cellular counterparts and some of these differences can be used to predict clinical properties of individual samples (Lu et al., 2005; Calin and Croce, 2006b; Pallante et al., 2006; Volinia et al., 2006; Yanaihara et al., 2006). In fact, in one example where a direct comparison between miRNA expression and mRNA expression in tumor samples was possible, the miRNA data was more reliable at predicting the clinical data (Lu et al., 2005). We speculate that this might be due to the fact that each miRNA is a regulatory molecule that specifically impacts the downstream expression of many proteins and will make a larger impact on the phenotype of a cell than a typical mRNA encoding a single protein that may not itself be regulatory in function. We have developed a unique microarray to measure the global expression patterns of miRNAs in HNSCC. The ar-
ray consists of triplicate spotting of most of the known human miRNAs from Ambion Inc. as well as the complete set described by the Croce laboratory which using our assay system allows measurement of both sense and anti-sense expression. In addition, a control oligonucleotide was spotted that allows measurement of labeling efficiencies and recovery of fluorescent probes. We have collected miRNA expression data from about a dozen tumor-normal pairs. These same clinical samples were used to generate global mRNA expression and CpG methylation data (see other sections of this review). We can conclude that like other tumor models, HNSCC samples each express altered patterns of miRNAs when compared to matched normal tissue. In addition, it is clear that there are distinct differences among the different tumor samples as well. It is these differences that we are currently using in an attempt to correlate with clinical parameters of the tumor samples. Additional samples will be studied to obtain statistically significant results. We are also investigating potential interactions between methylation and chromatin structure and miRNA expression. Jones and colleagues have observed that tumor cell lines treated with 5-azacytidine alone or in combination with the histone deacetylase inhibitor 4-phenylbutyric acid activate a limited subset of miRNAs and one in particular, miR-127, can inhibit the expression of the proto-oncogene BCL6 (Saito et al., 2006). We have treated the HNSCC cell line FaDu with 5-azacytidine and collected samples 24 and 48 hours post treatment for both mRNA expression microarrays and miRNA microarray analysis. Preliminary results demonstrate that two miRNAs previously shown to be underexpressed in breast cancer or having anti-apoptotic effects are activated in this HNSCC cell line. It is not uncommon to have important regulatory mechanisms interact with one another to fine-tune or feedback the regulation of downstream pathways. The ability to measure miRNAs, methylation patterns, mRNA levels and proteomics from the same samples should allow us to better understand the differences between HNSCC and its normal counterparts on a mechanistic level. Global proteomics The expression of many proteins has been studied and several of them have been proposed to have potential prognostic significance because of differential expression associated with clinical outcomes such as metastasis (van de Vijver et al., 2002) and overall survival (Villaret et al., 2000). In recent years, sophisticated analytic techniques have been applied not only to identify protein biomarkers that can discriminate tumor behavior (invasiveness, metastatic potential) but also to predict response to therapy and to determine the presence or recurrence of disease. The application of high throughput approaches for assessing tumor behavior permits the analysis of many proteins from a single cancer using antibody arrays (Xiao et al., 2002) or enables the measure of a potential biomarker in multiple cancers using tissue microarrays (Yonemura et al., 1998; Yarbrough et al., 2006). Perhaps the most elegant approach for identifying
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new prognostic biomarkers is the profiling and imaging of proteins directly in fresh frozen human cancer tissue by mass spectrometry (Yonemura et al., 1999). Alternatively, data from extracts of tissue analyzed by SELDI-TOF-MS (surface-enhanced laser desorption/ionization time-offlight mass spectrometry) have revealed differentially expressed proteins between healthy mucosa and HNSCC (Roesch-Ely et al., 2006). These new approaches should facilitate the identification of prognostic biomarkers. Currently no protein biomarker has been identified that can reliably detect the presence of HNSCC in serum or saliva. However, the recent application of high throughput mass spectrometry approaches has attempted to identify prognostic and predictive signatures/patterns in the serum to be used as a screen for individuals developing disease or recurrent disease (Yoo et al., 2002). While this approach has been fraught with problems in general (Yu et al., 2002), when properly applied, it holds great promise for identifying peptides/proteins that can distinguish tumor behavior and potentially response to therapy or early detection. Analysis and integration of data sets
Standard approaches Normalization of microarray data. Approaches to preparation and normalization of microarray hybridization data have been largely standardized now. One common approach, applied to RNA expression microarray data at our facility, is to compare gene expression patterns between epithelial mucosa (Cyanine 5-labeled sample) and a Universal Human Reference Sample (UHR) (Stratagene, CA, USA) (Cyanine 3-labeled sample) (van Rijnsoever et al., 2003; Novoradovskaya et al., 2004). The ratio of the fluorescence intensities of the two dyes therefore represents a measure of differential gene expression between the two samples of interest. Red (Cy5) and green (Cy3) signal intensities for each element on the array were calculated using GenePix Pro 3.0 software. This software gives an integrated intensity per spot for each channel in addition to an integrated background count. In order to establish consistent ratios, we first compute an intensity dependent normalization factor for each microarray experiment by finding the rank invariant subset of the spots (the spots that have equal or almost equal ranks in the two channels) (Tseng et al., 2001). Once this core of spots has been identified, a robust curve is fitted using the ‘lowess’ function from the R statistical package (Yang et al., 2002). We then compute the intensity dependent normalization factor where A is the geometric average intensity. Data designated to be of bad quality, which did not achieve a signal to noise ratio of at least two-fold, are discarded from subsequent analysis. Overview of microarray data analysis approaches and planning. One of the primary goals of the informatics part of the studies is the identification of epigenetic, proteomic or expression events that can serve as markers or predictors of various clinical subtypes. For initial statistical analysis of
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the microarray data, we use clinical features including histopathologic factors (i.e. advanced stage, lymph node metastasis, etc.) at diagnosis and clinical outcome (i.e., recurrence, progression-free and overall survival) as clinical indicators of disease aggressiveness. However, given the shear volume of data generated for each patient and cancer, it is not unusual to anticipate that a subset of genetic or epigenetic events will be significant with respect to patient outcome. The size of this subset is unknown; it may be the case that a single genetic event or hundreds of events may need to be simultaneously considered. Therefore, the first phase of supervised learning consists of training/learning of the statistical prediction model from a data set that has been classified; in other words, the class labels have been assigned to the samples. This data set is called a ‘training’ set. Once the model has been trained, it can then be used to predict (classify) data that has not been assigned to labels. In principle, one can first collect a training set, train the model and then collect additional data to be used as a ‘test’ set to see how well the model works. As a result, there are two very different error rates that can be calculated: the training error and the testing error (more often called the generalization error). The generalization error is the performance of the model on data that was not used in the training phase. It is this error that is the more important in that it indicates how well the model will work in prediction (classification) of data. One of the problems in supervised learning techniques, however, is the risk of overtraining or overfitting the model. The result would be a model that is overdetermined in terms of the parameters it has and, in a sense, overfits the specific data on which the algorithm was trained. In extreme cases, one can actually perfectly predict all of the training examples. What one then has learned is not some generic mapping of the input data (i.e., DNA methylation) to the target data (recurrence and survival) but rather specific features of a particular input dataset. Such overtrained models can have very poor ‘generalization’ error. The problem of overtraining or overfitting is a special concern in the supervised analysis of microarray data because of the relatively large number of input variables compared to the number of clinical samples that are practically available. Therefore, prior to training the model, one should first handle the problem of overfitting using a ‘feature selection process’ that will select on a subset of CpG island clones, expressed genes or proteins and peptides to be assessed by alternate methods for confirmation. Support vector machines, linear discriminant analysis, classification and regression trees (CART) and principal components analysis (PCA) are some approaches to achieve this. A review of some of these methods is provided in the next section. Finally, we validate the results of the supervised learning using cross validation procedures that enable us to estimate the generalization error of the selected genes. In fact, it is also possible to iterate over all of these steps (feature selection, training, cross validation) several times in order to improve the overall performance of the model.
Planning of studies employing microarray data. There are currently no widely accepted and comprehensive methods for planning sample sizes of microarray experiments. However, for the microarray analysis, a common approach is to employ a sample size determination method by Simon et al. (2002) for testing whether a particular gene is differentially expressed between two pre-defined classes. The basic approach is to apply the usual sample size formulas for comparing two means from normally distributed data, but with Type I (␣) and Type II () error rates that do not exceed 0.001 and 0.05, respectively. This ensures that if 1,000 CpG island clones are equivalent in two groups, the number of false positives will be at most one. Similarly, if 100 CpG island clones are differentially methylated, then the procedure will result in at most five false negative results. Other approaches for addressing the inflation in Type I error rate due to multiple testing will also be considered, including controlling for the false discovery rate (Hochberg and Benjamini, 1990; Reiner et al., 2003; Benjamini and Yekutieli, 2005). Alternative and advanced statistical approaches
Statistical approaches Statistical analysis of expression data might start with establishing the reliability of the approach employed. For instance, in our proteomics study, one of our objectives was to establish the reliability of the protein analysis. For this purpose we employed two HNSCC tumor cell lines (SCC25 and FADU). They provided a scale for the expected biological signal to be measured. This enabled us to determine whether the biological variability was much larger than the extraction and analytical variability, hence enabling us to test the reliability of the protein analysis. This experiment also helped us to identify those peptide fragments that are most robustly measured and that we would consider for further detailed analysis. In this experiment, three extractions were prepared from each cell line, the protein fraction from each of these extracts was fragmented into peptides and analyzed in triplicate by two-dimensional liquid chromatography-mass spectrometry (2D LC-MS) for global proteomics. Use of internal and external protein and peptide standards provided relative quantitation. Each peptide in the final pattern derived from 2D LC-MS had a characteristic mass, elution time from a reversed phase column and a salt concentration from which it is eluted from a strong cation exchange column. In order to quantify the reliability of our approach we employed a random effects ANOVA model. Based on the random effects model, we computed the ratio of the variation due to differences between cell lines for a given peptide to the total variation. We refer to this variation as the percentage biologic variation (Shrout and Fleiss, 1979). A peptide with 690% biologic variation has high reliability in differentiating the two cell lines. We identified peptides that were highly reliable, i.e., showing large biological variation hence discriminating the two cell lines (Shrout and Fleiss, 1979). The ratio of the vari-
ation due to differences between cell lines for a given peptide to the total variation is what we refer to as the biologic variation. A peptide with 690% biologic variation has high reliability. We found 150 peptides of 1 40,000 analyzed with a reliability index (intra class correlation coefficient) 690%. For these 150 peptides the median coefficient of variation was 0.165 (with inter-quartile range 0.14–0.20). This analysis of cell lines showed that the 2D LC-MS protocol could be used reliably to identify biologic variation in HNSCC. In assessing the predictive ability of expression data (microarray or proteomic) standard approaches need to account for the fact that hundreds and even thousands of molecular markers are being considered simultaneously. This leads to the problem of multiplicity of hypothesis which in turn leads to inflation of the Type I error rate, i.e., the probability of rejecting the null hypothesis when it is really true (also called a false positive). For instance, if we were simply to perform uncorrected hypothesis tests at the traditional 5% level, then 10,000 ! 0.05 = 500 false positives would be expected under the full null hypothesis if we were testing 10,000 genes or peptides. This is clearly unacceptable, so some procedure must be performed to control the false positive rate in a reasonable manner. For a single test, the false positive rate can be useful for measuring how likely it is for a true null case to be as significant as what has been observed, as shown in the above example. However, in this setting knowing what percentage of the significant tests is actually false is more informative and this is called the false discovery rate. For instance, if we are willing to incur a false discovery rate of 5%, then this means that among all tests we call significant, about 5% of them will be false positives. If there are 500 significant tests then this results in about 25 false positives. There are approaches that effectively control the false discovery rate (Storey, 2002). Equally important is ensuring reasonable study power, i.e., rejecting the null hypothesis when the alternative hypothesis is really true. This could be achieved by employing an appropriate test statistic as well as recruiting sufficient sample. In a preliminary proteomic analysis of HNSCC, we employed the q-value statistic, i.e., the lowest positive false discovery rate at which a peptide is called significant (Storey, 2002) in assessing whether a given peptide is related to clinical outcomes such as recurrence. It is also possible to refine this approach by taking into account not only statistical significance but also biological importance as assessed by magnitude of effect, i.e., larger fold change or odds ratio or hazard ratios. One may expect for a number of reasons that there may exist correlations between various groups of target biomarkers that collectively will be better predictors/discriminators of clinical outcome. For this reason, other standard approaches such as logistic regression and Cox proportional hazards model could be employed as appropriate in assessing simultaneously the predictive ability of such biomarkers. However, such analysis needs to be accompanied by cross-validation in order to establish the robustness of the results (Simon et al., 2002). Furthermore, such multivariable approaches also allow for the assessment of effect modification by other traditional risk factors and adjust-
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ment for potential confounding factors such as smoking. For instance, smoking itself may be correlated with stage and directly influence length of survival or disease-free survival. It becomes important to rule out confounding by smoking exposure when evaluating molecular studies in the larynx and pharynx in particular, and in many cases, may even require stratification on cumulative smoking status to assess accurately potential differential (interactive) associations with tobacco smoking (Koch et al., 1999). Similar arguments can also be extended to other etiological risk factors for head and neck cancers in general, e.g., alcohol, age and gender. While the evidence for alcohol in oral and pharyngeal cancers varies with respect to sub-site, increased risk associations with alcohol for laryngeal cancers also cannot be ruled out and have been documented by our group and others especially for supraglottic cancers (Schlecht et al., 1999; Kapil et al., 2005). Moreover, increased consumption has been shown to have an effect on the relationship between risk factors like HPV and disease risk (Smith EM et al., 2004). Furthermore, since age and gender can be correlated with stage and independently influence length of survival, it becomes important to see if there is a mediating effect or rule out confounding by such demographic factors in predicting prognosis. Although one may feel reassured that recent large biomarker studies have found independent associations with head and neck cancer after controlling for tobacco, alcohol, age and gender (Gillison et al., 2000; Ritchie et al., 2003; Eriksen et al., 2005), and in some cases among non-smokers (Koch et al., 1999), a concern for residual confounding may remain since these factors are also associated with co-morbid conditions and treatment regimen, which are not ascertained in most studies. As a large number of such potential biomarkers are expected, one might want to restrict the results to the top 5–15 biomarkers on the basis of a bivariate analysis that appropriately takes multiplicity into account. Then one might employ standard model building approaches such as all subset regression. The all subsets regression approach computes all possible models and allows examination of the multivariable effects of a set of peptides. In the multivariable model, one may want to restrict the model building to the best five subsets of candidate peptides depending on the sample size. This is because if the sample size is small, which is usually the case, then increasing the number of candidate peptides in the model would lead to model instability. Using this approach, it is probable that more than one model will be considered as a potential final model due to multicollinearity between the predictors. Dimensionality (a result of having a number of variables much larger than the number of observations) is an issue in this type of analysis and as a result there might be a need to consider other dimension reduction approaches such as principal component analysis (PCA) to determine whether the set of peptides may be reduced to several principal components that describe most of the variation in the data. A potential drawback of PCA is that the summary variables (i.e., principal components) do not necessarily have a clear biological interpretation.
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As an alternative solution to both the problems of dimensionality and biologic interpretation, one can employ classification and regression trees (CART) (Breiman et al., 1984) and their extensions (Ciampi et al., 1995). These approaches recursively partition the space of candidate peptide profiles into subgroups that are highly predictive of the outcome of interest such as metastasis or recurrence. These modeling approaches are more robust than the traditional log-linear models. Moreover, a large number of peptides can be considered simultaneously for searching for and isolating complex patterns and relationships; hence dimensionality is not a problem. These computationally intensive approaches are prone to overfitting. Therefore, it is important to carefully consider model selection approaches (Negassa et al., 2000). Other approaches are also possible, in fact, a wide variety of class prediction algorithms, including complex new algorithms, are being applied in the analysis of expression data. However, in settings where the number of candidate predictors is an order of magnitude greater than the number of cases, complex methods with many parameters often do not perform well when properly evaluated. Comparisons of class predictors varying in degree of complexity demonstrate this finding specifically for DNA microarray data (Dudoit and Fridlyand, 2002). In addition, a Bayesian modeling approach could also be employed in order to deal with the large number of candidate predictors and the uncertainty regarding the choice of the ‘best’ model (George and McCulloch, 1993). Since there are various approaches to the analysis of expression data, it is important to state from the outset whether the study is exploratory or confirmatory and interpret results accordingly. For exploratory studies, use of complete cross-validation of all findings derived from exploratory models is essential. Otherwise, results could be overly optimistic. Various statistical approaches could be interesting for exploration, however, reporting of such results needs to explicitly state this exploratory nature. For confirmatory studies, independent validation, sufficiently large sample size and adjustment for other classic risk factors of the disease of interest are essential. In addition, discussion of the successful adoption of biomarkers into clinical practice should also consider reproducibility in routine practice setting, clinical effects on patients’ outcome, and cost-effectiveness (McShane et al., 2005b). Alternative approaches Another alternative approach is to make use of the observation that most species maintain abundant genetic variation, yet the phenotypic variation of most wildtype complex traits is low, which does not reflect the underlying genetic variation present in a population. That is, development is robust to changes in genotype and environment. In the context of complex traits such as the head and neck cancer, the phenotype can be considered as the gene expression pattern; more specifically one would expect to observe a low variation in genes that are associated with the disease among healthy individuals. In the past we have shown (Siegal and Bergman, 2002) that such robust behavior among wildtype
Within group variance by node status 10 9 8 Node positive
population is the result of the complexity of the developmental process and selection, though important, might not be the primary force behind the observed phenomenon. We further demonstrated that the capacity to harbor genetic variation depends on the complexity of the genotype-phenotype mapping, and that higher mapping complexity can evolve to harbor larger variation, thus higher robustness. With this we have demonstrated that robustness may be an inevitable consequence of complex developmental-genetic processes. In a subsequent study (Bergman and Siegal, 2003), we demonstrated, using numerical simulations of complex gene networks, as well as genome-scale expression data from yeast single-gene deletion strains, that most, if not all, genes involved in the production of complex phenotypic traits reveal phenotypic variation when functionally compromised. This is in contrast to the more widely held hypothesis that there exist specific classes of genes, such as the Hsp90, that are unique and have been selected for by natural selection to act as evolutionary capacitors, that is, when functionality of such factors is impaired, or overwhelmed, the underlying genotypic variation is revealed phenotypically. These findings led us to assume that some genes may exist solely to stabilize the function of a developmental program and, when compromised, the underling genotypic variation is revealed which may result in higher variation in both expression level of genes in the same pathway as well as the complex phenotype. The existence in model organisms of a large number of gene knockouts without apparent phenotypic effect lends plausibility to this idea. Here we have applied this hypothesis to study revealed variation when comparing the variation among individuals at two different stages of the disease. We assess for indicators of cellular instability that may be indicative of metastatic potential by comparing the relative variance in microarray profiles between tumor tissues within node class and tumor stage categories with respect to presence of lymph node metastases at diagnosis or tumor stage. Genes can be ranked by degree of relative difference in variance between comparison groups and compared by F-test statistic. Absolute differences in variation exceeding two or even three standard deviations may therefore single out potential outlying genes of concern. The robustness of a wildtype organism can be extended to the study of any complex trait which is the result of a complex genotype to phenotype mapping. Here we assume that robustness is lost when a cell is transformed from its wildtype functionality to that of cancer, furthermore, as the tumor progresses loss of robustness is increased. In a preliminary study we took expression arrays from 55 patients, each of which contains the expression level of 28,700 genes. Data were obtained from primary tumors that were characterized as lymph-node positive and lymph-node negative. We hypothesize that genes in lymph-node positive tumors will exhibit higher variation between the different patients, given their ‘cell regulatory systems’ are disturbed as the stage of the disease advances. More specifically, genes directly involved in the pathways, both those that are novel as well as those that have already been implicated in the de-
MYL2
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SPRR2A Unknown KLK10 4 TNNT1 5
UPK1B
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4 5 6 Node negative TNNI2
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TNNI3K PKD2L1 TNNI3 PKD2
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PAPPA KCTD13
MRVI1 RGS2 RAF1
ZMYND19 HPCL2
PRKG1
PPP1R12A
DC284352 MGC2749
NPR1 GTF2I
TNNT1 TNNT2 TPM1ACTB TPM2 PSMC4 RORA PSMC3 PSMC5 KRT15 THRB ERCC3 SP1
KIAA0980
TNNC1
DIPA FXR2
b
PPFIA1
Fig. 5. Variant gene expression as a means to create protein interaction networks in HNSCC. (a) The within group variances for expression of each RNA (blue spot) for node positive tumors are plotted on the y-axis and for node negative tumors on the x-axis. Guidelines indicating no difference in variability, ± 2-times difference and ± 3-times difference in variability between groups are drawn on the diagonal. (b) Literature-based protein-protein interaction networks using Grid database and Osprey Network Visualization Systems. The network was originated from one of the proteins identified by the variance analysis described in part a.
velopment of the disease, are hypothesized to exhibit such behavior. When comparing the variation between the two tumor types, lymph-node positive and negative, we have identified five such genes. Four of the five genes that have been detected outside the two-fold range have already been implicated as being relevant for the development of the head and neck cancer (assuming there are approximately 100 genes involved in the disease, the probability for such an event to occur at random is excitingly low, P = 6 ! 10 –10). Figure 5a shows the comparison between variations observed within 14 patients with lymph-node negative to that of 41 patients with lymph-node positive disease. Further investigation can take place following the identification of the outliers. Investigation can be experimental
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Fig. 6. Systems biology approach to the study of complex traits. Our proposed approach integrates the different aspects and elements of clinical and experimental sciences involved in the study of HNSCC while applying system sciences to improve our understanding of this complex disease.
in nature as well as more theoretical. Experimental approaches will confirm the relationship of gene expression to clinical outcome and the dysregulation of particular pathways. Using theoretical approaches our understanding of the biological process can also be enhanced. For example, one of the genes identified has already been implicated in HNSCC. Using protein-protein interaction databases, an interaction network can be created. Here we focus on the gene to generate such a network (see Fig. 5b). As can be seen this gene appears to serve as a hub, i.e., a gene that is highly connected. Thus, it may have a pleiotropic effect when disrupted causing large phenotypic changes such as inducing the head and neck cancer phenotype. The protein interactions themselves may be obtained either from databases such as Osprey, a database based on reported interaction, or from databases such as DIP which are based on predicted interaction using computational approaches. Once such a network is in place, it provides a mechanistic model for the underlying genetic system of the trait in question. Such a model can in turn be manipulated to further predict its behavior under different genetic and environmental conditions. Integration of data sets In the future, we plan to make use of multiple types of data available for patients with head and neck cancer at different stages of the disease and its different types. The available data include: RNA expression, proteomic, epigenetic
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hypermethylation of CpG island promoter elements and the transcriptional silencing of corresponding genes, together with other biological and clinical information. An example of integration among the different modalities can be envisioned by the following example. In silico digestion of all proteins discovered using our increased variation analysis as well as their targets, discovered by the protein-protein interaction prediction, can be used to generate computationally derived mass spectrometry of the relevant proteins. This computationally derived signature can suggest particular proteins for detailed analysis using high resolution mass spectrometry, then be compared with that derived from cells at different stages of the disease and from different individuals. If a similar pattern of variation is observed in both modalities for the set of identified genes, our predictive power will dramatically increase. In addition, the increase in mRNA variation may be the result of transcriptional silencing in a subset of our patient population, thus, data obtained from epigenetic hypermethylation of CpG island promoter elements can be used not only to provide validation for our original observation, but may also provide a biological mechanism to explain the observed variation. Theoretical and systems biological approaches can be used to generate biological signatures and provide diagnostic tools, but more importantly, these approaches can also serve as strategies to generate hypotheses and plausible mechanisms for combinatorial and modifier effects that determine clinical outcome (Fig. 6). Acknowledgements The authors would like to acknowledge Ms. Annie D’Alauro for her editorial assistance.
References
Adrien LR, Schlecht NF, Kawachi N, Smith RV, Brandwein-Gensler M, et al: Classification of DNA methylation patterns in tumor cell genomes using a CpG island microarray. Cytogenet Genome Res 114: 16–23 (2006). Angus SP, Mayhew CN, Solomon DA, Braden WA, Markey MP, et al: RB reversibly inhibits DNA replication via two temporally distinct mechanisms. Mol Cell Biol 24: 5404–5420 (2004). Anneroth G, Batsakis J, Luna M: Review of the literature and a recommended system of malignancy grading in oral squamous cell carcinomas. Scand J Dent Res 95: 229–249 (1987). Bariol C, Suter C, Cheong K, Ku SL, Meagher A, et al: The relationship between hypomethylation and CpG island methylation in colorectal neoplasia. Am J Pathol 162: 1361–1371 (2003). Barnes L, Verbin R, Guggenheimer J: Cancer of the oral cavity and oropharynx, in Barnes L (ed): Surgical Pathology of the Head and Neck, 2nd ed, pp 370–438 (Marcel Dekker, New York 2001). Beitler JJ, Smith RV, Owen RP, Silver CE, Mazumdar M, Wadler S: Phase II clinical trial of parenteral hydroxyurea and hyperfractionated, accelerated external beam radiation therapy in patients with advanced squamous cell carcinoma of the head and neck: Toxicity and efficacy with continuous ribonucleoside reductase inhibition. Head Neck 29: 18–25 (2007).
Belbin TJ, Singh B, Barber I, Socci N, Wenig B, et al: Molecular classification of head and neck squamous cell carcinoma using cDNA microarrays. Cancer Res 62:1184–1190 (2002). Belbin TJ, Gaspar J, Haigentz M, Perez-Soler R, Keller SM, et al: Indirect measurements of differential gene expression with cDNA microarrays. Biotechniques 36: 310–314 (2004). Belbin TJ, Singh B, Smith RV, Socci ND, Wreesmann VB, et al: Molecular profiling of tumor progression in head and neck cancer. Arch Otolaryngol Head Neck Surg 131: 10–18 (2005). Benjamini Y, Yekutieli D: Quantitative trait loci analysis using the false discovery rate. Genetics 171:783–790 (2005). Bergman A, Siegal ML: Evolutionary capacitance as a general feature of complex gene networks. Nature 424:549–552 (2003). Bernier J, Domenge C, Ozsahin M, Matuszewska K, Lefebvre JL, et al: Postoperative irradiation with or without concomitant chemotherapy for locally advanced head and neck cancer. N Engl J Med 350:1945–1952 (2004). Bonner JA, Harari PM, Giralt J, Azarnia N, Shin DM, et al: Radiotherapy plus cetuximab for squamous-cell carcinoma of the head and neck. N Engl J Med 354:567–578 (2006). Brandwein-Gensler M, Teixeira MS, Lewis CM, Lee B, Rolnitzky L, et al: Oral squamous cell carcinoma: histologic risk assessment, but not margin status, is strongly predictive of local disease-free and overall survival. Am J Surg Pathol 29: 167–178 (2005). Breiman L, Friedman J, Olshen R, Stone C: Classification and Regression Trees (Wadsworth International Group, Belmont 1984). Brizel DM, Wasserman TH, Henke M, Strnad V, Rudat V, et al: Phase III randomized trial of amifostine as a radioprotector in head and neck cancer. J Clin Oncol 18: 3339–3345 (2000). Bryne M, Koppang HS, Lilleng R, Stene T, Bang G, Dabelsteen E: New malignancy grading is a better prognostic indicator than Broders’ grading in oral squamous cell carcinomas. J Oral Pathol Med 18:432–437 (1989). Bryne M, Koppang HS, Lilleng R, Kjaerheim A: Malignancy grading of the deep invasive margins of oral squamous cell carcinomas has high prognostic value. J Pathol 166: 375–381 (1992). Bryne M, Jenssen N, Boysen M: Histological grading in the deep invasive front of T1 and T2 glottic squamous cell carcinomas has high prognostic value. Virchows Arch 427: 277–281 (1995). Bundgaard T, Rossen K, Henriksen SD, Charabi S, Sogaard H, Grau C: Histopathologic parameters in the evaluation of T1 squamous cell carcinomas of the oral cavity. Head Neck 24: 656– 660 (2002). Calais G, Alfonsi M, Bardet E, Sire C, Germain T, et al: Randomized trial of radiation therapy versus concomitant chemotherapy and radiation therapy for advanced-stage oropharynx carcinoma. J Natl Cancer Inst 91: 2081–2086 (1999). Califano J, van der Riet P, Westra W, Nawroz H, Clayman G, et al: Genetic progression model for head and neck cancer: implications for field cancerization. Cancer Res 56: 2488–2492 (1996). Calin GA, Croce CM: MicroRNAs and chromosomal abnormalities in cancer cells. Oncogene 25:6202–6210 (2006a). Calin GA, Croce CM: MicroRNA signatures in human cancers. Nat Rev Cancer 6: 857–866 (2006b).
Cao QJ, Belbin T, Socci N, Balan R, Prystowsky MB, et al: Distinctive gene expression profiles by cDNA microarrays in endometrioid and serous carcinomas of the endometrium. Int J Gynecol Pathol 23: 321–329 (2004). Carvalho AL, Nishimoto IN, Califano JA, Kowalski LP: Trends in incidence and prognosis for head and neck cancer in the United States: a site-specific analysis of the SEER database. Int J Cancer 114:806–816 (2005). Cheung VG, Morley M, Aguilar F, Massimi A, Kucherlapati R, Childs G: Making and reading microarrays. Nat Genet 21: 15–19 (1999). Ciampi A, Negassa A, Lou Z: Tree-structured prediction for censored survival data and the Cox model. J Clin Epidemiol 48: 675–689 (1995). Condeelis J, Pollard JW: Macrophages: obligate partners for tumor cell migration, invasion, and metastasis. Cell 124:263–266 (2006). Cooper JS, Pajak TF, Forastiere AA, Jacobs J, Campbell BH, et al: Postoperative concurrent radiotherapy and chemotherapy for high-risk squamous-cell carcinoma of the head and neck. N Engl J Med 350:1937–1944 (2004). Costello JF, Fruhwald MC, Smiraglia DJ, Rush LJ, Robertson GP, et al: Aberrant CpG-island methylation has non-random and tumourtype-specific patterns. Nat Genet 24: 132–138 (2000). Couzin J: Breakthrough of the year. Small RNAs make big splash. Science 298: 2296–2297 (2002). Cummins JM, Velculescu VE: Implications of micro-RNA profiling for cancer diagnosis. Oncogene 25: 6220–6227 (2006). De Schutter H, Landuyt W, Verbeken E, Goethals L, Hermans R, Nuyts S: The prognostic value of the hypoxia markers CA IX and GLUT 1 and the cytokines VEGF and IL 6 in head and neck squamous cell carcinoma treated by radiotherapy +/– chemotherapy. BMC Cancer 5: 42 (2005). Dombrowski SM, Maglott D: Using the map viewer to explore genomes, in: The NCBI handbook [Internet], Chapter 19 (National Library of Medicine (US), National Center for Biotechnology Information, Bethesda 2003). Dudoit S, Fridlyand J: A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol 3:RESEARCH0036 (2002). Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95: 14863–14868 (1998). Epstein JB, Robertson M, Emerton S, Phillips N, Stevenson-Moore P: Quality of life and oral function in patients treated with radiation therapy for head and neck cancer. Head Neck 23: 389–398 (2001). Eriksen JG, Steiniche T, Overgaard J: The role of epidermal growth factor receptor and E-cadherin for the outcome of reduction in the overall treatment time of radiotherapy of supraglottic larynx squamous cell carcinoma. Acta Oncol 44:50–58 (2005). Ferlay J, Bray F, Pissani P, Parkin DM: GLOBOCAN 2000: Cancer Incidence, Mortality and Prevalence Worldwide, Version 1.0. IARC CancerBase No. 5 (IARC Press, Lyon 2001). Forastiere A, Koch W, Trotti A, Sidransky D: Head and neck cancer. N Engl J Med 345: 1890–1900 (2001). Forastiere AA, Goepfert H, Maor M, Pajak TF, Weber R, et al: Concurrent chemotherapy and radiotherapy for organ preservation in advanced laryngeal cancer. N Engl J Med 349: 2091–2098 (2003).
Gal TJ, Yueh B, Futran ND: Influence of prior hyperbaric oxygen therapy in complications following microvascular reconstruction for advanced osteoradionecrosis. Arch Otolaryngol Head Neck Surg 129: 72–76 (2003). George E, McCulloch R: Variable selection via Gibbs sampling. JASA 88: 881–889 (1993). Gillison ML, Shah KV: Human papillomavirus-associated head and neck squamous cell carcinoma: mounting evidence for an etiologic role for human papillomavirus in a subset of head and neck cancers. Curr Opin Oncol 13: 183–188 (2001). Gillison ML, Koch WM, Capone RB, Spafford M, Westra WH, et al: Evidence for a causal association between human papillomavirus and a subset of head and neck cancers. J Natl Cancer Inst 92:709–720 (2000). Ginos MA, Page GP, Michalowicz BS, Patel KJ, Volker SE, et al: Identification of a gene expression signature associated with recurrent disease in squamous cell carcinoma of the head and neck. Cancer Res 64: 55–63 (2004). Hanna E, Shrieve DC, Ratanatharathorn V, Xia X, Breau R, et al: A novel alternative approach for prediction of radiation response of squamous cell carcinoma of head and neck. Cancer Res 61: 2376–2380 (2001). Herrero R, Castellsague X, Pawlita M, Lissowska J, Kee F, et al: Human papillomavirus and oral cancer: the International Agency for Research on Cancer multicenter study. J Natl Cancer Inst 95: 1772–1783 (2003). Hochberg Y, Benjamini Y: More powerful procedures for multiple significance testing. Stat Med 9:811–818 (1990). Jemal A, Siegel R, Ward E, Murray T, Xu J, Thun MJ: Cancer statistics, 2007. CA Cancer J Clin 57: 43–66 (2007). Joost HG, Thorens B: The extended GLUT-family of sugar/polyol transport facilitators: nomenclature, sequence characteristics, and potential function of its novel members (review). Mol Membr Biol 18: 247–256 (2001). Kapil U, Singh P, Bahadur S, Dwivedi SN, Singh R, Shukla N: Assessment of risk factors in laryngeal cancer in India: a case-control study. Asian Pac J Cancer Prev 6: 202–207 (2005). Khuri FR, Jain SR: Novel agents and incremental advances in the treatment of head and neck cancer. Semin Oncol 31:3–10 (2004). Kim HC, Kusukawa J, Kameyama T: Clinicopathologic parameters in predicting cervical nodal metastasis in early squamous cell carcinoma of the oral cavity. Kurume Med J 40: 183–192 (1993). Kobayashi H, Sagara J, Kurita H, Morifuji M, Ohishi M, et al: Clinical significance of cellular distribution of moesin in patients with oral squamous cell carcinoma. Clin Cancer Res 10: 572–580 (2004). Koch WM, Lango M, Sewell D, Zahurak M, Sidransky D: Head and neck cancer in nonsmokers: a distinct clinical and molecular entity. Laryngoscope 109:1544–1551 (1999). Koh DM, Padhani AR: Diffusion-weighted MRI: a new functional clinical technique for tumour imaging. Br J Radiol 79: 633–635 (2006). Kudo Y, Kitajima S, Ogawa I, Hiraoka M, Sargolzaei S, et al: Invasion and metastasis of oral cancer cells require methylation of E-cadherin and/or degradation of membranous betacatenin. Clin Cancer Res 10:5455–5463 (2004). Kunkel M, Reichert TE, Benz P, Lehr HA, Jeong JH, et al: Overexpression of Glut-1 and increased glucose metabolism in tumors are associated with a poor prognosis in patients with oral squamous cell carcinoma. Cancer 97: 1015– 1024 (2003).
Cytogenet Genome Res 118:92–109 (2007)
107
Kyzas PA, Loizou KT, Ioannidis JP: Selective reporting biases in cancer prognostic factor studies. J Natl Cancer Inst 97: 1043–1055 (2005). Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T: Identification of novel genes coding for small expressed RNAs. Science 294:853–858 (2001). Lau NC, Lim LP, Weinstein EG, Bartel DP: An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294:858–862 (2001). Lee RC, Ambros V: An extensive class of small RNAs in Caenorhabditis elegans. Science 294: 862–864 (2001). Lee RC, Feinbaum RL, Ambros V: The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 75:843–854 (1993). Lee SH, Kwong AD, Pan ZQ, Hurwitz J: Studies on the activator 1 protein complex, an accessory factor for proliferating cell nuclear antigen-dependent DNA polymerase delta. J Biol Chem 266:594–602 (1991). Leethanakul C, Patel V, Gillespie J, Pallente M, Ensley JF, et al: Distinct pattern of expression of differentiation and growth-related genes in squamous cell carcinomas of the head and neck revealed by the use of laser capture microdissection and cDNA arrays. Oncogene 19: 3220– 3224 (2000). Leitha T, Glaser C, Lang S: Is early sestamibi imaging in head and neck cancer affected by MDR status, p53 expression, or cell proliferation? Nucl Med Biol 25: 539–541 (1998). Lindberg R: Distribution of cervical lymph node metastases from squamous cell carcinoma of the upper respiratory and digestive tracts. Cancer 29: 1446–1449 (1972). Lindel K, Beer KT, Laissue J, Greiner RH, Aebersold DM: Human papillomavirus positive squamous cell carcinoma of the oropharynx: a radiosensitive subgroup of head and neck carcinoma. Cancer 92: 805–813 (2001). List MA, Bilir SP: Evaluations of quality of life and organ function. Semin Oncol 31: 827–835 (2004). Liu ZJ, Maekawa M: Polymerase chain reactionbased methods of DNA methylation analysis. Anal Biochem 317: 259–265 (2003). Lothaire P, de Azambuja E, Dequanter D, Lalami Y, Sotiriou C, et al: Molecular markers of head and neck squamous cell carcinoma: promising signs in need of prospective evaluation. Head Neck 28:256–269 (2006). Lu J, Getz G, Miska EA, varez-Saavedra E, Lamb J, et al: MicroRNA expression profiles classify human cancers. Nature 435: 834–838 (2005). Madan R, Brandwein-Gensler M, Schlecht NF, Elias K, Gorbovitsky E, et al: Differential tissue and subcellular expression of ERM proteins in normal and malignant tissues: cytoplasmic ezrin expression has prognostic signficance for head and neck squamous cell carcinoma. Head Neck 28:1018–1027 (2006). Mangeat P, Roy C, Martin M: ERM proteins in cell adhesion and membrane dynamics. Trends Cell Biol 9: 187–192 (1999). Martinez I, Wang J, Hobson KF, Ferris RL, Khan SA: Identification of differentially expressed genes in HPV-positive and HPV-negative oropharyngeal squamous cell carcinomas. Eur J Cancer 43: 415–432 (2006). Martinez-Gimeno C, Rodriguez EM, Vila CN, Varela CL: Squamous cell carcinoma of the oral cavity: a clinicopathologic scoring system for evaluating risk of cervical lymph node metastasis. Laryngoscope 105:728–733 (1995).
108
Maruya S, Issa JP, Weber RS, Rosenthal DI, Haviland JC, et al: Differential methylation status of tumor-associated genes in head and neck squamous carcinoma: incidence and potential implications. Clin Cancer Res 10: 3825–3830 (2004). McShane LM, Altman DG, Sauerbrei W: Identification of clinically useful cancer prognostic factors: what are we missing? J Natl Cancer Inst 97: 1023–1025 (2005a). McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM: Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 97: 1180–1184 (2005b). Mellanen P, Minn H, Grenman R, Harkonen P: Expression of glucose transporters in head-andneck tumors. Int J Cancer 56: 622–629 (1994). Mineta H, Miura K, Takebayashi S, Misawa K, Araki K, et al: Prognostic value of glucose transporter 1 expression in patients with hypopharyngeal carcinoma. Anticancer Res 22: 3489– 3494 (2002). Mubashar M, Harrington KJ, Chaudhary KS, Lalani el-N, Stamp GW, Peters AM: Differential effects of toremifene on doxorubicin, vinblastine and Tc-99m-sestamibi in P-glycoproteinexpressing breast and head and neck cancer cell lines. Acta Oncol 43: 443–452 (2004). Negassa A, Ciampi A, Abrahamowicz M, Shapiro S, Boivin J: Validation of tree structured prediction for censored survival data: Its application to prognstic classification. J Statist Comput Simul 67:289–317 (2000). Neiderhuber J: First-Generation Guidelines for NCI-Supported Biorepositories. Federal Register 71:25184–25203 (2006). Ng IO, Lam KY, Ng M, Kwong DL, Sham JS: Expression of P-glycoprotein, a multidrug-resistance gene product, is induced by radiotherapy in patients with oral squamous cell carcinoma. Cancer 83:851–857 (1998). Novoradovskaya N, Whitfield ML, Basehore LS, Novoradovsky A, Pesich R, et al: Universal Reference RNA as a standard for microarray experiments. BMC Genomics 5: 20 (2004). Ogi K, Toyota M, Ohe-Toyota M, Tanaka N, Noguchi M, et al: Aberrant methylation of multiple genes and clinicopathological features in oral squamous cell carcinoma. Clin Cancer Res 8: 3164–3171 (2002). Oliver RJ, Woodwards RT, Sloan P, Thakker NS, Stratford IJ, Airley RE: Prognostic value of facilitative glucose transporter Glut-1 in oral squamous cell carcinomas treated by surgical resection; results of EORTC Translational Research Fund studies. Eur J Cancer 40: 503–507 (2004). Pallante P, Visone R, Ferracin M, Ferraro A, Berlingieri MT, et al: MicroRNA deregulation in human thyroid papillary carcinomas. Endocr Relat Cancer 13: 497–508 (2006). Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, et al: Molecular portraits of human breast tumours. Nature 406:747–752 (2000). Pignon JP, Burdett S: Comment on ‘Survival improvement in resectable non-small cell lung cancer with (neo)adjuvant chemotherapy: results of a meta-analysis of the literature’ by T. Berghmans, M. Paesmans, A.P. Meert, C. Mascaux, P. Lothaire, J.J. Lafitte, et al. (Lung Cancer 49 (2005) 13–23.) Lung Cancer 51: 261–262 (2006).
Cytogenet Genome Res 118:92–109 (2007)
Pignon JP, Bourhis J, Domenge C, Designe L: Chemotherapy added to locoregional treatment for head and neck squamous-cell carcinoma: three meta-analyses of updated individual data. MACH-NC Collaborative Group. Meta-Analysis of Chemotherapy on Head and Neck Cancer. Lancet 355:949–955 (2000). Po Wing YA, Lam KY, Lam LK, Ho CM, Wong A, et al: Prognostic factors of clinically stage I and II oral tongue carcinoma-A comparative study of stage, thickness, shape, growth pattern, invasive front malignancy grading, Martinez-Gimeno score, and pathologic features. Head Neck 24:513–520 (2002). Potti A, Dressman HK, Bild A, Riedel RF, Chan G, et al: Genomic signatures to guide the use of chemotherapeutics. Nat Med 12: 1294–1300 (2006). Puskas LG, Zvara A, Hackler L, Jr., Micsik T, van HP: Production of bulk amounts of universal RNA for DNA microarrays. Biotechniques 33: 898–900, 902, 904 (2002). Rabkin D, Chhieng DC, Miller MB, Jennings T, Feustel P, et al: P-glycoprotein expression in the squamous cell carcinoma of the tongue base. Laryngoscope 105:1294–1299 (1995). Ragin CC, Taioli E, Weissfeld JL, White JS, Rossie KM, et al: 11q13 amplification status and human papillomavirus in relation to p16 expression defines two distinct etiologies of head and neck tumours. Br J Cancer 95: 1432–1438 (2006). Reiner A, Yekutieli D, Benjamini Y: Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19:368–375 (2003). Reisser C, Eichhorn K, Herold-Mende C, Born AI, Bannasch P: Expression of facilitative glucose transport proteins during development of squamous cell carcinomas of the head and neck. Int J Cancer 80: 194–198 (1999). Rimm DL, Camp RL, Charette LA, Costa J, Olsen DA, Reiss M: Tissue microarray: a new technology for amplification of tissue resources. Cancer J 7:24–31 (2001). Ritchie JM, Smith EM, Summersgill KF, Hoffman HT, Wang D, et al: Human papillomavirus infection as a prognostic factor in carcinomas of the oral cavity and oropharynx. Int J Cancer 104:336–344 (2003). Robertson KD: DNA methylation, methyltransferases, and cancer. Oncogene 20: 3139–3155 (2001). Roesch-Ely M, Nees M, Karsai S, Ruess A, Bogumil R, et al: Proteomic analysis reveals successive aberrations in protein expression from healthy mucosa to invasive head and neck cancer. Oncogene 26: 54–64 (2006). Romond EH, Perez EA, Bryant J, Suman VJ, Geyer CE, Jr, et al: Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med 353: 1673–1684 (2005). Rosas SL, Koch W, da Costa Carvalho MG, Wu L, Califano J, et al: Promoter hypermethylation patterns of p16, O6-methylguanine-DNAmethyltransferase, and death-associated protein kinase in tumors and saliva of head and neck cancer patients. Cancer Res 61: 939–942 (2001). Saito Y, Liang G, Egger G, Friedman JM, Chuang JC, et al: Specific activation of microRNA-127 with downregulation of the proto-oncogene BCL6 by chromatin-modifying drugs in human cancer cells. Cancer Cell 9: 435–443 (2006). Schlecht NF: Prognostic value of human papillomavirus in the survival of head and neck cancer patients: an overview of the evidence. Oncol Rep 14:1239–1247 (2005).
Schlecht NF, Franco EL, Pintos J, Negassa A, Kowalski LP, et al: Interaction between tobacco and alcohol consumption and the risk of cancers of the upper aero-digestive tract in Brazil. Am J Epidemiol 150: 1129–1137 (1999). Schwartz GJ, Mehta RH, Wenig BL, Shaligram C, Portugal LG: Salvage treatment for recurrent squamous cell carcinoma of the oral cavity. Head Neck 22:34–41 (2000). Schwartz SM, Daling JR, Doody DR, Wipf GC, Carter JJ, et al: Oral cancer risk in relation to sexual history and evidence of human papillomavirus infection. J Natl Cancer Inst 90: 1626– 1636 (1998). Shrout P, Fleiss J: Intraclass correlation: Uses in assessing rater reliability. Psychol Bull 86: 420– 428 (1979). Siegal ML, Bergman A: Waddington’s canalization revisited: developmental stability and evolution. Proc Natl Acad Sci USA 99: 10528–10532 (2002). Simon R, Radmacher MD, Dobbin K: Design of studies using DNA microarrays. Genet Epidemiol 23: 21–36 (2002). Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL: Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235: 177– 182 (1987). Slamon DJ, Leyland-Jones B, Shak S, Fuchs H, Paton V, et al: Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med 344:783–792 (2001). Slebos RJ, Yi Y, Ely K, Carter J, Evjen A, et al: Gene expression differences associated with human papillomavirus status in head and neck squamous cell carcinoma. Clin Cancer Res 12: 701– 709 (2006). Smith EM, Ritchie JM, Summersgill KF, Hoffman HT, Wang DH, et al: Human papillomavirus in oral exfoliated cells and risk of head and neck cancer. J Natl Cancer Inst 96: 449–455 (2004). Smith EM, Ritchie JM, Pawlita M, Rubenstein LM, Haugen TH, et al: Human papillomavirus seropositivity and risks of head and neck cancer. Int J Cancer 120:825–832 (2006). Smith RV, Goldman SY, Beitler JJ, Wadler SS: Decreased short- and long-term swallowing problems with altered radiotherapy dosing used in an organ-sparing protocol for advanced pharyngeal carcinoma. Arch Otolaryngol Head Neck Surg 130:831–836 (2004). Smith RV, Schlecht NF, Childs G, Prystowsky MB, Belbin TJ: Pilot study of mucosal genetic differences in early smokers and nonsmokers. Laryngoscope 116:1375–1379 (2006). Spiro RH, Guillamondegui O, Jr, Paulino AF, Huvos AG: Pattern of invasion and margin assessment in patients with oral tongue cancer. Head Neck 21:408–413 (1999).
Storey JD: A direct approach to false discovery rates. J R Stat Soc B 64: 479–498 (2002). Strati K, Pitot HC, Lambert PF: Identification of biomarkers that distinguish human papillomavirus (HPV)-positive versus HPV-negative head and neck cancers in a mouse model. Proc Natl Acad Sci USA 103: 14152–14157 (2006). Takes RP, Baatenburg de Jong RJ, Schuuring E, Hermans J, Vis AA, et al: Markers for assessment of nodal metastasis in laryngeal carcinoma. Arch Otolaryngol Head Neck Surg 123: 412–419 (1997). Tibshirani R, Hastie T, Narasimhan B, Chu G: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 99:6567–6572 (2002). Tseng GC, Oh MK, Rohlin L, Liao JC, Wong WH: Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Res 29: 2549–2557 (2001). Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98: 5116–5121 (2001). Vandecaveye V, de Keyzer F, Vander Poorten V, Deraedt K, Alaerts H, et al: Evaluation of the larynx for tumour recurrence by diffusion-weighted MRI after radiotherapy: initial experience in four cases. Br J Radiol 79: 681–687 (2006). van de Vijver M, He YD, van ‘t Veer LJ, Dai H, Hart AA, et al: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999–2009 (2002). van Rijnsoever M, Elsaleh H, Joseph D, McCaul K, Iacopetta B: CpG island methylator phenotype is an independent predictor of survival benefit from 5-fluorouracil in stage III colorectal cancer. Clin Cancer Res 9: 2898–2903 (2003). van ‘t Veer L, Dai H, van de Vijver MJ, He YD, Hart AA, et al: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530–536 (2002). Villaret DB, Wang T, Dillon D, Xu J, Sivam D, et al: Identification of genes overexpressed in head and neck squamous cell carcinoma using a combination of complementary DNA subtraction and microarray analysis. Laryngoscope 110:374–381 (2000). Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, et al: A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA 103: 2257–2261 (2006). Wang J, Takashima S, Takayama F, Kawakami S, Saito A, et al: Head and neck lesions: characterization with diffusion-weighted echo-planar MR imaging. Radiology 220: 621–630 (2001). Warner GD, Reis PP, Jurisica I, Sultan M, Arora S, et al: Molecular classification of oral cancer by cDNA microarrays identifies overexpressed genes correlated with nodal metastasis. Int J Cancer 110:857–868 (2004).
Wei SH, Chen CM, Strathdee G, Harnsomburana J, Shyu CR, et al: Methylation microarray analysis of late-stage ovarian carcinomas distinguishes progression-free survival in patients and identifies candidate epigenetic markers. Clin Cancer Res 8:2246–2252 (2002). Xiao GH, Beeser A, Chernoff J, Testa JR: p21-activated kinase links Rac/Cdc42 signaling to merlin. J Biol Chem 277: 883–886 (2002). Yan PS, Perry MR, Laux DE, Asare AL, Caldwell CW, Huang TH: CpG island arrays: an application toward deciphering epigenetic signatures of breast cancer. Clin Cancer Res 6: 1432–1438 (2000). Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, et al: Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 9: 189–198 (2006). Yang YH, Speed T: Design issues for cDNA microarray experiments. Nat Rev Genet 3: 579–588 (2002). Yang YH, Dudoit S, Luu P, Lin DM, Peng V, et al: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 30:e15 (2002). Yarbrough WG, Slebos RJ, Liebler D: Proteomics: clinical applications for head and neck squamous cell carcinoma. Head Neck 28: 549–558 (2006). Yonemura S, Hirao M, Doi Y, Takahashi N, Kondo T, et al: Ezrin/radixin/moesin (ERM) proteins bind to a positively charged amino acid cluster in the juxta-membrane cytoplasmic domain of CD44, CD43, and ICAM-2. J Cell Biol 140:885– 895 (1998). Yonemura S, Tsukita S, Tsukita S: Direct involvement of ezrin/radixin/moesin (ERM)-binding membrane proteins in the organization of microvilli in collaboration with activated ERM proteins. J Cell Biol 145: 1497–1509 (1999). Yoo GH, Piechocki MP, Ensley JF, Nguyen T, Oliver J, et al: Docetaxel induced gene expression patterns in head and neck squamous cell carcinoma using cDNA microarray and PowerBlot. Clin Cancer Res 8: 3910–3921 (2002). Yu T, Robb VA, Singh V, Gutmann DH, Newsham IF: The 4.1/ezrin/radixin/moesin domain of the DAL-1/Protein 4.1B tumour suppressor interacts with 14–3-3 proteins. Biochem J 365: 783– 789 (2002). Zhang B, Pan X, Cobb GP, Anderson TA: microRNAs as oncogenes and tumor suppressors. Dev Biol 302:1–12 (2006). Zuo C, Ai L, Ratliff P, Suen JY, Hanna E, et al: O6methylguanine-DNA methyltransferase gene: epigenetic silencing and prognostic value in head and neck squamous cell carcinoma. Cancer Epidemiol Biomarkers Prev 13: 967–975 (2004).
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(MNA), loss of 1p, and gain of 17q represent a distinct subgroup, while loss of 11q, 3p and gain of 17q characterize another subgroup (Vandesompele et al., 1998; Guo et al., 1999; Breen et al., 2000; Plantaz et al., 2001). Each of these abnormalities have highly unfavorable prognoses, implying that they play important roles in the biology of the tumors (Brodeur et al., 1984; Luttikhuis et al., 2001; Lastowska et al., 2002; Spitz et al., 2003; Attiyeh et al., 2005; Vandesompele et al., 2005). In addition, there are many other recurrent, as well as random, imbalances found in neuroblastoma. Large-scale chromosomal imbalances alter the transcriptome of cancer cells
DNA copy number changes substantially alter the gene expression profiles of cancer cells, as determined by gene expression microarray analyses (Pollack et al., 2002; Wolf et al., 2004). Certainly, one might expect that the expression of the majority of genes mapping within a region of imbalance will be altered by the change in gene dosage. However, in addition to simple dosage effects, some chromosomal imbalances lead to the altered expression of genes mapping to other chromosomes, perhaps by altering the dosage of transcriptional activators and repressors. The global dysregulation of the transcriptome was evident following experimentally induced aneuploidy in cell culture models (Upender et al., 2004). Loss of 11q in neuroblastoma is also an example of an imbalance that can be associated with significant up or down regulation of genes located throughout the genome (McArdle et al., 2004). Enormous effort has gone into the search for tumor suppressor genes that are affected by large-scale genomic loss since the seminal discovery of the ‘two-hit’ model of allelic inactivation of a tumor suppressor gene in retinoblastoma (Knudson, 1971). However, the fact that some TSGs can convey a tumorigenic effect by haploinsufficiency has made the search for tumor suppressor genes more complicated in neuroblastoma and in many other forms of cancer. More recently, there is also an emerging view, particularly for neuroblastoma, that multiple genes in the region of imbalance may be contributing to tumor pathogenesis. Wang et al. (2006) have shown that expression of many candidate tumor suppressor genes in the region of 1p and 11q loss are altered in neuroblastoma, leading them to suggest that multiple genes are important in the pathogenesis of these tumors. It is also likely that chromosomal imbalances also are causing the dysregulation of nontranslated RNA sequences such as microRNAs that have recently been demonstrated to have major roles in tumorigenesis (reviewed by Esquela-Kerscher and Slack, 2006). Many microRNA loci, and other nontranslated RNAs that are involved with cancer remain to be discovered. Finally, it is very important to mechanistically connect imbalances on different chromosomes by identifying genes that operate together in common genetic pathways. An excellent example of connecting the genetic effects of multiple imbalances was described for the MYCN amplified subtype of neuroblastoma that exhibit 1p loss and low level gain of
17q (Valentijn et al., 2005). MYCN transcriptionally represses a gene, CDC4 that maps to chromosome 1p, which is involved with the induction of differentiation of neuroblastoma cell lines. MYCN also transcriptionally activates the NME1 (alias nm23-H1) gene mapping to the 17q region which undergoes low level gain. Most significantly, the nm23-H1 protein interacts with CDC42 protein to block differentiation. Thus, there are multiple ways of transcriptionally and post-translationally down-regulating CDC42 activity in neuroblastoma, along with multiple mechanisms for increasing the activity of NME1 (Valentijn et al., 2005). Many more complex pathways affected by multiple genomic abnormalities remain to be identified, such as the genetic pathway(s) affected by loss of 3p, 11q and gain of 17q in the 11q– neuroblastoma subtype. High resolution dissection of the neuroblastoma genome using oligonucleotide array CGH (oaCGH)
The development of comparative genomic hybridization (CGH) analysis in the early 1990’s by Kallioniemi et al. (1992) allowed for the genome-wide detection of chromosomal imbalances in virtually every form of cancer, which have been summarized at the site: http://www.progenetix. de/pgscripts/progenetix/Aboutprogenetix.html. Initially, CGH analysis involved competitive hybridization of tumor and normal DNA labeled with different fluorochromes to normal metaphase chromosomes on a microscope slide and had a five to ten megabase resolution. The metaphase CGH studies carried out on neuroblastoma are numerous and will not be individually cited here. The application of CGH to target sequences such as BACs or cDNAs on microarrays has led to dramatic increases in resolution. For example, the use of complete tiling-path BAC arrays containing 32,000 clones has allowed for an 80 to 100 kb resolution (Ishkanian et al., 2004). Only a very limited number of CGH analyses, however, using BAC or cDNA arrays have been carried out on neuroblastoma (Chen et al., 2004; Mosse et al., 2005). More recently, a number of commercially available CGH platforms utilizing long (60 mer) oligonucleotides as arrayed target molecules have been developed which provide substantially higher resolution than the lower density BAC or cDNA arrays (Barrett et al., 2004; Selzer et al., 2005). We have used a very recently developed high resolution oligonucleotide array comparative genomic hybridization (oaCGH) protocol (Selzer et al., 2005) to map the chromosomal breakpoints leading to imbalances in neuroblastoma tumors and cell lines (Stallings et al., 2006). The whole genome oaCGH platform from NimbleGen Systems contains 390,000 probes, allowing for a median probe spacing of 6 kb throughout the genome. The purpose of these analyses was to determine the highest resolution achievable by CGH, to provide high resolution mapping of breakpoints leading to large-scale chromosomal imbalances, and to identify cryptic abnormalities that were too subtle to be detected by other CGH array analyses of neuroblastoma tumors.
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Fig. 1. Map of breakpoints on chromosome 11q that generated loss of 11q material. Each breakpoint is the most distal position of the breakpoint interval in megabase (Mb) pairs on the DNA sequence map. The highest degree of clustering of breakpoint positions is indicated in the rectangular box, between coordinates 68.7 and 71.8 Mb.
Mapping breakpoint positions of large-scale chromosomal imbalances High resolution breakpoint mapping is important because it allows us to define a minimal region of deletion that can be used to further pinpoint potential tumor suppressor genes. CGH analysis of 56 tumors and cell lines with the whole genome oligonucleotide arrays led to the identification of 467 chromosomal breakpoints that generated imbalances greater than 2 Mb in length (Stallings et al., 2006). Although a matched constitutional DNA sample from the patient was not used as the reference DNA sample for the majority of experiments, imbalances in excess of 2 Mb should be somatically acquired abnormalities in the tumors, otherwise one might expect patients to have phenotypic abnormalities. Breakpoints were identified using both window averaging and non-window averaging methods, as described in Selzer et al. (2005). Larger window lengths provide greater accuracy in breakpoint assignment, but at the sacrifice of resolution. We have found that 25 kb window averaging allows for assignment of breakpoints to 50 kb intervals (25 kb 8 12.5 kb), providing an accurate assignment in a high proportion of cases (190%). For approximately 10% of breakpoint assignments, larger 50 kb window averaging is required. The quality of the results is directly proportional to the quality of the DNA. As loss of chromosome 11q is associated with a poor clinical outcome, and defines a major genetic subtype of neuro-
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blastoma, we will review our findings on the mapping of 11q breakpoints which were identified in 33 tumors and cell lines. As illustrated in Fig. 1, the chromosome 11q breakpoints were non-randomly distributed. Approximately 60% of these breakpoints clustered to a 3 Mb region between positions 68.8 to 71.8 Mb on the DNA sequence map. Within this region, five breakpoints mapped to a very narrow 225 kb region (positions 71.037 to 71.262) that spanned a region that was duplicated on many chromosomes. This region on chromosome 11q is clearly a hotspot for double strand DNA breaks. Other hotspots for breakage on chromosome 11q occurred around positions 73.4 Mb, 82 to 83 Mb and 98 to 99 Mb. Overall, the breakpoints on chromosome 11q occurred at a significantly higher than expected frequency in regions that were segmentally duplicated, suggesting that some segmentally duplicated regions behave like fragile sites (Stallings et al., 2006). There was, however, no statistically significant association between breakpoints on other chromosomes and segmental duplications. It will be interesting to determine if any other recurrent chromosome abnormalities in other types of cancer occur preferentially in segmentally duplicated regions. Mapping of chromosomal breakpoint intervals using focused tiling arrays The chromosomal mechanism generating loss of 11q and gain of 17q in neuroblastoma is usually an unbalanced t(11;
Fig. 2. Detection of a t(11;17) in neuroblastoma nuclei by FISH analysis. The red labeled probe is from a region on chromosome 17q mapping distal of the 17q breakpoint while the green labeled probe comes from a position mapping proximal of the 11q breakpoint. Arrows point to the translocation breakpoint, where red and green signals come in close proximity. Three copies of chromosome 17q are evident. Two signals for the 11q are present because the probe maps proximal of the region that was deleted.
Table 1. Breakpoints on chromosomes 11q and 17q in cases of unbalanced t(11;17)
Tumor/Cell line
11q Breakpointsa
17q Breakpointsa
SK-N-AS 10 17 31 37 40 41 45 48
71.262/71.312 77.662/77.687 82.187/82.212 70.287/70.337 71.262/71.312 73.437/73.462 80.112/80.137 69.612/69.637 69.837/69.862
38.262/38.287 30.087/30.112 30.287/30.312 27.712/27.737 27.637/27.662 40.887/40.912 31.487/31.512 37.837/37.862 34.237/34.262
a
The figures on either side of the slash represent the proximal/distal boundaries of the breakpoint positions on the DNA sequence map (Mb), 8 12.5 kb.
17), as observed by many different labs (Avet-Loiseau et al., 1995; Stark, 2003; Stallings et al., 2004). The chromosomes segregate so that a der(11) chromosome, along with a normal chromosome 11 and two normal chromosomes 17 are retained in the tumor cells. Due to the importance of this abnormality in neuroblastoma, we were interested in determining if there was any DNA sequence homology between the 11q and 17q breakpoint regions that might be mechanistically involved. Nine 11q– tumors or cell lines were determined to possess an unbalanced t(11;17) using FISH analysis (Stallings et al., 2004), as illustrated in Fig. 2 and Table 1.
As shown in Table 1, breakpoint positions on the 11q and on the 17q ranged over megabase sized regions. The breakpoint interval on the 11q was identical in only two tumors, and the breakpoint intervals for the 17q were unique in all tumors (although some mapped very close together). There was no sequence homology between the 11q and 17q regions for each translocation, other than high abundance repeats such as Alu, L1 etc. Thus, homlogous recombination between low abundance repeats or segmental duplications is apparently not involved with the formation of the t(11;17). Although low abundance copy number repeats play major roles in the generation of constitutional chromosome abnormalities (Shaffer and Lupski, 2000), very few examples for the role of low copy number repeats (LCR) in the generation of chromosome abnormalities acquired in cancer can be cited. LCRs may be implicated in the formation of an unusual Philadelphia chromosome found in CML (Saglio et al., 2002) and in the isochromosome 17q which is common to many cancers (Barbouti et al., 2004). Mapping breakpoints using higher density tiling arrays Could recombination between high abundance repeats play a role in generating the t(11;17), as has been reported for Alu repeats (Kolomietz et al., 2002; Elliot et al., 2005)? This question is not possible to answer with 50 kb breakpoint intervals because an interval of this size will have many of the same repeats on both chromosomes. CGH analysis of higher density tiling arrays focused on the 11q and 17q breakpoint regions (median probe spacing of 50 bp) led to more refined mapping of the breakpoints for three independent t(11; 17) (Selzer et al., 2005). Breakpoints for 11q could be refined to a 10 kb interval, while 17q breakpoints could be refined to 1 kb intervals. These intervals could not be further mapped because of the presence of high abundance repeats. The 11q and 17q breakpoint intervals did not contain the same repetitive elements, however, indicating that illegitimate homologous recombination is not involved with the formation of the t(11;17). Exactly why a recurrent t(11;17) takes place in neuroblast cells is unknown, but it seems likely that nonhomologous end joining of double strand breaks is involved. There is increasing evidence that non-random spatial proximity of certain translocation prone loci in the nucleus may be one cause of why translocations occur repeatedly (Roix et al., 2003), so it is tempting to speculate that chromosomes 11 and 17 are positioned in close proximity in the neuroblast nucleus – a possibility which requires further study. Identification of subtle abnormalities and CNPs using oaCGH analysis In addition to providing high resolution maps of the breakpoints generating large-scale chromosomal imbalances, oaCGH analysis has enormous potential for detecting subtle chromosomal imbalances that are less than 2 Mb in length. It has been our experience that imbalances that are as small as 100 kb can be routinely detected by oaCGH using the NimbleGen platform (Stallings et al., 2006). However, the high resolution achievable by oaCGH is also a ma-
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Fig. 3. Representative oaCGH plot of chromosome 9p illustrating microdeletion of the PTPRD locus. The vertical axis represents the fluorescence ratio between the tumor and reference DNA on a log2 scale, while the horizontal axis is genome position in Mb. Plots for both the primary tumor (upper) and metastatic cells from bone marrow (lower) are displayed. The deletion of PTPRD is only detected in cells from the metastatic site, consistent with the notion that PTPRD is involved with metastasis suppression.
jor weakness because large numbers of constitutional copy number variants, as described by Sebat et al. (2004) are detected. This is particularly true if a matched constitutional DNA sample from the patient is unavailable. Distinguishing CNPs from somatically acquired deletions and duplications then becomes a major challenge. Although databases cataloguing CNPs are not yet complete, we have nevertheless found that the majority of CNP variants can be identified using a bioinformatics approach (Stallings et al., 2006). Our approach involves determining if an imbalance is in a segmentally duplicated region of the genome using the UCSC Genome Browser, or is listed as a known polymorphic region in the Database of Genomic Variants (Toronto). If either is the case, we consider the imbalance detected in the tumor to be a constitutional variant, as opposed to a somatically acquired DNA copy number alteration. After identifying hundreds of probable CNP variants following the analysis of only 56 samples, we were left with 100 subtle imbalances that were likely somatically acquired in the neuroblastomas. In our studies, the most frequently occurring (six out of 56 cases) subtle abnormalities involved microdeletions of the protein tyrosine phosphatase receptor D locus (PTPRD), as illustrated in Fig. 3. In some instances, the deletions were homozygous, indicating a two-hit mechanism of allele inactivation. Interestingly, large-scale loss of chromosome 9p is a recurrent imbalance in neuroblastoma (Vandesompele et al., 2005), and the detection of these microdeletions suggest that PTPRD is at least one of the genes targeted by the larger-scale deletions. Analysis of constitutional DNA samples from some patients confirmed that these deletions were somatically acquired. This locus appears to play a rather general role in cancer, having been identified as being one of the most frequent homozygous deletions in lung cancer cell lines (Sato et al., 2005). PTPRD maybe more of a metastasis suppressor gene,
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rather than a tumor suppressor gene, given that PTPRD protein interacts with MTSS1 (alias MIM missing in metastasis) (Woodings et al., 2003), a likely metastasis suppressor gene. This is consistent with loss of 9p being more of a secondary abnormality in neuroblastoma. Summary
There is an emerging body of evidence that large-scale chromosomal imbalances significantly contribute to tumor pathogenesis – this is particularly true for neuroblastoma where several large-scale imbalances are associated with poor clinical outcome. The importance of these genomic gains and losses in neuroblastoma have led us to map large numbers of breakpoints using CGH analysis of both whole genome (median probe spacing 6 kb) and focused tiling (median probe spacing of 50 bp in breakpoint regions) oligonucleotide arrays. These arrays permit the mapping of breakpoints, such as the unbalanced t(11;17) generating 11q loss and 17q gain, to intervals ranging in length from 1 to 10 kb, dependent upon local genomic architectural features. Although breakpoints on chromosome 11q are preferentially associated with regions of segmental duplication, indicating that such regions can act as fragile sites, the breakpoints on other chromosomes were not preferentially associated with segmental duplications. No evidence for homologous recombination in the generation of the recurrent t(11;17) was obtained, and nonhomologous end joining of DNA double strand breaks appears to be a more likely mechanism, based on the wide distribution of 11q and 17q breakpoints. Finally, the identification of recurrent microdeletions at the same genetic locus (PTPRD) demonstrates the utility of oaCGH analysis for pinpointing genes that may have been targeted by large-scale chromosomal imbalances.
References Attiyeh EF, London WB, Mosse YP, Wang Q, Winter C, et al: Chromosome 1p and 11q deletions and outcome in neuroblastoma. N Engl J Med 353:2243–2253 (2005). Avet-Loiseau H, Venuat AM, Benard J, Leibovitch MP, Hartmann O, Bernheim A: Morphologic and molecular cytogenetics in neuroblastoma. Cancer 75:1694–1699 (1995). Barbouti A, Stankiewicz P, Nusbaum C, Cuomo C, Cook A, et al: The breakpoint region of the most common isochromosome, i(17q), in human neoplasia is characterized by a complex genomic architecture with large, palindromic, lowcopy repeats. Am J Hum Genet 74: 1–10 (2004). Barrett MT, Scheffer A, Ben-Dor A, Sampas N, Lipson D, et al: Comparative genomic hybridization using oligonucleotide microarrays and total genomic DNA. Proc Natl Acad Sci USA 101: 17765–17770 (2004). Breen CJ, O’Meara A, McDermott M, Mullarkey M, Stallings RL: Co-ordinate deletion of chromosome 3p and 11q in neuroblastoma detected by comparative genomic hybridization. Cancer Genet Cytogenet 120:44–49 (2000). Brodeur GM: Neuroblastoma: biological insights into a clinical enigma. Nat Rev Cancer 3: 203– 216 (2003). Brodeur GM, Seeger RC, Schwab M, Varmus HE, Bishop JM: Amplification of N-myc in untreated human neuroblastomas correlates with advanced disease stage. Science 224: 1121–1124 (1984). Chen QR, Bilke S, Wei JS, Whiteford CC, Cenacchi N: cDNA array-CGH profiling identifies genomic alterations specific to stage and MYCNamplification in neuroblastoma. BMC Genomics 5:1–13 (2004). Elliott B, Richardson C, Jasin M: Chromosomal translocation mechanisms at intronic Alu elements in mammalian cells. Mol Cell 17: 885– 894 (2005). Esquela-Kerscher A, Slack FJ: Oncomirs – microRNAs with a role in cancer. Nature Rev Cancer 6:259–269 (2006). Guo C, White PS, Weiss MJ, Hogaerty MD, Thompson PM, et al: Allelic deletion at 11q23 is common in MYCN single copy neuroblastomas. Oncogene 18:4948–4957 (1999). Ishkanian AS, Malloff CA, Watson SK, DeLeeuw RJ, Chi B, et al: A tiling resolution DNA microarray with complete coverage of the human genome. Nat Genet 36: 299–303 (2004). Kallioniemi A, Kallioniemi OP, Sudar D, Rutovitz D, Gray JW, et al: Comparative genomic hybridization: a rapid new method for detecting and mapping DNA amplification in tumors. Science 258:818–821 (1992). Knudson AG: Mutation and cancer: statistical study of retinoblastoma. Proc Natl Acad Sci USA 68:820–823 (1971).
Kolomietz E, Meyn MS, Pandita A, Squire JA: The role of Alu repeat clusters as mediators of recurrent chromosomal aberrations in tumors. Genes Chromosomes Cancer 35: 97–112 (2002). Lastowska M, Cotterill S, Bown N, Cullinane C, Variend S, et al: Breakpoint position on 17q identifies the most aggressive neuroblastoma tumors. Genes Chromosomes Cancer 34: 428– 436 (2002). Luttikhuis M, Powell JE, Rees SA, Genus T, Chughtai S, et al: Neuroblastomas with chromosome 11q loss and single copy MYCN comprise a biologically distinct group of tumors with adverse prognosis. Brit J Cancer 85: 531–537 (2001). McArdle L, McDermott M, Purcell R, Grehan D, O’Meara A, et al: DNA microarray analysis of gene expression in neuroblastoma displaying loss of 11q. Carcinogenesis 25: 1599–1609 (2004). Mosse YP, Greshock J, Margolin A, Naylor T, Cole K, et al: High resolution detection and mapping of genomic DNA alterations in neuroblastoma. Genes Chromosomes Cancer 43: 390–403 (2005). Plantaz D, Vandesompele J, Van Roy N, Lastowska M, Bown N, et al: Comparative genomic hybridization (CGH) analysis of stage 4 neuroblastoma reveals high frequency of 11q deletion in tumors lacking MYCN amplification. Int J Cancer 91:680–686 (2001). Pollack JR, Sorlie T, Perou CM, Rees CA, Jeffrey SS, et al: Microarray analysis reveals a major direct role of copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci USA 99: 12963–12968 (2002). Roix JJ, McQueen PG, Munson PJ, Parada LA, Misteli T: Spatial proximity of translocationprone gene loci in human lymphomas. Nat Genet 34:287–291 (2003). Saglio G, Storlazzi CT, Giugliano E, Surace C, Anelli L, et al: A 76-kb duplicon maps close to the BCR gene on chromosome 22 and the ABL gene on chromosome 9: possible involvement in the genesis of the Philadelphia chromosome translocation. Proc Natl Acad Sci USA 99:9882–9887 (2002). Sato M, Takahashi K, Nagayama K, Arai Y, Ito N, et al: Identification of chromosome 9p as the most frequent target of homozygous deletions in lung cancer. Genes Chromosomes Cancer 44: 405–414 (2005). Sebat J, Lakshmi B, Troge J, Alexander J, Young J, et al: Large-scale copy number polymorphism in the human genome. Science 305: 525–528 (2004). Selzer RR, Richmond TA, Pofahl NJ, Green RD, Eis PS, et al: Analysis of chromosome breakpoints in neuroblastoma at sub-kilobase resolution using fine tiling oligonucleotide array CGH. Genes Chromosomes Cancer 44: 305–319 (2005).
Shaffer LG, Lupski JR: Molecular mechanisms for constitutional chromosomal rearrangements in humans. Annu Rev Genet 34: 297–329 (2000). Spitz R, Hero B, Ernestus K, Berthold F: Deletions in chromosome arms 3p and 11q are new prognostic markers in localized and 4s neuroblastoma. Clin Can Res 9:52–58 (2003). Stallings RL, Carty P, McArdle L, Mullarkey M, McDermott M, et al: Molecular cytogenetic analysis of recurrent unbalanced t(11; 17) in neuroblastoma. Cancer Genet Cytogenet 154: 44–51 (2004). Stallings RL, Nair P, Maris JM, Catchpoole D, McDermot M, et al: High resolution analysis of chromosome breakpoints and genomic instability identifies PTPRD as a candidate tumor suppressor gene in neuroblastoma. Cancer Res 66:3673–3680 (2006). Stark B: der(11)t(11;17): a distinct cytogenetic pathway of advanced stage neuroblastoma (NBL) – detected by spectral karyotyping (SKY). Cancer Lett 197:75–79 (2003). Upender MB, Habermann JK, McShane LM, Korn EL, Barrett JC, et al: Chromosome transfer induced aneuploidy results in complex dysregulation of the cellular transcriptome in immortalized and cancer cells. Cancer Res 64:6941–6949 (2004). Valentijn LJ, Koppen A, van Asperen R, Root HA, Haneveld F, Versteeg R: Inhibition of a new differentiation pathway in neuroblastoma by copy number defects of N-myc, Cdc42, and nm23 genes. Cancer Res 65: 3136–345 (2005). Vandesompele J, Van Roy N, Van Gele M, Laureys G, Ambros P, et al: Genetic heterogeneity of neuroblastoma studied by comparative genomic hybridization. Genes Chromosomes Cancer 23:141–152 (1998). Vandesompele J, Baudis M, De Preter K, Van Roy N, Ambros P, et al: Unequivocal delineation of clinicogenetic subgroups and development of a new model for improved outcome prediction in neuroblastoma. J Clin Oncol 23: 2280–2299 (2005). Wang Q, Diskin S, Rappaport E, Attiyeh E, Mosse Y, et al: Integrative genomics identifies distinct molecular classes of neuroblastoma and shows that multiple genes are targeted by regional alterations in DNA copy number. Cancer Res 66: 6050–6062 (2006). Wolf M, Mousses S, Hautaniemi S, Karhu R, Huusko P, et al: High-resolution analysis of gene copy number alterations in human prostate cancer using CGH on cDNA microarrays: impact of copy number on gene expression. Neoplasia 6: 240–247 (2004). Woodings JA, Sharp SJ, Machesky LM: MIM-B, a putative metastasis suppressor protein, binds to actin and to protein tyrosine phosphatase . Biochem J 371: 463–471 (2003).
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Gross chromosomal aberrations
The development of cancer is generally believed to require the accumulation of multiple genetic aberrations. These aberrations range from single nucleotide mutations to cytogenetically detectable numerical and structural chromosomal alterations (Devilee and Cornelisse, 1994; Bieche and Lidereau, 1995). While human tissue specimens and immortal breast cancer cell lines represent an excellent resource for genomic studies, chromosomal alterations have not been well characterized until relatively recently, largely because of limitations inherent to conventional chromosome banding techniques. The development of new techniques which enable the accurate, high throughput analysis of specimens has enabled substantial progress in this field. Comparative genomic hybridization (CGH), introduced in 1992, has been the technique of choice for mapping DNA copy number changes in human tumors. The technique uses hybridization to compare abundance of specific genome sequences in tumor cell DNA relative to normal reference genomes (Kallioniemi et al., 1994). This opened new avenues in genomic investigation because it obviated the need to culture cells before their chromosomes could be analyzed. The latest generation of CGH analysis, array-CGH or matrix-CGH, uses ordered arrays of genomic DNA sequences and further increases the potential of CGH to provide insight into chromosomal aberrations present in cancer by enabling single-gene resolution. Recently developed multi-color chromosome imaging techniques such as spectral karyotyping (SKY) (Schrock et al., 1996) and multiplexFISH (M-FISH) (Speicher et al., 1996) utilize simultaneous visualization of each human chromosome with specific fluorochrome combinations and enable a far more rapid and detailed karyotypic analysis of solid tumors and cell lines (Macville et al., 1997; Kawai et al., 2002). The chromosomal aberrations in the most commonly used human breast carcinoma cell lines have recently been investigated using these techniques (Kytola et al., 2000; Xie et al., 2002; Watson et al., 2004; Goodison et al., 2005a; Shadeo and Lam, 2006). Karyotypic analysis has shown breast tumor cell lines to be either near diploid with simple rearrangements or highly aneuploid with multiple, complex rearrangements (Morris et al., 1997; Kytola et al., 2000). The most frequent gains detected by CGH in breast tumor cell lines are 1q, 8q, 20q, 7, 11q13, 17q, 9q, and 16p, whereas losses were most common at 8p, 11q14]qter, 18q, and Xq (Kytola et al., 2000). The comparison of CGH data from cell lines to CGH studies from primary breast tumors (Nishizaki et al., 1997; Tirkkonen et al., 1998) reveals that the most common gains and losses are the same. In breast tumor specimens, the most common chromosomal imbalances detected by CGH are gains of 1q, 8q, 16p, 17q, and 20q and losses involving 8p, 13q, 18q, and 16q (Isola et al., 1995; Ried et al., 1995). Although literally hundreds of articles have been published describing the pattern of copy number alterations in cancer, very few of the genes affected have been identified. Consequently, investigators are currently attempting to correlate CGH data with gene expression. Hy-
man et al. (2002) combined CGH with cDNA arrays to analyze breast tumor cell lines and found that a significant correlation does exist between gene amplification and gene expression across the genome. The same group has focused on the 17q21]q23 amplicon, which includes ERBB2 and is a common region of amplification in breast cancers with poor prognosis (Kallioniemi et al., 1994; Kauraniemi et al., 2001). These analyses are examples of the power achieved by combining genomic and expression approaches, and have identified several genes that are consistently overexpressed in breast cancer cell lines and advanced breast cancer. SKY analysis of breast tumor cell line karyotypes reveals that the chromosomes most frequently involved in translocations are 8, 1, 17, 16, and 20. These chromosomes contain gene amplicons, for example, at 8q24, 17q11]q12, and 20q12]q13, that are present in up to 30% of breast carcinomas (Devilee and Cornelisse, 1994; Bieche and Lidereau, 1995; Tanner et al., 1996; Barlund et al., 1997). Furthermore, studies using conventional cytogenetics report that primary and metastatic breast carcinomas contain aberrations of chromosome 8 in up to 40% of cases (Adeyinka et al., 2000; Popescu and Zimonjic, 2002). Databases that make data from SKY/M-FISH and CGH studies in cancer available have been created by the NCI (Knutsen et al., 2005). The SKY/M-FISH and CGH Database (http://www.ncbi.nlm. nih.gov/projects/sky/) enables investigators to submit and analyze clinical and research cytogenetic data. The Cancer Chromosomes database integrates the SKY/M-FISH & CGH Database with the Mitelman Database of Chromosome Aberrations in Cancer (http://cgap.nci.nih.gov/Chromosomes/ Mitelman) and the Recurrent Chromosome Aberrations in Cancer database (http://cgap.nci.nih.gov/Chromosomes/ RecurrentAberrations). Relatively few studies have explored whether specific cytogenetic abnormalities can be used to stratify breast tumors with clinical course, namely metastatic relapse. Blegen et al. (2003) performed CGH on tumors from patients who had early relapse and from patients who remained free from distant metastases for more than ten years. Tumors in relapse patients showed a higher average number of chromosomal copy alterations compared to the long-term survivors, including gains of chromosome 3q, 9p, 11p and 11q and loss of 17p (Blegen et al., 2003). This aligns well with a CGH study that evaluated 76 LN-negative breast carcinomas (median follow-up 46 months) and found that a gain of 3q is a stronger predictor of recurrence than grade, tumor size, and estrogen receptor status (Janssen et al., 2003). In a CGH study of 39 invasive breast carcinomas with a mean follow-up period of 99 months, Aubele et al. (2002) identified an independent prognostic value for chromosomal gains on 11q13, 12q24, 17 and 18p. More recently, an arrayCGH study of ER-positive breast cancer tissues reported that the most significant chromosomal alterations found more often in the group with metastatic recurrence within five years were loss of 11p15.5]p15.4, 1p36.33, 11q13.1, and 11p11.2 (Han et al., 2006). Array-CGH was also used by Yao et al. (2006) to show that the overall frequency of copy num-
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ber alterations in regions 1q, 8q24, 11q13, 12p13, 17q21]q23, 16p13 and 20q13, correlated with the advancing nature of the tumors. Amplification of candidate loci was confirmed by quantitative PCR, and expression level analysis of genes present in these loci identified many putative target genes (Yao et al., 2006). These CGH results have significant overlap and indicate that malignant breast adenocarcinomas are characterized by specific chromosomal copy number changes. The high divergence of karyotype data makes it difficult to define more specific aberrations involved in breast cancer etiology or progression, however, the study of copy number aberrations has proved to lead to the development of useful diagnostic markers. The tight correlation of amplification of ERBB2 with increased expression of the gene product provides the basis for the tests that measure the DNA copy number of ERBB2 for predicting response to Herceptin (Pauletti et al., 1996). Furthermore, combinations of FISH probes for regions of recurrent copy number aberration in other tumor types have proved useful for monitoring disease status (Sokolova et al., 2000) and for distinguishing benign from malignant skin lesions (Bastian et al., 1999). Tissue-based signatures of breast cancer progression
Large-scale microarray analyses of human tissue specimens are building consensus gene expression profiles of various tumors, including breast (Sorlie et al., 2001, 2003, 2006; van ‘t Veer et al., 2002; van de Vijver et al., 2002; Ma et al., 2003, 2004; Weigelt et al., 2003; Hu et al., 2006; Sotiriou et al., 2006). Breast cancer is clearly a heterogeneous disease and microarray analysis has been successfully used to derive distinct patterns of gene expression that correlate with molecular subtypes of breast cancers. Expression profiles that are associated with estrogen-receptor status, HER2 (ERBB2) expression, BRCA1 or BRCA2 mutations and with ‘basal type’ or ‘luminal types’ have been identified using unsupervised analyses (Sorlie et al., 2001; Hedenfalk et al., 2003; Mackay et al., 2003; Kristensen et al., 2005). These breast cancer subtypes also represent clinically distinct subgroups of patients, with differences in disease progression and overall survival. For example, ER-positive tumors tend to have the best outcome, whereas HER2+ tumors have a bad prognosis. The genes which show high variance across different tumors have been termed ‘intrinsic genes’ and a set of 1300 such genes has been reported to accurately subdivide the molecular subtypes across multiple array platforms and independent microarray studies (Hu et al., 2006). Studies using reduced lists of genes are beginning to be used in independent validation studies, and as few as 40 genes have been used to stratify subtypes of breast cancer using quantitative PCR assays (Perreard et al., 2006). In a study conducted in 2002, a supervised classification analysis of DNA-microarray data predicted prognosis better than clinical prognostic indicators such as grade, stage, and nodal status. The investigators queried samples from a co-
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hort of young (!53) breast cancer patients with LN-negative tumors. Reduction of the data identified a 70-gene signature that distinguished patient groups with good or poor prognosis with respect to the likelihood of the later development of clinical metastases (van ‘t Veer et al., 2002). Subsequent testing of the 70-gene prognosis gene set in an independent cohort of 295 patients confirmed the accuracy of the signature, regardless of LN status, in predicting the occurrence of distant metastases within five years of treatment (van de Vijver et al., 2002). The resulting gene-expression profile was a far more powerful predictor of the outcome of disease in young patients with breast cancer than the currently used St. Gallen or NIH consensus criteria based on clinical and histological characteristics. Importantly, the predictive power of the 70-gene signature was based upon metastasis to non-lymphatic tissues. More recently, a similar approach identified a 76-gene signature that was also successfully used to predict distant metastasis in patients with LN-negative primary breast cancer (Wang et al., 2005). The signature showed 93% sensitivity and 48% specificity when tested in an independent set of 171 LN-negative patients. As with the 70-gene signature described above, this signature outperformed the St Gallen criteria. Another study that included 159 samples obtained from both treated and untreated patients, with and without LN involvement, derived a 64gene signature set that identified genes associated with the occurrence of distant metastasis or death within five years (Pawitan et al., 2005). Once again, the signature was validated in an independent set of 289 patients and was found to outperform clinical criteria in the stratification of risk and overall survival. A number of studies have investigated the hypothesis that molecular programs of wound healing might be reactivated in cancer metastasis (Bissell and Radisky, 2001). Chang et al. identified consistent features in the transcriptional response of normal fibroblasts to serum, and used this so called ‘wound-response signature’ to reveal links between this phenomena and cancer progression in a variety of common epithelial tumors, including breast (Chang et al., 2004). The same group subsequently tested the accuracy of the wound-response signature in the same 295 patients with early-stage breast cancer used to identify and validate the 70-gene prognostic signature (van ‘t Veer et al., 2002). It was found that this signature could identify approximately 90% of patients who developed metastases, independently of clinical or pathological risk factors (Chang et al., 2005). The suggestion that the risk of metastasis for breast cancer patients can be predicted by the gene expression profile of its heterogeneous primary tumor has come as something of a surprise. The prevailing idea has been that metastatic potential is acquired relatively late in the multistep process of tumor progression (Fidler and Kripke, 1977), however, the recent microarray findings have reopened the debate on this topic by suggesting that the ability to metastasize to distant sites may be an early and inherent genetic property of breast tumors. An early study reported the derivation of a gene expression signature that distinguished pri-
mary from metastatic adenocarcinomas. Ramaswamy et al. (2003) compared expression profiles of a range of 64 primary adenocarcinomas (breast, prostate, lung, colon, uterus and ovary) to 12 unmatched metastases resulting from adenocarcinomas from the same spectrum of sites, but resected from a variety of end-organs. This comparison identified an expression pattern of 17 genes that best distinguished primary and metastatic adenocarcinomas. Notably, components of the protein translation apparatus were heavily represented in the 17-gene pan-metastasis signature. By re-applying this metastasis-associated gene expression pattern to data on 279 primary solid tumors of diverse types, including 78 stage I primary breast carcinomas, it was found that primary tumors carrying the 17gene expression signature were more likely to be associated with metastasis and poor clinical outcome (Ramaswamy et al., 2003). Conversely, in more recent studies utilizing sets of matched tissue specimens, Weigelt et al. (2003, 2005a) found that gene expression profiles in human primary breast carcinoma are preserved in the associated distant metastasis, with respect to both subtype profile and poor prognosis signature, even if metastases develop after a long interval. In breast cancer, it is the axillary LNs that are most often the first sites in which disseminated tumor cells can be detected (Stacker et al., 2002) and their presence or absence is currently one of the most important factors for disease course prediction for breast cancer patients (McGuire, 1987). While prognosis signatures for distant metastasis occurrence have been achieved, the same research groups have not been able to identify a classifier predicting the LN status of primary breast tumors. The comparison of gene expression profiles of 15 primary breast carcinomas and their matching LN metastases revealed no common subset of differentially expressed genes, and in the analysis of the Dutch 295 primary breast tumor profile dataset, no classifier predicting LN metastasis could be developed (Weigelt et al., 2005b). However, a study by Huang et al. (2003) did identify patterns of expression that were associated with the LN status of 89 Taipei breast cancer patients. These analyses were based on biopsy material and resulted in a predictive accuracy of approximately 90% for LN metastasis and relapse. None of the genes implicated in the overall recurrence of disease in this study were common to the 70-gene prognosis signature of van ‘t Veer et al. (2002), and cross-validation with a previous US study did not identify significant numbers of overlapping genes, presumably due to the different racial genetic backgrounds, but the authors concluded that LN metastasis and disease recurrence are distinct biologically. Collectively, these data suggest that proximal LN metastasis occurs independently of distant metastasis (Weigelt et al., 2005b), but it remains unclear whether metastasis to more distant sites proceeds sequentially from LN metastasis or in parallel via a hematogenous route (Chambers et al., 2000).
Current limitations and future prospects
Improved prognostic markers are clearly needed in order to better stratify patients with respect to their risk for developing metastases, and important advances towards this have been made. However, many limitations with microarray-based tumor classifications exist. The subtype and prognosis gene sets remain relatively broad and no quantitative information is available. Subsequent tests based on the qualitative presence or absence of a single gene, or even multiple genes are unlikely to be accurate enough for clinical assay development. Due to economic considerations, the number of samples tested to date remains very small relative to the feature dimensionality present on microarray chips. Consequently, as more data are added the current signatures can change dramatically, and another round of adjustment to tissue-based signatures will occur now that arrays that provide complete genome coverage have become available. Furthermore, studies that assay grossly homogenized tissues define an average gene expression signature that does not account for variations in tumor complexity, heterogeneity or non-tumor cell contributions. Gene expression signatures may reflect genetic and/or epigenetic phenomena, effects of growing in different tissue environments, or simply the composition of cell types. These problems have caused some debate regarding the timing of the next phase of current gene signature clinical testing (Brenton et al., 2005; Loi et al., 2005), but it is clear that independent validation of these signatures is the next step. Indeed, multiinstitutional studies are being organized (TransBIG in Europe (Tuma, 2004) and NCI PACCT in the US) to facilitate the evaluation of breast cancer genomic signatures in far greater numbers of clinical specimens. At this stage, microarray data in itself does not provide definitive answers. The inclusion of a gene in a prognostic list that is determined by a supervised classification method does not indicate the importance of that gene in cancer biology, nor does it provide functional insight into the underlying mechanisms of disease. However, these analyses have provided a wealth of candidate genes and pathways for further study. In order to investigate the role of these genes in the mechanisms of metastasis animal models are needed. Animal models of metastasis
Signatures of breast cancer metastasis The multistep nature of metastasis poses difficulties in both design and interpretation of experiments to unveil the mechanisms causing the process. Studies on excised human tissues are complicated by the variance of genetic background between individuals and by the cellular heterogeneity of a complex tissue mass. Critical to the experimental analysis of metastasis has been the isolation of human tumor cell lines and the ability to study their behavior in vivo by inoculation into immune-compromised mice. Several established human breast cancer cell lines with varying documented abilities of invasiveness and/or migration in
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vitro are available, and some are capable of spontaneous metastasis in vivo, i.e. dissemination from a primary tumor and proliferation in a distal site (Price et al., 1990). This xenograft model represents an experimental system in which the role of specific genes can be screened and tested. However, many breast cell lines, especially those isolated from pleural effusions, are polyclonal in nature and composed of cell populations that are heterogeneous in metastatic phenotype, thus in order to use cell lines as models in studies seeking to define genes causing metastasis it is optimal to isolate single cell progenies from the parental cell line source. Through in vivo selection of monoclonal cultures of the MDA-MB-435 breast tumor cell line we were able to characterize a pair of single cell progenies (M4A4 and NM2C5) which differ markedly in their ability to complete the metastatic process (Urquidi et al., 2002). When orthotopically inoculated into athymic mice, both cell lines form primary tumors, but only M4A4 is capable of metastasis to the lungs and lymph nodes (Urquidi et al., 2002; Goodison et al., 2003). These isogenic cell lines of opposite metastatic propensity constitute a stable and accessible model for the identification of genes involved in the process of tumor metastasis. We have performed multiple comparative analyses of these paired cell lines, including cytogenetic analyses and evaluation of the expression of a number of gene products previously implicated in cellular transformation and metastasis (Urquidi et al., 2002; Agarwal et al., 2003; Goodison et al., 2003, 2005a). To further elucidate the extent of the molecular changes associated with acquisition of the metastatic phenotype in this model, we recently employed a genomewide expression profiling approach. Intensity modeling and hierarchical clustering analysis revealed a subset of 85 genes (12-fold change) whose expression was statistically correlated with metastatic phenotype (Goodison et al., 2005b). Some genes in this group have been implicated in invasion, tumor cell proliferation and/or metastasis previously, but GTPase signaling components were one of the most-wellrepresented functional groups. Restoration of the expression of deleted in liver cancer-1 (DLC-1), a Rho-GTPase-activating protein, in metastatic M4A4 cells resulted in the inhibition of migration and invasion in vitro and a significant reduction in the ability of these cells to form pulmonary metastases in athymic mice. DLC-1 has specific GTPase activating protein functions for RhoA and Cdc42 (Wong et al., 2003), members of the Rho family that are consistently overexpressed in breast tumors (Fritz et al., 1999). The finding that DLC1 can act as a ‘metastasis-suppressor gene’ supports an influential role for GTPase signaling in tumor progression. Metastasis suppressor genes are potential candidates for marker development because, by definition, their loss should be associated with the acquisition of metastatic potential (Shevde and Welch, 2003). A similar methodological approach was used by Kang et al. (2003) to study breast tumor cell homing to specific organ sites. The investigators were able to derive monoclonal lines from the MDA-MB-231 breast tumor cell line that had differing degrees of ability to form tumor deposits in murine bone. The experimental system was necessarily differ-
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ent to that used in a lung metastasis model, in that cells need to be inoculated directly into the heart in order to get sufficient cell numbers to the arterial side of the murine body and to the skeleton. Using comparative microarray analyses of the parental MDA-MB-231 cell line and a variant selected for bone colonization, the investigators identified a bone metastasis gene expression signature of 102 genes. Interestingly, in line with our lung metastasis model, DLC-1 was found to be downregulated in breast cell populations which were highly metastatic to bone (Kang et al., 2003) suggesting that some signaling pathways may be pivotal to metastatic efficiency regardless of the target organ. Transfection of combinations of genes confirmed some of these genes (IL11, OPN/SPP1, CTGF and CXCR4) as being functionally involved in the efficiency of MDA-MB-231 cell bone colonization. Both IL11 and CTGF are known to be activated by TGF, suggesting a potential prometastatic role for this cytokine in bone. The same group continued to derive monoclonal lines from MDA-MB-231 and test their propensity to colonize other organs. Comparison of transcriptional profiles of cell populations highly or weakly metastatic to the murine lung resulted in a 54-gene lung metastasis signature (Minn et al., 2005). This gene set was distinct from the bone metastasis signature overall (Kang et al., 2003), but some genes were common to both MDA-MB-231 models, including MMP1 and CXCR4 (Minn et al., 2005). The MDA-MB231 lung metastasis signature gene set was also compared to expression profiles obtained from a cohort of 82 therapeutically excised primary breast carcinomas from patients with known metastatic status. This revealed that expression profiles in the primary tumors from patients with subsequent lung metastases, but not bone metastases, correlated to some extent with the lung metastasis signature (Minn et al., 2005). Another variation on clonal models of metastasis has been characterized by Lev et al. (2003). This model consists of a more aggressive metastatic clone (GILM2) derived from the weakly metastatic GI101A human breast cancer cell line which metastasizes to the lung and lymph nodes when inoculated orthotopically (Hurst et al., 1993). Microarraybased comparison with the parental line identified a list of 106 genes that were differentially expressed (12.5 fold) in the highly metastatic GILM2 variant (Kluger et al., 2005). Immunohistochemical confirmation in human breast tissue specimens of the expression of three markers, heat shock protein 70 (HSP70), chemokine ligand 1 (CXCL1), and secretory leukocyte protease inhibitor (SLPI), revealed that the expression of all three genes was correlated with lymph node involvement, and the expression of HSP70 and CXCL1 was associated with decreased overall patient survival. Interestingly, we have identified and functionally proven the role of SLPI in an invasion-independent model of metastasis (Sugino et al., 2004). A wholly murine model of spontaneous breast cancer metastasis to multiple sites has been characterized and used to identify genes involved in metastatic progression. Several syngeneic tumor lines with a spectrum of metastatic phenotypes were isolated from a spontaneous mammary tumor in a BALB/cfC3H mouse (Lelekakis et al.,
1999). When inoculated orthotopically, the resulting primary tumors are either nonmetastatic or produce spontaneous metastases to lymph node, lung and/or bone (Eckhardt et al., 2005). To identify metastasis-related genes, the investigators grouped the expression profiles of a weakly metastatic group and compared these with the profiles of a highly metastatic group using mouse cDNA arrays. A metastasis signature of 216 genes was derived, of which 125 were known genes. A significant proportion of genes belonging to extracellular matrix protein families had elevated expression in the highly metastatic tumors. The role of one of these genes, POEM (nephronectin), was further investigated using RNA interference technology, and decreased POEM expression in 4T1 tumors significantly inhibited spontaneous metastasis to the lung, kidney and bone. POEM is a secreted ECM molecule that has been reported to be involved in kidney morphogenesis and the development of bone (Brandenberger et al., 2001; Morimura et al., 2001), and so it is logical to assume that the expression of POEM by the tumor cells in the murine model is critical for the establishment of metastases in these organs. While genetic studies of clinical specimens will continue to be informative, they provide only a snapshot of a complicated disease state, and there are few experimental opportunities in such analyses. Thus, the study of breast cancer progression requires experimental models for the investigation of the links between genetic profiles and a more aggressive tumor phenotype. Animal models provide a powerful resource for the identification and investigation of genes essential in distinct steps of the metastatic cascade, in sitespecific homing, in complex tumor-host interactions, and enable the identification of targets that are optimal for therapeutic perturbation. The unique advantage of the clonal metastasis models described above is the ability to profile cells of opposing metastatic phenotype that originate from a common genetic background. The human origin of the cells and the spontaneous acquisition of the distinct phenotypes make them optimal for investigations into genetic changes that correlate with metastatic sufficiency. Investigators can alter the expression or activity of single, or multiplexes of candidate genes in the clonal lines with known metastatic characteristics and monitor which specific mechanisms are perturbed by comparison with isogenic controls. Many of these candidate genes will come from tissue-based analyses, such as those described above. There are certainly limitations regarding extrapolation from studies in a murine host to the human clinical situation, perhaps particularly acute when investigating tumor-stroma interactions, but in vivo functional analyses can distinguish which genes are essential in tumor progression, and better delineate pathway and signaling network interactions in tumor cells, and thus may in turn aid the prioritization of genes in prognostic signatures. In this way the two fields can overlap and mutually benefit. Signature overlap Given the differences in experimental design used in the production of the nine signatures described in this review,
the presence of specific genes in multiple tissue or model signatures, or better yet, in both, might implicate pathways or mechanisms for further research focus. The nine signatures contain a total of 683 non-redundant genes for which a unique gene identifier (Entrez Gene ID) is available to all signatures, and of these, 47 genes were found to appear in at least two of the nine signatures. The details of the distribution of these signature intersect genes is depicted in Table 1. Grouping genes by cellular location revealed that the most prevalent (34%) localization was extracellular. This is a common theme revealed in metastasis studies due to the complex interactions that need to occur with the secondary tissue environment and the requirement for extracellular matrix remodeling during the development of metastasis. This was reinforced by the fact that 23% of common genes were localized to the plasma membrane. The majority of genes were common to only two signatures, just a few appeared in three signatures. The overlap between tissue-based studies was weak (signature gene range 17–83). Only five genes appeared in two signatures resulting from tissue-based microarray studies (Table 1). There was no overlap between the 17-gene metastasis signature of Ramaswamy et al. (2003) with any other tissue-based study. Of the microarray studies discussed in this review, those performed by van ‘t Veer et al. (2002) and Wang et al. (2005) are the most aligned in many ways. While there were many differences between the studies with respect to population, clinical information availability, platform, and statistical treatment, both studies evaluated lymph-node negative samples and the platforms had thousands of genes in common, yet only one gene, cyclin E2 (CCNE2), was common to both signatures. Three genes (LOC51203, PRC1 and L2DTL) of the 64 genes in the study by Pawitan et al. (2005) were present in the 70-gene prognostic signature identified by van ‘t Veer and colleagues, and one gene (MLF1lP) was present in both the Pawitan and the Wang signatures. Although there are clear differences between studies, including selection of patients according to different inclusion and exclusion criteria, different gene expression platforms, and mathematical analysis of the data, thousands of genes were common to all studies and the overall goals were similar. The lack of convergence of breast cancer prognostic signatures to date makes it difficult to discern which signatures are the most accurate and optimal for prognosis and/ or potential therapeutic development going forward. There was significantly more overlap between model signatures (signature gene range 85–143) than that observed between tissue signatures. A total of 42 genes were found to be present in at least two model-based signatures. Although each model was derived from a different original tumor source, the clonality of the models represents a far simpler situation than that found in excised tumors consisting of complex mixtures of tumor and non-tumor cell components. The largest overlap (ten genes) was between the MDAMB-231 bone and lung metastasis signatures. This is likely due to the fact that both signatures are derived from the same parental cell line, MDA-MB-231. However, there was
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Table 1. Genes common to at least two breast cancer metastasis signatures amongst five animal models and four tissue-based expression microarray studies
ANXA2 CLU COL1A1 COL1A2 COL5A1 COL6A1 CTGF FN1 CXCL1 CYR61 LAMB1 LOXL2 LTBP1 MGP MMP1 SERPINE2 ANXA1 HLA-DPA1 HLA-DPB1 HLA-DRB1 LY6E PPL PTK7 CXCR4 ABCC3 TNFSF10 GPR56 EPHX1 ARHGDIB KRT86 TPM2 DLC1 CRYAB ID1 SOX4 HIST1H2AC PRC1 CCNE2 ARNT2 NUSAP1 GTSE1 DTL SPANX MLF1IP C10orf116 C14orf139 ANXA2P1
15q21–q22 8p21–p12 17q21.33 7q22.1 9q34.2–q34.3 21q22.3 6q23.1 2q34 4q21 1p31–p22 7q22 8p21.3–p21.2 2p22–p21 12p13.1–p12.3 11q22.3 2q33–q35 9q12–q21.2 6p21.3 6p21.3 6p21.3 8q24.3 16p13.3 6p21.1–p12.2 2q21 17q22 3q26 16q12.2–q21 1q42.1 12p12.3 12q13 9p13.2–p13.1 8p22 11q22.3–q23.1 20q11 6p22.3 6p21.3 15q26.1 8q22.1 15q24 15q15.1 22q13.2–q13.3 1q32.1–32.2 Xq27.1 4q35.1 10q23.2 14q32.13 4q21–q31
302 1191 1277 1278 1289 1291 1490 2335 2919 3491 3912 4017 4052 4256 4312 5270 301 3113 3115 3123 4061 5493 5754 7852 8714 8743 9289 2052 397 3887 7169 10395 1410 3397 6659 8334 9055 9134 9915 51203 51512 51514 *** 79682 10974 79686 303
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 3 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 6 6 NA
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E
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H
Human tumors
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G
MDA-231-LUNG
Sub- A B C D cell. loca- Animal models liz.b
GILM2
Entrez genea
MDA-435
Chromosomal location
MURINE-4T1c
Annexin A2 Clusterin Collagen, type I, alpha 1 Collagen, type I, alpha 2 Collagen, type V, alpha 1 Collagen, type VI, alpha 1 Connective tissue growth factor Fibronectin 1 Chemokine (C-X-C motif) ligand 1 Cysteine-rich, angiogenic inducer, 61 Laminin, beta 1 Lysyl oxidase-like 2 Latent transforming growth factor beta binding protein 1 Matrix Gla protein Matrix metallopeptidase 1 (interstitial collagenase) Serpin peptidase inhibitor Annexin A1 Major histocompatibility complex, class II, DP alpha 1 Major histocompatibility complex, class II, DP beta 1 Major histocompatibility complex, class II, DR beta 1 Lymphocyte antigen 6 complex, locus E Periplakin PTK7 protein tyrosine kinase 7 Chemokine (C-X-C motif) receptor 4 ATP-binding cassette, sub-family C (CFTR/MRP), member 3 Tumor necrosis factor (ligand) superfamily, member 10 G protein-coupled receptor 56 Epoxide hydrolase 1, microsomal (xenobiotic) Rho GDP dissociation inhibitor (GDI) beta Keratin, hair, basic, 1 Tropomyosin 2 (beta) Deleted in liver cancer 1 Crystallin, alpha B Inhibitor of DNA binding 1 SRY (sex determining region Y)-box 4 Histone 1, H2ac Protein regulator of cytokinesis 1 Cyclin E2 Aryl-hydrocarbon receptor nuclear translocator 2 Nucleolar and spindle associated protein 1 G-2 and S-phase expressed 1 Denticleless homolog (Drosophila) SPANX family, members A1, A2, B1, B2, C MLF1 interacting protein Chromosome 10 open reading frame 116 Chromosome 14 open reading frame 139 Annexin A2 pseudogene 1
Gene symbol
MDA-231-BONE
Gene name
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Studies in References: A: Kang et al. (2003); B: Eckhardt et al. (2005); C: Goodison et al. (2005); D: Kluger et al. (2005); E: Minn et al. (2005); F: Wang et al. (2005); G: Ramaswamy et al. (2002); H: Pawitan et al. (2005); I: Van ‘t Veer et al. (2002). a *** = Various SPANX family, members, Entrez Gene IDs: 30014; 64649; 64650; 64663; 64694. b Subcellular localization of gene products, 1: Extracellular space; 2: Plasma membrane; 3: Other membrane; 4: Cytoplasm; 5: Nucleus; 6: Other. c Homo sapiens homologous genes were considered for comparison (HomoloGene release 50.1).
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also significant overlap (seven genes) between the MDAMB-435 model and the GILM2 model. There was also considerable overlap (five genes) between the MDA-MB-435 model and the MDA-MB-231-bone signature, and this is in line with our recent observations that the M4A4 metastatic counterpart of the MDA-MB-435 model is also aggressively metastatic to bone when inoculated via an intra-cardiac route (article in preparation). Even though there was a relatively high intersection between the model signatures, there are major differences between the five models described in this review. The MDAMB-231 model is a xenograft model of ‘experimental’ metastasis involving the injection of human breast tumor cells directly into the circulatory system of immunocompromised mice, resulting in metastases in specific organs. This model has been valuable for analyzing the final stages of metastasis, and can identify genes that regulate the colonization of specific tissues (Kang et al., 2003; Minn et al., 2005), but does not encompass the initial stages of metastasis. Conversely, the MDA-MB-435 and the GILM2 model are xenograft models of ‘spontaneous’ metastasis involving the formation of a primary tumor in an orthotopic site, in this case breast, from which the cells must disseminate naturally. These spontaneous metastasis models recapitulate all of the steps of metastatic efficiency, from escape from the primary tumor to the establishment of distant metastases, and perhaps mimic more accurately the progression of breast cancer observed clinically. However, the MDA-MB231, MDA-MB-435 and the GILM2 models involve human tumor cells forming tumors in a mouse host and, therefore, may not best mimic important tumor-host interactions. The 4T1 mouse model has the advantage of being syngeneic and so may provide more insight into such interactions, but on the other hand, the genes identified as being associated with the tumor cell phenotype may not be functionally equivalent in human cells. Rather than being a hindrance to forming converging hypotheses of metastasis, the testing of candidate metastasis genes across these models with their inherent differences will provide powerful validation of specific gene functionality. A total of seven genes were found to be common to at least one tissue-based signature and one model-based signature (Table 1). Three to four such intersect genes were present in each of the tissue-based signatures, except for the 70-gene signature of van ‘t Veer et al. which had no intersect with any model signature. All model signatures were represented in the tissue vs. model intersect. A total of four genes were present in at least three signatures. Three of these (HLA-DPB1, ARHGDIB and C14orf139) were present in two model signatures and one tissue signature, whereas the CTGF gene was present in three model signatures but no tissue signature. Chromosomal location and alignment with CGH studies Alignment of the genes in the signature intersect list with chromosomal location reveals that they are randomly spread across the genome with extremes being an absence of any genes on chromosome 5, and seven genes being located on
chromosome 6. Yet, per chromosome the intersect genes appear to be tightly clustered in many cases. For example, five of the seven genes on chromosome 6 are located within 6p22]p21, both genes on chromosome 7 are located at 7q22, and all three genes on chromosome 11 are located at 11q22]q23. The NUSAP1 gene located at 15q15.1 is one of the few genes that were present in two of the tissue-based analyses. Interestingly, alignment of our 171-gene metastasis signature using ‘Genome View’ revealed that three of the genes more highly expressed in metastatic M4A4 cells were grouped together at 15q15 (Goodison et al., 2005b). Moreover, this region was revealed to be involved in cell-specific chromosomal rearrangements in the molecular cytogenetic analysis of MDA-MB-435 sub-lines (Goodison et al., 2005a). Although specific genes may not make the overlap lists, future meta-analyses of gene expression profiles with respect to chromosomal location may reveal metastasis-associated DNA hot-spots. Notably, many of the intersect genes are located at loci revealed by CGH to be perturbed in breast cancer. The one gene on chromosome 3 that was present in at least two signatures is TNFSF10, a member of the tumor necrosis factor ligand superfamily, located at 3q26. This region was found to be a strong predictor of breast cancer recurrence by CGH (Janssen et al., 2003). Furthermore, four clusters of intersect genes align very closely with recent reports of copy number alterations which correlate with the advancing nature of breast tumors. These include two genes located at 8q22]q24, two genes at 12p13.1]p12.3, one gene at 16p13, and the two genes on chromosome 17 at 17q21]q22. These four regions were all identified through the use of array-CGH to be amplified in late-stage breast cancer (Yao et al., 2006). The considerable alignment between the CGH and the gene expression microarray data suggests that the combination of DNAand RNA-based approaches can inform and guide the search for essential cancer-associated genetic events. Chemokines A common theme throughout the cell line model metastasis signatures are components of the human chemokine system. The chemokines and their receptors are a family of small secreted molecules that regulate the migration around the body of cells in the lymphoid system, a process that shares many characteristics with the successful dissemination of tumor cells in the body. Chemokine receptors are G-protein-coupled cell surface proteins expressed on leukocytes and many nonhematopoietic cells. These receptors bind various chemokines that are constitutively expressed in distinct tissue microenvironments. The chemokine CXCL1 and the chemokine receptor CXCR4 are present in the signature intersect gene list (Table 1). CXCR4 is the most widely expressed chemokine receptor in many different cancers. Primary breast tumors express the CXCR4 receptor, and target sites of breast cancer metastases, such as lung and bone, express more CXCL12, the CXCR4 ligand, than other organs (Muller et al., 2001). Antibody, or peptide-mediated blocking of the CXCR4 receptor decreased breast cancer cell invasiveness in vitro and also the bulk of metas-
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tases in the MDA-MB-231 xenograft mouse model (Muller et al., 2001; Liang et al., 2004; Lapteva et al., 2005). Smith et al. (2004) used stable RNAi to reduce expression of CXCR4 in murine 4T1 cells, a highly metastatic mammary cancer cell line that is a model for advanced human breast cancer. The knockdown of CXCR4 significantly limited the growth of orthotopically transplanted breast cancer cells in all mice and even prevented primary tumor formation in some. Most importantly, all mice transplanted with CXCR4 RNAi cells survived without developing macroscopic metastases (Smith et al., 2004). Recent studies have also identified a role for CXCL12 as a proliferation factor for breast tumor cells and in recruiting progenitor endothelial cells required for angiogenesis (Orimo et al., 2005). CXCR4 expression is also a prognostic marker in various types of cancer, including breast carcinoma. In a recent histological study of over 2000 breast carcinoma cases, CXCR4 was found to be expressed in the majority of cancers, and cytoplasmic expression was associated with parameters of tumor aggression, including grade and lymph node status, and had prognostic value (Salvucci et al., 2006). G protein-coupled receptors are considered among the most desirable targets for drug development (Li et al., 2005), thus, CXCR4 antagonists may become effective agents for the treatment of various malignancies. CCN family The connective tissue growth factor gene (CTGF) was present in three signatures, and the closely related CYR61 gene was present in two signatures. These genes encode proteins that belong to the CCN family of secreted regulatory factors involved in angiogenesis, chondrogenesis, and wound healing (Brigstock, 1999; Perbal, 2004). CTGF and CYR61 also promote endothelial cell growth, migration, adhesion, and survival in vitro, and many of these actions are mediated at least partly through interactions with integrins (Lau and Lam, 1999). The proangiogenic activity of CTGF and CYR61 supports a role in the establishment and functioning of the vasculature in metastasis. Recent studies have revealed an influence of CTGF in osteolytic bone metastases and its expression shown to be regulated by PTHrP (Shimo et al., 2006). In a large immunochemical study of human breast tumor specimens, high levels of CYR61, but low levels of CTGF were associated with poor prognosis and metastatic disease (Jiang et al., 2004). Interestingly, both genes are transcriptionally regulated by TGF (Bartholin et al., 2007), a multifunctional growth factor, which is thought to promote breast tumor metastasis and invasiveness (Beisner et al., 2006). ARHGD1B Another gene that was present in three metastasis signatures was the Rho GDP dissociation inhibitor (GDI) beta (ARHGD1B) gene, a member of the Rho GDI family which are major regulators of Rho GTPases. Evidence for the influence of Rho GTPase signaling in tumor metastasis is growing. The expression of the RhoC molecule was identified as being correlated with metastatic propensity in an increasingly metastatic series of the melanoma A375 cell
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line and the Rho-GTPases, RhoGD12 and Rac1, have been associated with aggressive phenotypes in experimental metastasis assays when testing bladder and breast cell line models respectively (Bourguignon et al., 2000; Seraj et al., 2000). In our own work, restoration of one of these GTPases, deleted in liver cancer-1 (DLC-1), in metastatic M4A4 cells resulted in the inhibition of migration and invasion in vitro and a significant reduction in the ability of these cells to form pulmonary metastases in athymic mice. Although RhoGDIs are known to inhibit Rho activities, recent studies indicate that RhoGDIs can also act as positive regulators through their ability to target Rho GTPases to specific subcellular membranes or to protect the GTPases from degradation by caspases. This dual role makes them promising cellular targets for novel anticancer drugs (Fritz and Kaina, 2006). While the signatures obtained from different microarray platforms might reveal different gene sets, they may be reporting related biological processes. Annotation of the 47 signature intersect genes using GenMapp (http://www. genmapp.org/) and KEGG (http://www.genome.jp/kegg/) pathway information summaries (Doniger et al., 2003; Kanehisa et al., 2006) revealed only one significant grouping. Three genes (FN1, LAMB1 and COL1A1) belong to the inflammatory response pathway. This finding perhaps reinforces the concept that gene expression programs related to normal physiological responses, as described above with respect to wound healing, may confer an increased risk of metastasis (Chang et al., 2004). Investigation of potential interaction between the gene products of the 47 signature intersect genes was queried using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database (http://string.embl.de/) (von Mering et al., 2005). Evidence for protein associations derived from experimental data and from the mining of databases and the literature was sought using the Protein Mode for maximum specificity. With the exception of C14orf139, DTL, and MLF1IP genes, and the pseudogene ANXA2P1, all gene products had annotations in the STRING database. Without entering into the detailed specific interactions here, it is worth noting that the analysis revealed that the transcription factor Sp1 may be a unifying factor for many of the proteins present in the signature intersect list. Sp1 was present in the node that connected a group that included CTGF, CYR61, MMP1, and several collagens with the chemokines CXCR4 and CXCL1. Sp1 protein expression is known to be elevated in breast carcinomas (Zannetti et al., 2000), and several studies show that Sp1 transcriptionally regulates many genes previously implicated in cancer progression, including VEGF (Ryuto et al., 1996; Abdelrahim et al., 2004), CCND1, FOS, and the antiapoptosis BCL2 gene (Safe and Abdelrahim, 2005). Furthermore, Sp proteins have been shown to regulate the expression of TGF receptors (Ji et al., 1997; Jennings et al., 2001) and thus will impact TGF signaling, which in turn regulates the CCN proteins discussed above. While additional research is required to determine direct linkages between the expression of Sp1 and its downstream factors in various tumor types, as a protein high in the regulatory hierarchy,
Sp1 may be an important prognostic factor and therapeutic target for metastatic disease. If a comparison of the intersect of just the nine signatures described in this review can implicate some common themes, a comprehensive meta-analysis of all available, unprocessed, metastasis-associated gene expression microarray data with more of a focus on biochemical and signaling pathways than gene identities may reveal much about the mechanisms of metastatic efficiency. Future directions
Genes revealed in analysis of multiple models, and those that are part of any overlap between models and tissuebased studies, will be a logical focus for functional investigation. As described above, relevant models for such investigations do exist, but it would be beneficial to develop more such models in order to identify those genes and pathways that are implicated across a number of platforms and are thus likely to be pivotal to metastatic efficiency. Likewise, more tissue-based studies are needed, both for discovery and validation of existing candidate genes. As discussed above, little overlap between tissue-based studies has been evident to date, presumably due to differences in tissue sources and processing and availability of consistent clinical information. The initiation of large-scale consortia such as TransBig and PACT will greatly facilitate this through standardization of all parameters. While there are economical and ease-of-use reasons to reduce the number of genes in diagnostic and prognostic signatures for routine clinical use, global gene expression profiling can have more specific roles. For example, based upon the expression of drug targets, and/or the expression of genes that metabolize these drugs, gene expression profiling may guide adjuvant therapy for individuals. Two studies that highlight this approach have reported an association in breast cancer patients between gene expression signatures and drug sensitivity to docetaxel or to a combination regimen containing paclitaxel, fluorouracil, doxorubicin and cyclophosphamide (Brown et al., 2004; Villeneuve et al., 2006). This use of gene expression profiling is likely to have a more rapid role in tailoring individual patient treatment. Prognostic signatures gleaned from gene expression data alone have been achieved through normalized microarray data comparison across literally hundreds of specimens. However, the heterogeneity of breast cancer as a disease, a fact confirmed by gene expression studies, plus the variation in patient genetic background makes the goal of predicting the outcome for an individual patient far from achievable at this stage. One way to improve upon this situation is to further stratify patients using clinical measures and current understanding of disease pathology. For example, the expression of the estrogen receptor (ER) and/or the Her2-neu gene at diagnosis is currently used to subdivide patients. ER and Her2-neu expression have a marked influence on the expression of many of the genes associated with breast cancer, and are thought to have an important impact
on survival and are used as independent prognostic factors (Hu et al., 2006; Nicolini et al., 2006). Investigators have begun to use clinical parameters to subdivide patients in order to further refine the prognostic classifiers for specific circumstances. For example, by combining expression data with ER status and age as clinical variables, Dai et al. (2005) identified a subgroup of patients in which a set of proliferation-associated genes was a strong predictor of poor outcome and occurrence of metastasis. The majority of expression studies to date have attempted to develop genetic marker-based prognostic systems that might replace the existing clinical criteria, rather than incorporating the valuable clinical information contained in established clinical markers. Given the complexity of breast cancer prognosis, a more promising strategy may be to combine both clinical and genetic marker information that may be complementary (Brenton et al., 2005). Along these lines, Pittman et al. (2004) have described a comprehensive modeling approach based on statistical classification tree models that evaluate the contributions of multiple forms of data, both clinical and genomic, in order to define multiple risk factors that associate with clinical outcome. A study of primary breast cancer recurrence demonstrated that models using ‘metagenes’ derived from microarray expression data combined with traditional clinical risk factors improved the accuracy of prediction on an individual basis over genomic or clinical data alone (Pittman et al., 2004). Furthermore, we have recently performed a computational study using the Dutch 70-gene prognosis signature and associated clinical information. The recently proposed I-RELIEF algorithm was used to identify a ‘hybrid signature’ through the combination of both genetic and clinical markers. The hybrid signature performed significantly better, with respect to specificity and odds-ratio, than the 70-gene signature, clinical markers alone and the St Gallen and NIH consensus criteria (Sun et al., 2007). One of the major differences between gene expressionprofile studies performed to date is in data processing. The advent of microarray technology has spawned a whole new field of bioinformatics which has the common goal of optimizing the accuracy and the applicability of the information gleaned from the wealth of data now available, but individual studies have often used quite different approaches which do not always facilitate inter-study comparisons. A number of groups have tackled this by designing algorithms that enable meta-analysis of publicly available data, including analysis across multiple array platforms (Bammler et al., 2005; Rhodes et al., 2005; Segal et al., 2005). These analyses seek to not only identify inter-study overlap but also to identify multidimensional interaction networks which in turn may implicate molecular hierarchies and regulatory mechanisms involved in cancer gene expression. Rhodes et al. (2004) analyzed compiled multiple data sets from some 3,700 cancer samples and developed an analytical strategy to assess the intersection between profiles. This approach identified components of transcriptional profiles which are similar across many cancer types, including breast. Specific to breast cancer, Shen et al. (2004) applied a Bayesian mod-
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eling strategy to analyze four independent microarray studies to derive a ‘meta-signature’ associated with breast cancer prognosis. The meta-signature had better prognostic performance than any of the classifiers of survival in each study, and which had minimal overlap with each other. These studies reveal that there are many legitimate ways to analyze array data sets, and it is hoped that the formation of consortia to monitor large-scale tissue-based studies will employ a standardized arsenal of bioinformatic and statistical analyses. Ongoing computational developments that enable inter-study comparison and the incorporation of distinct forms of data, including genomic and clinical data, will provide platforms for the refinement of cancer-related gene signatures and lead to more economical and accurate prognostic systems that may facilitate personalized patient evaluation and treatment decisions. These integrated analyses also have the potential to highlight pivotal genes and pathways that are likely part of the biological driving force of metastasis. Another way to significantly improve the quality of data resulting from tissue profile analyses is to provide more accurate histopathological details of each sample upfront. Concordance between two pathologists has been investigated and found to range from 50% to 85% (Robbins et al., 1995), and inaccuracy in sample evaluation may account for considerable error when array data is obtained from partial tissue specimens and subsequently related to parameters such as disease staging and histological grade. Furthermore, solid tumors are complex entities composed of malignant cells mixed and interacting with nonmalignant cells such as normal or benign epithelia, stromal counterparts and lymphocytic infiltrate. Thus, molecular analyses by standard gene expression profiling are limited when tissues are crudely homogenized causing loss of information on non-tumor cell types in sample preparation. Gene expression differences derived from such tumor samples may primarily reflect varying proportions of the non-neoplastic tumor components. In order to try to overcome these limitations we have previously employed a regression-based informatics approach for the identification of cell-type-specific patterns of gene expression in prostate cancer (Stuart et al., 2004). Through intense histological evaluation, we scored 88 prostate specimens for relative content of neoplastic and non-neoplastic components. The proportions of these cell types were then linked in silico to gene expression levels determined by microarray analysis, revealing unique cell-specific profiles. Gene expression differences for malignant and nonmalignant epithelial cells could be identified without being confounded by contributions from stroma that dominate many samples. Validation of selected cellspecific expression patterns confirmed that this analysis allowed segregation of molecular markers into more discrete and informative groups. This investigative approach is applicable to tumor marker discovery in any solid tumor and is an example of how more detailed evaluation of the tissue specimen can further refine expression profile data. Another way to separate the cells of a complex tissue from each other is through microdissection. This technique
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has most often been used to evaluate only several genes per sample (Wang-Rodriguez et al., 2003), but the advent of semi-automated, laser capture microdissection (LCM) systems now makes it feasible to use this procedure to obtain enough material for microarray analysis (Nishidate et al., 2004; Dahl et al., 2006; Schuetz et al., 2006; Yang et al., 2006). The combination of laser microdissection and gene expression profiling has been used to explore the gene expression changes that are associated with the early stages of breast cancer progression and ER-regulated genes (Ma et al., 2003; Yang et al., 2006). In a study by Dahl et al. the investigation of matched pairs of invasive ductal breast cancer and corresponding benign breast tissue by LCM and cDNA array profiling ultimately led to the identification of karyopherin alpha2 (KPNA2) expression as being associated with shorter overall survival and recurrence-free survival (Dahl et al., 2006). Finally, it has to be stated that genomic approaches are unlikely to be adequate as a sole prognostic and predictive platform in breast cancer. Most transcriptome array analyses implicitly treat mRNA expression as a surrogate for protein activity level, an assumption that does not account for processes such as mRNA stability, protein degradation and post-translational modifications. The key proteins driving tumor progression may very well undergo abnormal posttranslational modifications. While the animal models of metastasis described above lend themselves to proteomic analysis (Kreunin et al., 2004), proteomic analysis of complex tumor tissues is far more difficult. Proteomic techniques are far more limited with respect to high-throughput screening platforms. Current techniques cannot yet achieve high proteome coverage rates and they need large amounts of material given that there is no method for amplification. Due to the vastly superior gene screening advantages of microarrays, it is logical that transcriptome data should guide proteomics, for example, validation of array data should be performed at the protein level wherever possible.
References
Abdelrahim M, Smith R 3rd, Burghardt R, Safe S: Role of Sp proteins in regulation of vascular endothelial growth factor expression and proliferation of pancreatic cancer cells. Cancer Res 64:6740–6749 (2004). Adeyinka A, Kytola S, Mertens F, Pandis N, Larsson C: Spectral karyotyping and chromosome banding studies of primary breast carcinomas and their lymph node metastases. Int J Mol Med 5:235–240 (2000). Agarwal D, Goodison S, Nicholson B, Tarin D, Urquidi V: Expression of matrix metalloproteinase 8 (MMP-8) and tyrosinase-related protein-1 (TYRP-1) correlates with the absence of metastasis in an isogenic human breast cancer model. Differentiation 71: 114–125 (2003). Albertson DG: Profiling breast cancer by array CGH. Breast Cancer Res Treat 78: 289–298 (2003). Aubele M, Auer G, Braselmann H, Nahrig J, Zitzelsberger H, et al: Chromosomal imbalances are associated with metastasis-free survival in breast cancer patients. Anal Cell Pathol 24:77– 87 (2002).
Bammler T, Beyer RP, Bhattacharya S, Boorman GA, Boyles A, et al: Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2: 351–356 (2005). Barlund M, Tirkkonen M, Forozan F, Tanner MM, Kallioniemi O, Kallioniemi A: Increased copy number at 17q22]q24 by CGH in breast cancer is due to high-level amplification of two separate regions. Genes Chromosomes Cancer 20: 372–376 (1997). Bartholin L, Wessner LL, Chirgwin JM, Guise TA: The human CYR61 gene is a transcriptional target of transforming growth factor beta in cancer cells. Cancer Lett 246:230–236 (2007). Bastian BC, Wesselmann U, Pinkel D, Leboit PE: Molecular cytogenetic analysis of spitz nevi shows clear differences to melanoma. J Invest Dermatol 113:1065–1069 (1999). Beisner J, Buck MB, Fritz P, Dippon J, Schwab M, et al: A novel functional polymorphism in the transforming growth factor-beta2 gene promoter and tumor progression in breast cancer. Cancer Res 66: 7554–7561 (2006). Bieche I, Lidereau R: Genetic alterations in breast cancer. Genes Chromosomes Cancer 14: 227– 251 (1995). Bissell MJ, Radisky D: Putting tumours in context. Nat Rev Cancer 1: 46–54 (2001). Blegen H, Will JS, Ghadimi BM, Nash HP, Zetterberg A, et al: DNA amplifications and aneuploidy, high proliferative activity and impaired cell cycle control characterize breast carcinomas with poor prognosis. Anal Cell Pathol 25: 103– 114 (2003). Bourguignon LY, Zhu H, Shao L, Chen YW: Ankyrin-tiam1 interaction promotes Rac1 signaling and metastatic breast tumor cell invasion and migration. J Cell Biol 150: 177–191 (2000). Brandenberger R, Schmidt A, Linton J, Wang D, Backus C, et al: Identification and characterization of a novel extracellular matrix protein nephronectin that is associated with integrin alpha8beta1 in the embryonic kidney. J Cell Biol 154: 447–458 (2001). Brenton JD, Carey LA, Ahmed AA, Caldas C: Molecular classification and molecular forecasting of breast cancer: Ready for clinical application? J Clin Oncol 23:7350–7360 (2005). Brigstock DR: The connective tissue growth factor/cysteine-rich 61/nephroblastoma overexpressed (CCN) family. Endocr Rev 20: 189–206 (1999). Brown I, Shalli K, McDonald SL, Moir SE, Hutcheon AW, et al: Reduced expression of p27 is a novel mechanism of docetaxel resistance in breast cancer cells. Breast Cancer Res 6:R601– 607 (2004). Chambers AF, Naumov GN, Vantyghem SA, Tuck AB: Molecular biology of breast cancer metastasis. Clinical implications of experimental studies on metastatic inefficiency. Breast Cancer Res 2:400–407 (2000). Chang HY, Sneddon JB, Alizadeh AA, Sood R, West RB, et al: Gene expression signature of fibroblast serum response predicts human cancer progression: Similarities between tumors and wounds. PLoS Biol 2:E7 (2004). Chang HY, Nuyten DS, Sneddon JB, Hastie T, Tibshirani R, et al: Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci USA 102: 3738–3743 (2005). Dahl E, Kristiansen G, Gottlob K, Klaman I, Ebner E, et al: Molecular profiling of laser-microdissected matched tumor and normal breast tissue identifies karyopherin alpha2 as a potential novel prognostic marker in breast cancer. Clin Cancer Res 12: 3950–3960 (2006).
Dai H, van ‘t Veer L, Lamb J, He YD, Mao M, et al: A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients. Cancer Res 65: 4059– 4066 (2005). Devilee P, Cornelisse CJ: Somatic genetic changes in human breast cancer. Biochim Biophys Acta 1198:113–130 (1994). Doniger SW, Salomonis N, Dahlquist KD, Vranizan K, Lawlor SC, Conklin BR: Mappfinder: Using gene ontology and genmapp to create a global gene-expression profile from microarray data. Genome Biol 4:R7 (2003). Eckhardt BL, Parker BS, van Laar RK, Restall CM, Natoli AL, et al: Genomic analysis of a spontaneous model of breast cancer metastasis to bone reveals a role for the extracellular matrix. Mol Cancer Res 3: 1–13 (2005). Fidler IJ, Kripke ML: Metastasis results from preexisting variant cells within a malignant tumor. Science 197:893–895 (1977). Fritz G, Kaina B: Rho GTPases: Promising cellular targets for novel anticancer drugs. Curr Cancer Drug Targets 6:1–14 (2006). Fritz G, Just I, Kaina B: Rho GTPases are over-expressed in human tumors. Int J Cancer 81:682– 687 (1999). Goodison S, Kawai K, Hihara J, Jiang P, Yang M, et al: Prolonged dormancy and site-specific growth potential of cancer cells spontaneously disseminated from nonmetastatic breast tumors as revealed by labeling with green fluorescent protein. Clin Cancer Res 9: 3808–3814 (2003). Goodison S, Viars C, Urquidi V: Molecular cytogenetic analysis of a human breast metastasis model: Identification of phenotype-specific chromosomal rearrangements. Cancer Genet Cytogenet 156:37–48 (2005a). Goodison S, Yuan J, Sloan D, Kim R, Li C, et al: The RhoGAP protein DLC-1 functions as a metastasis suppressor in breast cancer cells. Cancer Res 65:6042–6053 (2005b). Han W, Han MR, Kang JJ, Bae JY, Lee JH, et al: Genomic alterations identified by array comparative genomic hybridization as prognostic markers in tamoxifen-treated estrogen receptor-positive breast cancer. BMC Cancer 6: 92 (2006). Hedenfalk I, Ringner M, Ben-Dor A, Yakhini Z, Chen Y, et al: Molecular classification of familial non-BRCA1/BRCA2 breast cancer. Proc Natl Acad Sci USA 100: 2532–2537 (2003). Hu Z, Fan C, Oh DS, Marron JS, He X, et al: The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 7: 96 (2006). Huang E, Cheng SH, Dressman H, Pittman J, Tsou MH, et al: Gene expression predictors of breast cancer outcomes. Lancet 361: 1590–1596 (2003). Hurst J, Maniar N, Tombarkiewicz J, Lucas F, Roberson C, et al: A novel model of a metastatic human breast tumour xenograft line. Br J Cancer 68:274–276 (1993). Hyman E, Kauraniemi P, Hautaniemi S, Wolf M, Mousses S, et al: Impact of DNA amplification on gene expression patterns in breast cancer. Cancer Res 62:6240–6245 (2002). Isola JJ, Kallioniemi OP, Chu LW, Fuqua SA, Hilsenbeck SG, et al: Genetic aberrations detected by comparative genomic hybridization predict outcome in node-negative breast cancer. Am J Pathol 147: 905–911 (1995).
Janssen EA, Baak JP, Guervos MA, van Diest PJ, Jiwa M, Hermsen MA: In lymph node-negative invasive breast carcinomas, specific chromosomal aberrations are strongly associated with high mitotic activity and predict outcome more accurately than grade, tumour diameter, and oestrogen receptor. J Pathol 201: 555–561 (2003). Jennings R, Alsarraj M, Wright KL, Munoz-Antonia T: Regulation of the human transforming growth factor beta type II receptor gene promoter by novel Sp1 sites. Oncogene 20: 6899– 6909 (2001). Ji C, Casinghino S, McCarthy TL, Centrella M: Multiple and essential Sp1 binding sites in the promoter for transforming growth factor-beta type I receptor. J Biol Chem 272: 21260–21267 (1997). Jiang WG, Watkins G, Fodstad O, Douglas-Jones A, Mokbel K, Mansel RE: Differential expression of the CCN family members Cyr61, CTGF and Nov in human breast cancer. Endocr Relat Cancer 11:781–791 (2004). Kallioniemi A, Kallioniemi OP, Piper J, Tanner M, Stokke T, et al: Detection and mapping of amplified DNA sequences in breast cancer by comparative genomic hybridization. Proc Natl Acad Sci USA 91:2156–2160 (1994). Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, et al: From genomics to chemical genomics: New developments in KEGG. Nucleic Acids Res 34:D354–357 (2006). Kang Y, Siegel PM, Shu W, Drobnjak M, Kakonen SM, et al: A multigenic program mediating breast cancer metastasis to bone. Cancer Cell 3: 537–549 (2003). Kauraniemi P, Barlund M, Monni O, Kallioniemi A: New amplified and highly expressed genes discovered in the ERBB2 amplicon in breast cancer by cDNA microarrays. Cancer Res 61: 8235– 8240 (2001). Kawai K, Viars C, Arden K, Tarin D, Urquidi V, Goodison S: Comprehensive karyotyping of the HT-29 colon adenocarcinoma cell line. Genes Chromosomes Cancer 34: 1–8 (2002). Kluger HM, Chelouche Lev D, Kluger Y, McCarthy MM, Kiriakova G, et al: Using a xenograft model of human breast cancer metastasis to find genes associated with clinically aggressive disease. Cancer Res 65: 5578–5587 (2005). Knutsen T, Gobu V, Knaus R, Padilla-Nash H, Augustus M, et al: The interactive online SKY/mFISH & CGH database and the Entrez cancer chromosomes search database: Linkage of chromosomal aberrations with the genome sequence. Genes Chromosomes Cancer 44:52–64 (2005). Kreunin P, Urquidi V, Lubman DM, Goodison S: Identification of metastasis-associated proteins in a human tumor metastasis model using the mass-mapping technique. Proteomics 4: 2754– 2765 (2004). Kristensen VN, Sorlie T, Geisler J, Langerod A, Yoshimura N, et al: Gene expression profiling of breast cancer in relation to estrogen receptor status and estrogen-metabolizing enzymes: Clinical implications. Clin Cancer Res 11:878s883s (2005). Kytola S, Rummukainen J, Nordgren A, Karhu R, Farnebo F, et al: Chromosomal alterations in 15 breast cancer cell lines by comparative genomic hybridization and spectral karyotyping. Genes Chromosomes Cancer 28: 308–317 (2000). Lapteva N, Yang AG, Sanders DE, Strube RW, Chen SY: CXCR4 knockdown by small interfering RNA abrogates breast tumor growth in vivo. Cancer Gene Ther 12:84–89 (2005).
Cytogenet Genome Res 118:116–129 (2007)
127
Lau LF, Lam SC: The CCN family of angiogenic regulators: The integrin connection. Exp Cell Res 248:44–57 (1999). Lelekakis M, Moseley JM, Martin TJ, Hards D, Williams E, et al: A novel orthotopic model of breast cancer metastasis to bone. Clin Exp Metastasis 17:163–170 (1999). Lev DC, Kiriakova G, Price JE: Selection of more aggressive variants of the gi101a human breast cancer cell line: A model for analyzing the metastatic phenotype of breast cancer. Clin Exp Metastasis 20:515–523 (2003). Li S, Huang S, Peng SB: Overexpression of G protein-coupled receptors in cancer cells: Involvement in tumor progression. Int J Oncol 27: 1329–1339 (2005). Liang Z, Wu T, Lou H, Yu X, Taichman RS, et al: Inhibition of breast cancer metastasis by selective synthetic polypeptide against CXCR4. Cancer Res 64: 4302–4308 (2004). Loi S, Desmedt C, Cardoso F, Piccart M, Sotiriou C: Breast cancer gene expression profiling: Clinical trial and practice implications. Pharmacogenomics 6:49–58 (2005). Ma XJ, Salunga R, Tuggle JT, Gaudet J, Enright E, et al: Gene expression profiles of human breast cancer progression. Proc Natl Acad Sci USA 100:5974–5979 (2003). Ma XJ, Wang Z, Ryan PD, Isakoff SJ, Barmettler A, et al: A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5: 607–616 (2004). Mackay A, Jones C, Dexter T, Silva RL, Bulmer K, et al: cDNA microarray analysis of genes associated with ERBB2 (HER2/neu) overexpression in human mammary luminal epithelial cells. Oncogene 22: 2680–2688 (2003). Macville M, Veldman T, Padilla-Nash H, Wangsa D, O’Brien P, et al: Spectral karyotyping, a 24colour FISH technique for the identification of chromosomal rearrangements. Histochem Cell Biol 108: 299–305 (1997). McGuire WL: Prognostic factors for recurrence and survival in human breast cancer. Breast Cancer Res Treat 10:5–9 (1987). Minn AJ, Gupta GP, Siegel PM, Bos PD, Shu W, et al: Genes that mediate breast cancer metastasis to lung. Nature 436: 518–524 (2005). Morimura N, Tezuka Y, Watanabe N, Yasuda M, Miyatani S, et al: Molecular cloning of POEM: A novel adhesion molecule that interacts with alpha8beta1 integrin. J Biol Chem 276: 42172– 42181 (2001). Morris JS, Carter NP, Ferguson-Smith MA, Edwards PA: Cytogenetic analysis of three breast carcinoma cell lines using reverse chromosome painting. Genes Chromosomes Cancer 20:120– 139 (1997). Muller A, Homey B, Soto H, Ge N, Catron D, et al: Involvement of chemokine receptors in breast cancer metastasis. Nature 410: 50–56 (2001). Nicolini A, Carpi A, Tarro G: Biomolecular markers of breast cancer. Front Biosci 11: 1818–1843 (2006). Nishidate T, Katagiri T, Lin ML, Mano Y, Miki Y, et al: Genome-wide gene-expression profiles of breast-cancer cells purified with laser microbeam microdissection: Identification of genes associated with progression and metastasis. Int J Oncol 25:797–819 (2004). Nishizaki T, DeVries S, Chew K, Goodson WH 3rd, Ljung BM, et al: Genetic alterations in primary breast cancers and their metastases: Direct comparison using modified comparative genomic hybridization. Genes Chromosomes Cancer 19: 267–272 (1997).
128
Orimo A, Gupta PB, Sgroi DC, Arenzana-Seisdedos F, Delaunay T, et al: Stromal fibroblasts present in invasive human breast carcinomas promote tumor growth and angiogenesis through elevated SDF-1/CXCL12 secretion. Cell 121: 335– 348 (2005). Pauletti G, Godolphin W, Press MF, Slamon DJ: Detection and quantitation of HER-2/neu gene amplification in human breast cancer archival material using fluorescence in situ hybridization. Oncogene 13: 63–72 (1996). Pawitan Y, Bjohle J, Amler L, Borg AL, Egyhazi S, et al: Gene expression profiling spares early breast cancer patients from adjuvant therapy: Derived and validated in two population-based cohorts. Breast Cancer Res 7:R953–964 (2005). Perbal B: CCN proteins: Multifunctional signalling regulators. Lancet 363: 62–64 (2004). Perreard L, Fan C, Quackenbush JF, Mullins M, Gauthier NP, et al: Classification and risk stratification of invasive breast carcinomas using a real-time quantitative RT-PCR assay. Breast Cancer Res 8:R23 (2006). Pittman J, Huang E, Dressman H, Horng CF, Cheng SH, et al: Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. Proc Natl Acad Sci USA 101:8431–8436 (2004). Popescu NC, Zimonjic DB: Chromosome and gene alterations in breast cancer as markers for diagnosis and prognosis as well as pathogenetic targets for therapy. Am J Med Genet 115: 142–149 (2002). Price JE, Polyzos A, Zhang RD, Daniels LM: Tumorigenicity and metastasis of human breast carcinoma cell lines in nude mice. Cancer Res 50:717–721 (1990). Ramaswamy S, Ross KN, Lander ES, Golub TR: A molecular signature of metastasis in primary solid tumors. Nat Genet 33: 49–54 (2003). Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, et al: Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA 101: 9309–9314 (2004). Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Barrette TR, Ghosh D, Chinnaiyan AM: Mining for regulatory programs in the cancer transcriptome. Nat Genet 37: 579–583 (2005). Ried T, Just KE, Holtgreve-Grez H, du Manoir S, Speicher MR, et al: Comparative genomic hybridization of formalin-fixed, paraffin-embedded breast tumors reveals different patterns of chromosomal gains and losses in fibroadenomas and diploid and aneuploid carcinomas. Cancer Res 55: 5415–5423 (1995). Robbins P, Pinder S, de Klerk N, Dawkins H, Harvey J, et al: Histological grading of breast carcinomas: A study of interobserver agreement. Hum Pathol 26: 873–879 (1995). Ryuto M, Ono M, Izumi H, Yoshida S, Weich HA, et al: Induction of vascular endothelial growth factor by tumor necrosis factor alpha in human glioma cells. Possible roles of Sp-1. J Biol Chem 271:28220–28228 (1996). Safe S, Abdelrahim M: Sp transcription factor family and its role in cancer. Eur J Cancer 41: 2438– 2448 (2005). Salvucci O, Bouchard A, Baccarelli A, Deschenes J, Sauter G, et al: The role of CXCR4 receptor expression in breast cancer: A large tissue microarray study. Breast Cancer Res Treat 97: 275– 283 (2006). Schrock E, du Manoir S, Veldman T, Schoell B, Wienberg J, et al: Multicolor spectral karyotyping of human chromosomes. Science 273: 494– 497 (1996).
Cytogenet Genome Res 118:116–129 (2007)
Schuetz CS, Bonin M, Clare SE, Nieselt K, Sotlar K, et al: Progression-specific genes identified by expression profiling of matched ductal carcinomas in situ and invasive breast tumors, combining laser capture microdissection and oligonucleotide microarray analysis. Cancer Res 66: 5278–5286 (2006). Segal E, Friedman N, Kaminski N, Regev A, Koller D: From signatures to models: Understanding cancer using microarrays. Nat Genet 37 Suppl: S38–45 (2005). Seraj MJ, Harding MA, Gildea JJ, Welch DR, Theodorescu D: The relationship of BRMS1 and RHOGDI2 gene expression to metastatic potential in lineage related human bladder cancer cell lines. Clin Exp Metastasis 18: 519–525 (2000). Shadeo A, Lam WL: Comprehensive copy number profiles of breast cancer cell model genomes. Breast Cancer Res 8:R9 (2006). Shen R, Ghosh D, Chinnaiyan AM: Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data. BMC Genomics 5: 94 (2004). Shevde LA, Welch DR: Metastasis suppressor pathways–an evolving paradigm. Cancer Lett 198: 1–20 (2003). Shimo T, Kubota S, Yoshioka N, Ibaragi S, Isowa S, et al: Pathogenic role of connective tissue growth factor (CTGF/CCN2) in osteolytic metastasis of breast cancer. J Bone Miner Res 21: 1045–1059 (2006). Smith MC, Luker KE, Garbow JR, Prior JL, Jackson E, et al: CXCR4 regulates growth of both primary and metastatic breast cancer. Cancer Res 64:8604–8612 (2004). Sokolova IA, Halling KC, Jenkins RB, Burkhardt HM, Meyer RG, et al: The development of a multitarget, multicolor fluorescence in situ hybridization assay for the detection of urothelial carcinoma in urine. J Mol Diagn 2: 116–123 (2000). Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, et al: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98: 10869–10874 (2001). Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, et al: Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100: 8418–8423 (2003). Sorlie T, Wang Y, Xiao C, Johnsen H, Naume B, et al: Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: Gene expression analyses across three different platforms. BMC Genomics 7: 127 (2006). Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, et al: Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 98:262–272 (2006). Speicher MR, Gwyn Ballard S, Ward DC: Karyotyping human chromosomes by combinatorial multi-fluor FISH. Nat Genet 12: 368–375 (1996). Stacker SA, Achen MG, Jussila L, Baldwin ME, Alitalo K: Lymphangiogenesis and cancer metastasis. Nat Rev Cancer 2: 573–583 (2002). Stuart RO, Wachsman W, Berry CC, Wang-Rodriguez J, Wasserman L, et al: In silico dissection of cell-type-associated patterns of gene expression in prostate cancer. Proc Natl Acad Sci USA 101:615–620 (2004). Sugino T, Yamaguchi T, Ogura G, Saito A, Hashimoto T, et al: Morphological evidence for an invasion-independent metastasis pathway exists in multiple human cancers. BMC Med 2: 9 (2004).
Sun Y, Goodison S, Li J, Liu L, Farmerie W: Improved breast cancer prognosis through the combination of clinical and genetic markers. Bioinformatics 23: 30–37 (2007). Tanner MM, Tirkkonen M, Kallioniemi A, Isola J, Kuukasjarvi T, et al: Independent amplification and frequent co-amplification of three nonsyntenic regions on the long arm of chromosome 20 in human breast cancer. Cancer Res 56: 3441–3445 (1996). Tirkkonen M, Tanner M, Karhu R, Kallioniemi A, Isola J, Kallioniemi OP: Molecular cytogenetics of primary breast cancer by CGH. Genes Chromosomes Cancer 21: 177–184 (1998). Tuma RS: A big trial for a new technology: TransBIG project takes microarrays into clinical trials. J Natl Cancer Inst 96: 648–649 (2004). Urquidi V, Sloan D, Kawai K, Agarwal D, Woodman AC, et al: Contrasting expression of thrombospondin-1 and osteopontin correlates with absence or presence of metastatic phenotype in an isogenic model of spontaneous human breast cancer metastasis. Clin Cancer Res 8:61–74 (2002). van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, et al: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530–536 (2002). van de Vijver MJ, He YD, van ‘t Veer LJ, Dai H, Hart AA, et al: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999–2009 (2002).
Villeneuve DJ, Hembruff SL, Veitch Z, Cecchetto M, Dew WA, Parissenti AM: cDNA microarray analysis of isogenic paclitaxel- and doxorubicin-resistant breast tumor cell lines reveals distinct drug-specific genetic signatures of resistance. Breast Cancer Res Treat 96: 17–39 (2006). von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, et al: String: Known and predicted proteinprotein associations, integrated and transferred across organisms. Nucleic Acids Res 33:D433– 437 (2005). Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, et al: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365: 671–679 (2005). Wang-Rodriguez J, Urquidi V, Rivard A, Goodison S: Elevated osteopontin and thrombospondin expression identifies malignant human breast carcinoma but is not indicative of metastatic status. Breast Cancer Res 5:R136–143 (2003). Watson MB, Bahia H, Ashman JN, Berrieman HK, Drew P, et al: Chromosomal alterations in breast cancer revealed by multicolour fluorescence in situ hybridization. Int J Oncol 25:277– 283 (2004). Weigelt B, Glas AM, Wessels LF, Witteveen AT, Peterse JL, van ‘t Veer LJ: Gene expression profiles of primary breast tumors maintained in distant metastases. Proc Natl Acad Sci USA 100:15901– 15905 (2003). Weigelt B, Hu Z, He X, Livasy C, Carey LA, et al: Molecular portraits and 70-gene prognosis signature are preserved throughout the metastatic process of breast cancer. Cancer Res 65: 9155– 9158 (2005a).
Weigelt B, Wessels LF, Bosma AJ, Glas AM, Nuyten DS, et al: No common denominator for breast cancer lymph node metastasis. Br J Cancer 93: 924–932 (2005b). Wong CM, Lee JM, Ching YP, Jin DY, Ng IO: Genetic and epigenetic alterations of DLC-1 gene in hepatocellular carcinoma. Cancer Res 63: 7646–7651 (2003). Xie D, Jauch A, Miller CW, Bartram CR, Koeffler HP: Discovery of over-expressed genes and genetic alterations in breast cancer cells using a combination of suppression subtractive hybridization, multiplex fish and comparative genomic hybridization. Int J Oncol 21: 499–507 (2002). Yang F, Foekens JA, Yu J, Sieuwerts AM, Timmermans M, et al: Laser microdissection and microarray analysis of breast tumors reveal ERalpha related genes and pathways. Oncogene 25:1413–1419 (2006). Yao J, Weremowicz S, Feng B, Gentleman RC, Marks JR, et al: Combined cDNA array comparative genomic hybridization and serial analysis of gene expression analysis of breast tumor progression. Cancer Res 66: 4065–4078 (2006). Zannetti A, Del Vecchio S, Carriero MV, Fonti R, Franco P, et al: Coordinate up-regulation of Sp1 DNA-binding activity and urokinase receptor expression in breast carcinoma. Cancer Res 60: 1546–1551 (2000).
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GEJ adenocarcinomas were characterized by several comparative genomic hybridization studies. Gains were detected most frequently at 8q and 20q, and less frequently at 1q, 3q, 6p, 7pq, 15q and 17q. Recurrent losses were found at 18q and also, less frequently, at 4pq, 5q, 9p, 14q and 17p. 8q gain was found in 47–79% of GEJ adenocarcinomas, with high-level amplifications occurring in 3–32%. The minimal overlapping region for gain at 8q was 8q23.3]q24.22 (Moskaluk et al., 1998; van Dekken et al., 1999, 2001, 2006; Walch et al., 2000; El-Rifai et al., 2001; Riegman et al., 2001; Stocks et al., 2001). Gain of 8q has been shown to be a progression marker in other cancer types, such as prostate and breast adenocarcinomas (Isola et al., 1995; Alers et al., 2000). Furthermore, gain of 8q has also been detected in precursor stages of GEJ adenocarcinoma, suggesting that this genetic aberration may also play a role in progressive behavior of this type of cancer (Walch et al., 2000; Riegman et al., 2001; van Dekken et al., 2001). In GEJ cancer, as in other types of cancer, copy number increase of 8q24 has often been related to the oncogene MYC. It has, however, been proposed that other target genes may exist on 8q (Cher et al., 1996; Garnis et al., 2004; van Duin et al., 2005a). To address the question whether genomic regions in addition to the MYC region are involved, array comparative genomic hybridization (aCGH) was performed using a tile-path array specific for the whole long arm of chromosome 8 (van Duin et al., 2005b). To this end, 37 GEJ cancer specimens were analysed, including 22 primary cancer samples, 13 cell lines and two xenografts. Subsequently, candidate genes were selected of the most frequently gained regions, and real-time RT-PCR was performed to study the role of these genes in GEJ adenocarcinomas. Materials and methods Tissue specimens GEJ adenocarcinoma specimens were obtained from 22 patients (Barrett’s adenocarcinoma, n = 11; gastric cardia adenocarcinoma, n = 11), of which 19 were male and three female (mean patient age 66, range 50–85 years). For fourteen cases, DNA samples were obtained from fresh-frozen tissue; for the remaining eight cases formalin-fixed, paraffin-embedded tissue was used. In addition, cell lines OEC19, OEC33, BE-3, BIC-1, ESO-26, ESO-51, FLO-1, KYAE-1, P4, SEG-1, OACM5.1C, SK-GT-4 and SK-GT-5, and xenografts OACM2.1X and OACM4.1X were included (Shimada et al., 1992; Altorki et al., 1993; Hughes et al., 1997; Rockett et al., 1997; de Both et al., 2001; Stiles et al., 2003). The cell lines and xenografts were derived from gastric cardia and oesophageal adenocarcinoma in five and ten cases, respectively. As a reference for RT-PCR, nine normal stomach samples were available. Arrays Genomic target DNA was isolated from bacterial cultures and arrayed as described previously (Watson et al., 2004; van Duin et al., 2005b). At the time of construction, clones and clone positions were derived from the UCSC genome browser June 2002 freeze. For this study, an update based on the May 2004 freeze was used. This resulted in the exclusion of 86 clones, which were not present in the new freeze. Hybridization to microarrays Isolation of DNA from the formalin-fixed, paraffin-embedded tumor material was performed as described previously (van Dekken et al., 2006), using the Puregene DNA isolation kit (Gentra systems, Min-
neapolis, MN). For DNA isolation from the fresh-frozen tissue, the DNA extraction kit (Qiagen, Valencia, CA, USA) was used. 400 ng of test DNA (xenograft or tumor) and reference genomic DNA was labeled with Cy3 and Cy5 (Amersham Pharmacia Biotech, Piscataway, NJ, USA), respectively, according to a modified random priming protocol from the Bio-Prime labeling system (Invitrogen, Carlsbad, CA, USA). Briefly, random primer (random DNA octamers) was added to the DNA to a final concentration of 300 ng/l. After denaturation for 10 min at 100 ° C, the sample was put on ice and dNTPs (dATP, dGTP and dCTP: final concentration 200 M; dTTP: final concentration 50 M), cyanine dye-labeled dUTP (either Cy3-dUTP or Cy5-dUTP: final concentration 40 M) and 40 units of Klenow fragment were added to a final volume of 25 l. The reaction mixture was incubated overnight at 37 ° C. Unincorporated nucleotides were removed using microspin columns according to the recommendations of the manufacturer (Amersham Pharmacia Biotech, Piscataway, NJ, USA). Reference and test DNA for each array were then pooled. The mixture of labelled reference and test DNA was precipitated in the presence of 60 g COT human DNA (Roche, Basel, Switzerland) and resuspended in 50 l hybridization solution containing 50% formamide, 10% dextran sulfate, 2! SSC, 4% SDS and 10 g/l yeast tRNA. The probes were denatured for 10 min at 74 ° C, upon which pre-annealing of the COT human DNA took place for 60 min at 37 ° C. Preparation of the slides consisted of cross-linking the slides (GS Gene Linker UV chamber, BioRad laboratories, Hercules, CA; 260 mJ) and placing a dam of rubber cement around each array, at least 60 min before use to allow the rubber cement to set. The hybridization mixture (50 l) was added to the array. A rubber gasket and a glass microscope slide fastened to the slide provided an enclosed chamber for the hybridization. Hybridization was carried out for 48 h at 37 ° C on a unidirectional tilting platform (8 3 rpm) placed within an incubator (Robbins Scientific, Sunnyvale, CA, USA). After hybridization, slides were washed for 15 min in 50% formamide, 2! SSC, pH 7.0 at 50 ° C, and then for an additional 20 min in 2! SSC, 0.1% SDS at 50 ° C. Finally, slides were washed in PN buffer (0.1 M sodium phosphate buffer, 0.1% NP40, pH 8.0) for 10 min at room temperature and rubber cement dams were removed.
Image and data analysis A ScanArray Express HT (Perkin Elmer Life Sciences, Boston, MA, USA) was used to collect 16-bit TIF images through Cy3 and Cy5 filter sets. Images were analysed using custom software as described (Snijders et al., 2001; Jain et al., 2002). Thus, a ratio of Cy3 and Cy5 intensities, averaged for the quadruplicate spots, was obtained per clone. Clones were excluded for analysis if the standard deviation of the replicate spots exceeded 0.2 or if none or a single spot had contributed to the mean of the replicate spots. For analysis and presentation log2 values of fluorescence ratios were used. Two types of analysis were used for this data set. Firstly, for depiction of gains and losses, aberrant log2 values were defined by an algorithm using flexible thresholds based on the standard deviation (SD) of the data sets of the specimens. SD’s over windows of five consecutive BACs were averaged, sliding along chromosome arm 8q one BAC at a time. The resulting SD was multiplied by + or –2.5 to create the threshold value for gains and losses, respectively. The factor of 2.5 was chosen on the basis of prior experience (van Duin et al., 2005a). Samples with less than 20 altered clones gained or lost were excluded from Fig. 3. A high-level amplification was defined as a distinct peak with a log2 fluorescence ratio 11. Secondly, to identify common regions of amplification the average was calculated for each clone for the full set of gastroesophageal specimens tested (n = 37). Subsequently, a simple smoothing procedure was applied by calculating the average over windows of nine consecutive BACs, sliding along 8q one BAC at a time. Quantitative RT-PCR RNA was isolated using the RNAbee reagent (Campro Scientific, Veenendaal, The Netherlands). Assays-On-Demand were obtained from Applied Biosystems. cDNA was generated using oligo(dT) primers and random hexamer primers (Amersham Pharmacia Biotech, Piscataway, NJ, USA). The primers were annealed to 5–10 g of
Cytogenet Genome Res 118:130–137 (2007)
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Table 1. Genes within three regions of frequent amplification in a series of gastroesophageal specimens and EXT1 found within a specific amplification of cell line SK-GT-5
Region
Chromosome band
Genomic distance
Gene
I
8q24.13 8q24.13
125 Mb 126 Mb
ANXA13 MTSS1
Annexin A13 isoform b Missing in metastasis (MIM)
II
8q24.21 8q24.21
128 Mb 129 Mb
FAM84B MYC
Breast cancer membrane protein 101 Myelocytomatosis viral oncogene
III
8q24.3 8q24.3 8q24.11
141 Mb 142 Mb 119 Mb
C8orf17 PTK2 EXT1
Molt-4 sequence tag-1 Focal adhesion kinase 1 Exostosin 1
total RNA in a total volume of 25 l by incubation at 65 ° C for 5 min and cooling down to 0 ° C. Reverse transcription was performed by the addition of 20 l RT mix such that the final concentration was 1! buffer, 0.5 mM dNTP, 2.5 mM DTT, and 200 U Superscript II (Invitrogen, Carlsbad, CA, USA). The mixture was incubated at 25 ° C, subsequently incubated at 42 ° C for 50 min, heated to 70 ° C for 15 min, and cooled to 4 ° C. Assays were carried out according to the instructions of the manufacturer using an ABI PRISM 7700 Sequence Detection System (Applied Biosystems, Foster City, CA, USA). Briefly, PCR was performed using the Taqman Universal PCR master mix, which contains AmpliTaq Gold DNA polymerase. The final PCR mixture contained 1! master mix, 5 l of 20! diluted cDNA and assay mix, containing unlabeled primers (900 nM final reaction concentration) and Taqman probe (250 nM final reaction concentration) in a volume of 25 l. The HPRT, HMBS , GAPDH and B2M genes were used to generate a normalization factor for each sample using the Genorm software (Vandesompele et al., 2002). Relative quantitation of gene expression was done according to recommendations of ABI, using the standard curve method based on serial dilution of a pool of gastroesophageal cell lines, i.e. SK-GT-4, FLO-1, SEG-1 and OEC33.
Fluorescent in situ hybridisation (FISH) FISH of fresh cell preparations was accomplished as described by us before (van Duin et al., 2005b). DNA probes for bicolor FISH of the chromosome 8 centromere and 8q BAC DNA clones were labeled with biotin and digoxigenin, respectively, using a Nick Translation Reagent Kit (Vysis, Downers Grove IL, USA). The biotin-labeled probes were detected with FITC-conjugated avidin (Vector Laboratories, Burlingame CA, USA), the digoxigenin-labeled probes were visualized with rhodamin-conjugated, anti-digoxigenin antibody (Roche, Basel, Switzerland). For validation purposes, the following 8q BAC clones were chosen: RP11-728K22 (EXT1), RP11-580G9 (ANXA13), RP11-746D17 (MTSS1), RP11-177G8 (FAM84B), RP11-440N18 (MYC), RP11-910I20 (C8orf17) and RP11-1126O23 (PTK2). Briefly, after 48 h hybridization the cells were counterstained with DAPI in antifade solution (Vectashield; Vector, Burlingame CA, USA). Two investigators scored up to 100 interphase cells per specimen for the 8q BAC probe signals. Images of each of the three fluorochromes were collected using an epifluorescence microscope (Leica DM, Rijswijk, The Netherlands) equipped with appropriate excitation and emission filter sets (Leica), and a cooled CCD camera (Photometrics, Tucson AZ, USA). The green, red and blue images were collected sequentially by changing the excitation filter using CW4000 FISH software (Leica). Statistics Expression levels in normal and cancer specimens were compared using the Mann-Whitney U test. The average correlation of FISH with aCGH over the seven genomic locations was calculated (Pearson correlation coefficient, r) and the significance of the correlation was assessed using mixed model ANOVA (SAS PROC MIXED). A P value of !0.05 was considered significant.
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Results
Genomic array analysis We have genomically analyzed a series of 37 GEJ adenocarcinoma specimens, of which 21 originated from the esophagus and 16 from the gastric cardia. An 8q contig array was employed to define the region(s) of common gain. Previously, samples were shown to have 8q gain by chromosomal CGH (n = 15), genome wide aCGH (n = 16) or MYC amplification as demonstrated by FISH analysis (n = 6). Using the 8q contig array, 8q gain was confirmed in all these samples with substantially higher resolution. Single copy gain of the whole arm was found in three of 37 cases (8%). Partial single copy gain was found in essentially all cases, i.e. in 36 out of 37 (97%). Amplifications, defined by a clear, sharp increase in copy number with a log2 value of more than 1, were detected in 11 cases (30%). Furthermore, deletions were found in ten cases (27%). Specific MYC amplification was found in ten cases (27%). Representative examples are shown in Figs. 1 and 2. To determine common regions of genomic gain, a cumulative 8q profile of all 37 GEJ adenocarcinoma specimens was constructed (Fig. 3). Using this analysis, three commonly overrepresented regions were identified. The most distinct region of amplification encompasses the MYC gene at 8q24.21 (127–128 Mb), indicated as region II in Fig. 3. Regions of frequent gain were further found proximal to MYC at 124–125 Mb (8q24.13) and distal to MYC at 141–142 Mb (8q24.3). mRNA expression analysis Seven known genes were chosen from the UCSC Genome Browser, on the basis of relevance to cancer for expression analysis by RT-PCR (Table 1). In region II, in addition to MYC, the gene FAM84B was included. Known genes in the regions I and III include ANXA13 (125 Mb; 8q24.13), MTSS1 (126 Mb; 8q24.13), C8orf17 (141 Mb; 8q24.3) and PTK2 (142 Mb; 8q24.3). In addition to these genes, EXT1 (119 Mb; 8q24.11) was included since it was found in a specific amplification in both cell line SK-GT-5 (Fig. 2C) and a primary adenocarcinoma (data not shown). For all of these gene locations, FISH was performed to confirm aCGH results (Fig. 4). A significant relationship was found between aCGH and FISH (average Pearson correlation factor = 0.66; p = 0.02). Quantitative RT-PCR analysis of these seven genes
Fluorescence ratio (log2)
2
A
1
0
–1 46
56
66
76
86
96
106
116
126
136
146
56
66
76
86
96
106
116
126
136
146
56
66
76
86
96
106
116
126
136
146
B Fluorescence ratio (log2)
2
1
0
–1 46
C Fluorescence ratio (log2)
3 2 1 0 –1 46
24.3
24.23
24.22
24.21
24.13
24.11 24.12
23.3
23.2
23.1
22.3
22.2
22.1
21.3
21.2
21.13
21.12
21.11
13.3
13.2
13.1
12.3
12.2
12.1
11.21 11.22 11.23
11.1
Genomic distance (Mb)
Fig. 1. 8q aCGH profiles of primary GEJ adenocarcinoma specimens. The genomic distance is plotted on the X-axis, whereas the Y-axis shows the log2 fluorescence ratio of test and reference DNA. (A) Log2 ratios of a paraffin-embedded cardia adenocarcinoma sample showing a single copy gain from 8q24.12]qter (120–146 Mb). (B) Log2 ratios of a fresh-frozen cardia cancer specimen demonstrating a complex pattern of single copy gains (8q11.21]q21.12 (52–79 Mb), 8q21.13 (83–84 Mb) and 8q24.13]q24.22 (123–132 Mb)). Two high-level am-
plifications were found at 8q24.21 (129 Mb and 130 Mb). In addition, a deletion was detected at 97 Mb (8q22.1). (C) Log2 ratios of a fresh-frozen Barrett’s adenocarcinoma sample showing various single copy gains between 8q21.3 and 8q24.3 (89–146 Mb) and four high-level amplifications, i.e. 8q21.13 (82 Mb), 8q24.13 (125 Mb), 8q24.21 (129 Mb) and 8q24.22 (132 Mb). A chromosomal ideogram of 8q can be found at the bottom of the figure.
was performed on a panel of 24 gastroesophageal samples, including 13 cell lines, two xenografts and nine normal stomach controls. The mRNA expression data are shown in Fig. 5. For two of the seven genes, MYC (8q24.21) and EXT1 (8q24.11), a significantly increased expression was found in
GEJ adenocarcinoma cell lines and xenografts compared to normal controls (both P = 0.02). Expression of the genes MTSS1 (P ! 0.001; 8q24.13), FAM84B (P = 0.02; 8q24.21) and C8orf17 (P ! 0.001; 8q24.3) was found to be significantly decreased in this set of cell lines and xenografts.
Cytogenet Genome Res 118:130–137 (2007)
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Fluorescence ratio (log2)
2
A
1
0
–1 46
56
66
76
86
96
106
116
126
136
146
56
66
76
86
96
106
116
126
136
146
56
66
76
86
96
106
116
126
136
146
B Fluorescence ratio (log2)
2
1
0
–1 46
C Fluorescence ratio (log2)
5 4 3 2 1 0 –1 46
Fig. 2. 8q aCGH profile of GEJ adenocarcinoma cell lines. The genomic distance is plotted on the X-axis, whereas the Y-axis shows the log2 fluorescence ratio of test and reference DNA. (A) Log2 ratios of cell line FLO-1 showing distal single copy gain (8q24.21]qter; 128–146 Mb); breakpoint is separated by approximately 700 kb from MYC.
Discussion
In this study, three regions of frequent gain on distal chromosome 8q in GEJ adenocarcinoma were found. Conventional chromosomal CGH of GEJ adenocarcinomas revealed a minimal overlapping region of gain on 8q23.3] q24.22 (van Dekken et al., 1999; Walch et al., 2000; Riegman et al., 2001). In addition, a study of gastric cardia adeno-
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Cytogenet Genome Res 118:130–137 (2007)
24.3
24.23
24.22
24.21
24.13
24.11 24.12
23.3
23.2
23.1
22.3
22.2
22.1
21.3
21.2
21.13
21.12
21.11
13.3
13.2
13.1
12.3
12.2
12.1
11.21 11.22 11.23
11.1
Genomic distance (Mb)
(B) Log2 ratios of xenograft OACM2.1X showing genomic loss at 8q21.11]q21.12 (77–79 Mb), single copy gains at 8q21.2]q21.3 (86–88 Mb) and at 8q22.1]qter (95–146 Mb). (C) Log2 ratios of cell line SKGT-5. Two high-level amplifications were found, at 8q24.12 (119 Mb; EXT1) and at 8q24.21 (129 Mb; MYC).
carcinomas demonstrated a frequently gained region on 8q21.2]q24.13 (van Dekken et al., 2001). In our study, these amplified regions are worked out in more detail and illustrate the increased resolution of aCGH compared to chromosomal CGH. Moreover, separate cumulative plots of gastric cardia and esophageal adenocarcinoma specimens did not show significant differences of the region between 8q24.13 and 24.3 (data not shown).
0.75 I
II
III
0.5
0.25
0
–0.25 146
24.3
24.23
24.22
136
24.21
126
24.13
116
23.1 23.2 23.3 24.11 24.12
22.2 22.3
86 96 106 Genomic distance (Mb)
22.1
76
21.3
66
21.11 21.12 21.13 21.2
56
12.1 12.2 12.3 13.1 13.2 13.3
46
11.1 11.21 11.22 11.23
Fig. 3. High-resolution definition of amplified regions by average log2 ratios of the GEJ adenocarcinoma samples and cell lines. The mean fluorescence ratio for each BAC clone calculated over 37 samples is shown. Frequently amplified regions are indicated by the Roman numerals I to III. The first region is positioned at 8q24.13 (124–127 Mb), the second region at 8q24.21]q22 (128–132 Mb) and the third region at 8q24.3 (142–146 Mb). A chromosomal ideogram of 8q can be found at the bottom of the figure.
Fig. 4. Examples of bicolor FISH with a centromeric chromosome 8-specific DNA probe (green) and 8q-specific BAC DNA clones (red) to interphase nuclei of GEJ adenocarcinoma cell lines. (A) FISH with BAC clone RP11-440N18 (8q24.21; 129 Mb) to FLO-1 interphase cells illustrating single copy gain of 8q. In most cell nuclei five red BAC-related spots and four green centromeric spots are seen. (B) FISH with BAC clone RP11-1126O23 (8q24.3; 142 Mb) to OACM2.1X interphase cells. Two extra BAC-related red spots are seen relative to the green centromeric spots. (C) High-level amplification of SKGT-5 at 8q24.21 (129 Mb), visualized with BAC clone RP11-440N18. Clusters of red FISH signal represent highlevel amplification.
Copy number increase of MYC was found frequently in this study. Specific MYC amplification was detected in ten cases. Consequently, MYC was found to be overexpressed in GEJ adenocarcinoma cell lines and xenografts. Although there was no clear correlation between increased MYC expression and copy number in all cases, the amplification in SK-GT-5 did correspond to the highly increased mRNA ex-
pression, i.e. 30-fold higher than the median level in normal controls (data not shown). In line with our results, both amplification and overexpression of MYC have been reported to occur in Barrett’s adenocarcinoma and in precursor lesions of this disease (Walch et al., 2000; Tselepis et al., 2003). mRNA expression analyses of region II also included FAM84B, which was found to be downregulated in this
Cytogenet Genome Res 118:130–137 (2007)
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Fig. 5. Quantitative expression analysis of candidate genes. Relative expression levels are shown for each candidate gene in normal (left of each panel) and cancer specimens (right of each panel). Gene names are shown below the panel. Asterisks denote significantly increased expression, i.e. overexpression of EXT1 and MYC.
Relative expression levels
100,000
10,000
*
*
1,000
100
10
1
study. FAM84B (alias BCMP101) was identified in a proteomics study of breast cancer membranes, and, in contrast to our findings, found to be overexpressed in this type of cancer (Adam et al., 2003). Tissue-specific expression patterns may underlie this difference. Two other regions of genomic overrepresentation were found, although none of the genes within these regions were found to be upregulated. In contrast, significant downregulation was found for MTSS1 and C8orf17. MTSS1, also known as missing in metastasis (MIM), was identified by differential display using bladder cell lines and was found not to be expressed in metastatic cell lines (Lee et al., 2002). In the present study, only metastatic cell line OACM5.1C demonstrated absence of MTSS1 expression. The MTSS1 gene product is an actin-binding protein and has been suggested to be involved in control of cellular proliferation (Lee et al., 2002; Woodings et al., 2003). No putative function has been described for C8orf17, but overexpression of this gene has been reported in prostate and breast cancer (Tan et al., 2003). EXT1 was found to be overexpressed in this set of GEJ cancer specimens as compared to normal controls. The gene product of EXT1 is a glycosyltransferase, involved in heparan sulfate proteoglycan (HSPG) biosynthesis. HSPGs are involved in several aspects of cancer biology, including
EXT1
ANXA13
MTSS1
FAM84B
MYC
C8orf17
PTK2
tumorigenesis, tumor progression and metastasis (Sasisekharan et al., 2002). Moreover, mutations of the EXT1) gene have been shown to be the cause of the disease of multiple osteochrondromas (Ahn et al., 1995; Wells et al., 1997). This suggests that EXT1 is a tumor suppressor at least in some types of cancer. However, EXT1 was found to be overexpressed in cancer of the salivary gland and head and neck squamous cell carcinoma (www.oncomine.org; Frierson et al., 2002; Cromer et al., 2004; Rhodes et al., 2004). In conclusion, we have evaluated chromosomal and expression patterns of chromosome 8q, a genomic region often described to be amplified in progressive cancer. We found that besides the oncogene MYC also other genes, such as EXT1, contribute to this genetic phenomenon. These findings do not only enlarge our understanding of biological mechanisms underlying GEJ adenocarcinomas. They might also prove useful in the surveillance of patients with Barrett’s esophagus, since 8q amplification has also been detected in pre-neoplastic lesions. Acknowledgements We would like to acknowledge L. Roque and P. Chaves for the possibility to include cell lines ESO-26 and ESO-51 in our study. We further thank A. Sieuwerts for excellent advice.
References Adam PJ, Boyd R, Tyson KL, Fletcher GC, Stamps A, et al: Comprehensive proteomic analysis of breast cancer cell membranes reveals unique proteins with potential roles in clinical cancer. J Biol Chem 278: 6482–6489 (2003). Ahn J, Ludecke HJ, Lindow S, Horton WA, Lee B, et al: Cloning of the putative tumour suppressor gene for hereditary multiple exostoses (EXT1). Nat Genet 11:137–143 (1995). Alers JC, Rochat J, Krijtenburg PJ, Hop WC, Kranse R, et al: Identification of genetic markers for prostatic cancer progression. Lab Invest 80: 931–942 (2000).
136
Altorki N, Schwartz GK, Blundell M, Davis BM, Kelsen DP, Albino AP: Characterization of cell lines established from human gastric-esophageal adenocarcinomas. Biologic phenotype and invasion potential. Cancer 72: 649–657 (1993). Botterweck AA, Schouten LJ, Volovics A, Dorant E, van Den Brandt PA: Trends in incidence of adenocarcinoma of the oesophagus and gastric cardia in ten European countries. Int J Epidemiol 29:645–654 (2000). Cameron AJ, Ott BJ, Payne WS: The incidence of adenocarcinoma in columnar-lined (Barrett’s) esophagus. N Engl J Med 313: 857–859 (1985).
Cytogenet Genome Res 118:130–137 (2007)
Cher ML, Bova GS, Moore DH, Small EJ, Carroll PR, et al: Genetic alterations in untreated metastases and androgen-independent prostate cancer detected by comparative genomic hybridization and allelotyping. Cancer Res 56: 3091–3102 (1996). Cromer A, Carles A, Millon R, Ganguli G, Chalmel F, et al: Identification of genes associated with tumorigenesis and metastatic potential of hypopharyngeal cancer by microarray analysis. Oncogene 23:2484–2498 (2004).
de Both NJ, Wijnhoven BP, Sleddens HF, Tilanus HW, Dinjens WN: Establishment of cell lines from adenocarcinomas of the esophagus and gastric cardia growing in vivo and in vitro. Virchows Arch 438: 451–456 (2001). El-Rifai W, Frierson HF Jr, Moskaluk CA, Harper JC, Petroni GR, et al: Genetic differences between adenocarcinomas arising in Barrett’s esophagus and gastric mucosa. Gastroenterology 121:592–598 (2001). Frierson HF Jr, El-Naggar AK, Welsh JB, Sapinoso LM, Su AI, et al: Large scale molecular analysis identifies genes with altered expression in salivary adenoid cystic carcinoma. Am J Pathol 161:1315–1323 (2002). Garnis C, Coe BP, Ishkanian A, Zhang L, Rosin MP, Lam WL: Novel regions of amplification on 8q distinct from the MYC locus and frequently altered in oral dysplasia and cancer. Genes Chromosomes Cancer 39: 93–98 (2004). Hughes SJ, Nambu Y, Soldes OS, Hamstra D, Rehemtulla A, et al: Fas/APO-1 (CD95) is not translocated to the cell membrane in esophageal adenocarcinoma. Cancer Res 57:5571–5578 (1997). Isola JJ, Kallioniemi OP, Chu LW, Fuqua SA, Hilsenbeck SG, et al: Genetic aberrations detected by comparative genomic hybridization predict outcome in node-negative breast cancer. Am J Pathol 147: 905–911 (1995). Jain AN, Tokuyasu TA, Snijders AM, Segraves R, Albertson DG, Pinkel D: Fully automatic quantification of microarray image data. Genome Res 12:325–332 (2002). Lee YG, Macoska JA, Korenchuk S, Pienta KJ: MIM, a potential metastasis suppressor gene in bladder cancer. Neoplasia 4: 291–294 (2002). Menke-Pluymers MB, Schoute NW, Mulder AH, Hop WC, van Blankenstein M, Tilanus HW: Outcome of surgical treatment of adenocarcinoma in Barrett’s oesophagus. Gut 33: 1454– 1458 (1992). Morales TG, Sampliner RE, Bhattacharyya A: Intestinal metaplasia of the gastric cardia. Am J Gastroenterol 92:414–418 (1997). Moskaluk CA, Hu J, Perlman EJ: Comparative genomic hybridization of esophageal and gastroesophageal adenocarcinomas shows consensus areas of DNA gain and loss. Genes Chromosomes Cancer 22: 305–311 (1998). Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, et al: ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6: 1–6 (2004).
Riegman PH, Vissers KJ, Alers JC, Geelen E, Hop WC, et al: Genomic alterations in malignant transformation of Barrett’s esophagus. Cancer Res 61:3164–3170 (2001). Rockett JC, Larkin K, Darnton SJ, Morris AG, Matthews HR: Five newly established oesophageal carcinoma cell lines: phenotypic and immunological characterization. Br J Cancer 75: 258– 263 (1997). Sasisekharan R, Shriver Z, Venkataraman G, Narayanasami U: Roles of heparan-sulphate glycosaminoglycans in cancer. Nat Rev Cancer 2: 521–528 (2002). Shimada Y, Imamura M, Wagata T, Yamaguchi N, Tobe T: Characterization of 21 newly established esophageal cancer cell lines. Cancer 69: 277–284 (1992). Snijders AM, Nowak N, Segraves R, Blackwood S, Brown N, et al: Assembly of microarrays for genome-wide measurement of DNA copy number. Nat Genet 29: 263–264 (2001). Spechler SJ, Zeroogian JM, Antonioli DA, Wang HH, Goyal RK: Prevalence of metaplasia at the gastro-oesophageal junction. Lancet 344:1533– 1536 (1994). Stiles BM, Bhargava A, Adusumilli PS, Stanziale SF, Kim TH, et al: The replication-competent oncolytic herpes simplex mutant virus NV1066 is effective in the treatment of esophageal cancer. Surgery 134:357–364 (2003). Stocks SC, Pratt N, Sales M, Johnston DA, Thompson AM, et al: Chromosomal imbalances in gastric and esophageal adenocarcinoma: specific comparative genomic hybridization-detected abnormalities segregate with junctional adenocarcinomas. Genes Chromosomes Cancer 32:50–58 (2001). Tan JM, Tock EP, Chow VT: The novel human C8ORF17 (C8orf17 ) gene exhibits tissue specific expression, maps to chromosome 8q24.2, and is overexpressed/amplified in high grade cancers of the breast and prostate. Mol Pathol 56: 109–115 (2003). Tselepis C, Morris CD, Wakelin D, Hardy R, Perry I, et al: Upregulation of the oncogene c-myc in Barrett’s adenocarcinoma: induction of c-myc by acidified bile acid in vitro. Gut 52: 174–180 (2003). van Dekken H, Geelen E, Dinjens WN, Wijnhoven BP, Tilanus HW, et al: Comparative genomic hybridization of cancer of the gastroesophageal junction: deletion of 14q31]q32.1 discriminates between esophageal (Barrett’s) and gastric cardia adenocarcinomas. Cancer Res 59: 748–752 (1999).
van Dekken H, Alers JC, Riegman PH, Rosenberg C, Tilanus HW, Vissers K: Molecular cytogenetic evaluation of gastric cardia adenocarcinoma and precursor lesions. Am J Pathol 158: 1961–1967 (2001). van Dekken H, Wink JC, Vissers KJ, van Marion R, Koppert LB, et al: Genomic analysis of early adenocarcinoma of the esophagus or gastroesophageal junction: Tumor progression is associated with alteration of 1q and 8p sequences. Genes Chromosomes Cancer 45: 516–525 (2006). van Duin M, van Marion R, Vissers K, Watson JE, van Weerden WM, et al: High-resolution array comparative genomic hybridization of chromosome arm 8q: evaluation of genetic progression markers for prostate cancer. Genes Chromosomes Cancer 44: 438–449 (2005a). van Duin M, van Marion R, Watson JE, Paris PL, Lapuk A, et al: Construction and application of a full-coverage, high-resolution, human chromosome 8q genomic microarray for comparative genomic hybridization. Cytometry A 63: 10–19 (2005b). Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, et al: Accurate normalization of re
