Cancer Drug Discovery and Development
Series Editor Beverly A. Teicher Genzyme Corporation, Framington, MA, USA
For further volumes: http://www.springer.com/series/7625
Vitaly A. Polunovsky · Peter J. Houghton Editors
mTOR Pathway and mTOR Inhibitors in Cancer Therapy
Editors Vitaly A. Polunovsky Professor of Medicine Department of Family Medicine University of Minnesota Minneapolis, Minnesota 55455 USA
[email protected]
Peter J. Houghton Director, Center for Childhood Cancer Elizabeth M. and Richard M. Ross Chair The Research Institute Nationwide Children’s Hospital 700 Children’s Drive Columbus, OH 43205
[email protected]
ISBN 978-1-60327-270-4 e-ISBN 978-1-60327-271-1 DOI 10.1007/978-1-60327-271-1 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010930694 © Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Humana Press, c/o Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of going to press, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Humana Press is part of Springer Science+Business Media (www.springer.com)
A theory is the more impressive the greater the simplicity of its premises, the more different kinds of things it relates, and the more extended its area of applicability Albert Einstein
Preface
The main objective of this book is to provide an up-to-date survey of the rapidly advancing field of cancer therapy. Moreover, since our knowledge in this area rapidly evolves, some data have got obsolete during the process of book editing. Our understanding of the mechanisms involved in cancer genesis and progression underwent unprecedented expansion during the last decade, opening a new era of cancer treatment – targeted therapy. The surge in this area results in no small part from studies conducted jointly by basic health scientists and clinical investigators. It is our hope that this book will help foster even further collaboration between investigators in these two disciplines. The target of rapamycin (TOR) was first identified in Saccharomyces cerevisiae and subsequently in mammals (mTOR) as a conserved atypical serine/threonine kinase. In mammalian cells, mTOR exists in at least two multi-protein complexes that have critical roles in regulating cellular homeostasis and survival. As with many other areas of science, discovery of TOR signaling was fortuitous. Rapamycin was isolated as a product of the soil bacteria Streptomyces hygroscopicus, identified in a soil sample taken from the island of Rapa Nui (Easter Island). Rapamycin was first discovered to be a potent antifungal agent and next as an immune suppressive drug. It was only later that it was found to be active as an antitumor agent in non-clinical models; although it was not developed for this indication. The history of rapamycin presents one of the first examples of chemical genetics. TOR was identified in a yeast screen designed to find genes conferring rapamycin resistance. Identification of mammalian TOR (mTOR) activation pathways and their roles in regulating cap-dependent translation, transcription, growth, proliferation, and survival continues to be a dynamic field of research. Dysregulation of mTOR is associated with several human diseases including cancer-prone syndromes, such as tuberous sclerosis and Peutz–Jegher, Cowden’s, and Lhermitte–Duclos disease; most adult human malignancies; and potentially with autism. Undoubtedly, the list is not complete. Rapamycin or derivatives have been approved for use as immunosuppressive agents for organ transplantation, for treatment of both renal cell carcinoma and mantle cell lymphoma, and have shown glimpses of activity in a broad range of human cancers. Critical to optimizing the use of these rapalogs as pharmacological agents will be a more comprehensive understanding of pathways that activate vii
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mTOR; which of these pathways are critical for cancer cell genesis, maintenance, and progression survival; and how the cellular consequences of inhibiting mTOR signaling, either in the mTORC1 or mTORC2 complex, interact with transforming events that characterize human neoplasias. In this work, experts in TOR signaling have contributed in two thematic areas: mTOR signaling and cancer therapy (chapters “mTORC1: A Signaling Integration Node Involved in Cell Growth”, “The Regulation of the IGF-1/mTOR Pathway by the p53 Tumor Suppressor Gene Functions”, “mTOR Signaling in Angiogenesis”, “mTORC1 Signaling and Hypoxia”, “mTOR Signaling in Glioblastoma: Lessons Learned from Bench to Bedside”, “mTOR and Cancer Therapy: General Principles”, “mTOR and Cancer Therapy: Clinical Development and Novel Prospects”, and “Drug Combinations as a Therapeutic Approach for mTORC1 Inhibitors in Human Cancer”) and therapeutic targeting downstream of mTOR (chapters “Downstream Targets of mTORC1”, “Downstream of mTOR: Translational Control of Cancer”, “Genome-Wide Analysis of Translational Control”, “Translational Control of Cancer: Implications for Targeted Therapy”, and “Downstream from mTOR: Therapeutic Approaches to Targeting the eIF4F Translation Initiation Complex”). All chapters are completely new or have been extensively updated by their authors and we are indebted to all authors who have exemplified the links between these two thematic areas. We hope that this book will attract a diverse audience, reflecting the broad range of scientific and clinical disciplines focused on current problems in cancer etiology and therapy – and future perspectives of drug development. Consequently, we have brought together biochemists, cancer biologists, and clinicians to share their unique perspectives on the role of mTOR signaling pathway in cancer genesis and contemporary therapeutic approaches.
Contents
mTORC1: A Signaling Integration Node Involved in Cell Growth . . . Neil Kubica and John Blenis The Regulation of the IGF-1/mTOR Pathway by the p53 Tumor Suppressor Gene Functions . . . . . . . . . . . . . . . . . . . . . . . . . Zhaohui Feng and Arnold J. Levine
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mTOR Signaling in Angiogenesis . . . . . . . . . . . . . . . . . . . . . . Henry Mead, Mirjana Zeremski, and Markus Guba
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mTORC1 Signaling and Hypoxia . . . . . . . . . . . . . . . . . . . . . . James Brugarolas
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mTOR Signaling in Glioblastoma: Lessons Learned from Bench to Bedside . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . David Akhavan and Paul S. Mischel mTOR and Cancer Therapy: General Principles . . . . . . . . . . . . . Peter J. Houghton
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mTOR and Cancer Therapy: Clinical Development and Novel Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sandrine Faivre, Thomas Decaens, and Eric Raymond
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Drug Combinations as a Therapeutic Approach for mTORC1 Inhibitors in Human Cancer . . . . . . . . . . . . . . . . . . . . . . . . Madlaina Breuleux and Heidi A. Lane
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Downstream Targets of mTORC1 . . . . . . . . . . . . . . . . . . . . . Bruno D. Fonseca and Christopher G. Proud
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Downstream of mTOR: Translational Control of Cancer . . . . . . . . Ryan J.O. Dowling and Nahum Sonenberg
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Genome-Wide Analysis of Translational Control . . . . . . . . . . . . . Ola Larsson and Peter B. Bitterman
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Translational Control of Cancer: Implications for Targeted Therapy . . Peter B. Bitterman and Vitaly A. Polunovsky
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Downstream from mTOR: Therapeutic Approaches to Targeting the eIF4F Translation Initiation Complex . . . . . . . . . . Jerry Pelletier and Jeremy R. Graff
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Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Contributors
David Akhavan UCLA Medical Scientist Training Program, Departments of Pathology and Laboratory Medicine and Molecular and Medical Pharmacology, The David Geffen UCLA School of Medicine, Los Angeles, CA 90095-1732, USA,
[email protected] Peter B. Bitterman Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA John Blenis Department of Cell Biology, Harvard Medical School, Boston, MA 02155, USA,
[email protected] Madlaina Breuleux Basilea Pharmaceutica AG, Basel 4005, Switzerland,
[email protected] James Brugarolas Department of Developmental Biology and Internal Medicine, Oncology Division, University of Texas Southwestern Medical Center, Dallas, TX 75390-9133, USA,
[email protected] Thomas Decaens Department of Hepatology and Liver Transplantation, APHP and INSERM U841, Hôpital Henri-Mondor, Créteil, France,
[email protected] Ryan J.O. Dowling Department of Biochemistry, Rosalind and Morris Goodman Cancer Centre, McGill University, Montreal, QC H3A 1A3, Canada,
[email protected] Sandrine Faivre Department of Medical Oncology, APHP and INSERM U728, Beaujon University Hospital, Service de Cancérologie, Clichy, France,
[email protected] Zhaohui Feng Department of Radiation Cancer Biology, UMDNJ-Robert Wood Johnson Medical School, The Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA,
[email protected] Bruno D. Fonseca Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; Department of Biochemistry, Goodman Cancer Centre, McGill University, Montreal, QC H3A 1A3, Canada,
[email protected]
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Jeremy R. Graff Cancer Growth and Translational Genetics, Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA,
[email protected] Markus Guba Multi-Organ Transplantation Program, Toronto General Hospital, University of Toronto, Toronto, ON M5G2N2, Canada,
[email protected] Peter J. Houghton The Research Institute, Center for Childhood Cancer, Nationwide Children’s Hospital, Columbus, OH 43205, USA,
[email protected] Neil Kubica Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA,
[email protected] Heidi A. Lane Basilea Pharmaceutica AG, Basel 4005, Switzerland,
[email protected] Ola Larsson Department of Oncology-Pathology, Karolinska Institute, Stockholm, 171 76, Cancer Center Karolinska R8:01, Sweden,
[email protected] Arnold J. Levine Cancer Institute of New Jersey, Princeton, NJ 08540, USA; School of Natural Sciences, Institute for Advanced Study, Princeton, NJ 08540, USA,
[email protected] Henry Mead Wyeth Research, Collegeville, PA 19426, USA,
[email protected] Paul S. Mischel Lya and Harrison Latta Professor of Pathology, Departments of Pathology and Laboratory Medicine and Molecular and Medical Pharmacology, The David Geffen UCLA School of Medicine, Los Angeles, CA 90095-1732, USA,
[email protected] Jerry Pelletier Department of Biochemistry, McGill University, Montreal, QC H3G 1Y6, Canada,
[email protected] Vitaly A. Polunovsky Department of Medicine, Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA,
[email protected] Christopher G. Proud Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; School of Biological Sciences, University of Southampton, Southampton SO16 7PX, UK,
[email protected] Eric Raymond Department of Medical Oncology, APHP and INSERM U728, Beaujon University Hospital, Clichy, France,
[email protected] Nahum Sonenberg Department of Biochemistry, Rosalind and Morris Goodman Cancer Centre, McGill University, Montreal, QC H3A 1A3, Canada,
[email protected] Mirjana Zeremski Wyeth Pharmaceuticals – Scientific Affairs, Toronto, ON L3T 7Y2, Canada,
[email protected]
mTORC1: A Signaling Integration Node Involved in Cell Growth Neil Kubica and John Blenis
Abstract The mammalian target of rapamycin (mTOR) is an atypical serine /threonine kinase that plays an indispensable role in the control of cell growth. When localized with the interacting proteins raptor and mLST8 in the mammalian target of rapamycin complex 1 (mTORC1), mTOR serves as an integrator of cellular signals to control the balance between cellular anabolism and cellular catabolism. Under conditions that promote cell growth, or in the presence of common genetic lesions associated with cancer, mTORC1 signals to the effectors 4E-BP1 and S6K1 resulting in ribosomal biogenesis and enhanced mRNA translation. The positive effects of mTORC1 on mRNA translation involve a dynamic molecular process that results in an increase in bulk protein synthesis, including more dramatic effects on a subset of mRNA species encoding pro-growth, anti-apoptotic proteins. Recent data also suggest a role of mTORC1 in the “pioneer” round of mRNA translation, in addition to the more established effects on “steady-state” protein biosynthesis. Growth control by mTORC1 is required in physiological and developmental settings for proper maintenance of cellular homeostasis, cell survival, and embryonic development, while inappropriate regulation of mTORC1 signaling is observed in the overwhelming majority of human cancers. This review will discuss the current view of the signaling network upstream of mTORC1 and the regulation of protein biosynthesis by this evolutionarily conserved, clinically relevant cell signaling node. Keywords mTOR · mTORC1 · S6K1 · 4E-BP1 · Protein metabolism · mRNA translation · Ribosomal biogenesis · Cell growth · Cell proliferation · Cancer
J. Blenis (B) Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA e-mail:
[email protected] V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_1,
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1 Introduction Since the discovery of the mammalian target of rapamycin (mTOR) [1–5], considerable progress has been made toward understanding the signaling network that feeds into and emanates from this evolutionarily conserved serine/threonine kinase. As is true in yeast model systems, mTOR is now recognized to be a member of two distinct protein complexes termed mTORC1 and mTORC2. mTORC1 serves as an integration hub for a variety of upstream growth factor, nutrient, and stress signals and modulates a variety of anabolic (e.g., protein biosynthesis) and catabolic (e.g., autophagy) processes to suit the status of the cellular environment. mTORC1 plays a critical role in determining progression of the intimately linked processes of cell growth and cell division through the action of its downstream targets 4E-BP1 and S6K1. Careful control of these fundamental biological processes is required for maintenance of cellular homeostasis, cell survival, embryonic development, and tissue/body patterning, while aberrant mTORC1 signaling is associated with a number of pathophysiological conditions, including human cancer. A plethora of known oncogenes (e.g., EGFR, PDGFR, PI3K, Akt, Ras, Raf, Rheb, S6K1, and eIF4E) and tumor suppressors (e.g., PTEN, NF1, LKB1, REDD1, TSC1/2, PDCD4) are members of the mTORC1 signaling network. Mutations in several of these network components are causative in a number of inherited tumor pre-disposition syndromes, such as tuberous sclerosis complex (TSC), lymphangioleiomyomatosis (LAM), neurofibromatosis (NF), Cowden’s disease (CD), and Peutz–Jeghers syndrome (PJS), that share the common clinical feature of non-metastatic large-celled hamartomas. Hyperactivation of mTORC1 signaling is observed in nearly all sporadic human cancers and results in a variety of cellular phenotypes that confer a selective growth advantage to cancer cells compared to their non-malignant counterparts. The importance of mTORC1 in cancer biology is further reinforced by the ability of the specific mTORC1 inhibitor rapamycin to inhibit some tumorigenic phenotypes. Several rapamycin analogues are currently approved, or being evaluated in clinical trials, for treatment of numerous cancer subtypes. This chapter will focus on signaling upstream of mTORC1 and the role of mTORC1 in protein biosynthesis, cell growth, and proliferation.
2 The Domain Structure and Protein Complex Assembly of mTOR Yeast TOR1 (yTOR1) and TOR2 (yTOR2) are the founding members of the PI3Krelated kinase (PIKK) superfamily that includes mTOR, ATM, ATR, and DNA-PK [6–9]. Members of this atypical protein kinase family are characterized by their high molecular weight (mTOR ~289 kDa) and an unusual C-terminal kinase domain with significant homology to the PI3K lipid kinase domain. Despite the obvious homology to lipid kinases, mTOR functions as a protein serine/threonine kinase. The N-terminus of mTOR contains a series of 20 HEAT repeats, domains that
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are composed of tandem anti-parallel α-helices known to mediate protein–protein interactions. C-terminal to these HEAT repeats is a FAT domain of unknown function found in all PIKK family members [10], followed by an FRB domain that binds the FKBP12–rapamycin complex [4], the atypical kinase domain, a putative NRD (negative regulatory domain) [11], and finally an FATC domain, another FAT domain at the extreme C-terminus of the protein. The mTORC1 inhibitor rapamycin acts through a novel mechanism that requires interaction of the small molecule with an endogenous protein known as an immunophilin (FKBP12) in order to interact with and repress its protein target. This molecular arrangement confers a high degree of specificity, such that TOR is the only described target of the FKBP12– rapamycin complex. Interestingly, the serine residue associated with the dominant point mutations (TOR1 S1972A and TOR2 S1975I) that allowed for the genetic identification of yTOR isoforms [12, 13] is conserved in the mTOR (Ser-2035) FRB domain and mutation of this residue ablates the interaction between TOR and the FKBP12–rapamycin complex [14, 15]. A 2.7 Å crystal structure of the FKBP12– rapamycin–FRB domain ternary complex reveals that interactions rely on the ability of rapamycin to bind hydrophobic pockets on both proteins simultaneously [16]. The FATC domain is required for mTOR kinase activity [17, 18] and, although the mechanism is not formally established, it is proposed that the FAT and FATC domains interact in a manner that exposes the catalytic region in the kinase domain. In yeast, the two TOR genes localize to distinct protein complexes termed yTORC1 and yTORC2 [19]. yTORC1 is a rapamycin-sensitive complex composed of TOR1 or TOR2 and the interacting proteins KOG1, LST8, and Tco89p. Despite the fact that mammals have only a single TOR gene, mTOR protein complex architecture appears to be conserved. mTOR forms two distinct protein complexes termed mTORC1 and mTORC2. mTORC1 is a heterotrimeric protein complex that contains mTOR bound to two associated proteins, namely raptor (regulatory-associated protein of mTOR; KOG1 ortholog) and mLST8 (LST8 ortholog) [20–22]. Tco89p does not appear to have a mammalian ortholog. The cellular functions of mTORC1 seem to be largely conserved as this complex, similar to yTORC1, regulates cell growth by modulation of protein synthesis, ribosomal biogenesis, autophagy, and metabolism [23–25]. Raptor is a 150 kDa protein with a conserved N-terminal domain, three HEAT domains, and seven WD40 motifs. The spatial expression pattern of mTOR and raptor shows a striking correlation and evidence exists to support the notion that raptor is involved in mTOR protein stability [20, 21]. Raptor is also involved in mediating the ability of mTOR to phosphorylate the well-described downstream targets 4E-BP1 and S6K1. Overexpression of exogenous raptor results in increased mTORC1 signaling in vitro [20], while knockdown of raptor expression leads to decreased S6K1 phosphorylation and a reduction in cell size in vivo [21]. These effects seem to be explained by a model in which raptor serves as a scaffold to recruit 4E-BP1 and S6K1 to mTORC1 where they can be efficiently phosphorylated by mTOR. 4E-BP1 and S6K1 physically interact with raptor [20] via a TOS (TOR signaling) motif found in both downstream effectors [26]. Mutations in the TOS motif decrease 4E-BP1 and S6K1 binding to raptor and subsequent phosphorylation of these downstream effectors by mTOR [26–29]. Interestingly, raptor
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knockout mice exhibit early embryonic lethality at ~E6.5 days [30], a result that phenocopies timing of prenatal death observed in mTOR–/– mice [31, 32]. Taken together, these results suggest the interdependence of raptor and mTOR function in regulating cell growth and embryogenesis. mLST8 binds to the kinase domain of mTOR leading to the hypothesis that mLST8 may play a positive role in regulating mTORC1 signaling. Overexpression of mLST8 enhances mTORC1 signaling [22], while RNAi-mediated knockdown of mLST8 represses mTORC1 signaling [19, 22, 33] and results in reduced cell size [22]. The mechanism by which mLST8 positively effects mTORC1 signaling is less clear, although Kim et al. propose a model by which mLST8 interprets cellular inputs and regulates the stability of the mTOR–raptor interaction [22]. Surprisingly, mLST8–/– knockout embryos display lethality at a later stage (~E10.5) when compared to mTOR–/– and raptor–/– animals, but similar to knockouts of the mTORC2-specific components rictor and mSin1 [30]. mLST8–/– MEFs show no change in mTORC1 function, as S6K1 and 4E-BP1 phosphorylation levels are unchanged in these cells in vivo and mTORC1 purified from these cells can phosphorylate S6K1 in vitro [30]. Furthermore, mLST8–/– MEFs exhibit a marked reduction in Akt Ser-473 phosphorylation, a biomarker for mTORC2 function. The knockout animal models suggest that mLST8 may be more important in mTORC2 function and dispensable with regard to its role in the mTORC1 complex. The discrepancies between the initial observations in established cell lines and the knockout animal models could be explained by the ability for cellular compensation of mLST8 in early embryonic development that is lost in immortalized cell lines or differences in the genetic background between otherwise normal knockout animals and the highly aberrant genomes of established human cell lines [34]. Despite the nuances of the roles of various mTORC1 components recent work has elucidated a myriad of upstream signaling events that regulate the activity of the mTORC1 complex.
3 Cellular Signaling Upstream of mTORC1: Integration of Anabolic and Catabolic Cues The activity of yTORC1 is predominantly regulated by the availability of nutrients. In addition to these evolutionarily ancient nutrient inputs, cell signaling networks in higher eukaryotes have developed connections between mTORC1 and inputs from growth factors and stress signaling pathways (Fig. 1).
3.1 Growth Factor Signaling Growth factors play an important role in mammalian cell growth and proliferation. Growth factor binding (e.g., insulin/IGF-1) to their receptor tyrosine kinases (e.g., IR/IGF-1R) promotes recruitment of adaptor molecules such as IRS-1. IRS-1 recruits PI3K, which then phosphorylates PIP2 leading to the generation of PIP3 , a
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Fig. 1 Cellular signaling pathways upstream of mTORC1. mTORC1 integrates a diverse set of anabolic and catabolic cellular cues to establish appropriate rates of mRNA translation, cell growth, and cell proliferation. Upstream inputs include growth factor signaling pathways (e.g., PI3K/Akt and MAPK), nutrients (e.g., amino acids), and stress signals (e.g., energy deprivation and hypoxia). Many of these signals regulate the GAP activity of the TSC1/2 complex toward the small G protein Rheb. Several lines of evidence suggest that Rheb-GTP serves as the proximal upstream activator of mTORC1 via direct binding. Proper co-localization of mTORC1 and Rheb to late endosomes is maintained by amino acid-mediated regulation of the Rag family of GTPases (RagA-D). Growth factor signaling also promotes mTORC1 signaling via inactivation of the inhibitory protein PRAS40 via Akt and direct phosphorylation of raptor by RSK. Repressive phosphorylation events on raptor are mediated by AMPK subsequent to energetic stress. The upstream inputs to mTORC1 include many known oncogenes and tumor suppressors that play a role in sporadic cancers and a variety of tumor pre-disposition syndromes
process antagonized by the lipid phosphatase PTEN. PIP3 recruits PDK1 and Akt to the plasma membrane through their respective pleckstrin homology (PH) domains. Based on co-localization at the membrane, PDK1 efficiently phosphorylates Akt on Thr-308 resulting in partial Akt activation, sufficient to promote downstream signaling to numerous Akt target proteins [30, 35]. Knockin mice expressing PDK1 with a mutated PH domain have reduced Akt signaling, a small body phenotype and exhibit insulin resistance [36]. Early efforts to elucidate the role of Akt in promoting mTORC1 signaling focused on direct phosphorylation of mTOR on Ser2448 [11, 37–39]. While phosphorylation of this residue appears to correlate with mTORC1 signaling, the role of this phosphorylation event was called into question, as substitution of this residue with alanine does not affect mTORC1 signaling
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[11]. A key breakthrough in the understanding of PI3K/Akt signaling to mTORC1 was the discovery of the TSC1/TSC2 complex as a negative regulator of mTORC1 signaling. Genetic linkage studies in families with tuberous sclerosis complex (TSC), an autosomal dominant disorder characterized by the development of non-metastatic tumors with enlarged cells [40], mapped the causative loss-of-function mutations to two genetic loci subsequently named TSC1 and TSC2 [41–43]. In the years following this discovery, in seemingly unrelated work, several groups employed classic Drosophila genetic screens in an effort to discover genes involved in cell/body size determination. When combined with epistatic analysis, these screens revealed a critical role for numerous components in the insulin/IGF-1 pathway as regulators of cell size. dInr (insulin receptor homolog) [44], chico (IRS1–4 homolog) [45], dPI3K [46, 47], dAkt [47, 48], dTOR [49], and dS6K [50] form a linear genetic axis that promotes cell size, while dPTEN [47, 51] serves as a repressor of cell size lying upstream of dAkt. Subsequent efforts in this experimental system demonstrated that dTSC1 and dTSC2 were also negative regulators of cell size [52–54], functioning downstream of dAkt and upstream of dS6K [53]. The results prompted studies in mammalian systems showing that cells lacking TSC1 or TSC2 exhibit rapamycinsensitive [55] hyperactivation of mTORC1 [56, 57]. Furthermore, overexpression of TSC1 and TSC2 reduces phosphorylation of 4E-BP1 and S6K1 and RNAi-mediated knockdown of TSC2 increases phosphorylation of S6K1 [58, 59]. Taken together, the evidence supports the conclusion that TSC1/TSC2 serves as a negative regulator of mTORC1 signaling. TSC1 and TSC2 form a tight heterodimeric complex in which both proteins are required for proper function. TSC1 (also known as hamartin) is a 140 kDa protein that contains several coiled-coil domains. TSC2 (also known as tuberin) is a 200 kDa protein with a coiled-coil loop domain and a C-terminal GTPaseactivating protein (GAP) domain. The crucial function of the TSC1/TSC2 complex in inhibiting mTORC1 appears to be GAP activity toward the small G protein Rheb. Drosophila genetic epistasis experiments were again employed to place Rheb in the cell size network downstream of dTSC1/dTSC2 but upstream of dTOR [60, 61], and an RNAi screen in Drosophila S2 cells demonstrated that Rheb knockdown inhibits S6K1 phosphorylation [62]. Biochemical approaches in mammalian cells show that GTP-loaded Rheb stimulates mTORC1 activity both in vitro and in vivo, while GAP activity of the TSC1/TSC2 complex results in GTP hydrolysis and formation of inactive Rheb-GDP [63–66]. Interestingly, there are several Akt consensus phosphorylation motifs in TSC2 that are conserved between Drosophila and Homo sapiens and experimental evidence confirms Akt-dependent phosphorylation of Ser-939, Ser-1130, and Thr-1462 in human TSC2 [58, 67, 68]. These phosphorylation events inhibit TSC2 GAP activity leading to derepression of Rheb and activation of mTORC1 signaling. In support of an inhibitory role for Akt phosphorylation of TSC2, several studies demonstrate that exogenous expression of a TSC2 mutant with alanine substitutions at Akt-sensitive serine/threonine residues acts as a dominant-negative inhibitor of growth factor-mediated mTORC1 signaling [58, 67].
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Several unresolved issues remain in the current understanding of PI3K/Akt/TSC2/Rheb signaling pathway. First, the mechanism for TSC2 GAP inhibition via Akt-mediated phosphorylation remains unclear. Several studies suggest that phosphorylation by Akt disrupts the integrity of the TSC1/TSC2 complex, leading to subsequent degradation of TSC1 and TSC2 [58, 68]; however, other reports have not supported this conclusion [67, 69]. Binding of 14-3-3 proteins to Akt-dependent phosphorylation sites on TSC2 has also been proposed to inhibit GAP activity [70, 71], but others have demonstrated that 14-3-3 binds to TSC2 via phosphorylated sites other than those targeted by Akt [72, 73]. Discovery of the GAP activity of the TSC1/TSC2 complex has also prompted the search for an activating Rheb-GEF. The initial observation that Rheb has low intrinsic GAP activity and is predominantly found in a GTP-bound form in vivo prompted the hypothesis that a putative Rheb-GEF was either unregulated or entirely absent. A relatively recent publication [74] provides evidence that TCTP serves as the Rheb-GEF in Drosophila. Interestingly, exogenous expression of human TCTP appears to rescue the dTCTP mutant phenotypes; however, GEF activity of dTCTP toward dRheb is noticeably low in biochemical exchange assays [74]. Data from mammalian cells strongly question a functional role for TCTP as the GEF for Rheb [75, 76] and Rheb-GEF discovery remains an active area of inquiry in the field. Additionally, it is uncertain whether mTORC1 activation by Rheb is direct or indirect. A search for intermediates between Rheb and mTOR was initially unsuccessful, leading to the hypothesis that Rheb is the immediate upstream activator of mTOR. Long et al. demonstrate that exogenously overexpressed GST-Rheb binds directly to the kinase domain of mTOR; however, binding of Rheb to mTOR is independent of GTP-loading status [77, 78], a surprising result considering Rheb binds to TOR in Schizosaccharomyces pombe in a GTP-dependent manner [79]. The authors propose that while nucleotide-free Rheb is capable of binding mTOR, GTP-Rheb is unique in the ability to induce conformational changes in mTORC1 required for activation [77, 78]. Additional work will be required to confirm the nature of the Rheb interaction with mTOR and the mechanism by which this interaction stimulates mTORC1 signaling. Alternatively, several recent publications suggest candidates for the elusive targets downstream of Rheb and upstream of mTORC1. Bai et al. [80] report that Rheb regulates mTOR through an interaction with the FK506-binding protein family member FKBP38. The authors of this study propose a model where FKBP38 inhibits mTORC1 by binding to mTOR in a manner similar to FKBP12–rapamycin, a function antagonized by Rheb-GTP binding to FKBP38. Other groups confirm the FKBP38–Rheb interaction, but do not support the inhibitory role of FKBP38 on mTORC1 function [76]. Sun et al. demonstrate that Rheb directly binds to and activates phospholipase D1 (PLD1). PLD1 is an enzyme that functions to generate the lipid second messenger phosphatidic acid (PA). PA activates mTORC1 signaling [81] via direct binding to the mTOR FRB domain [82] in a manner that is competitive to FKBP12–rapamycin binding [83]. It is also not clear that Akt-mediated repression of TSC2 is sufficient to maximally activate mTORC1 signaling. Mutation of the Akt sites in dTSC2 does not result in the expected effect on Drosophila cell growth [84]. Furthermore,
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Akt1/Akt2 double-knockout MEFs exhibit a dramatic decrease in 4E-BP1 phosphorylation without a concomitant decrease in TSC2 phosphorylation [85]. This point of controversy may be at least partially explained by the recent discovery of several parallel Akt-dependent and Akt-independent mechanisms for mTORC1 activation. PRAS40 (proline-rich Akt substrate 40 kDa) was recently identified as a novel growth factor-sensitive repressor of mTORC1 [86, 87]. PRAS40 binds directly to the mTOR kinase domain [87] and/or to raptor [86]. High salt concentrations weaken the association of PRAS40 and mTOR resulting in increased mTORC1 kinase activity in vitro. Growth factors inhibit PRAS40 function via activation of Akt, which phosphorylates the C-terminal Thr-246 residue in PRAS40 resulting in mTORC1 derepression. Interestingly, several groups report the presence of a TOS motif in PRAS40 [88, 89], supporting the conclusion in Sancak et al. that PRAS40 interacts with mTORC1 via raptor, but conducive to the alternative hypothesis that PRAS40 may be a novel mTORC1 substrate. Consistent with this idea, mTORC1 was shown to phosphorylate PRAS40 on Ser-183 in a rapamycin-sensitive manner. The interaction between PRAS40 and raptor appears to be highly stable and overexpression of PRAS40 suppresses phosphorylation of 4E-BP1 and S6K1. These data suggest that PRAS40 may serve as a competitive inhibitor of substrate recruitment to raptor. PRAS40 antagonizes mTORC1 activation by Rheb in vitro [86], suggesting that the dynamic interplay between negative regulation by PRAS40 and positive regulation by Rheb can determine mTORC1 signaling strength. Interestingly, Akt has the dual role of inhibiting PRAS40 directly and activating Rheb indirectly via repression of TSC1/2 GAP activity, events that collaborate to activate mTORC1 signaling. The MAPK pathway has also been shown to directly activate the mTORC1 pathway through Akt-dependent and Akt-independent mechanisms. Growth factors (e.g., EGF) and tumor-promoting phorbol esters (e.g., PMA) activate the potent oncogene Ras. Ras can directly activate PI3K [90, 91] presumably leading to mTORC1 activation via the Akt/TSC2 pathway described earlier. Additionally, Ras activates the canonical MAPK pathway that includes Raf, MEK, ERK, and RSK [92]. PMA activates mTORC1 in a wortmannin-insensitive, UO126-sensitive manner, while overexpression of TSC1 and TSC2 represses PMA stimulation of mTORC1, suggesting a role for MAPK signaling in the regulation of TSC2 GAP activity [93]. Indeed, PMA treatment promotes TSC2 phosphorylation in a PI3Kindependent, MAPK-dependent manner [93]. Subsequent investigations revealed that both ERK [94, 95] and RSK [96] directly phosphorylate and inactivate GAP activity of TSC2 against Rheb. ERK appears to preferentially phosphorylate TSC2 on Ser-664 in vitro and in vivo, an event that leads to TSC1/TSC2 complex disassembly and derepression of mTORC1 signaling [94]. Similar to Akt, RSK phosphorylates Ser-939 and Thr-1462 residues on TSC2 along with the unique Ser-1798 site, which also dramatically represses GAP activity of TSC2 [96]. Quantitative analysis of phosphorylation using stable isotope labeling by amino acids in cell culture (SILAC) coupled with liquid chromatography tandem mass spectrometry (LC-MS/MS) confirms the existence of numerous ERK/RSK-sensitive sites on TSC2 [97]. Subsequent work demonstrates that loss-of-function mutations
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in the inhibitory Ras-GAP NF1 also lead to repression of TSC1/TSC2 function and hyperactivation of mTORC1 signaling [98]. Recent data suggest that RSK can also promote mTORC1 signaling via direct phosphorylation of raptor [99]. Taken together, the data suggest that the PI3K/Akt and MAPK pathways collaborate to transmit growth factor signals to mTORC1 via TSC2-dependent and TSC2-independent mechanisms. Finally, the mTORC1/S6K1 signaling axis contains a number of described feedback loops that can influence the pathway. S6K1 phosphorylates the Ser-2448 site on mTOR directly [100, 101], but as mentioned above, the functional consequences of this event are not known. mTORC1/S6K1 signaling also promotes a negative feedback loop that represses insulin signaling. Insulin-mediated Akt activation is repressed by chronic rapamycin treatment [102] and excess amino acid availability can inhibit insulin signaling in a rapamycin-sensitive manner [103]. Furthermore, TSC2–/– Drosophila larvae exhibit reduced Akt activity, which can be rescued by deletion of dS6K [104], while loss of TSC1/TSC2 function in mammalian cells leads to decreased Akt phosphorylation [57, 105]. Subsequently, S6K1 was shown to directly phosphorylate IRS-1 on several inhibitory sites, resulting in degradation of the adaptor molecule, explaining loss of PI3K/Akt function in response to elevated mTORC1 signaling [102, 106–108]. S6K1–/– mice have decreased pancreatic β cell mass, but display normal fasting glucose levels due to insulin hypersensitivity as a result of loss of the mTORC1/S6K1 negative feedback loop [109]. These mice are also resistant to obesity when challenged with a high-fat diet due to heightened insulin sensitivity, suggesting that negative feedback loop signaling may contribute to the insulin resistance phenotype associated with type II diabetes and metabolic syndrome [109, 110]. The negative feedback loop may also explain the observation that TSC1/TSC2-null tumors, such as the hamartomas seen in tuberous sclerosis complex, are typically benign (i.e., non-metastatic) in nature. While TSC lesions would be predicted to drive tumor growth via mTORC1, the S6K1 negative feedback loop would simultaneously restrain pro-survival, pro-growth, and pro-metastatic signaling from Akt. Consistent with this hypothesis, feedback loop signaling in TSC2+/– heterozygous mice correlates with limited tumor growth, and crossing these animals with PTEN+/– mice hyperactivates Akt and results in a severe tumorigenic phenotype [111, 112]. Unfortunately, the negative feedback loop may also explain the limited success of rapamycin analogue monotherapy observed in clinical trials for a variety of human cancers. While rapalogs potently inhibit mTORC1 signaling, they also repress the negative feedback loop resulting in Akt activation. At this time it is not clear whether IRS-1 is the sole target of the negative feedback loop. Interestingly, a murine model system expressing a non-phosphorylatable form of ribosomal protein S6, known as rpS6(P–/– ) knockin mice, also exhibits decreased β cell mass [113], suggesting an undefined role for S6K1 phosphorylation of S6 in this process. TSC1/TSC2 deletion also suppresses PDGFR expression in a rapamycin-sensitive manner through an undefined mechanism [114], suggesting the involvement of other receptor tyrosine kinase pathways in mTORC1-dependent feedback mechanisms. Finally, recent data support a role for the unfolded protein response (UPR) in negative feedback signaling in TSC1–/– and TSC2–/– MEFs [115].
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3.2 Nutrients TORC1 serves as a nutrient sensor in yeast where exogenous supply of metabolic precursors, particularly high-quality nitrogen sources such as glutamine [116], is usually rate limiting for growth and division. Nutrient sensing remains an important input in the regulation of mTORC1 function in mammalian cells which seems advantageous to cellular fitness, as it is not economical for the cell to promote cell growth and proliferation in the absence of an adequate supply of energy and molecular building blocks. Consistent with the critical role of mTORC1 in protein biosynthesis (see below), mTORC1 function appears to be particularly sensitive to amino acid availability. Amino acid inputs to mTORC1 appear to be dominant over growth factor inputs, as growth factors such as insulin are unable to stimulate 4E-BP1 and S6K1 phosphorylation in the absence of amino acids [117]. Amino acid deprivation in mammalian cells results in the rapid dephosphorylation of the mTORC1 targets 4E-BP1 and S6K1, while resupplementation of amino acids stimulates 4E-BP1 and S6K1 phosphorylation in a rapamycin-sensitive manner [117–123]. Mammalian cells appear to be particularly sensitive to deprivation of branch chain amino acids, particularly leucine [121, 124–129]. The mechanism(s) by which amino acid signals are propagated to mTORC1 are not clearly defined; however, roles for amino acid sensing have been ascribed to TSC1/TSC2, Rheb, and signals that appear to be parallel to the TSC1/TSC2–Rheb axis. One study demonstrates that inactivation of TSC2 leads to resistance of mTORC1 signaling to amino acid withdrawal in Drosophila and mammalian cells [130]. However, amino acid starvation still results in mTORC1 repression in TSC2–/– cells [131, 132], leading to the hypothesis that Rheb functions as the proximal amino acid sensor for mTORC1. Overexpression of Rheb rescues TORC1 signaling in the face of amino acid withdrawal [60, 64–66] and binding of Rheb to mTOR is regulated by amino acids in some [78] but not all [131] reported studies. Amino acid withdrawal from TSC2–/– cells leads to mTORC1 repression in the absence of a change in Rheb-GTP levels [132], the Rheb-binding state proposed to be required for mTORC1 activation [77]. Furthermore, Saccharomyces cerevisiae lacks TSC1 and TSC2 orthologs, while S. pombe lacks homologs of TSC1, TSC2, and Rheb, yet yTORC1 in both species responds to nutrient availability arguing for an evolutionarily conserved mechanism for TORC1 nutrient sensing that does not require these network components. One possible mechanism consistent with this observation may be direct nutrient sensing by the mTORC1 complex. Nutrient deprivation promotes a high-affinity interaction between mTOR and raptor in a manner that inhibits mTORC1 activity [21], likely by preventing recruitment of 4E-BP1 and S6K1 to raptor. mLST8 also plays a positive role in nutrient-dependent activation of mTORC1, possibly by stabilizing the low-affinity mTOR–raptor interaction that promotes substrate recruitment [22]. The mechanism by which amino acids induce these conformational changes in mTORC1 requires further investigation. Recently published data also support a role for the class III PI3K Vps34 in communicating amino acid levels to mTORC1 independently of TSC1/TSC2 [132, 133].
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Amino acid stimulation of mTORC1 signaling is sensitive to treatment with the PI3K inhibitor wortmannin despite the fact that Akt is not activated by amino acids. RNAi-mediated knockdown of class I PI3K, a well-established wortmannin target, has no effect on amino acid stimulation of mTORC1 function [132]. Several approaches demonstrate that amino acids modulate the activity of the class III PI3K hVps34 in a manner required for amino acid stimulation of mTORC1 [132–134]; however, overexpression of hVps34 is not sufficient to prevent reduction in S6K1 phosphorylation following amino acid deprivation. Gulati et al. [134] demonstrate that amino acids induce elevation in intracellular Ca2+ , leading to increased binding of Ca2+ /calmodulin (CaM) to hVps34, an event required for lipid kinase activity and the subsequent increase in mTORC1 signaling. The molecular events downstream of hVps34 and mTORC1 activation are not clearly defined. hVps34 phosphorylates phosphatidylinositol (PtdIns) generating phosphatidylinositol-3-phosphate (PtdIns3-P), a regulator of membrane trafficking and membrane fusion. Interestingly, the vacuolar membrane-associated EGO protein complex has been suggested to play a role in yTORC1 activation [135]. The vacuole is the major amino acid reservoir in yeast. Overexpression of the EGO complex constituents GTR2 (RagA-D homolog) or EGO3 promotes rapamycin resistance [136] and transcriptional profiling of the yeast ego3 mutant shows similarity to profiles from rapamycin-treated cells [137]. A recent synthetic lethal screen in yeast designed to find interactions with TOR1 identified EGO proteins, Vps34, Vps15, and components of the class C Vps complex [138]. In yeast, Vps34 and Vps15 are members of the PAS protein complex that regulates autophagy and protein sorting [139, 140], while the class C Vps complex functions in the recognition and fusion of vesicles with vacuolar and secretory membranes. Importantly, supplementation of glutamine or glutamate was sufficient to rescue growth of single class C Vps mutants or the tor1/pep3 double mutant. Taken together, the results suggest an important interaction between TORC1 signaling and proper vacuolar function. One hypothesis is that these proteins are required to promote autophagy and for proper liberation of amino acids from intracellular vesicles, disruption of which reduces intracellular amino acid levels and inhibits TORC1. Defects in this process may be lethal in the context of TOR1 loss of function where cells are unable to properly downregulate protein biosynthesis in response to reduced supply of amino acid building blocks. Alternatively, recent work shows that the Rag family of GTPases (RagA-D) interacts with mTORC1 in an amino acid-dependent manner, leading to co-localization of mTORC1 with Rheb in Rab7-positive late endosomal and/or lysosomal compartments [141]. Finally, a role for the sterile 20 family protein kinase MAP4K3 as another possible player in amino acid stimulation of mTORC1 was recently reported [142]. Resupplementation of amino acids to starved cells induces an increase in MAP4K3 activity that is required for activation of mTORC1. It is not clear if hVps34 and MAP4K3 are linear members of the same pathway or parallel sensors that propagate amino acid signals to mTORC1. While elucidation of amino acid inputs to mTORC1 remains an elusive goal of the field, apparent discrepancies in the available data may be explained by the existence of multiple layers of intracellular regulation of mTORC1 by amino acids including distal regulation of intracellular amino acid
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concentrations from membrane-bound vesicles and proximal interpretation of amino acid levels by TSC1/TSC2, Rheb, and protein–protein interactions within mTORC1. Ongoing investigations in numerous laboratories are currently being undertaken in an effort to present a unifying model for amino acid regulation of mTORC1.
3.3 Stress Signals An ever-growing number of cellular stresses appear to communicate to the mTORC1 signaling hub including energy stress, hypoxia, glucocorticoid signaling, DNA damage, reactive oxygen species, viral infection, and osmotic stress. In addition to the aforementioned role of amino acids, glucose deprivation also leads to repression in mTORC1 signaling; however, particular glucose breakdown products that specifically mediate this effect have not been identified. Instead, this effect may be mediated through a general reduction in cellular energy status. Treatment of cells with the non-hydrolyzable glucose analogue 2-deoxyglucose (2-DG) leads to depletion of intracellular energy status and recapitulates the effect of glucose deprivation on mTORC1 [143]. Early studies suggested a role of mTOR itself as a sensor of cellular ATP levels [143]; however, repression of mTOR activity requires a drastic reduction in ATP concentrations far below normal physiological levels. The tight homeostatic regulation of cellular ATP suggested the existence of another sensor for energy stress. An alternative mechanism for transmission of energy signals to mTORC1 involves the 5 -AMP-activated protein kinase (AMPK). AMPK is activated by numerous mechanisms including allosteric regulation via binding by AMP [144, 145] and activation loop phosphorylation by LKB1. Under conditions that promote energy stress, AMP/ATP ratios can be dramatically elevated making AMP sensing a more sensitive readout of energy stress compared to a mechanism that monitors ATP levels. AMPK was initially implicated in regulation of mTORC1 signaling by activation of this kinase by the AMP analogue AICAR, which promotes a reduction in 4E-BP1 and S6K1 phosphorylation [146–148]. Overexpression of an activated allele of AMPK represses S6K1 phosphorylation while exogenous expression of a dominant-negative AMPK construct leads to elevated S6K1 phosphorylation [148]. AMPK directly phosphorylates TSC2 in vitro and in vivo on Thr-1227 and Ser-1345 [149], presumably causing an increase in TSC2 GAP activity and subsequent repression of Rheb/mTORC1 activation. Mutation of AMPK-dependent sites on TSC2 to alanine leads to partial insensitivity of mTORC1 signaling following energy depletion, the same result observed in TSC2–/– cells [149]. Furthermore, TSC2–/– cells and cells overexpressing AMPK phospho-resistant TSC2 mutants undergo apoptosis in response to energy deprivation [149]. AMPK can also directly phosphorylate raptor in response to AICAR treatment, leading to 14-3-3 binding and contributing to repression of mTORC1 signaling in response to severe energy stress [150]. Maximal
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activation of AMPK requires phosphorylation of the activation loop residue Thr-172 by LKB1, a serine/threonine kinase that localizes to a protein complex with the adaptor proteins STRAD and MO25 [151, 152]. LKB1–/– MEFs have a deficiency in AMPK activity in response to energy deprivation [153] and exhibit elevated mTORC1 signaling [153–156]. Loss of LKB1 function is causative in Peutz– Jeghers syndrome (PJS), an autosomal dominant disorder with clinical similarities to TSC including the development of non-malignant hamartomas. LKB1+/– mice phenocopy the manifestations of PJS, and S6K1 phosphorylation is elevated in tissue samples from the spontaneous hamartomas that develop in these animals [156]. Liver-specific LKB1–/– mice display diabetic phenotypes such as hyperglycemia [157] and the diabetes drug metformin activates AMPK in an LKB1-dependent manner [157, 158]. AMPK is also involved in regulation of cell growth in response to genotoxic stress. Upon DNA damage, p53 inhibits mTORC1 activity via association with LKB1 and activation of the AMPK–TSC2 signaling pathway [159]. Surprisingly, mTORC1 was subsequently shown to play a positive role in p53 expression in TSC1/TSC2-deficient cells [160], suggesting the possibility of another negative feedback loop in the mTORC1 signaling axis. In addition, AMPK also plays a role in the newly described link between Wnt signaling and mTORC1 regulation [161]. Wnt binds to the Frizzled family of cell surface receptors and plays an important role in cell growth, animal development, and cancer [162, 163]. The canonical Wnt signaling pathway involves repression of GSK-3 followed by stabilization and translocation of β-catenin to the nucleus where it can induce a progrowth gene program [164–167]. Wnt activates mTORC1 in a GSK-3-dependent β-catenin-independent manner in mammalian cells and mouse models [168]. AMPK phosphorylation of TSC2 on Ser-1345 serves as a priming event that is a prerequisite for subsequent GSK-3β-mediated activating phosphorylation of TSC2 on Ser-1341 and Ser-1337. Wnt repression of GSK-3 results in activation of mTORC1 and rapamycin dramatically reduces tumor formation by Wnt-1-expressing cells in an immunodeficient mouse xenograft model [168]. Interestingly, there appear to be a number of interactions between AMPK and Akt signaling with regard to regulation of mTORC1. Akt appears to play a role in regulating energy levels and repressing AMPK. Akt1/Akt2 double-knockout MEFs exhibit an elevated AMP/ATP ratio and an increase in AMPK activity [169]. Conversely, cells expressing a constitutively activate Akt mutant display a decreased AMP/ATP ratio and a subsequent decline in AMPK activity [169]. The mechanism by which Akt regulates AMP/ATP levels was not formalized in this study, but Akt is known to regulate nutrient uptake in an mTORC1-dependent manner [170] and promotes translocation of the glucose transporter GLUT-4 to the plasma membrane [171–173]. Furthermore, Akt activates the ubiquitin ligase MDM2 resulting in degradation of p53 [174] and is also known to phosphorylate and repress GSK-3 [175]. These data suggest that Akt functions to repress TSC2 GAP activity both by direct inhibitory phosphorylation of TSC2 and via repression of stimulatory signals
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to TSC2 by AMPK through modulation of AMP/ATP levels, p53 protein expression, and GSK-3 activity. In addition to the repressive inputs from energy and genotoxic stress, the culturing of mammalian cells under low oxygen conditions (hypoxia) potently inhibits mTORC1 signaling. Repression of mTORC1 by hypoxia requires TSC1/TSC2 because TSC1–/– and TSC2–/– cells do not exhibit a decrease in S6K1 phosphorylation under low-oxygen conditions [176]. A Drosophila genetic screen identified the Scylla and Charybdis genes as repressors of the dPDK1/dAkt-driven increase in cell size. Epistatic analysis places these genes downstream of dAkt and upstream of dTSC [177]. Importantly, Scylla and Charybdis are upregulated in response to hypoxia [177]. Similarly, the mammalian orthologs of these proteins, Redd1 and Redd2, negatively regulate mTORC1 downstream of Akt and upstream of TSC1/TSC2 [176, 178]. Redd is transcriptionally upregulated by HIF-1α in response to hypoxia [179] and Redd1–/– MEFs cannot repress mTORC1 signaling in response to hypoxia [176]. Interestingly, Redd proteins also appear to be induced by other stress signals that repress mTORC1, including energy deprivation [180], glucocorticoid treatment [181], DNA damage [182], reactive oxygen species [183], and alcohol intoxication [184]. Redd1 appears to have a very short half-life (~5 min) and rapid turnover of the protein is responsible for activation of mTORC1 observed after inhibition of protein synthesis with cyclohexamide [185]. Redd1 expression is elevated in response to chronic energy deprivation and can repress S6K1 phosphorylation independently of AMPK [180]. Loss of Redd1 function has no effect on AMPK-mediated TSC2 phosphorylation, but it does repress S6K1 activation in response to long-term energy stress. This AMPK-independent input to TSC2 in response to energy deprivation was predicted by the absence of conserved AMPK activation sites in dTSC2 [186] and the aforementioned incomplete nature of S6K1 phosphorylation rescue in response to energy stress in TSC2–/– cells and cells overexpressing TSC2 mutants that cannot be phosphorylated by AMPK [149]. The mechanism by which Redd proteins activate TSC1/TSC2 and repress mTORC1 is not clearly defined. Redd1 lacks any known functional domains with the exception of a C-terminal coiled-coil domain [182]; therefore, it is unlikely to act as a kinase to phosphorylate TSC2 like other known upstream activators/repressors. Recently, DeYoung et al. demonstrated that Redd1 suppresses mTORC1 by releasing TSC2 from growth factor-induced association with inhibitory 14-3-3 proteins in response to hypoxia [187]. In addition to these Redd-dependent mechanisms, the Bcl-2 homology 3 domain-containing protein Bnip3 can also repress mTORC1 signaling in response to hypoxia by directly interacting with Rheb, thus reducing Rheb GTP-loading status [188]. Finally, recent data suggest a role for the pro-inflammatory TNFα signaling pathway in mTORC1 activation via post-translational modification of TSC1. Lee et al. demonstrate that IKKb directly interacts with and phosphorylates TSC1 at Ser-487 and Ser-511 resulting in repression of TSC1/TSC2 function, activation of mTORC1, increased VEGF expression, enhanced angiogenesis, and tumor development [189]. These data support a novel role for the mTORC1 function in inflammation-mediated increases in angiogenesis in human cancer.
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4 Downstream Targets of mTORC1 Regulate Cell Growth Control Under cellular conditions that are conducive to cell growth, mTORC1 modulates a number of downstream effectors that promote cell anabolism while simultaneously repressing catabolic processes. The most well-understood targets of mTORC1 signaling are 4E-BP1 and S6K1. Upon phosphorylation by mTORC1, 4E-BP1 and S6K1 collaborate to promote the intricately controlled process of mRNA translation initiation. While the discussion below will focus on the initiation phase of mRNA translation, it is important to note that mTORC1/S6K1 also appears to affect translation elongation through repressive phosphorylation of eEF2K and subsequent derepression of eEF2 [190–192]. In addition, S6K1 stimulates ribosomal biogenesis and represses the catabolic process macroautophagy. Taken together, regulation of these molecular processes by mTORC1 results in cellular level effects on growth (i.e., cell size) and proliferation. In addition to other pathophysiological outcomes, aberrant regulation of these processes can contribute to a tumorigenic phenotype. The discussion below will focus on pro-anabolic regulation of protein biosynthesis by mTORC1.
4.1 mRNA Translational Control mRNA translation is a fundamental biological process by which genetic information encoded in messenger RNA is used to manufacture cellular proteins by a massive molecular machine known as the ribosome. mRNA translation is subdivided into three distinct phases: translational initiation, translational elongation, and translational termination. Translational initiation is the process in which the ribosome is recruited to the mRNA and scans the translational start site. This carefully orchestrated operation serves as the rate-limiting process in protein biosynthesis. Translation initiation itself appears to have two rate-limiting steps: delivery of the eIF2-GTP-Met-tRNAi ternary complex to the 40S ribosomal subunit to form the 43S pre-initiation complex (PIC) and subsequent recruitment of messenger RNA via the eIF4F complex to form the 48S PIC (Fig. 2). The eIF4F complex is composed of the mRNA 5 -m7GppN cap-binding protein eIF4E, the scaffolding protein eIF4G, and the RNA helicase eIF4A. The protein expression level of eIF4E makes this initiation factor limiting for eIF4F complex formation. Additionally, bioavailability of eIF4E is antagonized by the action of a family of inhibitory-binding proteins known as the 4E-BPs. This family is comprised of three members named 4E-BP1, 4E-BP2, and 4E-BP3. The overwhelming majority of the literature is focused on 4E-BP1 and thus we will focus our discussion on this 4E-binding protein. 4E-BP1 binds to eIF4E on a region that overlaps with the binding surface of the eIF4F complex member eIF4G such that these interactions are mutually exclusive [193, 194]. Hypophosphorylated 4E-BP1 binds to eIF4E with high affinity, while ordered, hierarchical phosphorylation of 4E-BP1
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Fig. 2 Control of mRNA translation initiation by mTORC1/S6K1 through dynamic protein– protein interactions and ordered phosphorylation events. Under conditions such as serum starvation and nutrient deprivation mTORC1 signaling is repressed and mRNA translational initiation is downregulated. Hypophosphorylated 4E-BP1 interacts with the cap-binding protein eIF4E, while inactivated S6K1 binds to eIF3. Upon stimulation with growth factors or re-addition of nutrients mTORC1 is activated and binds to eIF3 displacing S6K1. From this platform mTORC1 phosphorylates 4E-BP1 and S6K1. Phosphorylation of 4E-BP1 disrupts the interaction with eIF4E, allowing binding of eIF4G, assembly of the eIF4F complex, and formation of the 48S pre-initiation complex (PIC). Liberated S6K1 is fully activated by PDK1-mediated phosphorylation and acts on local substrates such as the ribosomal protein S6 and eIF4B. S6K1 plays an additional role in the pioneer round of translation on newly synthesized mRNAs. The CBP80/20 complex serves as the cap-binding protein on newly minted mRNAs. These mRNAs are also bound at exon–exon boundaries by the exon junction complex (EJC). The S6K1-specific interacting protein SKAR is a novel EJC-interacting protein required for splicing-dependent enhancement of mRNA translation. S6K1 phosphorylates several proteins in CBP80-bound mRNPs in a SKAR-dependent manner
sequentially on Thr-37, Thr-46, Thr-70, and Ser-65 results in disruption of the interaction between 4E-BP1 and eIF4E [195]. A structural model of the 4E-BP1–eIF4E interaction shows that these 4E-BP1 phosphorylation sites are in proximity to a series of acidic amino acids in eIF4E, suggesting that negatively charged phosphate groups may disrupt the interaction of 4E-BP1 and eIF4E by electrostatic repulsion [196]. mTORC1 can phosphorylate 4E-BP1 on Thr-37 and Thr-46 in vitro [195,
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197, 198], events that are required for subsequent phosphorylation of Thr-70 and Ser-65 and disassociation of 4E-BP1 and eIF4E. Thus, mTORC1 action increases the availability of eIF4E and promotes eIF4F complex formation, driving the ratelimiting recruitment of mRNAs to the forming translation pre-initiation complex and subsequent cap-dependent translation. Importantly, one potential mechanism for rapamycin resistance in some cancer cell lines is re-phosphorylation of 4E-BP1 despite the presence of the inhibitor [199]. The other well-studied mTORC1 target, the ribosomal protein S6 kinase (S6K), also plays an important role in translation initiation via phosphorylation of numerous downstream effectors. Mammalian cells have two S6K isoforms, S6K1 [200] and S6K2 [201, 202], that have significant homology (~70% identity) including conservation of functional domains and phosphorylation sites. Based on alternative splicing S6K1 is found in a short predominantly cytoplasmic isoform (70 kDa) and a longer isoform (85 kDa) that contains a nuclear localization signal (NLS). These isoforms appear to be regulated in an identical manner by mTORC1 and the significance of the localization of these variants is not known. The discussion below will primarily focus on S6K1 consistent with availability of information in the published literature; however, it is important to note that S6K2 is the predominant isoform in multiple cell and tissue types [203] and S6K2 can partially compensate for the loss of S6K1–/– knockout mice [201]. Activation of S6K1 is a complicated process and an active area of investigation that is beyond the scope of the current chapter; however, this process seems to require phosphorylation of the C-terminal hydrophobic motif at Thr-389 by mTORC1 and the Thr-229 residue in the T-loop region by PDK1 [8]. Upon activation, S6K1 phosphorylates numerous downstream targets, many of which play a role in mRNA translation and protein biosynthesis. Two known S6K1 targets also influence proper eIF4F complex formation suggesting that S6K1 collaborates with 4E-BP1 in regulation of this critical cellular process. First, S6K1 phosphorylates the tumor suppressor PDCD4. Reminiscent of the competitive binding of eIF4E by 4E-BP1 and eIF4G, PDCD4 binds to eIF4A [204] via two tandem MA3 domains [205–207] preventing interaction of eIF4A with eIF4G, repressing eIF4A helicase activity, and inhibiting cap-dependent translation [204]. Phosphorylation of PDCD4 on Ser-67 by S6K1 promotes the recruitment of the ubiquitin ligase bTRCP and subsequent degradation of PDCD4 [208]. Overexpression of a PDCD4 mutant deficient in binding bTRCP results in translational repression of an mRNA with a highly structured 5 -UTR that would normally inhibit efficient 48S PIC scanning and results in reduced cell size and cell proliferation [208]. Interestingly, recent data show that the oncogenic fusion protein BCR-ABL activates mTORC1/S6K1 and leads to reduction in PDCD4 expression [209]. S6K1 also phosphorylates eIF4B on Ser-422 in vitro [210] and in vivo [210, 211]. eIF4B is an RNA-binding protein that promotes the RNA helicase activity of eIF4A [212]. Phosphorylation of eIF4B by S6K1 promotes its recruitment to the PIC through an interaction with the heteromeric scaffolding complex eIF3 [211], enhances binding of the PIC to mRNAs containing highly structured 5 -UTRs [213], and increases the translation of mRNAs with highly structured 5 -UTRs [214].
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Recent work also demonstrates a role for mTORC1/S6K1 in the “pioneer” round of translation, the first passage of the ribosome along the length of a newly minted mRNA [215]. Unlike steady-state translation where eIF4E serves as the 5 -capbinding protein, the nascent pre-mRNA 5 -m7GppN cap is co-transcriptionally bound by the nuclear cap-binding complex (CBC), a heterodimer of the proteins CBP80 and CBP20. CBC binding is required for efficient mRNA splicing [216]. In turn, splicing imprints the mRNA with several protein complexes, including the exon junction complex (EJC), which is deposited ~20 nt upstream of each exon– exon junction [217]. The EJC is involved in proper nuclear export of mRNA and is only removed by the first passage of the ribosome [218–221], suggesting a potential role for the EJC in regulation of the “pioneer” round of translation [222–224]. Interestingly, the presence of introns enhances subsequent protein expression in a splicing-dependent manner [225–229] and deposition of the EJC is necessary and sufficient to mediate this result at least partially by increasing the association of spliced mRNAs with actively translating polysomes [222, 223]. Rapamycin treatment inhibits the increase in translational efficiency gained by mRNA splicing [215] implicating mTORC1 in this process. The S6K1-specific interacting protein SKAR [230] was recently shown to be a novel EJC-interacting protein that is required for the splicing-dependent increase in protein synthesis [215]. S6K1 appears to phosphorylate several proteins in CBP80-bound mRNPs in a SKAR-dependent manner. Future work will be required to elucidate the identity and function of CBP80-bound S6K1 phospho-proteins in regulating the “pioneer” round of mRNA translation. In addition to the mTORC1-dependent mechanisms that drive global mRNA translation described earlier, mTORC1 signaling also appears to play a role in preferential regulation of specific RNA species. The first example of such regulation involves phosphorylation of the small ribosomal protein S6 by S6K1. S6K1 phosphorylates S6 on a cluster of C-terminal residues including Ser-235, Ser-236, Ser-240, Ser-244, and Ser-247. Early reports supported a role for S6K1-mediated phosphorylation of S6 in the translation of mRNAs containing a 5 -terminal oligopyrimidine tract (5 -TOP) immediately adjacent to the 5 -m7GppN cap structure. Interestingly, 5 -TOP-containing messages include numerous components of the translational machinery such as ribosomal proteins, elongation factors, and the poly(A)-binding protein (PABP). Rapamycin treatment represses 5 -TOP translation, an effect that is rescued by overexpression of rapamycin-resistant S6K1 [231, 232]. More recently, several publications have called the sufficiency of S6K/S6 in regulation of rapamycin-sensitive 5 -TOP translation into question. Tang et al. demonstrated that overexpression of dominant-negative S6K1 was unable to repress stimulation of 5 -TOP translation by amino acid stimulation, a result repeated in S6K1–/– S6K2–/– double-knockout MEFs [233] and ES cells [203, 234]. Additionally, knockin mice expressing S6 with all five phosphorylation sites mutated to alanine (rpS6(P–/– )) have no defect in 5 -TOP translation [113]. MEFs from these animals have a small size phenotype explained by defects in cell growth; however, they had elevated protein synthetic rates and accelerated cell division compared to rpS6(P+/+ ) cells. The search to identify the mTORC1-dependent,
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S6K1/S6-independent mechanism for 5 -TOP translational control continues, as do efforts to uncover the physiological role of the first described target of S6K1. mTORC1 also affects the translation of specific mRNAs through the aforementioned role on eIF4F complex formation and eIF4A/eIF4B activation. While the availability of a functional eIF4F complex is thought to modestly effect global rates of protein synthesis, the effect on individual messages is unequal. As discussed previously, mRNAs that contain highly structured 5 -UTRs have an increased requirement for the helicase function of eIF4A and eIF4B for efficient translation. Koromilas et al. fused the CAT reporter mRNA to artificial 5 -UTRs with increasing degrees of secondary structure to show that eIF4E overexpression results in elevated translation of structured messages [235]. This result is sensible because, as the limiting member of the eIF4F complex that is required for cap binding, overexpression of eIF4E should lead to a general increase in eIF4F function. Similar results might not be expected for eIF4F members that are expressed in excess of stoichiometric levels. The result of Koromilas et al. prompted the discovery of numerous “non-competitive” endogenous mRNAs [236] that are preferentially translated in eIF4E-overexpressing model systems including ornithine decarboxylase (ODC) [214, 237–239], cyclin D1 [240], c-myc [241], fibroblast growth factor (FGF) [242], vascular endothelial growth factor (VEGF) [243], Bcl-2 [244], Pim1 [245], and ribonucleotide reductase [246]. Interestingly, recently reported data show that eIF2B mRNA is translationally controlled in a rapamycin-dependent manner in rat skeletal muscle following recovery from resistance exercise [247]. This message encodes the catalytic subunit of the guanine nucleotide exchange factor (GEF) complex for eIF2. eIF2B controls GTP loading on eIF2, thus regulating the delivery of eIF2-GTP-Met-tRNAi to the 40S ribosomal subunit. This process represents the other rate-limiting step in mRNA translation initiation. These mTORC-dependent mRNAs all contain a long, highly structured 5 -UTR and the proteins they encode are known to have pro-growth and/or anti-apoptotic functions, consistent with known mTORC1 phenotypes. Many of these mRNAs code for proteins that are bona fide oncogenes or are overexpressed in human cancers. A recent report by Holz et al. [248] provides insights into the spatial and temporal nature of mTORC1 regulation of translation initiation in which eIF3 serves as a central scaffold. eIF3 is the largest eukaryotic initiation factor, composed of 13 unique subunits (eIF3a-m) encoded on distinct genomic loci. eIF3 binds to free 40S ribosomal subunits and is required for assembly of the eIF2-GTP-Met-tRNAi ternary complex, recruitment of the eIF2-GTP-Met-tRNAi ternary complex and the other members of the 43S PIC, eIF4F-mRNA recruitment, and scanning of the 48S PIC to the AUG start codon [249]. In addition to these functions, eIF3 also appears to be a scaffold for the mTORC1 translational control signaling axis [248]. Under conditions where mTORC1 signaling is repressed, S6K1 associates with eIF3. Stimulation with growth factors activates mTORC1 promoting S6K1 Thr-389 phosphorylation, which leads to a dynamic interchange whereby S6K1 is released from eIF3 and the mTORC1 complex binds to the eIF3 scaffold. From this vantage point, mTORC1 is in close proximity to 4E-BP1 and can phosphorylate this downstream target promoting eIF4E release and eIF4F complex formation. S6K1
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liberation allows further activation of the kinase by PDK1 and subsequent phosphorylation of, or interaction with, the local targets S6, eIF4B, PDCD4, and SKAR promoting translation initiation by the mechanisms described above.
4.2 Ribosomal Biogenesis In addition to the effects of mTORC1-mediated phosphorylation events on mRNA translation and 5 -TOP-mediated translation of many protein components of the translation apparatus, mTORC1 also plays an established role in transcriptional regulation of ribosome-related genes via all three major DNA polymerases. Indeed, coordinated regulation of Pol I, Pol II, and Pol III is mandatory to economically generate the required amount of rRNA, tRNA, and ribosomal proteins to support ribosome generation. Exponentially growing HeLa cells generate ~7,500 ribosomes/min requiring the transcription of ~200 unique genes and the synthesis of ~300,000 ribosomal proteins [250]. In yeast, the synthesis of rRNA represents ~60% of total cellular transcriptional investment, while production of ribosomal protein encoding mRNAs equals roughly the same percentage of all Pol II-mediated transcriptional events [251]. Based on the massive cellular energy investment in ribosomal biogenesis and protein biosynthesis these processes need to be tightly coupled to amino acid availability, mitogenic status, and ATP concentrations, a role for mTORC1 that is conserved from yeast to man. rRNA synthesis appears to be the rate-limiting process in ribosomal biogenesis; therefore, sustained Pol I transcription is required for ribosome production, sustained mRNA translation, and subsequent cell growth/proliferation. Deprivation of essential amino acids (especially leucine) [252] and rapamycin treatment [253–256] result in rapid repression of Pol I transcription. Pol I transcription requires a minimum of three basal factors, namely TIF-IA, TIF-IB, and UBF. Rapamycin-mediated repression of Pol I transcription can be rescued by exogenous overexpression of TIF-1A, mTOR, and wild-type S6K1, but not kinase-dead S6K1 [257]. Rapamycin treatment represses TIF-1A activity by decreasing inhibitory Ser-44 phosphorylation and promoting stimulatory Ser-199 phosphorylation [257], events partially regulated by mTORC1-dependent regulation of PP2A. The yeast homolog of TIF1A (Rrn3p) is also involved in yTORC1-mediated Pol I regulation [258]. Rapamycin treatment results in repression of the interaction between TIF-1A and TIF-1B [257] disrupting assembly of the Pol I transcription initiation complex [258] and promoting translocation of TIF-1A from the nucleolus to the cytoplasm where it is functionally sequestered. UBF is also regulated by the mTORC1/S6K1 signaling pathway. Rapamycin treatment represses protein expression levels of UBF. Growth factor-stimulated C-terminal phosphorylation of UBF, which promotes the interaction between UBF and TIF-1B, is rapamycin sensitive and can be rescued by exogenous expression of constitutively active S6K1 [256]. This work relied on the use of nuclear extracts and did not resolve whether S6K1-dependent UBF phosphorylation was direct or indirect. Subsequent work by Nader et al. [259] shows that
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CDK4/cyclin D1 promotes mTORC1-dependent increase in rRNA transcription in myotubes suggesting an ongoing role for cell cycle proteins in terminally differentiated cells. The authors suggest a model by which mTORC-dependent translation of cyclin D1 (see above) promotes an increase in CDK4 activity, phosphorylation of Rb, an increase in UBF availability/phosphorylation leading to enhanced assembly of the basal Pol I transcription initiation complex. Interestingly, in addition to these transcriptional mechanisms, TORC1 is also reported to play a role in 35S precursor rRNA processing in yeast through a mechanism involving rRNA stability [255]. Although poorly defined, TORC1 appears to play a role in the stability of many RNA species in yeast [260] and mammalian cells [261, 262]. Ribosome biogenesis also involves regulation of a large number of ribosomal proteins (RPs). Each ribosomal protein is encoded by a unique gene and RP loci are scattered across various chromosomes in the human genome. Co-transcriptional regulation of mRNAs encoding these proteins, as well as many other transcripts involved in protein biosynthesis, is achieved via shared enhancer sequences for common cis- and trans-acting factors in their respective promoters. These socalled Ribi-regulons generate mRNAs that have the common feature of 5 -TOP sequences involved in mTOR-mediated translational regulation as discussed earlier. In yeast, repression of yTORC1 by rapamycin leads to a profound and global repression in expression of ribosomal protein transcription [263, 264] by regulating at least two transcription factors, SFP1 and FHL1. Under conditions conducive for growth, yTORC1 is activated promoting SFP1 nuclear localization by an indirect mechanism involving PKA where it associates with RP gene promoters and positively regulates RP gene transcription. Conversely, when TORC1 is repressed by environmental conditions or rapamycin treatment SFP1 is shuttled to the cytoplasm and RP gene transcription is repressed [265, 266]. Similarly the forkhead DNA-binding domain-containing transcription factor FHL1 mediates transcription of RP genes in a TORC1-dependent manner. FHL1 is constitutively bound to the RP gene promoter where its function is modulated by dynamic exchange between the co-activator IFH1 and the co-repressor CRF1 [267–270]. yTORC1 promotes FHL1 function by repressing the kinase YAK1 via PKA. When yTORC1 is inactivated, activated YAK1 phosphorylates CRF1, inducing nuclear translocation and repression of FHL1. While a detailed description of transcriptional regulation by yTORC1 is beyond the scope of this chapter, a common feature highlighted by the above examples is re-distribution of transcription factors between the nuclear and cytoplasmic compartments. In addition to the positive role of yTORC1 on transcription factors, yTOR also modifies RP gene expression by regulating chromatin remodeling. These epigenetic mechanisms include yTOR-mediated modulation of Rsc9 (RSC chromatin remodeling complex component), activation of the histone acetyltransferase ESA1 (NuA4 subunit), and repression of the histone deacetylase RPD3 (SIN3 complex member), which collaborate to regulate access to RP gene promoters [271–273]. The importance of these chromatin-based mechanisms on RP gene expression is supported by the observation that RPD3 deletion mutants exhibit resistance to rapamycin-mediated repression of RP gene expression.
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Finally, while all of the mechanistic details are not clearly defined, yTORC1 also promotes Pol III transcription of the 5S rRNA and tRNA species. Repression of Pol III transcription is observed following rapamycin treatment in wild-type yeast and in a temperature-sensitive TOR mutant resulting in decreased 5S rRNA and Leu-tRNA expression [254]. The proposed model for TORC1 modulation of Pol III involves phosphorylation of Pol III itself and the TBP-containing transcription factor TFIIIB. A similar result is observed in mammalian cells where repression of Pol III transcription is concomitant with decreased association of TFIIIB and TFIIIC with 5S rRNA promoters [274] indicating that this is a conserved function of mTORC1. One of the major questions in the field of ribosomal biogenesis is the identity of the unifying mechanism by which Pol I, Pol II, and Pol III are coordinately regulated in order to generate stoichiometric amounts of the myriad of gene products that must be integrated to execute the process. If this challenge is not great enough, it must also be carried out in a parsimonious manner due to the massive investment of cellular resources that are required. Based on the role of mTORC1 as an integrator of cellular energy and amino acid availability, coupled with its downstream role involving regulation of all three DNA polymerases required for ribosomal biogenesis, mTOR is an excellent candidate for an overarching conductor of this cellular orchestra. A downstream target of interest in this coordination is the unconventional prefoldin URI. URI associates with RPB5, a core component of all three nuclear RNA polymerases, responds to nutrient signals, participates in TORC1-mediated gene expression [275], and is phosphorylated by S6K1 on Ser-371 [276]. URI will be of great interest in future efforts to understand coordination of ribosomal biogenesis by mTORC1.
5 Conclusion mTORC1 serves as a central integration hub for a diverse array of anabolic and catabolic inputs. Upstream inputs including nutrient deprivation, energy insufficiency, and hypoxia are common in tumor microenvironments so the ability to cope with these severe conditions is important for the survival and fitness of cancer cells. In physiological and pathological settings alike, mTORC1 interprets regulated or aberrant upstream cues and modulates its downstream effectors 4E-BP1 and S6K1 to regulate processes that determine cell size and proliferation. These processes are interrelated, as cells must obtain a critical mass prior to undergoing cellular division [277]. mTORC1-mediated cell growth is partially explained by increased ribosomal biogenesis and enhanced rates of mRNA translation that collaborate to upregulate protein biosynthesis. Regulation of the protein synthetic machinery has been proposed to play an important role in cancer formation and progression [278]. In addition to the role for mTORC1 in promotion of global rates of protein synthesis, 4E-BP1 repression and S6K1 activation work in concert to promote the translation of specific mRNA species with highly structured 5 -UTR elements. These proteins include several known oncogenes that can promote hallmarks of cancer [279] such
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as self-sufficiency in growth signals, evasion of apoptosis, and sustained angiogenesis. Thus far, work on specific mTORC1-sensitive messages has focused on the most dramatic examples, in terms of both altered translation and cellular phenotypes, which are particularly amenable to reductionist approaches. The advent of systems biology will allow for the study of more subtle changes in the translation of suites of mRNAs that operate in common cellular networks to affect particular cellular outcomes. In support of such integrative experiments, a recent study demonstrates that mTORC1 regulates cellular oxygen consumption through broad, but often subtle, changes in a wide variety of mitochondrial genes through promoting the interaction of the YY1-PGC-1a transcriptional complex [280]. The application of similar approaches to mTORC1-dependent translational networks is of great interest. In addition, despite the already dizzying complexity of the mTORC1 signaling network, many components of the signaling pathway likely remain undiscovered. The identification of such novel players will involve many approaches, but the application of high-throughput RNAi screening and modern proteomic techniques will surely join more established tools like yeast two-hybrid screening in this effort. Together these classical and modern experimental approaches will hopefully lead to resolution of some of the major unresolved issues in the field. The prominent clinical role of mTORC1 in human diseases like cancer is a strong catalyst that will drive basic research in this exciting field for the foreseeable future.
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The Regulation of the IGF-1/mTOR Pathway by the p53 Tumor Suppressor Gene Functions Zhaohui Feng and Arnold J. Levine
Abstract The tumor suppressor p53 plays an important role in maintaining genomic stability and tumor prevention by responding to a wide variety of stress signals and initiating a transcriptional program to produce several different cellular responses. These stress signals all interfere with the cellular homeostatic mechanisms that monitor and control the fidelity of DNA replication, chromosome segregation, and cell division. The IGF-1 and mTOR pathways regulate cell growth and division and coordinate it with nutrient availability and energy demands during both development and throughout the life span of the organism. To protect cells from errors introduced into both cell growth and division by such stress signals, p53 negatively regulates the IGF-1/mTOR pathways. In this chapter the mechanisms that coordinate the regulation between p53 and IGF-1/mTOR pathways are presented. The impact of the p53 pathway upon glycolysis and oxidative phosphorylation, ribosomal and mitochondrial biogenesis, and autophagy are explored. Keywords mTOR · p53 · Stress signals · Cell proliferation · Energy metabolism
1 The p53 Pathway The p53 tumor suppressor gene is the most frequently mutated gene in a wide variety of human tumors. Over 50% of all tumors harbor mutations in the p53 gene and over 80% of tumors have a poorly functional p53 signaling pathway. The p53 protein plays an important role in maintaining genomic stability by responding to a wide variety of intrinsic and extrinsic stress signals resulting in an increase in its concentration in a cell and initiating the transcription of selected genes [1]. The stress signals that will activate the p53 protein include DNA damage, hypoxia, the
A.J. Levine (B) School of Natural Sciences, Institute for Advanced Study, Princeton, NJ 08540, USA e-mail:
[email protected] V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_2,
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shortening of chromosome telomere lengths, mitotic spindle damage, the inhibition of ribosome biogenesis, poor nutritional supplements (low levels of glucose or amino acids), depletion of ribonucleotide triphosphates, and even the activation of selected oncogenes (e.g., myc, ras, E2F-1, and beta-catenin) or the inactivation of a tumor suppressor gene (APC helps degrade beta-catenin and Rb inactivates the functions of E2F-1) in a cell [1]. These stress signals all interfere with the cellular homeostatic mechanisms that monitor and control the fidelity of DNA replication, chromosome segregation, and cell division. A stress signal is detected and communicated to the p53 protein via a wide variety of enzymes that mediate protein modifications such as phosphorylation, acetylation, methylation, ubiquitination, summolation, and neddylation of the p53 protein and its negative regulator MDM-2. MDM-2 binds to the p53 protein blocking directly its ability to function as a transcriptional activator and in addition it degrades the p53 protein by poly-ubiquitinating it so as to lead to its degradation [2]. In response to gamma radiation, which results in single- and double-strand breaks in the DNA, the ATM protein kinase is activated [3]. The phosphorylation of MDM-2 by this kinase leads to its auto-polyubiquitination and degradation, increasing the levels of the p53 protein while the ATM kinase also phosphorylates the p53 protein [4]. Thus the p53 protein is regulated at the post-translational level by altering its half-life. Once p53 is activated it gains the ability to bind to a p53-responsive DNA sequence element in the genome and selectively transcribes a set of target genes to initiate various cellular responses. The p53 DNA consensus sequence has been defined as PuPuPuC(A/T) (T/A)GPyPyPy (N)0–14 PuPuPuC(A/T) (T/A)GPyPyPy, where Pu stands for a purine, Py stands for a pyrimidine, and N stands for any nucleotide [5]. These consensus sequences are often located 5 to the gene or in the first or second intron of the gene regulated by p53. Different stress signals result in different modifications of the p53 protein which in turn result in different transcriptional programs and outcomes for the cell. A large number of p53-responsive genes (e.g., p21, WIP-1, SIAH-1, PTEN, TSC-2, IGF-BP-3, cyclin G, p73delta N, MDM-2, COP1, and PIRH-2) initiate a variety of negative and positive feedback loops within the p53 pathway that then alters p53 protein activity [6]. In addition some of the p53-regulated gene functions interact with and inhibit or activate a wide variety of other signal transduction pathways altering the functions of the endosomal compartment, secreting proteins that modify the extracellular matrix or turning off the IGF-1/mTOR pathways [7, 8]. Depending upon the cell type, the transformed state of a cell, and the type or degree of stress placed upon a cell, the p53 protein induces either cell cycle arrest, apoptosis, or senescence. When a cell attempts to duplicate itself with damaged DNA or a defective microtubule assembly system (a stress) a high rate of mistakes or mutations ensue. To prevent the propagation of these mutant cells that could potentially become cancerous the p53 protein eliminates this cell or stops cell cycle progression so as to permit time for repair. Thus, the p53 protein plays a crucial role in maintaining genomic stability by eliminating cells with damaged and mutated genomes before they become nascent tumor cells. In this sense the p53 protein functions to enhance fidelity in cellular processes that lead to replication (see Fig. 1).
The Regulation of the p53 Pathway Signals
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Fig. 1 The p53 pathway: A wide variety of stress signals are detected by the cell and communicated to the p53 protein, which results in the degradation of the MDM2 protein and the increase of the levels and activity of the p53 protein. The p53 activation initiates the transcription of selected target genes and in turn leads to various cellular responses
The disruption of the normal p53 function commonly leads to the development or progression of tumors. It has been shown that p53 null mice all develop tumors within several months and heterozygous p53 mutant mice develop sarcomas and other tumors over a period of a year or more [9, 10]. The human Li–Fraumeni syndrome patients, who are heterozygous for the p53 gene, display a 50% cancer incidence by the age of 30 and 100% of these individuals develop one to five independent tumors over their life times [11]. A single nucleotide polymorphism in the MDM2 (SNP309) promoter resulting in the higher expression levels of MDM2 and the attenuation of the p53 pathway responses contributes to an earlier age of onset of tumors in humans and in some cases a higher incidence of tumors in a population [12, 13]. While cell cycle arrest, apoptosis, and senescence are traditionally thought of as the major outputs of the p53 pathway, some recent studies are beginning to define additional functions of the p53 pathway. The p48 and p53R2 proteins are encoded by p53-responsive genes and these proteins function in DNA repair. The sestrins are a set of p53-regulated genes that counter the presence of reactive oxygen species in the cell to prevent DNA damage [14]. The p53-regulated TSAP-6 gene was shown to enhance the rate of exosome production from cells undergoing a p53 response to stress so as to communicate with the cells of the immune system [15, 16]. The p53-regulated genes that encode PTEN, IGF-BP3, TSC2, and the beta-subunit of AMP kinase negatively regulate IGF-1/mTOR pathways and prevent cell growth
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and division after a stress signal [8, 17]. More recently, p53 has been shown to be directly involved in the energy metabolism of the cell through regulation of the SCO2 (the synthesis of cytochrome c oxidase-2) employed in oxidative phosphorylation and TIGAR, a protein that slows the rate of glycolysis by inhibiting the formation of fructose 1, 6-bisphosphate [18, 19]. Most recently, the p53 protein was shown to function in maternal reproduction through regulation of transcription of the LIF gene in the uterus of mice at the time of implantation of the embryo [20]. Clearly then the p53 response to stress is a very integrated one mobilizing the sources of energy metabolism, modulating down signals for cell growth and division, responding to cell damage and even communicating with the innate and the adaptive immune response, and ultimately initiating a program of cell death or senescence if the need arises to cope with mutant clones that predispose a cell to become cancerous or dysfunctional.
2 The Coordinate Regulation Between the p53 and IGF-1/mTOR Pathways Cell growth and proliferation each requires environmental or external signals demonstrating the dual availability of adequate glucose and amino acid levels as well as the presence of mitogens that signal for cell maintenance and/or division. In response to high levels of nutrients such as glucose, insulin is secreted which results in the production of the insulin-like growth factor 1 (IGF-1) and the uptake of glucose by cells via specific transporters. IGF-1 acts to engage its receptor (IGFR) at the cell surface. The receptor autophosphorylates itself which results in the binding of an adaptor and the lipid kinase, PI3 kinase, all localized at the plasma membrane. PI3 kinase produces a second messenger, PIP-3 (phosphoinositol 3phosphate), which in turn positively regulates two lipid-activated (PIP-3) protein kinases, mTOR–rictor (TORC-1) and PDK-1. These kinases phosphorylate and activate the AKT-1 kinase. AKT-1 moves from the plasma membrane to the nucleus where it in turn phosphorylates several transcription factors from the Forkhead family, termed FOXO transcription factors [21, 22]. The phosphorylated FOXO proteins then leave the nucleus which results in a transcriptional program that enhances oxidative phosphorylation (efficient energy production), increases the levels of protein folding chaperones (the HSP proteins), and produces proteins that lower the levels of DNA damaging reactive oxygen species. The removal of FOXO from the nucleus also turns off the production of p27, a tumor suppressor protein that inhibits cyclin D-cdk-4/6 protein kinases required for entry into the cell cycle by phosphorylating the Rb protein and liberating E2F-1 transcription factor required to enter the cell cycle. Finally the removal of FOXO from the nucleus lowers the signaling for cellular apoptosis, so that the result of this pathway (IGF-1) is a mitogen-driven entry into the cell cycle. The IGF-1 pathway coordinates with the mTOR pathway by the AKT-1 phosphorylation and inhibition of the TSC-1/TSC-2 GTPase. This
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GTPase inhibits the RHEB G protein which is required along with PIP-3 to activate the mTOR–raptor protein kinase (TORC-2) [23, 24]. The IGF-1-AKT and mTOR pathways are monitored by the p53 pathway because stress (responded to by the p53 pathway) makes the process of cell growth and cell division (AKT and mTOR pathways) subject to high error rates. Thus in responding to stress the p53 pathway shuts down the IGF-1-AKT and mTOR pathways so as to limit the error frequency during cell growth and division [17]. This increases the fidelity of these processes over the life time of an organism. p53 positively regulates the expression of four p53 target genes in the IGF-1-AKT and mTOR pathways and all four of these gene products negatively regulate the IGF-1-AKT and mTOR pathways in response to stress signals (Fig. 2) [8]. They are PTEN, TSC2, the beta-subunit of AMPK, and IGF-BP3. p53 induces the synthesis of PTEN, a PIP-3 phosphatase that degrades PIP-3 to PIP2 which no longer activates mTOR– rictor, PDK-1, AKT-1, nor mTOR–raptor. The loss of the AKT-1 activity increases the TSC-1/TSC-2 activity which in turn lowers the mTOR activity. p53 also directly regulates an increase in TSC-2 concentrations which has the same negative impact on the mTOR activity. p53 increases the concentrations of the beta-subunit of
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Fig. 2 The coordinate regulation of the p53 and IGF-1/mTOR pathways: Several different p53regulated genes act in concert to turn off the IGF-1/mTOR pathways in response to a stress signal. p53 transcribes IGF-BP3, which combines with IGF-1 to prevent it from engaging its receptor. Within some cell types p53 transcribes the PTEN phosphatase, which degrades PIP-3 and inactivates the AKT and mTOR lipid kinases. p53 also induces the transcription of the TSC2 protein, which turns off mTOR by turning off Rheb signaling. p53 also increases the rate of transcription of the beta-subunit of AMP kinase, increasing its activity and turning off mTOR. Thus, p53 regulates four gene products that act to shut down growth and division signaling from the IGF-1/mTOR pathways during stress
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AMP kinase. AMP kinase is a heterotrimeric protein where the alpha-subunit is the kinase catalytic subunit, the gamma-subunit is the AMP-binding subunit, and the beta-subunit functions as a scaffold for the binding of alpha- and gamma-subunits. Increasing the concentration of the beta-subunit increases the AMP kinase activity, increases the activity of the TSC-1/2 complex, and shuts down mTOR [25, 26]. The fourth p53-regulated protein to increase after a p53-mediated stress signal is the IGF-BP3 protein which binds to free IGF-1 and prevents it from interacting with the receptor shutting down IGF-1-AKT signaling (Fig. 2). Thus p53 shuts down this critical growth response pathway in the event of a stress that would lower the fidelity of cell division. All four of these p53-regulated genes that modulate down the IGF-1-AKT and mTOR pathways do so in a cell-type and tissue-type restricted fashion. This regulation of the IGF-1/mTOR pathways only occurs in a selected set of tissues in the body. The tissues where these genes act (in mice) under the control of the p53 transcription factor are the same tissues that require insulin for glucose uptake (fat, muscle, liver, intestine, kidney) [8]. These types of tissue-restricted regulatory patterns of expression can help to explain some of the tissue preferences in the patterns of mutations in oncogenes or tumor suppressor genes in different types of cancers. Thus four p53-regulated gene products, PTEN, IGF-1-BP3, TSC2, and the beta-subunit of AMP kinase, negatively regulate the IGF-1 and mTOR pathways creating an inter-pathway network that permits cells under stress (p53) to shut down cell growth and division (IGF-1), nutritional sensing and metabolic regulation (mTOR) for the entry into the cell cycle. Furthermore, the p53 inhibition of mTOR activates autophagy [17, 27], a conserved lysosomal-mediated catabolic pathway involved in the turnover of macromolecules and organelles, which often proceeds with apoptosis to supply nutrients to the surviving (undamaged) cells [28]. DRAM (damage-regulated autophagy modulator) has recently been identified as a p53-regulated gene through which p53 regulates autophagy. DRAM is a lysosomal protein with six membrane-spanning regions. Its exogenous expression leads to the accumulation of autophagosomes, whereas a knockdown of DRAM mRNA prevents the p53-mediated accretion of autophagosomes [27]. This balance between a response to cellular stress and a commitment to cell growth and division is modulated by regulatory loops between p53 and the IGF-1AKT–mTOR pathways. mTOR phosphorylates and activates the alpha-4 subunit of the PP2A phosphatase which then acts to remove a phosphate group from serine-15 on the p53 protein [29]. During nutrient deprivation of cells the AMPK adds a phosphate group to serine-15 on the p53 protein (the start of activating the p53 protein) and the failure to remove it by the alpha-4-PP2A phosphatase during glucose starvation (where mTOR is now turned off) has been observed experimentally. When these events transpire in normal mouse embryo fibroblast cells (MEF) the p53 protein, while phosphorylated by the AMP kinase, is not stabilized or activated and the cell eventually slows its growth rate due to glucose starvation. Thus in normal cells nutrient deprivation is communicated to p53 (via Ser-15 phosphorylation) by the AMPK sensing low glucose, but this is not in itself sufficient to activate p53 functions. When this same experiment is carried out in an adenovirus E1A-transformed MEF, p53 Ser-15 is phosphorylated and p53 is stabilized and activated and the cells
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die of a p53-mediated apoptosis [8]. In this case the E1A protein binds to the Rb protein which frees the E2F-1 transcription factor. E2F-1 transcribes the p14 ARF gene which makes the p14 ARF protein which binds to MDM-2 and inhibits its activity. Now when nutrient levels are very low the phosphorylation of p53 Ser-15 by the AMPK results in an increase in p53 levels and these cells undergo apoptosis. This is a classical p53 positive feedback loop between loss of Rb function and activation of p53 which senses and titrates the presence of activated oncogenes (E1A, myc, ras, β-catenin, etc.) in cells and informs the p53 pathway to focus upon apoptosis as an outcome of nutrient stress. In this case glucose deprivation is the stress signal, sensed in the presence of an activated oncogene, which is communicated to the p53 pathway via the AMP kinase and the LKB-1 gene products. A second feedback loop between p53 and the IGF-1 pathway also exists. PTEN concentrations rise after a p53 stress-driven activation which in turn inhibits the AKT-1 kinase. The AKT-1 kinase can phosphorylate and activate the MDM-2 protein (increasing its activity) which in turn lowers p53 levels and activity. So that low AKT-1 activity decreases the MDM-2 activity which increases p53 functions. This p53–PTEN– AKT-1–MDM-2 loop positively regulates p53 activity after stress and higher levels of p53 favor an apoptotic response. External factors acting on these loops in a positive or negative fashion will favor one response or the other. The places at which p53 chooses to shut down the mTOR and IGF-1-AKT pathways likely point to the rate-limiting steps or central nodes that have an impact upon these networks. These interconnections (Fig. 2) help to elucidate the relationships between stress, longevity, and the control over metabolic networks and pathways involved in cancer and diabetes (both late in life onset diseases).
3 The p53 Regulation of Energy Metabolism Metabolic changes have been suggested to be a hallmark of tumor cells and have been recently identified as possible contributors to malignant progression [30, 31]. Virtually all tumor cells display altered energy metabolism, primarily utilizing glycolysis rather than the much more efficient aerobic respiration for their energy needs, a switch known as the Warburg effect [32, 33]. Because glycolysis produces ATP less efficiently than aerobic respiration, tumor cells compensate by having a much higher rate of glucose uptake than normal cells. One view of this observation is that the increased dependence upon glycolysis is an adaptation to hypoxia that develops as tumor cells grow progressively further away from their blood supply [32]. Interestingly, cells derived from tumors continue to utilize glycolysis in culture under normoxic and hyperoxic conditions, which suggests that some stable genetic or epigenetic changes might account for this metabolic shift to glycolysis. Although the physiological significance of the Warburg effect has been controversial since its discovery over 80 years ago, and the underlying molecular mechanisms for Warburg effect are not well understood, based on the observation of increased glycolysis in cancer, positron emission tomography (PET) is widely used in clinic
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for tumor detection because some tumor cells take more of the glucose analog 18 flurodeoxyglucose than normal cells. Several changes in cancer cells have been shown to contribute to the Warburg effect, such as the activation of several oncogenes, including Myc and Akt, and hypoxia-inducible factor (HIF). Activation of the Akt pathway is critical to the increase in both glucose uptake and metabolism [34, 35], whereas Myc transcriptionally activates many of the glycolytic enzymes [36, 37]. Activation of HIF is also involved in mediating the switch to aerobic glycolysis through its ability to increase the expression of genes encoding glucose transporters and glycolytic enzymes [38]. HIF-1 has also been shown to suppress both the TCA cycle and aerobic respiration by inducing pyruvate dehydrogenase kinase 1 (PDK1), which phosphorylates and inactivates the TCA cycle enzyme, pyruvate dehydrogenase [39, 40]. Some recent studies revealed a new function for p53 in the regulation of energy metabolism. Matoba et al. [18] showed that p53 loss in cells or tissues resulted in a defect in oxygen consumption, which suggested a decreased function of mitochondrial respiration. Overall ATP production is not altered and lactic acid levels increase with loss of p53, which indicate that glycolysis compensates for the reduction in aerobic energy production. Interestingly, p53–/– mice, while having nearly identical body composition and overall ATP production as wild-type mice, have a marked decrease in exercise stamina as shown by a swim stress test. These results demonstrate that cells and tissues that lack functional p53 show decreased mitochondrial respiration and a shift to glycolysis for the production of energy, thereby contributing to the Warburg effect. Matoba et al. further identified SCO2 (cytochrome c oxidase 2) as a direct transcriptional target of the p53 protein, which for the first time links p53 with mitochondrial respiration. SCO2 is a key regulator of the cytochrome c oxidase complex that is essential for mitochondrial respiration and the utilization of oxygen to produce energy. The p53 protein positively regulates the expression of SCO2, which ensures the maintenance of the cytochrome c oxidase complex in normal tissues. p53 loss leads to reduced levels of SCO2 expression. Rescue of endogenous SCO2 levels by expression of exogenous SCO2 in p53-deficient cells restores aerobic respiration. These results demonstrate a direct role of p53 in ensuring efficient ATP production by aerobic respiration. More recently, TIGAR (TP53-induced glycolysis and apoptosis regulator) has been shown to be another novel p53 target gene which is directly involved in energy metabolism [19]. TIGAR shares functional sequence similarities with the bisphosphatase domain (FBPase-2) of the bifunctional enzyme PFK-2/FBPase-2 (6-phosphofructo-2-kinase/fructose-2, 6-bisphosphatase), which degrades fructose-2,6-bisphosphate (Fru-2,6-P2). Fru-2, 6-P2 stimulates 6-phospho-1-kinase to convert fructose-6-phosphate to fructose-1, 6-bisphosphate at the third step in glycolysis; when Fru-2, 6-P2 decreases, the formation of fructose-6-phosphate is favored. Indeed, TIGAR functions to lower the intracellular levels of fructose-2, 6-bisphosphate, thereby blocking glycolysis and directing glucose to an alternative pathway, the pentose phosphate pathway (PPP), to produce NADPH. One consequence of the pentose phosphate shunt and increased NADPH generation is an increase in glutathione (GSH) levels, which promote
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the scavenging of reactive oxygen species (ROS). Interestingly, the expression of TIGAR has been shown to protect cells from ROS and modestly protect cells from DNA damage-induced apoptosis [19]. In addition to SCO2 and TIGAR, p53 modulates glycolysis through the transcriptional regulation of some other key enzymes along the glycolytic pathway. For example, p53 lowers the transcriptional expression of phosphoglycerate mutase (PGM), an enzyme that is part of the glycolytic pathway. Loss of p53 is associated with increased PGM expression, which can enhance glycolysis and thus contribute to the Warburg effect [41]. It has also been suggested that p53 may regulate glycolysis through other factors such as plasma membrane glucose transporters [42]. These findings, together, demonstrate a new function of p53 in energy metabolism, which provides a partial mechanism for the Warburg effect and suggests a novel mechanism of p53 in tumor suppression. The increased dependence of cancer cells on the glycolytic pathway for ATP generation provides a biochemical basis for the design of therapeutic strategies to preferentially kill cancer cells by pharmacological inhibition of glycolysis. By switching to glycolysis, tumor cells use a much less efficient pathway of energy production, and so have a much higher requirement for glucose than their normal counterparts. Inhibition of glycolysis and glucose uptake is therefore likely to adversely impact upon cancers more than normal tissues. Indeed the inhibition of glycolysis has been shown to suppress the tumorigenicity of malignant cells in mice [43, 44].
4 Summary While the p53 gene and its protein are not essential for the life of the organism (the knockout mouse for the p53 gene is viable), it is essential for a high quality of life and the fidelity of the replication of the organism. The p53 gene has been found in both worms and flies where it is primarily responsible for the surveillance of genome fidelity in the germ line exposed to stress signals such as DNA damage. This is because the somatic tissues of the adults of these organisms are largely post-mitotic. With the development of the vertebrate body plan, where stem cells for the constant reproduction of many somatic tissues were employed, the p53 fidelity factor was similarly employed to prevent cancers from arising in the organism. The IGF-1 and mTOR pathways are utilized to control cell growth and division and coordinate it with nutrient availability and energy demands during both development and throughout the life span of the organism (indeed this pathway affects the life span). Because a wide variety of stresses introduce errors into both the growth and division of cells it is reasonable to link the IGF-1/mTOR pathways under the negative regulation of p53 as it senses and responds to stress. Indeed four important gene products in the IGF-1/mTOR pathways are regulated by the p53 transcription factor and all four negatively regulate the functioning of this pathway. The redundancy of these four genes in their regulation of the IGF-1/mTOR pathway is informative but not yet fully understood (tissue differences, rate limiting steps, etc.). Further both the
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IGF-1/mTOR and the p53 pathways encode functions that regulate glycolysis and oxidative phosphorylation, ribosomal and mitochondrial biogenesis, and destruction of defective mitochondria (autophagy). While p53 responding to stress signals negatively regulates the IGF-1/mTOR pathways, an active IGF-1/mTOR pathway negatively regulates the pro-apoptotic predisposition of the p53 pathway. The ability of AKT-1 to phosphorylate and increase the activity of MDM-2 results in lower p53 activity. An active mTOR– raptor phosphorylates and activates the PP2A phosphatase which acts to remove a phosphate from p53 Ser-15, lowering p53 activity. Thus communication between these two pathways flows in both directions and coordinates the cellular response in growth, division, and stress. Disruption of these functions and their communication can impact upon the life span of the organism, its growth and development, and the maintenance of its functioning as an adult. Such disruptions can result in cancers and type 2 diabetes late in life.
References 1. Levine AJ, Hu W, Feng Z (2006) The p53 pathway: what questions remain to be explored? Cell Death Differ 13(6):1027–1036 2. Bond GL, Hu W, Levine AJ (2005) MDM2 is a central node in the p53 pathway: 12 years and counting. Curr Cancer Drug Targets 5(1):3–8 3. Bakkenist CJ, Kastan MB (2003) DNA damage activates ATM through intermolecular autophosphorylation and dimer dissociation. Nature 421(6922):499–506 4. Khosravi R, Maya R, Gottlieb T, Oren M, Shiloh Y, Shkedy D (1999) Rapid ATM-dependent phosphorylation of MDM2 precedes p53 accumulation in response to DNA damage. Proc Natl Acad Sci USA 96(26):14973–14977 5. el-Deiry WS, Kern SE, Pietenpol JA, Kinzler KW, Vogelstein B (1992) Definition of a consensus binding site for p53. Nat Genet 1(1):45–49 6. Harris S, Gil G, Robins H et al (2005) Detection of functional single-nucleotide polymorphisms that affect apoptosis. Proc Natl Acad Sci USA 102(45):16297–16302 7. Yu X, Harris SL, Levine AJ (2006) The regulation of exosome secretion: a novel function of the p53 protein. Cancer Res 66(9):4795–4801 8. Feng Z, Hu W, de Stanchina E et al (2007) The regulation of AMPK beta1, TSC2, and PTEN expression by p53: stress, cell and tissue specificity, and the role of these gene products in modulating the IGF-1-AKT-mTOR pathways. Cancer Res 67(7):3043–3053 9. Donehower LA, Harvey M, Slagle BL et al (1992) Mice deficient for p53 are developmentally normal but susceptible to spontaneous tumours. Nature 356(6366):215–221 10. Jacks T, Remington L, Williams BO et al (1994) Tumor spectrum analysis in p53-mutant mice. Curr Biol 4(1):1–7 11. Malkin D, Li FP, Strong LC et al (1990) Germ line p53 mutations in a familial syndrome of breast cancer, sarcomas, and other neoplasms. Science 250(4985):1233–1238 12. Bond GL, Hu W, Bond EE et al (2004) A single nucleotide polymorphism in the MDM2 promoter attenuates the p53 tumor suppressor pathway and accelerates tumor formation in humans. Cell 119(5):591–602 13. Bond GL, Hirshfield KM, Kirchhoff T et al (2006) MDM2 SNP309 accelerates tumor formation in a gender-specific and hormone-dependent manner. Cancer Res 66(10):5104–5110 14. Budanov AV, Sablina AA, Feinstein E, Koonin EV, Chumakov PM (2004) Regeneration of peroxiredoxins by p53-regulated sestrins, homologs of bacterial AhpD. Science 304(5670):596–600
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15. Passer BJ, Nancy-Portebois V, Amzallag N et al (2003) The p53-inducible TSAP6 gene product regulates apoptosis and the cell cycle and interacts with Nix and the Myt1 kinase. Proc Natl Acad Sci USA 100(5):2284–2289 16. Amzallag N, Passer BJ, Allanic D et al (2004) TSAP6 facilitates the secretion of translationally controlled tumor protein/histamine-releasing factor via a nonclassical pathway. J Biol Chem 279(44):46104–46112 17. Feng Z, Zhang H, Levine AJ, Jin S (2005) The coordinate regulation of the p53 and mTOR pathways in cells. Proc Natl Acad Sci USA 102(23):8204–8209 18. Matoba S, Kang JG, Patino WD et al (2006) p53 regulates mitochondrial respiration. Science 312(5780):1650–1653 19. Bensaad K, Tsuruta A, Selak MA et al (2006) TIGAR, a p53-inducible regulator of glycolysis and apoptosis. Cell 126(1):107–120 20. Hu W, Feng Z, Teresky AK, Levine AJ (2007) p53 regulates maternal reproduction through LIF. Nature 450(7170):721–724 21. Blume-Jensen P, Hunter T (2001) Oncogenic kinase signalling. Nature 411(6835):355–365 22. Brunet A, Bonni A, Zigmond MJ et al (1999) Akt promotes cell survival by phosphorylating and inhibiting a Forkhead transcription factor. Cell 96(6):857–868 23. Zhou BP, Liao Y, Xia W, Zou Y, Spohn B, Hung MC (2001) HER-2/neu induces p53 ubiquitination via Akt-mediated MDM2 phosphorylation. Nat Cell Biol 3(11):973–982 24. Levine AJ, Feng Z, Mak TW, You H, Jin S (2006) Coordination and communication between the p53 and IGF-1-AKT-TOR signal transduction pathways. Genes Dev 20(3):267–275 25. Yoo L, Chung D, Yuan J (2002) LKB1-A master tumour suppressor of the small intestine and beyond. Nat Rev Cancer 2:529–535 26. Shaw R, Kosmatka M, Bardeesy N et al (2004) The tumor suppressor LKB1 kinase directly activates AMP-activated kinase and regulates apoptosis in response to energy stress. Proc Natl Acad Sci USA 101:3329–3335 27. Crighton D, Wilkinson S, O’Prey J et al (2006) DRAM, a p53-induced modulator of autophagy, is critical for apoptosis. Cell 126(1):121–134 28. Lum JJ, Bauer DE, Kong M et al (2005) Growth factor regulation of autophagy and cell survival in the absence of apoptosis. Cell 120(2):237–248 29. Kong M, Fox CJ, Mu J et al (2004) The PP2A-associated protein alpha4 is an essential inhibitor of apoptosis. Science 306(5696):695–698 30. Garber K (2006) Energy deregulation: licensing tumors to grow. Science 312(5777): 1158–1159 31. Shaw RJ (2006) Glucose metabolism and cancer. Curr Opin Cell Biol 18(6):598–608 32. Gatenby RA, Gillies RJ (2004) Why do cancers have high aerobic glycolysis? Nat Rev Cancer 4(11):891–899 33. Warburg O (1956) On the origin of cancer cells. Science 123(3191):309–314 34. Elstrom RL, Bauer DE, Buzzai M et al (2004) Akt stimulates aerobic glycolysis in cancer cells. Cancer Res 64(11):3892–3899 35. Plas DR, Thompson CB (2005) Akt-dependent transformation: there is more to growth than just surviving. Oncogene 24(50):7435–7442 36. Shim H, Dolde C, Lewis BC et al (1997) c-Myc transactivation of LDH-A: implications for tumor metabolism and growth. Proc Natl Acad Sci USA 94(13):6658–6663 37. Osthus RC, Shim H, Kim S et al (2000) Deregulation of glucose transporter 1 and glycolytic gene expression by c-Myc. J Biol Chem 275(29):21797–21800 38. Semenza GL (2003) Targeting HIF-1 for cancer therapy. Nat Rev Cancer 3(10):721–732 39. Papandreou I, Cairns RA, Fontana L, Lim AL, Denko NC (2006) HIF-1 mediates adaptation to hypoxia by actively downregulating mitochondrial oxygen consumption. Cell Metab 3(3):187–197 40. Kim JW, Tchernyshyov I, Semenza GL, Dang CV (2006) HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia. Cell Metab 3(3):177–185
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mTOR Signaling in Angiogenesis Henry Mead, Mirjana Zeremski, and Markus Guba
Abstract An important feature of tumor blood vessels is that they are in a state of constant new tumor blood vessel growth. In endothelial cells, the PI3K/Akt pathway has been shown to play an important role in mediating cell survival, proliferation, and migration. The serine/threonine kinase, mammalian target of rapamycin (mTOR), an important downstream target of PI3K/Akt and mTOR signaling, has been shown to be involved in the control of cell growth and proliferation. To grow beyond a certain size primary tumor and metastases are dependent on the formation of new blood vessels or angiogenesis. In angiogenesis mTOR serves as a central regulator. There is growing evidence in support of the hypothesis that mTOR acts as a critical switch for endothelial cellular catabolism and anabolism, thus determining whether these cells grow and proliferate. This is especially critical in cancer cells bearing disturbances in the TOR pathway. There are several human cancers whereby the PI3K/Akt pathway is dysregulated. Gain or loss mutations of this pathway lead to neoplastic transformation. mTOR inhibitors downregulate hypoxia-inducible factor 1α (HIF1α)-mediated production of pro-angiogenic cytokine, vascular endothelial growth factor (VEGF), by tumor cells and the resulting activation of vascular endothelial growth factor receptors (VEGF-Rs) on endothelial and lymphatic precursor cells inhibiting survival and growth-promoting signals that support tumor vascularization and tumorigenesis. The antiangiogenic and antilymphangiogenic effects of mTOR inhibition may well translate into a reduced incidence of clinically apparent malignancies through reduced tumor growth and lymphatic metastasis, respectively. Keywords Akt · Angiogenesis · Endothelial cells · Lymphangiogenesis · Mammalian target of rapamycin (mTOR) · Phosphatidylinositol 3,4,5 triphosphate (PI3P) · PTEN vascular endothelial growth factor (VEGF)
H. Mead (B) Wyeth Research, Collegeville, PA 19426, USA e-mail:
[email protected] V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_3,
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Abbreviations AMPK ANG bFGF CNI EGF eIF-4E ECM ERK1/2 FGFs Flk-1/KDR GβL GSK 3β HUVEC HIFs IKKβ IGF IL-8 KS LPA mTORC mTOR mSin1 MAPK NF1 PTEN PI3K PI3P PDK1 PGF PDGF PKC PKD1 RHEB RTK RCC p70S6K1 STK TF TGF 4E-BP TSC TAMs TGBβ
AMP-dependent protein kinase angiopoietin basic fibroblast growth factor calcinurin inhibitor epidermal growth factor eukaryotic translation factor 4E extracellular matrix extracellular signal-regulated kinase fibroblast growth factors fetal liver kinase receptor G protein β-subunit-like protein glucagen synthase kinase -3β human umbilical cord vein endothelial cell hypoxia-inducible transcription factors I kappa B kinase β insulin like growth factor interleukin-8 Kaposi’s sarcoma lysophosphatic acid mammalian target of rapamycin complex mammalian target of rapamycin mitogen-activated protein kinase-associated protein 1 mitogen-activated protein kinase neurofibromin 1 phosphatase and tensin homolog on chromosome 10 phosphatidylinositol 3 kinase phosphatidylinositol 3,4,5-triphosphates phosphoinositol-dependent protein kinase 1 placental growth factor platelet-derived growth factor protein kinase C pyruvate dehydrogenase kinase 1 RAS homolog enriched in brain receptor tyrosine kinase renal cell carcinoma ribosomal p70S6 kinase serine/theronine kinase tissue factor transforming growth factor translation initiation factor 4E-binding protein tuberous sclerosis complex tumor-associated macrophages tumor growth factor-β
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VCAM1 VEGF VSMCs VEGF-R VHL
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vascular cell adhesion molecule 1 vascular endothelial growth factor vascular smooth muscle cells VEGF receptor von Hippel–Lindau
1 mTOR Signaling in Angiogenesis 1.1 Tumor Angiogenesis During embryonic vasculogenesis, there is a de novo formation of blood vessels from endothelial cell precursors (angioblasts). These angioblasts assemble into a primary capillary plexus. This primitive network then differentiates and new blood vessels sprout and branch from existing capillaries – the process of angiogenesis [1]. The vasculature is usually quiescent in the adult and endothelial cells are among the longest lived cells outside the nervous system. During normal physiological angiogenesis, there is rapid maturation and stabilization of new blood vessels. An important and discrete step, termed the ‘angiogenic switch’ [2], in tumor propagation and progression is the induction of a tumor vasculature. In tumordriven angiogenesis this induction is dependent on physiological stimuli such as hypoxia, which results from increased cell mass [3], and oncogenic activation or tumor suppressor mutation. An important feature of tumor blood vessels is that they fail to become quiescent, thus enabling a state of constant new tumor blood vessel growth to exist. A consequence of this unbalanced signaling milieu is the development of a vasculature that has unique characteristics distinct from the normal blood supply [4]. Tumors, which stimulate the growth of new vasculature on their own, tend to generate a tangle of vessels. The vascular architecture is irregular in shape, dilated, tortuous, and dead-ended. The vessels are highly aberrant in most aspects of their structure and function. The vascular network in tumor-driven angiogenesis is often leaky and hemorrhagic, partly due to overproduction of vascular endothelial growth factor (VEGF). Perivascular cells, which are usually in close contact with the endothelium, are often less abundant or more loosely associated [5]. In tumor development the embryonic vasculogenic process is adapted. Endothelial precursor cells are mobilized from the bone marrow and transported through the blood stream to become incorporated into the walls of growing blood vessels [6]. Induction of angiogenesis depends on tipping the balance between proand antiangiogenic molecules in favor of process activators [7] and can occur at different stages of tumor development. In appreciating the implications of tipping the angiogenic balance toward vascular production, attention is focusing on activators of endothelial cell proliferation and migration. These signaling compounds are mainly tyrosine kinase ligands such as vascular endothelial growth factor (VEGF), fibroblast growth factors (FGFs), platelet-derived growth factor (PDGF), and epidermal
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growth factor (EGF), but they can also be of very different origin, such as lysophosphatic acid (LPA). LPA upregulates VEGF levels, whereas EGA upregulates VEGF, FGR, and interleukin-8 (IL-8). Fibroblast growth factor (FGF-2) and transforming growth factor (TGF-β1) also play important roles in the process [8]. Furthermore, the angiogenic response in the adult may involve inflammation. The scheme of vascular development involving VEGF, angiopoietin, PDGF, and ephrin families are represented below (Fig. 1).
Endothelial cell Differentiation Proliferation Tube formation
Vascular branching and remodeling
Pericyte recruitment
VEGF TGF2 TGF- β 1
VEGF TGF ANG 1
VEGF ANG 1 PDGF-BB
Maintenance of mature vessels
Ischemia induced angiogenesis
ANG 1
EGF FGF 2 ANG 2 PDGF-BB PLGF
Fig. 1 The scheme of vascular development
1.2 mTORC1 Signaling: Upstream Activation of Angiogenesis Tumor angiogenesis is one of many discrete steps in metastatic progression. Angiogenesis relies on a complex interplay between tumor cells, endothelial cells, and the surrounding mesenchymal cells such as pericytes in microvessels and vascular smooth muscle cells in large vessels. The culmination of this interplay is the activation of endothelial cell proliferation and recruitment of migrating endothelial cells and pericytes to form new vessels and capillaries through vascular remodeling and maturation. Tumor angiogenesis at the molecular level depends on shear stress and the coordinated interactions between endothelial growth factors (e.g., VEGF, ANG1, ANG2, bFGF, PDGF-B, ephrin-B2, and members of the TGBβ superfamily), intracellular signaling molecules (e.g., NOTCH1 and COUP-TFII), and intercellular contacts (e.g., connexins and vascular cell adhesion molecule 1 (VCAM1)). All of these factors can activate the PI3K/Akt/mTOR pathway in cancer cells, endothelial cells, or pericytes [9]. The molecular pathways whereby environmental signals regulate mTORC1 function are now partially understood. Stimulatory growth signals, either nutrient or cytokine in origin, involve the PI(3)K/Akt axis. The positioning of mTOR as a downstream target of the PI(3)K/Akt pathway provides a clear link to oncogenesis (Fig. 2). Many cancers feature a deregulation of signaling through the PI(3)K pathway [10]. An important alteration leading to hyperactivation of the PI(3)K pathway involves the loss through either mutation or epigenetic mutation of PTEN gene
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Fig. 2 PI3K/Akt/mTOR pathway
function. PTEN lipid phosphatase catalyzes removal of the D3 phosphate from phosphatidylinositol 3,4,5-triphosphates, the reverse of the reaction catalyzed by PI(3)K, to limit and ultimately terminate PI(3)K signaling in cells [11]. The downstream targets for Akt are tumor suppressors, tuberous sclerosis complex 2 (TSC2) protein, which functions in a heterodimeric complex with TSC1. TSC1 (also known as hamartin; 130 kDa) can stabilize TCS2 (also known as tuberin; 200 kDa) by binding with TSC2, which prevents TSC2 from ubiquitination and degradation. TSC2 acts as a GTPase-activating protein to regulate the GTPase function of Ras homolog enriched in brain (RHEB) by decreasing the ratio of GTP to GDP bound on RHEB. The cellular proliferation, survival, and migration required for vascular sprouting and endothelial cell differentiation leading to tube formation are primarily driven by VEGF/VEGF receptor (VEGF-R) activation that in turn activates the PI3K/Akt/mTOR pathway. One way in which the mTORC1 is activated by growth factors and inhibited by nutrient shortage seems to be through the phosphorylation of TSC2 by several upstream kinases. Signals that inhibit TSC2 and consequently activate mTORC1 include the kinases ERK, RSK, and Akt, all of which directly phosphorylate TSC2 [12]. TSC2 is directly phosphorylated by Akt on at least two sites (S939 and T1462). Conversely, AMPK phosphorylation of TSC2 activates its ability to inhibit mTORC1, even in the presence of active ERK and Akt [10]. An important stimulus of tumor angiogenesis is hypoxia. Hypoxic stress activates hypoxia-inducible transcription factors (HIFs) which ultimately induce the expression of VEGF, VEGF-R, bFGF, PDGF, and ANG2 [13]. mTOR can facilitate the translation of HIF1α mRNA, enhancing the expression of VEGF, a process that is influenced by PTEN [14]. HIF1α is transiently expressed in normal cells due to the
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action of HIF-prolyl-hydroxylase which targets HIF1α to an ubiquitin ligase complex containing von Hippel–Lindau (pVHL), which marks it for destruction by the proteasome. In cancer cells, a number of factors stabilize HIF1α and translocate HIF1α into the nucleus. For example, BCL2, a family of proteins that determine whether or not a cell commits apoptosis by regulating the exit of cytochrome c from mitochondria, and hypoxia synergize to induce HIF1α and VEGF expression in melanoma cells [15]. In renal cell carcinoma, loss of function mutations of VHL gene cause HIF1α stabilization, which leads to the induction of overexpression of VEGF and PDGF and the consequent sustained tumor angiogenesis [16]. The anticancer effects seen with rapamycin, a universal mTORC1 inhibitor, may be partially or primarily due to the antiangiogenic consequences on endothelial cells and pericytes as opposed to the cancer cells themselves. mTOR inhibition can block angiogenesis by disrupting several signaling pathways including inhibition of HIF1α translation, VEGF/VEGF-R, and/or PDGF/PDGF receptor (PDGF-R) cascade. In a proof of concept experiment, inactivation of mTOR in hypoxia-activated endothelial cells and pericytes induced a G0 –G1 cell cycle block that was associated with reduction of cyclin D1 expression and p27 accumulation rather than apoptosis [17]. Guba reported that the antiangiogenic effect of mTOR inhibition by rapamycin is related to VEGF antagonism at two distinct levels. At one level VEGF production was significantly reduced. Reduced VEGF mRNA levels in tumor cells corroborated this reduction. On a second level, rapamycin–mTOR inhibition markedly reduced VEGF human umbilical cord vein endothelial cell (HUVEC) proliferation and VEGF-induced tubular formation [18]. In cells exposed to hypoxia, levels of HIF1α increase and, via an HIF1α target gene, facilitate the expression of VEGF. mTORC1 regulates the translation and activity of HIF1α [19]. There are two clinical examples that support the hypothesis of inhibition of mTORC1, being a critical regulator of VEGF production and angiogenesis. Kaposi’s sarcoma (KS) is a tumor characterized by high degree of vascularization and increased VEGF signaling. Stallone demonstrated a very successful outcome in the treatment of KS tumors by treating patients with mTORC1 inhibition using rapamycin. Via immunofluorescence techniques they demonstrated that the KS lesions had a significant increase in both Akt and P70S6 kinase phosphorylation, suggesting both upstream and downstream activation of mTOR. Also compared to normal controls, KS lesions had increased expression of VEGF and the endothelial receptor Flk-1/KDR. After 3 months of treatment with mTOR inhibitor rapamycin, no cutaneous Kaposi sarcoma lesions could be identified [20]. A second clinical setting in support of this hypothesis is renal cell carcinoma (RCC). Many cases (50–60%) exhibit loss of the von Hippel–Lindau (VHL) tumor suppressor, which encodes a negative regulator of HIF1α [21]. Loss of VHL expression correlates with elevated HIF1α levels, an increased vascular network and sensitivity to rapamycin [22]. In a xenograft model using human RCC, rapamycin appears to function by inhibiting the translation of HIF1α, which correlates with a decrease in VEGF expression and a reduction in angiogenesis.
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1.3 The Role of mTOR Signaling in Downstream Endothelial Cell Signaling 1.3.1 VEGF/VEGF-R-Mediated Signaling in Endothelial Cells Angiogenesis and vascular remodeling occur throughout growth and development and involve both proliferation and regression of vascular endothelial cells. The development and maturation of blood vessels result from a complex interplay of pro- and antiangiogenic regulators [23]. Deregulation of the balance between these factors is thought to result in the formation of pathological blood vessels, such as those found in tumors [24]. Tumor cells respond to microenvironmental changes in pH, oxygen concentration, and vascular pressures by secreting proteins that promote neovascularization and decreasing expression of proteins that inhibit angiogenesis. Additionally, endothelial cells release pro-angiogenic molecules that induce migration of mural cells (pericytes) to blood vessels and alter the composition of the extracellular matrix (ECM). Although not the only one, hypoxia is a primary inducer and regulator of angiogenesis, through transcriptional regulation of pro-angiogenic factors such as VEGF. First recognized as an endothelial cell-specific mitogen, VEGF is a key positive regulator of normal and abnormal angiogenesis [25]. VEGF-A is a major pro-angiogenic factor that belongs to a gene family, also including VEGF-B, VEGF-C, VEGF-D, and VEGF-E and placental growth factor (PGF). Several of these factors, notably VEGF-A, exist in five different, alternatively spliced isoforms (121, 145, 165, 189, and 206), which appear to have unique biological functions determined by their heparin-binding affinities [26]. The VEGF family proteins exert their biological effects by binding in a distinct pattern to three structurally related receptor tyrosine kinases (RTK), known as VEGF-R1(Flt), VEGF-R2 (Flk-1/KDR), and VEGF-R3 [27]. VEGF-R1 (Flt-1) and VEGF-R2 (Flk-1/KDR), both of which are expressed on endothelial cells, specifically mediate activation of signaling pathways necessary for angiogenesis, whereas VEGF-R3 regulates lymphangiogenesis. A large body of data, such as those on gene inactivation, indicate that VEGF-R1 exerts a negative regulatory effect on VEGF-R2, at least during embryogenesis. Although recent data implicate VEGF-R1 in pathological angiogenesis, the molecular mechanisms are unclear. A number of in vitro studies suggest that endothelial cells respond to VEGF-A mainly through VEGF-R2-mediated signaling [27, 28]. The VEGF proteins are in general poor mitogens, but binding of VEGF-A to VEGF-R2 leads to survival, migration, and differentiation of endothelial cells and mediation of vascular permeability. Stimulation of endothelial cells with VEGF-A induces phosphorylation of a variety of proteins; however, multiple studies have confirmed that VEGEF A/VEGF-R2-mediated activation of PI3K/Akt/mTOR signaling pathway is a critical regulator of multiple steps in angiogenesis.
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1.3.2 Regulation and Function of the PI3K/Akt/mTOR Pathway in Endothelial Cells In endothelial cells, the PI3K/Akt pathway has been shown to play an important role in mediating cell survival, proliferation, and migration [29, 30]. The binding of cytokines and growth factors to their respective RTK on vascular endothelial cells increases PI3K activity. Akt is a serine/threonine protein kinase that is activated by a number of growth factors and cytokines in a PI3K-dependent manner. Akt enhances protein synthesis through increasing the phosphorylation of mTOR kinase. Activation of Akt by PI3K is the central event in the signaling cascade and results from the accumulation of phosphatidylinositol 3,4,5-triphosphates (PI3P) formed by PI3K, which leads to phosphorylation of Akt. Akt is phosphorylated by phosphoinositol-dependent protein kinase 1 (PDK1) at the threonine residue T-308 and by another putative PDK2 at the serine residue Ser473. Although, several candidates have been proposed to function as the putative PDK2, recent studies have demonstrated that the rictor–mTOR complex might function as a PDK2 [31–33]. The serine/threonine kinase, mTOR, is an important downstream target of PI3K/Akt and mTOR signaling has been shown to be involved in the control of cell growth and proliferation [34]. At least two functionally distinct, mTOR-containing complexes are expressed in mammalian cells. mTORC1 is a heterotrimeric protein kinase that consists of the mTOR catalytic subunit and two associated proteins, raptor (regulatory-associated protein of mTOR) and G protein β-subunit-like protein (GβL). mTORC1 is known to be rapamycin sensitive [33]. mTORC2 also contains mTOR and GβL but instead of raptor it contains two proteins, rictor (rapamycin-insensitive companion of mTOR) and a mitogen-activated protein kinase-associated protein 1 (mSin1). mTORC2 is not directly susceptible to inhibition by rapamycin [35]. The mTORC1 controls central events in cell growth and metabolism, such as protein synthesis, ribosome biogenesis, and transcription, via phosphorylation of ribosomal p70S6 kinase (S6K1) and phosphorylation of the translation initiation factor 4E-binding protein (4E-BP), inhibiting binding of 4E-BP1 to eukaryotic translation factor 4E (eIF-4E), leading to formation of the active eIF4F complex. The mTORC2 is involved in actin cytoskeleton organization and probably regulates Akt activity via phosphorylation of the Ser-473 residue in a rictor- and mTOR-dependent manner [36, 37]. Akt is a multifunctional protein kinase. Upon activation Akt functions as follows: (a) Critical regulator of PI3K-mediated cell survival via inhibition of apoptosis regulatory molecules including BAD, FOXO1, and FOXO3a family of forkhead transcription factors and Ikkα [38]. (b) Mediator of cell proliferation and migration in part via phosphorylation and activation of the endothelial nitric oxide synthase and glucagen synthase kinase
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3β (GSK 3β) known to be functional in cell cycle progression, as well as via inactivation of p21 and p27 [39–41]. (c) Activator of protein synthesis by increasing the phosphorylation of mTOR, 4EBP1, and p70S6K [42]. Signaling functions of VEGF-stimulated PI3K/Akt/mTOR pathway are widely studied in preclinical models and are relatively well understood due in large part to the availability of the specific inhibitors of that signaling pathway such as rapamycin and its analogs – rapalogs, known to be an exclusive mTOR kinase inhibitors. The Akt/mTOR pathway is not a simple “linear pathway.” In a transgenic mice model, where endothelial cell Akt signaling could be tightly controlled, inhibition of mTOR by rapamycin not only inhibited the downstream effects of activated endothelial Akt but also reduced phosphorylation of myrAkt1 in endothelial cells. This observation suggests that mTOR’s effects are both upstream and downstream in the Akt pathway [43]. Recent studies using rapamycin to modulate activity of Akt/mTOR pathway in HUVEC confirmed that mTOR regulates Akt activity in endothelial cells and that the inter-relationship between Akt and mTOR involves positive and negative regulatory feedback loops [44, 45]. Akt plays an important role in VEGF-mediated endothelial cell proliferation and migration [39]. mTORC1 not only mediates biological effects downstream of Akt but also facilitates a negative feedback loop and inhibits PI3K-mediated Akt activation. Although, the precise mechanism involved in this negative feedback loop has not been fully characterized, it is postulated that p70S6 kinase can in part inhibit phosphorylation and activation of PI3K/Akt pathway [46]. It was found that the short exposure of endothelial cells to rapamycin inhibited the proliferation and migration of endothelial cells as a result of inhibition of mTORC1 – downstream-mediated signaling, while increasing Akt phosphorylation. The effect of rapamycin on cell motility and endothelial cell proliferation involved inhibition of p70S6 kinase and 4E-BP1 and was independent of its effect to Akt phosphorylation [47, 48]. Akt is typically recognized as a major pro-survival kinase and its ability to protect cells from undergoing apoptosis is mediated by inhibition of the FoxO subfamily of forkhead transcription factors as well as BAD. FOXO 1 and FOXO 3 are known to be expressed in mature endothelial cells [23, 38, 42, 49]. mTORC2 was found to activate Akt by phosphorylation of Ser-473 residue in the carboxyl domain. Rapamycin potentiated serum withdrawal-mediated apoptosis and attenuated the protective effects of cytokines and growth factor-inducible responses in endothelial cells. These effects are known to be mediated via upstream inhibition of Akt phosphorylation. Although mTORC2 is not directly sensitive to rapamycin, a long-term exposure of endothelial cells to rapamycin inhibited association between mTOR and rictor and subsequent Akt phosphorylation and activation. Impaired re-assembly of mTORC2 is presumably the result of long-term depletion of mTOR molecules within endothelial cells by rapamycin [45, 50, 51].
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These and similar findings confirmed multiple, complex interactions among elements of PI3K/Akt/mTOR pathway and positioned mTOR as a key regulatory kinase that is of critical importance in endothelial cell survival, migration, and cell proliferation (Fig. 3).
Fig. 3 Akt/mTOR positive and negative feedback loops
1.3.3 PI3K/Akt/mTOR Signaling Pathway in Tumor Angiogenesis The effects of PI3K/Akt/mTOR signaling pathway and its inhibition of tumorinduced angiogenesis have been extensively studied in numerous preclinical in vitro and animal model systems. To grow beyond a certain size, primary tumor and metastases are dependent on the formation of new blood vessels or angiogenesis. In many of these processes mTOR serves as a central regulator [52]. VEGF-A/VEGEF-R2-mediated activation of endothelial cells Akt leads to the abnormal structure and function of vasculature in tumors. Sustained endothelial activation of Akt by conditional expression of constitutively activated myristoylated Akt1 (myr Akt1) in mice led to the formation of enlarged and hyperpermeable blood vessels that essentially recapitulated the complex structure and functional abnormalities of tumor vessels, thought to be secondary to VEGF signaling. Moreover, the effects of Akt activation of the vascular bed were completely reversible upon reversal of Akt activation or with rapamycin treatment, suggesting that inhibition of endothelial cells Akt/mTOR signaling could be a promising approach for blocking the effects of tumor-derived angiogenic factors such as VEGF-A. These findings clearly emphasized the importance of the
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PI3K/Akt/mTOR signaling pathway in modulating endothelial cells integrity and angiogenesis in growing tumors [43]. In endothelial cells of tumor tissues mTOR inhibition has been shown to result in a decrease of vessels proliferation. Experiments in immunocompetent mice have shown that treatment with rapamycin abrogated tubular formation, endothelial cell migration, and sprouting of newly formed vessels and associated growth of primary and metastatic tumor implants [18]. When endothelial cell signaling is inhibited the formation of new blood vessel growth is not the only process being prevented. In preclinical studies, anti-VEGF therapy or Akt/mTOR inhibition reversed the existing vascular abnormalities induced by growing tumors and radically altered the function of tumor-derived vascular network. Jain has termed this phenomenon “vascular normalization” [18, 53]. In addition to mTOR-mediated direct effect on tumor angiogenesis, recent data suggested that mTOR regulates expression of tissue factor (TF) in established tumor vessels. Under normal circumstances TF is not expressed on endothelial cells but is rapidly induced in response to inflammatory stimuli including VEGF [54]. TF production depends on the activation of VEGF/VEGF-R1/mitogenactivated protein kinase (MAPK) pathway and it is negatively regulated through PI3K/Akt/mTOR signaling. The potential deleterious effects of TF upregulation are controlled by simultaneous phosphorylation of the p70S6 kinase via relief of pseudo-substrate suppression [55]. This negative loop is further stabilized by VEGF activation of PI3K pathway via VEGF-R2, leading to mTOR-dependent p70S6K phosphorylation. In vitro experiments as well as studies done on tumor-bearing mice have shown that mTOR inhibition by rapamycin resulted in increased expression of TF in endothelial cells and vascular smooth muscle cells, causing tumor-specific thrombosis of originally functional tumor vasculature [56, 57].
Fig. 4 Mechanisms of mTOR inhibition of angiogenesis
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This mechanism could help explain why tumors which usually express VEGF in large amounts do not undergo early auto-thrombosis and auto-necrosis and why long-term treatment with rapamycin not only prevents further tumor growth but causes tumor necrosis as a result of tumor vessel-specific thrombosis [56, 58] (Fig. 4).
1.4 mTOR Kinase as a Therapeutic Target in Tumor Angiogenesis There is a considerable amount of evidence from experiments and preclinical data to propose a central role of VEGF-mediated PI3K/Akt /mTOR signaling pathway in inducing and sustaining tumor angiogenesis as a critical step in development of human cancers. Tumor angiogenesis offers a uniquely attractive therapeutic target that is shared by most and perhaps all types of human tumors. Therapeutic strategies that target and disrupt the already-formed vessel network of growing tumors are actively pursued. Increasing experimental and clinical evidence established mTOR inhibition as a promising rationale for targeting human malignancy. Rapamycin analogs such as temsirolimus (Wyeth) and everolimus (Novartis) are determined to be valid therapeutic agents in a treatment algorithm of highly vascular tumors and tumors with hyperactive Akt. Further evaluation of these agents in oncology setting is ongoing. It is possible that the anticancer effects of mTOR inhibitors involve antiangiogenic processes mediated by effects on endothelial cells and tumor microenvironment rather than on cancer cells themselves. Endothelial cells are one of the clearest examples of a cell type in which Akt phosphorylation is susceptible to mTORC2 inhibition by prolonged rapamycin treatment [50]. The dual inhibitory action of rapamycin on both mTORC1 and mTORC2 in endothelial cells may be the key to its antiangiogenic properties [51]. However, the clinical application of mTOR inhibitors in oncology will probably continue to raise provocative new questions regarding the role of mTOR signaling in tumorigenesis, as well as the mechanism of action of the inhibitors themselves. The biological impact of mTOR inhibition may depend on several factors such as (1) cell type (endothelial cell vs. tumor cell); (2) the dosing regimen; and (3) duration of treatment [59]. Paradoxical increase in Akt activation as a result of mTORC1 inhibition is not simply a preclinical phenomenon, as an increase was also noted in tumor Akt phosphorylation in patients treated with the mTOR inhibitor everolimus [60]. Although this paradoxical increase in Akt activation has not been shown to correlate with resistance to mTOR inhibitors it clearly reinforces the need for continued studies to better understand the functioning of this complex signaling pathway. Identifying and elucidating the signaling intermediates that regulate tumor vascular function will provide new avenues for therapeutic intervention and predictive markers of efficacy.
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1.5 Malignant Diseases Associated with Activated Angiogenesis Due to Disturbance of mTOR Signaling Signal transduction in cancer cells frequently involves the constitutive or conditional activation of RTKs. RTKs activate multiple cytoplasmic kinases, which are mostly serine/threonine kinases. These cellular signaling pathways appear to operate independently, in parallel and/or through interconnections to promote angiogenesis and cancer development. Three major signaling pathways have been identified as important in cancer biology: the PI3K/Akt kinase pathway [61], the protein kinase C (PKC) family [62], and the MAPK/Ras signaling cascades [63]. There is growing evidence in support of the hypothesis that mTOR acts as a critical switch for cellular catabolism and anabolism, thus determining whether cells grow and proliferate. This is especially critical in cancer cells bearing disturbances in the TOR pathway. Numerous growth factors and cytokines, such as VEGF, EGF, and IGF, are triggered via PI3K through their RTK and G protein coupled receptors [64]. PI3K activation leads to an accumulation of PI3P and subsequent activation of Akt via pyruvate dehydrogenase kinase 1 (PKD1). PTEN, a tumor suppressor gene, counteracts Akt activation through PI3K elimination (Fig. 5). This segment of the pathway is directly upstream of mTOR and is constitutively active in various cancers such as Cowden syndrome [65]. Dysregulation often originates from aberrations of the PTEN gene, where deletions or methylations are associated with the development of malignancies [66].
Fig. 5 mTOR upstream signaling pathway
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Tumors cannot grow beyond a volume of several cubic millimeters without establishing a vascular supply because cells must be within 100–200 μm of a blood vessel to survive [67]. Signaling pathways that are upstream and downstream of TOR are deregulated in human cancers indicating that this pathway has a critical role in maintaining the transformed phenotype and potentially sensitizes the tumor to mTOR inhibition. mTOR regulates essential signal transduction pathways and is involved in the coupling of growth stimuli to cell cycle progression. Quiescent cells in response to growth induction signals increase the translation of a subset of mRNAs, the protein product required for the progression through the G1 phase of the cell cycle. PI3K and Akt are the key elements of the upstream pathway that links the stimulation of growth factor receptors to the phosphorylation and activation of mTOR. There are several genetic syndromes with well-characterized somatic gene mutations affecting PTEN or the PI3K/Akt/mTOR pathway (Table 1). Table 1 Tumor-prone syndromes with disturbances in the mTOR signaling pathway
PTEN
Clinical presentation
Syndrome
Etiology
Hamartomatous tumor syndromes
Cowden disease Cowden syndrome Cowden syndrome-like phenotype Bannayan–Zonana syndrome Bannayan–Riley–Ruvalcaba syndrome Lhermitte–Duclos disease Endometrial carcinoma Prostate carcinoma Malignant melanoma Tuberous sclerosis complex
PTEN is a negative regulator of PI3K
Malignancies
TSC1
Hamartomas in multiple organs
TSC2
Abnormal proliferation of smooth muscle cells in the lung Benign and malignant peripheral nerve sheath tumors Cardiomyopathy
Lymphangioleiomyomatosis
Gastrointestinal hamartomas
Peutz–Jeghers syndrome
NF1
AMPK
LKB1
Neurofibromatosis 1
Familial hypertrophic cardiomyopathy
TSC1 is part of a heterodimer (with TSC2) that negatively regulates mTOR TSC2 is part of a heterodimer (with TSC1) that negatively regulates mTOR Loss of NF1 causes AKT activation via PI3K and Ras On ATP depletion, AMPK can activate TSC2 LKB1 (STK11) phosphorylated and activated AMPK on ATP depletion
AMPK, AMP-dependent protein kinase; STK11, serine/theronine kinase 11; mTOR, mammalian target of rapamycin; NF1, neurofibromin 1; PI3K, phosphatidylinositol 3-kinase; PTEN, phosphatase and tensin homolog on chromosome 10; TSC1, tuberous sclerosis 1.
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Table 2 PI3K signaling disturbance in human malignancies Cancer type
Type of alteration
Glioblastoma Ovarian
PTEN mutation Allelic imbalance and mutations of PTEN gene Elevated AKT1 kinase activity AKT2 amplification and overexpression PI3K p110α amplification PI3K p85α mutation Elevated AKT1 kinase activity AKT2 amplification and overexpression RSK amplification and overexpression Loss of heterozygosity at PTEN locus PI3K and AKT2 overactivation PTEN mutation PTEN silencing PTEN mutation PTEN mutation PTEN silencing Aberrant PTEN transcripts PI3K p85α mutation PTEN inactivation PTEN mutations PTEN mutations AKT overexpression and overactivation PTEN mutations
Breast
Endometrial Hepatocellular carcinoma Melanoma Digestive tract Lung Renal cell carcinoma Thyroid Lymphoid
There are several human cancers whereby the PI3K/Akt pathways are dysregulated (Table 2). Gain or loss mutations of the pathway lead to neoplastic transformation. There are several pathway steps of importance. PI3K catalytic activity is tightly regulated in normal cells. Currently it is hypothesized that preformed, inactive p85–p110 complex is present in the cytoplasm of resting cells, ready for activation in response to appropriate signals (Fig. 6). For RTKs, this signal comes
Fig. 6 Phosphatidylinositol 3-kinase (PI3K)
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from ligand-mediated activation of kinase activity and transphosphorylation of the RTK cytoplasmic tail. This precedes the recruitment of the p85–p110 complex to the receptor by interaction of the SRC homology 2 (SH2) domain of p85 with consensus phosphotyrosine residues on the RTK. There are two reasons that PI3K becomes active. First, the p110 catalytic subunit is in close proximity to its lipid substrate in the cell membrane. Second, the RTK–p85 interaction might relieve an inhibitory effect of p85 on p110 kinase activity. RTKs can also activate PI3K indirectly through Ras, which can bind and activate the p110 subunit [68]. Dysregulation of the PI3K/Akt/mTOR pathway can result from exogenous or endogenous activation (Table 3). Exogenous factors include activation by Ras, mostly restricted to gastrointestinal malignancies, whereas receptor tyrosine kinase activation has been reported in a broad variety of hematological and solid tumors. Endogenous factors include either kinase activation resulting from gene mutation/amplification or PTEN loss of function. Table 3 PI3K/Akt/mTOR exogenous or endogenous activation Protein
Disturbance/effect
Tumor type
K-Ras Receptor tyrosine kinases p110
Mutation resulting in activation Receptor activation
Pancreatic, gastric, colon Many tumor types
Gene amplification Gene mutation Gene mutation Gene mutation, deletion, or promoter methylation (loss of function) Gene amplification Protein overexpression Gene mutation Gene amplification Protein overexpression Gene amplification
Head and neck, ovarian Gastrointestinal, brain Colon, ovarian Endometrial, glioblastoma, thyroid, HCC, Cowden syndrome
p85 PTEN
AKT TSC1/2 4EBP1 and eIF3E S6K1
Breast, ovarian, colon Ovarian, breast TSC syndrome Breast Squamous cell, adenocarcinoma Breast, ovarian
PI3K/Akt/mTOR activation affects many tumor types. RTK activation is frequent in malignancies. Activation of PI3K is mediated by K-Ras mutations in certain gastrointestinal cancers, and frequently in pancreatic, gastric, and colon cancers. Loss of PTEN function via gene mutation, deletion, or promoter methylation has been reported in a subset of tumors that include endometrial cancer, glioblastoma, prostate, ovarian, thyroid carcinoma, and less frequently in hepatocellular carcinoma, breast, lung, renal cell carcinoma, and melanoma. Tumors associated with PTEN inactivation are particularly susceptible to the therapeutic effects of mTOR inhibition. There is also data suggesting that PTEN suppresses the hypoxia-mediated stabilization of HIF1 [69]. HIF1, when stabilized, upregulates the expression of VEGF, a potent stimulator of new blood vessel formation. Therefore, loss of PTEN or activation of PI3K/Akt may further endow tumors with angiogenic properties (Table 4).
Early mutation (30–50%)
Loss of expression (30–40%) No alteration in most cases
Mutation uncommon in sporadic colon cancer; methylation of PTEN promoter in MSI-H sporadic colon cancer Infrequent loss of function (PTEN mutation in sporadic endocrine pancreatic tumors) Loss of function infrequent (11%)
Endometrial carcinoma
Glioblastoma Head and neck cancer
Colon cancer
Lung cancer
Prostate cancer
Mutation rare Reduced protein expression (24–74%), presumably by promoter silencing
Loss of PTEN in 30% of sporadic tumors of which 60% have a methylated promoter Frequent in Cowden disease Low expression in advanced tumors
Breast cancer
Ovarian cancer
Mutation Promoter inactivation Rare cases of mutation
Hepato-carcinoma
Gastric cancer
Pancreatic cancer
Molecular events PTEN
Tumor type
Increased PI3K activity
Increased PI3K activity
PI3KCA mutation (18–26%), mostly if PTEN functional
Gene amplification
Increased PI3K activity Overexpression associated with lymph node metastasis: • EGFR independent • Involved in angiogenic switch • Involved in cisplatin resistance Activity not altered in most cases Low incidence of PI3KCA mutation (13.6%) Increased activity caused by Ras mutation Infrequent upregulation or mutation (4.3–10.6%) of PI3KCA Concurrent K-Ras mutation Increased PI3K activity
Increased PI3K activity
PI3K
Increased expression or activation of AKT2 (gene amplification) S6K1 gene amplification Increase expression or activation of AKT S6K1 gene amplification Increased expression or activation of AKT Increased expression or activation of AKT, activation of S6K1
Increased expression or activation
Increased expression or activation (28.9%)
Increased expression or activation
Increased expression or activation
Early activation (66% in hyperplasia) Increased expression or activation Increase activation (57–81%)
AKT
Table 4 Classification of the major disturbances in selected tumor types that might be candidates for therapeutic intervention with mTOR inhibitors mTOR Signaling in Angiogenesis 65
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PI3K transmits signals not only to Akt but also to a number of downstream effectors including the Rho family of GTPases (Rho, RAC1, and cdc42) that are key mediators of membrane ruffling, cell motility, and cell spreading [70]. As a result, loss of one gene copy of PTEN increases the motility and invasiveness of murine embryonic fibroblasts or mammalian cancer cells. Re-expressing PTEN abrogates this effect [71]. This data support the hypothesis that the substantial increase in the PTEN mutation rate in metastatic tumors may result from a selective metastatic advantage acquired through the loss of PTEN regulation of motility and invasion.
1.6 mTOR: Integrating Inflammation and Tumor Angiogenesis Tumor-associated macrophages (TAMs) are tumor-promoting factors in inflammation-mediated tumor development. The signaling molecules involved in TAMs-mediated tumor angiogenesis are not well understood. Therefore, it is urgent to elucidate the cross talk between inflammatory cells and cancers and to explore the precise pathways involved in TAMs-induced tumor angiogenesis. Recently, Lee and colleagues showed that IKKβ, which is a major downstream kinase in TNFα signaling pathway, activates the mTOR pathway and promotes tumor angiogenesis through inactivation of the TSC1–TSC2 complex. IKKβ physically interacts with and phosphorylates TSC1 at Ser487 and Ser511, resulting in suppression of TSC1. This IKKβ-mediated TSC1 suppression in turn activates the mTOR pathway, enhances angiogenesis, and results in tumor development. They further showed that expression of activated IKKβ is associated with TSC1 Ser511 phosphorylation and VEGF production in multiple tumor types and correlates with poor clinical outcome of breast cancer patients [72]. This principal idea that inhibition of the mTOR pathway may interact with inflammatory-triggered angiogenesis was also confirmed in benign diseases. Sola-Villa et al. showed earlier in rat mesangial cells that rapamycin strongly inhibits both IL-1β- and TNFα-induced VEGF formation in a concentration-dependent manner [73]. The results of both studies provide some insight in the complex interactions between mTOR and common inflammatory pathways resulting in angiogenesis in benign and malignant diseases.
1.7 mTOR and Lymphangiogenesis The physiological function of lymphatic vascular networks in the body is to collect extravasated fluid, macromolecules, and leukocytes at regional lymph nodes for immune surveillance, and then transport them to the blood vessels for circulation. Lymphatic microvessels consist of a thin endothelium, which is usually sparsely coated with pericytes and vascular smooth muscle cells (VSMCs). Although accumulating evidence shows that intratumoral lymphatic networks are vital for
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lymphatic metastasis, little is known about possible structural and functional differences between healthy lymphatic vessels and those present in tumors. Similar to the blood vasculature, lymphatic vessels in most adult tissues and organs are quiescent under physiological conditions. Based on what we have learnt from hemangiogenesis, it seems plausible that a lymphangiogenic phenotype must be switched on to initiate lymphatic vessel growth. The “lymphangiogenic switch” might represent a mirror image of the hemangiogenic switch, with overproduction of lymphangiogenic factors and downregulation of lymphangiogenic inhibitors. A range of lymphangiogenic factors that are produced by tumor cells, stromal cells, or inflammatory cells have recently been identified. These findings indicate that lymphangiogenesis is a complex process that is controlled by multiple factors produced by various cell types and that the functional outcome might be dependent on the combined effect of these factors. Among known lymphangiogenic factors, the VEGF-C/VEGF-D-VEGF-R3 pathway is the best-characterized signaling system. It has a vital role in the budding of initial lymphatics from PROX1-expressing vein endothelium. Elimination of either VEGF-C or PROX1 genes in mice results in failure to form the initial lymphatic vasculature in embryos. In addition to members of the VEGF family, other factors, FGF, PDGF, and angiopoietin families, have interdependent or collaborative roles in establishing functional lymphatics. For example, VEGF-C is required for the formation and growth of initial lymphatic vessels, whereas angiopoietins and their receptors are essential for later stages of remodeling and maturation. Without ANG-2, VEGF-C is unable to establish functional lymphatic vessels in the adult. These studies highlight that the regulation of lymphatic vessel formation and growth is complex and requires collaborative action among these different factors [74]. Interestingly some of the side effects of a maintenance immunosuppression with mTOR inhibitors could be well rationalized by a potential antilymphangiogenic activity of mTOR inhibitors. Specifically, impaired wound healing and formation of lymphoceles have been associated with rapamycin-based immunosuppression in kidney transplant patients [75]. Spontaneous formation of severe lymphedema has also been observed in a number of patients, which regressed after conversion to mTOR inhibitor-free immunosuppressant [76]. Huber et al. could show that VEGF-A- and VEGF-C-triggered lymphangiogenesis is dependent on mTOR. Inhibition of mTOR by rapamycin inhibits proliferation and migration of LYFE+ lymphendothelial cells in vitro in concentrations as low as 10 ng/ml [77]. It was further shown that VEGF-A, VEGF-C, and FGF-2 phosphorylates the p70S6 kinase in lymphendothelial cells and that rapamycin could block this phosphorylation [78]. This effect seems to be a class effect of mTOR inhibitors since it was reproducible with other mTOR inhibitors like RAD001. This antilymphangiogenic effect also translates into the in vivo situation, where rapamycin treatment of mice prevented regenerative lymphangiogenesis in a skin flap model and malignant lymphangiogenesis in a rodent lymphangioma model [77]. Moreover, mTOR inhibitors do not only interact with downstream signaling in lymphendothelial cells upon activation but also seem to prevent upregulation and production of pro-lymphangiogenic factors like VEGF-C in cancer cells [79].
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Thus, the antilymphangiogenic effects of mTOR inhibition complicating the early phase after transplantation may actually be advantageous during later stages to prevent the development, progression, and metastasis of certain tumor entities. Indeed, increasing evidence suggests that mTOR inhibitors may reduce the increased rate of de novo malignancies after organ transplantation [80, 81]. First clinical evidence for an antilymphangiogenic effect of rapamycin relates to the therapeutic experience in Kaposi sarcomas (Fig. 7). Kaposi sarcomas are characterized by a high expression of VEGF-R3 suggesting a lymphatic origin of these tumors. Sivakumar and coworkers could show that infection with Kaposi sarcomaassociated Herpes virus induces sustained levels of VEGF-C and VEGF-A in human microvascular endothelial cells [82]. Both, the lymphendothelial origin and the high VEGF-A and VEGF-C levels make Kaposi sarcomas susceptible for an mTOR inhibitor treatment. In fact, several case series could show that conversion from a calcinurin inhibitor (CNI) to an mTOR inhibitor-based immunosuppression in most cases results in regression of these Kaposi sarcomas [20, 83]. Therefore, the antilymphangiogenic as well as antiangiogenic effects of mTOR inhibition may well translate into a reduced incidence of clinically apparent malignancies through reduced tumor growth and lymphatic metastasis, respectively.
Fig. 7 Neoplastic lymphangiogenesis occurs in Kaposi sarcomas. In other malignant tumors lymphatic vessels facilitate lymphogenic metastasis of tumor cells. mTOR inhibitors block the upregulation of pro-lymphangiogenic mediators and inhibit activating downstream signaling in lymphendothelial cells
1.8 Targeting Angiogenesis by mTOR Inhibitors Rapamycin is a naturally occurring macrocyclic lactone antibiotic that was initially recognized as a potent antifungal agent and an effective immunosuppressant. Subsequent preclinical studies have further validated rapamycin as a highly specific inhibitor of the serine–threonine protein kinase mTOR and demonstrated its antiproliferative and antiangiogenic properties in several tumor types, which provided a
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rationale for the development of mTOR inhibitors as a novel class of anticancer agents. Promising preclinical data and early-phase clinical studies have led to the development of several rapamycin analogs with more favorable pharmacokinetic properties than the parent compound. Currently there are three rapamycin analogs (rapalogs) that are being developed for the treatment of several human tumor types: Temsirolimus (CCI-779, Wyeth Pharmaceuticals) is an ester derivative of rapamycin, with improved solubility, which is available in i.v. formulation. Temsirolimus is the first in class mTOR inhibitor that has been approved by several regulatory agencies for indications in advanced renal cell carcinoma, after being evaluated in a large phase III clinical trial that has shown improved overall and progression-free survival in patients with advanced renal cell carcinoma with poor prognostic factors [84, 85]. Further clinical development of this agent in various tumor types is ongoing. Everolimus (RAD-001, Novartis) is an orally available hydroxyethyl derivative of rapamycin which is being evaluated in advanced phase clinical trials in renal cell carcinoma and some other tumor entities. AP23574 (Ariad Pharmaceuticals) is the newest mTOR inhibitory agent and is also a rapamycin analog, and early-phase clinical studies with this agent are underway. The clinical data, taken together, positioned mTOR inhibitors as generally well-tolerated anticancer agents, with asthenia, stomatitis, hyperglycemia, hyperlipidemia, and thrombocytopenia being the most commonly reported adverse events. mTOR inhibitors mediate their effect by forming a complex with the intracellular immunophilin FKBP 12; the resulting complex interacts with mTOR and inhibits mTOR-mediated cell signaling. By inhibiting mTOR-mediated cell signaling, mTOR inhibitors block translation of several key proteins that regulate cell cycle progression and angiogenesis, thereby blocking cellular responses to growth factors, nutrients, and hypoxic stress [86]. The exact mechanism by which FKBP 12–rapamycin inhibits mTORC1 function is still controversial. Some studies have shown that FKBP 12–rapamycin dissociates mTOR–raptor complex, subsequently limiting the access of mTOR to its substrates [86, 87], while other studies suggested that FKBP12–rapamycin inhibits intrinsing mTOR kinase activity [88]. Within the context of preclinical data, it has been indicated that apart from the direct suppressive effects of rapamycin and its analogs on tumor cell proliferation, invasion, and metastases, these agents have a potent antiangiogenic properties related to the suppression of VEGF-mediated signal transduction. The relative contribution of these two effects in any given tumor may be difficult to delineate [89]. mTOR inhibitors downregulate HIF1α-mediated production of pro-angiogenic cytokine VEGF by tumor cells and the resulting activation of VEGF-Rs on endothelial cells and lymphatic precursor cells, inhibiting survival and growth-promoting signals that support tumor vascularization and tumorigenesis [16, 18, 90, 91].
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Consistent with these mechanisms, the significant therapeutic activity of temsirolimus in renal cell carcinoma may be at least partially explained by its ability to suppress VEGF-mediated tumor angiogenesis. The most prevalent subtype of RCC, clear RCC, is characterized by loss of function mutations in VHL protein that leads to abnormal HIF1α accumulation and subsequent overexpression of VEGF and VEGF/VEGF-R signaling [21]. Another striking clinical example of potent antiangiogenic effects of mTOR inhibitors is the regression of Kaposi sarcoma, a tumor characterized by high vascularization and increased VEGF signaling [92, 93]. Clinical development of mTOR inhibitors for the treatment of hematopoietic, mesenchymal, and epithelial neoplasms where angiogenesis is an important component of tumorigenesis is progressing. Considering that the use of mTOR inhibitors as a monotherapy seems to be insufficient to effectively control tumor progression in most tumor entities further research efforts are focusing on defining the most effective combination therapy protocols to improve overall clinical benefit of these drugs. The most promising non-cytotoxic combination therapies involve rapalog administration with VEGF-targeted anticancer agents such as tyrosine kinase inhibitors (TKI) and VEGF inhibitors, with expected additive or synergistic antiangiogenic efficacy of these drugs. mTOR inhibitors have emerged as a promising class of novel anticancer agents, however adequate surrogate and response parameters based on individual tumor determinants remain to be characterized to develop patient selection models and utilize the full potential of these drugs in cancer patients.
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mTORC1 Signaling and Hypoxia James Brugarolas
Abstract mTORC1 is an important regulator of cell growth and cell proliferation and its activity is adjusted in response to multiple cues including oxygen levels. Under conditions of hypoxia, mTORC1 is inhibited. Hypoxia-induced mTORC1 inhibition typically involves the REDD1 protein. REDD1 is transcriptionally induced in response to hypoxia, and in fibroblasts and many other cell types, REDD1 is necessary for mTORC1 inhibition by hypoxia. Moreover, REDD1 overexpression is sufficient to inhibit mTORC1. REDD1 is a member of a family that includes also REDD2, which appears to have arisen through a gene duplication event occurring independently in insects and humans. REDD1 is a 25 kDa protein with no known structural or functional domains, and a novel structural fold. Despite that hypoxia would predictably lead over time to decreased energy stores, mTORC1 inhibition by hypoxia can occur independently of the energy signaling kinases AMPK and LKB1. REDD1-induced mTORC1 inhibition requires a functional TSC1/TSC2 complex and can be blocked by Rheb overexpression, but how REDD1 signals remains to be elucidated. The TSC1/TSC2 complex is also required for mTORC1 inhibition by hypoxia in many cell types and failure to downregulate mTORC1 in TSC1/TSC2deficient fibroblasts in response to hypoxia results in increased cell proliferation. How tumors sustain mTORC1 activity despite hypoxia remains to be elucidated, but in normal cells, the downregulation of mTORC1 by hypoxia likely represents an adaptative mechanism whereby energy demanding processes, such as protein translation, are downregulated. Keywords REDD1 · DDIT4 · REDD2 · HIF · AMPK · LKB1
J. Brugarolas (B) Department of Developmental Biology and Internal Medicine, Oncology Division, University of Texas Southwestern Medical Center, Dallas, TX 75390-9133, USA e-mail:
[email protected] V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_4,
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1 Introduction In most organisms, the energy required for biosynthetic processes and overall cell function is stored in phosphoanhydride bonds in adenosine triphosphate (ATP). Through the oxidation of dietary products (e.g., sugars and fatty acids), electrons become available that will be transferred in a stepwise manner from higher to lower energy states by membrane-bound electron carriers through a process coupled to the generation of an electrochemical proton gradient. This proton gradient is subsequently utilized by ATP synthase to generate ATP. This process ultimately requires an electron acceptor and in non-photosynthetic cells this is typically oxygen. However, when oxygen becomes limiting, electron transfer stalls, and the main venue of ATP generation in the cell is compromised. While oxygen is necessary to sustain ATP production rates in many cell types, oxygen levels fluctuate across species and among cell types within a species. In primates, partial pressures of oxygen (pO2 ) in tissues range between 160 and 5 mmHg [1] (as a reference, pO2 of ambient air at sea level is approximately 160 mmHg corresponding to an atmospheric pressure of 760 mmHg and O2 content of 20.9%). When oxygen levels decrease below a ‘normal’ threshold, which varies across cell types, an adaptative response may be elicited to (1) increase oxygen delivery, (2) upregulate alternative routes of ATP generation, and (3) limit ATP consumption [2]. This response has many components, but a critical regulator at the cellular level is the hypoxia-inducible factor (HIF). HIF is a heterodimeric transcription factor composed of two subunits, a labile alpha (α) and a stable beta (β) subunit [3]. While other modes of regulation exist, a particularly important mechanism whereby HIF activity is regulated by oxygen involves the hydroxylation of either of two specific prolyl residues in HIF-α subunits [4–7], which allows recognition by an ubiquitin ligase complex containing the tumor suppressor protein von Hippel–Lindau (pVHL) [8–11]. When oxygen abounds, HIF-α is hydroxylated, polyubiquitylated, and degraded by the proteasome. In contrast, when oxygen is limiting, the prolyl residues remain unmodified and HIF-α escapes pVHL recognition leading to increased formation of HIF-α/β heterodimers, nuclear translocation of the complex, and the expression of genes containing a hypoxia-response element (HRE) [12]. In humans, there are three different genes encoding α-subunits (HIF1α, HIF-2α, and HIF-3α) and both HIF-1α and HIF-2α function as transcriptional activators of hypoxia-inducible genes (albeit with both shared and distinct targets) [13]. Among the genes induced by hypoxia there are genes encoding for proteins involved in increasing oxygen-carrying capacity, angiogenesis, glucose uptake, and glycolysis [14].
2 mTORC1 Signaling Is Regulated by Oxygen Levels The mammalian target of rapamycin complex 1 (mTORC1) plays a critical role in the regulation of cell growth, and mTORC1 is regulated by a multiplicity of signals, including signals from nutrients, growth factors, energy stores, and oxygen. The
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regulation of mTORC1 by oxygen was first reported by Tinton and Buc-Calderon [15]. Using cultures of primary rat hepatocytes, these investigators showed that a shift from supraphysiological 95% O2 (720 mmHg) to ∼ 5% (40 mmHg) resulted in decreased phosphorylation of the mTORC1 substrate the eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1). A change in 4E-BP1 phosphorylation was observed even after 15 min, and it correlated with an increase in 4E-BP1 binding to the eukaryotic translation initiation factor 4E (eIF4E). Of note, the increase in 4E-BP1 binding to eIF4E was very similar in magnitude to that observed upon treatment with the mTORC1 inhibitor rapamycin. Importantly, whereas hypoxia led to a very rapid (within 15 min) inhibition of protein synthesis (as determined by [14 C]Leu incorporation), rapamycin (at high concentrations, 500 nM) did not significantly affect the rate of [14 C]Leu incorporation over the course of the experiment (60 min). Thus, it appears that mTORC1 inhibition cannot account for the effects of hypoxia on global protein synthesis, at least in the early stages. Subsequently, studies were conducted that examined the effects of hypoxia on the activation of mTORC1 by multiple stimuli [16]. Stimulation of serum-starved human embryonic kidney 293 (HEK293) cells with insulin led, as expected, to a robust activation of mTORC1, but in cells exposed to 1.5% O2 , the addition of insulin did not increase mTORC1 activity. In fact, the level of mTORC1 activity in HEK293 cells exposed to hypoxia and stimulated with insulin was lower than that of serum-starved HEK293 cells at baseline. Similarly, stimulation of hypoxic serum-starved HEK293 cells with leucine failed to activate mTORC1. To a lesser extent, hypoxia also interfered with mTORC1 activation by phorbol 12-myristate 13-acetate (PMA). Taken together these data suggest that oxygen is necessary for mTORC1 to be in a permissive state such that it can be activated by insulin, amino acids, and phorbol esters.
3 mTORC1 Regulation by Hypoxia Requires the TSC1/TSC2 Complex Signaling by mTORC1 is regulated by the small GTPase Ras homologue enriched in the brain (Rheb). While the mechanism whereby Rheb regulates mTORC1 remains to be fully elucidated, Rheb interacts with mTORC1 [17] and GTP-bound Rheb can activate purified mTORC1 complexes in vitro [18]. Rheb is in turn regulated by a heterodimeric complex formed by the proteins tuberous sclerosis complex 1 and 2 (TSC1/TSC2), which functions as a GTPase activating protein (GAP) [19–25]. The TSC1/TSC2 complex is a critical regulator of mTORC1 involved in the relay of many signals including those from growth factors. Stimulation of receptor tyrosine kinases by their ligands leads to the recruitment and activation of Ras resulting in the activation of the downstream effectors extracellular signal-regulated kinase (ERK) and p90 ribosomal protein S6 kinase (RSK). Phosphatidylinositol 3-kinase (PI3K) is also recruited to the plasma membrane following the activation of receptor tyrosine kinases, where it phosphorylates phosphatidylinositol 4,5-biphosphate
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Fig. 1 Regulation of protein translation by hypoxia. In response to hypoxia, the hypoxia-inducible factor-1α (HIF-1α) protein becomes stabilized and it interacts with its β-subunit (HIF-1β) to form an active transcription factor that translocates to the nucleus, where it binds to hypoxia-response elements in, among others, the promoter of the gene regulated in development and DNA damage response 1 (REDD1). REDD1 overexpression is sufficient to inhibit the protein complex composed of the atypical serine/threonine kinase mammalian target of rapamycin (mTOR), regulatoryassociated protein of mTOR (raptor), and mammalian lethal with sec-thirteen protein 8 (mLST8), a complex referred to as mTOR complex 1 (mTORC1). Inhibition of mTORC1 by REDD1 requires the tuberous sclerosis complex 1 (TSC1) and 2 (TSC2) proteins, which form a protein complex (TSC1/TSC2), that functions as a GTPase activating protein toward the small GTPase Ras homologue enriched in brain (Rheb), which is required for mTORC1 activation. In response to mTORC1 activation, several eukaryotic initiation factors (eIFs) are activated. mTORC1 phosphorylates the eIF4E-binding protein 1 (4E-BP1), and this releases 4E-BP1 inhibition of eIF4E, thereby allowing eIF4E interaction with eIF4G and the assembly of a translation preinitiation complex at the 5 -end of mRNAs (at a 7-methylguanylate moiety referred to as the cap). mTORC1 also phosphorylates and contributes to the activation of S6 kinase-1 (S6K1), which in turn phosphorylates, among other substrates, the small ribosomal protein S6 and eIF4B. eIF4B phosphorylation promotes its assembly onto the translation preinitiation complex and activates eIF4A, an RNA helicase that is important to unwind secondary structure of the 5 -untranslated mRNA. At lower O2 concentrations, the levels of cellular AMP rise and AMP binds to the γ-subunit of the AMP-activated protein kinase (AMPK), a heterotrimeric serine/threonine kinase that contains also a scaffolding β-subunit and a catalytic α-subunit. Activation of AMPK by AMP requires also phosphorylation of the α-subunit by the complex formed by the serine/threonine kinase LKB1 (also called STK11), the STE20-related adaptor (STRAD), and mouse protein 25 (Mo25). AMPK activation leads to TSC2 phosphorylation and primes TSC2 for phosphorylation by the glycogen synthase kinase 3 β (GSK3β), and this results in mTORC1 inactivation. Also at low O2 concentrations, perhaps mediated through the accumulation of misfolded proteins in the endoplasmic reticulum (ER), the ER
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to generate phosphatidylinositol 3,4,5-triphosphate, which, in turn, mediates to the recruitment of Akt (also called PKB) to the plasma membrane, where it is activated [26]. ERK, RSK, and Akt have all been shown to directly phosphorylate TSC2, and TSC2 phosphorylation is important for mTORC1 activation by growth factors [27– 32]. While TSC1/TSC2-independent mechanisms of mTORC1 regulation by growth factors have been recently described [18, 33], the importance of TSC1/TSC2 is illustrated by the fact that mouse embryo fibroblasts (MEFs) deficient for Tsc2 fail to downregulate mTORC1 in growth factor-poor conditions [34–36]. Signals from energy stores are also relayed to mTORC1 through the TSC1/TSC2 complex [37]. As cellular ATP is utilized, AMP levels raise and AMP binds to the AMP-activated protein kinase (AMPK) [38], which is phosphorylated and activated by the LKB1 kinase [39–41]. AMPK directly phosphorylates the TSC2 protein [37] and primes TSC2 for phosphorylation by glycogen synthase kinase 3 β (GSK3β) [42] leading to mTORC1 inactivation. The importance of TSC1/TSC2 for energy signaling is underscored by the observation that cells deficient for Tsc1/Tsc2 fail to downregulate mTORC1 in response to energy deprivation [37, 43] and are predisposed to undergo apoptosis [37]. Recently, it was reported that AMPK can also directly phosphorylate regulatory-associated protein of mTOR (raptor) and this phosphorylation contributes to mTORC1 inhibition [44]. However, as for growth factor signaling, the TSC1/TSC2 complex plays a critical role in signal relay, and the context in which raptor phosphorylation is important for AMPK signaling remains to be established. In contrast, nutrient signaling to mTORC1 occurs independently of TSC1/TSC2. Whereas mTORC1 inhibition by signals from low growth factors and depleted energy stores requires TSC1/TSC2, the TSC1/TSC2 complex is actually dispensable for mTORC1 inhibition by low amino acids [36, 45, 46]. How amino acids regulate mTORC1 remains to be established, but Rag GTPases were recently implicated in this process [47]. Hypoxia signaling to mTORC1 requires the TSC1/TSC2 complex (see Fig. 1). Unlike wild-type MEFs, MEFs deficient for Tsc2 (or Tsc1) fail to inhibit mTORC1 in response to hypoxia (1% O2 ), but mTORC1 inhibition can be restored by reintroducing wild-type TSC2 [48]. These results have been extended to other cell types, and RNAi-mediated depletion of TSC2 has been shown to block hypoxia-induced
Fig. 1 (continued) transmembrane protein kinase RNA (PKR)-like ER kinase (PERK) is activated leading to the phosphorylation of the α-subunit of eIF2, which functions as a GTPase. eIF2α phosphorylation results in the sequestration and inactivation of its guanine nucleotide exchange factor, the pentameric eukaryotic translation initiation factor 2B (eIF2B). Failure to exchange GDP in eIF2γ for GTP inhibits the loading of the Met-tRNAi Met onto the small ribosomal subunit, and thereby inhibits translation initiation. Protein translation elongation is also inhibited by hypoxia. AMPK phosphorylates the eukaryotic elongation factor 2 (eEF2) kinase (eEF2K), which in turn phosphorylates and inactivates the eEF2, which is required for the sequential translocation of the ribosome along the mRNA. Depicted in red and blue, respectively, are sensors and effectors of the hypoxic response
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mTORC1 inhibition in both HeLa cervical carcinoma and HEK293 cells [48], as well as in spontaneously immortalized human breast epithelial cells (MCF10A) [49]. In addition, Rheb overexpression also blocks the inhibition of mTORC1 by hypoxia [50]. Importantly, failure to inhibit mTORC1 by hypoxia results in abnormal cell proliferation. In response to 1% O2 , cell proliferation is downregulated in fibroblasts, but this process is impaired in Tsc2-deficient cells [48]. While cell proliferation is inhibited by hypoxia in both Tsc2+/+ and Tsc2–/– MEFs, the level of inhibition is dampened in the absence of Tsc2. Because these experiments involved cells that were also deficient for p53, these data also indicate that the inhibition of cell proliferation triggered by hypoxia is, at least in part, p53-independent. Similar results have been observed under conditions of anoxia [51], as well as in the ERC15 cell line, a cell line derived from a renal cell carcinoma from the Eker rat, which harbors a retrotransposon insertion disrupting the Tsc2 gene [50]. In these experiments, ERC15 cells reconstituted with wild-type TSC2 were more competent to induce a cell cycle arrest in response to hypoxia than those reconstituted with a loss-of-function TSC2 mutant. Thus, it appears that oxygen availability controls cell proliferation through a mechanism that involves the TSC1/TSC2 complex.
4 The Energy Signaling Kinase AMPK Is Dispensable for mTORC1 Inhibition by Hypoxia How the TSC1/TSC2 complex is involved in hypoxia signaling is not clear. Evidence suggests that oxygen signals are transduced to TSC1/TSC2 independently of growth and energy signaling pathways. mTORC1 activation by insulin and phorbol esters can be blocked by pretreatment with hypoxia [16], and hypoxia does not affect TSC2 phosphorylation at S939 and T1462 , sites phosphorylated by Akt [52]. In addition, while hypoxia would predictably lead to energy depletion and activation of AMPK, which in turn would be expected to phosphorylate TSC2 and inactivate mTORC1, mTORC1 inhibition by hypoxia, at least in the early stages, occurs independently of AMPK [48]. AMPK is not activated in MEFs by 1% O2 , and inactivation of AMPK with the AMPK inhibitor compound C, while blocking mTORC1 inhibition by the AMPK activator (5-aminoimidazole-4-carboxamide 1-β-D-ribofuranoside [AICAR]), which is converted in cells to an AMP analogue, does not block mTORC1 inhibition by hypoxia [48]. Similarly, mTORC1 inhibition by hypoxia occurs independently of the AMPK kinase LKB1, which is required for mTORC1 inhibition by energy depletion states [43, 53]. Whereas Lkb1-deficient MEFs are unable to inhibit mTORC1 in response to AICAR treatment, Lkb1-deficient fibroblasts are competent for mTORC1 inhibition by hypoxia [48]. Furthermore, HeLa cells which are deficient for LKB1 [54] and are therefore refractory to mTORC1 inhibition by energy depletion signals inhibit mTORC1 in response to hypoxia [48]. While AMPK and LKB1 may play a role in mTORC1
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inhibition under conditions of extreme or prolonged hypoxia (or when cells have been previously serum starved) [50], these data indicate that mTORC1 inhibition by hypoxia can occur independently of both AMPK and LKB1.
5 The REDD1 Protein Is an Important Mediator of mTORC1 Inhibition by Hypoxia 5.1 Identification of the REDD1 Orthologues Scylla and Charybdis Insight into the mechanism of hypoxia-induced mTORC1 inhibition came from studies in Drosophila. Reiling and Hafen conducted a sensitized screen to identify genes that when overexpressed inhibited an increase in cell size resulting from the simultaneous expression in the eye of two kinases involved in insulin signaling, 3-phosphoinositide-dependent protein kinase-1 (PDK1), and Akt [55]. Two enhancer/promoter (EP) insertions suppressing a ‘big eye’ phenotype were found that mapped to the scylla locus, and a genome search for genes with homology led to the identification of a highly similar gene, charybdis (38% identity; 49% similarity). When individually overexpressed, both scylla and charybdis reduced organ size (or body size/weight, if ubiquitously overexpressed), an effect that was associated with a moderate reduction in cell size but not an appreciable change in cell number. Conversely, flies simultaneously deficient for scylla and expressing a hypomorph charybdis allele exhibited an ∼15% increase in body weight. In addition, overexpression of scylla (or charybdis) decreased the mortality associated with ubiquitous Akt overexpression. Epistasis analyses suggested that Scylla and Charybdis while functioning downstream of Akt were upstream of Tsc1/Tsc2. A clue that Scylla and Charybdis may be involved in hypoxia signaling was obtained from previous studies in mammalian cells which had shown that the mammalian orthologue, the gene regulated in development and DNA damage response 1 (REDD1; also called DNA-damage-inducible transcript 4 [DDIT4], dexamethasoneinduced gene 2 [Dig2], and RTP801), was regulated by hypoxia. The REDD1 gene had been shown to be upregulated by hypoxia and to contain a HRE in its promoter [56]. Similarly, both scylla and charybdis loci were found to have sequences conforming to consensus HREs, and the expression of both genes was induced when larvae were exposed to hypoxia, albeit in a tissue-restricted manner [55]. In addition, the expression of a constitutively active form of the Drosophila hypoxia-inducible factor α (sima), along with its β-subunit (tgo), in the fat body, induced the expression of scylla. In contrast, charybdis was not induced under these conditions. However, in the fat body, charybdis is not upregulated even in response to hypoxia. Thus, scylla and charybdis are hypoxia-inducible negative regulators of insulin signaling in Drosophila.
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5.2 REDD1 Is Induced by Hypoxia and Is Both Necessary and Sufficient for mTORC1 Inhibition In humans, likewise there are two genes with similarity to scylla and charybdis, REDD1 and REDD2 (also called DNA-damage-inducible transcript 4-like [DDIT4L] or RTP801-like [RTP801L]. REDD1 and REDD2 appear to have originated from a gene duplication event that occurred independently in humans and insects [57]. REDD1 was originally identified in a screen for mRNAs induced by hypoxia using a subtractive hybridization approach in the rat C6 glioma cell line. In these studies, the REDD1 transcript was found to be induced approximately fivefold after 4 h of 0.5% O2 [56]. Within 500 bp upstream of the transcription start site a sequence conforming to an extended HRE was found, which was also conserved between humans and mice. This sequence was specifically bound by a complex containing Hif-1α in electrophoretic mobility shift assays (EMSA) when extracts were used of hypoxic (but not normoxic) cells [56]. Importantly, embryonic stem (ES) cells deficient for Hif-1α were markedly impaired in their ability to upregulate Redd1 in response to hypoxia, and nuclear extracts of these cells lacked the Hif-1α complex observed in wild-type ES cells [56]. Besides hypoxia, REDD1 expression can also be upregulated by the hypoxia mimetic CoCl2 , and this effect can be significantly blunted by HIF-1α knockdown [58, 59]. The upregulation of REDD1 in response to hypoxia (or hypoxia mimetics) may also involve the SP1 transcription factor [59, 60], which has been previously implicated in the regulation of other hypoxia-responsive genes [61]. In addition, PI3K has also been implicated in the regulation of REDD1 by CoCl2 ; in the PC3 prostate cancer cell line the induction of REDD1 by CoCl2 was significantly inhibited by pharmacological inhibitors of PI3K and RNAi-mediated downregulation of the PI3K catalytic subunit p110β [58]. Dominant negative Akt also impairs REDD1 induction by CoCl2 in HeLa cells [59]. Besides tissue culture systems, Redd1 has also been shown to be upregulated in animal models of ischemia. Cerebrovascular artery ligation results in a marked territorial induction of Redd1 mRNA levels [56]. While in a model involving arterial ligation it is difficult to ascertain the relative contribution of low oxygen per se to Redd1 induction, Redd1 expression was also upregulated in the retinas of neonate rodents exposed to relative hypoxia (shift from 75% O2 to room air [20.9%]) [62]. Thus, the REDD1 gene is transcriptionally induced in response to hypoxia (and hypoxia mimetics) and at least in some contexts, this requires HIF-1α. Human REDD1 encodes a 232 amino acid protein with a predicted molecular weight of 25 kDa that is devoid of any recognizable structural or functional domains. REDD1 is a very unstable protein with a half-life estimated at ∼5 min [63]. Recent data suggest that REDD1 half-life is regulated by the E3 ubiquitin ligase complex CUL4A–DDB1–ROC1–β-TRCP [64]. The authors propose that REDD1 degradation is regulated by GSK3β-dependent phosphorylation of T23/25 , but results were limited by the lack of phospho-REDD1-specific antibodies and the results remain to be confirmed. Where REDD1 localizes is controversial; REDD1 was reported
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to be diffusely cytoplasmic in one study [65], and both nuclear and cytoplasmic in another [66], but both studies were based on overexpression analyses. In crude biochemical fractionation studies a portion of endogenous REDD1 protein was found to be associated with membranes [52]. Importantly, REDD1 overexpression is sufficient to inhibit mTORC1. mTORC1 inhibition was observed following ectopic expression of REDD1 in multiple cell types, including U2OS, HeLa, and HEK293 cells [48]. Similarly, REDD2 overexpression leads to mTORC1 inhibition [48]. Structure–function analyses have revealed that whereas the first 84 amino acids, which are poorly conserved, are dispensable for mTORC1 inhibition, deletions at the C-terminus are poorly tolerated [57]. REDD1 forms a sandwich with two antiparallel α-helices packed against a mixed β-sheet containing an unusual psi-loop motif (two antiparallel β-strands separated by an intervening strand making hydrogen contacts with both flanking strands) and exhibits a unique fold [57]. Conservation mapping onto the surface of the protein reveals a conserved area which is required for function and is probably involved in effector protein interactions. mTORC1 inhibition by REDD1 requires the TSC1/TSC2 complex, but occurs independently of AMPK (see Fig. 1). Depletion of TSC2 with RNAi blocks REDD1-induced mTORC1 inhibition [48]. Similarly, REDD1 overexpression fails to inhibit mTORC1 in Tsc2-deficient MEFs [67]. Of note, even a modest downregulation of the TSC2 protein is sufficient to block mTORC1 inhibition by REDD1 [48]. As expected, REDD2 similarly requires the TSC1/TSC2 complex for function [68], and mTORC1 inhibition by REDD1 (or REDD2) can also be blocked by overexpression of Rheb [67, 68]. Thus, a functional TSC1/TSC2–Rheb axis is needed for mTORC1 inhibition by the REDD proteins. However, REDD1 (or REDD2)induced mTORC1 inhibition does not require AMPK [67, 68], which is consistent with the idea that hypoxia-induced mTORC1 inhibition can occur independently of AMPK [48]. The importance of REDD1 in hypoxia signaling is illustrated by the analysis of Redd1-deficient MEFs. Unlike normal MEFs, Redd1–/– MEFs are substantially impaired in their ability to inhibit mTORC1 by 1% O2 [48]. In addition, REDD1 knockdown has also been shown to block hypoxia-induced mTORC1 inhibition in the histiocytic lymphoma cell line U-937 [69]. Besides hypoxia, REDD1 has also been implicated in mTORC1 regulation by energy signals downstream of AMPK [67]. However, the link between AMPK and REDD1 is unclear. In contrast, growth factor signaling occurs independently of REDD1. Whereas Tsc2–/– MEFs are impaired in transducing growth factor signals, the response of Redd1–/– MEFs to growth factor deprivation is undistinguishable from that of wild-type MEFs [48]. How REDD1 inhibits mTORC1 is not clear. It was recently proposed that REDD1 acts by sequestering 14-3-3 proteins away from TSC2 [52]. However, how 14-3-3 proteins, which are very abundant and are involved in interactions with over 100 different proteins [70], would be sequestered from TSC2 by REDD1 is uncertain. In addition, structure-based docking studies have shown that REDD1 does not conform to any 14-3-3 binding mode known and REDD1 does not seem to bind to 14-3-3 proteins either in vitro or in vivo [57].
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5.3 Hypoxia-Independent Regulation of REDD1 Ectopic expression of REDD1 is sufficient to inhibit mTORC1 in multiple cell types [48, 67, 68], and REDD1 induction by hypoxia leads to mTORC1 inhibition [48]. A variety of other stimuli have been shown to upregulate REDD1 (see Fig. 2), and under these conditions REDD1 may similarly be involved in mTORC1 regulation. In addition, it cannot be excluded that REDD1 may be involved in other processes besides the regulation of mTORC1. REDD1 expression is induced by the glucocorticoid analogue dexamethasone. Redd1 was identified in a microarray experiment involving three different cell types (two murine lymphoma cell lines and primary thymocytes) in search for mRNAs that were upregulated following exposure to dexamethasone [71]. Dexamethasone treatment of primary thymocytes induced Redd1 expression within 2 h, and Redd1 upregulation could be blocked by both the RNA polymerase II inhibitor actinomycin D and the glucocorticoid receptor antagonist RU486. Dexamethasone was also shown to upregulate Redd1 in thymocytes in mice in a manner that could be largely blocked by RU486 [72]. While the molecular mechanism and functional significance of REDD1 upregulation by dexamethasone remains unclear, it has been suggested that REDD1 exerts an antiapoptotic effect, and ectopic expression of Redd1 in the lymphoma cell line WEHI7.2 reduced dexamethasone-induced cell death [71]. In rodents, dexamethasone induced Redd1 expression also in skeletal
Dexamethasone
Ionizing radiation
GR Hypoxia
p53
HIF-1α HIF-1β
Elk-1
REDD1
Arsenite MMS
SP1 C/EBP Cell confluency
p63 ER stress Thapsigargin Tunicamycin β-mercaptoethanol
Differentiation
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Ethanol Heat shock
Fig. 2 Proposed mechanisms leading to transcriptional upregulation of REDD1. HIF-1α (hypoxiainducible factor-1α), HIF-1β (hypoxia-inducible factor-1β), GR (glucocorticoid receptor), and C/EBP (CCAAT/enhancer-binding protein)
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muscle [73], and Redd1 upregulation in muscle was similarly blocked by pretreatment with RU486 [74]. Treatment with dexamethasone of L6 myoblasts was associated with mTORC1 inhibition and it has been proposed that this effect is mediated by Redd1 [73]. Thus, Redd1 expression can be induced by treatment with dexamethasone in both thymocytes and skeletal muscle; however, whether REDD1 is induced in response to physiological levels of endogenous glucocorticoids and whether REDD1 is involved in glucocorticoid receptor signaling remains to be established. Redd1 expression has also been shown to be regulated by ethanol. Acute exposure of rats to high concentrations of ethanol led to Redd1 induction in skeletal muscle (specifically fast twitching) [74]. However, experiments involving the administration of ethanol to isolated limbs as well as to C2C12 myocytes in culture suggested that the effects of ethanol on Redd1 expression were indirect. However, acute administration of ethanol was associated with mTORC1 inhibition [74], and this process may be mediated by Redd1. REDD1 is also upregulated in response to treatment with agents that cause DNA damage including ionizing radiation, methyl methane sulfonate (MMS, an alkylating agent), and the topoisomerase II inhibitor etoposide. Ionizing radiation induces REDD1 expression in a manner that requires p53, and p53 overexpression is sufficient to upregulate REDD1 [65]. A p53 consensus binding site was found within 1 kb of the transcriptional start site of human REDD1 and a sequence containing this site was shown to be sufficient to confer p53-inducibility in reporter assays [65]. REDD1 expression is also upregulated by MMS [65], but this occurs, at least in part, in a p53-independent manner [65]. The effects of MMS might be mediated by the transcription factors Elk-1 and CCAAT/enhancer-binding protein (C/EBP). The REDD1 promoter contains consensus binding sites for both Elk-1 and C/EBP and in reporter assays, disruption of these sites dampened the effects of MMS [75]. In addition, extracts from MMS-treated cells formed complexes with an oligonucleotide containing the C/EBP-binding site, and complexes could be supershifted with antibodies against C/EBPβ [75]. C/EBPβ and Elk-1 have also been proposed to mediate the induction of REDD1 observed following treatment with arsenite [66]. In addition, ectopic expression of C/EBPε has been shown to induce REDD1 [69]. Finally, REDD1 is also upregulated by the topoisomerase II inhibitor, etoposide [71]. Thus, REDD1 expression is upregulated in response to DNA damage in both a p53-dependent and a p53-independent manner. The expression of the REDD1 gene is also increased in response to a variety of other stress conditions. In three different cell lines (two murine lymphoma cell lines and a breast cancer cell line), the drugs thapsigargin and tunicamycin, which cause ER stress, were shown to induce REDD1 [71]. Importantly, Redd1 induction in response to thapsigargin and tunicamycin has been recently shown to require both protein kinase RNA (PKR)-like ER kinase (PERK) and activating transcription factor 4 (Atf4) [76]. In addition, β-mercaptoethanol (which also causes ER stress), heat shock, and osmotic stress induce Redd1 expression in S49.A2 lymphoma cells [71]. The upregulation of REDD1 by multiple cellular stress conditions suggests that REDD1 may function as a stress-response gene. However, what the role of
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REDD1 is in the stress response appears to be context dependent and REDD1 has been proposed to both promote [56, 77, 78] and suppress apoptosis [56, 58, 71]. REDD1 has been proposed to play a role in the process of normal development and in cell differentiation. The expression of a reporter driven by ∼4 kb of sequences upstream of the Redd1 transcription start site was found to resemble that of p63 [65], a transcription factor related to p53 that is essential for limb and skin morphogenesis [79, 80]. Importantly, in p63-deficient embryos Redd1 expression was markedly downregulated suggesting that p63 plays an important role in regulating Redd1 during development [65]. But what the role of Redd1 is in morphogenesis is unclear and Redd1 is dispensable for normal development [62, 67]. In the histiocytic lymphoma cell line U-937, REDD1 expression was induced by all-trans-retinoic acid (ATRA), which promotes cell differentiation, and downregulation of REDD1 via an shRNA reduced the expression of cell differentiation markers [69]. Notably, the expression of the REDD1 orthologues scylla and charybdis is also developmentally regulated during Drosophila embryogenesis [81]. In blastoderm stage embryos, the expression of scylla is restricted to the dorsal region, and this pattern is abrogated by disruption of the gene encoding the ventral morphogen dorsal. Similarly, charybdis is also expressed in the dorsal region. Based on RNAi experiments, the authors propose that the function of scylla and charybdis is required for embryogenesis, but that scylla and charybdis act in a redundant fashion [81]. Previous studies reported, however, that embryos were viable with loss-of-function mutations in scylla and a partial loss-of-function charybdis allele estimated to downregulate gene expression to 25% of normal [55]. Thus, the precise role for scylla and charybdis during development remains to be fully determined. In Drosophila, REDD1 orthologues have been shown to control lipid stores and affect survival under conditions of nutrient deprivation [55]. Both scylla and charybdis are induced in response to nutrient starvation, and simultaneous overexpression of scylla and charybdis increased total body lipid content and prolonged life expectancy in starved flies. Conversely, flies expressing a partial loss-of-function charybdis allele had reduced survival under conditions of starvation. In addition, in mice, a microarray study showed that caloric restriction resulted in a profound induction of Redd1 in the liver [82]. REDD1 is regulated by other processes. REDD1 expression has also been shown to be regulated by cell confluency; REDD1 is induced at high cell densities and REDD1 induction can be markedly blunted by knockdown of the SP1 transcription factor [59]. Putative binding sites have also been reported in the REDD1 promoter for NFκb and hepatocyte nuclear factor 4 (HNF-4) [66].
5.4 REDD1 in Cancer The REDD proteins are negative regulators of mTORC1 and thus may function as tumor suppressors. Negative regulators of mTORC1 have been extensively implicated in tumor development and when disrupted by mutation in the germline are
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often associated with inherited tumor predisposition syndromes [83]. Tuberous sclerosis complex, a syndrome characterized by the development of hamartomas in multiple tissues [84] and which confers an increased predisposition to renal cell carcinomas [85], results from mutations disrupting the TSC1 or TSC2 gene [86, 87]. Peutz–Jeghers syndrome, which is characterized by pigmented mucocutaneous lesions, gastrointestinal hamartomatous polyps, and an increased predisposition to a variety of malignant tumors, results from mutations in LKB1 [88, 89], and LKB1 mutations have also been found in sporadic tumors, particularly non-small cell lung cancer, where they can be present in up to 30% of patients [90]. Finally, mutations in phosphatase and tensin homolog (PTEN) are associated with a variety of syndromes, including Cowden, Bannayan–Riley–Ruvalcaba, Proteus, and Proteus-like which are associated with an increased predisposition to malignancy, and mutations in PTEN occur frequently in sporadic tumors [91]. The importance of mTORC1 in tumor development has been recently highlighted by clinical studies with mTORC1 inhibitors. Rapamycin, which is an inhibitor of mTORC1 that is highly specific, has shown activity against astrocytomas and angiomyolipomas (AMLs) in tuberous sclerosis complex patients [92, 93]. In addition, mTORC1 inhibitors may also be active against epithelioid angiomyolipomas which, like AML, exhibit activation of the mTORC1 pathway [94, 95]. The mTORC1 inhibitor temsirolimus, which is largely a rapamycin prodrug [96–99], is active against renal cell carcinomas (RCC) and has been shown in a phase III clinical trial to prologue progression-free and overall survival in patients with advanced disease who are in a poor-prognosis category [100]. A second rapamycin analogue, everolimus, has shown efficacy against RCC following progression to the small molecule kinase inhibitors sorafenib and sunitinib [101]. In addition, in phase II trials, mTORC1 inhibitors have shown evidence of activity against several other tumor types [102]. Since REDD1 is induced by hypoxia, REDD1 would be expected to be upregulated in solid tumors (which not infrequently outgrow their blood supply and are often hypoxic) and this would be predicted to inhibit mTORC1 and thereby impair cell growth and proliferation. While this could conceivably be advantageous to the cell by disrupting a negative feedback loop and allowing the activation of Akt, there may be a selective pressure to downregulate REDD1 in tumors. Indeed, in a small cohort of breast adenocarcinomas, REDD1 mRNA levels were found to be downregulated in ∼30% of tumors compared to matched normal breast tissue from the same patients [52]. However, mutations in REDD1 have not to-date been found in tumors (http://www.sanger.ac.uk/genetics/CGP/cosmic/). In addition, results from overexpression and loss-of-function studies in cell lines have not been uniform. Whereas Redd1-deficient fibroblasts expressing both SV40 large T-antigen and a constitutively active form of Akt were reported to exhibit an increased tumorigenic potential in xenograft assays compared to similarly transformed wild-type controls [52], knockdown of REDD1 in the prostate cancer cell line PC3 was reported to diminish tumor growth in an orthotopic xenograft model [58]. Thus, whether REDD1 plays a role in tumor suppression remains to be established.
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6 Other Hypoxia Effector Pathways The mechanism whereby mTORC1 is inhibited by hypoxia is likely to be more complex than the pathway presented herein in which HIF-1 activation by hypoxia leads to increased expression of REDD1, which in a TSC1/TSC2-dependent manner inhibits mTORC1. In part, because of the rapidity of the response, the role of HIF-1α in hypoxia-induced mTORC1 inhibition has been questioned [16, 50]. However, at least in some settings, HIF-1α is necessary for REDD1 induction by hypoxia and hypoxia mimetics [56, 58, 59]. Nevertheless, there are likely to be REDD1-independent (and possibly TSC1/TSC2-independent) mechanisms of mTORC1 inhibition by hypoxia and in fact, some level of mTORC1 inhibition can be appreciated in Redd1-deficient fibroblasts following hypoxia [48]. In addition, most studies to-date have been performed in established cell lines or fibroblasts, and other primary cells may respond differently to hypoxia [103]. Finally, other proteins have been proposed to play a role in mTORC1 inhibition by hypoxia including promyelocytic leukemia (PML) [104] as well as the BCL2/adenovirus E1B 19 kDa interacting protein 3 (BNIP3) [105], but how these proteins function in hypoxia signaling remains to be fully elucidated. While mTORC1 is involved in many cellular processes, mTORC1 is thought to play a particularly important role in controlling the assembly of a translation preinitiation complex at the 5 -end of mRNAs (at a 7-methylguanylate molecule referred to as the cap) [106], and while translation initiation can also occur at internal ribosomal entry sites (IRES) [107], cap-dependent translation is thought to be critical for the translation of most nuclear-encoded mRNAs. mTORC1 is inhibited by hypoxia, but the functional significance of this inhibition remains to be fully established. Data from Tsc2-deficient fibroblasts suggest that mTORC1 inhibition is important for a cell proliferation arrest [48], but how mTORC1 inhibition contributes to the inhibition of cell proliferation or affects protein translation under conditions of hypoxia is unknown. In mammalian cells, rapamycin does not appear to affect global protein translation rates [15, 106] suggesting that the inhibition of protein translation observed following hypoxia is primarily mTORC1-independent. Both the eukaryotic initiation factor 2 (eIF2) and the eukaryotic elongation factor 2 (eEF2) have been implicated in the inhibition of protein translation following hypoxia (see Fig. 2). eIF2 is a heterotrimeric complex composed of a regulatory α-subunit, a βsubunit, and a catalytic γ-subunit with GTPase activity. eIF2, when bound to GTP, interacts with and recruits the initiator methionyl-tRNA (Met-tRNAi Met ) to the 40S ribosomal subunit. Upon interaction of the Met-tRNAi Met with the mRNA initiator codon (AUG), the GTP molecule bound to eIF2 is hydrolyzed to GDP and eIF2 is released [108]. GDP-bound eIF2 will be acted on by the heteropentameric eIF2B, which functions as a guanine nucleotide exchange factor (GEF), to generate GTPbound eIF2, which then becomes available to load another Met-tRNAi Met onto a 40S ribosomal subunit. However, in response to a variety of stress conditions, including severe hypoxia, eIF2B is inhibited [109]. eIF2B is inactivated through sequestration by its substrate eIF2, a process that occurs when eIF2α is phosphorylated at Ser51 [110]. Several kinases have been identified that phosphorylate eIF2α at Ser51 ,
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and PERK is required for eIF2α phosphorylation by hypoxia [111]. PERK is a type I transmembrane protein with an ER luminal domain, which senses the presence of unfolded proteins in the ER, and a catalytic domain facing the cytosol. When unfolded proteins accumulate in the ER lumen, PERK oligomerizes, it autophosphorylates [112], and it phosphorylates eIF2α at Ser51 [113, 114] resulting in the sequestration of eIF2B, reduced levels of GTP-bound eIF2, and a decrease in the translation of most, albeit not all, mRNAs [108]. In addition, severe hypoxia (or moderate hypoxia in serum-starved cells) [50] also leads to the inactivation of eEF2. eEF2 catalyzes the sequential translocation of the ribosome along the mRNA after the incorporation of each amino acid onto the growing polypeptide chain. The activity of eEF2 is regulated by phosphorylation, and phosphorylation at T56 by the eEF2 kinase (eEF2K) leads to eEF2 inactivation [115]. In response to hypoxia, eEF2 is phosphorylated and this process can be blocked by RNAi-mediated depletion of eEF2K [49]. The mechanism whereby eEF2K is activated in response to hypoxia appears to involve AMPK. AMPK has been shown to be able to phosphorylate and activate eEF2K [116, 117], and AMPK is required for eEF2 phosphorylation in serum-starved HEK293 cells exposed to hypoxia [50]. Similarly, eEF2K activation by oligomycin, an inhibitor of mitochondrial respiration, also requires AMPK [116]. Thus, in response to AMPK activation, eEF2K is phosphorylated and activated leading to the phosphorylation and consequent inactivation of eEF2 and the inhibition of peptide chain elongation. The downregulation of protein translation under severe or prolonged hypoxia decreases ATP consumption, making ATP available for processes more essential for cell survival [2].
7 A Negative Feedback Loop: HIF-1 Regulation by mTORC1 Heretofore a model has been presented wherein HIF-1 functions upstream of mTORC1. However, mTORC1 has also been shown to regulate HIF-1. Thus, there appears to be a loop with HIF-1 functioning both upstream and downstream of mTORC1. HIF-1α protein levels are regulated by growth factors. Insulin and mitogens have been shown to induce HIF-1α protein levels and to increase HIF-1 activity in a variety of cell types [118–120]. HIF-1α upregulation by growth factors can be blocked with inhibitors of PI3K as well as of mTORC1 [121–125], and it involves the TSC1/TSC2 complex [126]. Cells deficient for Tsc2 fail to downregulate HIF1α protein levels under conditions of serum starvation (0.5% serum), but HIF-1α protein levels can be normalized in these cells by treatment with rapamycin [83]. Multiple mechanisms have been proposed for the regulation of HIF-1α by mitogens including transcriptional [123, 126] as well as post-transcriptional [124, 125, 127], involving both increased HIF-1α mRNA translation rates [125] and inhibition of HIF-1α protein degradation [118, 128].
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Besides in response to growth factors, mTORC1 appears to be involved in the regulation of HIF-1α by persistent hypoxia. In response to hypoxia, HIF-1α protein levels are upregulated, but HIF-1α upregulation is transient, and after a period of time, HIF-1α levels are downregulated [126]. The transient nature of HIF-1α upregulation by hypoxia has also been documented in the mouse [129]. Exposure of mice to 6% O2 results in a profound upregulation of HIF-1α in multiple tissues including the brain, kidney, and liver, and while the kinetics of upregulation are different in different tissues, in all tissues examined, HIF-1α levels were downregulated following prolonged hypoxia and returned to baseline between 6 and 12 h. Importantly, the normal downregulation of HIF-1α under conditions of persistent hypoxia requires the inhibition of mTORC1 (which normally occurs in response to hypoxia; see above) [48, 126]. Failure to downregulate mTORC1 in Tsc2-deficient cells in response to chronic hypoxia results in an abnormally prolonged elevation of Hif-1α protein and in sustained expression of Hif-1 target genes [48, 126]. Importantly, Hif-1α levels can be normalized in Tsc2-deficient cells under chronic hypoxia by treatment with rapamycin [48]. Thus, mTORC1 appears to regulate HIF-1α not only in response to growth factors but also in response to chronic hypoxia. While there are multiple mechanisms whereby mTORC1 may contribute to tumor formation, HIF-1α may also be involved in this process. It is noteworthy that the first tumor type in which mTORC1 inhibitors have been shown to be clinically beneficial is RCC, a tumor type in which HIF plays a critical role [130]. The most common type of RCC, clear-cell RCC, is characterized by inactivation of the VHL tumor suppressor gene [131, 132], which leads to inappropriate activation of HIF [133] and increased expression of HIF target genes [134]. The importance of HIF in RCC development has been evaluated in a cell line derived from a clearcell RCC tumor, in which it was shown that HIF-2α downregulation using RNAi markedly impaired tumor growth in xenografts [135, 136]. While the relative contribution of HIF-1α to RCC development and the role of mTORC1 in the regulation of HIF-2α remain to be fully established, it would not be surprising that mTORC1 inhibitors acted on RCC, at least in part, by downregulating HIF transcription factors. Acknowledgments This work was supported by the grants K08NS0518431 and RO1CA129387. I would like to thank the members of my laboratory for their reading of the manuscript and their comments.
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mTOR Signaling in Glioblastoma: Lessons Learned from Bench to Bedside David Akhavan and Paul S. Mischel
Abstract Phosphatidyl-inositol-3 kinases (PI3Ks) are a family of intracellular lipid kinases that are frequently hyperactivated in glioblastoma. The PI3K complex links growth factor signaling with cellular proliferation, differentiation, metabolism, and survival. mTOR (mammalian target of rapamycin) acts as both downstream effector and upstream regulator of PI3K, thus highlighting its importance in glioblastoma. This review will highlight laboratory and clinical evidence of mTOR’s role in glioblastoma. Mechanisms of escape from mTOR inhibition will also be discussed, as well as future clinical strategies of mTOR inhibition. Keywords Glioblastoma · PI3K · mTOR
1 Introduction: mTOR Signaling in Glioblastoma Glioblastoma is the most common malignant primary brain tumor of adults and one of the most lethal of all cancers [1]. Glioblastomas invade the surrounding brain making complete surgical complete excision impossible. Glioblastomas are also among the most radiation and chemotherapy-resistant type cancers with median survival of 12–15 months from the time of initial diagnosis [2]. New therapeutic approaches are needed. The mammalian target of rapamycin (mTOR), a key mediator of phosphatidyl-inositol-3-kinase (PI3K) signaling, has emerged as a compelling molecular target in glioblastoma patients, although to date, efforts to target mTOR in the clinic have yet to be successful. Here we outline the evidence demonstrating that mTOR is a compelling molecular target in glioblastoma patients. We focus P.S. Mischel (B) Lya and Harrison Latta Professor of Pathology, Departments of Pathology and Laboratory Medicine and Molecular and Medical Pharmacology, The David Geffen UCLA School of Medicine, Los Angeles, CA 90095-1732, USA e-mail:
[email protected]
V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_5,
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on the role of mTOR, both within PI3K signaling and its larger role in metabolic cellular programs in glioblastoma. We summarize the current status of mTOR inhibition in the clinic, paying particular attention to the lessons learned about resistance through studying glioblastoma patients treated with the mTORC1 complex inhibitor rapamycin in phase I/II clinical trials. We describe how, by studying glioblastoma patients, an increasing understanding of the complexity of mTOR signaling is emerging. Finally, we describe new strategies for targeting mTOR in glioblastoma patients.
2 Constitutive PI3K Pathway Activation Is a Hallmark of Glioblastoma phosphatidyl-inositol-3-kinases (PI3Ks) are a family of highly conserved intracellular lipid kinases that regulate cellular proliferation, differentiation, metabolism, and survival [3]. Class IA PI3Ks are activated by growth factor receptor tyrosine kinases (RTKs), either directly or through interaction with insulin receptor substrate family of adaptor molecules [3]. The activity of PI3K results in phosphatidylinositol-3,4,5trisphospate (PIP3), a critical regulator of the serine/threonine kinase Akt to link growth factor signaling with cellular growth, proliferation, metabolism, and survival [3, 4]. Tightly regulated PI3K signaling pathway is essential for normal development and persistently activated PI3K signaling is associated with the development of cancer [5]. PI3K-activating mutations are found in nearly all patients with glioblastoma [6, 7] as is evidenced for phosphorylation of key signaling proteins in the PI3K pathway [8]. EGFR amplification is detected in 45% of GBM patients, providing one common route toward PI3K pathway activation. EGFR-activating mutations are also commonly detected in EGFR-amplified glioblastomas [9–11]. EGFRvIII, the most common EGFR mutation occurring in 20–30% of glioblastomas, results from genomic deletion of exons 2–7 [12, 13]. It lacks a ligand-binding domain, yet it is persistently activated and fails to be internalized normally, resulting in a constitutive signal with preferential effect on PI3K signaling relative to the wild-type receptor [8, 14]. Other receptor tyrosine kinases also contribute to PI3K pathway activation, including c-MET and PDGFRα, both of which can be co-activated in EGFR-amplified tumors [1], promoting resistance to EGFR inhibitors by providing an alternative route for maintaining PI3K signal flux [15]. Amplification and/or activating mutations of the catalytic and regulatory subunits of PI3K also contributes to PI3K pathway activation in up to 10% of glioblastomas [6]. Loss of the PTEN tumor suppressor protein (phosphatase and tensin homologue deleted on chromosome 10), the major negative regulator of the PI3K pathway that antagonizes PI3K signaling by dephosphorylating PIP3, occurs in greater than 50% of glioblastomas, providing a third mechanism for constitutive PI3K pathway activation [8, 16]. In addition, NF1 loss, either through genetic inactivation [6] or enhanced proteosomal degradation [17], is also a relatively common event
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in GBM that can lead to enhanced PI3K pathway activity [18]. Therefore, multiple PI3K-activating genetic lesions occur in nearly all GBM patients highlighting the biological importance of this pathway. Consistent with the mutational data from patients, mouse genetic models indicate that constitutive PI3K pathway activation may be required for malignant glioma formation and progression [19]. In these models, constitutive PI3K signaling cooperates with alterations in other signaling pathways that are also commonly mutationally activated in GBM patients, including RAS/MEK/ERK, p53, and pRB pathways, to promote glioma formation and progression. A series of mouse genetic models have highlighted the role for concurrent Ras and Akt activation in glioma formation [20–22]. Other mouse genetic models demonstrate alternative routes toward malignant glioma formation involving PI3K hyper-activation, including mutant EGFR in combination with Ink4a/arf [23], Ras activation [24], or p53 loss [25]. Further, PTEN loss cooperates with pRB loss to promote glioma formation and progression [26, 27]. Perhaps most impressively, concurrent alterations of the same pathways identified in human GBMs by mutational profiling, including NF1 loss, p53 loss, and PTEN loss (including haploinsufficiency), promote the formation and progression of malignant gliomas. Thus, mouse genetic models directly complement the human mutational analyses by suggesting a causative role for persistent PI3K pathway activation, in cooperation with the other commonly altered pathways, in the development and progression of malignant gliomas.
3 mTOR as a Therapeutic Target in GBM mTOR acts through the canonical PI3K pathway via two distinct complexes each characterized by different binding partners each with distinct activities. In complex with PRAS40, raptor, and mLST8/GβL mTOR acts as a downstream effector of PI3K/Akt signaling, linking growth factor signals with protein translation, cell growth, proliferation, and survival. This complex is known as mTORC1. In complex with rictor, mSIN1, protor, and mLST8 as part of mTORC2, mTOR acts as an upstream activator of Akt [28]. The recently identified negative regulator of mTOR, DEPTOR, is a component of both mTORC1 and mTORC2 complexes [29]. Upon growth factor receptor-mediated activation of PI3K, Akt is recruited to the membrane through interaction of its pleckstrin homology (PH) domain with PIP3, thus exposing its activation loop and enabling phosphorylation at Threonine 308 (Thr308) by the constitutively active phosphate-dependent kinase (PDK1) [30–32]. For maximal activation, Akt is also phosphorylated on Serine 473 (Ser473) of its C-terminal hydrophobic motif by mTORC2. Akt activates mTORC1 by inhibitory phosphorylation of TSC2, which along with TSC1, negatively regulates mTORC1 by inhibiting the Rheb GTPase, a positive regulator of mTORC1. mTORC1 has two well-defined substrates, p70S6K (referred to hereafter as S6K1) and 4E-BP1, both of which critically regulate protein synthesis [32]. Thus, mTORC1 is an important downstream effector of PI3K linking growth factor signaling with protein
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translation and cellular proliferation [33]. Surprisingly, an Akt-independent, PKCαdependent mechanism linking PI3K with mTORC1 in glioblastoma has also been recently described [34]. In a serial cell line transformation model, mTORC1 signaling through S6K1 was required for malignant glioma formation, highlighting its functional importance [35]. The compelling nature of mTORC1 as a glioma target, coupled with the relative efficacy of the mTORC1 complex inhibitor rapamycin and its derivatives in preclinical mouse GBM models [36–38], generated considerable hope for more effective targeted therapy for malignant glioma patients. However, a number of factors suggested that rapamycin-based treatments may not be the best choice for effectively targeting mTOR in patients. First, mTORC1 plays a dual role, as both a positive regulator of PI3K/Akt signaling from growth factor receptors and a negative regulator of PI3K pathway activation when signal flux through PI3K is high. This ensures homeostatic regulation of this critical signal in healthy cells [28]. mTORC1 impairs PI3K activation in growth factor-stimulated cells by downregulating IRS1 and IRS2 and PDGFR [39–41]. Its role in regulating PI3K signaling downstream of EGFR is less well understood, although recent work suggests that an activated EGFRvIII allele enhances feedback activation of PI3K signaling in glioblastoma cells treated with rapamycin [42]. Potentially de-repression of mTORC1-mediated feedback by treatment with rapamycin could paradoxically result in more rapid clinical progression in glioblastoma patients treated with rapamycin, as will be described below [43]. Furthermore, rapamycin and its derivatives are not mTOR kinase inhibitors, but rather disrupt mTORC1 function in an unknown manner. Recent work suggests that there may be rapamycin-insensitive activities of mTORC1 [44]. Furthermore, rapamycin and its derivatives do not consistently target mTORC2, whose function and importance are discussed below [29]. The recent development of true mTOR kinase inhibitors therefore offers a whole new set of therapeutic options that will need to be evaluated in the clinic [28]. mTORC2 signaling in glioblastoma is less well understood. mTORC2, which is activated by PI3K, phosphorylates Akt on Serine 473 (Ser473) of its C-terminal hydrophobic motif to promote maximal Akt activity. mTORC2 also activates additional kinases including serum glucocorticoid-induced protein kinase (SGK1) and PKCα, all of which may also play important roles in regulating cellular proliferation and growth. mTORC2 activity has been shown to be elevated in glioma cell lines and clinical isolates, and rictor overexpression has been shown to enhance motility and promote glioma cell proliferation in vitro [45]. Furthermore, in a recent drosophila model of gliomagenesis promoted by concurrent EGFR-Ras-Akt, mTORC2 activity was required for glial proliferation. These studies suggest that mTORC2 signaling may be essential for growth in the context of enhanced signal flux through the PI3K pathway [46]. Future studies, using both genetic and pharmacological approaches, will be needed to better understand the role of mTORC2 signaling in malignant gliomas. Clearly, elucidating the molecular circuitry underlying mTORC1 and mTORC2 signaling could be very important for developing more effective glioblastoma treatments. In fact, an enriched picture of these signaling networks has begun to emerge during the time when the first phase of rapamycin-based
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clinical trials has been conducted. Below, we discuss the results of these trials, highlighting efforts to study mechanisms of resistance in glioma patients treated with rapamycin.
4 Targeting the EGFR/PI3K/mTOR Signaling Pathway in Glioma Patients in the Clinic – Lessons Learned A number of malignant glioma phase I and phase II clinical trials of rapamycin (and its analogue CCI-779) as monotherapies have been conducted. In addition, a small number of EGFR tyrosine kinase inhibitor and EGFR tyrosine kinase inhibitor/rapamycin analogue clinical trials have been reported [43, 47–50]. Although results with these agents have been disappointing, a considerable amount has been learned by studying tumor tissue from these patients, potentially pointing the way toward more effective treatment strategies. Initial results with the EGFR tyrosine kinase inhibitors gefitinib and erlotinib suggested relatively low response rates, on the order of 10–15% [48, 51]. This was difficult to reconcile with the perceived importance of the EGFR target. Work from our group demonstrated that expression of the constitutively active mutant EGFRvIII sensitized tumors to EGFR inhibitors in vitro and in patients on clinical trials, but only if the PTEN tumor suppressor protein was intact. In fact, loss of PTEN-uncoupled inhibition of EGFR with inhibition of downstream PI3K signaling demonstrating that PTEN loss was a critical factor in promoting upfront resistance to EGFR inhibitors [47]. Concurrently, Haas-Kogan et al. demonstrated that in vitro and in glioma patients, high levels of EGFR coupled with low levels of activated Akt (a critical effector of PI3K signaling) were associated with favorable response [48]. These two studies demonstrated that intact regulation of PI3K signaling appears to be critical for effective response to EGFR, a finding that has also been shown in the human serially passaged xenograft model of GBM [52] as well as in other cancer types [53]. Maintained signal flux through PI3K, whether through PTEN loss or receptor tyrosine kinase coactivation, is a common mechanism of EGFR inhibitor resistance [15] and mTORC1 appears to be its critical effector [34, 54]. Preclinical studies demonstrate the efficacy of dual EGFR/mTOR inhibition for targeting EGFR-activated, PTEN-deficient tumors [54–56], although a small rapamycin plus gefitinib clinical trial for recurrent malignant glioma patients failed to demonstrate any durable responses [50]. A number of possibilities could underlie the lack of efficacy observed in the clinic with rapamycin and its analogues, as monotherapy or in combination with EGFR kinase inhibitors [51, 57]. Typically, when a new cancer drug first enters the clinic, its development typically proceeds empirically by defining the maximum tolerated dose, then assessing clinical activity across a range of diseases. This approach may not be adequate for determining optimal dose or assessing efficacy of target inhibition when using a drug-like rapamycin. First, it is anticipated that targeted agents such as rapamycin will be effective primarily in those patients whose tumors are dependent on the molecular lesion that is specifically targeted
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by the new agent. For example, work from multiple investigators [58–63] suggests that activation of PI3K signaling through PTEN loss sensitizes tumor cells to rapamycin in pre-clinical models, although other pathways modulating sensitivity have been described [33]. This suggests the possibility of stratifying patients for treatment based on PTEN status. Second, for targeted agents that inhibit the activity of specific signaling pathways such as the mTORC1/S6K1/S6 signaling axis, assays to assess adequacy of pathway inhibition in patients need to be incorporated into the design, interpretation, and implementation of such clinical trials. Adequate doses need to be determined based primarily on the efficacy of pathway inhibition, rather than on maximal tolerated dose. Tumor tissue must be assessed at relevant time points during treatment, which presents significant challenges for clinical trial design. Salvage surgical resection is often part of the clinical management of glioblastoma patients who relapse after standard upfront therapy (which typically consists of surgical resection followed by adjuvant radiation and chemotherapy). This presented an opportunity to design a molecularly guided clinical trial that included molecular selection criteria and that enabled molecular analysis of the effect of rapamycin on mTOR signaling in vivo. In collaboration with Drs. Tim Cloughesy, Charles Sawyers, Ingo Mellinghoff and colleagues, we conducted a small pilot phase I/II neoadjuvant clinical trial of rapamycin in patients with relapsed, PTEN-negative glioblastoma at three dose cohorts. Rapamycin was orally administered to patients prior to a scheduled tumor resection with the primary goals of defining a dose required for mTOR target inhibition and assessing potential anti-proliferative effects on tumor cells. Upon initial clinical presentation and biopsy evaluation, PTEN status within tumor tissue was determined. Upon relapse after standard upfront therapy, patients whose tumors were determined to be PTEN deficient at initial biopsy received a 10-day course of rapamycin, at three dose cohorts (2, 5, 10 mg/day B.I.D.) followed by surgical excision. Intratumor drug levels, mTORC1 signaling (as measured by S6 phosphorylation), and cellular proliferation were measured and compared between surgery 1 and surgery 2 in rapamycin-treated patients, and in a set of similarly matched “control” patients with relapsed glioblastomas who did not receive rapamycin treatment. Intratumoral rapamycin concentrations sufficient to inhibit mTOR in vitro were achieved in all patients, even those at the lowest dose. However, the magnitude of mTORC inhibition in tumor cells varied significantly from patient to patient (from 10 to 80% inhibition of S6 phosphorylation). Reduction in tumor cell proliferation (as measured by Ki67 staining) in vivo was significantly related to the degree of inhibition of mTORC1 signaling. Inhibition of greater than 50% resulted in significantly inhibited tumor cell proliferation; in contrast, lower levels of mTOR inhibition did not translate into cytostatic response in patients. Further, tumor cells removed from rapamycin-“resistant” patients that we cultured ex vivo were found to be highly sensitive to the drug. Thus, resistance to rapamycin was not cell autonomous, but rather that lack of cytostatic response in patients represented a failure of the drug to fully access its target in vivo. These results suggest a different interpretation of the clinical failure of rapamycin in glioblastoma patients: the lack of efficacy may
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be a consequence of an inefficient and incomplete targeting rather than injudicious choice of molecular target. These results also demonstrate the importance of using biomarkers of pathway inhibition to interpret clinical trial data.
5 Pathway Cross Talk and Feedback Loops in Patients To determine whether rapamycin treatment could potentially promote tumor cell growth in patients by de-repressing this negative feedback loop to reactivate PI3K signaling [64, 65], rapamyin treatment was reinstituted in these patients following surgery and patients were monitored for progression. Strikingly, rapamycin treatment led to Akt activation in 7 of 14 patients, presumably due to loss of negative feedback, which was associated with significantly shorter time to progression during post-surgical maintenance rapamycin therapy [43]. Thus, beginning to understand the complex role of mTOR in regulating signal transduction, and cellular metabolism (as will be discussed below), will be critical for developing more effective mTOR-targeted therapies.
6 Dual PI3K/mTOR and a Role for mTOR/Erk Inhibition? The finding of PI3K pathway reactivation following rapamycin treatment suggests that dual PI3K/mTOR inhibitors may be more effective by preventing reactivation of PI3K signaling and by more effectively targeting TORC2 (as well as TORC1) signaling. A dual PI3K/mTOR inhibitor (PI-103) was shown to be efficacious at blocking the growth of glioblastoma cells in vitro and in vivo, independent of PTEN status [55]. Significant benefit in combination with erlotinib or gefitinib was also found in EGFR-activated tumor cells [56]. A number of such dual PI3K/mTOR inhibitors are in early phase clinical trials, including NVP-BEZ235, which demonstrated efficacy against a panel of glioblastoma cell lines [66]. Somewhat surprisingly, in glioblastoma preclinical models, the dual PI3K/mTOR inhibitors have failed to promote tumor cell apoptosis [66]. It is not clear whether this apoptotic failure is mediated through coactivation of another key signaling pathway such as ERK, but investigations are underway to determine whether these dual PI3K/mTOR inhibitors will promote apoptosis when used in conjunction with other pathway-targeted agents, cytotoxic chemotherapies, or radiation. The remarkable plasticity of tumor cells and their capacity for rewiring have recently been demonstrated by recent papers showing that mTORC1 inhibition leads to ERK pathway activation through a PI3K-dependent mechanism [12]. These studies raise the possibility that dual PI3K/mTOR inhibitors, if they sufficiently block PI3K signaling, could also suppress activation of ERK signaling. Clinical studies will be needed to assess whether these agents also suppress ERK signaling. A similar “biopsy-treat-biopsy” strategy [43] may be required to assess the effect
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of dual PI3K/mTOR inhibitors on PI3K, mTORC1, mTORC2, Akt, and ERK signaling to determine their role in regulating cellular proliferation and/or apoptosis and to assess their clinical benefit. Such studies will help determine the potential use of these agents alone or in combination with other pathway-targeted therapies, cytotoxic therapies, and/or radiation.
7 mTOR at the Interface of Signal Transduction and Cellular Metabolism Up to this point, the focus on mTOR has been on its role as a key effector and regulator of PI3K signaling. However, mTOR also plays a critical role in integrating cellular metabolism with signal transduction. Elegant work by Sarah Kozma and George Thomas’ groups demonstrates that class 3 PI3K (vps34) provides an amino acid sensing mechanism to activate mTORC1 signaling in a process entirely independent of class I PI3K and its canonical signaling pathway [67]. This places mTOR directly as a key node integrating growth factor signaling with cellular metabolism. It also raises the possibility that class 3 PI3K signaling to mTORC1 could promote escape from mTOR and/or dual class I PI3K/mTOR inhibitors. Future work will be needed to address the clinical relevance of class 3 PI3K signaling to mTOR in glioblastoma patients and to assess its contribution toward promoting resistance to RTK/PI3K/mTOR inhibitors in the clinic. mTORC1 has also emerged as a critical effector downstream of the tumor suppressor LKB1 (liver kinase B1) (STK11). LKB1 is the tumor suppressor whose germ line loss is responsible for the Peutz–Jeghers-inherited cancer syndrome and whose sporadic loss is detected in a variety of cancers [68]. LKB1 is thought to enact its tumor suppressor functions by negatively regulating mTORC1 signaling via the central metabolic switch, AMP-activated protein kinase (AMPK). LKB1– AMPK pathway acts as a metabolic checkpoint arresting cell growth when nutrients are scarce. LKB1 phosphorylates and activates AMPK, which in turn negatively regulates mTORC1 signaling by activating TSC2 and by direct inhibitory phosphorylation of the mTOR-binding partner raptor [69–71]. Further, an emerging role for mTOR inhibition is being suggested in tumors caused by LKB1 loss [68, 72] indicating a key role for mTOR downstream of LKB1/AMPK metabolic signaling. Other classical growth factor signaling pathways may also interact with LKB1/AMPK. Recent work demonstrates that the BRAF V600E mutation, a common hyperactivating mutation in melanoma and other cancer types [73–75], forms a complex with Erk and LKB1 promoting phosphorylation and inactivation of LKB1 by ERK, thus functionally relieving LKB1’s ability to activate AMPK and thus inhibit mTORC1 [76]. Strikingly, nonphosphorylatable LKB1 at positions identified to be V600E BRAF targets (s325 and s428) prevents V600E-driven proliferation, suggesting that the LKB1/AMPK axis is a critical mediator of oncogenic RAF signaling [76]. As yet, the role of LKB1/AMPK signaling and the importance of mTORC1 as its mediator in malignant gliomas remain to be elucidated. Recent work
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from our group shows that the AMPK agonist AICAR is very effective at blocking the growth of EGFR-activated glioblastomas in vitro and in vivo [42]. Surprisingly, this was only partially mediated through inhibition of mTORC1 signaling. Rather, the striking anti-growth effect of AICAR on EGFR-activated tumor cells was mediated primarily by inhibiting lipogenesis. Future studies will be needed to assess the molecular mechanisms by which EGFR signaling through the PI3K pathway regulates tumor cell metabolism in glioblastoma. Further, studies to examine the role of LKB1/AMPK signaling in malignant gliomas and to determine the role of mTORC1 as an effector of this pathway are needed. Understanding how AMPK regulates lipogenesis and how it interacts with mTORC1 signaling, as well as with EGFR signaling through the PI3K/Akt and RAS/ERK pathways, may prove to be important for developing more effective treatment strategies.
8 Concluding Thoughts mTOR, by virtue of its role as a key mediator of phosphatidyl-inositol-3-kinase (PI3K) signaling and as in integrator of signal transduction and cellular metabolism, represents an attractive therapeutic target for glioblastoma patients. Despite the relative failure of rapamycin and its derivatives in the clinic, an enormous amount has been learned from studying mTOR signaling in well-controlled experimental glioma models and from studying the effects of rapamycin in patients treated in phase I/II clinical trials. A number of challenging questions have been raised. Do we fully understand the importance of mTORC2 signaling in malignant gliomas and is dependence on this pathway enhanced in the context of persistent PI3K signaling? What is the role for class III PI3K signaling to mTORC1 in malignant gliomas and does this ancient pathway play a role in promoting resistance to growth factor receptor and class I PI3K-targeted therapies? To what extent is mTORC1 the critical effecter of LKB1/AMPK signaling in malignant gliomas, and if so, is dependence of AMPK on mTORC1 by cell context? Answers to these questions, along with emerging data about the underlying signal transduction circuitry and the development of more specific and effective mTOR-targeted drugs, will facilitate the testing of a series of new treatment approaches. Will true mTOR kinase inhibitors prove to be more effective than rapamycin and its derivatives? Will PTEN status matter? Will dual PI3K/mTOR inhibitor treatments be needed and will these agents suppress feedback activation of ERK signaling or will ERK inhibitors be needed as well? Finally, will it be possible to develop better AMPK agonists that cross the blood–brain barrier and will they be effective at blocking mTORC1 signaling and glioblastoma tumor growth? Answers to these questions, along with emerging data about the underlying molecular circuitry, as well as the development of true mTOR kinase inhibitors, will inform an array of new treatment approaches. Perhaps most importantly, well-designed clinical trials will be needed, in which tissue will be assessed at relevant time points during treatment and for which biomarkers of pathway activation can be analyzed.
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44. Thoreen CC, Kang SA, Chang JW et al (2009) An ATP-competitive mammalian target of rapamycin inhibitor reveals rapamycin-resistant functions of mTORC1. J Biol Chem 284:8023–8032 45. Masri J, Bernath A, Martin J et al (2007) mTORC2 activity is elevated in gliomas and promotes growth and cell motility via overexpression of rictor. Cancer Res 67: 11712–11720 46. Read RD, Cavenee WK, Furnari FB, Thomas JB (2009) A drosophila model for EGFR-Ras and PI3K-dependent human glioma. PLoS Genet 5:e1000374 47. Mellinghoff IK, Wang MY, Vivanco I et al (2005) Molecular determinants of the response of glioblastomas to EGFR kinase inhibitors. N Engl J Med 353:2012–2024 48. Haas-Kogan DA, Prados MD, Tihan T et al (2005) Epidermal growth factor receptor, protein kinase B/Akt, and glioma response to erlotinib. J Natl Cancer Inst 97:880–887 49. Reardon DA, Quinn JA, Vredenburgh JJ et al (2006) Phase 1 trial of gefitinib plus sirolimus in adults with recurrent malignant glioma. Clin Cancer Res 12:860–868 50. Kreisl TN, Lassman AB, Mischel PS et al (2009) A pilot study of everolimus and gefitinib in the treatment of recurrent glioblastoma (GBM). J Neurooncol 92:99–105 51. Doherty L, Gigas DC, Kesari S et al (2006) Pilot study of the combination of EGFR and mTOR inhibitors in recurrent malignant gliomas. Neurology 67:156–158 52. Sarkaria JN, Yang L, Grogan PT et al (2007) Identification of molecular characteristics correlated with glioblastoma sensitivity to EGFR kinase inhibition through use of an intracranial xenograft test panel. Mol Cancer Ther 6:1167–1174 53. Sos ML, Koker M, Weir BA et al (2009) PTEN loss contributes to erlotinib resistance in EGFR-mutant lung cancer by activation of Akt and EGFR. Cancer Res 69:3256–3261 54. Wang MY, Lu KV, Zhu S et al (2006) Mammalian target of rapamycin inhibition promotes response to epidermal growth factor receptor kinase inhibitors in PTEN-deficient and PTENintact glioblastoma cells. Cancer Res 66:7864–7869 55. Fan QW, Knight ZA, Goldenberg DD et al (2006) A dual PI3 kinase/mTOR inhibitor reveals emergent efficacy in glioma. Cancer Cell 9:341–349 56. Fan QW, Cheng CK, Nicolaides TP et al (2007) A dual phosphoinositide-3-kinase alpha/mTOR inhibitor cooperates with blockade of epidermal growth factor receptor in PTEN-mutant glioma. Cancer Res 67:7960–7965 57. Chang SM, Wen P, Cloughesy T et al (2005) Phase II study of CCI-779 in patients with recurrent glioblastoma multiforme. Invest New Drugs 23:357–361 58. Podsypanina K, Lee RT, Politis C et al (2001) An inhibitor of mTOR reduces neoplasia and normalizes p70/S6 kinase activity in Pten+/– mice. Proc Natl Acad Sci USA 98: 10320–10325 59. Yu K, Toral-Barza L, Discafani C et al (2001) mTOR, a novel target in breast cancer: the effect of CCI-779, an mTOR inhibitor, in preclinical models of breast cancer. Endocr Relat Cancer 8:249–258 60. Shi Y, Gera J, Hu L et al (2002) Enhanced sensitivity of multiple myeloma cells containing PTEN mutations to CCI-779. Cancer Res 62:5027–5034 61. Majumder PK, Febbo PG, Bikoff R et al (2004) mTOR inhibition reverses Akt-dependent prostate intraepithelial neoplasia through regulation of apoptotic and HIF-1-dependent pathways. Nat Med 10:594–601 62. Wendel HG, De Stanchina E, Fridman JS et al (2004) Survival signalling by Akt and eIF4E in oncogenesis and cancer therapy. Nature 428:332–337 63. Yilmaz OH, Valdez R, Theisen BK et al (2006) Pten dependence distinguishes haematopoietic stem cells from leukaemia-initiating cells. Nature 441:475–482 64. O‘Reilly KE, Rojo F, She QB et al (2006) mTOR inhibition induces upstream receptor tyrosine kinase signaling and activates Akt. Cancer Res 66:1500–1508 65. Breuleux M, Klopfenstein M, Stephan C et al (2009) Increased AKT S473 phosphorylation after mTORC1 inhibition is rictor dependent and does not predict tumor cell response to PI3K/mTOR inhibition. Mol Cancer Ther 8:742–753
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mTOR and Cancer Therapy: General Principles Peter J. Houghton
Abstract mTOR (mammalian target of rapamycin) is a serine/threonine kinase that plays a pivotal role in coordinating cell cycle progression in response to intracellular and extracellular queues. Pathways upstream of mTORC1, a complex that controls cap-dependent translation, are dysregulated in most human cancers. Dysregulation downstream of mTORC1 also occurs in many human cancers. In model systems activation of mTORC1 increases the incidence and penetrance of cancer, as does overexpression of eIF4E, the RNA cap-binding protein that is regulated by mTORC1 signaling. The mTORC1 complex regulates translation of cell cycle regulators, angiogenic factors, as well as factors that control cell motility. However, the role of the mTORC2 complex is less well defined, but activation of mTORC2, and phosphorylation of at least one substrate AKT(S473), may activate survival pathways. Taken together, there is compelling data to support development of cancer therapies that abrogate mTORC1 and/or mTORC2 signaling either directly or at sites upstream or downstream of this complex. Keywords Rapamycin · mTOR · Cancer therapy · Drug resistance
1 Introduction Malignant disease is characterized by genetic mutations or compensatory changes in cells that result in unregulated population growth due to increased proliferation or decreased cell death. Understanding pathways known to be critical to the growth and survival of tumors is essential to developing potentially selective treatments. Validation of this concept has been met with compounds such as imatinib (Gleevec, STI-571; Novartis AG). This compound inhibits tyrosine kinases such as P.J. Houghton (B) The Research Institute, Center for Childhood Cancer, Nationwide Children’s Hospital, Columbus, OH 43205, USA e-mail:
[email protected] V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_6,
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Fig. 1 Structure of rapamycin
Bcr/Abl in chronic myeloid leukemia and c-kit in gastrointestinal stromal tumors. Evidence that Bcr/Abl is indeed the primary target for imatinib is the observation that resistance is characterized by mutations in this kinase that restrict access of the drug. Less success has been met by targeting other activated kinases. Rapamycin (sirolimus, Rapamune; Wyeth-Ayerst Laboratories; Fig. 1), an immunosuppressant, has emerged as a potent inhibitor of a signaling pathway that may be deregulated in some forms of cancer, leading to both increased growth and malignant characteristics of cells. Rapamycin is a lipophilic macrolide that selectively inhibits the serine/threonine kinase, mTOR. mTOR lies downstream of phosphatidylinositol 3 -kinase (PI3K) in the PI3K/AKT signaling pathway, Fig. 2. Rapamycin was originally isolated as a fungicide from the soil bacteria Streptomyces hygroscopicus [1, 2]. While rapamycin was being developed as an immunosuppressant, it was also found to exert potent antitumor activity in vitro and in vivo [3–5]. However, its mechanism of action was unknown at that time, and rapamycin was not developed as a cancer therapeutic. Rapamycin and its analogs, CCI-779 (temsirolimus), RAD001 (everolimus), and AP23675 (deferolimus), are the most potent and selective inhibitors of mTOR reported so far. The three agents share a common mechanism of antitumor action, inhibiting mTORC1 signaling which links mitogen stimulation to protein synthesis and cell cycle progression. Temsirolimus is approved for treatment of advanced refractory renal cell carcinoma and has demonstrated significant activity against mantle cell lymphoma and endometrial carcinoma. Everolimus has demonstrated single agent activity against renal cell carcinoma after failure of anti-VEGF therapy, and deferolimus has shown clinical activity against various sarcomas. Thus, mTORC1 is a validated target for cancer treatment. Recently
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Fig. 2 Signaling pathways that activate mTOR complexes and regulation of c
two mTOR kinase inhibitors (OSI-027 and AZD 8055) have entered clinical trials. Unlike the rapalogs, these agents target the ATP-binding domain of mTOR and directly inhibit its kinase function whether in the mTORC1 (raptor) or mTORC2 (rictor) complexes. In this chapter we will consider some of the evidence that points to mTOR signaling in the genesis and progression of cancer and as an important therapeutic target alone or as a component of multi-targeted therapy in the treatment of cancer.
2 Activation of the PI3K/mTOR Pathway in Cancer 2.1 Amplification/Overexpression of Growth Factor Receptors One of the criteria assumed to relate to sensitivity of cells to mTOR inhibitors is that the mTOR pathway is indeed activated and cells are dependent on this pathway for proliferation and survival, the so-called oncogene addiction [6]. The PI3K pathway upstream of mTORC1 is activated downstream of both receptor tyrosine kinases (RTKs) upon ligand binding and dimerization, as well as downstream of Ras. Amplification and overexpression of growth factor receptors occur frequently in human cancer. Some of the best-documented examples are Her2/neu (the c-erb-B2 receptor in breast carcinoma and gastric carcinoma), c-MET (the receptor for hepatocyte growth factor in gastric carcinoma), αPDGFR (glioblastoma), and c-erb-B1 (glioblastoma, non-small cell lung cancer). Each of these receptors activates multiple pathways including the PI3K signaling cascade. The importance of
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RTK activation of PI3K is shown by the observation that small molecule inhibitors of c-erb-B1 (gefitinib and erlotinib) elicit antitumor activity only if they inhibit the activation of PI3K. For example, resistance to gefitinib is characterized by amplification of c-MET and maintained signaling in the presence of the RTK inhibitor [7, 8]. Similarly, resistance to RTK inhibitors appears to correlate with K-Ras mutations that activate PI3K downstream of the RTK [9].
2.2 Activation of the PI3K Catalytic Subunit p110α The p110α catalytic subunit of PI3K, encoded by the PIK3CA gene, is mutated frequently in many human cancers (Table 1) and amplified in many other cancers (Table 2). PIK3CA mutations appear to occur almost exclusively in invasive tumors, whereas upstream mutations that activate the PI3K pathway (Ras, cerb-B2/c-erb-B3, or PTEN) are detected in both early-stage (non-invasive) and late-stage tumors [11]. These findings support the idea that PIK3CA mutation is a restricted late-stage event that augments earlier activation of the PI3K pathway [12]. ‘Hot spot’ mutations in PIK3CA cluster in exon 20 (the kinase domain) and exon 9 (the helical domain) are non-synonymous missense mutations constitutively activating the kinase activity [11]. No PIK3CA mutations were found in acute myeloid leukemia (AML), myelodysplastic syndromes (MDS), and nonHodgkin lymphomas (NHL), as well as in osteosarcomas, prostate, and ovarian cancer samples [13]. Of interest, mutations in other p110 isoforms have not been identified in human cancer, suggesting that p110α has greater oncogenic function. Amplification of the PIK3CA gene has been reported in squamous cell carcinoma of the head and neck and lung cancers, as well as gastric and a high percentage of cervical cancers (Table 2) [10]
Table 1 Frequency of PIK3CA mutations in human carcinoma Tumor
Frequency (%)
Mammary carcinoma Endometrial carcinoma Hepatocellular Colon carcinoma Urinary tract carcinoma Ovarian Glioma Lung Gastric
26a 23b 36a 26a 17b 10a 8a 2a 7a
a From b From
Engelman et al. [13] Yuan and Cantley [10]
mTOR and Cancer Therapy Table 2 Frequency of PIK3CA amplification inhuman cancer
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Frequency (%)
Head and neck Thyroid Lung: squamous Lung: adenocarcinoma Breast Gastric Esophageal (adeno) Cervical
42 9 66 5 9 36 6 69
From Engelman et al. [13]
2.3 PTEN Mutation/Deletion/Silencing The lipid phosphatase PTEN (phosphatase and tensin homolog deleted on chromosome 10) acts as a major negative regulator of the PI3K/AKT signaling pathway [14–16]. Germ line mutations in PTEN predispose to autosomal dominant hamartomatous tumor syndromes, phenotypically diverse disorders (Cowden syndrome, Bannayan–Riley–Ruvalcaba syndrome, Proteus syndrome, and Proteus-like syndrome) that share several overlapping clinical features. Both Cowden syndrome and Bannayan–Riley–Ruvalcaba syndrome are familial diseases characterized by benign tumors and an increased risk of cancer [17]. Table 3 Alterations in PTEN in human cancer
Loss of heterozygosity
Mutations/homozygous deletions
Tumor
Frequency
Glioblastoma Prostate Breast Gastric Glioblastoma
54 35 23 47 28
Prostate Breast Melanoma Gastric
12 0 8 0
From Engelman et al. [10]
Loss of PTEN by deletion or mutation occurs in as many as 50% of all solid human tumors [18] (Table 3), resulting in activation of AKT. In mice loss of heterozygosity results in development of spontaneous multifocal complex atypical hyperplasia of the uterine secretory epithelium that progresses to neoplastic transformation. Tumor cells demonstrate elevated levels of phosphorylated AKT and activated S6K1. In this model, while temsirolimus (CCI-779) had no effect on
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AKT activation, as anticipated, it normalized S6K1 activity and inhibited neoplastic transformation [19]. Further, expression of a myristoylated AKT enhances the rate and penetrance of lymphoma in the eμ-Myc mouse model [20]. Several studies suggest that PTEN-mutated or PTEN-deficient cancer cells are more sensitive to temsirolimus [19, 21, 22]. Conceptually, this would activate mTOR-dependent pathways, hence forming the basis for drug hypersensitivity of PTEN-deficient cells. Whether, loss of PTEN function consistently sensitizes tumor cells to rapamycin analogs remains to be demonstrated, as initial results from a clinical trial of temsirolimus in treatment of endometrial cancer did not show increased responses in patients with PTEN-negative tumors.1 Similarly, while rapamycin has activity against PTEN-negative glioblastoma [24], it is unclear whether the response rate is higher than for the non-selected population.
2.4 AKT Amplification Amplification of AKT2 is notably at high frequency in head and neck (30%), pancreatic and gastric cancers (20%) but rarely in breast carcinoma (3%) [10]. AKT2 mutations and IRS2 amplifications have been reported in colon carcinoma [25]. AKT1 amplification has been reported in lung carcinoma cells selected in vitro for resistance to cisplatin [26], and high-level expression in non-small cell lung cancer correlated with poor response to cisplatin. Amplification of AKT1 appears to be very rare in human cancer with single reports in gastric carcinoma and glioblastoma [27, 28]. Overexpression of AKT3 mRNA has been reported in samples from breast and prostate cancers [28]. A somatic missense mutation of AKT1 (E17K) in the plextrin homology (PH) domain that results in constitutive association with the plasma membrane and prolonged activation of AKT has been reported in breast, colorectal, and ovarian cancers [29]. In vitro the E17K mutant causes transformation.
2.5 TSC/LKB Mutations Mutations in the tuberous sclerosis complex (TSC) protein, TSC2, which inhibits mTOR function under hypoxic conditions as well as conditions of energy deprivation lead to tuberous sclerosis syndrome [30, 31]. This syndrome is associated with well-vascularized hamartomas (benign lesions) as well as an increased risk of renal cell carcinoma [32, 33]. Loss of TSC function leads to the activation of mTORC1 signaling. Recently, several clinical trials have started to evaluate rapamycin as a potential therapeutic in these patients. Interestingly, rapamycin reverses learning impairment in TSC+/– mice [34], hence may benefit TSC patients who also suffer from learning disorders. Inactivating mutations in the LKB1 kinase gene are associated with the Peutz–Jeghers cancer-prone syndrome [35]. The LKB1 kinase activates the
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Oza et al. [23].
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AMP-dependent kinase (AMPK) which in response to low energy levels inhibits the function of mTOR via TSC2 activation [36]. The observed mutations in Peutz– Jeghers result in a failure to inhibit mTOR under low energy conditions [36] and predispose to gastrointestinal and other neoplasms. Inactivating mutations in LKB1 appear to be frequent in non-small cell lung cancer.
3 Rheb Amplification/Overexpression Rheb (Ras-like protein enriched in brain) is considered to be the proximal activator of mTOR in the mTORC1 complex. Active Rheb-GTP is regulated by the TSC complex which acts as a GTPase-activating protein (GAP) to maintain Rheb in the inactive GDP-bound form. Recently, amplification of Rheb has been identified in prostate carcinoma and overexpression of Rheb in the mouse prostate leads to hyperplasia and a low-grade neoplastic phenotype [37]. Importantly, PTEN haploinsufficiency cooperates with Rheb overexpression to markedly promote prostate tumorigenesis. Rheb is also highly expressed in some human lymphomas, and as anticipated overexpression of Rheb enhances lymphoma formation in the eμ-Myc transgenic mouse lymphoma model [38].
3.1 Alterations Downstream of mTORC1 in Cancer Cancer-related changes in mTOR kinase substrates and their associated proteins have also been reported. The gene coding for eukaryotic initiation factor 4E (eIF4E) is altered in a number of tumors. Progressive amplification of the eIF4E gene is associated with late-stage head and neck carcinoma, ductal cell breast carcinoma, and thyroid carcinoma [39–41]. eIF4E protein levels are elevated in some colon carcinomas in comparison to normal colon cells [42, 43]. The levels of eIF4E are also increased in some bladder and breast cancers that have a poor outcome [44, 45]. In these cancers, a corresponding increase in VEGF was also observed [45, 46]. In the eμ-Myc lymphoma mouse model, eIF4E cooperates with c-Myc in the genesis of B-cell lymphoma, accelerating the formation of tumors [20]. When the Myc mice were crossed with p53+/– mice the incidence of lymphoma was increased due to the loss of the remaining wild-type p53 allele, unless eIF4E was overexpressed. When eIF4E was overexpressed in the p53+/– mice tumorigenesis was enhanced but the wild-type p53 allele was maintained [20]. So a moderate increase in the level of eIF4E appears to abrogate the requirement for suppressing p53mediated apoptosis in this model system. Overexpression of eIF4E also results in increased frequency of late-onset cancers [47]. These data point to the possibility that at least under some conditions eIF4E may act as an oncogene. Of interest overexpression of other components of the preinitiation translation complex (eIF4A, eIF4B, eIF4G) or the constitutively active eIF2α(S51A) mutant failed to enhance the rate of tumor onset or penetrance of lymphoma formation in the eμ-Myc mouse model (48).
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S6K1 is overexpressed or constitutively active in tumor cell lines and in early stages of transformation in ovarian surface epithelium associated with BRCA1 mutations [49]. S6K1 is also amplified in some breast carcinomas [50]. Generally, for tumors that have an amplification of S6K1, there is a corresponding increase in the level of S6K1 protein [50]. In certain cancer histotypes the relationship between the levels of mTORC1 substrate 4EBP1 and the protein whose function it inhibits, eIF4E, might be a predictor of metastasis. In colon carcinoma both eIF4E and 4EBP1 are frequently overexpressed, but the 4EBP1 levels are the most elevated in patients that have little or no metastatic disease [51]. Acquired resistance to rapamycin in vitro was associated with decreased 4E-BP1 and increased malignant characteristics (anchorage-independent growth) in sarcoma cells [52]. Further, overexpression of 4E-BP1 suppresses tumor growth [53].
4 Cooperation Between the PI3K/mTORC1 Pathway and Other Oncogenes in Tumorigenesis When Myc is overexpressed in lymphoid tissue, eIF4E cooperates in the genesis of B-cell lymphoma, accelerating the formation of tumors [20] and overexpression of eIF4E also results in increased frequency of late-onset cancers [47]. These data point to eIF4E acting as an oncogene, in part through activation of Ras [54, 55]. eIF4E also cooperates with two immortalizing genes, v-myc and E1A, to cause transformation of rat embryo fibroblasts [55]. Activation of the PI3K pathway, for example, through loss of PTEN and amplification of Rheb, also promotes tumorigenesis in mouse models. Further, the levels of mTOR are regulated by the tumor suppressor FBXW7 which ubiquitinates mTOR targeting it for degradation [56]. Interestingly, human breast cancer cell lines and primary tumors demonstrate a reciprocal relationship between loss of FBXW7 and deletion or mutation of PTEN. Overall a picture is emerging that suggests that dysregulated signal flux through mTORC1 ultimately leads to enhanced tumor formation.
5 mTORC1 Signaling in Solid Tumors 5.1 Regulation of mTORC1 by Cellular Stress mTORC1 signaling is suppressed by adverse cellular conditions such as nutritional deprivation (amino acids or glucose), DNA damage, and hypoxia, conditions that pertain to most, if not all, solid malignancies. Interestingly, mTORC1 suppression following DNA damage is p53-dependent [57], through induction of sestrin-1 and sestrin-2 activating AMPK and subsequent TSC complex function [58]. Regulation of mTORC1 in these cellular environments is covered in detail in several other chapters. However, it is worth noting that mTORC1 signaling is elevated in many
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solid tumors, and maintenance of this signaling pathway is probably necessary for G1- to S-phase progression. Thus, continued proliferation may depend upon maintained mTORC1 signaling even under conditions of cellular stress. However, the consequences of continued signaling may be disastrous to the cell. For example, in cells defective in TSC2 function, extreme glucose deprivation results in maintained mTORC1 signaling and induction of p53-dependent apoptosis that was abrogated by rapamycin [59]. Similarly, we found that suppression of mTORC1 by hypoxia is dependent on the ataxia telangiectasia mutated (ATM) protein kinase. In cells lacking active ATM, mTORC1 signaling is not suppressed under hypoxic conditions and directs p53-dependent apoptosis via activating the extrinsic death pathway.2 A survey of 64 human tumor models in scid mice showed that all solid tumor models had significantly reduced levels of ATM compared to leukemia models, and mTORC1 signaling was significantly upregulated in solid tumors compared to their respective normal tissue. These results would suggest that for cancer cells to maintain proliferation under stress conditions that pertain to solid tumor growth (glucose restriction, DNA damage, and hypoxia), p53 function must be abrogated. The in vitro results also indicate that p53 functions intimately both upstream and downstream of mTORC1.
5.2 mTORC1 Signaling in Survival The results discussed present a paradox with respect to mTORC1 signaling and survival. For example, overexpression of AKT or Rheb upstream or eIF4E downstream of mTORC1 enhances the rate of tumor onset and penetrance of tumor formation in the eμ-Myc lymphoma model despite maintenance of a wild-type p53 allele. Similarly, dysregulated NOTCH1 signaling protects against p53-dependent apoptosis induced by cytotoxic agents that is blocked by rapamycin [61]. In contrast, glucose starvation or hypoxia and continued mTORC1 signaling appear incompatible, inducing p53-dependent apoptosis. In the lymphoma model it suggests that mTORC1 signaling suppresses p53-dependent apoptosis, whereas in the cell lines continued signaling under stress induces p53-dependent death. These disparate results suggest that it is the context of mTORC1 signaling that dictates the cellular response. In p53-wild-type murine embryo fibroblasts (MEFs), grown under serumfree conditions, rapamycin induces growth arrest, but cells maintain viability [62]. In contrast, p53–/– MEFs undergo apoptosis under identical conditions when mTORC1 signaling is inhibited. Re-expression of p53 or p21CIP1 abrogates apoptosis induced by rapamycin. Similar results were obtained using human sarcoma cell lines harboring p53 mutations; however, exogenous insulin-like growth factors (IGF-1, -2) (but not other growth factors) completely protected against apoptosis induced by rapamycin [63, 64], indicating that modifiers regulate cellular fate when mTORC1 signaling is inhibited.
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Cam et al. [60].
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5.3 Role of mTORC1/C2 Signaling in Motility and Invasion Malignant disease is characterized not only by dysregulated proliferation but by metastasis which is the end result of invasion, dissemination, and re-establishment at some site distant to the primary tumor. The role of mTORC1 is well established in regulating cancer cell motility [64, 65], although the role for mTORC2 is becoming better understood. Rapamycin inhibits motility of a variety of neoplastic cells in vitro, preventing F-actin reorganization following IGF-1 stimulation [66, 67], and inhibits phosphorylation of focal adhesion proteins including FAK, paxillin, and p130 (Cas). Rapamycin also inhibits migration of breast cancer cells induced by formation of the tissue factor–FVIIa–FXa complex [68]. However, it is unclear whether protracted rapamycin treatment exerts these effects through downregulation of mTORC2 signaling. The mTORC2 complex phosphorylates AKT at S473 and regulates the actin cytoskeleton via Rho GTPases. Masri et al. [69] reported that decreased motility of glioblastoma cells is mediated through mTORC2 signaling and hepatocyte growth factor promotes migration of human myeloma cells independent of mTORC1 [70]. mTORC2 also appears to regulate PGE2-mediated endothelial cell migration [71]. Signaling from mTORC2 to activation of Rho GTPases appears to be linked by P-Rex1 and P-Rex2 [72].
6 mTOR Signaling in Angiogenesis Several signaling pathways, such as MAPK and the PI3K–mTOR pathway, have been implicated in cellular hypoxic response [73–77]. Support for a role of mTORC1 signaling in production of vascular endothelial growth factor (VEGF) includes suppression of hypoxia-induced increases of HIF-1α by rapamycin and increased VEGF in cells deficient in the TSC complex that negatively regulates mTOR via Rheb [78–80]. Rapamycin has been reported to have anti-angiogenic activities, decreasing vessel density in several tumor models, linked to a decrease in VEGF production and to inhibited response of vascular endothelial cells to stimulation by VEGF [81–83]. Rapamycin also targets vascular mesenchymal cells (pericytes, smooth muscle cells, adventitial fibroblasts) inhibiting sustained VEGF and hepatocyte growth factor (HGF) expression, via silencing of the PDGFRα– S6K1 pathway. Protracted treatment with rapamycin impairs endothelial function and reduces cell viability through downregulation of mTORC2 activity [84]. Other studies support a role mainly for PI3K/AKT signaling in insulin-induced HIF-1α expression, and mTOR being required to a lesser extent [74]. VEGF levels are decreased by PI3K inhibitors, whereas expression of constitutively active AKT reverses this effect. These data implicate AKT as a regulator of VEGF production. In sarcoma cells, inhibition of AKT had far greater effect in blocking hypoxia-induced increases in VEGF than inhibiting mTORC1 with rapamycin, again suggesting that control of tumor-derived VEGF under hypoxic conditions was regulated through AKT [85]. As inhibition of mTORC1 by rapamycin leads to
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the anecdotal hyperphosphorylation of AKT(S473), rapamycin may actually stimulate VEGF production in some tumors. In normal tissues hypoxia causes rapid and reversible inactivation of mTORC1 [86] and requires the TSC complex and the hypoxia-inducible gene REDD1 [79]. However, regulation of mTORC1 in solid tumors by hypoxia may be dysregulated and mTORC1 signaling is maintained. Thus, targeting mTOR complexes should reduce tumor-derived VEGF, alter the response of vascular endothelial cells, and potentially lead to their loss of viability.
7 mTOR in Tumor Stem Cells Although the concept of the tumor stem cell is still somewhat controversial, there is some indication that rapamycin may selectively inhibit tumor stem cell proliferation, although mTORC1 signaling may be necessary for maintenance of both cancer stem cells and normal tissue stem cells. For example, overexpression of AKT1 in embryonic mouse cortex increases the proportion of stem cells and this activity is blocked by rapamycin [85]. The importance of the PI3K/mTOR pathway in maintenance of malignant stem cells has been reported for MCF7 breast cancer ‘side population’ (SP) cells (analogous to ‘stem cells’) where signaling of PI3K/mTOR, STAT3, and PTEN appears to be critical for MCF7 SP cell survival and proliferation [87]. On the other hand, alterations in the PI3K/mTOR pathway may distinguish between normal and cancer stem cells. In a mouse model where PTEN was conditionally deleted from hematopoietic cells, adult mice immediately developed myeloproliferative disease and go on to develop leukemia in 4–6 weeks [88]. Immediately after the conditional deletion of PTEN, there was a dramatic but transient proliferation of hematopoietic stem cells. When these cells were transplanted into irradiated mice, they were unable to stably reconstitute the hematopoietic program indicating that the PTEN-deleted stem cell population is limited in its ability to self-renew. If the transfer of the ‘stem cell marker positive’ cells was delayed until the donor mice had developed leukemia the SCID recipient mice also develop leukemia, whereas inoculation of the ‘non-stem leukemic cells’ resulted in a much lower rate of leukemic development in the recipient mice. Surprisingly, the inhibition of mTOR alone using rapamycin was sufficient to dramatically reduce the effects on stem cell proliferation and leukemia development resulting from the PTEN deletion. Both the myeloproliferative phenotype and the development of leukemias were blocked in mice treated with rapamycin. The transient proliferation of hematopoietic stem cells upon PTEN deletion was also abrogated. Of importance was the finding that PTEN-deleted hematopoietic stem cells transplanted into irradiated mice treated with rapamycin were able to reconstitute the hematopoietic population in the donor animals indicating that there was a selective inhibition of the cancer stem cells and not a general inhibition of all stem cells. These results imply that it may be possible to selectively target certain populations of cancer stem cells that have alteration in PI3K/AKT signaling with mTOR inhibitors. However, whether these findings extend to other organ systems needs to be defined [89].
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8 mTOR Signaling in Drug Resistance 8.1 Resistance to Cancer Chemotherapeutic Agents Constitutive activation of the PI3K/mTOR pathway has been associated with resistance to traditional cancer chemotherapeutic agents, differentiation agents (retinoids), anti-estrogens, and molecularly targeted agents such as the c-erb-B2directed antibody trastuzumab and cytokines such as TRAIL [90, 91]. AKT activation has been associated with resistance to multiple chemotherapeutic agents [92–94]. AKT activation can directly phosphorylate and inactivate the pro-apoptotic protein BAD and stimulates transcription of glucose transporter genes and translocation to the plasma membrane of both glucose and other nutrient transporters [95, 96]. Maintenance of nutrient uptake to promote survival under growth factor-deficient conditions appears to be dependent on mTORC1 signaling [96] as it is in response to antimicrotubule agents [97]. The role of mTOR signaling in resistance to cancer chemotherapeutic drugs appears to be cell-context specific. For example, in vitro rapamycin can either antagonize the effect of proliferation-dependent cytotoxic agents or enhance their activity. Further, inhibition of mTORC1 in tumors has effects on both tumor cells and their surrounding stroma. For example, as with other anti-angiogenic agents, rapamycins may initially ‘normalize’ tumor vasculature [98, 99], leading to reoxygenation and better drug access, followed by reduced vasculature and potentially enhanced hypoxia. Thus, the effects in vitro may be quite dissimilar to those occurring in vivo. This was shown recently in a comprehensive in vitro cell culture study where rapamycin was combined with cisplatin, vincristine (antimicrotubule agent), etoposide (topoisomerase 2 poison), or a bifunctional alkylating agent (melphalan). The predominant effect was sub-additive or additive activity, with clear antagonism between etoposide and rapamycin. In contrast, when the combinations were evaluated in vivo against a panel of solid tumor models, the combination of rapamycin with cyclophosphamide (bifunctional alkylating agent), cisplatin, or vincristine had predominantly additive or greater than additive antitumor activity and the drug combinations demonstrated a high frequency of therapeutic synergy. At the cellular level mTORC1 is negatively regulated by DNA damage and this response is p53-dependent [57]. The consequences of continued mTORC1 signaling following DNA damage are somewhat controversial. MEFs deficient in TSC2 cannot suppress mTORC1 signaling in the presence of DNA damage and are more sensitive to the DNA damaging agent methyl methane sulfonate (MMS) than cells wild type for TSC2 [59]. In this model, continuing mTORC1 signaling sensitizes cells to DNA damage, putatively by increasing translation of p53, with concomitant induction of apoptosis. Thus, it was shown that inhibiting mTORC1 by rapamycin, for example, protected cells from DNA damage. The converse was found in the yeast Saccharomyces cerevisiae, where TORC1 signaling protected cells from DNA damage as they traversed S-phase and inhibition of TORC1 sensitized cells to MMS treatment and prevented MMS-induced mutations [101]. The proposed mechanism
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for rapamycin action is that mTORC1 signaling is required for the synthesis of DNA damage-inducible subunits of ribonucleotide reductase required to increase deoxyribonucleotide pools necessary for error-prone translesion repair by DNA polymerases. Rapamycin, or the rapalog temsirolimus, have been shown previously to potentiate apoptosis induced by cisplatin in several tumor cell models [102, 103] and to potentiate cisplatin antitumor activity in vivo [104]. Beuvink et al. [105] have reported increased apoptosis activity when cisplatin was combined with the everolimus only in tumor cells with wild-type p53. The purported mechanism of enhanced cell killing was through rapamycin blocking p53-mediated induction of p21CIP1, leading to decreased G1 arrest and enhanced apoptosis. Resistance to antimicrotubule agents has been linked to the activation of AKT–mTORC1 signaling pathway and augmentation of glucose utilization [97]. Vincristine was also reported to be synergistic against several mantle cell lymphoma cell lines in vitro when combined with everolimus [106].
8.2 Resistance to Molecularly Targeted Agents An emerging picture of intrinsic or acquired resistance to receptor tyrosine kinase inhibitors, such as gefitinib or erlotinib, that inhibit the epidermal growth factor receptor, c-erb-B1, is failure to inhibit the PI3K pathway downstream. Resistance is often associated with amplification of the hepatocyte growth factor receptor, c-MET, or IGF-1 receptor signaling which maintains PI3K signaling in the presence of gefitinib [8, 107]. Resistance to trastuzumab (herceptin), an antibody that blocks the c-erb-B2 receptor amplified in about 25% of breast cancers, is through compensation by increased expression of the IGF-1 receptor maintains PI3K activation [108]. Similarly, resistance to agents that target receptor tyrosine kinases appears frequent in tumors harboring constitutively active K-Ras mutants as a consequence of receptor-independent activation of PI3K [9]. At least conceptually, blocking mTORC1 or mTORC2 would partially abrogate this form of resistance. Indeed, combination of erlotinib with rapamycin was found to be synergistic against some cell lines both in vitro and as xenograft models [109]. Similarly, the PI3K–mTOR inhibitor NVP-BEZ235 overcame acquired resistance to the c-erb-B2 inhibitor lapatinib [110].
9 Concluding Remarks mTOR, whether in the mTORC1 or mTORC2 complex, serves a pivotal role in human cancer. Activation of AKT and cap-dependent translation downstream of mTORC2 occurs very frequently in human malignancies. While rapamycin and its derivatives have demonstrated some activity against clinical cancer, notably renal cell carcinoma and mantle cell lymphoma, these agents seem to slow tumor growth
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rather than induce tumor regression. Whether the anecdotal activation of AKT protects cells from apoptosis induced by rapalogs needs to be fully evaluated. Inhibition of mTORC1 may de-repress autophagy, a cellular process that may also protect against death under conditions of nutrient starvation. Preliminary data suggest that direct mTOR kinase inhibitors may more effectively inhibit mTORC1/mTORC2 signaling compared to rapamycin, hence may have greater therapeutic potential. However, it is likely that inhibition of mTORC1 or mTORC2 in the context of inhibition of other pathways will ultimately be an effective therapeutic strategy. The challenge will be to identify such synthetic lethal interactions that are tumor-specific.
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mTOR and Cancer Therapy: Clinical Development and Novel Prospects Sandrine Faivre, Thomas Decaens, and Eric Raymond
Abstract mTOR, a pivotal signal transduction protein involved in multiple cellular functions in tumors at the level of cancer and stroma cells, represents an attractive target for cancer therapy. Rapamycin and several rapamycin derivatives (rapalogs), inhibiting mTOR function by a kinase-independent mechanism, have been tested in clinical trials in several tumor types. The proof of principle that rapalogs can improve survival has been obtained in patients with advanced poor prognosis renal cell carcinoma and mantle cell lymphoma. How to further explore potential novel indications of rapalogs in the clinic remains challenging and may help accelerating the development of novel mTOR kinase inhibitors. In this review, current data and limitations of rapalogs in terms of doses, schedules, and pharmacokinetics are summarized. Novel pharmacodynamic endpoints that may help the development of rapalogs in clinical trials are also reviewed. Finally, based on current knowledge of tumor biology and clinical results in phase I–II trials, we update potential novel indications for mTOR inhibitors given either as single agent or in combination with other anticancer drugs. Keywords Hepatocellular carcinoma · Endometrial cancer · Breast cancer · Neuroendocrine tumors · Lung cancer · Colon cancer · Mantle cell lymphoma · Rapalogs · mTOR kinase inhibitors · Combination therapy
1 Introduction mTOR is a highly conserved serine/threonine kinase that plays a central role in cellular metabolism, protein synthesis, apoptosis, cell survival, and tumor angiogenesis, thereby representing an original target for cancer therapy [1]. Cell signaling of mTOR depends upon the binding to multimolecular complexes S. Faivre (B) Department of Medical Oncology, Service de Cancérologie, APHP and INSERM U728, Beaujon University Hospital, Clichy, France e-mail:
[email protected] V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_7,
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mTORC1 and mTORC2 that drive distinct cellular functions. For instance, mTORC1 activation by tyrosine kinase receptors, such as the vascular endothelial growth factor receptor (VEGFR), the platelet-derived growth factor receptor (PDGFR), the epidermal growth factor receptor (EGFR), and the insulin growth factor receptor (IGFR), ignites cell proliferation and angiogenesis in several tumor types. Rapamycin and several rapamycin derivatives (rapalogs) inhibit preferentially mTORC1 function by a kinase-independent mechanism that requires FKBP12 as a cofactor. mTORC2 appears more multifaceted and was shown to participate upstream to mTORC1 in inducing the phosphorylation of AKT on serine 473, a mechanism that is thought to participate in cell survival and may account in resistance to rapalogs [2]. Rapalogs may exert direct antitumor effects either by inducing dose-dependent G1/S cell cycle arrest, apoptosis, and autophagy in cancer cells or by blocking endothelial cells and pericytes proliferation, thereby also inhibiting tumor angiogenesis [1]. Which mechanism accounts most in clinical efficacy remains poorly understood but is likely to be dependent on dose, schedule, pharmacokinetics, and specific (still to decipher) pharmacodynamic molecular parameters depending on individual tumor types. Ten years ago, rapalogs have been the first-generation mTOR inhibitors demonstrating efficacy in the treatment of several tumors such as renal cell carcinoma. More recently, ATP-binding molecules inhibiting the kinase function of mTOR have entered clinical trials. Those chemicals may sometimes have affinity to other mTOR family kinases, reducing specificity. Promises offered by this second-generation mTOR kinase inhibitors may broader the spectrum of activity and eventually counteract resistance to current rapalogs. In this chapter, we will summarize published data on dosing and schedules of mTOR inhibitors in clinical trials and will attempt to address pharmacokinetic/pharmacodynamic issues toward prospects and combinability.
2 mTOR Inhibitors Entered in Clinical Trials Rapamycin (also named sirolimus, Wyeth) and rapalogs including temR R , Wyeth), everolimus (RAD001, Afinitor , Novartis sirolimus (CCI-779, Torisel Pharmaceutical), and deforolimus (AP23573, Merck Serono/Ariad Pharmaceutical) share the same molecular scaffold with substitution of the lactonic macrocycle, making the compound either suitable for intravenous (temsirolimus and deforolimus) or oral (everolimus and deforolimus) formulation, depending on drug solubility. Main differences between rapalogs include distinct metabolism features, temsirolimus being considered as a prodrug that is bioconverted into sirolimus, whereas everolimus and deforolimus are readily available as active compounds that do not require biotransformation for activity. So far, no clinical information is available concerning mTOR kinase inhibitors and, thus, the following update will only concern clinical data obtained with rapalogs. Although limited experiments have been carried out to benchmark and address cross-resistance between rapalogs, similarities
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in terms of chemical structures, mechanisms of action, affinity for the target, and overall spectrum of activity in laboratory experiments strongly suggest that currently developed rapalogs are similar in many ways, main differences belonging to pharmacokinetic properties rather than antitumor potency. Sensitivity and resistance to rapalogs may depend on duration of drug exposure. Short exposure to rapalogs results in the inhibition of mTORC1 that blocks the downstream S6K1, resulting in the inhibition of the S6K1 feedback loop that in turn may help activating T308AKT [2]. For this reason, while mTORC1 is inhibited, mTORC2 may still remain efficient to activate S473-AKT and maintain cancer cell survival. Interestingly, sustained exposure to rapamycin was shown to secondarily inhibit mTORC2 since most of mTOR bounded to rapamycin/FKBP12 become unavailable to complex with rictor [2]. Those data suggest that resistance to rapamycin may be associated with the activation of AKT, a mechanism at least in part prevented by using sustained exposure to rapamycin that blocks both mTORC1 and mTORC2. Thereby, antitumor activity may depend not only on the type of rapalogs and doses used in the clinic but also on the duration of drug administration/exposure. Sustained exposure may increase the potency of rapalogs by inhibiting mTORC1 as well as mTORC2.
3 Dose and Schedule Impact on Toxicity of Rapalogs Rapalogs were studied in clinics using three main intravenous and oral schedules: five times daily dosing every 2 weeks, once weekly dosing, or daily continuous dosing. Dose-limiting toxicities (DLT) were consistent between the three compounds and consisted of reversible mucositis, asthenia, and thrombocytopenia, mainly observed with the five-time daily schedule [3–6]. Comparing different schedules, the weekly administration appeared as better tolerated than the five times daily every 2 weeks and daily administration schedules since the maximal tolerated dose (MTD) was not reached, even hitting the highest dose levels using weekly dosing. In most phase I trials, MTD was not reached and recommended doses for phase II studies were based on compromising between side effects, pharmacokinetic and pharmacodynamic data. Overall, the most commonly encountered side effects at recommended doses with the weekly schedule of temsirolimus were asthenia (about 51% of the patients), skin toxicity (maculopapular rash in about 47% of the patients), and mucositis (about 20% of the patients) [7]. Hyperglycemia and hyperlipidemia were also reported at the highest dosage in the weekly schedule of temsirolimus and everolimus [3]. Rapamycin being known as an immunosuppressive agent, this parameter was carefully evaluated but no significant immunosuppression was found across trials. Long-term effects of rapalogs are still under investigation and include interstitial and alveolar pulmonary infiltrates that were not dose dependant but were reported in patients treated for long periods of time [8]. In most cases, pneumonitis shall resolve upon treatment discontinuation or corticosteroids. For patients treated with temsirolimus, interstitial pneumonitis has been reported in 1–36% at doses ranging 25–250 mg/week. The onset of pneumonitis was 16 weeks. Pneumonitis was
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asymptomatic in half of patients, mainly diagnosed on chest CT scan performed for tumor evaluation. Data with everolimus mainly came from its use in heart transplantation in which interstitial pneumonitis occurred in 3.3% of patients. Additional data on incidence and severity of pneumonitis are expected from prospective survey of ongoing phase II–III trials.
4 Pharmacokinetics of Rapalogs Rapamycin and rapalogs (including temsirolimus) have complex pharmacokinetics. Adding to the complexity, temsirolimus is primarily converted into sirolimus, sirolimus being prevalent in plasma after a single infusion of temsirolimus [3, 4] and sustaining at relatively high concentrations for several days. Pharmacokinetic studies show that temsirolimus and other rapalogs are primarily metabolized in the liver by the cytochrome CYP450 3A4/5. Bioconversions of temsirolimus into sirolimus appear to be less than the dose proportional suggesting a saturation of the CYP3A4 capacity at higher dosing [3]. A dose-proportional increased exposure to everolimus has been shown following weekly or daily administration [5]. Similarly, deforolimus exposure increases dose proportionally at lower doses, but exposure appears to be less than the dose proportional at higher doses, with a plateau reached about the 12.5 mg/day dose [6]. For the three compounds, half-life of parental drug ranges between 22 and 74 h, but temsirolimus-derived sirolimus half-life reaches 40–102 h. Overall, rapalogs display predictable pharmacokinetics with a high distribution volume and low interpatient variability for intravenous administration of temsirolimus and deforolimus. In contrast, oral absorption, biodisponibility, and concomitant medication may interfere with pharmacokinetics of oral rapalogs. In the case of everolimus, the absorption and bioavailability that depend on the expression of ATPbinding cassette membrane transporters in the gut may participate to interpatient variability for exposure [5]. Recent pharmacokinetic studies also demonstrate that exposure to rapalogs may be modified by the concomitant administration of drugs that are substrates, activators, and inhibitors of cytochrome P450 (CYP3A4/5) such as rifampicin, anticonvulsants, and immunosuppressive drugs such as cyclosporine [9, 10]. Considering the long pharmacokinetic and biological half-life of those agents, balancing toxicity and dose intensity, advantages of daily versus weekly schedules are not fully evident. For those compounds, threshold daily doses associated with antitumor effects of rapalogs average 10 mg while weekly doses recommended for phase II studies are ranging from 20 to 50 mg.
5 Current Imaging of the Antitumor Effects of Rapalogs Mechanisms by which mTOR inhibitors are able to control tumor growth in cancer patients remain complex and incompletely elucidated. Laboratory experiments using cancer cell lines have shown that antiproliferative effects of mTOR inhibitors
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may result from the direct inhibition of critical survival pathways, blockage of cell cycle, as well as induction of apoptosis and autophagy. Effects of mTOR inhibitors may also be observed in several other cellular types such as endothelial cells and are likely to play an important role in several highly vascularized tumors such as renal carcinoma. While most of the effects of mTOR inhibitors have been characterized in preclinical experiments, clinical trials aimed to better define which of those potential mechanisms of action account the most for efficacy in patients. Antitumor effects of mTOR inhibitors, including objective response and tumor stabilization, have been reported consistently in patients with renal cell carcinoma and other tumor types in several trials. Computerized tomography scans are usually a reliable method to assess reduction in tumor size and modification of tumor density that might reflect a direct effect on cancer cells and on tumor angiogenesis, respectively. Regarding tumor sensitivity, sporadic objective responses may be obtained at doses well below the MTD, suggesting that tumor or endothelial cells may display adequate signaling parameters and functional apoptosis pathways, circumstances that render cancer cells highly vulnerable to rapalogs by triggering apoptosis. However, tumors sensitive to single-agent rapalogs only represent a limited number of cases. In most situations, tumor types are only marginally sensitive because of redundant signal transduction pathway or lack of functional apoptosis. In this particular situation, a dose-dependent cell cycle inhibition can be achieved with mTOR inhibitors. This may match clinical situations in which rapamycin derivatives do not induce tumor shrinkage on CT scans, but were shown to induce sustained tumor stabilization or delayed tumor progression. For this reason, classical imaging techniques may be insufficient to identify treatment benefit in phase II trials. Timeto-tumor progression and/or progression-free survival may be considered more appropriate for identifying antitumor effects of rapalogs in phase II–III trials. A large number of trials have tried identifying the antitumor effects of rapalogs using 18-FDG PET scanning. However, since rapalogs are known to interfere with glucose metabolism independently of their antitumor effects, 18-FDG PET scan may appear inappropriate to reliably monitor the effects of rapalogs [5].
6 Monitoring the Biological Activity of Rapalogs Monitoring the biological activity of rapalogs to determine the biologically active dose rapidly appeared crucial for patients participating in clinical trials to optimize therapeutic efficacy. Rapamycin and rapalogs are known for a long time to induce effects on glucose and lipid metabolisms. Despite the fact that rapalogs do modify blood level of cholesterol and triglycerides, these parameters were never fully considered to monitor the biological effects of rapalogs in trials in patients with cancer. Since peripheral blood mononuclear cells (PBMC) were easily available, most studies were done looking at the phosphorylation of S6K and 4EBP1 in those cells [4–6, 11]. Those data consistently showed a dose-dependent effect of rapalogs in
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inducing a dephosphorylation of S6K and/or 4EBP1 in PBMC. In a recent study, we showed that modeling the S6K dephosphorylation in PBMC in animals and humans under exposure to everolimus allows predicting a threshold level of activity of this drug in humans and helps selecting dosing for phase II studies [11]. Similarly, another study addressed the direct effects of the everolimus on phosphorylation of several kinases including S6K and AKT in skin and tumor tissue biopsies [12]. The authors showed that exposure to continuous everolimus more readily induced inhibition of S6K phosphorylation and activation of S473-AKT at doses above 10 mg daily and 50 mg weekly. In these studies, daily schedule from 10 mg dose appears to induce more profound and durable S6K1 and 4EBP1 than weekly schedule [11, 12]. Unfortunately, due to the limited number of patients entered in those studies, no correlation was made between molecular changes, toxicity, and activity of everolimus. Thus, it is unclear whether these molecular markers are reliable for addressing the efficacy of rapalogs in tumor or whether they may only reflect unspecific molecular changes induced by these compounds. For this reason, those parameters should not be used routinely in patients treated with rapalogs. Furthermore, none of the currently tested biomarker reliably addressed the antiangiogenic effects of rapalogs. For instance, little is known on the effects of rapalogs on VEGF and sVEGFR2 in plasma of patients treated for renal cell carcinomas. Since no clear correlation between antiproliferative/antitumor effects and inhibition of phosphorylation of S6K and 4EBP1 has been demonstrated, recommendations that are based on currently studied surrogate biomarkers may misestimate the appropriate dose of mTOR inhibitors.
7 Potential Novel Indications Beyond Renal Cell Carcinoma mTOR inhibitors have been extensively investigated in renal cell carcinoma and clinical results are presented by Gary Hudes in another chapter. A number of other tumor types may represent attractive candidates for this therapeutic class of agents. In this chapter, we will review emerging novel indications that should be and are being currently investigated in clinical trials.
7.1 Hepatocellular Carcinomas (HCC) Several studies identified the key role of mTOR pathway in HCC carcinogenesis. In HCC, it is estimated that mTOR pathway is activated in 30% of cases (cf. infra). Many oncogenic events may contribute to the activation of mTOR in HCC either due to the overexpression of tyrosine kinase receptors (VEGFR, PDGFR, or IGFR), activating mutation of PIK3CA, or mutation inactivating the function of suppressor genes PTEN or TP53 that are negative regulators of the mTOR pathway [13]. Pathological studies reported the activation of PI3K/AKT/mTOR pathway in human HCC with a frequency varying 15–50% [14–18]. Moreover, recent work
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by Zucman-Rossi et al. [19] demonstrated that an increase of AKT expression is observed in about one-third of patients with HCC. In addition, several studies underline that activation of AKT is more frequently observed in patients with either HBV infection and IGF2 overexpression (75%) or PIK3CA mutations (25%) as compared to other groups (13%). Published data suggested that mTOR inhibitors may induce both antiproliferative and antiangiogenic effects in preclinical models of HCC. Zhang et al. [20] reported the effect of rapamycin on hepatocellular carcinoma cells BEL-7402 and HepG-2. They demonstrated that rapamycin has significant antiproliferative effect by inducting apoptosis via activation of caspase-3 and disruption of mitochondrial membrane potential, as well as by downregulation of anti-apoptotic protein Bcl-2 and upregulation of pro-apoptotic protein Bcl-xl. Moreover, Semela et al. [21] reported the effect of sirolimus against HCC in a rat model. They demonstrated that rats treated with sirolimus had significantly longer survival with smaller tumor burdens and less extrahepatic metastases than untreated controls. This was in part associated with a decrease of intratumoral microvessel density and inhibition of endothelial cell proliferation. Together, those studies supported the use of mTOR inhibitors in HCC. One of the first clinical trials was performed in 21 patients with advanced HCC [22]. In this study, sirolimus was administered once daily with a dose adjusted to the serum trough levels ranging 4–15 ng/ml. Evidences of antitumor activity were reported using tumor measurement by either CT scan or MRI according to RECIST criteria. One patient experienced a partial response and five patients had stable disease. We had similar experience in a pilot study evaluating the safety and the activity of sirolimus in cirrhotic patients with advanced HCC [23]. In this trial, sirolimus was given orally at weekly doses ranging 20–30 mg in 14 patients. Interestingly, rapamycin did not impair the hepatic function of cirrhotic patients and was well tolerated in this patient population with no grade 3–4 hematological toxicity and only one occurrence of grade 3 stomatitis. Among patients who were evaluable for response according to RECIST criteria, one complete response and four partial responses (Fig. 1), five stable diseases, and two tumor progressions were observed. Exposure to rapamycin was not significantly influenced by variations in liver function parameters, body mass, and body surface area. These results suggesting activity of rapamycin in advanced HCC encourage further investigations of rapalogs in larger studies.
7.2 Endometrial Cancers The frequent loss of PTEN in endometrial cancer supports the investigation of rapalogs in this disease [24, 25]. However, clinical experience in endometrial cancer remains somehow limited with only one phase II trial investigating the activity of temsirolimus in recurrent or metastatic endometrial cancer [26]. Eighteen patients received temsirolimus at the dose of 250 mg/week for a median duration of treatment of 6 months. Five of the 16 evaluable patients showed partial responses
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Fig. 1 Antitumor activity of rapamycin in two patients with advanced hepatocellular carcinoma (a: complete response, b: partial response) Decaens, personal communication [23]
(31%) and 10 of 16 patients (63%) had stable disease, with only 1 patient showing progressive disease. Another trial explored everolimus at a daily dose of 10 mg in advanced or recurrent endometrial carcinoma [27]. Objective response and sustained tumor stabilization were also reported. These preliminary results suggest that rapalogs either given as single agents or in combinations should be further explored for the treatment of endometrial carcinoma.
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7.3 Breast Cancers Loss of PTEN has been reported in 30% of sporadic breast cancer while PI3K mutations were observed in 18–26% of patients. Evidences of activity were reported in phase I trials with temsirolimus. Temsirolimus given as single agent also showed activity in a phase II trial in 109 patients with advanced or metastatic pretreated breast cancer [28]. Patients were randomized to receive 75 or 250 mg per week. Efficacy was similar for both doses with 9.2% objective response rate. Given the favorable safety profile, it was therefore logical combining rapalogs with other agents commonly used for the treatment of breast cancer such as letrozole or vinorelbine. The combination of everolimus with letrozole was feasible with no pharmacokinetic interaction and some evidence of activity in patients experiencing progression under single-agent letrozole [29]. Another study explored the clinical and biological responses to everolimus with letrozole versus letrozole alone in a large neoadjuvant phase II trial in estrogen receptor positive advanced breast cancer. The combination significantly improved local response (68% versus 59%) in all subsets of tumors, including PTEN positive and PI3K wild-type breast cancers [30]. Other trials combining everolimus with vinorelbine and trastuzumab are ongoing in HER2 overexpressing metastatic breast cancer [31].
7.4 Neuroendocrine Tumors PTEN loss has been described in neuroendocrine tumors (NET), especially in advanced and poorly differentiated NET, warranting the investigation of rapalogs in this type of disease. A phase II trial of temsirolimus was performed in patients with islet cell NET and carcinoids [32]. Sustained tumor stabilizations and partial responses in both pathological subtypes were reported with only 25–28% of patients presenting primary tumor progression. The median time-to-tumor progression was 10.6 and 6.0 months for islet cell NET and carcinoids, respectively. Those data were obtained in patients heavily pretreated with chemotherapy. Similar results were observed with everolimus in several histological subtypes of NET with promising survival parameters in a recent phase II study [33]. Ongoing phase III trials should provide more definitive evidences on the efficacy of rapalogs in NET.
7.5 Non-small Cell Lung Cancers Most non-small cell lung carcinoma (NSCLC) cell lines demonstrate PI3K/AKT/mTOR activation [34]. In tumor specimens from patients, 24–74% of samples showed no PTEN protein expression [35]. Mutations or reduced PTEN expression, along with increased PI3K activity, has been reported in NSCLC. Temsirolimus, everolimus, and deforolimus were all shown to yield objective response in NSCLC during phase I trials [4–6]. Given as single agent in a phase
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II trial, temsirolimus was associated with a median overall progression-free survival of 2 months as maintenance treatment after induction chemotherapy [36]. Combination of everolimus with gefitinib and erlotinib was investigated in phase I trials and appeared to be safe for future evaluations in phase II studies [37, 38].
7.6 Colon Cancers In colon cancer, K-Ras-induced PI3K/AKT activation was reported as an early event in carcinogenesis and has been associated with increased tumorigenic potential. Evidences of sporadic responses were reported in patients with heavily pretreated colorectal cancer treated with rapalogs in phase I trials [6]. Recent studies have shown that K-Ras mutation may activate PI3K signaling especially in tumor lacking PTEN expression. While no clinical trials were conducted in patients with advanced colorectal cancers, rapalogs may require further investigations in colorectal cancer with K-Ras mutations and/or PTEN deletion, a molecular pattern associated with clinical resistance to epidermal growth factor receptor inhibitors.
7.7 Gliomas PTEN has been found to be inactivated in 30–40% glioblastomas, loss of PTEN being associated with activation of mTOR. Expression of the mutant epidermal growth factor receptor VIII was also tightly correlated with activation of the PI3K pathway [39]. Among 92 patients with glioma, the levels of p-PI3K, pAKT, and p-S6K1 all correlated inversely with the levels of cleaved caspase 3, indicating that PI3K pathway activation suppresses apoptosis. Importantly, activation of the PI3K/AKT/mTOR pathway was associated with increasing tumor grade and reduced patient survival [40]. PTEN gene inactivation or loss has been correlated with poor prognosis in high-grade astrocytomas. In particular, the PI3K/AKT pathway might be involved in glioma cell migration and stimulated by the urokinase-type plasminogen activator in glioma cells [41]. Two phase II studies have been performed on patients with recurrent glioblastoma who were treated with temsirolimus at the weekly dose of 250 mg [42, 43]. The results of both studies are consistent, showing limited antitumor activity as measured by a CT scan and/or MRI with a median time to progression of disease of 2.3 months. Thus, singleagent temsirolimus seems to have only limited activity in recurrent glioblastoma. However, perspectives might aim to combine mTOR inhibitors with other treatment modalities such as radiotherapy and temozolomide chemotherapy in gliomas.
7.8 Mantle Cell Lymphomas Preclinical studies revealed that mTOR inhibitors could downregulate cyclin D1 in mantle cell lymphoma, a disease driven by a chromosomal translocation
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t(11;14)(q13;q32) that places the cyclin D1 gene under the influence of the immunoglobulin heavy-chain enhancer region, resulting in cyclin D1 overexpression. A phase II trial in 35 patients with mantle cell lymphoma who had relapsed after chemotherapy and rituximab treatment indicated that temsirolimus resulted in a remarkable overall response rate of 38%, with a median duration of responses of 6 months. This suggested that mTOR inhibitors can mediate sustained antilymphoma effects [44]. The effect appeared regardless of the dose level (250 or 25 mg weekly) in this phase II trial [45]. A recent phase III study compared two temsirolimus regimen: 175 mg weekly for 3 weeks followed by either 75 mg (175/75-mg) or 25 mg (175/25-mg) weekly, or investigator’s choice therapy. This trial was positive since temsirolimus 175 mg weekly for 3 weeks followed by 75 mg weekly significantly improved PFS (primary endpoint) and objective response rate compared with investigator’s choice therapy in patients with relapsed or refractory mantle cell lymphoma [46].
8 Optimizing Activity of Rapalogs Using Combinations with Other Anticancer Drugs Although rapalogs have displayed activity in a number of malignancies, their antitumor effects appear to be limited as single agent, since primary or acquired resistances to mTORC1 inhibitors are observed in the vast majority of tumor types. Key factors for primary resistance involve cell survival and apoptosis signaling pathways. Tumor/endothelial cells may maintain survival and proliferation by using redundant cross-signaling pathways, involving particularly MAPK. In addition, tumor/endothelial cells may have non-functional apoptotic pathways, especially when expressing Bcl-2 [47]. In such situations, strategy to overcome resistance to rapalogs may be based on combinations with other anticancer agents, especially potent apoptosis inducers. Combinations of rapalogs with conventional chemotherapy aimed to enhance pro-apoptotic effects and to broader the spectrum of activity in tumor types only marginally sensitive to single-agent rapalogs. However, combinations of rapalogs with 5-fluorouracil, paclitaxel, or gemcitabine were associated with limiting toxicities that were difficult to manage in clinical applications [48]. Thus, combinations of rapalogs with other anticancer agents have not been extensively pursued. Other avenues have combined rapalogs with targeted agents to circumvent mechanisms related to alternative survival signaling pathways. The goal was to either optimize inhibition of the PI3K/AKT/mTOR pathway or inhibit multiple potentially redundant signaling pathways. Recent laboratory studies combined rapamycin with inhibitors of EGFR or KIT receptors, showing synergistic effects [49]. Combination of epidermal growth factor receptor (EGFR) inhibitors with rapalogs offers a strong potential for further clinical investigations in tumor type responding to EGFR
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inhibitors such as lung, colon, and head and neck carcinomas [50]. It will be interesting to investigate whether these combinations have a potential in tumors not only sensitive to but also resistant to EGFR inhibitors. Finally, another strategy is to combine rapalogs with drugs targeting angiogenesis. The antiangiogenic properties of both interferon alpha and temsirolimus along with their activity as single agent in renal cell carcinoma provided a strong rationale for investigating their combinations in clinical trials. Phase I/II trials showed that the combination of temsirolimus with interferon was feasible with a safe toxicity profile [51]. However, surprisingly, survival of the combination of temsirolimus with interferon alpha was no superior to temsirolimus given as a single agent [7]. Other antiangiogenic agents such as bevacizumab, sorafenib, and sunitinib recently showed potent antitumor efficacy in patients with renal cell carcinoma. Combinations of temsirolimus with sorafenib and sunitinib in clinical trials are anticipated to be associated with possible pharmacokinetic interactions since those agents are expected to be catabolized by the same cytochromes in the liver. Conversely, recent unpublished data suggested that combining bevacizumab with temsirolimus was feasible and may have clinical potential to enhance survival of patients with advanced kidney cancer.
9 Non-rapalog mTOR Kinase Inhibitors One of the key factors that might be involved in primary resistance to mTORC1 inhibitors is the target itself. The rapamycin-insensitive complex mTORC2 may limit the efficacy of rapalogs by activating AKT. AKT activation may stimulate several survival pathways that stimulate proliferation and inhibit apoptosis. In this case, it might be interesting to investigate the new generation of mTOR inhibitors that are designed to block the kinase of mTOR, thereby inhibiting both mTORC1 and mTORC2. Drugs that are ATP mimetics (some developed by Astra-Zeneca, Celgene, and Exelixis) that target the mTOR kinase are expected to be at least in part non-cross-resistant with rapamycin and to broader the spectrum of activity of current rapalogs. Acute and long-term toxicity and combinability may also be different from that of rapalogs. mTOR kinase inhibitors are currently in preclinical development and some of them recently entered clinical trials.
10 Conclusions Rapalogs display pleiotropic effects, inhibiting tumor cell and endothelial cell proliferation and survival, finally yielding direct antitumor and/or antiangiogenic effects. In addition to renal cell carcinoma, a number of tumor types may benefit from rapalogs. Future studies should aim at identifying biological parameters that may predict the antitumor activity of mTOR inhibitors, distinguishing which of the possible mechanisms, i.e., cell cycle blockage, apoptosis, and autophagic induction
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in cancer cells versus antiangiogenic effects, are predominant. Inhibition of multiple signaling pathways using rapalogs in combination with other targeted agents and development of mTOR kinase inhibitors may represent interesting novel approaches for cancer therapy.
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Drug Combinations as a Therapeutic Approach for mTORC1 Inhibitors in Human Cancer Madlaina Breuleux and Heidi A. Lane
Abstract A central sensor of the availability of growth factors, nutrients, and energy sources, the mammalian target of rapamycin complex 1 (mTORC1) is an intracellular serine/threonine kinase which plays a key role in mammalian cell growth, proliferation, survival, and metabolism. Inhibitors of the mTORC1 kinase, including rapamycin (sirolimus), RAD001 (everolimus), CCI-779 (temsirolimus), and AP23573 (deforolimus), have been shown to have broad antitumor activity preclinically in experimental tumor models as well as clinically in cancer patients. The central role of mTORC1 in cell growth and metabolism suggests a promising drug combination potential, with mTORC1 inhibition sensitizing tumor cells to other anticancer agents. In this chapter we review the pre-clinical data supporting the combination of targeted and cytotoxic therapeutics with mTORC1 inhibitors. We assess this in the context of molecular aspects of mTORC1 inhibition and cross talk between different signaling pathways involved in malignant transformation. The known role of mTORC1 in the development of resistance to cancer therapies, as well as the potential for bypass mechanisms which may contribute to resistance to mTORC1 inhibitors, is presented in the context of preventing resistance through rationale drug combinations. Where full manuscripts are available, reference to supporting clinical data is provided. Keywords Targeted anticancer agents · Combination strategies · Cytotoxic combinations · mTORC1 signaling · Autophagy · Metabolism · Angiogenesis · Antitumor therapy
H.A. Lane (B) Basilea Pharmaceutica AG, Basel 4005, Switzerland e-mail:
[email protected] V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_8,
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1 Why Target the mTORC1 Pathway in Cancer? The mammalian target of rapamycin complex 1 (mTORC1) is an intracellular serine/threonine kinase, downstream of the phosphatidylinositol 3-kinase (PI3K)/AKT pathway, which plays a key role in cell growth, proliferation, survival, and metabolism [1]. mTORC1 acts as a sensor for nutrient, amino acid, and growth factor availability, monitoring intracellular energy status (Fig. 1) [2, 3]. As a reg-
Fig. 1 Cross talk between mTORC1 and different signaling pathways as a rationale for combination therapy. mTORC1 acts as a convergence point for sensing the energy/nutrient status of cells and integrating signals derived from activation of transmembrane receptor tyrosine kinases (RTKs) and downstream signaling cascades controlling cell proliferation, survival, and angiogenesis. Inhibition of mTORC1 was shown to sensitize cells toward targeted inhibitors directed against transmembrane RTKs and intracellular signaling cascades (such as the E2/ER, the PI3K/AKT, and the Ras/MAPK pathways) as well as toward cytotoxic agents interfering with DNA replication, mitotic spindle formation, and cellular metabolism. AMPK: AMP-activated protein kinase; ATP: adenosine triphosphate; E2: estrogen; ER: estrogen receptor; Grb2: growth factor receptorbound adaptor protein 2; HIF1α: hypoxia-inducible transcription factor 1, alpha subunit; MAPK: mitogen-activated protein kinase; MEK1/2: mitogen-activated protein kinase kinase; mTORC1: mammalian target of rapamycin complex 1; PI3K: phosphoinositide-3 kinase; PTEN: phosphatase and tensin homologue; Raf: v-Raf murine sarcoma viral oncogene homologue; Ras: rat sarcoma viral oncogene homologue; RTK: receptor tyrosine kinase; SHC: src homology 2 domaincontaining adaptor protein; SOS: son of sevenless guanine nucleotide exchange factor; TSC2: tuberous sclerosis complex 2; VEGF: vascular endothelial growth factor; Vps34: vacuolar protein sorting 34; 2-DG: 2-deoxyglucose; 3-BrOP: 3-bromo-2-oxopropionate-1-propyl ester
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ulator of the S6 ribosomal protein kinase 1 (S6K1) and the eukaryotic initiation factor 4E (eI4E)-binding protein (4E-BP1) [3], mTORC1 plays an essential role in the regulation of mRNA translation. Influencing the expression of a number of proteins known to be involved in cell proliferation and survival [1, 4], the close connection of this kinase with cell growth was recently highlighted by the finding that serum- and glucocorticoid-inducible kinase 1 (SGK1) represents another mTORC1 substrate, making a further link to cell cycle regulation through regulation of the phosphorylation and localization of the cyclin-dependent kinase inhibitor p27Kip1 [5]. Consistent with the central role played by this kinase in human cell biology, it is not surprising that the mTORC1 pathway is upregulated in many human cancers. Hence, mTORC1 has been pursued as an attractive target in the development of novel anticancer therapeutics [6]. Inhibitors of the mTORC1 kinase currently under evaluation as anticancer drugs are derivatives of rapamycin (sirolimus) and include RAD001 (everolimus), CCI-779 (temsirolimus), and AP23573 (deforolimus). Rapamycins exert their effects by binding to the immunophilin FK506-binding protein (FKBP12) [7]. The FKBP12/rapamycin complex binds mTORC1, preventing phosphorylation of downstream effectors involved in the regulation of global mRNA translation [8]. In cancer cell lines, in nutrient replete conditions, this principally results in a delay in progression through the G1 phase of the cell cycle [1], although apoptosis can be induced in the absence of nutrients [9–11]. In terms of antitumor activity, the broad inhibitory effects mTORC1 inhibition has on tumor cell growth is proposed to be complemented by direct effects on tumor vascularization [12–15]. Indeed, treatment of animal tumor models with mTORC1 inhibitors has been shown to reduce the expression of vascular endothelial growth factor (VEGF) and hypoxia-response genes such as HIF1α (hypoxia-inducible transcription factor 1, alpha subunit) [13–15], inhibiting tumor neovascularization by directly affecting both endothelial and smooth muscle cell biology [12, 13]. The application of mTORC1 inhibitors for treating various types of human cancer has been actively studied both pre-clinically and clinically [1, 16]. In cancer patients, mTORC1 inhibitors are generally well tolerated and induce disease stabilization with tumor regression in a subset of patients [17–19]. However, results from pre-clinical studies indicate that some mTOR-dependent functions are rapamycin-insensitive. This is based on the observation that the mTOR kinase exists in two mutually exclusive complexes. The rapamycin-sensitive mTORC1 complex contains raptor [1]. However, a rapamycin-insensitive, rictor-containing complex (mTORC2) also exists, reported to be implicated in the regulation of the actin cytoskeleton as well as the direct phosphorylation of AKT on Ser473 [1, 20, 21]. This suggests that direct inhibitors of the mTOR kinase domain may display a different spectrum of anticancer activity than observed with rapamycins. The genetic heterogeneity of cancer renders it a huge therapeutic challenge. In most tumor types, targeting a specific effector of a signaling pathway is unlikely to confer prolonged therapeutic benefit. Indeed, although clinical trial results in cancer patients treated with mTORC1 inhibitors are encouraging, highlighted by
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a survival advantage in metastatic renal cancer patients treated with RAD001 or CCI-779 [17–19], objective response rates are infrequent in some tumor types [22–24]. Moreover, pre-clinical data suggest that mTORC1 inhibitors have cytostatic activity rather than cytotoxic activity [1, 4]. Due to the central role of mTORC1 in cell growth and metabolism, inhibition of this kinase has been shown to sensitize tumor cells to enter a cell death program as a result of adverse conditions, such as nutrient deprivation [9, 10] or combination therapy with other anticancer agents [4]. As combination therapies are generally considered superior to monotherapy, and hence represent the standard approach for the treatment of most cancers, the combinatorial potential of mTORC1 inhibitors has been extensively addressed with both conventional cytotoxic and targeted anticancer therapeutics. Data have been obtained which illustrate a clear potential for the treatment of human cancer, in terms of the enhancement of antitumor efficacy as well as the ability to overcome drug resistance [4]. This chapter will review the rationale behind and the current status of pre-clinical combination experimentation, with reference to translation to the clinical setting where appropriate.
2 Combination with Receptor Tyrosine Kinase (RTK) Inhibitors The accessibility of growth factor receptor tyrosine kinases (RTKs) for therapeutic intervention, based on cell membrane localization and a pivotal role in the development of human cancer, has resulted in a multitude of both antibody- and small molecule-based treatment strategies [25, 26]. Clinically successful therapeutics aim at targeting both the tumor cell (e.g., epidermal growth factor (EGF) receptor (EGFR)/ErbB2 receptor-directed therapies such as trastuzumab, cetuximab, panitumumab, lapatinib, gefitinib, and erlotinib) [27–29] and the vascular compartment (e.g., inhibitors of VEGF receptor (VEGFR) signaling such as bevacizumab, sorafenib, and sunitinib) [30] or potentially both (e.g., cetuximab, panitumumab, gefitinib, erlotinib, bevacizumab, and sorafenib) [31, 32]. If one considers the relative number of registered therapeutics, RTK inhibition could be considered as one of the most successful targeted approaches so far. However, the phenomenon of clinical resistance to these agents would suggest a need for improvement, which may be facilitated by rational drug combination approaches [33–36]. For example, lack of response to EGFR/ErbB2-directed therapeutics is associated with deregulation of signaling elements downstream of these growth factor receptors, which could influence the activation status of (and hence dependency on) the mTORC1 pathway [4, 37]. Specifically, activation of the phosphoinositide-3 kinase (PI3K) pathway through insulin-like growth factor 1 (IGF-1) receptor (IGF-1R) RTK signaling, loss of PTEN (phosphatase and tensin homologue) function, or oncogenic mutation of PIK3CA (phosphoinositide-3 kinase, catalytic, alpha polypeptide) is clearly associated with poor prognosis of breast cancer patients after therapy with the ErbB2-directed monoclonal antibody trastuzumab [34, 38, 39]. Moreover, colorectal patients with PTEN loss or activating K-ras mutations are less likely to respond
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to EGFR-directed monoclonal antibody-based therapies such as panitumumab and cetuximab [40–43]. Given the facts that mTORC1 activation state is regulated through the PI3K/AKT pathway, that loss of PTEN or hyperactivation of AKT has been suggested to sensitize tumors to the effect of mTORC1 inhibitors [1], and that cross talk between the PI3K and Ras pathways is well established (see Fig. 1), a role for mTORC1 in these non-responsive tumor phenotypes may not be unexpected.
2.1 Combination with ErbB Receptor Tyrosine Kinase Inhibitors Members of the ErbB receptor tyrosine kinase family (ErbB1/EGFR, ErbB2, ErbB3, and ErbB4) are implicated in the development of many types of cancer. In particular EGFR and ErbB2 are aberrantly activated in a wide range of human tumors and are established drug targets for anticancer therapies [27]. An important mediator of ErbB receptor signaling is the PI3K/AKT/mTORC1 pathway (Fig. 1) [27]. Moreover, analysis of patient-derived invasive breast cancers has demonstrated a positive association between ErbB2 overexpression and the phosphorylation levels of both AKT and mTOR. Survival analysis showed that phosphorylation of each of these three markers was associated with poor disease-free survival, suggesting a causal relationship between ErbB2 overexpression and mTORC1 activation which was confirmed in vitro [44]. Furthermore, combined EGFR/phospho-mTOR expression was identified as an independent poor prognostic factor for recurrence and death in patients with biliary tract cancer [45]. This demonstrated crosstalk between the ErbB and mTORC1 pathways is complemented by evidence that mTORC1 can also signal independently from the ErbB receptors [46] and vice versa [47]. In this latter context, exogenous ErbB ligands have been shown to bypass the antiproliferative effects of mTORC1 inhibition in some EGFR- and ErbB2-overexpressing tumor lines [47]. Additionally, rapamycin treatment can induce transactivation of the EGFR, activating the pro-survival kinases Erk1/2 (MAPK) and p90RSK [48]. These points, together with the association between PI3K/mTORC1 pathway activation and clinical resistance to ErbB-directed therapeutics (see above), support the hypothesis that targeting mTORC1 in combination with anti-ErbB therapeutics might lead to more profound effects on tumor cell biology even in the setting of resistance [49]. Increasing evidence suggests that mTORC1 inhibitors have activity alone and in combination in both EGFR inhibitor-sensitive and inhibitor-resistant tumor models, restoring the ability of EGFR inhibitors to inhibit growth and survival in the resistant setting [50–59]. Indeed, rapamycin in combination with the EGFR inhibitor EKI-785 was shown to have synergistic antiproliferative and pro-apoptotic effects on tumor cells in vitro by disrupting parallel and complementary signaling pathways [60]. Additionally, combinations of RAD001 with the EGFR/ErbB2 inhibitor AEE788 in glioma, renal cell and breast cancer models resulted in a significant potentiation of antiproliferative effects [47, 61, 62]. Similar interactions were also
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reported for erlotinib and cetuximab in combination with rapamycin in biliary tract cancer cells [45]. Strikingly, and in agreement with the need to avoid any potential for ‘bypass’ mechanisms, the study of Stephan et al. [47] illustrated that profound effects on cell viability in ErbB2-overexpressing breast cancer lines were only evident when both mTORC1 and ErbB receptor inhibition were optimal [47]. Studies in transgenic mouse models of ErbB2-dependent human breast cancer also support a therapeutic potential for mTORC1 inhibitors in ErbB2-positive breast cancers [63], with RAD001 shown to overcome trastuzumab resistance caused by PTEN deficiency in vitro and in xenograft models [64]. Overall, pre-clinical studies combining mTORC1 inhibitors with anticancer drugs targeting EGFR and/or ErbB2 receptor tyrosine kinases suggest that this treatment modality may prove to be effective in the treatment of human cancer. Although a number of clinical trials have been initiated to assess the combination potential of both RAD001 and CCI-779 with EGFR/ErbB2 antagonists, there is as yet limited information published as full manuscripts. However, a phase I trial evaluating RAD001 with the EGFR inhibitor gefitinib in patients with advanced progressive NSCLC (non-small cell lung cancer) has suggested clinical activity, with two radiographic responses identified among eight response-evaluable patients [65]. In contrast, prospective assessment of the effects of reinitiation of erlotinib or gefitinib treatment in 10 patients with acquired resistance to either agent, followed by the addition of RAD001, suggested no benefit of the combination [66]. Clearly, these studies are only preliminary, and the field awaits more extensive clinical information in order to guide future development of this combination. 2.1.1 IGF-1 Receptor Tyrosine Kinase Activation of the PI3K/AKT/mTORC1 signaling pathway can occur through mutation of pathway components or through activation of upstream signaling molecules. A major upstream RTK regulating the PI3K/AKT pathway is the IGF-1R, a member of the insulin receptor subclass of RTKs which is frequently overexpressed in human tumors, playing a central role in transformation, invasion, and protection from apoptosis [67, 68]. Consequently, a number of monoclonal antibodies and small molecule IGF-1R inhibitors are currently in the late pre-clinical or clinical settings [67, 68]. Although, potent antitumor activity has been demonstrated in pre-clinical models after treatment with IGF-1R inhibitors alone, the IGF-1R pathway has additionally been implicated as a key player in the development of resistance to cytotoxic therapies, such as radiation and chemotherapy, as well as targeted therapies, such as ErbB receptor inhibitors, farnesyltransferase inhibitors (FTIs), and antiestrogens [34, 57, 67, 69]. This would suggest that combinations with IGF-1R inhibitors may delay the onset of resistance or enhance the response of tumors originally indifferent to therapy. A connection between IGF-1R and mTORC1 signaling has been known for some time. As mentioned earlier, IGF-1R is upstream of mTORC1, and it is now well established that the mTORC1 pathway itself negatively regulates insulin and IGF-1R signaling via S6K1-mediated proteasomal degradation of the insulin
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Fig. 2 Inhibition of the PI3K/AKT/mTORC1 pathway and its effect on AKT phosphorylation. Box I: Activation of the PI3K/AKT pathway, activating mTORC1, induces a negative feedback loop involving S6K1 which inhibits PI3K signaling. mTORC2 is suggested to act as a PDK2 (pyruvate dehydrogenase kinase, isoenzyme 2), inducing basal AKT phosphorylation on Ser473. Box II: Inhibition of mTORC1 leads to a G1 cell cycle arrest. At the same time the negative feedback loop is abolished which in some tumor lines results in the induction of AKT phosphorylation through PI3K and mTORC2. Box III: Concomitant inhibition of mTORC1 and the IGF-1R or PI3K/mTORC2 inhibits AKT activation. This is associated with more profound effects on tumor cell proliferation and survival. IRS1: insulin receptor substrate 1; IGF: insulin-like growth factor; IGF-1R: insulin-like growth factor receptor 1; InsR: insulin receptor; mTORC1: mammalian target of rapamycin complex 1; mTORC2: mammalian target of rapamycin complex 2; PI3K: phosphoinositide-3 kinase; S6K1: ribosomal protein S6 kinase, 70 kDa, polypeptide 1; S473: Serine 473; T308: Threonine 308
receptor substrate 1 (IRS-1) (see Fig. 2, Box I) [68, 70–72]. Recent studies have demonstrated that mTORC1 inhibition can lead to enhanced AKT phosphorylation in treated tumors from patients and in tumor lines, a phenomenon regulated to some extent through the IGF-1R/IRS-1 signaling modality (see Fig. 2, Box II) [73, 74]. An association between IGF-1R signaling and rescue of the effects of rapamycin on cell survival has also been made in in vitro models of rhabdomyosarcoma and breast cancer [72, 75, 76]. Interestingly, combinations of rapamycin or RAD001 with antibody (h7C10 and α-IR3) – or small molecule (NVP-AEW541) – based IGF-1R inhibitors have been shown to attenuate the effects of mTORC1 inhibition on AKT phosphorylation (see Fig. 2, Box III) [72, 77, 78], associated with enhanced effects on the proliferation and survival of rhabdomyosarcoma, breast cancer, and myeloma cell lines [72, 78, 79]. Moreover, enhanced effects on neuroblastoma cell growth and survival have been observed with α-IR3 in combination with either rapamycin or CCI-779 [80, 81].
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Another connection that has recently elicited interest is the relationship between IGF-1/mTORC1 signaling and survivin. Survivin is a member of the family of ‘inhibitor of apoptosis’ proteins, and is overexpressed in most human cancers. Regulating mitosis and programmed cell death, survivin is also involved in the promotion of angiogenesis and chemoresistance [82]. In vitro IGF-1 treatment elicited increased survivin expression in prostate cancer cells, resulting from translation of a pool of survivin mRNA in an mTORC1/S6K1-dependent manner [83]. Activation of IGF-1R also induced survivin expression in NSCLC cells, which was associated with resistance to the apoptotic activities of erlotinib and the FTI SCH66336 in an mTORC1-dependent manner [57, 69]. In both cases, rapamycin treatment abolished resistance, inducing apoptosis [57, 69]. Hence, taking all these data together, there is a clear combination potential which may be broadly applicable in cancer, consistent with deregulated IGF-1R and mTORC1 signaling in many tumor histotypes [1, 67, 68, 82].
2.2 Synergistic Inhibition of the PI3K/AKT/mTOR Pathway The class I PI3K/AKT pathway is a prototypic survival pathway that is constitutively activated in many types of human cancer. Mechanisms of pathway activation include loss of PTEN function, amplification or mutation of PI3K, amplification or mutation of AKT, and activation of growth factor receptors [84]. Because of its central role as a convergence point for many growth and survival signals (see Fig. 1) [73], this pathway is an attractive target for cancer therapy [85]. Interestingly, constitutive or residual activation of the PI3K/AKT pathway has been associated with resistance to a number of treatment modalities, with evidence that combination treatment with PI3K/AKT pathway inhibitors may sensitize tumor cells to chemotherapy or targeted drugs [86]. In contrast, hyperactivation of the class I PI3K/AKT pathway has been associated with enhanced sensitivity to mTORC1 inhibitors [1]. The suggestion that disruption of PTEN function may be the important factor in defining response to mTORC1 inhibitors [87–90] was recently supported through work in breast cancer models [91]. However, loss of PTEN function was found to be insufficient to adequately predict responsiveness to RAD001 in a comprehensive panel of 17 glioblastoma multiforma mouse models [92]. With this in mind, it is interesting to note that a phase I trial assessing rapamycin in PTEN-deficient glioblastoma patients recorded a reduction in tumor cell proliferation in 7 of 14 patients treated with rapamycin for 1 week [93]. Strikingly, tumor cells harvested from the non-responders retained sensitivity to rapamycin ex vivo [93], suggesting greater complexity in the patient setting than can be predicted by PTEN status/PI3K pathway activation alone. As mentioned in the previous section, a number of studies have shown that mTORC1 inhibition induces AKT phosphorylation in a subset of human cancer cell lines and patient-derived tumor material [1, 73, 74, 77]. This modulation of AKT activity has been suggested to have a potentially negative impact on mTORC1
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inhibitor efficacy through attenuation of effects on tumor cell proliferation and apoptosis [1, 73]. In this context, we have recently demonstrated that induction of AKT phosphorylation following RAD001 treatment of a large panel of mixed histotype tumor lines does not correlate with effects on proliferation, suggesting that this phenomenon cannot be used as a general surrogate biomarker predictive of response [94]. Intriguingly, in the PTEN-deficient glioblastoma phase I trial mentioned above [93], rapamycin treatment led to AKT activation in half of the patients, as judged by increased phosphorylation of the AKT substrate PRAS40 (proline-rich AKT substrate, 40 kDa). This phenomenon was associated with a significantly shorter time to progression during post-surgical rapamycin maintenance therapy. Hence, induction of the AKT pathway following mTORC1 inhibition may prove in some tumor types to be a strong rationale for combination therapy approaches directly targeting this pathway. The mTORC2 (rictor/mTOR) kinase is now accepted as the factor responsible for AKT phosphorylation on Serine 473 (S473) (see Fig. 2, Box I) [1, 73]. Although it has been suggested that rictor does not play a role in the induction of AKT phosphorylation after acute rapamcyin treatment [95], we have recently performed an extensive siRNA screen on five mixed histotype tumor cell lines with longer treatment times, which has clearly demonstrated that RAD001-induced prolonged AKT S473 phosphorylation is dependent on the maintenance of rictor expression (see Fig. 2, Box II) [94]. A role for rictor-containing kinase complexes suggests that logical drug combinations aimed at attenuation of this response may not only include PI3K and/or AKT inhibitors but also compounds directly targeting rictor complexes, particularly as mTORC2 activity appears to be needed in vivo to permit high-level PI3K/AKT signaling [96]. In this respect, recent publications have suggested that dual mTORC1/mTORC2 inhibitors do have the potential for activity in rapamycin-resistant tumor cell lines [97, 98]. It is also of note that concomitant inhibition of mTORC1 and p110δ PI3K activity with RAD001 and IC87114, respectively, induced additive inhibition of the proliferation of blast cells derived from newly diagnosed AML (acute myeloid leukemia) patients [77]. Moreover, using in vitro glioma models, inhibition of p110α PI3K activity, mTORC1, and mTORC2 using the PI-103 inhibitor produced a cell cycle arrest similar to that observed when combining a p110α inhibitor (PIK-90) with rapamycin. Both treatment modalities caused a more profound growth arrest than observed with PIK-90 or rapamycin alone [99]. Interestingly, treatment of glioma xenografts with PI-103 resulted in stabilization of tumor growth which was associated with a block in tumor cell proliferation and no increase in apoptosis, an observation consistent with the cytostatic effect of PI-103 in vitro [99]. Although a similar cytostatic effect was observed after PI-103 treatment of AML cell lines, a potent induction of apoptosis was induced in primary blast cells derived from AML patients as well as in T-cell acute lymphoblastic leukemia cells [100, 101]. Additionally, PI-103 was shown to induce autophagy-mediated cell death rather than apoptosis in malignant peripheral nerve sheath tumor (MPNST) in vitro models [102]. These data suggest that cellular context plays a role in defining response to dual PI3K/mTOR inhibitors.
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Taking this point one step further, NVP-BEZ235 is a pan-class I PI3K inhibitor (inhibiting p110α, β, δ, and γ) which also inhibits mTORC1 and mTORC2, resulting in a potent antiproliferative effect in vitro and in animal tumor xenograft models [103, 104]. Although this agent has a cytostatic effect in some tumor lines, we have recently demonstrated in vitro cytotoxic activity with this agent in mouse embryo fibroblasts with deregulated mTORC1 activity, as well as in the breast cancer cell line MCF7. Loss of cell viability was far more profound than observed after treatment with the mTORC1 inhibitor RAD001 and could not be augmented at sub-optimal NVP-BEZ235 concentrations by combination of the two agents [94]. Taken together these studies demonstrate that dual inhibition of the PI3K/mTOR pathway can result in more profound antiproliferative/cytotoxic effects on tumor cell biology (see Fig. 2, Box III). However, activity against all the class I PI3Ks and mTORC1/mTORC2 may be the preferential approach and the cellular context (e.g., primary versus lines) may be an important consideration when profiling these inhibitors pre-clinically. Although there are limited studies performed so far, and more information is needed on the potential of this approach, the in vitro activity of small molecule inhibitors with dual PI3K/mTOR activity at optimal doses does not seem to be augmented through the addition of rapamycin-based mTORC1 inhibitors [94, 99]. Hence, inhibitors like NVP-BEZ235 [104], which is already in phase I trials in cancer patients, may already provide the answer with regard to improving on mTORC1 inhibitors through dual targeting of the PI3K and mTOR kinases.
2.3 Targeting Multiple Kinases Cancer progression is dependent on deregulated signal transduction involving multiple growth factor RTKs as well as a plethora of intracellular signaling cascades. Work with a number of tumor models identified several key signaling pathways promoting cancer development, which act independently, in parallel or are interconnected. Understanding this complexity provided the rationale to simultaneously inhibit multiple kinases in order to improve therapeutic benefit. Pre-clinical and clinical data indicate potent antitumor activity of single drugs inhibiting multiple molecular targets, such as sorafinib (VEGFR, PDGFR (platelet-derived growth factor receptor), KIT (v-Kit Hardy-Zuckerman 4 feline sarcoma oncogene homologue), FLT3 (FMS-related tyrosine kinase 3), RAF (v-Raf murine sarcoma viral oncogene homologue)) and sunitinib (VEGFR, PDGFR, KIT, FLT3, RET (rearranged during transfection proto-oncogene)), or combination therapy involving drugs with selective target specificity [105]. The PI3K/AKT/mTOR (PI3K), the protein kinase C (PKC), and the RAS (rat sarcoma viral oncogene homologue)/RAF/MAPK (RAS) signaling pathways play a major role in tumor development and progression [73, 106, 107] and cooperation of these pathways has been observed [108]. However, clinical trials aiming at highly selective inhibition of one of these signaling cascades were associated with limited or sporadic response rates [105]. The PI3K and RAS pathways intersect at various points, providing potential for redundancy. In this regard, inhibition
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of an intracellular kinase located at the convergence of two signaling pathways, such as mTORC1 (see Fig. 1), presents a promising therapeutic strategy. In many tumor types, including melanoma, glioblastoma, lung, and kidney cancer, both pathways are activated through multiple mechanisms [73, 84, 106]. Consistent with the above hypothesis, treatment with RAF inhibitors (e.g., sorafinib, LBT613) in combination with low-dose rapamycin or RAD001 synergistically inhibited serum-stimulated melanoma cell proliferation and invasive growth in organotypic culture [109, 110]. Moreover, combination of RAD001 with LBT613 significantly decreased glioma cell proliferation, migration, and invasion [111]. In human lung cancer cell lines, inhibitors of MEK1/2 (mitogen-activated protein kinase kinase) (e.g., CI-1040 or PD0325901) in combination with rapamycin or AP23573 also exhibited dose-dependent synergistic suppression of proliferation associated with decreased protein translation [112]. Likewise, combination of MEK1/2 inhibitors (UO126, PD184352) with rapamycin led to greater tumor cell growth inhibition than either drug alone [113, 114]. Recent data from animal studies [115], together with the report that mTORC1 inhibition can activate MAPK in patient tumors and tumor cell lines [48, 116, 117], suggest that this combination scenario is worthy of further investigation. Taking this point further, heat shock protein 90 (HSP90) is a molecular chaperone essential for the conformational maturation, stability, and transport of many oncogenic signaling proteins such as the RAF, ErbB2, and IGF-1R kinases [118]. In breast cancer, high expression of HSP90 is associated with high ErbB2 and estrogen receptor levels, large tumors, and high nuclear grade and lymph node involvement. Furthermore, high HSP90 expression in primary breast cancer was suggested to predict a population of patients with decreased survival rates [119]. In multiple myeloma, the PI3K pathway and the heat shock protein family are also frequently upregulated [120, 121], regulating the cyclin D/retinoblastoma pathway, a critical pathway in the development of multiple myeloma [122]. Combination of rapamycin with 17-allylamino-17-demethoxygeldanamycin (17-AAG), an HSP90 inhibitor in late clinical development, was demonstrated to have more profound effects on the proliferation of breast cancer cells [123] and to synergistically inhibit proliferation and induce apoptosis in multiple myeloma cell lines [122]. Taken together, the results of these studies support the hypothesis that simultaneous inhibition of multiple intracellular signaling pathways has the potential to enhance antitumor efficacy. Rather than treating tumor cells directly, a number of therapies have been developed which target host processes supporting tumor growth such as vascular endothelial cells and tumor-associated angiogenesis. Neovascularization, the process of developing new blood vessels from existing ones, is critical for tumor cell growth, survival, invasion, and metastasis [31]. It is tightly controlled by the concerted action of pro-angiogenic and anti-angiogenic factors released by tumor and stromal cells in the tumor microenvironment. Of these, VEGF is a key factor regulating multiple steps in the angiogenic process [31]. VEGF-specific anticancer therapy is clinically applied, with inhibitors targeting VEGF directly (e.g., bevacizumab, a humanized anti-VEGF mAb) or the VEGFR kinase domain (e.g., PTK787, sunitinib
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and sorafinib small molecule kinase inhibitors). The PI3K/mTORC1 pathway is also involved in tumor angiogenesis, principally regulating the expression of HIF1α and VEGF (see Fig. 1) [124], as well as some aspects of VEGFR signaling in endothelial cells [125, 126]. It has been known for some time that rapamycin inhibits tumor angiogenesis [12]. However, in a recent study, the molecular analyses were taken further based on the observation that RAD001 elicited potent antitumor activity in animal tumor models, irrespective of the intrinsic sensitivity of the tumor cell lines used [13]. RAD001 was shown to inhibit the vascularization of tumors derived from both sensitive and resistant lines. Moreover, a comparison of RAD001 with PTK787 indicated that VEGFR and mTORC1 inhibitors elicited similar but also distinct effects on tumor vascular biology. Although RAD001 inhibited VEGF-induced endothelial cell proliferation and tumor cell VEGF production, unlike PTK787 RAD001 did not inhibit VEGF-induced endothelial cell migration or blood vessel leakiness, and tumor vascular permeability was also unaffected. In contrast, RAD001 had more profound effects on tumor vessel maturation than PTK787 [13]. Hence, the fact that mTORC1 inhibitors and VEGFR antagonists can have overlapping as well as distinct effects on tumor angiogenesis is a clear rationale for combining these agents, but may also explain the prolongation of progression-free survival observed with RAD001 in patients with metastatic renal cell carcinoma whose disease had progressed on VEGF-targeted therapy [19]. With respect to the former, as compared to the single agents, combination of the specific VEGF inhibitory antibody bevacizumab with rapamycin resulted in a significantly greater antitumor effect in six xenograft models of patient-derived hepatocellular carcinoma (HCC), associated with more pronounced reductions of VEGF expression and tumor microvessel density [127]. Positive therapeutic effect was even more striking in an intraperitoneal model of HCC, with more profound antitumor effects associated with reduced ascite levels and prolonged survival [127]. These combination data are clearly very promising and should be followed up clinically.
2.4 Combination with E2 Antagonists Therapeutic agents that interfere with estrogen receptor (ER) function, including antiestrogens such as tamoxifen and inhibitors of the aromatase involved in estrogen biosynthesis such as anastrozole and letrozole, have contributed to a dramatic reduction in breast cancer mortality. However, despite initial responses, many tumors will eventually recur [128–130]. A lot of work has been performed to investigate the mechanisms involved in lack of response or the development of resistance to endocrine therapy, with altered expression of ER (and coregulators), altered metabolism of endocrine agents, as well as increased growth factor signaling and estrogen hypersensitivity, being heavily implicated [130, 131]. It has become clear that the estrogen/ER pathway is more complex than initially anticipated, exhibiting pleiotropic effects through non-genomic interactions with growth factor signaling pathways. Several levels of interaction between ER and growth factor pathways,
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including the ErbB2, PI3K/AKT, MAPK, and mTORC1 pathways, have been documented (see Fig. 1) [130, 131]. Indeed, AKT pathway activation is observed in many breast cancers, being associated with a more aggressive clinical phenotype [84], a worse clinical outcome in endocrine-treated patients [132], and an increased risk of relapse and death in ER-positive patients treated with tamoxifen [133]. Breast tumors can also adapt to endocrine deprivation therapy, as a result of treatment with aromatase inhibitors, for example, by developing hypersensitivity to estrogen through upregulation of ER expression as well as activation of the MAPK, PI3K, and mTORC1 pathways [134]. This may contribute to both the failure of therapy as well as the development of drug resistance. There is increasing evidence that mTORC1 is required for estrogen-driven proliferation of breast cancer cells in vitro [1, 135–137]. In this context, dual inhibition of mTORC1 and ER signaling in vitro, using rapamycin, RAD001, or CCI-779 in combination with tamoxifen or letrozole, has been demonstrated to cause more profound effects on the proliferation of endocrine-sensitive, ER-positive breast cancer lines. This was defined as synergy in some cases [135, 136, 138, 139]. Increased accumulation in G1 phase of the cell cycle was noted for the combination versus single-agent treatment [135, 138]. Furthermore, loss of cell viability was evident at optimal drug combinations [135, 139], with superior antitumor activity shown in an MCF-7 breast tumor xenograft model [138]. Strikingly, resistance to endocrine therapy, elicited by the overexpression of myristoylated AKT in MCF-7 cells, was associated with upregulation of the mTORC1 pathway both in vitro and in tumor xenografts, as well as with more potent effects of CCI-779 on tumor growth [140]. This observation is consistent with the fact that increased PI3K signaling in breast cancer cells confers sensitivity to rapamycin [87]. In this context, inhibition of mTORC1 activity with a number of agents effectively restored susceptibility to estrogen antagonists (including tamoxifen, letrozole, and fulvestrant) both in vitro and in animal xenografts [140, 141]. This phenomenon was related to mTORC1 inhibition in the aberrant AKT setting restoring the normal apoptotic response of breast cancer cells to hormone therapy [140]. Recently, a similar combination effect was observed in a tamoxifen-resistant breast cancer xenograft model selected from a patient-derived breast carcinoma (named 3366) by continuous treatment of xenograft-bearing animals with the antiestrogen [142, 143]. In this case also, increased AKT phosphorylation was associated with the development of resistance, and a significantly superior effect on tumor growth was obtained when combining tamoxifen with RAD001 as compared to the single agents [143]. Interestingly, in the same study, analysis of xenografts derived from a patient-derived breast cancer with inherent tamoxifen resistance (named 4049) showed a potent antitumor response to RAD001 alone, which was not augmented through combination with tamoxifen [143], suggesting a greater degree of complexity exists in terms of response to single versus combination therapy in this context. Taken together, all these data clearly point to a potential for combining estrogen antagonists with mTORC1 inhibitors in breast cancer patients, in both endocrinesensitive and endocrine-resistant settings. Consequently, a number of clinical trials
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have been initiated in breast cancer patients with both RAD001 and CCI-779 [131, 144–146]. Although phase II trials with CCI-779 combined with letrozole in locally advanced or metastatic breast cancer patients suggested promising response rates, phase III trials (first-line hormonal therapy) were terminated due to an absence of benefit over letrozole alone [145, 147]. With regard to RAD001, a phase 1b study, assessing daily therapy with letrozole in metastatic breast cancer patients stable or progressing on letrozole alone, suggested antitumor efficacy with no pharmacokinetic interactions and an acceptable safety margin [144]. This prompted a phase II randomized neoadjuvant study in operable ER-positive breast cancer, where tumor biopsies were taken for biomarker analysis at baseline and after 15 days treatment [148]. Patients were treated for 4 months with RAD001 and letrozole or placebo and letrozole, with a primary end-point of clinical response by palpation. Strikingly, there was a significant increase in response rate in the RAD001 arm as compared to letrozole alone, a finding corroborated by a more profound antiproliferative response (reduction in tumor Ki67 expression at day 15) in the former. Supportive of an association between PI3K/AKT/mTORC1 signaling and insensitivity to endocrine therapies, tumors with gain-of-function mutations in exon 9 of the PI3K, catalytic, alpha (PIK3CA) gene were found to have a reduced antiproliferative response to letrozole alone as compared to the combination [148]. Although the numbers of patients with these mutations were relatively small in this study, such genetic changes appear predictive of poor prognosis [149]. Hence, this preliminary observation, together with the demonstrated clinical potential of combining mTORC1 inhibitors with letrozole, could have implications for future therapeutic and patient selection decisions.
2.5 Combination with Cytotoxic Agents Cytotoxic therapies in combination have been used for decades in the treatment of cancer, and represent standard-of-care regimens routinely used in oncology today. This strategy has more recently been extended to take advantage of the sensitizing effect a targeted therapeutic can have on the activity of a conventional cytotoxic, and offers the opportunity to prevent resistance development or even restore sensitivity to tumor cells that have acquired resistance. Pre-clinical combination studies indicate that mTORC1 inhibitors have great potential to have synergistic effects with standard cytotoxics, theoretically allowing dose reductions and opening up a therapeutic window of opportunity. 2.5.1 Combination with DNA-Damaging Agents Many tumors are insensitive to DNA-damaging agents or relapse after acquiring chemoresistance; phenomena associated with activation of survival pathways such as the PI3K/AKT/mTORC1 pathway. Gemcitabine (2,2-difluorodeoxycytidine), a deoxycytidine-analogue antimetabolite with broad activity against a variety of solid tumors and lymphoid malignancies, plays an emerging role in combination with
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novel targeted agents [150]. Combination of CCI-779 with gemcitabine has synergistic effects in pancreatic cancer models in vitro [151] and in vivo [152], as well as in breast cancer models in vitro [153]. Doxorubicin, a DNA-intercalating agent inhibiting topoisomerase II, also elicits additive antitumoral activity when administered with rapamycin to animal models of hepatocellular carcinoma [154]. This was related, in part, to accentuation of antiproliferative effects on vascular endothelial cells leading to a more profound decrease in tumor microvessel density, suggesting that enhanced efficacy may be at the level of both tumor and host cells. Striking combination effects have also been reported for mTORC1 inhibitors combined with platin-based DNA cross-linking agents, such as cisplatin and carboplatin, in a number of tumor types including breast, ovarian, endometrial, and melanoma [8, 153, 155–157]. S6K1, acting downstream of mTORC1, mediates the translation of proteins essential for cell growth and proliferation [1, 3]. mTORC1 inhibitors block the activation of S6K1, thus compromising the cell’s ability to progress through G1 phase of the cell cycle [1, 3, 158]. Cell death caused by cisplatin has been associated with decreased activity of S6K1 [159]. Consistent with this observation, protection from cisplatin-induced apoptosis can be mediated through activation of the AKT/mTORC1/S6K1 pathway [159–162] and reversed by mTORC1 inhibition [160–163], which may be due to reduced expression of growth and antiapoptotic proteins [162]. Beuvink et al. demonstrated that RAD001 sensitizes cells to cisplatin-induced cell death in vitro by attenuating p53-dependent expression of the cell cycle regulator p21Waf1/Cip1 , through a global inhibition of protein translation [8]. This observation was supported by studies showing that cells lacking p21Waf1/Cip1 display enhanced sensitivity toward DNA-damaging agents [164, 165], whereas tumors expressing high levels of p21Waf1/Cip1 are prone to be resistant [166, 167]. Taken together, these observations support the proposition that attenuation of p21Waf1/Cip1 expression in malignant cells subverts the normal repair process induced by sub-optimal DNA damage, potentially making DNA-damaging agents more effective [168]. Hence, mTORC1 inhibitors could be considered as p21Waf1/Cip1 attenuators, providing a clear molecular rationale for this combination and suggesting that p53 genetic status and/or p21Waf1/Cip1 expression status could be valuable in terms of patient selection [8, 168]. Although there are indications that combination therapy with rapamycin, radiation, and cisplatin may be well tolerated in NSCLC patients [169], the clinical validation of the potential of combining mTORC1 inhibitors with DNA-damaging agents has yet to be comprehensively reported.
2.5.2 Microtubule-Targeted Agents Microtubule-targeted agents, such as the taxanes (microtubule stabilizing) and vinca alkaloids (microtubule destabilizing), are widely and successfully used for the treatment of human cancer [170]. Recent studies have shown that inhibition of mTORC1 using rapamycin or RAD001 increases the in vitro chemosensitivity of lymphoma
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and solid tumor models to microtubule inhibitors [171–174], including an ovarian cancer line considered taxane resistant [171]. Interestingly, combination of RAD001 with docetaxel was more effective in decreasing tumor volume in a bone model of prostate cancer, with RAD001 also having a significant positive impact on body weight losses caused by tumor cachexia, suggesting an additional benefit of adding mTORC1 inhibitors to standard therapies [173]. Moreover, CCI-779 administered between docetaxel treatments resulted in a greater tumor growth delay than docetaxel alone in prostate cancer xenografts, indicating that mTORC1 inhibition can prevent repopulation of cancer cells between courses of therapy [175]. Despite these positive reports, there have been discussions within the field which may have implications with regard to the molecular basis of these combination effects. For example, the BCL-2 family of pro-survival proteins are overexpressed in a variety of tumors and have been directly related to unfavorable prognosis of human cancer [176]. Consequently, attempts have been made to target this family of proteins for cancer therapy [176]. Although it has been suggested that disruption of microtubule function by treatment with paclitaxel exerts an antitumor effect, at least in part, via inhibition of S6K1 [177], microtubule-targeting agents (such as paclitaxel and nocodazole) have been shown to activate mTORC1 [178, 179], resulting in BCL-2 phosphorylation/inactivation and apoptosis [179]. Consequently, rapamycin pretreatment was shown to rather inhibit the increased apoptosis associated with microtubule damage in human follicular B-cell lymphoma lines expressing high levels of BCL-2 [179]. This association of mTORC1 inhibition with activation of an antiapoptotic program is at odds with the positive combination data described in [171–173, 175, 180], as well as with the observation that AKT upregulation can increase resistance to microtubule-targeted agents through activation of mTORC1 [181], a phenomenon reversed by combination with rapamycin [181]. Although more experimentation is required to solve this molecular issue, one could speculate that choice of tumor model or dosing schedule may explain the differences observed in the outcome of such combinations. In this regard, consistent with the proposal that drug schedule may be important when combining mTORC1 and microtubuletargeted agents [179, 180], it is interesting to note that we have demonstrated a clear schedule dependency when combining RAD001 with paclitaxel in human tumor xenograft mouse models. Antagonism was observed when either drug was administered prior to the other, with positive antitumor interactions only seen with concomitant administration [182]. These points clearly should be expanded upon and taken into account when designing clinical dosing schedules, even if the significance of the demonstrated interaction between microtubule disruption, mTORC1 inhibition, and BCL-2 function is currently unclear.
2.6 Interfering with Tumor Cell Metabolism In contrast to normal cells, cancer cells exhibit an increased dependency on the glycolytic pathway for ATP generation. This metabolic difference provides a biochemical basis for the design of therapeutic strategies, one of which is to inhibit
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glycolysis and thus preferentially impact tumor metabolism [183]. Another means to impact tumor metabolism is to target control mechanisms that affect the expression and/or function of metabolic regulators. Whereas full mTORC1 activation requires positive signals from both nutrients (glucose and amino acids) and growth factors, energy stress leads to rapid suppression of mTORC1 activity [184]. This phenomenon is modulated through the AMP-activated protein kinase (AMPK) pathway [185], leading to a reduction in cell growth, and has been suggested to contribute to the suppression of tumorigenesis [186]. AMPK achieves this by directly phosphorylating and activating a negative regulator of mTORC1, the tuberous sclerosis complex 2 (TSC2) tumor suppressor (Fig. 1). Moreover, another AMPK substrate is the mTORC1-binding partner raptor, the phosphorylation of which is necessary for the full engagement of the AMPK-mediated metabolic checkpoint [184]. The central positioning of mTORC1 in the regulation of metabolism, together with the fact that mTORC1 inhibitors cause a relatively moderate cytostatic effect in optimal growth conditions, raises the question of whether inhibition of mTORC1 and glycolysis would synergistically impact energy metabolism in cancer cells. Strikingly, in hematological malignancies, Xu et al. provided evidence that combining mTORC1 inhibition with blockade of glycolysis, using 3-bromo-2oxopropionate-1-propyl ester (3-BrOP), synergistically suppressed glucose uptake and severely depleted cellular ATP pools. This resulted in a significant enhancement of tumor cell killing [187]. 2-Deoxyglucose (2-DG), a synthetic analogue of glucose that blocks the first step in glycolysis, is currently in early clinical trials in solid tumors based on the rationale that targeting hypoxic tumor regions will raise treatment efficacy when combined with standard chemotherapy [188]. Using in vitro and in vivo models, 2-DG was shown to modulate AMPK and mTORC1 activities by limiting energy availability [189]. Interestingly, high levels of HIF-1α were shown to confer resistance to 2-DG [190]. As HIF-1α expression is downstream of the mTORC1 pathway [1] (see Fig. 1), it is not surprising, therefore, that a recent study demonstrated that CCI-779 hypersensitizes hypoxic tumor cells to 2-DG, with CCI-779-induced HIF-1α downregulation coinciding with increased tumor cell killing [191]. The same study concluded that intrinsically low mTORC1 and HIF-1α expression may be important predictors of the sensitivity of hypoxic tumor cells to 2-DG alone or in combination with an mTORC1 inhibitor [191]. These data indicate that the addition of mTORC1 inhibitors to clinical protocols that include 2-DG (or other glycolytic inhibitors) may potentiate tumor cell killing in hypoxic regions with a reduced HIF-1α response.
2.7 Inhibiting Autophagy Cell death can be achieved not only by apoptosis (type I programmed cell death) but also by necrosis, mitotic catastrophe, and autophagy. Autophagy represents a catabolic process by which the cell recycles its components through selfconsumption of cellular organelles and bulk cytoplasm. In times of stress, it serves
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to generate much needed nutrients [192]. Tumorigenesis is associated with downregulation of autophagy. At the same time, basal autophagy has been harnessed by some tumor cells as a survival mechanism to protect against ischemia and signals that induce apoptosis [193]. Hence, autophagy has been suggested as an alternative cell death pathway to target for cancer therapy [192]. The class III PI3K Vps34 (vacuolar protein sorting 34) is required for induction of autophagy during nutrient deprivation (Fig. 1) [194]. Autophagy is negatively regulated by mTORC1 [195] and can be induced in all mammalian cell types by mTORC1 inhibitors or ionizing radiation [196]. In malignant glioma cells, inhibitors of the PI3K/AKT pathway greatly enhanced the effectiveness of mTORC1 inhibitors by synergistically augmenting rapamycin-induced autophagy, sensitizing even rapamycin-resistant tumor cells to undergo autophagy [197]. This suggests that combinations of PI3K and mTORC1 inhibitors may elicit other antitumor responses than simply represented by augmentation of apoptosis. One example of pro-autophagic chemotherapy, aimed at overcoming resistance of cancer cells to apoptosis, comes from the use of temozolomide. This cytotoxic drug provides therapeutic benefit in glioblastoma patients and is undergoing clinical testing against several apoptosis-resistant cancer types [198]. Thus, further success in certain aggressive tumors may be achieved through combination of pro-autophagic cytotoxics such as temozolomide with mTORC1 inhibitors. In this respect, it is interesting to refer to a preliminary publication demonstrating a dramatic interaction between RAD001 and temozolomide in an orthotopic model (U87MG) of glioblastoma [199]. Strikingly, a profound positive antitumor interaction was observed particularly in larger (established) tumors, an observation worthy of further investigation.
3 Perspectives The extensive pre-clinical package discussed in this chapter clearly substantiates the premise of combining mTORC1 inhibitors with chemotherapeutic agents or other targeted anticancer drugs. These studies indicate that mTORC1 inhibitors can sensitize tumor cells to other agents or modulate resistance phenotypes, resulting in more profound antitumor effects. Additionally, silencing of red flags associated with mTORC1 inhibition, such as the upregulation of AKT and MAPK activity observed in the tumors of some treated patients, may be achieved through rational combination strategies. However, although there are already some interesting clinical observations, it is still early days with regard to the clinical evaluation of mTORC1 combination strategies. This raises the question of how can one prioritize which combination, in which patient population, holds the most promise in the clinic? The decision could be based on efficacy alone, but clearly therapeutic window is a major issue. Although tumors appear to be more dependent on certain signaling pathways, such as the mTORC1 pathway, the role of the target in normal
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cells is an important consideration. For example, although combination of mTORC1 inhibitors with gemcitabine has shown promise pre-clinically, unexpected toxicity in a RAD001 phase I clinical trial led to an early discontinuation of the study [200]. A similar situation was observed with CCI-779 in combination with 5-fluorouracil and leucovorin [201]. Up until now, most pre-clinical work has aimed to show improved efficacy with little consideration for a thorough investigation of potentially negative interactions which would affect tolerability in patients. It may be advisable to redirect some pre-clinical studies to concentrate on expanding the therapeutic window while maintaining clinical benefit. In this regard, analysis of the effect of drug administration schedule could provide potentially beneficial information before entry into the clinic. However, this is not only in terms of therapeutic window but also for potentially enhancing antitumor effect, particularly with agents which cause cellular defects at different points in the cell cycle (e.g., G1/S versus G2/M). The latter is highlighted by the clear antagonism observed for combinations of RAD001 and paclitaxel if the two agents are not administered concomitantly [182]. Hence, although the predictability of mouse models of human cancer can be (and is often) debated, more extensive pre-clinical experimentation could be informative for the clinic. One major point for all cancer therapeutics is ‘are we targeting the right patient population?’ This is particularly difficult in the case of combination therapy, where the mechanism of action of two drugs has to be taken into account. Although, more biomarker work has been incorporated into clinical trial designs, finding a marker which stratifies patients with regard to potential for response to combination therapies is difficult enough pre-clinically; translating to the clinical setting is an even higher hurdle. With this in mind analysis of mutations which are known to activate the mTORC1 pathway and be involved in resistance to the co-administered therapeutic could be a simple solution. This was highlighted in the RAD001/letrozole phase 1b neoadjuvant breast cancer trial, where tumors with gain-of-function mutations in exon 9 of the PIK3CA gene were found to have a reduced antiproliferative response to letrozole alone as compared to the combination [148]. As we have outlined earlier, this observation is consistent with the activation of the PI3K/AKT pathway being implicated in predicting response to mTORC1 inhibitors, as well as being associated with resistance to endocrine therapies. The correlation between PIK3CA genetic status and patient response in this trial was based on a small number of patients, but is an example that simply searching for molecular connections between sensitivity to one agent and resistance to the other may provide valuable insight into patient stratification for combination therapy with mTORC1 inhibitors. Taken together, the great potential to use mTORC1 inhibitors in therapeutically beneficial combination treatment strategies is undisputed. What is needed is careful pre-clinical evaluation and well-designed clinical trials, based on an understanding of the molecular mechanisms of action of each agent and, most importantly, based on what not to combine.
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Downstream Targets of mTORC1 Bruno D. Fonseca and Christopher G. Proud
Abstract The best-understood targets for mTOR complex 1 (mTORC1) are proteins that are involved in mRNA translation or in its control. They include the ribosomal protein S6 kinases and the eukaryotic initiation factor (eIF) 4E-binding proteins (4E-BPs). The latter regulate the availability of the cap-binding protein eIF4E and the formation of eIF4F complexes which promote the translation of certain mRNAs. The physiological roles of the S6 kinases are less clear. 4E-BPs and S6 kinases all contain a TOR-signaling (TOS) motif which allows them to interact with the mTORC1 component raptor and facilitates their phosphorylation by mTORC1. Two other proteins, the transcriptional regulator hypoxia-inducible factor (HIF)-1α and proline-rich Akt substrate 40 kDa (PRAS40), also contain TOS motifs and are regulated by mTORC1. Several other processes are also controlled by mTORC1; these include translation elongation (through the regulation of eukaryotic elongation factor (eEF2) kinase), autophagy, and the transcription of genes involved in mitochondrial function. Here, we review current understanding of signaling downstream of mTORC1. Keywords Mammalian target of rapamycin · Protein synthesis · Translation factor · Transcription · Cell cycle · Protein kinase
1 Introduction Signaling through mammalian target of rapamycin complex 1 (mTORC1) is the focus of a high level of interest, reflecting the major advances that have been made in recent years in understanding mTORC1 and its control. There
C.G. Proud (B) Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; School of Biological Sciences, University of Southampton, Southampton SO16 7PX, UK e-mail:
[email protected] V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_9,
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is also an enormous interest in the role of mTORC1 signaling in human disease, particularly in cancer. Impetus for this was provided by the discovery that rapamycin very potently inhibits transformation by phosphatidylinositide 3-kinase (PI 3-kinase) and Akt [1] and the proliferation of certain cancer cell lines, e.g., ones with dysregulated signaling through the phosphatidylinositide 3-kinase/Akt pathway, is very sensitive to inhibition by rapamycin [2, 3]. A number of pharmaceutical companies have developed mTORC1 inhibitors, based on rapamycin, known as ‘rapalogs’ [4]. These compounds are in clinical trials for various solid tumors, hematopoietic malignancies, and sarcomas (see [4] for further details). CCI779 has FDA approval for use in advanced renal cell carcinoma [5]. More recently, ATP-competitive inhibitors of mTOR’s kinase activity have been developed [6–8]. There are numerous recent and excellent review articles discussing mTORC1 (see chapter “mTORC1: A Signaling Integration Node Involved in Cell Growth”) or its regulation [9]. This chapter does not therefore attempt to give a comprehensive overview of the field, but rather to highlight recent research findings related to signaling downstream of mTORC1 and also the insights and questions emerging from them.
2 Signaling Downstream of mTORC1 mTOR is a large multidomain protein which possesses a protein kinase domain (related to lipid kinases of the phosphoinositide (PI) kinase-related kinase family) which binds a number of other proteins to form mTOR complexes (for a comprehensive review see [10]). There are two major types of mTOR complex (mTORC1 and mTORC2) with different (protein) components, characteristics, and cellular targets. mTORC1 contains raptor, mLST8 (also called GβL), and the recently identified protein deptor [11]. Many, but not all, of the signaling events mediated by mTORC1 are blocked by short-term treatment of cells with nanomolar concentrations of rapamycin, which binds as a complex with FK506 and rapamycin-binding protein (FKBP12) to the FKBP12/rapamycin-binding domain (FRB) domain of mTOR (Fig. 1). Rapamycin has been shown to disrupt the binding of mTOR to raptor [12], although another report appears to conflict with this [13] and the exact mechanism whereby FKBP12-rapamycin impairs the function of mTORC1 is still poorly understood. Interestingly, a recent report showed that a widely used rapamycin analogue (CCI-779) can inhibit mTORC1 kinase activity in an FKBP12-independent manner at high (μmolar) concentrations [14]. Such concentrations of CCI-779 exert a much stronger inhibitory effect on protein synthesis than the lower (nanomolar) concentrations which are usually used and which do require FKBP12. Like mTORC1, mTORC2 also contains deptor and mLST8 but in addition includes rictor, mSIN1, and protor/PRR5, which are absent from mTORC1 [15–17]. The functions of mTORC2 appear not to be impaired by short-term treatment of cells with rapamycin although prolonged treatment does affect mTORC2 at least in some cell types [18].
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Fig. 1 Downstream signaling from mTORC1 through proteins that contain TOR-signaling (TOS) motifs. All examples are discussed in the text. Dashed ‘blocked’ arrow indicate alleviation of inhibition. (P) indicates a phosphorylation event. The question mark indicates that it is not yet established that HIF1α is a direct substrate for mTORC1. It is also unclear whether PRAS40 positively or negatively regulates cell survival. ‘N’ and ‘C’ indicate the N- and C-termini of the proteins depicted here. IRS1 = insulin receptor substrate 1
3 Raptor Mediates the Phosphorylation of mTORC1 Substrates That Contain TOR-Signaling (TOS) Motifs The protein raptor (KOG1 in budding yeast) is a specific component of mTORC1 [10, 12, 19] and may determine its specificity toward the phosphorylation of certain proteins. The first mTORC1 substrate proteins to be shown to interact with raptor were the S6 kinases (S6Ks) and the eukaryotic initiation factor (eIF) 4E-binding proteins (4E-BPs) [20, 21] (Fig. 1). Each of them contains similar short sequences termed TOR-signaling (TOS) motifs which bind raptor (although it is not certain that the interaction is direct). Mutation of the TOS motif or knockdown of raptor impairs the control of the S6Ks and/or 4E-BP1 (see, e.g., [21–25]).
4 The eIF4E-Binding Proteins (4E-BPs) The best-understood 4E-BP is 4E-BP1, which is a well-established substrate for phosphorylation by mTORC1. Its partner, eIF4E, binds the cap structure that is
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present at the 5 -end of all eukaryotic mRNAs that are encoded by nuclear genes [26]. The cap structure contains a 7-methyl-guanosine triphosphate (m7 GTP) moiety. The 4E-BPs interact with eIF4E through a conserved motif, which is similar to the one through which the scaffold protein eIF4EI/II bind to eIF4E [27]. The 4E-BPs and eIF4G proteins thus compete with one another for binding to eIF4E. The eIF4G proteins mediate the assembly of protein:protein complexes that recruit 40S ribosomal subunits to the mRNA and can thus play a key role in the initiation of mRNA translation [26]. Among the other partners for eIF4G are the ATP-dependent RNA helicases eIF4AI/II . These proteins can unwind RNA duplexes and are considered to be important for the efficient translation of mRNAs, whose 5 -untranslated regions (5 -UTRs) contain stable secondary structure, which impedes the scanning of the ribosomal initiation complexes from the 5 -cap to locate the start codon. The eIF4E/eIF4G/eIF4A complex is often termed eIF4F; such complexes are likely to be especially important for ensuring the efficient translation of mRNAs with structured 5 -UTRs (substrates for eIF4A). The mTORC1-dependent phosphorylation of the 4E-BPs leads to their release from eIF4E permitting the formation of eIF4F complexes. This provides a mechanism by which mTORC1 signaling can promote the translation of mRNAs with structured 5 -UTRs that are particularly dependent upon eIF4F. The human 4E-BP family comprises three members: 4E-BP1, 4E-BP2, and 4EBP3. 4E-BP1 undergoes phosphorylation at several sites. Seven phosphorylation sites have been identified in 4E-BP1, four of which directly or indirectly control its binding to eIF4E. The phosphorylation of the two sites nearest in the primary sequence to the eIF4E-binding site (Ser65, Thr70 in the human protein; numbering of all phosphorylation sites differs by minus one in the rodent proteins) decreases the association of 4E-BP1 with eIF4E, although their relative roles are controversial (see, e.g., [28]). Two more N-terminal sites, Thr37/46, are required for phosphorylation of Thr70 and Ser65, i.e., they act as ‘priming’ sites [29–31]. Phosphorylation of Thr70 and Ser65 within cells also requires the TOS motif, but mutation of residues in the TOS motif only has a modest effect on the phosphorylation of Thr37/46 (see, e.g., [32, 33]). In accordance with this, transient depletion of raptor by siRNA markedly decreases the phosphorylation of 4E-BP1 at Ser65 and Thr70 with only a modest effect on Thr37/46 phosphorylation [25]. Interestingly, stimuli such as insulin often only have a small effect on the phosphorylation of Thr37/46, which are already substantially phosphorylated in serum-starved cells [29, 32, 34]. Starvation of cells for amino acids results in the dephosphorylation of these sites. In contrast, amino acids alone are generally not enough to promote the phosphorylation of Ser65 or Thr70, which also usually requires a stimulus such as insulin. The regulation of 4E-BP1 differs somewhat between cell types. For example, in Chinese hamster ovary cells, the equivalent of Thr70, Thr69, is not affected by amino acid starvation or insulin [32, 34]. Furthermore, phosphorylation of Thr37/46 is also generally not affected greatly by treating cells with rapamycin [32, 34]. This could suggest that these sites are not actually controlled by mTORC1. However, various lines of evidence suggest that they are, including the observations that knocking down mTOR [34] or raptor
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decreases their phosphorylation within cells and that they are efficiently phosphorylated by mTORC1 immunoprecipitates in vitro. Such in vitro phosphorylation of these sites is also quite insensitive to rapamycin [35]. The recently developed mTOR kinase inhibitors do effectively block phosphorylation of these sites in 4E-BP1 [6–8] indicating that they are targets for a rapamycin-insensitive output from mTOR pathway mTORC1 [34]. Phosphorylation of Thr37/46 requires a region in the N-terminal part of 4E-BP1 which contains the residues Arg-Ala-Ile-Pro (hence ‘RAIP’ motif [36]). Recent data show that this region plays a role in the interaction of 4E-BP1 with raptor: although variants of 4E-BP1 where the RAIP motif is replaced by four alanines (AAAA) can still bind to raptor, such binding is markedly decreased as compared to the wild-type protein [33]. Taken together with other findings (see, e.g., [22, 23]) these data are consistent with the idea that the RAIP motif mediates the phosphorylation of Thr37/46 in 4E-BP1 via an interaction with raptor, in a rapamycin-insensitive but amino acid-dependent manner. A RAIP motif is also found in 4E-BP2 where again it mediates an amino acid-dependent input [34], while 4E-BP3 has a modified motif [36].
5 The Roles of eIF4E and 4E-BP1 in Cell Transformation 4E-BP1 (and probably 4E-BP2) are strong candidates for linking mTORC1 signaling to cell transformation. They negatively regulate eIF4E, as described above. Whereas overexpression of eIF4E has a well-established ability to transform cells in culture [37, 38] and to generate tumors in transgenic animals (when expressed together with, e.g., c-myc [39, 40]), 4E-BP1 and 4E-BP2 can each inhibit the transforming ability of eIF4E in cell culture [41]. eIF4E is also expressed at high levels in many tumors [42, 43]: such levels may exceed the capacity of 4E-BPs to sequester eIF4E, giving rise to ‘constitutively active’ eIF4F complexes. The links between eIF4E and tumorigenesis have been the subject of several recent and informative reviews [43–48]. eIF4E may drive transformation by enhancing the translation of specific mRNAs, e.g., ones whose 5 -untranslated regions contain extensive secondary structure. A recent study identified multiple mRNAs whose translation (entry into polyribosomes) is promoted by increased levels of eIF4E [49]. eIF4E can also promote the transport of certain mRNAs from the nucleus to the cytoplasm [50, 51]. The phosphorylation of eIF4E (at Ser209) may be required for eIF4E’s function in mRNA transport [52] and recent data suggest it is also important for the translation of certain mRNAs [53]. Early studies showed that overexpression of eIF4E led to enhanced expression of cyclin D1 (which is involved in G1/S-phase progression) through both transcriptional and post-transcriptional mechanisms [54]. The latter likely involve effects on both the export of the cyclin D1 mRNA from the nucleus and its translation in the cytoplasm [55, 56]. The control of cyclin D1 expression through eIF4E may well play a role in the ability of rapamycin affect the cell cycle (but see below for additional links between mTORC1 and the cell cycle).
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eIF4E has also been linked to the control of the translation of numerous other mRNAs (for a recent study of this see [49]). Since this topic has been the subject for several recent reviews (e.g., [43, 57]), these mRNAs will not be discussed in detail again here. eIF4E promotes cell survival (see, e.g., [39] and the reviews cited above). Inhibition of eIF4E, e.g., by non-phosphorylatable (‘constitutively active’) mutants of 4E-BP1 [58], a small molecule that inhibits eIF4G/eIF4E binding [59] or cellpermeant peptides that block the eIF4G-binding site in eIF4E [60], promotes cell death. eIF4E enhances the translation of the mRNA for Mcl-1, an anti-apoptotic protein, and the expression of Mcl-1 correlates with phosphorylation of eIF4E at Ser209 [53] which is necessary for eIF4E’s oncogenic properties [52]. Rapamycin also causes a shift of the Mcl-1 mRNA out of polysomes, indicating that Mcl1 mRNA may, in some instances, also be regulated at the translational level by mTORC1 [61]. In principle, rapamycin could affect the functions of eIF4E both in mRNA transport and in translation, and either or both might underlie the effects of rapamycin on cancer cell proliferation or survival. However, the role of eIF4E in mRNA transport does not require a key residue (Trp73) through which eIF4E binds eIF4G. Since this residue is also required for the interaction of eIF4E with 4E-BP1 [62], it follows that rapamycin cannot interfere with the function of eIF4E in mRNA export through sequestration of eIF4E by 4E-BPs. The protein 4E-T was originally reported as a nucleocytoplasmic shuttling factor for eIF4E [63]: it also interacts with eIF4E through Trp73. The fact that eIF4E(Trp73Ala) still functions in mRNA export (and can transform cells) [64, 65] implies that eIF4E does not need this residue to enter the nucleus and that, at least in these experiments where eIF4E is artificially overexpressed, 4E-T is not required for its nuclear entry either. This is consistent with the finding that the nucleocytoplasmic distribution of eIF4E(Trp73Ala) is similar to that of wild-type eIF4E [66]. There is so far no evidence that the mTORC1 pathway controls the nucleocytoplasmic export of the cyclin D1 mRNA: for example, manipulations that impair mTORC1 signaling do not affect the distribution of this mRNA [46]. So, does inhibition of mTORC1 signaling affect the translation of mRNAs that are believed to be sensitive to the availability of eIF4E? Although impairment of mTORC1 signaling (due to rapamycin treatment or starvation of cells for leucine) did not affect the levels of cyclin D1 mRNA [46], levels of cyclin D1 protein did decrease in response to rapamycin. This could reflect decreased synthesis of this protein due to the impaired translation of its mRNA. Knocking down 4E-BP1, which diminishes the reduction of eIF4F levels that occurs in response to impaired mTORC1 signaling, attenuated the drop in cyclin D1 protein levels [46]. These data are consistent with the idea that impaired mTORC1 signaling decreases cyclin D1 levels by inhibiting the eIF4F-dependent translation of its mRNA rather than by affecting the transport of this mRNA to the cytoplasm. Both tumors and tumor cell lines display a wide spectrum of sensitivity/insensitivity to rapamycin and rapalogs (see [4] for a review). It is possible that this may, in some cases, be related to insensitivity of the eIF4E/4E-BP1 axis
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to rapamycin. This could arise in two main ways: First, if eIF4E is expressed at high levels, as it is in many tumors [42, 43], its molar levels might exceed those of the 4E-BPs, such that their dephosphorylation and activation may not suffice to sequester all the eIF4E molecules. In this case, agents that target eIF4E directly, e.g., by preventing its interaction with eIF4G, may be of value. Such a compound was described recently [59]. Alternatively, the phosphorylation of 4E-BP1 may be resistant to rapamycin, as is seen in certain types of cells (see, e.g., [34, 46, 67]). The reasons for this insensitivity to rapamycin are not known: in the same cells, the phosphorylation of S6 kinases 1/2 is blocked by rapamycin, so it does not reflect an insensitivity of mTORC1 itself to this drug. Where studied, starving cells of amino acids or specifically of leucine does elicit the dephosphorylation of 4E-BP1. Further studies are needed to elucidate why rapamycin does not affect 4E-BP1 phosphorylation in some cell types. Last, it is conceivable that high levels of eIF4E phosphorylation might somehow counter the effects of rapamycin. The phosphorylation site in eIF4E (Ser209) appears to be required for its role in cell transformation [52, 53]. High levels of eIF4E phosphorylation could result from activation of the MEK/ERK pathway which activates the Mnk1a isoform [68–70] (e.g., by oncogenic Ras) or from expression of Mnk2a (Mnk2 in mice), which has high basal activity [71]. It is possible that combined inhibition of Mnks and mTORC1 signaling may be more effective than the latter alone.
6 The Ribosomal Protein S6 Kinases As their name suggests, the S6 kinases phosphorylate ribosomal protein S6, a component of the small (40S) ribosomal subunit, at five sites in its C-terminus (reviewed in [72, 73]). There are two distinct S6 kinase genes (S6K1/2) in mammals, which give rise (via alternative splicing) to four proteins. The N-termini of all the isoforms contain a TOS motif (Fig. 1). S6Ks can be directly phosphorylated by mTORC1 (Fig. 1), principally at a conserved Thr in a hydrophobic sequence context (Thr389 in the short form of S6K1 and Thr412 in the longer isoform [73]). The activation of the S6Ks also requires their phosphorylation at a number of other sites, including several in its C-terminal region (Fig. 1). Their phosphorylation appears to precede that of Thr389 and may be required for phosphorylation of Thr389: since the phosphorylation of these sites is also sensitive to rapamycin it also depends on mTORC1, but the link between mTORC1 and the control of the phosphorylation of these sites remains to be established. They do not seem to be direct targets for mTORC1 (for a review, see [73]). Knockout of the sole S6K genes in Drosophila and of the S6K1 genes in mice leads to decreased animal size compared to controls [74, 75]. The mechanisms underlying this are so far unclear. Although S6 has been known to undergo phosphorylation for more than two decades, the role of this modification remains unclear (see, e.g., [76]). These and data from mice in which both S6K1 and S6K2 genes have been ‘knocked out’ demonstrated that, contrary to earlier conclusions, neither the S6 kinase nor S6 phosphorylation are important for the mTORC1-mediated control
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of the translation of ribosomal protein mRNAs [76, 77]. Cells (mouse embryonic fibroblasts) expressing the mutated S6 lacking the phosphorylation sites actually divide faster than wild-type cells, indicating that S6 phosphorylation is not required for normal cell division [76]. Several other substrates for the S6 kinases have been identified, including other proteins that are involved in mRNA translation: eIF4B (eukaryotic initiation factor 4B), [78, 79]), PDCD4 (programmed cell death protein 4), an inhibitor of eIF4A [80], and eEF (eukaryotic elongation factor) 2 kinase [81]. These phosphorylation events likely contribute to the activation of mRNA translation. S6K1 is actually recruited to translation initiation complexes [82] and also to newly synthesized mRNA, by its interaction partner and substrate, SKAR (S6K1 Aly/REF-like substrate [83, 84]; Fig. 1). In contrast to the abundant evidence that the eIF4E/4E-BP axis plays a role in transformation and oncogenesis, there are fewer indications of a role for the S6 kinases (for a recent review, see [85]), although the S6 kinase 1 gene is amplified in breast tumors [86]. S6 kinases can impair PI 3-kinase/Akt signaling through the phosphorylation of insulin receptor substrate 1 (IRS1) [87, 88]. Treating cells with rapamycin can therefore actually promote Akt signaling by abrogating this feedback. Given the oncogenic properties of Akt, mTORC1 inhibition might actually promote tumorigenesis in some contexts. However, a recent study [89] went some way to allaying these concerns by providing data indicating that mTORC1 signaling may be the major route by which Akt promotes cell proliferation and tumorigenesis.
7 Other Recently Discovered Proteins That Are Regulated via TOS Motifs Two further proteins were recently reported to contain functional TOS motifs and to be controlled via mTORC1. The first of these is PRAS40 (proline-rich Akt substrate, 40 kDa; Fig. 1). PRAS40 was first identified as a protein that bound to 14-3-3 proteins [90, 91]. 14-3-3 proteins interact with partner proteins that are phosphorylated on Ser or Thr residues [92]. 14-3-3 proteins function as dimers which can interact with two different phosphorylated residues in the same ‘target’ protein. In addition to identifying PRAS40 as a 14-3-3 partner, the first studies showed that PRAS40 was a substrate for Akt (also termed protein kinase B, PKB [91]). Akt/PKB phosphorylates PRAS40 at Thr246 (phosphorylation of this residue being required for binding of PRAS40 to 14-3-3). Binding of PRAS40 to 14-3-3 was found to require both growth factors and nutrients (amino acids [90]). The latter characteristics are typical of proteins that are controlled by mTORC1, although rapamycin was found only to have modest inhibitory effect on binding of 14-3-3 to PRAS40 [90]. Indeed, PRAS40 was subsequently shown to interact with mTORC1 [93, 94]. These studies suggested that PRAS40 functioned as a negative regulator of mTORC1 and that the phosphorylation of PRAS40 by Akt/PKB relieved
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this inhibitory effect, perhaps because the resulting binding of PRAS40 to 14-3-3 weakened its interaction with mTORC1 and thereby diminished its inhibitory effect. These studies placed PRAS40 ‘upstream’ of mTORC1 but were rapidly followed by three reports which showed that PRAS40 is actually a substrate for mTORC1 and contains a functional TOS motif [25, 95, 96]. Unlike the TOS motifs in S6Ks or 4EBPs, which are located close to their N- or C-termini, respectively, the TOS motifs in PRAS40 lie almost exactly in the middle of the polypeptide (Fig. 1). mTORC1 can phosphorylate PRAS40 at Ser183 [95] and Ser212/221 [97]. Interestingly, while phosphorylation of Ser183 and Ser221 was sensitive to rapamycin, Ser212 was not: as we have seen for 4E-BP1, certain phosphorylation sites for mTORC1 are apparently insensitive to this compound. The binding of PRAS40 to 14-3-3 is decreased by mutation of Ser183 or (less strikingly) Ser221 to alanine [97]: thus, the bidentate binding of 14-3-3 to PRAS40 appears to involve phosphorylation at mTORC1-regulated sites (Ser183/221) and at the Akt site (Thr246), likely explaining why 14-3-3 binding requires both nutrients and growth factors which activate Akt [90]. Several of these reports also found that overexpression of PRAS40 can inhibit the regulation of other targets of mTORC1 [17, 25, 95, 96]. This property was attributed to competition between PRAS40 and other TOS-motif-containing proteins for binding to raptor [95, 96], i.e., to inhibition by PRAS40 of binding of other substrates. However, one study found that mutating the highly conserved Phe residue’s TOS motif in PRAS40 did not eliminate this inhibitory effect [25]. This may reflect the existence of other contacts between PRAS40 and, e.g., raptor, although an intact TOS motif is needed for its stable association with mTORC1. Together, this group of reports identifies PRAS40 as a target for mTORC1 and thus as being ‘downstream’ of this complex. If the inhibitory effect of PRAS40 on mTORC1 signaling reflected its action as an upstream negative regulator that is overcome by binding of PRAS40 to 14-3-3, one might expect that other stimuli that turn on mTORC1 would also elicit the phosphorylation of PRAS40 at Thr246 and its binding to 14-3-3. mTORC1 signaling is also activated via the MEK/ERK (classical MAP kinase) pathway [98–100]. However, agents that stimulate this pathway without activating Akt do not induce either the phosphorylation of PRAS40 at Thr246 or its binding to 14-3-3 [101]. These data indicate that binding of PRAS40 to 14-3-3 is not obligatory for the activation of mTORC1. Further studies are required to elucidate the precise role of PRAS40 in mTORC1 signaling, e.g., whether it truly functions as an upstream regulator of mTORC1 or as a substrate which can impair mTORC1 signaling to other targets by simply competing for raptor. Furthermore, it is important to establish whether PRAS40 has other roles in addition to its ability to modulate mTORC1 signaling. Several studies have suggested that PRAS40 plays a positive role in cell survival [102–105]. For example, the enhanced expression of PRAS40 in mouse brain exerted neuroprotective effects in a model of transient focal cerebral ischemia [102]. Conversely, siRNA-mediated knockdown of PRAS40 increased the sensitivity of melanoma cells to pro-apoptotic agents and decreased their ability to form tumors in nude mice [105]. Targeting
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PRAS40 may conceivably be of value in inhibiting the development of melanoma and perhaps other cancers. The available data for the role of mTORC1 in apoptosis are quite extensive and in some regards conflicting, but a number of studies do indicate that mTORC1 signaling is anti-apoptotic [106–110]. Puzzlingly, knockdown of PRAS40 was shown to prevent the induction of apoptosis by the protein synthesis inhibitor cycloheximide or by tumor necrosis factor-α [17]. This study provided evidence that the function of PRAS40 in apoptosis may be independent of its control by mTORC1 [17]. Interestingly, Lobe, the Drosophila protein that is most similar to PRAS40, plays a role in eye development and specifically in cell survival, for which it is required [111–113]. It should be noted that Lobe is a much larger protein than PRAS40 (562 vs. 257 residues) and that their mutual sequence similarity is restricted to the C-terminal part.
8 Hypoxia-Inducible Factor 1α Oxygen deprivation induces the expression of the transcription factor subunit HIF1α, which promotes the transcription of proteins involved in angiogenesis (e.g., vascular endothelial growth factor (VEGF)-A) and angiopoietin-2 (Ang-2) as well as many other genes involved in apoptosis, energy metabolism, and metastasis [114]. Inhibition of HIF-1α or its downstream effects may be valuable in blocking tumor vascularization and/or metastasis. The expression of VEGF, a downstream target of HIF1α, is inhibited by rapamycin indicating that the production of this proangiogenic factor may be regulated by mTORC1 signaling [115–117]. One strategy to block tumor vascularization could therefore be to target mTORC1. But how does mTORC1 exerts its pro-angiogenic effects? One possibility is that mTORC1 does so through HIF1α. Indeed, in addition to blocking VEGF expression, rapamycin has also been shown to block the expression of HIF1α [117] (this was the first study to implicate mTORC1 signaling in the control of HIF1α protein levels). Subsequent work supported this finding: hypoxia induces a transient increase in HIF1α protein levels which subsequently decline to basal levels. Consistent with a role for mTORC1 in promoting HIF1α expression, mouse embryo fibroblasts lacking the tumor suppressor TSC2 (which negatively regulates mTORC1) show increased sustained HIF1α protein levels and increased VEGF expression [118, 119]. Earlier studies suggested that mTORC1 signaling may act to stabilize HIF-1α [118]. Land and Tee [120] have shown that HIF-1α itself contains a functional TOS motif (Fig. 1) and interacts with raptor. Mutation of this motif (Phe99Ala mutant) makes HIF-1α transcriptionally inactive and unresponsive to activation of mTORC1 signaling. The Phe99Ala mutant acts in a dominant-interfering manner with respect to cells’ endogenous HIF-1α, but is expressed (ectopically) at similar levels to the wild-type protein, indicating that the TOS motif does not function to help stabilize the HIF-1α protein.
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These data indicate that HIF-1α is likely subject to direct regulation by mTORC1. It remains to be established whether the regulation of HIF-1α involves its direct phosphorylation by mTORC1. These data reinforce the view that inhibition of mTORC1 may be a valuable approach to repressing angiogenesis.
9 Regulation of Translation Elongation by mTORC1 All the proteins that are established as direct substrates for phosphorylation by mTORC1 possess TOS motifs. A number of other processes are also controlled by mTORC1, but the mTORC1-proximal components involved in their regulation are unknown or incompletely understood (Fig. 2). These processes include translation elongation, autophagy, and other transcriptional events including those involved in mitochondrial biogenesis. Of these, autophagy may be the one of most direct relevance to tumorigenesis and cancer. On the other hand, the best understood of these is translation elongation, which can be regulated through the phosphorylation of
Rheb. GTP
deptor mLST8 (Gβ L)
mTOR P
P
lipin-1
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eEF2 kinase
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FIP200 Phosphorylation of eEF2 (inhibition of translation elongation) Autophagy
Fig. 2 Other signaling events downstream of mTORC1. All examples are discussed in the text. Dashed arrow indicates a poorly understood link between mTORC1 and cdc2/cyclin B. Dashed ‘blocked’ arrows indicate alleviation of inhibition. The dotted arrow from eEF2K to autophagy indicates a probable link between eEF2 kinase and autophagy. The dashed arrow from mTORC1 to cdc2 indicates that the mechanism linking mTORC1 to control of cdc2 is unclear. The question mark indicates that it is not yet clear how mTORC1 controls YY1. Gray arrows/connectors denote that there is relatively little information of the links between mTORC1 and lipin 1/lipid synthesis or CLIP-170/microtubule organization. (P) denotes a phosphorylation event
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eukaryotic elongation factor 2 (eEF2) which inhibits its activity (reviewed [121, 122]). Agents such as insulin induce the dephosphorylation (and activation) of eEF2 and the inactivation of eEF2 kinase, the enzyme that phosphorylates eEF2 (Fig. 2). Both these effects are blocked by rapamycin, indicating they require signaling via mTORC1 [121–123]. How does mTORC1 bring about the inactivation of eEF2 kinase? Three phosphorylation sites in eEF2 kinase are known to be regulated in an mTORC1-dependent manner (Ser78 [124], Ser359 [125], and Ser366 [81] in the human protein), and phosphorylation of each of them inhibits the activity of eEF2 kinase. However, eEF2 kinase does not interact with raptor and is not a direct substrate for mTORC1 in vitro [126]. No TOS motif is evident in its primary sequence. Thus, additional components must be involved in linking mTORC1 to the control of eEF2 kinase. The phosphorylation of eEF2 kinase at Ser78 inhibits the association of eEF2 kinase with calmodulin, an essential activator of eEF2 [124] (Ser78 lies immediately next to the calmodulin-binding site). This site becomes phosphorylated in response to insulin and this effect is eliminated by rapamycin. This indicates that the kinase and/or phosphatase acting on this site are/is regulated by mTORC1, but these components have yet to be identified. Ser359 was recently shown to be a substrate for cdc2/cyclin B [126] (Fig. 2). This study also revealed that the activity of cdc2/cyclin B against eEF2 kinase is regulated by amino acids and TSC2, in a manner consistent with positive regulation by mTORC1. These findings further imply that mTORC1 may regulate cell cycle progression from G2 into M-phase. Such a link makes good physiological sense and would mean that mTORC1 was involved in controlling the cell cycle at G1/S and G2/M. Further work is needed to confirm this. Ser365, the third inhibitory site in eEF2 kinase that is linked to mTORC1 signaling, is a substrate for S6 kinases (and also for p90RSK , a kinase that lies downstream of ERK) [81].
10 mTORC1 and the Control of Autophagy Macroautophagy (often referred to simply as autophagy) is a process of cellular ‘self-eating’ whereby damaged cytoplasmic components are engulfed and targeted for lysosomal degradation [127]. It evolved, presumably, to help single-celled organisms cope with starvation, but also plays an important role in many aspects of the physiology and pathology of metazoan animals [128, 129]. Although autophagy may justifiably be regarded as facilitating cell survival, it actually plays a protective role against cancer. This is illustrated, for example, by the enhanced tendency of mice lacking beclin 1 (a component of the autophagic machinery) to develop spontaneous tumors [130, 131]. Autophagy may help restrict DNA damage and preserve genomic stability [132]. However, there is also evidence that inhibition of autophagy may enhance the efficacy of anti-cancer chemotherapeutic agents (reviewed in [128, 129]).
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Consistent with its role in cellular physiology, starvation activates autophagy while nutrients such as amino acids repress it. The finding that the repression of autophagy by amino acids in hepatocytes was partially alleviated by rapamycin provided the first indication that mTORC1 played a role in controlling (inhibiting) autophagy [133]. The stimulatory effect of rapamycin on autophagy has since been observed in other types of cells (reviewed in [127]). Subsequent studies in yeast, where the autophagic machinery is best understood, showed that rapamycin and nutrients exert opposing effects on the phosphorylation of the Atg1p/Atg13p/Atg17p complex, which modulates autophagosome formation (reviewed in [134]). For example, under nutrient-rich conditions (where mTORC1 is active), Atg1p and Atg13p are phosphorylated. Rapamycin treatment or starvation leads to its/their dephosphorylation (and presumably activation). The identity of the Atg1/Atg13/Atg17 complex in mammals remained elusive until recently. Several different research groups [135–139] have now independently identified the different components of the mammalian Atg1p/Atg13p/Atg17p complex (Fig. 2). This complex includes UNC-51-like kinase (ULK)1 or ULK2 (the Atg1p homologues in mammals), mAtg13 (KIAA0652), and FIP200 (FAK familyinteracting protein of 200 kDa), the proposed functional counterpart of Atg17p [136, 138]. Analogous to the situation in yeast, mTORC1 binds to ULK1 and phosphorylates both ULK and mAtg13 to inhibit the formation of the nascent autophagosome [135–137]. The precise role of Atg1 in autophagosome formation and the identity of its substrates (besides mAtg13) are not yet clear. In fact, there are conflicting data regarding the importance of Atg1’s kinase activity in autophagy [140–143]. There also appears to be a role for S6 kinases in controlling autophagy (at least in Drosophila [144]), but whether S6Ks function to inhibit or activate autophagy remains controversial (see, e.g., [143]). There is also evidence that eEF2 kinase participates in the control of autophagy: for example, RNA interference-mediated knockdown of eEF2 kinase inhibited autophagy [145–146] (Fig. 2). While it is an attractive idea that eEF2 kinase may both promote (protein) degradation and inhibit protein synthesis, further studies are needed to establish its function in autophagy. Additional mechanisms for the control of autophagy by mTORC1 probably exist. The class III phosphatidylinositol 3 (PI(3)) kinase Vps34 plays a positive role in the control of protein degradation by autophagy in budding yeast. Vps34 is a lipid kinase that phosphorylates phosphatidylinositol to give rise to PI(3)P. Early studies suggested that hVps34 (the Vps34 human homologue) positively regulated mTORC1 signaling by acting upstream of mTORC1 [147, 148]. The fact that mTORC1 inhibits autophagy and hVps34 plays a positive role in autophagy did not, however, support the model that hVps34 acted as a positive regulator of mTORC1. An elegant study has recently shed light into this paradox. This study showed that Vps34 actually functions downstream of TORC1 in Drosophila [149]. Overexpression of TSC1/2 in Drosophila leads to induction of autophagy. Deletion of Vps34, however, impairs the ability of TSC1/2 to induce autophagy.
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11 mTORC1 and the Control of Transcription mTORC1 signaling, and the corresponding pathway in other organisms, also controls gene transcription, as we have already seen in the case of HIF-1α. mTORC1 also regulates the transcription of genes involved in ribosome biogenesis (reviewed in [150]). One further recent example is the ability of mTORC1 to regulate the expression of genes involved in mitochondrial oxidative phosphorylation. Rapamycin decreases the expression of the transcriptional regulator PGC-1α and rates of oxygen consumption in skeletal muscle and related cells [151]. Analysis of genes whose expression is repressed by rapamycin led to the identification of YY1 (yin-yang 1) as a likely player in the control of these genes by mTORC1 (Fig. 2). YY1 binds directly to the promoters of these genes and was found to interact with raptor (and, perhaps indirectly, with mTOR itself). PGC-1α functions as a transcriptional co-activator for YY1 in an mTORC1-dependent manner: the interaction between YY1 and PGC-1α is disrupted by treating cells with rapamycin. This link between mTORC1 and mitochondrial gene expression could allow a positive feedback activation of mitochondrial activity in response to high nutrient levels. However, it remains to be elucidated how mTORC1 controls the YY1/PGC-1α interaction.
12 mTORC1 and Lipin 1 Lipin 1 is a phosphatidic acid phosphatase which produces diacylglycerol, used, e.g., as a precursor in the synthesis of phospholipids. Huffman et al. [152] have shown that lipin 1 undergoes phosphorylation in response to insulin and that rapamycin inhibits this, thus identifying lipin 1 as a novel target for control by mTORC1 (Fig. 2), although it is not clear whether it is a direct substrate for mTORC1. Insulin affects the subcellular localization of lipin 1 [152] although this was not affected by rapamycin. The relevance of the mTORC1-dependent phosphorylation of lipin 1 for the control of lipid metabolism thus remains to be established.
12.1 mTORC1 and CLIP-170 Choi et al. [153] reported that mTOR (which is also sometimes termed ‘FRAP’) interacts with and can phosphorylate CLIP-170 (Fig. 2), a protein that associates with microtubules and is involved in regulating microtubule dynamics. Interestingly, CLIP-170 undergoes phosphorylation at multiple sites in cells, and phosphorylation at some sites is sensitive to rapamycin. Rapamycin also inhibited the association of CLIP-170 with microtubules. Furthermore, mTOR immunoprecipitates could phosphorylate CLIP-170 in vitro. The significance of these observations for the control of the cytoskeleton or cancer cell biology remains to be established. Given the efficacy of drugs that affect microtubule dynamics as anti-cancer agents (e.g., paclitaxel, docetaxel), further studies on this appear to be warranted.
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13 SGK1, mTORC1, and mTORC2 SGK1 (serum- and glucocorticoid-inducible kinase 1) is a member of the AGC family of protein kinases, which includes – among many others – the S6 kinases. Like the S6 kinases, the activation of SGK1 requires phosphorylation within its activation loop (at Thr256) by PDK1 (which also phosphorylates the S6 kinases) and phosphorylation within a hydrophobic motif similar to that containing Thr389 in S6K1 (catalyzed by mTORC1) [154]. A study by Hong et al. proposed that in addition to phosphorylating S6Ks, mTORC1 phosphorylates Ser422 within the hydrophobic motif in SGK1 [155]. This finding has, however, been recently disputed in a comprehensive study leading to the conclusion that mTORC2, not mTORC1, catalyzes phosphorylation of Ser422 in SGK1 [156].
14 Concluding Remarks In this review, we provide a brief overview of our current understanding of signaling downstream of mTORC1. S6Ks together with 4E-BPs were the very first targets, and substrates, of mTORC1 to be identified but recent years have seen the identification of new targets of mTORC1 involved primarily in transcription control and autophagy. In addition to controlling transcription, autophagy, and protein synthesis, mTORC1 is also understood to promote ribosome biogenesis, although the mechanisms that link mTORC1 to ribosome biogenesis await detailed elucidation. Identification of novel targets of mTORC1 will further our understanding of these and other mTORC1-dependent processes. Many of these processes (mRNA translation, ribosome synthesis, cell survival) play key roles in cell proliferation and tumorigenesis, so a better understanding of their control by mTORC1 is likely to lead to new insights into how targeting mTORC1 signaling may be valuable in cancer therapy. Acknowledgments BDF gratefully acknowledges financial support from a Morton Trust Fellowship, a University of Dundee School of Life Sciences Alumnus Fund, and a Canadian Institute for Health Research Cancer Consortium Training Grant Fellowship Award. The CGP laboratory was previously funded by the Canadian Institutes for Health Research, the Heart and Stroke Foundation of British Columbia, and the Yukon and the Ajinomoto Amino Acid Research Program for their work on mTORC1 signaling and is currently supported by the Welcome Trust, Biotechnology and Biological Sciences Research Council, Royal Society, British Heart Foundation, and European Union.
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Downstream of mTOR: Translational Control of Cancer Ryan J.O. Dowling and Nahum Sonenberg
Abstract mTOR is a key regulator of a number of critical cellular processes including growth, proliferation, cytoskeletal organization, and differentiation. mTOR mediates its effects on these processes by regulating mRNA translation initiation via phosphorylation of its major downstream targets: the 4E binding proteins (4E-BPs) and the ribosomal protein S6 kinases. Dysregulation of mTOR signalling leads to increased cellular growth and proliferation and is implicated in a number of human cancers. In particular, increased mTOR signalling is associated with human cancers that are characterized by loss or mutations in tumour suppressors such as LKB1, PTEN, and TSC1/2, which are responsible for suppressing the PI3K/AKT pathway. The regulation of mRNA translation by mTOR will be the focus of this chapter. In particular, the role of the translational machinery downstream of mTOR in oncogenesis will be discussed. Keywords mRNA translation · mTOR · Protein synthesis · Cell growth · Cell proliferation · Oncogenesis · Cancer · Cell signalling
1 Introduction The PI3K/AKT/mTOR signalling pathway regulates a number of critical cellular processes including growth, proliferation, differentiation, and survival. The serine/threonine kinase mTOR integrates a wide range of extra- and intra-cellular signals to control these processes through the regulation of mRNA translation. mTOR mediates its effects on mRNA translation through phosphorylation of its major downstream targets: the 4E binding proteins (4E-BPs) and the ribosomal protein S6 kinases (S6K1 and S6K2). The 4E-BPs suppress translation initiation
N. Sonenberg (B) Department of Biochemistry, Rosalind and Morris Goodman Cancer Centre, McGill University, Montreal, QC H3A 1A3, Canada e-mail:
[email protected] V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_10,
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and are inhibited via mTOR-mediated phosphorylation, while the S6Ks enhance translation upon activation by mTOR by targeting components of the translational machinery. Inappropriate activation of mTOR signalling can promote tumorigenesis through an increase in the translation of mRNAs encoding growth factors, pro-survival proteins, cell cycle regulators, and angiogenic factors. Consequently, dysregulation of the mTOR pathway is implicated in a number of human cancers. The role of mTOR and its downstream targets in mRNA translation initiation will be the focus of this chapter. In particular, the regulation of the translational machinery via mTOR-mediated phosphorylation will be described and the importance of the downstream targets of mTOR in the development of human cancer will be addressed.
2 Translation Initiation The process of translation is divided into three stages: initiation, elongation, and termination. Initiation is rate limiting under most circumstances and as a result is a primary target of translational control. Translation initiation is a complex, highly ordered process that culminates in the assembly of the 80S ribosome at the initiation codon of an mRNA. The rate-limiting step is thought to be the formation of the eIF4F (eukaryotic initiation factor 4F) complex, which mediates the recruitment of ribosomal subunits to the mRNA [1]. eIF4F is composed of eIF4E, which binds the 7-methylguanosine “cap” (m7 GpppX, where X is any nucleotide and m is a methyl group) found on the 5 -end of all nuclear-transcribed cellular mRNAs, the helicase, eIF4A, and the large scaffolding protein, eIF4G (Fig. 1) [2, 3]. While eIF4E is required for recognition and binding of the 5 -cap, eIF4G bridges the ribosome and the mRNA by interacting with eIF3, which binds the 40S ribosome. eIF4A unwinds any 5 -mRNA secondary structure to facilitate binding of the 40S subunit and scanning of the mRNA 5 -untranslated region (UTR). The ATPase and helicase activities of eIF4A are stimulated by eIF4B, a small RNA-binding protein that is involved in
Fig. 1 eIF4F complex formation: The eIF4F complex mediates the recruitment of ribosomal subunits to the mRNA and is composed of the cap-binding protein eIF4E, the large scaffolding protein eIF4G, and the helicase eIF4A. eIF4G is involved in circularization of the mRNA through an interaction with PABP and bridges the 40S ribosome and the mRNA by interacting with eIF3. eIF4B is an RNA-binding protein that stimulates the ATPase and helicase activities of eIF4A
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ribosomal recruitment to mRNA [2, 4]. The 40S ribosome with its associated initiation factors scans the 5 -UTR until it encounters an initiation codon (AUG or a cognate thereof). Once the initiation codon is encountered, the 60S ribosome joins to form the active 80S ribosome.
3 TOR Complex Formation Two mTOR containing complexes exist in mammalian cells, namely mTORC1 and mTORC2. mTORC1 is rapamycin sensitive, regulated by nutrients and growth factors, and composed of mTOR, raptor (regulatory-associated protein of TOR), and mLst8 (also known as GβL) [5, 6]. Raptor functions as an adaptor protein that is responsible for recruiting the mTORC1 substrates 4E-BP1, S6K, and PRAS40 (proline-rich AKT substrate 40 kDa) [7, 8]. Raptor is required for mTOR-mediated phosphorylation of S6K and 4E-BP1 and interacts with mTOR targets through a TOR signalling (TOS) motif. The TOS motif is located in the NH2 terminus of S6K (FDIDL for S6K1 and FDLDL for S6K2) and the COOH terminus of 4E-BP1 (FEMDI) [9, 10]. Mutation of this motif greatly reduces mTOR-mediated phosphorylation of S6K and 4E-BP1 [9–12]. mLst8 interacts with the kinase domain of mTOR and stabilizes the mTOR–raptor interaction [13]. Recently, it was reported that PRAS40 associates with mTORC1 in cells and negatively regulates mTOR activity [14, 15]. The inhibitory effects of PRAS40 on mTORC1 signalling are due to competition with S6K and 4E-BP1 for binding to raptor, as PRAS40 contains a TOS motif (FVMDE) [16–18]. The activity of PRAS40 is regulated by phosphorylation such that AKT-mediated phosphorylation of PRAS40 on Thr246 reduces its inhibitory effects on mTOR signalling [14, 15]. In addition, PRAS40 was also recently identified as an mTORC1 substrate [8, 17]. In contrast, mTORC2 is composed of mTOR, mLst8, rictor (rapamycininsensitive component of TOR), and mSIN1 (mammalian stress-activated protein kinase (SAPK)-interacting protein). mTORC2 is rapamycin insensitive, regulated by growth factors, and is implicated in cytoskeletal organization. The only substrate of mTORC2 is believed to be AKT (PKB); however, mTORC2 also regulates the phosphorylation of PKC-α (protein kinase C) and potentially other AGC-family kinases [19–21].
4 Regulation of Translation Initiation by mTOR Signalling mTOR integrates signals from nutrients, growth factors, cellular energy stores, and other cues to control a variety of important cellular processes including growth, proliferation, differentiation, transcription, and mRNA translation [4, 22, 23]. mTOR is a serine/threonine kinase that co-ordinates these processes via phosphorylation of its downstream targets. The major targets of mTORC1 are integral components of the translational machinery, particularly those involved in translation initiation.
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Consequently, this chapter will focus on mTORC1 signalling, unless otherwise noted. The eIF4F components eIF4G and eIF4E are regulated by mTORC1, with the activity of eIF4E being controlled by mTORC1-mediated phosphorylation and inactivation of its repressors, the 4E-BPs. In addition, S6K1 and S6K2 are direct targets of mTORC1. S6K1 plays an important role in translational control via regulation of the ribosomal protein S6 (rpS6), eukaryotic elongation factor 2 (eEF2) kinase, and eIF4B.
4.1 The 4E-BPs The assembly of the eIF4F complex is controlled by a family of small translational repressor proteins known as the 4E-BPs. Three 4E-BPs exist in mammals (4E-BP1, 4E-BP2, and 4E-BP3), each encoded by a separate gene, while Drosophila only express one 4E-BP [24–26]. The 4E-BPs negatively regulate eIF4F assembly by competing with eIF4G for binding to a shared site on eIF4E. The binding of the 4EBPs to eIF4E is controlled by mTORC1-mediated phosphorylation. The majority of studies on 4E-BP regulation have focussed on 4E-BP1. Consequently, 4E-BP1 is the best characterized member of the 4E-BP family. In its hypophosphorylated form, 4E-BP1 binds to eIF4E with high affinity, preventing eIF4F complex formation and suppressing cap-dependent translation. However, upon stimulation by growth factors, nutrients, or hormones, mTORC1 phosphorylates and inhibits 4E-BP1 leading to the release of eIF4E and an increase in translation initiation (Fig. 2) [24, 27]. Seven phosphorylation sites have been identified in 4E-BP1 (Thr 37, Thr 46, Ser 65, Thr 70, Ser 83, Ser 101, and Ser 112) and four of which (Thr 37, Thr 46, Ser
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Fig. 2 Regulation of eIF4E-BP1 by phosphorylation: Hypophosphorylated 4E-BP1 binds to eIF4E with high affinity and inhibits cap-dependent translation. Upon stimulation, mTORC1 phosphorylates 4E-BP1 on Thr 37 and Thr 46, which acts as a priming event that is required for the subsequent phosphorylation of residues Ser 65 and Thr 70. The hyperphosphorylated form of 4EBP1 is released from eIF4E, allowing formation of the active eIF4F complex and an increase in cap-dependent translation
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65, and Thr 70) are linked to mTORC1. The importance of these individual phosphorylation sites in the regulation of 4E-BP1–eIF4E binding is not completely clear. However, the four mTORC1-specific sites are known to be involved in the release of eIF4E from 4E-BP1 [4, 27–29]. The phosphorylation of 4E-BP1 is a complex process that proceeds in a hierarchical manner (Fig. 2). Upon stimulation, mTOR phosphorylates 4E-BP1 on Thr 37 and Thr 46. The phosphorylation of these sites is believed to act as a priming event that is required for the subsequent phosphorylation of residues Thr 70 and Ser 65 [27, 28]. Phosphorylation of 4E-BP1 on residues Thr 37 and Thr 46 does not disrupt binding of eIF4E by 4E-BP1, and phosphorylation of Ser 65 and Thr 70 alone is insufficient to block eIF4E binding [27, 28]. Therefore, it is likely that in combination, these phosphorylation events co-operate to cause the dissociation of eIF4E from 4E-BP1.
4.1.1 S6 Kinase The ribosomal protein S6 kinases are direct targets of mTORC1 and play important roles in the regulation of mRNA translation. Two S6 kinase proteins (S6K1 and S6K2) are expressed in mammalian cells, and both proteins are phosphorylated and activated by mTORC1 [30]. Despite being encoded by separate genes, the phosphorylation sites on S6K1 and S6K2 are conserved. S6K1, the most characterized of the two kinases, is involved in the regulation of cell growth in Drosophila and mammalian cells [31, 32]. Complete activation of S6K1 requires two phosphorylation events. Phosphorylation at Thr 389 is mediated by mTORC1 and is required for subsequent phosphorylation of Thr 229 by PDK1 (phosphoinositidedependent kinase 1) [33–35]. S6K1 is believed to promote cell growth by increasing mRNA translation via phosphorylation of its downstream targets rpS6, eIF4B, and eEF2 kinase (Fig. 3). The best characterized substrate of S6K1 is rpS6; however, the functional significance of rpS6 phosphorylation is somewhat unclear. In the past, the phosphorylation of rpS6 correlated well with the translational activation of mRNAs that contain a 5 -terminal oligopyrimidine tract (TOP). However, studies in cells lacking S6K1 and S6K2 have demonstrated that rpS6 phosphorylation is not required for translation of 5 -TOP mRNAs [36–38]. These studies indicate that rpS6 may not be the major target through which the S6 kinases mediate their effects on mRNA translation and cell growth. Despite its unknown function, the phosphorylation of rpS6 is routinely used as an indicator of S6K and mTORC1 activity. While the physiological relevance of rpS6 phosphorylation remains unclear, S6K1-mediated phosphorylation of eIF4B plays an important role in eIF4F activity and the recruitment of ribosomes to mRNA. eIF4B is an RNA-binding protein that stimulates the ATPase and helicase activities of eIF4A. S6K1 phosphorylates eIF4B on Ser 422, leading to an increase in mRNA translation [39]. This increase in translation is likely due to an enhanced interaction between phosphorylated eIF4B and eukaryotic translation initiation factor 3 (eIF3) [40, 41]. Phosphorylation may also
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Fig. 3 S6 kinase 1 targets and their role in mRNA translation. Phosphorylation of S6K1 by mTORC1 enhances its kinase activity towards its downstream targets (ribosomal protein S6; rpS6, eukaryotic initiation factor 4B; eIF4B, programmed cell death protein 4; PDCD4, insulin receptor substrate-1; IRS-1, S6K1 Aly/REF-like target; SKAR, and eukaryotic elongation factor-2 kinase; eEF2K), which are involved directly or indirectly in the regulation of mRNA translation. eIF4B, rpS6, and SKAR are activated by S6K1-mediated phosphorylation, whereas PDCD4, eEF2K, and IRS-1 are inhibited by phosphorylation
increase the activity of eIF4B towards the helicase eIF4A, leading to more efficient translation of mRNAs containing large amounts of 5 -UTR secondary structure. For example, footprinting assays have demonstrated that eIF4B is required for the binding of ribosomes to an mRNA-containing secondary structure, and knock-down of eIF4B by RNA interference results in reduced translation of highly structured mRNAs [4, 42, 43]. The phosphorylation of eIF4B is increased by a variety of extracellular stimuli that induce cell growth including serum, insulin, and phorbol esters [4, 44]. In addition to S6K1, eIF4B is also phosphorylated on Ser 422 by the p90 ribosomal protein S6 kinase (RSK) in response to stimulation of the ERK1/2 MAPK pathway [40]. S6K1 may also indirectly affect eIF4F activity via regulation of the eIF4A inhibitor PDCD4 (programmed cell death protein 4) [19]. PDCD4 is a tumour suppressor that binds to and inhibits the helicase activity of eIF4A, thus negatively affecting the unwinding of 5 -mRNA secondary structure and translation initiation. Phosphorylation of PDCD4 by S6K1 targets it for ubiquitination and degradation, relieving its inhibitory effect on eIF4A function and protein synthesis [45].
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S6K1 also plays an important role in the regulation of translation elongation via phosphorylation of eEF2 kinase. eEF2 kinase is a negative regulator of protein synthesis as it phosphorylates and inactivates eEF2, leading to inhibition of the translocation step in translation elongation [46]. Upon activation, S6K1 phosphorylates eEF2 kinase on Ser 366, which inhibits its kinase activity towards eEF2 and relieves the suppression of translation elongation [47]. An additional target of S6K1 is SKAR (S6K1 Aly/REF-like target). SKAR is phosphorylated on residues Ser 383 and 385 specifically by S6K1, not S6K2 [48]. SKAR is involved in the control of cell growth and, in co-operation with S6K1, is believed to enhance the translation of spliced mRNAs [49]. In addition to being downstream of mTORC1, S6K1 is also involved in the regulation of mTOR activity by controlling insulin signalling. Insulin or IGF (insulin-like growth factor) binds to their receptor, leading to phosphorylation and activation of the insulin receptor substrate-1 (IRS-1). Activated IRS-1 recruits and activates PI3K, which in turn activates AKT and consequently, mTOR. Upon stimulation by mTORC1, S6K1 phosphorylates IRS-1, marking it for degradation and causing a suppression of PI3K/AKT signalling [50–53]. This negative feedback loop is particularly active in cells exhibiting enhanced mTOR activity due to genetic mutations or prolonged nutrient exposure. The regulation of insulin signalling by S6K1 poses a challenge for anti-cancer therapies that target mTORC1 (such as rapamycin and its derivatives) since mTORC1 inhibition reduces S6K1 activity, which results in increased IRS-1 activation and PI3K-AKT signalling that itself plays a role in the control of cell proliferation. As a result, a great deal of research has focussed on the use of mTORC1 inhibitors in combination with inhibitors of insulin signalling for the treatment of cancer [54]. 4.1.2 eIF4G The large scaffolding protein eIF4G is an integral component of the eIF4F translation initiation complex. eIF4G interacts with the other eIF4F components, eIF4E and eIF4A, and is responsible for bridging the 40S ribosomal subunit to the mRNA through an interaction with the ribosome-associated initiation factor eIF3. eIF4G also contains binding sites for PABP (poly-A-binding protein) and the Mnk kinases (mitogen-activated protein kinase signal-integrating kinase; Mnk1, 2) [2]. The interaction between eIF4G and PABP is important for circularization of the mRNA, while the Mnks regulate the phosphorylation of eIF4E on Ser 209 [55–57]. All eukaryotes express two related eIF4G proteins, eIF4GI and eIF4GII, which are encoded by separate genes [2]. Both eIF4GI and eIF4GII are phosphorylated on multiple sites; however, the phosphorylation of eIF4GI is better characterized. eIF4GI is phosphorylated in response to extracellular stimuli that promote cell growth including serum, insulin, and growth factors [58, 59]. The phosphorylation of eIF4GI is mediated by mTORC1 as a number of rapamycin-sensitive phosphorylation sites including Ser 1108, Ser 1148, Ser 1192 have been identified [58]. However, the functional significance of eIF4G phosphorylation remains unclear. Phosphorylation does not affect the activity of eIF4G or its ability to
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associate with other initiation factors, but may change its structural conformation and alter the translation of specific mRNAs. 4.1.3 Other Targets of mTORC1 No other direct targets of mTORC1 have been identified; however, a number of additional cellular proteins contain putative TOS motifs, indicating that other mTORC1 substrates may exist. For example, HIF1-α, STAT3, and some PKC isoforms contain potential TOS motifs that may target them for raptor recruitment and mTORC1-mediated phosphorylation [60]. In fact, some reports indicate that STAT3 phosphorylation is sensitive to rapamycin treatment, supporting a role for mTORC1 in the regulation of STAT3 activity [61]. 4.1.4 mTORC2 Regulation of AKT While the mTORC1 phosphorylation targets are well characterized, very few mTORC2 substrates have been identified. The mTORC2 complex is rapamycin insensitive and involved in cytoskeletal regulation. Recently, mTORC2 was identified as the kinase responsible for phosphorylating Ser 473 in AKT. AKT is a positive regulator of mTOR signalling that phosphorylates and inhibits the TSC1/2 (tuberous sclerosis complex 1 and 2) complex, which negatively controls mTOR activity. Complete activation of AKT requires phosphorylation on Thr 308 within the activation loop and Ser 473, which lies in the hydrophobic motif [5]. PDK1 is responsible for AKT Thr 308 phosphorylation, and recent studies demonstrate that mTORC2 targets Ser 473. mTORC2 phosphorylates AKT on Ser 473 in vitro and knock-down of rictor in cells reduces Ser 473 phosphorylation, thus establishing mTORC2 as the PDK2 kinase involved in AKT regulation [20, 21]. In addition to AKT, mTORC2 may be involved in the regulation of PKC-α phosphorylation. PKC-α is involved in a number of cellular processes including growth, apoptosis, cellular structure, and motility. Knock-down of rictor reduces PKC-α phosphorylation and stability; however, this effect may not be direct [62].
5 Translation and Cancer The translation of mRNA is a process that is important for a number of critical cellular processes including growth, survival, proliferation, and differentiation. Therefore, it is not surprising that mRNA translation is implicated in the development of cancer. Increased levels of translation are believed to promote transformation and tumorigenesis through an increase in the expression of proteins involved in growth, proliferation, and survival. Many of these proteins are encoded by a subset of mRNAs that contain long, highly structured 5 -UTRs. For example, the mRNAs encoding survivin, cyclin D1, TGF-β (transforming growth factor), CDK4 (cyclin-dependent kinase), and IGF-II (insulin-like growth factor) contain structured 5 -UTRs [83, 86]. Efficient translation of these mRNAs requires high levels of the
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eIF4F complex, which through the actions of its helicase eIF4A unwinds the extensive mRNA secondary structure and facilitates translation initiation. Consequently, increased expression or activation of translation initiation factors, particularly components of the eIF4F complex, can promote synthesis of oncogenic proteins and tumorigenesis. As mentioned above, a number of key translation initiation factors, as well as other important regulators of translation, are targets of the mTOR signalling pathway. Increased mTOR signalling can contribute to higher levels of translation and the development of cancer. A number of mechanisms can lead to over-activation of mTOR. Mutational loss or inactivation of tumour suppressors that negatively regulate mTOR can cause hereditary hamartomatous diseases and human cancers. For example, mutation or loss of TSC2, LKB1, and PTEN (phosphatase and tensin homologue deleted on chromosome 10) is associated with cancer-like syndromes in which patients exhibit hamartomatous polyps in multiple organs and an increased risk of cancer development [63–66]. Loss of these tumour suppressors leads to overactive mTOR signalling and an increase in the phosphorylation of S6K and 4E-BP1 [67–70]. In addition, genetic amplification of positive regulators of mTOR, such as AKT and PI3K, has been reported in breast, ovarian, and head and neck cancers [71, 72]. Regardless of the mechanism, enhanced mTOR signalling contributes to tumorigenesis via increased phosphorylation of its downstream targets and elevated mRNA translation.
6 Downstream Targets of mTOR and Their Role in Cancer 6.1 The 4E-BPs A key mechanism by which mTORC1 signalling contributes to tumorigenesis is increased phosphorylation of the 4E-BPs. The 4E-BPs are believed to act as tumour suppressors by negatively regulating formation of the eIF4F complex that is required for mRNA translation initiation. In support of their role as tumour suppressors, over-expression of 4E-BP1 or 4E-BP2 causes a reversion of the transformed phenotype in cells transformed by eIF4E, ras, or src [73]. As mentioned above, mTORC1-mediated phosphorylation of 4E-BP1 leads to its release from eIF4E and a subsequent increase in eIF4F formation and mRNA translation. Consequently, inactivation of the 4E-BPs by phosphorylation is likely a key step in the development of cancer. For instance, ectopic expression of a non-phosphorylatable 4E-BP1 mutant, which remains capable of binding eIF4E regardless of mTOR activation, suppressed the tumorigenicity of human breast cancer cells [74]. Increased levels of 4E-BP1 phosphorylation have been observed in human breast, prostate, and ovarian tumours, supporting a role for elevated mTORC1 signalling and subsequent eIF4F complex formation in the development of these cancers [75–79]. In fact, 4E-BP1 may be useful as a prognostic indicator in human cancer as increased 4E-BP1 phosphorylation correlated with malignant progression and poor prognosis.
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While the 4E-BPs are considered tumour suppressors, their binding partner, eIF4E, acts as an oncoprotein. The cap-binding protein eIF4E is the least abundant component of the eIF4F complex, and its over-expression can lead to oncogenesis. Over-expression of eIF4E transforms rodent fibroblasts and causes the malignant transformation of primary embryo fibroblasts in co-operation with the immortalizing proteins myc and E1A [80, 81]. eIF4E expression also transforms human mammary epithelial cells [74]. Consistent with these data, mice over-expressing eIF4E develop lymphomas, angiosarcomas, lung adenocarcinomas, and hepatocellular adenomas [82, 83]. In humans, elevated eIF4E levels have been reported in colon, breast, bladder, lung, and prostate tumours highlighting its importance in tumour development and progression [84–86]. In fact, eIF4E has emerged as a major target for anti-cancer therapies. Reduction of eIF4E levels by administration of anti-sense oligonucleotides reduced the growth of tumours in mice and use of these anti-sense oligonucleotides is currently under investigation in phase I clinical trials (Eli Lilly Co.) [87, 88].
6.2 S6 Kinase The S6 kinases are important regulators of protein synthesis as well as cell growth [32]. Phosphorylation of S6K1 by mTORC1 enhances its kinase activity towards its downstream targets, rpS6, eIF4B, eEF2 kinase, PDCD4, and SKAR, leading to increased mRNA translation. The role of S6K1 in oncogenesis is not as well characterized as eIF4E or the 4E-BPs. However, the involvement of S6K1 in the regulation of cell growth strongly suggests that it may be involved in cancer. Over-expression of S6K1 causes increased cell growth and expression of a constitutively active form of S6K1 induces invasive and migratory phenotypes in ovarian cancer cells [89, 90]. Furthermore, it has been reported that S6K1 is important for the epithelial to mesenchymal transition (EMT) of ovarian cancer cells, a key process involved in tumour invasion, metastasis, and progression [91]. S6K1 is believed to promote migration and EMT in ovarian cancer cells via up-regulation of proteins involved in migration and invasion, such as matrix metalloproteinases and Snail [89, 91]. Increased S6K1 activity has been observed in breast cancer cells as well as a number of other cancer cell lines [92–95]. S6K1 is also involved in glial transformation as knock-down of S6K1 by siRNA or shRNA reduces anchorage-independent growth of human astrocytes in culture as well as the growth of intracranial tumours in mice [96]. In humans, S6K1 levels are elevated in 16% of primary breast tumours and the phosphorylation of S6K1 correlates with higher tumour grade in ovarian cancer patients [91, 93, 95, 97–99].
6.3 eIF4G The functional significance of mTORC1-mediated eIF4G phosphorylation is somewhat unclear; however, due to its importance in eIF4F complex formation and mRNA translation initiation, eIF4G is likely implicated in the development
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of cancer. Over-expression of eIF4GI causes malignant transformation of rodent fibroblasts and a number of human breast cancer cells contain elevated levels of eIF4G [74, 100]. Recent work by Braunstein and colleagues has demonstrated that together with 4E-BP1, eIF4G plays an important role in causing a hypoxia-activated switch to cap-independent translation, which favours the translation of mRNAs encoding angiogenic factors, and pro-survival proteins [101]. eIF4GI levels are up-regulated in squamous cell lung carcinomas as well as advanced breast tumours [101–103]. Elevated levels of eIF4G likely contribute to tumorigenesis via increased formation of the eIF4F complex and subsequent translation of highly structured mRNAs encoding growth factors and other oncogenic proteins.
6.4 AKT Regulation by mTORC2 in Cancer mTORC2 is the kinase responsible for phosphorylating and activating AKT. Rapamycin specifically inhibits mTORC1 by disrupting the interaction between mTOR and raptor; however, mTORC2 is not affected by rapamycin. Consequently, mTORC2-AKT signalling represents a significant challenge to the treatment of cancers with rapamycin and its derivatives since AKT is involved in the regulation of cell growth and survival. However, some research indicates that mTORC2 signalling is inhibited by prolonged treatment with rapamycin [104]. Further research is required to elucidate the role of mTORC2 in tumorigenesis, and the development of mTORC2 inhibitors as anti-cancer therapies represents an area of research that requires further examination.
7 Conclusions Due to its importance in the regulation of cellular growth and proliferation, mTOR has emerged as a key factor in the process of tumorigenesis. Over-active mTOR signalling due to mutation or loss of its negative regulators is implicated in a number of hereditary diseases and human cancers. Consequently, a great deal of research has focussed on the regulation of mTOR and its downstream targets. The majority of mTOR targets are components of the translational machinery, highlighting its importance in the regulation of mRNA translation. The major targets of mTORC1, the 4E-BPs and the S6Ks, have been extensively characterized and their roles in protein synthesis are well described. However, their involvement in the development of cancer is an area of research that requires further attention. In addition, a great deal of work will be required to identify additional substrates of mTORC1 and to elucidate the potential role of the mTORC2 complex in transformation and cancer. Future studies focussing on the function of mTOR and its downstream targets in cancer will not only aid in the understanding of the biochemical regulation and function of mTOR but also provide an opportunity for the identification and development of novel anti-cancer therapies.
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Genome-Wide Analysis of Translational Control Ola Larsson and Peter B. Bitterman
Abstract The importance of translational deregulation of specific transcripts in cancer has emerged during the last decade. For a more complete understanding of translational control in cancer, genome-wide studies of translational control are essential. In this chapter we describe methods that make such analysis possible and identify several key aspects of experimental design and data analysis which are essential for an informative study. We further review several studies that have used such approaches to gain insights in genome-wide translational control in cancer models with particular focus on genome-wide translational deregulation downstream of eIF4E. Finally we introduce “integrative translatomics” as a means to link genome-wide translational patterns to molecular mechanisms originating from the RNA sequences. This information may prove to be essential for a comprehensive understanding of how pathological translational control mediates the acquisition of a malignant phenotype. Keywords Polysome · Polyribosome · Translation · Genome-wide · Microarray · Gene expression · Systems biology · Translatomics · RNA element · Regulatory element
1 Introduction Transcriptomics – driven by enabling technologies – has been a clear focus in genome-wide biomedical research during the last decade. The tool used, gene expression microarray, allows researchers to study differential transcript abundance between conditions at the global, genome-wide level. These approaches have revolutionized our understanding of biology (reviewed, e.g., in Larsson et al. [1]). Recently, several new variations of microarray methodology have been introduced. O. Larsson (B) Department of Oncology-Pathology, Karolinska Institute, Stockholm, 171 76, Cancer Center Karolinska R8:01, Sweden e-mail:
[email protected] V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_11,
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These include tiling arrays, which not only measure the expression of predicted genes or ESTs but also measures all transcription by using probes targeting the whole genome [2, 3], and exon arrays in which each exon of the transcript is targeted and therefore has the potential to provide data on alternative splicing [4]. Such methodologies have led to new conceptual advances. Many researchers would argue that the proteome is of higher biological importance than the transcriptome, and the current placement of proteomics as the Holy Grail among the “omics’s” is based on the assumption that protein expression will more accurately mirror the phenotypes under investigation. However, from a gene expression systems perspective it is important to note that proteome data provide limited mechanistic insights into gene regulation. As a consequence, proteomics is further away from a detailed understanding of how regulation of gene expression is organized compared to current transcriptional profiling. This is because more mechanisms can contribute to the steady-state level of the protein (including transcription, RNA splicing, RNA transport, RNA stability, translational efficiency, and protein stability) than can influence transcriptional profiling (which in principle measures transcript synthesis and transcript stability, with possible contributions from alternative splicing). Thus data obtained from proteomics are less precise regarding mechanisms for differential regulation compared to transcriptional analysis. It appears that detailed genome-wide understanding of these different processes which can affect the protein level will be necessary to understand how genes are regulated at a systems level. In this direction, progress has been made toward genome-wide understanding of some mammalian non-transcriptional processes, e.g., alternative splicing [4], RNA transport [5], and RNA stability [6, 7]. In this chapter we will focus on the emerging field of genome-wide analysis of translational activity or “translatomics.” The current literature on genome-wide translational activity indicates that differential translational efficiency plays a major role in determining the steady-state level of the protein and represents a layer of regulation that can generate substantial additional complexity compared to regulation of transcription. It also appears to be a step in the gene expression pathway that is pathologically regulated during several important biological conditions such as cancer and tissue fibrosis. Thus a general understanding of genome-wide translational regulation is motivated both from a systems and biological perspective. In addition, the importance of understanding translational control will not diminish if methods for genome-wide proteomics become available, but will remain an integral part of a systems understating of how gene expression is regulated and an essential key to solving the cancer enigma.
2 Methods to Assess Ribosome Recruitment Genome Wide There are several mechanisms that can lead to changes in the relationship between transcript and protein levels, including RNA transport, translational efficiency, and protein stability. Among these, regulation of translational efficiency is the most energy-consuming step and would hence be expected to be under significant control. Regulation of translational efficiency could be achieved by modulating how many
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ribosomes associate with each transcript and/or changing the velocity of the elongation/termination phase of protein synthesis. Of these, ribosome recruitment appears to be rate limiting in most situations [8]. Methods to study differential translational activity should therefore enable estimates of how many ribosomes are associated with all transcripts under different conditions. It is possible to stratify the RNA population based on the number of bound ribosomes using poly(ribo)some preparations (Fig. 1). During polysome preparations, cycloheximide is used to immobilize ribosomes on the mRNA. All cytosolic RNA, bound to ribosomes or free, is isolated and loaded onto a sucrose density gradient. The sample is ultracentrifuged which causes those RNAs attached to many ribosomes to sediment faster. The sample is collected in a manner so that different fractions, defining RNAs bound to different numbers of bound ribosomes, are isolated. Microarrays can then be used to quantify the amount of each mRNA species in each fraction or pools of fractions. Using each gradient fraction separately gives the highest information content but is associated with large costs as many microarrays are needed. Despite this, one early study used each fraction to study ribosome occupancy of different transcripts in Saccharomyces cerevisiae [9]. The methodology has been described in detail in [10]. One of the major problems of the polysome microarray approach is that it is technically demanding, very labor intensive, and hard to scale up. It is therefore not surprising that most studies have been small, and more focus needs to be directed
Fig. 1 The polysome microarray approach to study global changes in translational activity. The sample (A) is separated for total RNA (B) and polysome–RNA (C) preparation. During the polysome–RNA preparation, the RNA is ultra-centrifuged (D) and fractionated (E). During the fractionation an A254 tracing is recorded as a function of sedimentation and individual polysome peaks as well as ribosome peaks are visualized (F). Individual fractions, or more commonly pools of fractions, can then be purified (G) and, in parallel with the total RNA sample, labeled for microarray assays (H). The obtained polysome–RNA- and total RNA-derived data are analyzed to identify translational events genome wide (I)
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toward improving reproducibility and scalability. Wang et al. proposed a method for large-scale sample preparation based on a 96-well format [11]. In this approach, step gradients are prepared in a 96-well plate (2 ml wells) which can be centrifuged in a standard centrifuge for an extended time (40 h). Both the pouring of the gradient and the elution of the samples are performed using robotic systems. Shortcomings include the extended processing time which could lead to RNA degradation and the need for robotic equipment. So far, no data set has been generated using this approach and its applicability remains uncertain. A second approach to obtain translationally active RNAs which could be performed on a larger scale is antibody-mediated pull-down of those mRNAs that are involved in translation. One obvious target for such pull-downs would be the ribosome, although other general translation factors could be used. However, pull-downs would, in comparison to polysome preparations, result in reduced resolution. During polysome preparations the researcher has the choice to stratify in different ways, so that, e.g., fractions with mRNA bound to greater than three ribosomes can be compared to those with three or less (or to the total RNA level – see below). The antibody-mediated approaches would only allow detection of transcripts that shift from a translationally inactive pool to a translationally active pool, thus missing potentially significant regulation. Another possible problem is that the antibody-based preparation could produce non-linear data as more ribosomes would give more potential binding sites which could result in more efficient isolation. So far no data set has been published using antibody approaches to study translation. In most studies, polysome preparations have been used to study in vitro systems (e.g., cell cultures) and it is not clear whether it is possible to routinely obtain highquality RNA from tissue samples. There are several problems with tissue samples compared to in vitro materials. First, to obtain sufficient amounts of polysomebound RNA, a lot of tissue is often needed (depending on tissue type), which may not be obtainable. However, current amplification protocols during labeling of samples for microarrays can often overcome this challenge. Second, tissue samples often suffer from RNA degradation that occurred prior to sample cryopreservation; and since the polysome process is time consuming, samples may be further degraded compared to a situation when total RNA is isolated. So far, to our knowledge, only two studies have been published that utilized tissue samples for genome-wide polysome microarray analysis [12, 13]. In both cases the samples were obtained from model organisms under controlled settings. It is therefore still an open question how successful the polysome procedure will be in other contexts (e.g., with samples from bio-banks).
3 Some Important Aspects of Polysome Microarray Data Analysis The next step after preparation of the polysomal RNA is quantification of all RNA species in the translationally active pool. When the goal is to compare sample classes such as cancer vs. normal, the estimate obtained from the ribosome fraction needs to be corrected for total transcript levels. This is because an increase in
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total mRNA abundance will be distributed in a transcript-specific pattern across the polysome profile, and thus leads to a corresponding increase of mRNA in the polysome fractions. A parallel sample from unstratified total RNA can be used to correct the estimate obtained from the ribosome-bound stratum for such effects (i.e., by taking the ratio between polysomal and total RNA), but this type of ratio-based correction can be problematic. First, if one attempts to perform statistical analysis of the corrected estimate, both the variance from the polysomal and total RNA pool will contribute to the noise and thus diminish the number of differentially translated genes identified. Second, although the goal of the correction is to negate any effect of the total RNA to the polysomal pool, the resulting corrected values may still correlate to the total RNA data depending on approach and variances of the total and polysomal estimates. Such correlations would indicate that the correction has not worked uniformly for the entire data set. These shortcomings indicate the need for new approaches to analyze polysome data and it is likely that single-channel microarrays or two-channel microarrays using a reference design will be most suitable for polysome microarray analysis (to obtain non-relative measures of polysome and total RNA levels in each sample). It is also possible to perform the statistical analysis on the polysome samples and later, using the data from the total RNA, try to determine which of the differences are related to transcription and which are related to translation. However, this approach will be largely threshold driven and therefore not ideal. A second issue is related to sample preparation, bias, and random variation. These aspects were not of such paramount importance previously when the overall polysome profile was assessed or RNA from individual fractions was used to study the profile of a favorite gene(s) using northern blotting or quantitative realtime PCR. Under these settings, the experiment can be relatively well controlled and the demand for a highly reproducible setup is reduced. This is partly because the researcher often studies a gene where there is already data suggesting such regulation and/or where a large effect is expected; thus there is prior knowledge of what is reasonable and expected. The relatively low cost of the experiment also allows the experiment to be repeated many times in combination with confirmatory experiments using western blotting. Thus, one is likely to arrive at a correct conclusion regarding the regulation of the single gene under study. However, the use of microarrays adds substantial demands on the integrity and the independence of the samples and it is currently not entirely clear how well the polysome microarray studies performed to date meet these challenges. The problem emerges in part because tens of thousands of genes are assessed with minimal prior expectation about the regulation of each individual gene and without the capacity to validate a large fraction of the identified genes. The problem is compounded by the natural tendency of investigators to not randomly select genes to validate, but rather picking “genes of interest” that are likely to be regulated based on prior knowledge about other similar genes. These two factors make it difficult to estimate how well the polysome microarray procedure can be validated across all studies that have been performed. While this may sound pessimistic, our experience is that polysome microarray data can be validated at a high rate if extreme care is taken during both the planning and execution of the initial study as well as the validation experiments. To characterize important
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issues during experiment planning and data analysis we examined a large proportion of the available data sets derived from polysome–RNA. From such analyses it appears there are substantial risks for study bias and false conclusions regarding the magnitude of translational regulation compared to transcriptional regulation for technical reasons as we detail below. A. Run-to-Run Bias. Technical issues, related to the reproducibility of sucrose gradients and the elution of fractions, generate run-to-run bias. In some of our studies we have solved this run-to-run bias by processing only one sample at a time. If the study is performed so that all conditions are included in each run, one could circumvent the problem indirectly, but it will be hard to add additional sample type(s) at a later step (although there are high-level analysis approaches that can be used in such cases, see, e.g., [1] for review). In all other setups there is a large risk that run-to-run bias is part of the output or dramatically restricts the output. Our conclusion is that currently, the best way to reduce the impact of this bias and maintain the option of adding additional samples later is to run the samples one by one, at least in the standard setting. B. Increased Random Variation. A second technical concern is variable levels of RNA degradation due to the extensive time needed for sample preparation (2–5 h depending on protocol), leading to increased levels of random variation. Given the number of steps during the preparation, there is also risk of small mistakes/inconsistencies compared to standard RNA preparations – resulting in even more random variation. As a result of run-to-run bias, it is common that samples processed within the same run are more similar to each other compared to the sample categories analyzed over several runs (Fig. 2a). Similarly, it also appears that polysome microarray data contain more random variation compared to transcriptional data [14]. Both of these data set characteristics need to be kept in mind during the data analysis phase as it limits the validity of several common approaches. The main limitation from run-torun bias in the most common setting (all sample conditions were prepared in each polysome preparation run, and the runs were repeated) is that application of statistics will be problematic due to large variances for replicates across runs. One acceptable
Fig. 2 (continued) independent replicates of both polysome and total RNA levels (all in different runs). As these samples are identical, no differential expression would be expected. The data set obtained is analyzed using fold changes of either a single sample (i.e., one C vs. one C, top two graphs) or using means (i.e., mean from three Cs run on different days vs. mean from three Cs run on different days, bottom two graphs). In the example we have simulated the random noise seen in total and polysome microarray data and added this to the expression obtained from a real experiment (thus all samples are identical but with added noise similar to what was observed in [14] so that polysome data have a higher mean standard deviation). The population of genes that show differential expression (the number of genes that pass a twofold change, indicated by the dashed lines) is shown in each graph. Substantially more genes are identified as differentially expressed in the polysome arm of the analysis (due to the increased random variation). The effect is reduced – but clearly persists – when three replicates are used (the recommended analysis in the text would have correctly shown no significant differential expression)
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Fig. 2 Issues in polysome microarray data analysis. (a) Run-to-run bias. Two different sample classes, cancer (C) and normal (N), are subjected to total and polysome–RNA preparations on different days (run 1 and run 2), i.e., two replicates of each sample. When analyzing data from the total RNA using hierarchical clustering of all detected genes, the samples cluster according to their biological class. However, when analyzing polysomal RNA in the same manner the samples cluster according to when the RNA was isolated. Thus significant run bias has confounded the study. (b) Effect of increased polysome data variance on analysis involving fold changes. The preparation of one single cancer (C) sample is repeated on different days to generate two or six
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approach is to regard each run as a pair and perform a paired analysis using methods that account for the extensive multiple testing. Here it should also be noted that if one set of sample classes (e.g., breast cancer cell lines) were prepared together – but not in parallel with a second set of sample classes (e.g., different controls) – any comparison between sample types that were not prepared together will be obscured by run-to-run bias. The main implication from increased random variation is that it will be difficult to compare the magnitude of regulation between transcription and translation using fold changes. It is often of biological interest to compare translational and transcriptional regulation, and this has been attempted in several published polysome microarray studies. However, if the variance within the polysome data is higher, this will lead to more random false-positive fold changes compared to situations when the data set variance is low (which is common in the transcriptional arm of the analysis). Thus, a comparison between transcriptional and translational regulation using fold changes would be expected to produce more substantial “differential” expression in the polysome pool due to differences in data set variance (Fig. 2b). One approach to solve this problem is to use a statistical approach, e.g., a nonparametric permutation-based approach [15, 16] in which the internal data set variance determines the thresholds for significance to identify differential translation and transcription. The genes that emerge from such analysis can then be compared at the fold change level to get an overview of the contribution of transcription and translation [14]. The problems from run-to-run bias and increased random variation in polysome data sets are not limited to these examples. Thus, one needs to be careful when planning and analyzing polysome microarray studies due to large run-to-run bias and the increased random variation that is intrinsic to polysome microarray data.
4 What Insights Have We Gathered from Genome-Wide Analysis of Translational Activity? Until now only a handful of studies with a genome-wide perspective on ribosome recruitment have been performed. Most investigators have used the polysome preparation method with a pooled fraction approach and corrected for total RNA levels as discussed above. These studies compare the level of each transcript in the actively translated pool to the level in total RNA within each sample and then compare this relative measure across different sample types.
4.1 Global Translational Regulation Downstream of eIF4E The interest in eIF4E and its effects on transcript-specific translational control is due to the connection between this general translation factor and cancer. The observation that eIF4E can transform cells [17] unexpectedly indicated that a general translation factor that is necessary for all cap-dependent translation could be somehow
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involved in carcinogenesis. It was suggested that different transcripts have different requirements for eIF4E to achieve maximal translational efficiency and that some transcripts would be more affected by changes in eIF4E levels than others [18]. The population of transcripts which could only achieve maximum translational activity when the level of active eIF4E is high was suggested to display such regulation due to long-structured 5 -UTR sequences. Thus the theory that emerged was that translation of a set of transcripts with long-structured UTRs encoding proteins relevant to cancer was dramatically increased as a consequence of increased eIF4E activity. These changes would be specific in the sense that the mRNA levels remained unchanged and the levels of most other proteins were only marginally affected [19]. These hypotheses were supported by single gene examples until polysome microarray analysis was used as a tool to globally investigate the impact of increased levels of eIF4E on both transcription and translation. To date, three different systems have been used. In the first report, a mouse cell line (NIH-3T3) which stably expresses ectopic eIF4E (at a level about twofold control) was compared to one that only expresses endogenous eIF4E [20]. About 250 genes were identified as showing increased ribosome recruitment in cells that express ectopic eIF4E. From this study it became clear that the effect of eIF4E was not restricted to a very limited subset of transcript only related to malignant transformation, but that a plethora of functions appeared to be regulated by eIF4E. However, the stable expression of ectopic eIF4E has the disadvantage that many secondary effects could contribute to the observed phenotype despite efforts to identify effects directly related to eIF4E. In a second study an inducible system was used to avoid such effects [21]. Very few genes were found to be differentially expressed at the transcriptional level (27 unique genes), while many differences could be observed at the translational level (294 showed a relative increase in ribosome recruitment). In this study focus was shifted toward ribosomal proteins as many were found to be translationally activated in cells abruptly overexpressing eIF4E. Several important genes related to apoptosis were also identified and validated as being differentially translationally regulated. In the third study human immortalized (using hTERT) primary breast epithelial cells (HMEC) with and without ectopic eIF4E were used [22]. Once again, relatively few genes were found to be under transcriptional regulation (141 unique genes) while many genes were found to be under translational control (1,518 unique genes). This study confirmed the translational activation of transcripts encoding ribosomal proteins and the diversity of gene functions observed as responsive to eIF4E in the two previous studies. One surprising observation emerged from this study. In addition to the translational activation of many genes in response to eIF4E, there was also substantial translational inactivation upon eIF4E expression. This unexpected finding – which would intuitively be deemed as a cellular response to the increased amount of eIF4E – was examined by studying the UTRs. It was found that transcripts that were translationally inactivated when eIF4E was overexpressed were enriched for micro-RNA-binding sites in their 3 -UTRs (over 30 micro-RNAs were enriched targeting ∼50% of all inactivated transcripts). Thus there appeared to be cross talk between the translational machinery and the micro-RNA system (although the micro-RNA sites could act as a marker for another mechanism as no
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direct studies were performed). This response would be predicted to be indirect, as increasing the concentration of the eIF4F complex in in vitro systems counteracts the inhibitory effect from the let-7 micro-RNA on a reporter construct [23] and thus the opposite regulation pattern would be expected. In this context, it is interesting to note that MYC has recently been reported to silence micro-RNA expression [24] and that MYC together with eIF4E (MYC also transactivates eIF4E transcription) is efficient for transforming cells [25–29]. Thus, MYC may unlock the translational effects of eIF4E which are tightly controlled in normal cells. Several other indications that the cell is actively counteracting the effect of eIF4E were observed in the HMEC model. First, translational inhibitors were translationally activated by eIF4E (e.g., translational activation of EIF4EBP1 and PAIP2). When examining key genes related to proliferation and apoptosis, pro- and anti-proliferation genes as well as pro- and anti-apoptosis genes were found to be translationally activated. In contrast, growth factors were primarily translationally activated. These results indicate that one of the major goals in the future will be to sort out the direct and indirect effects upon induction of eIF4E and how eIF4E-mediated differential translation depends on the activity of other cancer-related genes. From these three studies some general genome-wide conclusions can be formed. (i) It appears that the major effect from eIF4E on gene expression programs is regulation of ribosome recruitment. (ii) The functional spectrum of regulation downstream of changes in eIF4E activity is diverse. (iii) Ribosomal proteins appear to be strong targets of eIF4E, although indirect effects cannot be fully excluded. (iv) While initial studies of eIF4E overexpression looking at a few key target genes indicated unidirectional activation of oncogenic drivers [19], genome-wide studies indicate that the translational landscape is affected in a more complex manner with both oncogenic drivers and inhibitors being under translational regulation downstream of eIF4E [22]. (v) In addition to this, there seems to be other mechanisms which protect the cell from high eIF4E activity such as micro-RNA-mediated translational repression.
4.2 Other Cancer-Related Systems That Have Been Characterized Genome Wide at the Translational Level In addition to the direct studies of eIF4E, several studies of factors or pathways that could activate eIF4E have been performed. Two main pathways have been investigated: the RAS pathway and the mTOR pathway. These studies address different aspects of eIF4E activity. Studies with activated RAS examine the impact of increased eIF4E phosphorylation. In contrast, studies involving changes in the mTOR pathway examine eIF4E activation by virtue of phosphorylation of the EIF4EBPs. In an early study of how these pathways affect translational regulation, RAS, AKT, as well as chemical inhibitors of the RAS pathway (U0126), PI3K (Ly294002), and mTOR (rapamycin and CCI-779) were assessed in a mouse
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glioblastoma model [30]. The authors identified genes that followed defined regulation patterns across all conditions and a set of genes that were altered at the translational level after correction for transcriptional differences. Importantly, this study established translational regulation as a major effect downstream of RAS and AKT. The large effect on translational control after introduction of RAS was confirmed by a second study in a prostate cell line [31]. These findings were recently followed up in MEFs derived from TSC1 and TSC2 knockout mice [32]. Here, genes that were dependent on TSC1 or TSC2 for translational activation upon serum stimulation and genes whose translational activity was sensitive to rapamycin treatment were identified. Because both conditions involve differential activity of the mTORC1 complex, which phosphorylates S6K and EIF4EBP1 and hence modulates eIF4E activity, the study would be expected to show similarities to the studies examining the effect of increased eIF4E levels. Accordingly, many ribosomal proteins were found to be translationally regulated upon serum treatment in the WT cells. In agreement with a role for the mTOR pathway for this effect, most of these were not regulated in cells lacking TSC1 or TSC2. However, while these three eIF4E studies identified genes related to cell cycle progression and apoptosis, there were relatively few such genes identified as regulated by serum and rapamycin. The reason for this discrepancy is unclear but the result for rapamycin parallels an earlier study of translational activity in cells treated with rapamycin in which many ribosomal – but relatively few cell cycle or apoptosis related – genes were identified [33]. A large set of genes that were regulated by serum and by rapamycin in the TSC1/TSC2 model was identified. Interestingly, this set of genes possessed short 5 -UTRs, thus suggesting additional mechanisms other than the long complex 5 -UTR-mediated regulation (see further below). Several other studies have investigated the role of translational regulation in cancer models. In one study, several aspects of cancer biology were studied by comparing pairs of polarized mammary epithelial cells exhibiting cancer-related biological differences [34]. By comparing the regulation pattern across eight cell/condition pairs, the authors identified sets of genes showing differential polysomal RNA levels (total RNA levels were not assessed in this part of the study and some of the changes observed may be transcriptional) and patterns that agreed with the biologically defined differences between the eight pairs. Thus sets of genes related to scattering (a migratory phenotype), oncogene deregulation, metastasis, and EMT were identified. However, from a translational perspective, the lack of a correction for total RNA limits the conclusions from the study. In another study the transcriptome and the translatome were studied in a model of colorectal cancer progression [35]. The model utilizes two cell types, one obtained from the primary tumor and one obtained from a lymph node metastasis. These cell lines show differences in both proliferation and apoptosis so that the cells from the metastasis require a lower concentration of serum growth factors to proliferate and are less susceptible to inducers of apoptosis. The study showed that a large part of all changes in gene expression could only be seen at the ribosome recruitment level, thus highlighting that translational regulation may be an important factor during metastasis. The authors also found that while some cellular processes seemed to be regulated at
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both the transcriptional and translational levels, others such as regulation of apoptosis were only modulated at the translational level. The authors observed increased 5 -UTR length of those transcripts that were regulated at the translational level; however, both translationally repressed and translationally activated transcripts showed this difference. It also appeared that both translational repression and activation involved genes whose effect could be plausibly linked to the phenotypes associated with the model. Thus the study indicated complex regulatory patterns at the translational level. Cellular stress is an area where translational control has been shown to be important on a gene-by-gene basis. As the microenvironment in cancer lesions involves more or less constant cellular stress, it is likely that deregulation of cellular stress responses at a translational level could contribute to cancer phenotypes. There have been a few studies comparing the translatome of cancer cells to normal cells after different stress stimuli. In one such study a remarkable regulation of ribosome recruitment was observed after ionizing radiation in human brain tumor cells [36]. These authors studied both transcription and translation in three cell lines after treatment with ionizing radiation. Ten times more genes were found to be affected at the translational level compared to the transcriptional level, suggesting that regulation of ribosome recruitment is the main effect of ionizing radiation. The authors also identified a shared radiation-regulated gene set across the three cell lines, and compared this set to translational differences in normal astrocytes when these were treated similarly – thus attempting to identify cancer-related differences in the stress response. The comparison indicated shared and non-shared regulation patterns which will need further follow-up. Another stress situation that is highly relevant to cancer and which has been investigated genome wide at the translational level is hypoxia. In one study the effect of hypoxia on both transcription and translation was studied in HeLa cells [37]. Whereas overall translation is inhibited during hypoxia, the authors identified a subset of mRNAs whose translation is induced without changes in the mRNA levels. These mRNAs encode proteins which are important for the cellular response to hypoxia. In a study from the same laboratory the importance of one of the stresssensing protein kinases – Perk – was investigated in the context of hypoxia using cells derived from Perk knockout mice [38]. Here a subset of genes thought to be important for angiogenesis showed Perk-dependent translational changes. This was of particular interest as the cells that lacked Perk showed reduced activity in several cancer models, indicating that Perk is needed for cancer progression and, by association, translational regulation downstream of Perk. Genome-wide translational regulation has also been investigated in fragile-X syndrome [39]; endoplasmic reticulum stress [40]; differentiation [41]; development [42, 43]; cell cycle [44]; eIF4E-independent translation [45]; mitogen stimulation [46]; heat shock [47]; and fibrosis [14]. Most of these studies were recently reviewed [48]. In summary, we argue that there are substantial genome-wide data to suggest that translational regulation of specific transcripts will be an integral part of the
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cancer phenotype and will affect important cancer characteristics including survival, proliferation, metastasis, and responses to stress. Many future studies will be necessary to further investigate the role of differential ribosome recruitment under such conditions in different tumor types and models. Such studies can then be used for integrative translatomics with the goal of understanding how such regulation is organized.
5 What Is Integrative Translatomics and What Will We Learn from It? Integrative translatomics is an approach that uses information from many genomewide studies of translational regulation to arrive at a more holistic picture of the system and a better understanding of the individual study. Although each of the above studies gives insights into translational regulation under specific situations, we can only gain very limited insights into how translation regulation is organized, its mechanisms, and how these are deregulated in cancer. This is a consequence of the approaches and the limited information in single studies where the result is often presented as a list(s) of genes which is sometimes used in ontology/pathway/bibliometric analysis [49, 50] to look for functional patterns of regulation. Such analyses reduce the complexity and the initially large list of genes is transformed to a shorter list of biological themes. It is rare, however, that this sort of analysis gives rise to a new, unexpected hypothesis. This is related to two problems which are philosophical in their nature: (i) due to the large number of genes that are identified, the investigator halts the attempted exploration at what was known and fails to unravel the unexpected and (ii) even though the number of pathways or gene categories that are compared to the list obtained is large, they are inevitably limited by current knowledge. This restricts the findings to interpretations within the current biological framework. One way of extending the framework is by constructing new combinations of genes whose functions may be thought to be under regulation (for example, see [14, 51]). However, while this approach can be successful in the sense that an unexpected (but predictable) pattern emerges, it is also highly unsatisfactory in the sense that we are now limited to what the investigator can imagine. Such problems prompted us to identify four approaches for using transcriptional data in a more constructive manner [1]. These approaches can be easily extended to genome-wide translational data: 1. The extended microarray study: This approach is characterized by comparisons to related or sometimes unrelated data sets or added controls within the initial study that are not of immediate interest to the central hypothesis. In the simplest case, a gene list from one case can be compared to a gene list from a second. While compelling in terms of simplicity the gene list approach has severe limitations because different studies have used different methods to generate the list of “differentially expressed genes”; because it suffers from low sensitivity;
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because an overlap can be expected by chance; and because an apparent absence of similarity is never proof of absence of similarity. The final point is often violated when Venn diagrams are used to compare two or more populations of genes. It is common that genes that do not overlap in such analyses are regarded as specific for either condition. Here diagnostics and direct comparisons are necessary to show that these are true differences that are not to a large extent a function of the variance in the data sets and/or the thresholds applied. 2. A summarizing microarray analysis: Here the goal is to identify a more accurate set of genes that are associated with a phenotype (e.g., overexpression of eIF4E). The new discoveries can derive from the increased statistical power that is obtained by increasing the normally low number of samples in each individual study. However, this approach may be problematic in, e.g., the eIF4E scenario unless the same or very similar models have been used. This is because eIF4E activity will primarily modulate the translation of the basic repertoire of transcripts already present within the cell. If the repertoire differs between two cell types, the effect of eIF4E will be different. In addition, if other mechanisms influence and restrict the effect of eIF4E in a cell-type-dependent manner, such as micro-RNA-mediated gene silencing, the meta signature will be even less informative. Technical factors also restrict the applicability. In the eIF4E example it would appear that there are three data sets that could be compared. However, two of them were performed on old small platforms which will result in diminishing overlapping gene populations which most likely make them unsuitable for such analyses [1, 52]. Thus, the currently available data sets do not seem to allow meaningful direct integration of eIF4E-mediated translational regulation. 3. Hypothesis-driven meta analysis: This type of analysis is very similar to how standard biomedical research is conducted in the sense that the experimentalist has defined a hypothesis. The hypothesis is tested by integration of studies of different conditions and assessed using appropriate statistics. One might, e.g., ask whether the translational response to ionizing radiation involves modulation of eIF4E activity by comparing the eIF4E data sets to the ionizing radiation data sets or whether drugs that target the mTOR pathway lead to normalization of the translational deregulation induced by high eIF4E activity. It is strongly recommended to use more sophisticated methodology than comparisons of gene lists and pay attention to variations in data quality that can severely bias or limit the output from such comparisons [1]. Otherwise, it is likely that the output will contain many false patterns (random and/or not above what would be expected by chance) and missing patterns (due to low sensitivity, for method examples see [1] and current status of comparability in transcriptional analysis see [52, 53]). 4. Exploratory microarray analysis: In the exploratory analysis, the investigator is not directly motivated by a specific hypothesis. Rather, multi-study microarray data sets are used to find previously uncharacterized patterns of translation. The requisite is an idea about the principles behind these patterns which leads to the formulation of approaches and methods that are applicable. There are several examples discussed in [1] including attempts to functionally annotate genes and discovery of modules that are co-regulated and deregulated in cancer.
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In summary, we think that the way forward for understanding global translational regulation in any context lies in integration of genome-wide studies of translational control. It appears that many more studies in specific model systems are necessary before these extremely powerful approaches can be applied to get a better systemslevel understanding of translational regulation.
6 How to Advance from Integrative Translatomics to Mechanisms? In order for genome-wide research on translational control to move from studying associations and correlations to assessing causal mechanisms, additional approaches will be needed. These approaches rely on the conceptual framework proposed by Jack Keene [54] in the form of the post-transcriptional operon hypothesis. According to this theory, transcripts will share translational regulation as a function of their mRNA sequences and such groups of genes will define functional units within the cell that can be independently regulated in a combinatorial manner. Using this framework as a working hypothesis for understanding translational regulation indicates that we should attempt to identify which genes are regulated as groups and then look in their sequences for the answers to their co-regulation. However, linking global patterns of translational control to distinct mechanisms present both conceptual and technical challenges. The conceptual problems can be highlighted by reviewing the attempts to understand the mechanisms of eIF4E regulation from the above-mentioned global studies. In each of the studies there were attempts made to explain or characterize the underlying mechanisms. In the first study [20], a de novo identification approach was used to look for RNA elements that were enriched among the eIF4E translationally activated genes. A putative element which could mediate the expected regulation pattern was identified using an element identification algorithm called Bioprospector [55]. However, this element was only present in a small fraction of all transcripts that were activated by eIF4E. A search was also performed to identify known elements as an explanation of the observed regulation but this failed to identify any significant element. A known element (5 -terminal oligopyrimidine tract [TOP]) was identified as enriched among the transcripts that were translationally activated in the inducible eIF4E study [21]. However, only a fraction of the transcripts contained the TOP element and only some TOP sequences seemed to be targeted by eIF4E while others were not. In the final eIF4E study using HMECs, a trend for enrichment of genes carrying TOP sequences was observed among genes that were translationally activated. However, unexpectedly another strong bias was identified – transcripts that were translationally inactivated by eIF4E were enriched for micro-RNA target sites. Again, not all transcripts that were translationally repressed carried enriched micro-RNA target sites and not all genes that carried any of these micro-RNAs were translationally repressed [22]. Finally, the study that examined the effect of the mTOR pathway on translational regulation after serum stimulation identified
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TOP as enriched in genes that were serum responsive and rapamycin sensitive [32]. However, many genes that carried TOP did not change their translation and all transcripts that did change their translation did not carry a TOP. Thus, it appears that only partial and incomplete clues have emerged from such analyses, which is not unexpected. Given the role of eIF4E in translation initiation, one would expect that it could interact and be part of many mechanisms that influence translational regulation, where TOP-mediated regulation seems to be one. It is therefore apparent that identification of regulatory mechanisms from currently available and new data sets will be limited – because in general, more than one mechanism or operon will be differentially regulated in the condition under study and each gene may belong to more than one operon. From a single study perspective, this dilutes the signal from each mechanism, severely reducing the success rate when trying to identify mechanisms. As a consequence, regulation of a single transcript will not always be predictable from a single mechanism perspective and methods to assess active mechanisms in a certain system as well as defining which mechanisms can target which transcripts will be necessary to start dissecting this complexity. If we assume that these theoretical considerations are at least partially true, it indicates that comparative translatomics, which has the potential to more clearly define genes that are co-regulated across several systems, is the way forward. The second aspect lies in the technical challenges involved in identification of RNA regulatory elements. It is common to use cross-species comparative genomics to identify, e.g., promoters and such approaches have been applied to identify conserved sequences in both 5 -UTR and 3 -UTR sequences [56]. This set of sequences represents one class of putative novel elements that can be tested for activity in any study; however, it is not known whether they will influence translational regulation. Instead of comparative genomics, the most commonly applied approach is to look for sequences or structures that are overrepresented in the UTRs within the regulated set compared to the test set (Fig. 3). Such approaches rely on correct annotation of the UTRs in, e.g., the RefSeq database [57], which is lacking for many transcripts. This approach has been applied not only to translational data as indicated above [20] but also in several other studies involving post-transcriptional regulation. In a study of the yeast Puf proteins, an RNA element defining a distinct subset of transcripts was identified using genome-wide data [58]. In another study, data from global RNA stability were used to identify a regulatory element within transcripts that show differential stability properties [59]. Such examples indicate that it is possible to go from a genome-wide assessment of post-transcriptional regulation to functional elements which at least explain a part of the observed regulation. It is, however, unclear how efficiently the currently available methods can identify such elements. In an attempt to investigate this we compared how well current computational methods can identify regulatory elements if we assume that they show similar characteristics to those already described [60]. We showed that a set of three methods can identify about 50% of known elements when these are present at frequencies of 20%. We also highlighted that combination of algorithms, integration of outputs, and development of novel methods are important aspects for future element identification. Another challenge lies in the plethora of mechanisms by which regulation of
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Fig. 3 Identification of regulatory mechanisms from polysome microarray studies. A study where run-to-run bias has been controlled for is performed and used to identify translationally regulated genes between the two conditions (cancer and normal). The UTR sequences are collected and utilized in element identification and a candidate element is found. This candidate element is tested in functional experiments to validate biological activity
ribosome recruitment can occur. A recent example showed an intricate mechanism involving the 5 -UTR, the 3 -UTR, and a binding protein [61]. In this case, translation is normally repressed through an element in the 5 -UTR. This repression can be relieved through binding of a protein to an element in the 3 -UTR which somehow interacts with the ribosome to relieve the 5 -UTR-mediated repression. Thus the level of the binding protein determines the level of translational derepression and hence the level of the encoded protein. Even if a set of transcripts with such a shared mechanism was to be identified and the correct element indicated, the high complexity might, despite a true element having been identified, obfuscate the mode of regulation in a screening setting which is usually used to study element activity.
7 Conclusions Studies of genome-wide translational regulation will be integral for understanding how translation modulates cancer-related functions in a transcript-specific manner and how this is organized on a systems level. While individual studies have provided important insights into such processes, our understanding at a systems-wide level is still in its infancy. Essential prerequisites for progress in the field include technical improvements, correct analytical approaches, and integration of data across many
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conditions. With these improvements, we will be able to use translatomics for identification of mechanisms which may lie on the causal pathway to cancer genesis and progression. Acknowledgments O.L. is supported by a fellowship from the Knut and Alice Wallenberg Foundation.
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Translational Control of Cancer: Implications for Targeted Therapy Peter B. Bitterman and Vitaly A. Polunovsky
Abstract The limitations of contemporary cytotoxic therapy and the unveiling of signaling networks that govern the decision of a cancer cell to propagate or die have spawned the era of targeted anticancer therapy. However, treatment of patients with agents targeting specific oncoproteins frequently leads to acquired treatment resistance. What is the next step in anticancer therapy? One robust approach to drug discovery might be to target the few key pathways activated by oncogenes at vulnerable steps, i.e., at nodal points where oncogenic pathways converge. This concept embraces the unfortunate reality that several hundred genetic alterations – no one of which is obligatory for carcinogenesis – are able to usurp a few critical regulatory hubs converting cells from normal to malignant. It also powerfully motivates the search for those components of the cellular machinery that are ubiquitous and absolutely essential parts of the oncogenic circuitry – and yet are resilient enough in normal cells to tolerate perturbation. Two decades of experimental data summarized here indicate that eIF4F – the machinery mediating the initiation step in the translation of mRNA into protein – is an obligatory regulatory hub of multiple oncogenic pathways and thus is a bona fide candidate molecular target for cancer drug discovery. Keywords Cancer therapy · Translational control · eIF4F
1 Challenges in Cancer Drug Discovery About a decade ago, several novel strategies emerged transforming the development of anticancer therapeutics from a largely empirical endeavor to one featuring rational structure-based design of compounds directly targeting cancer-related processes. V.A. Polunovsky (B) Department of Medicine, Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA e-mail:
[email protected] V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_12,
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These approaches have evolved from a greater understanding of the pathways regulating cell proliferation, survival, and metastasis. Despite some improvements in patient survival from this new generation of anticancer therapeutics, overall success has been mixed. Only 5% of drugs entering clinical trials are approved for use, many limitations of standard chemotherapy still apply, and the overall prognosis for patients with the most common cancers remains poor [1]. The major impediment to successful anticancer therapy remains its toxicity to normal tissues and the narrow therapeutic index of most anticancer drugs. Ideally, antineoplastic interventions should target only cancer cells. The first generation of effective cancer drugs was comprised of agents considered to be cytostatic for normal cells and cytotoxic for their neoplastic counterparts – a concept that still forms the basis of most treatment regimens today [2]. The cell cycle antagonists, such as alkylating agents, platinum compounds, topoisomerase inhibitors, antimetabolites, and microtubule inhibitors, reversibly arrest non-malignant cells at the G1-S, G2, and mitosis checkpoints in the normal cell cycle [3], whereas in neoplastic cells, these interventions potentiate the intrinsic antioncogene programs [4] and cause “failsafe” responses including apoptotic or autophagic death and/or irreversible growth cessation [5–8]. However, these agents are also genotoxic. They induce irreversible damage in chromosomes in both normal and malignant cells either by directly damaging DNA (radiation, alkylating agents), inhibiting DNA synthesis (DNA antimetabolites and topoisomerase inhibitors) or inhibiting mitosis [9]. The other major limiting factor in antineoplastic therapies is the failure of some tumor types to respond to anticancer treatments and the appearance of resistant cell populations in originally responsive malignancies upon relapse. Despite aggressive treatments, many cancers remain resistant to current treatment protocols due to their original genetic constitution or acquired genetic lesions [10]. Escape from normal intrinsic tumor-suppressive mechanisms leading to suppression of apoptosis, premature senescence, destructive autophagy, and other anticancer programs produces a chemo- and radioresistant state in many tumors – accounting in large part for the failure of many current anticancer therapies [4, 8, 11, 12].
2 Rise of Targeted Cancer Therapy – and Its Limitations A paramount question in cancer biology is whether different malignancies share at least one common genetic lesion necessary for neoplastic growth. The commonly accepted paradigm of oncogenesis as a multistep process implies that cell malignant conversion is initiated by the stochastic accumulation of genetic lesions that drive evolution of a resulting cell population from benign clonogenic expansion toward invasive and metastatic neoplasia [13, 14]. One consequence of these concepts is that cancer is an always-changing target, which might be impossible to destroy. However, a series of recent observations indicating that neoplastic growth can be reversed by inactivating a pivotal oncogenic insult [15–17] has revived the idea of an “Achilles heel” in cancer. This idea in part has been conceptualized in the recent
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hypothesis of oncogene addiction [18, 19], oncogenic shock [20, 21], and oncogene amnesia [22]. These models predict that tumorigenesis can be overturned by pharmacological inhibition of a single obligatory oncogenic alteration [21]. Do all or many forms of cancer share a common Achilles heel? Failure to improve conventional cytotoxic therapy over a period of several decades has led cancer researchers to search for completely novel approaches to minimize general drug toxicity. The unveiling of signaling networks that govern the decision of a cancer cell to propagate or die has spawned the era of targeted anticancer therapy [23–25]. More than 30 agents targeting cancer-related gene products are approved for clinical use, while others are in the midst of evaluation [1]. Due to technical problems with membrane permeability and intracellular metabolic vulnerability of drugs, the list of available druggable targets consists mostly of cell surface receptors and secreted regulators including the vascular endothelial cell growth factor (VEGF), epidermal growth factor receptor (EGFR and the related receptor protein HER-2), plateletderived growth factor receptor (PDGFR), and oncogenes Bcr-Abl, c-Kit [1, 24]. A major advantage of targeted cancer therapeutics is their low toxicity, relatively high therapeutic index, and selectivity for neoplastic targets. Their target selectivity, however, can also be their undoing. Unlike testicular cancer or certain forms of leukemia in which tumorigenesis is driven by a single, targetable oncogene, e.g., chronic myeloid leukemia and acute promyelocytic leukemia [26, 27], most solid tumors result from genetic alterations of a large number of genes providing a plethora of collaborating, complex oncogenic circuits [28, 29] – and we still lack suitable systems-level models and rules to predict how these circuits can be dismantled. Thus, the aberrant expression of a putative target does not by itself predict that a drug disabling that target will be effective. Moreover in single-target treatments, cancer cells typically adapt by activating or up-regulating alternative pathways. As a result, the clinical application of targeted agents frequently leads to acquisition of treatment resistance [10] and the available single-target agents are not having a major impact on long-term survival for most common malignancies [24, 30, 31].
3 The Paradigm: Targeting a Nexus of Cancer Pathways What is the next step in anticancer therapy and rational drug discovery? One broadly accepted option is further development of combinatorial multitarget therapy by using a tailored combination of several molecular-targeted drugs and safe doses of cytotoxic agents [1, 32]. However, current simplistic models of linear signaling pathways would argue that a genetic lesion driving tumor growth should impact drug responsiveness as well. In fact, most naturally occurring tumors are genetically complex and each individual neoplasia has its own genetic composition and metabolic features. Therefore, it is unlikely that a combination of drugs targeting several individual oncogenes will be efficient against different types of tumors – or even against a specific category of tumors. Tumors result from the accumulation of
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different genetic alterations of a large number of genes (more than 300) that pervert a few key signaling pathways to incite tumorigenesis [33]. Thus, rather than seeking agents that target individual mutated genes, a more robust possibility for drug discovery may be to develop agents that hit critical nodes and hubs in the oncogenic circuitry [29]. The principle of convergence of oncogenic pathways – the ability of several hundred genetic alterations – no one of which is obligatory for carcinogenesis – to usurp a few critical regulatory nodes – motivates the search for those components of the cellular machinery that are necessary for oncogenic signaling. Recent experimental data summarized here and in other chapters of this book (chapter “mTORC1: A Signaling Integration Node Involved in Cell Growth”, “The Regulation of the IGF-1/mTOR Pathway by the p53 Tumor Suppressor Gene Functions”, and “Downstream Targets of mTORC1”) indicate that the proximal arm of the PI3K/Akt/mTOR/translation axis functions as a regulatory hub in key cancer pathways and that the mTORC1-regulated eIF4F-mediated translational apparatus is a bona fide candidate for targeting by the next generation of anticancer therapeutics.
4 Receptor Signaling Networks Include the Initiation Stage of Translation as a Regulatory Hub Translation of mRNA into protein is the most energy expensive step in the flow of genetic information from the genome to the proteome. The canonical cap-dependent initiation step of translation is conventionally subdivided into four consecutive stages: (1) Formation of the pre-initiation ribosome complex 43S; (2) cap recognition by the initiation complex eIF4F and recruitment of the 43S complex to the 5 -end of the capped mRNA; (3) scanning of the 5 -untranslated region (UTR) to the start codon; (4) and formation of the 80S ribosome complex [34] (chapter “Downstream of mTOR: Translational Control of Cancer”). While control of mRNA translation occurs at each step, the primary target for regulation is the cap recognition process. It is exerted either by microRNA (miRNA) targeting of [35–37] or by modulation of eIF4F activity through a battery of signal transduction pathways [38, 39] (chapters “mTORC1: A Signaling Integration Node Involved in Cell Growth”, “The Regulation of the IGF-1/mTOR Pathway by the p53 Tumor Suppressor Gene Functions”, and “Downstream Targets of mTORC1”). At least three potentially oncogenic pathways downstream of growth factor receptors, Myc-Max, PI3K/Akt/mTOR, and Ras/MAPK/Erk, converge on eIF4F to activate cap-dependent translation (Fig. 1). The primary regulation of eIF4F activity is exerted through the PI3K/Akt/mTOR kinase cascade, which is positively regulated by Ras and leads to phosphorylation of the translational repressors 4E-BPs on several serine/threonine sites. Hyperphosphorylated 4E-BPs have a markedly decreased affinity for eIF4E, the cap-binding constituent of the eIF4F heterotrimer, which is free to associate with eIF4G and initiate translation (chapter “Downstream of mTOR: Translational Control of Cancer”). When activated, Ras also signals phosphorylation of eIF4E itself at Ser209 [40, 41] resulting in increased eIF4E
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Fig. 1 The translational complex eIF4F promotes tumor progression through the integration of multiple cancer pathways. The primary regulation of eIF4F activity is exerted through the PI3K/Akt/mTOR kinase cascade, which is positively regulated by Ras and leads to phosphorylation of the translational repressors 4E-BPs on several serine/threonine sites. Transcriptional control of eIF4F occurs through c-Myc – a transcription factor with pleiotropic oncogenic effects. Among the pro-oncogenic eIF4F downstream effectors are also several key upstream eIF4F activators such as Akt, Ras, and Myc. Thus, eIF4E mediates a series of self-amplifying signaling loops promoting cell proliferation and survival, thereby integrating and intensifying the potentially oncogenic growth signaling circuitry downstream of growth factor receptors
affinity for capped mRNA and enhanced translation rates for some transcripts. Phosphorylation of eIF4E is mediated by the MAP kinase-interacting protein kinase-1 (Mnk-1) [42], which is activated through the Ras-regulated MAPK/ERK and p38/Jnk kinase cascades. Transcriptional control of eIF4F occurs through c-Myc – the transcription factor with pleiotropic effects on oncogenesis [43–45]. When deregulated, c-Myc promotes transcriptional transactivation of eIF4E operating through two functional Myc-binding sites (E-boxes) in the eIF4E promoter [46– 48]. Thus, the translation initiation apparatus couples the cellular growth/survival circuit to a spectrum of trophic extracellular cues.
5 Translational Control of the Cell Cycle Machinery Growth factor-regulated translational control has a specific effect on cell proliferation by integrating the successive chain of events leading to transit through cell cycle checkpoints [49]. Growth factor-deprived, non-proliferating cells have diminished levels of global translation, whereas synthesis of some proteins is increased
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[50, 51]. Cell fusion experiments demonstrate that quiescent cells actively synthesize inhibitors that block progression through G1 [52]. These observations inspired the idea that cellular quiescence is not a passive property but rather an active metabolic state maintained by selective translation of negative regulators of prereplicative events [53]. The molecular mechanism involves the transcript encoding p27kip – the inhibitor of G1 cyclin-dependent kinases (CDKs) - whose, abundance is selectively increased in quiescent cells and downregulated in response to mitogenic signals [54]. Phosphotyrosine signaling downstream of a variety of cell growth pathways shares the common ability to stimulate cell cycle progression and activate the translation initiation apparatus. In addition to driving the translational machinery through phosphorylation of S6K1 and 4E-BPs, the PI3K/Akt/mTOR pathway controls progression of cells through G1. Activation of eIF4F and S6K is required for mTOR to drive G1 transit, suggesting that the PI3K/Akt/mTOR cascade signals cell cycle progression through activation of the translational machinery [55]. Conversely, post-replicative progression of cells through the G2 and mitotic checkpoints relies on cap-independent translation driven by the internal ribosome entry sites (IRES) of mRNAs encoding impotent promoters of the cell cycle and oncogenesis, c-Myc and ornithine decarboxylase (ODC) [49, 56]. The switch from cap-dependent translation to the IRES-mediated mechanism is tightly regulated by a mechanism that remains encrypted. Oncogenic insults, such as deregulated c-Myc, may promote aberrant activation of eIF4F-mediated translation in post-replicative cells [57]. Oncogene-induced breaching of the translational switch results in failure of IRES-mediated translation of mRNA essential for proper chromosome segregation leading to genome instability. Together, these findings establish cap-dependent translational control as an essential component of physiological proliferation, which determines whether a post-mitotic cell progresses toward the next round of DNA synthesis or enters a quiescent state and whether non-proliferating cells remain quiescent or resume proliferation. Thus, oncogenes may disrupt normal cell cycle control by commandeering the translational machinery.
6 Translational Control of the Antitumor Defense Systems To mitigate the risk of neoplasia, evolution conferred eukaryotic organisms with a constellation of defense programs. In addition to promoting cell cycle transit and other cancer-related capabilities, the prototypical oncogenes also trigger irreversible growth arrest and self-destruction programs [4]. Established examples include the ability of c-Myc [58, 59] and E2F1 [60] to stimulate apoptosis and the proclivity of oncogenic Ras and Akt to promote proliferative senescence [61, 62] or destructive autophagy [63]. The classical paradigm of oncogene cooperation states that activated oncogenes require supporting signals from a cadre of collaborating oncogenes [64, 65]. There is compelling evidence that collaborating oncogenic lesions curb the antiproliferative function of each other by down-regulating the senescence and death pathways imbedded in their programs [4, 66]. Up-regulated eIF4F collaborates with
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deregulated Myc in cell transformation [67]. This effect is mediated by the ability of eIF4F to block Myc-induced apoptosis [68, 69] by translational activation of negative regulators of the apoptotic machinery such as Bcl-XL [70]. Along these lines, enforced expression of the eIF4E repressor 4E-BP1 sensitizes cells to drug-induced apoptosis in a manner strictly dependent on expression of oncogenic Ras and the ability of 4E-BP1 to sequester eIF4E from a translationally active complex with eIF4G [71, 72]. Senescence is a stable proliferative arrest resulting from telomere erosion during iterative cell divisions [73]. A telomere length-independent growth arrest termed “premature senescence” can be induced by activated oncogenes such as ras [61]. As a result, Ras-induced transformation requires cooperation from immortalizing oncogenes that overcome the senescence barrier [64]. Enforced expression of eIF4E does not rescue primary human mammary epithelial cells (HMEC) from senescence associated with loss of telomeres; however, HMECs overexpressing eIF4E bypass the premature senescence barrier to uncontrolled proliferation [74]. These observations indicate that eIF4E can collaborate with a spectrum of oncogenes by inactivating their embedded antitumor programs and that cells harboring activated eIF4F have advanced along the cancer pathway.
7 Translational Addiction of Cancer In the past decade, several causal links have emerged between deregulated translation and cancer, providing strong support for the idea that hyper-activated cap-dependent translation is a bone fide oncogenic pathway [39, 75, 76] (chapters “Downstream of mTOR: Translational Control of Cancer”, and “Downstream from mTOR: Therapeutic Approaches to Targeting the eIF4F Translation Initiation Complex”). Since eIF4F funnels and augments multiple oncogenic pathways, it is not surprising that cap-dependent translation is commonly deregulated in most – if not all – human malignancies [77]. The most pronounced increase of eIF4GI is found in lung carcinomas, whereas eIF4E is upregulated in lung and breast cancers and head and neck squamous cell carcinomas [78–80]. Increased expression of eIF4E is one of the early events in breast tumorigenesis [81] where it serves as an independent prognostic factor [82–84]. In lung neoplasia, the level of eIF4E increases with morphological aberrancy: ranging from low levels in atypical adenomatous hyperplasia, intermediate levels in bronchioloalveolar carcinoma up to high levels in invasive papillary adenocarcinoma [85]. Recent findings in mouse models of cancer [69, 86] confirmed earlier seminal studies [87, 88] that overexpression of the cap-binding protein eIF4E is sufficient to transform rodent cells. Moreover, normalizing deregulated translation in cancer cells by genetic [89, 90] or pharmacological [91–95] (chapter “Translational Control of Cancer: Implications for Targeted Therapy”) interventions reverses the transformed phenotype and is associated with sustained regression of xenograft tumors. These findings indicate that the downstream translation step provides a major contribution
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to cancer genesis induced by activated oncogenic pathways – and that malignancies become irrevocably addicted to hyper-activated translation. Why do cancer cells switch to the hyper-activated mode of translation? One explanation is that any actively propagating population of cells relies on increased global translation to meet their intrinsic need to replicate all cellular contents during frequent cell divisions. This, however, is not the case for actively proliferating normal or cancer mammalian cells, which express up-regulated eIF4F but do not reveal dramatic changes in general translation. Instead, activated eIF4F predominantly stimulates recruitment of ribosomes to a specific subset of mRNA, suggesting that the sustained activation of eIF4F switches the translational repertoire of cells rather than increasing overall translational rates uniformly. This supports an alternative explanation that cancer cells have unique translational requirements that extend beyond the need for increasing total protein mass in order to divide.
8 What Triggers Pro-oncogenic Recruitment of Ribosomes to mRNA? The initiation factor eIF4E regulates gene expression on several post-transcriptional levels including differential export of nuclear mRNA and selective recruitment of cytoplasmic mRNAs to ribosomes. When overexpressed, eIF4E facilitates nuclear export of mRNAs encoding key components of the cell cycle machinery including cyclins D1, E1, B1, c-Myc, Pim1, and ODC [96]. Microarray analysis of polyribosome-bound mRNA populations shows that the principal target of activated eIF4F is a subset of transcripts that impact cell proliferation and survival [74, 97–99]. Among the pro-oncogenic eIF4F effectors are also several key upstream activators such as Akt [100], Ras [43, 74, 88], and Myc [43, 47] (Fig. 2). Thus,
Fig. 2 Ectopic expression of eIF4F in hTERT-immortalized human breast carcinoma cells alters recruitment of ribosomes to mRNAs encoding proteins implicated in the cancer-related phenomena
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eIF4F mediates a series of self-amplifying signaling loops promoting cell proliferation and survival, thereby integrating and intensifying the potentially oncogenic growth signaling circuitry downstream of growth factor receptors – unless the normal pathways restraining eIF4E are intact. How does the activated translational apparatus select transcripts for differential recruitment into or out of polyribosomes? 5 -Untranslated regions (UTRs) of these transcripts are relatively long, often contain GC-rich sequences, and are thus predicted to have a complex secondary structure. These features make recruitment of these mRNAs to ribosomes highly sensitive to the activity of eIF4F and highly dependent upon the abundance of the cap-binding protein eIF4E – the rate-limiting component of eIF4F (chapter “Downstream Targets of mTORC1”). The other common feature of mRNAs regulated by eIF4E is a cis-regulatory element in their 5 -UTR which functions as a putative structural code for coordinated post-transcriptional control [74, 96, 98, 101], findings in accord with the post-transcriptional regulon model of gene expression regulation [102].
9 Strategy to Target eIF4F Normalizing eIF4F function might be accomplished in at least three ways: (1) decreasing abundance of a key constituent of the eIF4F complex; (2) disrupting association of monomeric components of the eIF4F heterotrimer; (3) antagonizing the interaction of the cap-binding component of eIF4F with the 7-methylguanosine cap at the 5 -mRNA terminus of pro-oncogenic mRNA. A major expectation for targeted cancer therapeutics is that they manifest increased efficacy and low toxicity. To this end, all three anti-eIF4F approaches are attractive conceptually and their feasibility has been proved experimentally (Fig. 3).
Fig. 3 Strategies to target the integrity of eIF4F (see the text for details)
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Decreasing abundance of eIF4E: Graff and colleagues from the Lilly Research Laboratory successfully applied an ASO-based strategy to downregulate eIF4E and suppress tumorigenesis [94]. They were able to circumvent technical complications typical for ASOs in vivo by using second-generation ASOs modified to penetrate the cell membrane and enhance stability. Treatment of breast carcinoma cell lines with ASOs decreases the abundance of eIF4E and reduces the abundance of oncogenic eIF4E and its downstream effectors. The 4E-ASO increased apoptosis in vitro and suppressed tumorigenicity in xenograft models of breast cancer without any signs of general toxicity. It is now in early phase clinical trials. Disrupting eIF4E/eIF4G association: To pharmacologically mimic the antieIF4F effect of the 4E-BPs, a research group led by Gerhard Wagner identified a small molecule compound designated 4EGI-1 in a high-throughput screen which binds to eIF4E, increases affinity of eIF4E for 4E-BP1, and disrupts eIF4E/eIF4G association [95]. When applied in vitro, 4EGI-1 ablates eIF4F-dependent neoplastic properties in cancer cells including expression of the c-Myc and Bcl-XL oncoproteins, apoptosis resistance, and clonogenic expansion. Disrupting association of eIF4A and eIF4G: The other approach for disrupting eIF4F was applied by three groups of researchers who found that the marine natural product pateamine A binds eIF4A and stabilizes its association with mRNA thereby sequestering it from association with eIF4G [103–105]. Recently, the plant cyclopenta[b]benzofuran flavagline silvestrol was found to interact with eIF4A and suppress translation and cancer cell growth through a mechanism similar to pateamine A – but with less toxicity [106, 107]. Antagonizing the eIF4E-to-cap interaction: An alternative approach that we have taken to normalize aberrant cap-dependent translation is direct – using synthetic nucleotide derivatives such as 7-benzyl guanosine monophosphate (7-BnGMP) to antagonize the binding of eIF4E to the mRNA cap. We have developed a library of phosphoramidated derivatives of 7-Me-GMP analogues with favorable drug-like properties, designed to displace capped mRNAs from the eIF4F complex. Using a multistep in vitro/in vivo test system, a group of compounds that are a new class of potentially cell-permeable inhibitors of the eIF4E–cap interaction have been identified [108–110].
10 Molecular Biomarkers of Successful Translational Therapy A means to objectively evaluate the pharmacological response to therapeutics targeting the translation initiation machinery remains an unmet challenge. Compelling evidence shows a strong correlation between the status of the eIF4F-mediated translational apparatus and intracellular levels of some cancer-related proteins such cyclin D1, c-Myc, Ras, and VEGF [111]. However, the abundance of these proteins is determined by many other physiological and biochemical factors and therefore their levels may not serve as reliable biomarkers of anti-eIF4F treatment efficacy. Gene expression-based drug discovery has the potential to identify compounds that
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convert malignant cells, as defined by its transcriptional gene expression signature, to a less malignant and/or chemosensitive state [112]. However, there are several drawbacks to transcriptional gene expression profiling as a biological marker for cellular responses [102, 113]. First, many transcripts present in a cell are translationally silent. Second, the effect of translational modulators on recruitment of ribosomes to the available cellular mRNAs is much greater than alterations in total mRNA profiles. Third, and most importantly for anti-eIF4F drug discovery, microarray analysis of polyribosome-bound mRNAs suggest that translational modulators affect the cellular phenotype by differentially altering translational efficiency of physiologically important transcripts that are preferentially selected by the modified translational apparatus from among the pool of available mRNAs (see previous section for details). For these reasons, we plan to develop a novel type of gene expression signature-based strategy for drug discovery designated the “translational signature,” which may serve as a surrogate biomarker for efficacy of anticancer agents. This approach is based on identifying categories of genes in cancer cells that are actively translated or silenced in response to drug treatments. This strategy aims to define translational signatures of those anti-eIF4F interventions that result in the cessation of eIF4F-dependent capabilities in cancer cells including resistance to apoptosis and deregulated cell proliferation.
11 Why Would Not Inhibition of eIF4F Simply Kill All Cells – Not Just Cancer? Significant suppression of the eIF4F-mediated translation should be toxic, since both normal and cancer cells require eIF4F-mediated translation. The rational approach to eIF4F-targeted therapy is not complete inhibition, but rather reversal of eIF4F hyperactivation in cancer with proper dosing. Accepting this, what is the theoretical and experimentally determined therapeutic margin between normal cells and cancer? Experimental data show that cancer cells manifest about a 2.0-fold increase in cap-dependent translation and that suppressing eIF4F-mediated activity 50% by genetic interventions is sufficient to promote apoptotic death in cancer cells while being absolutely harmless for non-transformed cells [114]. In vivo, eIF4E ASO suppresses eIF4E-mediated translation by 50–60%. This intervention inhibits tumor growth in mice without any general deleterious effects [94, 115]. Detailed analysis of protein expression in these mice documents a selective decrease in the abundance of oncogenic proteins with minimal effect on physiological protein expression.
12 Can Antitranslational Therapeutics Kill All Tumor Cells? Targeting proliferating tumor cell populations: To devise efficient cancer therapy, it is important to know whether the given therapeutic approach eliminates
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all potentially tumorigenic cells. Pertinent to anti-eIF4F interventions, all potentially tumorigenic cells should be addicted to aberrant eIF4F-mediated translation to be targets for successful treatments. Compelling data suggest that cell populations in most tumors are hierarchically organized into tumorigenic and non-tumorigenic fractions [116, 117]. Consistent with the traditional clonal evolution model of carcinogenesis, the vast majority of tumor cells are highly malignant and successful therapeutic interventions have sought to eliminate all tumor cells to prevent tumor re-growth. Experimental data show that normalizing aberrant eIF4F-mediated translation in cells of carcinoma lines efficiently suppresses xenograft tumor growth by eliminating a “critical mass” of potentially tumorigenic cells by apoptosis [90]. This suggests that single-agent translational therapy might have great clinical impact on cancers in which tumorigenic cells represent most of the cells in a tumor cell population. Targeting cancer stem cells: Available data indicate that many cancers including colon, breast, brain, head and neck, pancreatic, and hematopoietic malignancies contain minor populations of tumor-initiating cells or cancer stem cells (CSC) [117–119]. In accord with this model, cancer therapy must specifically focus on this population of cells. There is evidence that CSCs might be intrinsically more resistant to therapy than other cells in tumors [120]. The status of the translational machinery in CSC remains unknown and CSC susceptibility to anti-eIF4F therapy remains to be defined. Targeting quiescent tumor cells: The other unresolved problem for targeting tumorigenic cells is the presence of non-proliferating cell populations. These cells are viable but in a reversible state of growth arrest [121, 122]. Deregulated translation is conventionally associated with increased cell proliferation [123] and suppression of cell growth by antitranslational interventions is most efficient in cell populations in which deregulated cell proliferation is driven by oncogenes [71]. The multiple stress signaling pathways induced by noxious microenvironmental factors including inadequate vasculature and hypoxia operate through the p38MAPK regulated network to reprogram tumor cells toward acquisition of a quiescence gene expression program, which often precedes the onset of metastasis [124] and poses a barrier to many cancer therapies [122]. These alterations contribute to decreasing translational activity in quiescent cells. Hypoxia, for example, promotes disassembly of eIF4F by dephosphorylation of 4E-BPs, decreased expression of eIF4E, and by sequestration of eIF4E from its association with other eIF4F constituents by relocation of eIF4E into the nuclear compartment [125, 126]. The potential insensitivity of non-proliferating cells to eIF4F inhibitors may therefore contribute to tumor re-growth through recruitment of quiescent cells into the proliferating fraction. Moreover, although restoration of normal levels of cap-dependent translation in cancer cells leads to cell death, not to cell cycle arrest [57, 68, 90], the possibility of inducing the quiescence program in some types of cancer cells cannot be excluded, and the induction of the quiescent state may render cells refractory to other drug interventions. Thus, it is of paramount importance to gain insight into efficacy of antitranslational therapies for targeting tumor quiescent cells using appropriate cancer models such as hypoxia of solid tumors.
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13 Anticipated Therapeutic Limitations: Signaling Feedback Loops Downstream of Deregulated eIF4F One theoretical limitation of single-agent eIF4F inhibitors is activation of prooncogenic feedback loops. Genome-wide analysis of transcripts recruited to ribosomes in response to eIF4F activation shows that coordinated activation of mRNAs encoding positive regulators of cell proliferation and inhibitors of apoptosis is accompanied by recruitment of ribosomes to transcripts encoding potential tumor suppressors [74]. Decreasing eIF4F activity, therefore, might alleviate homeostatic repression of some pro-survival and cell growth pathways. The role of signaling feedback loops in anti-eIF4F therapy remains a subject for future investigations by methodologies that include combinations of translational gene expression profiling and bioinformatics in cells subjected to antitranslational interventions.
14 Concluding Remarks Despite improvements in cancer therapies over the past decades, many cancers remain incurable. In its essence and etiology, cancer is a genetic disease and isolated primary human cells require only five or six genetic alteration before they acquire tumorigenic competence [127]. In contrast to in vitro model systems, the process of multistep human tumorigenesis evolves progressively through multiple intermediate stages and analysis of genetic variations and gene expression patterns in naturally existing tumors reveal an extremely complicated picture of genetic control of tumorigenesis. However, while different cancers and even cancers of the same type show great genetic variations, they manifest common features at the protein pathway level sharing a core group of about a dozen perturbed pathways. This supports the view that at the functional level, cancer is a protein pathway disease. The clear implication of this view for cancer therapy is that instead of targeting individual genetic alterations one by one, the next generation of cancer therapeutics will target regulatory nodes and hubs in the cancer network. Accumulating evidence indicates a prominent role for the initiation stages of translation in integrating multiple cancer-related pathways into a self-amplifying oncogenic system – whose integrity is essential for cancer cell proliferation and survival. These studies have culminated in the development of low molecular weight inhibitors of eIF4Fmediated translation as cancer therapies. Before these compounds move into clinical trials, highly focused laboratory efforts are needed to evaluate which cancers and which cancer-related properties are vulnerable to agents normalizing the function of eIF4F.
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Downstream from mTOR: Therapeutic Approaches to Targeting the eIF4F Translation Initiation Complex Jerry Pelletier and Jeremy R. Graff
Abstract Protein synthesis, which proceeds through three distinct phases – initiation, elongation and termination – is the most energetically expensive process in the cell and is accordingly tightly regulated. In mammals, translational control is primarily exerted at the level of ribosome recruitment during the initiation phase. This critical regulatory step is often targeted during viral infection, in developmental processes, in the regulation of cell proliferation and growth, and in malignant progression. Under most circumstances, mRNA recruitment to the 43S translation initiation complex is rate limiting and represents a major node for regulating cellular gene expression. Mechanisms regulating translation initiation have the distinct advantage of being much more rapid in onset than those targeting nuclear events and are also capable of exerting spatial control over the expression of specific proteins. mRNA recruitment to ribosomes is governed by the assembly and activity of the eIF4F translation initiation complex, which is under control of the mTOR kinase. A wealth of evidence from studies in experimental cancer models and human cancer tissues has now accumulated invoking a role for elevated eIF4F activity in cancer development and malignant progression and highlighting this complex as a target for the development of novel anti-cancer therapeutics. Keywords Molecular targeted therapy · eIF4F · Translation initiation · mTOR
1 The Ribosome Recruitment Phase of Translation Initiation The ribosome recruitment phase of translation initiation is generally thought to be the rate-limiting step of protein synthesis (for a discussion of this, see [1–3]). This phase consists of 43S pre-initiation complex (40S ribosome and associated factors) recruitment to mRNA templates by a set of specialized translation factors from the J. Pelletier (B) Department of Biochemistry, McGill University, Montreal, QC H3G 1Y6, Canada e-mail:
[email protected] V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1_13,
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Fig. 1 Schematic diagram of the ribosome recruitment phase of eukaryotic translation initiation showing points of action of the inhibitors discussed in this review. See text for details
eukaryotic initiation factor (eIF) 4 class (Fig. 1). The 40S ribosome is thought to traverse the 5 -untranslated region (in a process known as scanning) until an appropriate initiation codon is reached, at which point a 60S ribosomal subunit joins and polypeptide elongation begins. There are variations on this basic model, such as internal recruitment of ribosomes or repositioning (shunting) of ribosomes to downstream sequences without scanning, but discussion of these is outside the scope of this current review. The cap structure (m7 GpppX; where X is any nucleotide) is present at the 5 end of cytoplasmic cellular transcripts. It is added to mRNA soon after transcription initiation and is important for nuclear processes, such as polyadenylation, splicing, and nucleocytoplasmic export [4]. In the cytoplasm, the cap plays a role in mRNA stability and in facilitating translation initiation. The role of this structure in
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stimulating ribosome recruitment is mediated by eIF4F, a heterotrimeric complex, composed of (a) eIF4E, a cap-binding protein; (b) eIF4A, an RNA helicase; and (c) eIF4G, a large polypeptide that has RNA-binding activity and interacts with eIF4E, eIF4A, and the poly(A)-binding protein (PABP) (Fig. 1). In order to appreciate the consequences of targeting various steps of the translation initiation pathway, we provide below brief introductions of trans-acting factors and cis-acting elements that are involved in this process.
2 Trans-Acting Factors in Ribosome Recruitment 2.1 eIF4E eIF4E is responsible for delivering eIF4F to the 5 -end of mRNAs by binding to the cap structure in an ATP-independent process. eIF4E is phylogenetically conserved from Saccharomyces cerevisiae to mammals [5]. Deletion of eIF4E in S. cerevisiae is lethal, a phenotype that can be rescued by mammalian eIF4E (which is 32% identical to the yeast protein) [6]. A significant amount of eIF4E is in the nucleus (12–33%) where it may participate in cap-dependent nuclear mRNA metabolism [7].
2.2 eIF4A eIF4A (DDX2) is the prototype member of the DEAD-box family of helicases, which is comprised of >35 members in mammals. There are two highly related eIF4A gene products, eIF4AI (DDX2A) and eIF4AII (DDX2B) (90–95% identical), both implicated in translation and functionally interchangeable in vitro [8, 9]. eIF4A exists as a free form (referred to herein as eIF4Af ) and as a subunit of eIF4F (eIF4Ac ) [10, 11]. As part of the eIF4F complex, eIF4Ac is delivered to the mRNA template in the vicinity of the cap structure by eIF4G in a high-affinity RNA-binding state [12]. eIF4A’s helicase activity is ∼20-fold more efficient when it is a subunit of the eIF4F complex than when it exists as a free form [13, 14]. Structural analysis revealed that the middle domain of eIF4G binds to the C-terminal domain of eIF4A adjacent to motifs implicated in RNA binding, ATP hydrolysis, and RNA unwinding [15]. eIF4Af is believed to recycle through eIF4F during the initiation process [9, 16–18]. Whether eIF4A stimulates ribosome recruitment by unwinding mRNA secondary structure, dissociating protein–RNA and/or protein–protein complexes remains to be established. Are there other helicases involved in the ribosome recruitment step of translation initiation? eIF4AIII (DDX48) is a related helicase (66% identical to eIF4AI in humans) that was reported to inhibit ribosome recruitment and translation in vitro [19]. eIF4AIII can bind to the middle domain of eIF4G and is found associated with eIF4F in HeLa extracts [19]. However, it is present at ∼1/10th the concentration
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of eIF4AI in extracts [19] and is predominantly a nuclear protein implicated in nonsense-mediated decay [20]. Thus its putative role in regulating translation needs to be better defined. In yeast, another DEAD-box protein, Ded1, is required for translation initiation [21–23]. A mutant allele of Ded1 that does not impair yeast growth is capable of inhibiting Brome mosaic virus (BMV) RNA replication [24]. This was found to be a consequence of selective inhibition of BMV RNA2 translation whereas general cellular translation remained unaffected [24]. The human homologue, DDX3, has been reported to inhibit translation, disrupt polysomes, interact with eIF4E, and inhibit eIF4E:eIF4G interaction [25]. Recently, the RNA helicase DHX29 has been invoked as being necessary for 48S complex formation on mRNAs containing highly structured 5 -UTRs [26]. These findings suggest that helicases other than eIF4A may participate in translation initiation, although their precise roles remain to be defined.
2.3 eIF4G There are two eIF4G isoforms in mammals, eIF4GI and eIF4GII, that are 46% identical and possess similar functional properties [27–29]. The amino terminal third fragment of eIF4G has a small ∼10 amino acid region that interacts with eIF4E [30, 31]. A separate domain at the amino terminus of eIF4G also interacts with PABP [27, 32]. The middle domain of eIF4G contains binding sites for eIF3 and eIF4A [31, 33] and possesses RNA-binding activity [34]. The RNA-binding activity may stabilize eIF4F on the mRNA following cap recognition by eIF4E. eIF4G appears to perform an mRNA/ribosome bridging function by interacting with the 40S-bound eIF3 complex, PABP, and the mRNA. The C-terminal region of eIF4G also contains a second eIF4A-binding site and a binding site for MAP-kinase-interacting kinases 1 and 2 (Mnk1 and Mnk2), which in turn target bound eIF4E for phosphorylation (see below). p97 (also known as DAP5, NAT1, or eIF4G2) is homologous to eIF4GI with the exception that it lacks the N-terminal domain of eIF4GI required for eIF4E binding [35–38]. It contains a binding site for eIF4A and eIF3 and is thought to sequester these into inactive complexes, thereby negatively regulating translation [37, 39]. Given that eIF3 and eIF4A appear to be in excess of eIF4GI, it will be important to assess the fractional amount of eIF3 and eIF4A that actually participates in translation initiation in support of this hypothesis [40, 41]. Suggesting a different role for p97 in translation initiation, it has recently been shown to bind to eIF2β and to stimulation translation [42].
2.4 eIF4B and eIF4H eIF4B and eIF4H co-operate with eIF4A during ribosome recruitment to render its helicase activity more processive [13, 43–45]. eIF4B contains two RNA recognition motifs (RRMs), one near its amino terminal domain (NTD) and the other in the
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carboxyl-terminal half of the protein, that are important for its activity [46–49]. eIF4B can self-associate and interact with eIF3, an event that might aid in recruiting the 43S pre-initiation complex to mRNAs [50]. eIF4B is a phosphoprotein and its phosphorylation status is regulated by both the PI3K/mTOR and MAPK pathways, a modification that increases the interaction between eIF4B and eIF3 [51]. eIF4B can also act as a discriminatory factor, with some mRNAs being less dependent than others on the presence of this factor for initiation [52]. It is essential for mRNAs that have even moderate base pairing within their 5 -untranslated regions (UTRs) and may differentially affect the translation of cap-dependent mRNAs [53]. Sequestration of eIF4B by 14-3-3σ (a negative regulator of the cell cycle) correlates with the reduction in protein synthesis typically observed during mitosis [54]. eIF4H is 39% identical and 62% similar to eIF4B [43]. Like eIF4B, eIF4H contains an RRM with RNP-1 and RNP-2 domains [44]. The presence of these RNA-binding motifs in eIF4H is consistent with data showing that eIF4H can bind to RNA and polymerizes on the 5 -UTR of mRNAs during the cap recognition process [55]. Downregulation of eIF4H by siRNA-mediated knockdown exerts anti-proliferative effects in cancer cells [56]. eIF4H (as well as eIF4B and eIF4A) interacts with the virion host shutoff (Vhs) protein of herpes simplex virus to accelerate degradation of host and viral mRNAs and facilitate sequential differential expression of viral genes [57, 58].
2.5 Poly(A)-Binding Protein The poly(A) tail is an important contributor to both mRNA stabilization and translation initiation [59]. It is generally found associated with the 71 kDa polypeptide PABP, of which several molecules are present per poly(A) tail. PABP is thought to stimulate translation by promoting mRNA circularization through simultaneous interactions with eIF4G and the 3 -poly(A) tail [32]. PABP has four RRMs, of which RRMs1/2 can support many of its biochemical functions, including binding to the poly(A) tail [60–62] and eIF4G interaction [27, 63]. The crystal structure of PABP RRM1/2 with poly(A) has been reported [64]. Consistent with PABP playing an important role in translation initiation, depletion of PABP from extracts reduces translation rates, reduces 48S and 80S ribosomal complex formation, and impairs eIF4E–cap interaction [65].
3 Cis-Acting mRNA Elements That Impact on the Efficiency of Ribosome Recruitment 3.1 The m7 G Cap Structure Detailed understanding into the interaction between eIF4E and the 7-methyl guanosine cap structure has been obtained from structure/activity studies utilizing
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cap analogues and from structural studies of eIF4E bound to cap analogues [66, 67]. 7-Methyl guanine recognition is mediated by stacking between two conserved tryptophans, as well as hydrogen bonding and van der Waals contacts between the N7-methyl group and a third conserved tryptophan [66, 67]. The stacking interaction is significantly strengthened because of charge transfer between the electron-rich indole groups and the electron-deficient 7-methyl guanine (which carries a delocalized positive charge due to methylation) [68]. The presence of a cap structure at the 5 -end of an mRNA template stimulates translation, presumably by enhancing recruitment of eIF4F to the mRNA 5 -end via eIF4E, followed by RNA binding of eIF4Ac and/or eIF4G. However, equally plausible scenarios are that eIF4E binds to m7 G cap structures followed by recruitment of eIF4G and eIF4A [69, 70] or that eIF4F first is incorporated into the 43S pre-initiation complex which then binds to the cap structure (these models are discussed in detail in Gingras et al. [1]). The ability of eIF4E to access the cap structure is a determinant of an mRNA’s inherent translational efficiency [16]. However, less efficient translation of unmethylated, capped (e.g., GpppGterminated) or uncapped (e.g., pppG-terminated) transcripts can occur as well. One set of experiments indicates that the central domain of eIF4G can bind to RNA in a 5 -end-dependent manner to stimulate translation of uncapped mRNAs [71] obviating the need for eIF4E and providing an explanation for why scanning is also observed on uncapped transcripts [72]. Whether the 5 -end-dependent initiation on uncapped transcripts is a consequence of a default mechanism occurring because the body of the mRNA is masked by RNA-binding proteins or eIF4G has inherent affinity for a free 5 -end remains to be established (these issues were discussed in De Gregorio et al. [71]).
3.2 5 -Terminal Oligopyrimidine (5 -TOP) Tracts A structural hallmark of mRNAs encoding for ribosomal proteins and some translation factors (eEF1A and eEF2) is the presence of 5 -TOP sequences. Transcription of TOP mRNAs is initiated with cytosine followed by 4–14 pyrimidine residues (reviewed in [73, 74]). TOP mRNAs are exquisitely sensitive to the growth rate of a cell, with growth arrest leading to the inhibition of TOP mRNA translation. The position of the TOP tract adjacent to the cap structure as well as its integrity is required for this response [75]. Relief of TOP mRNA repression in vitro by a synthetic TOP-containing RNA oligonucleotide suggests the presence of a titratable repressor protein [76]. The RNA-binding protein La has been proposed as a candidate repressor since it can interact with 5 -TOP elements [77, 78] and co-sediments with TOP-containing mRNA in polysomes [79]. General RNA-binding proteins, such as the La autoantigen, have been shown to render translation of mRNAs more cap-dependent presumably by suppressing internal ribosome recruitment and forcing 5 -end-mediated eIF4F–mRNA engagements [80].
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3.3 mRNA Secondary Structure When secondary structure is present near the 5 -cap, this appears to be more inhibitory to translation than when it is positioned further downstream [81–84]. Such a structure inhibits 48S complex formation [18, 83], does not appear to affect eIF4E–cap interaction [85, 86], but can inhibit eIF4B–mRNA interaction [86]. Since eIF4B–mRNA interaction is dependent on eIF4A activity, it would be interesting to probe eIF4A–mRNA interactions in these systems. Presumably, increased secondary structure near the mRNA 5 -end impairs eIF4Ac’s ability to prepare a ribosome landing pad and is responsible for the observed decrease in translational efficiency.
3.4 Protein–mRNA Interactions Protein–mRNA interactions present in the 5 -UTR are also inhibitory to translation initiation. This was nicely shown by experiments demonstrating that binding of the iron regulatory protein-1 (IRP-1) to its binding site in the 5 -UTR of the ferritin mRNA prevents ribosome recruitment [87]. The binding of eIF4F to the mRNA was not affected by IRP-1/mRNA interactions, rather the bridging interaction between eIF4F and eIF3 was blocked [87]. Forcing protein/mRNA interactions near the 5 -end of mRNAs using small molecule chemical inducers of dimerization also decreases ribosome binding and inhibits translation initiation [88].
3.5 Poly(A) Tail The majority of cytoplasmic mRNAs possess a 3 -poly(A) tail (ranging in length from 50 to >200 bases long). Like the cap structure, this element also plays an important role in stimulating translational efficiency – an effect that is mediated by PABP. The prevailing thought is that PABP’s ability to interact with the 3 -poly(A) tail and the eIF4G subunit of eIF4F leads to circularization of eukaryotic mRNAs, known as a closed-loop mRNP [32, 89]. PABP binding to eIF4F affects cap recognition and ATPase activity and these events may contribute to the poly(A)-m7 G synergy [90–93]. As well, the poly(A) tail can facilitate binding of the 43S complex to mRNA, but not efficiently to the 5 -end in the absence of a cap [94]. A circularized mRNA may deposit newly terminated ribosomes in vicinity of the 5 -end, increasing the effective concentration of ribosomes in this locale and stimulating reinitiation – an effect that might be aided by interaction between eukaryotic release factor (eRF)1, eRF3, and PABP [95]. Indeed two distinct closed-loop mRNP states that are resistant to cap analogue inhibition have been described – one requiring the 48S ribosomal complex and the other requiring an 80S ribosome and eRF3 and eRF1 [96].
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4 Signal Transduction Pathways Regulating eIF4F Complex Assembly and Activity Assembly of the eIF4F complex is dependent upon the availability of eIF4E and eIF4A to interact with the scaffolding protein eIF4G. The regulation of eIF4E and eIF4A availability is controlled by essentially the same process – both are liberated from an inhibitory binding protein as a consequence of mTOR-mediated phosphorylation. eIF4A is normally bound to the tumor suppressor protein PDCD4 (programmed cell death protein 4). This causes the consequence that PDCD4 blocks translation initiation, particularly of mRNAs with stable 5 -UTR secondary structures. By binding directly to eIF4A and also by binding eIF4G, PDCD4 inhibits the helicase activity of eIF4A and prevents the association of eIF4A with eIF4G [97]. Recent work indicates that PDCD4 blocks translation specifically by displacing both eIF4G and RNA from eIF4A [98]. PDCD4 binding to eIF4A is regulated by phosphorylation at serine 67, which promotes rapid degradation of PDCD4, enabling eIF4A function and enhancing translation. Phosphorylation of PDCD4 at serine 67 is rapamycin sensitive and is directly mediated by the kinase S6K1 [99]. Thus signaling through mTOR to S6K1 phosphorylates PDCD4, promoting its degradation and liberating eIF4A from inhibition. The availability of eIF4E to interact with eIF4G is specifically regulated by binding to one of the three inhibitory 4E-binding proteins (4E-BPs) [1]. The binding of eIF4E to 4E-BP or eIF4G is mutually exclusive and occurs through a similar site on 4E-BP and eIF4G that encompasses a “core” sequence “YXXXXL” (in which X is any amino acid and is a residue possessing an aliphatic portion, most often L, but sometimes M or F). The eIF4E-binding motifs of 4E-BPs are disordered and undergo a disorder-to-order transition upon binding to eIF4E [1]. As with the uncoupling of eIF4A from PDCD4, eIF4E is liberated from the 4E-BPs by mTOR-driven phosphorylation. Specifically, 4E-BP1 is phosphorylated in a hierarchical manner. The first phosphorylation events, threonine 37 and threonine 46, are considered to be constitutive and are relatively insensitive to the effects of rapamycin. However, these sites are required to “prime” 4E-BP1 for subsequent phosphorylation at threonine 70 and serine 65. Phosphorylation of these latter sites then causes the release of 4E-BP1 from eIF4E [100]. Once released from 4E-BP1, eIF4E is free to bind eIF4G and, along with eIF4A, forms the eIF4F complex. As noted, the release of both eIF4E and eIF4A from their respective inhibitory binding proteins is a consequence of signaling through mTOR. Signaling through mTOR also appears to phosphorylate eIF4G directly, though the functional consequence of these phospho-events is unclear. Moreover, mTOR-mediated signaling promotes the association of eIF4G with the eIF3 translation complex, thereby facilitating the connection of eIF4F with the 40S ribosome and increasing the rate at which the small ribosome is positioned on the mRNA [101]. Once bound to eIF4G, eIF4E is now in spatial proximity to the kinases MNK1 and MNK2, which interact specifically with eIF4G [29, 102]. The MNKs phosphorylate eIF4E at serine 209 [143]. In general, phosphorylation of eIF4E correlates
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with increased translation rates and cellular growth status (for a review, see [103]). It has been proposed that phosphorylated S209 forms a salt bridge with K159 in eIF4E to clamp down on bound mRNA, strengthening the eIF4F–mRNA interaction and increasing the efficiency of initiation [103]. However, ablation of eIF4E phosphorylation, by homologous inactivation of MNK1 and MNK2, does not alter global translation rates nor does it affect development in the mouse indicating that this post-translational modification is not essential for eIF4E activity in normal tissues [104]. Phosphorylation of S209, however, is required for the transformation properties of eIF4E in cell lines [105] and in Eμ-myc-driven lymphomagenesis [106]. Phosphorylated eIF4E promotes tumorigenesis primarily by suppressing apoptosis and enhancing the expression of the anti-apoptotic protein MCL-1 [106]. These data demonstrate that phosphorylation of eIF4E by MNKs may be particularly important for the transformed phenotype but dispensable in normal tissues, suggesting that pharmacologic inhibition of MNKs is an attractive therapeutic approach. A small molecule inhibitor of the MNK kinases, CGP57380, prevents phosphorylation of eIF4E [107], but unfortunately also targets other kinases [108].
5 Impact of Elevated eIF4F Activity in Cancer Progression 5.1 The eIF4F Complex and mRNA Discrimination Enhanced eIF4F complex formation increases the translation of all cap-dependent mRNAs and thereby increases total protein synthesis rates. The extent to which specific mRNAs are translated varies substantially between different mRNAs and is largely dependent upon sequence elements within each mRNA, such as the length and structure of that mRNA’s 5 -UTR and the presence of discrete hairpin structures in the 5 -UTR [83, 109, 110]. The majority of cellular mRNAs are characterized by relatively short, unstructured 5 -UTRs (e.g., β-actin, GAPDH) that are easily traversed by the eIF4F complex to form a ribosome landing pad. These mRNAs (i.e., strong mRNAs) are translated efficiently even when eIF4F complex activity is limiting, as they require only minimal eIF4F activity. In contrast, a select group of mRNAs are extremely sensitive to, and dependent upon, eIF4F for translation. These “weak” mRNAs typically harbor lengthy, G+C-rich, highly structured 5 -UTRs that encumber efficient RNA unwinding by the eIF4F complex and subsequently prevent efficient ribosome loading [110]. Alternately, these mRNAs may have discrete sequence elements in the 3 -UTR that specifically require eIF4E for the transport of that mRNA from the nucleus to the cytoplasm [111]. These weak mRNAs are most sensitive to eIF4E availability and eIF4F activity and are poorly translated under normal conditions when eIF4F complex formation is limiting. In cancers, where eIF4E availability and eIF4F activity are commonly elevated, the translation of these weak mRNAs is preferentially and disproportionately
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enhanced. These mRNAs typically encode potent growth and survival factors that have well-recognized roles in malignancy, including the angiogenesis factors VEGF and FGF-2, the proto-oncoproteins cyclin D1 and c-myc, and the anti-apoptotic survival factors BCL-2, MCL-1, and survivin [112].
5.2 eIF4E in Cancer Elevated eIF4E availability and eIF4F activity drive the selective, disproportionate, and coordinated increase in the translation of weak mRNAs – i.e., those encoding the potent growth and survival factors that, in concert, enable tumor formation, angiogenesis, and malignant progression [110, 113]. Not surprisingly then, forced eIF4E overexpression in cultured fibroblasts or epithelial cells can induce cellular transformation and tumorigenesis [114, 115]. Moreover, in transgenic mice, ectopic eIF4E expression increases the incidence of lymphomas, lung adenocarcinomas, hepatomas, and angiosarcomas [116] and accelerates lymphomagenesis [116, 117]. eIF4E overexpression also facilitates the establishment of autocrine stimulatory loops [115, 118], suppresses apoptosis [119], and imparts drug and radioresistance to tumor cells [110, 117] – phenotypic alterations integral to malignant progression. Whereas enhanced eIF4E function promotes malignancy in experimental models, reducing or inhibiting eIF4E function suppresses malignancy, in concert with reduced expression of potent growth and angiogenesis factors. Decreasing the level of translation initiation factor 4E with antisense RNA causes reversal of ras-mediated transformation and tumorigenesis of cloned rat embryo fibroblasts [120–124]. Peptides designed specifically to block the interaction of eIF4E with eIF4G rapidly induce apoptosis in MRC5 cells [123]. Overexpression of 4E-BP1 also suppressed tumorigenesis and growth in Src-transformed cells [122] and in breast cancers [124]. Likewise, antisense RNA-mediated reduction of eIF4E in both epithelial and fibroblast tumor models suppressed tumor growth, invasion, and metastasis in concert with reduced translation of key malignancy-related molecules such as ODC, VEGF, and FGF-2 [110, 125–127]. Short interfering RNA-mediated knockdown of eIF4E can also suppress myc-dependent proliferation programs by decreasing the translational efficiency of c-myc mRNA [128]. In human cancers, the levels of free eIF4E are commonly elevated consequently to increased eIF4E expression or release of eIF4E from 4E-BPs, a result of signaling through the AKT/mTOR and ras signaling pathways (Fig. 2), which are frequently activated in a diverse range of human cancers [32]. Increased eIF4E expression has been documented repeatedly in association with malignant progression in multiple human cancers, including leukemias and lymphomas and cancers of the breast, colon, bladder, lung, prostate, and head and neck (reviewed in [32]). Moreover, recent work has demonstrated that 4E-BP phosphorylation is also increased and associated with malignant progression in breast, ovarian, prostate, and colon cancers [129].
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Tumor formation and malignant progression Fig. 2 eIF4F complex formation is increased in human cancer and contributes to malignancy. Assembly of the eIF4F complex is dependent upon the availability of eIF4E. In cancer, eIF4E availability is increased as a consequence of enhanced signaling through mTOR, which phosphorylates 4EBPs, releasing eIF4E. Alternately, eIF4E availability is increased as a function of increased eIF4E expression. Enhanced eIF4F complex assembly then preferentially and disproportionately enhances the translation of many diverse, potent proteins involved in all aspects of malignancy from deregulated growth control (c-myc, ODC, cyclin D1), to angiogenesis (VEGF, FGF-2), cell survival (Bcl-2, Mcl-1, survivin), and metastasis (MMP-9, osteopontin)
6 Targeting eIF4F Activity for Cancer Therapy 6.1 Blocking eIF4E–m7 G Cap Recognition with Cap Analogues Cap analogues (e.g., m7 GDP, m7 GTP, m7 GpppG) have been extensively utilized to study the ribosome recruitment step of translation initiation in complete translation assays [130–134], in partial reactions with purified initiation factors [10, 135–138], and in direct binding assays with eIF4E [139]. Several structure–activity relationships have emerged from studies with cap analogues: (i) N7 alkyl and alicyclic substituents larger than ethyl decrease the inhibitory activity [131, 133, 140]; (ii) aryl substitution at N7 improves the efficacy of inhibition [134]; (iii) replacement of the ribose by (hydroxyethoxy)methyl ether (but not by other pentoses) [138] or ring opening [134] decreases inhibitory activity; and (iv) derivatization of the α-phosphate moiety by O-methylation decreases the potency of m7 GMP [137]. In
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addition, there is preference for the anti-conformation in ligand binding [133], there is improved inhibition with increasing phosphate residues [134], and the second nucleotide residue in analogues of the form m7 GpppN affects inhibitory activity in the order G>C>U>A [134]. There is a general trend between the affinity of cap analogues for eIF4E and their potency to inhibit translation in vitro. However, cap analogues are not cell permeable and cannot be used to easily inhibit eIF4E binding to mRNA in vivo. mRNAs that translate via a closed-loop model are particularly resistant to cap analogue inhibition [96], and so it remains important to determine the extent to which translation could be inhibited in vivo by cell-permeable cap analogues. One study used transient treatment of cells with digitonin to permeabilize them to m7 GpppG, causing release of eIF4E from the nucleus [141]. The effect of delivering m7 GpppG by this approach on cytoplasmic translation has not been assessed. One way of making cap analogues cell permeable might be to link bile acids, long-chain fatty acids, or a cholesterol moiety to the m7 GpppG, as has been achieved for optimization of in vivo delivery of lipophilic siRNAs [142]. It has been recently suggested that ribavirin is a cap analogue and can be used in vivo to inhibit eIF4E-mediated processes [143]. However, these results have not been corroborated by two other studies [144, 145].
6.2 Disrupting the eIF4F Complex An alternative approach to targeting the eIF4E–cap interaction is to selectively disrupt the interaction of eIF4E with eIF4G, thereby disabling the formation of the eIF4F complex. Wagner and colleagues recently identified an inhibitor of the eIF4E:eIF4G interaction, called 4EGI-1 [146]. Treatment of transformed cells in culture with this agent elicited the expected reduction in expression of eIF4Eregulated proteins, such as c-myc and cyclin D1 [146]. Intriguingly, this inhibitor blocked only the eIF4E:eIF4G interaction but did not block the eIF4E:4E-BP1 interaction. However, 4EGI-1 inhibited HCV IRES-mediated translation, which does not require eIF4E for activity, suggesting “off-target” effects and therefore this compound needs to be optimized before further preclinical studies are undertaken [146]. A second HTS assay has recently been used by Pelletier and colleagues to screen for small molecules that interfere with eIF4E:eIF4G interaction [147, 148]. The discovery of these inhibitors highlights the feasibility of selectively disrupting the eIF4F complex with small molecules to target translation initiation.
6.3 Reducing eIF4F Activity by Targeting eIF4A There is much precedence for regulating translation by modulating eIF4A availability. BC1, a small brain-specific noncoding RNA at synaptodendritic microdomains, can sequester eIF4A (and PABP) to repress translation [149, 150]. RNA aptamers targeting eIF4A have also been described that impair its ATPase
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activity and inhibit cap-dependent translation [151]. As described above, PDCD4 is a well-characterized modulator of eIF4A activity and availability. As well, p97 can function as a general repressor of translation by forming translationally inactive complexes that include eIF4A and eIF3, but not eIF4E. A dominant-negative mutant of eIF4A has been described that associates more strongly with eIF4G than wild-type eIF4A and subsequently inhibits eIF4E crosslinking to the cap structure [18]. Translation inhibition by this dominant-negative mutant is thought to be a consequence of inhibiting eIF4A recycling [18]. Three small molecule modulators of eIF4A activity have been identified from an in vitro screen designed to score for modulators of cap-dependent and HCV IRES-mediated initiation [147, 152]. 6.3.1 Pateamine – A Chemical Inducer of Dimerization Pateamine (PatA) was isolated off the shores of New Zealand by Northcote et al. [153] from the marine sponge Mycale sp. PatA has been reported to be a more potent inducer of apoptosis in ras- or bcr/abl-transformed 32D myeloid cells than in non-transformed cells [154]. The total synthesis of this complex molecule has been reported [155, 156]. PatA inhibits eukaryotic protein synthesis in vitro and in vivo and does not affect prokaryotic protein synthesis [157]. PatA can also selectively inhibit cap-dependent protein synthesis in vivo [157]. However, inhibition of translation by PatA is irreversible [157]. PatA causes disruption of polysomes, as expected for an initiation inhibitor, and also causes re-distribution of eIF4A into heavier sedimenting complexes [158, 159], which is RNA dependent [159]. PatA induces the formation of stress granules (SGs) [160] – cellular aggregates that form as a consequence of stress and that contain stalled translation pre-initiation complexes [158, 161]. Using an affinity chromatography approach, four biological targets of PatA have been identified: eIF4A, cytokeratin, tubulin, and serine/threonine kinase receptorassociated protein (STRAP) [157, 158]. Western blotting of eluents from the affinity matrix indicated that eIF4AI, eIF4AII, and eIF4AIII were specifically retained by PatA, whereas eIF4E, eIF4B, eIF2α were not [157]. Consistent with eIF4A being a relevant biological target of PatA, its overexpression in cells increased the IC50 for translation inhibition by PatA, whereas overexpression of STRAP had no effect [158]. The significance of cytokeratin or tubulin binding to PatA has not been investigated. PatA stimulates eIF4AI-mediated ATP hydrolysis, RNA-dependent ATP binding, and ATP-dependent and ATP-independent RNA binding [157, 158]. PatA induces global conformation changes in eIF4A and its activity is dependent on the nature of the linker domain that connects the N-terminal and C-terminal domains [162]. PatA does not stimulate ATP binding to eIF4Ac suggesting that the PatAbinding site is masked or perturbed by the association of eIF4A with eIF4G. PatA also stimulates the helicase activity of eIF4Af , allowing it to unwind duplexes with G’s that would otherwise be too stable to be substrates for this helicase [157]. PatA has also been reported to stimulate the interaction between eIF4A and eIF4B [158], although this may be an indirect RNA-mediated phenomenon [159]. Although it is difficult to assess whether the activity of other cellular helicases is affected by
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PatA, none appeared to be retained on a PatA affinity resin [163]. PatA also did not affect the kinetics of in vitro splicing reactions (which requires >7 helicases) or Ded1p-mediated helicase activity, suggesting specificity of action toward eIF4A [157]. How does a compound that stimulates eIF4A ATP hydrolysis and RNA binding cause inhibition of translation? One model is that PatA behaves as a chemical inducer of dimerization (CID) – forcing an engagement between eIF4A and RNA in a non-sequence-dependent manner and titrating eIF4A away from the translation apparatus, making it unavailable to participate in the initiation phase (Fig. 1). Although eIF4A is very abundant (three copies/ribosome) [8], its delivery to mRNA is mediated by eIF4F, in a mechanism that ensures coupling of cap recognition, RNA unwinding, and ribosome recruitment. Binding of eIF4A to eIF4G is weaker than that of eIF4E to eIF4G. eIF4A can be readily dissociated from mammalian eIF4G on ion exchange columns [16] and can be exchanged easily with free eIF4A [9]. Purified eIF4F complexes from plant [164], Drosophila [165], and yeast [166] lack eIF4A, presumably due to its dissociation during isolation. By sequestering eIF4A to RNA, PatA would reduce the levels of eIF4Af available for recycling into the eIF4E:eIF4G complex. Since eIF4F complexes devoid of eIF4Ac cannot recruit 43S pre-initiation complexes on mRNAs [16, 167], this would impose a block at the initiation level. Consistent with this, PatA has been shown to indeed reduce the amount of eIF4Ac in the eIF4F complex [158, 159]. In one study, PatA prevented 40S ribosome loading onto mRNA templates or rendered them unstable to sucrose gradient sedimentation [157], whereas in a second study, PatA was reported to trap 48S pre-initiation complexes on the mRNA template, a result that is surprising given that PatA reduces eIF4F levels [158, 159]. IRESes that require eIF4A for ribosome recruitment are inhibited by PatA [157, 158]. The HCV IRES does not require eIF4A for initiation and PatA does not prevent ribosome loading on this IRES nor inhibit HCV IRES-mediated translation at concentrations sufficient to abolish cap-dependent translation [157, 158]. In vitro, high concentrations (>10 μM) of pateamine slightly inhibit HCV- and CrPV-dependent translation, an effect attributed to a secondary consequence of PatA forcing engagements between eIF4A and the IRESes, which could non-specifically block ribosome loading and reduce translational efficiency [157]. PatA thus appears to behave as a CID to enhance protein–RNA interactions. Pelletier and colleagues had previously engineered bivalent small molecules with RNA- and protein-interacting sites to act as CIDs and artificially recruit streptavidin, a protein that typically does not bind RNA, to the 5 -UTR of specific reporter mRNA transcripts [88]. This logic to modulate gene expression at the level of translation remains largely unexplored, but nature seems to have taken advantage of this approach to block gene expression at the level of translation. 6.3.2 Inhibition of eIF4A RNA Binding by Hippuristanol Hippurins are cytotoxic polyoxygenated steroids that were isolated from the marine gorgonian Isis hippuris [168, 169]. These compounds were found to be cytotoxic
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to cell growth and the activity of a number of structurally related entities has been compared [163, 168, 170]. Some polyoxygenated steroids have been reported to reverse multidrug resistance [171] although the mechanistic details are not known. Hippurins are potent inhibitors of eukaryotic (not prokaryotic) protein synthesis. Hippuristanol, one member of the family, inhibits the RNA-dependent ATPase, RNA binding, and helicase activity of eIF4Af [163]. It also inhibits RNA binding of eIF4Ac [163] (Fig. 1). As expected for an inhibitor of eIF4A, hippuristanol blocks translation of IRESes that require this factor for initiation and does not affect expression from the HCV or CrPV IRESes [163, 172]. Hippuristanol functions in vivo to mainly inhibit protein synthesis, with a minor effect on DNA replication [163]. Partial inhibition of DNA synthesis has been previously observed with other protein synthesis inhibitors [173]. The binding site for hippuristanol on eIF4A has been mapped [174]. It interacts with amino acids within and adjacent to conserved motifs V and VI of eIF4AI, two regions at the CTD that are implicated in RNA, ATP, and making interdomain contacts [175, 176]. The binding site is conserved between eIF4AI and eIF4AII and contains three key amino acid differences between eIF4AI and eIF4AIII that might explain the reduced sensitivity (∼10-fold) of eIF4AIII ATPase activity to hippuristanol [174]. There is extensive sequence variation in the binding site present in other DEAD-box RNA helicases [174] and this provides a framework for understanding the selectivity of hippuristanol toward eIF4AI and eIF4AII. Consistent with this, hippuristanol does not inhibit ATPase activity of human DDX52 and DDX19, does not inhibit the helicase activity of yeast Ded1p, and does not affect nuclear splicing [163, 174]. Hippuristanol-resistant alleles of eIF4AI and eIF4AII have been generated and allowed rescue of hippuristanol-mediated inhibition of translation in vitro and in vivo [174]. 6.3.3 Cyclopenta[b]benzofurans (CBFs) – Modulators of eIF4A Activity CBFs were initially isolated from components of plants from Aglaia sp. These compounds showed a variety of biological effects, including reversion of K-ras-NRK morphology [177], insecticidal activity [178], inhibition of cell growth ex vivo and in vivo [179], anti-fungal activity [180], and antiplatelet aggregation activity [181]. CBFs have also been reported to inhibit mdm2, NF-AT, and NF-κB activity [182–184]. Some of these pleiotropic effects are likely secondary consequences of these compounds inhibiting protein synthesis [177, 179, 185, 186]. Indeed, we have recently shown that, like pateamine, these compounds act as CIDs – forcing an engagement between eIF4A and RNA [186]. Using a murine lymphoma model in which mTOR signaling is elevated (due to PTEN deletions), we showed that one member of this class of compounds is able to reverse resistance to doxorubicin as effectively as rapamycin [186]. Importantly, tumors overexpressing eIF4E were resistant to rapamycin, but responded to the sensitizing effects of CBF [186], consistent with eIF4E being a genetic modifier of the rapamycin response in vivo [187]. Silvestrol was shown to also function as a single-agent chemotherapy in xenograph studies of acute lymphoblastic leukemia (697 ALL) in SCID mice [188] as well as
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prostate cancer (PC-3) and breast cancer (MDA-MB-231) xenographs in nude mice [189]. These results provide in vivo validation for a strategy aimed at increasing drug sensitivity in cancers by targeting translation initiation.
7 Reducing eIF4F Activity by Decreasing eIF4E Expression An alternative approach to targeting eIF4E would be to reduce eIF4E protein expression. Taking advantage of recent advances in antisense oligonucleotide (ASO) chemistry, we have now delivered to clinical trials a second-generation ASO specifically targeting eIF4E [190]. Early-generation ASOs lacked the nuclease resistance and tissue stability necessary for systemic therapy. In addition, these first-generation ASOs also triggered substantial immune stimulatory activity. These liabilities have been engineered out of the second-generation ASOs to avoid immune stimulation, increase plasma stability and tissue half-life, and target RNA affinity. Consequently, these second-generation ASOs (e.g., the eIF4E ASO) are markedly more tractable systemic therapies than earlier generation ASOs. Indeed, data from recent human clinical trials have demonstrated that second-generation ASOs effectively distribute to tumor tissue and, in a dose-dependent manner, reduce target RNA and protein [191]. The eIF4E ASO effectively reduced both eIF4E RNA and protein in a wide array of transfected human and murine cells, subsequently reducing the expression of the malignancy-related proteins – specifically cyclin D1, VEGF, c-myc, survivin, and BCL-2. Importantly, ASO-mediated reduction of eIF4E did not affect the expression of β-actin, a protein encoded by a “strong” mRNA, nor did it reduce overall protein synthesis substantially. These data substantiate the notion that eIF4E inhibition selectively impacts the translation of a limited pool of mRNAs but has minimal impact on global translation rates and is thereby qualitatively and quantitatively distinct from the inhibition of global protein synthesis by elongation inhibitors [120]. As in other cell and tumor models, the most prominent biologic consequence of ASO-mediated eIF4E reduction was the substantial induction of apoptosis as measured by both caspase 3 activation and TUNEL staining. This effect is evident not only in breast and non-small cell lung cancer cells [190] but in all other tumor cell lines we have examined (Graff et al., unpublished observations). The proapoptotic effect of the eIF4E ASO was also evident in treated xenograft tumor tissue as well. In PC-3 prostate cancer xenografts, wherein eIF4E expression was reduced by ∼50%, TUNEL staining was increased nearly ninefold relative to tumors from mice treated with a mismatch control ASO. In these same tumors, the proliferative, Ki-67-positive cell fraction was decreased roughly fourfold by the eIF4E ASO [190]. These data indicate that a significant pro-apoptotic effect in tumor tissue is realized by a 50% reduction in eIF4E protein expression after intravenous dosing of the eIF4E ASO [120]. In addition to having a profound impact on the induction of apoptosis, the modulation of eIF4E impacts on the expression of the potent angiogenic factors,
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VEGF and FGF-2 [125, 126]. This strongly implies that, by decreasing translation of these pro-angiogenic proteins, reduction of eIF4E may have a pronounced anti-angiogenic effect. This effect is recapitulated in eIF4E ASO-treated xenograft tumors and in in vitro models of angiogenesis. The vascularity of PC-3 xenografts was markedly affected by treatment with the eIF4E ASO. Endothelial content, as measured by the examination of the endothelial marker von Willebrand’s factor, was reduced substantially. Moreover, the vessels in the eIF4E ASO-treated xenograft tissues were more constricted. Consistent with this anti-angiogenic effect, these eIF4E ASO-treated xenografts showed a substantial loss of VEGF expression as measured by immunohistochemistry [190]. These data support the notion that reducing eIF4E expression causes a decrease of potent angiogenic factors, like VEGF, and affects tumor-induced angiogenesis [110]. In this manner, the effect of the eIF4E ASO on tumor-related angiogenesis is indirect – that is, it is a consequence of affecting angiogenesis factor production by the tumor. However, it is also feasible that eIF4E reduction could affect the response of endothelial cells directly. Accordingly, we transfected human umbilical vein endothelial cells (HUVECs) with the eIF4E ASO and then plated these cells on a dermal fibroblast bed to evaluate the formation of vessel-like structures. These results showed that eIF4E depletion directly inhibited the formation of these vessel structures. Importantly, this was not simply a consequence of reduced HUVEC cell number or cell survival post-transfection. In each experiment, identical numbers of eIF4E ASO and control transfected, and viable HUVECs were plated onto dermal fibroblasts. In parallel, HUVECs from the same harvest were plated under standard tissue culture conditions and continued to grow [110]. As such, these data indicate that the effect of eIF4E reduction in HUVECs was to block the response of these endothelial cells to a pro-angiogenic stimulus. Together with the anti-angiogenic effects of the eIF4E ASO on treated xenografts, these data suggest that depleting eIF4E expression profoundly impacts tumor-related angiogenesis by directly affecting the endothelial response and, indirectly, by decreasing angiogenic factor expression by the tumor. eIF4E is a general protein synthesis factor, serving as a the main conduit for mRNA trafficking into ribosomes. Yet, numerous studies over the last two decades have demonstrated that eIF4E modulation disproportionately affects a limited pool of “weak” mRNAs (e.g., cyclin D1, VEGF, Bcl-2). The accumulated data therefore suggest that, by selectively and disproportionately reducing the translation of a limited pool of mRNAs, targeting eIF4E may preferentially affect tumor tissues, where eIF4E function is routinely elevated. To address this question, the eIF4E ASOs were engineered to target both human and murine eIF4E so that we could simultaneously evaluate the impact of eIF4E reduction in the human tumor xenograft tissue and in mouse normal tissues after intravenous (IV) dosing. Intravenous dosing of the eIF4E ASO strongly reduced eIF4E levels in both xenograft tumor tissue and the mouse liver tissue. Despite reducing eIF4E by 80% in the liver of these xenograft-bearing mice, there was no effect on body weight nor any signs of illness or distress. In the xenografted tumors from these same mice, ∼50% reduction of eIF4E levels dramatically suppressed tumor growth in both breast and
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prostate cancer models. Similarly, in normal mice dosed with four different eIF4E ASOs, eIF4E was reduced by ∼80% in liver without any signs of illness or distress nor any significant change in body weight, spleen weight, liver weight, or plasma levels of liver transaminases [190]. Taken together, these studies demonstrated for the first time that tumor tissues may be more sensitive to the effects of eIF4E inhibition than normal tissues, a differential effect consistent with the concept that eIF4E activity is elevated in, and required by, tumor tissue to sustain the expression of key growth and survival factors that drive malignancy. These studies thereby demonstrated the plausibility of using a second-generation ASO for systemic therapy targeting eIF4E. Furthermore, these preclinical xenograft tumor studies showed that eIF4E could be targeted with a systemic therapy to treat cancer without eliciting toxicity. These studies thereby prompted the initiation of eIF4E ASO clinical trials in cancer patients starting in 2006 [112].
8 Concluding Remarks A significant role for eIF4E and the eIF4F complex in tumor formation and malignant progression has been established in both experimental cancer models and studies with primary human cancer tissues. Recent studies have now clearly demonstrated the plausibility of developing novel anti-cancer therapeutics that target eIF4E directly, the helicase activity of eIF4A or the binding of eIF4E to eIF4G [146, 186, 190]. The predominant biology resulting from the disruption of eIF4F activity seems to be the induction of programmed cell death, although the mechanism by which eIF4E and the eIF4F complex regulate apoptosis has not been fully elucidated. It is likely that the reduction of eIF4E or eIF4F activity would subsequently reduce the translation of multiple anti-apoptotic proteins, leading to robust induction of programmed cell death. Studies with a second-generation eIF4E ASO have demonstrated that depleting eIF4E expression selectively suppressed the expression of eIF4E-regulated proteins and substantially repressed tumor growth in preclinical tumor models, inducing a robust apoptotic response within the xenografted tumor tissue in vivo. Work with the eIF4E ASO has now also revealed that modulating eIF4E levels directly and indirectly affects the tumor-related angiogenesis response. Perhaps of greatest significance, these studies revealed that targeting eIF4E in normal tissues (i.e., liver) can be well tolerated by xenograft-bearing athymic nude mice and by normal immunocompetent mice [190]. The accumulated literature over the past 18 years has clearly demonstrated that the function of eIF4E, and by extension the eIF4F complex, is elevated in multiple human cancers and plays a pivotal role in driving malignant progression. Indeed, these studies continue to support the notion that the function of the eIF4F complex is commonly elevated in many human cancers, particularly those that are most advanced. As a consequence, tumors may be more dependent upon (i.e., addicted to) enhanced eIF4F activity to sustain the expression of the many diverse, potent proteins critical for malignancy – from deregulated growth control (c-myc, ODC,
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cyclin D1) to angiogenesis (VEGF, FGF-2), cell survival (Bcl-2, Bcl-Xl, survivin, MCL-1), and metastasis (MMP-9, osteopontin). In this manner, the eIF4F complex may represent an Achilles’ heel for cancer. Indeed, preclinical data with the eIF4E ASO show that tumor tissues are preferentially more susceptible to the inhibition of eIF4E than normal tissues [120]. It appears that targeting the eIF4Ac component of the eIF4F complex can yield similar results [186, 189]. The continued push to discover inhibitors of the eIF4F complex will most assuredly provide further insight into an evermore attractive, plausible, and potentially effective therapy for cancer. Acknowledgments We wish to thank Drs. R. Cencic and F. Robert for their critical comments on the manuscript. Work in J.P.’s lab on this topic was supported by grants from the Canadian Institutes of Health Research, National Cancer Institutes of Canada, and the National Institutes of Health (USA).
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Index
Note: The letters ‘f’ and ‘t’ following locators refer to figures and tables respectively.
A “Achilles heel,” 238–239, 275 Adenosine triphosphate (ATP), 12–14, 20, 43–45, 76, 79, 89, 115, 134, 136, 144, 150f, 164–165, 180, 182, 259, 269–271 Akhavan, D., 99–107 AMP-activated protein kinase (AMPK), 5, 12–14, 41–43, 53, 63t, 78f, 79–81, 83, 89, 106–107, 119–120, 150f, 165 AMPK, see AMP-activated protein kinase (AMPK) AMP kinase, 39, 41f, 42–43 Angioblasts, 51 Angiogenesis, 14, 23, 49–70, 51, 76, 122–123, 133–134, 137, 144, 150f, 156, 159–160, 188–189, 228, 266, 267f, 273–275 ‘Angiogenic switch,’ 51 Antitranslational therapeutics, cancer, 247–248 Antitumor defense systems, 242–243 Antitumor therapy, 153 AP23574 (Ariad Pharmaceuticals), 69 ATP, see Adenosine triphosphate (ATP) Autophagy, 2–3, 11, 15, 41f, 42, 46, 126, 134, 137, 157, 165–166, 189–191, 189f, 238, 242 See also mTORC1 and control of autophagy B β-actin, 272 BCL2/adenovirus E1B 19 kDa interacting protein 3 (BNIP3), 14, 88 Bioprospector, 231 “Biopsy-treat-biopsy” strategy, 105 Bitterman, P. B., 237–249 Blenis, J., 1–23
BMV, see Brome mosaic virus (BMV) BNIP3, see BCL2/adenovirus E1B 19 kDa interacting protein 3 (BNIP3) Breast cancer combination therapies, benefits, 141 letrozole/vinorelbine treatment, 141 loss of PTEN/PI3K mutations, 141 temsirolimus treatment, phase II trial, 141 Breuleux, M., 149–167 Brome mosaic virus (BMV), 260 3-bromo-2-oxopropionate-1-propyl ester (3-BrOP), 150f, 165 3-BrOP, see 3-bromo-2-oxopropionate-1propyl ester (3-BrOP) Brugarolas, J., 75–90 C Calcinurin inhibitor (CNI), 68 Cancer chemotherapeutic agents, 124 Cancer therapy, 113–126 Cap-binding complex (CBC), 18 CBC, see Cap-binding complex (CBC) CBFs, see Cyclopenta[b]benzofurans (CBFs) CD, see Cowden’s disease (CD) Cell growth/proliferation, 4, 5f, 10, 15, 17, 20, 40 Cellular anabolism, 2 Cellular catabolism (autophagy), 2 Cellular signaling pathways upstream of mTORC1, 5f growth factor signaling Akt phosphorylation in Drosophila and Homo sapiens, 6 MAPK pathway, 8–9 mTORC1/S6K1 signaling, 9 PA activation of mTORC1 signaling, 7 PI3K/Akt signaling, 4–6 PI3K/Akt/TSC2/Rheb signaling pathway, issues, 7
V.A. Polunovsky, P.J. Houghton (eds.), mTOR Pathway and mTOR Inhibitors in Cancer Therapy, Cancer Drug Discovery and Development, C Springer Science+Business Media, LLC 2010 DOI 10.1007/978-1-60327-271-1,
287
288 Cellular signaling pathways (cont.) PRAS40/Rheb activation of mTORC1 signaling, 8 regulation of cell size, genetic linkage studies in TSC families, 6 TSC1/TSC2 complex, inhibition of mTORC1 signaling, 6 nutrients amino acid deprivation/resupplementation in mammalian cells, results, 10 class III PI3K Vps34, role in amino acid simulation, 10–11 MAP4K3, role in amino acid simulation, 11–12 nutrient sensing, importance, 10 nutrient sensing in S. pombe, mechanism, 10 stress signals AMPK and Akt signaling, role in mTORC1 regulation, 13–14 AMPK, role in Wnt signaling and mTORC1 regulation, 13 cellular stresses, types, 12 glucose deprivation on mTORC1, 12 mTORC1 activation via TNFα signaling pathway, 14 repression of mTORC1 by hypoxia, 14 transmission of energy signals by AMPK mechanism, 12–13 Cisplatin, 66t, 118, 124–125, 163 Class III PI3K (Vps34), 10–11, 107, 166 CNI, see Calcinurin inhibitor (CNI) Colon cancers, 64, 66t, 142, 266 Combination strategies, see Drug combinations, therapeutic approach for mTORC1 inhibitors Combination therapy, see Rapalogs; individual cancers Cowden’s disease (CD), 2 Cyclopenta[b]benzofurans (CBFs), 271–272 Cytotoxic combinations, 162–164 Cytotoxic therapies, 106, 154, 162, 239 D Damage-regulated autophagy modulator (DRAM), 42 Decaens, T., 133–145 Dexamethasoneinduced gene 2 (Dig2), see REDD1 protein DLT, see Dose-limiting toxicities (DLT) DNA-damage-inducible transcript 4 (DDIT4), see REDD1 protein
Index DNA-damage-inducible transcript 4-like (DDIT4L), see REDD2 protein DNA-damaging agents, 162–163 Dose-limiting toxicities (DLT), 135 Dowling, R. J. O., 201–211 Downstream of mTOR, translational control of cancer downstream targets of mTOR and their role in cancer AKT regulation by mTORC2 in cancer, 211 4E-BPs, 209–210 eIF4G, 210–211 S6 kinase, 210 PI3K/AKT/mTOR signalling pathway in cellular processes, 201 regulation of translation initiation by mTOR signaling 4E-BPs, 204–208 TOR complex formation mTORC1/mTORC2, role in, 203 translation and cancer, 208–209 efficient translation of mRNAs, requirements, 208–209 increased mTOR signalling, cause of cancer, 209 loss/inactivation of tumour suppressors, effects, 209 translation initiation eIF4F complex formation, rate-limiting step, 202, 202f translation process, stages, 202 Downstream targets of mTORC1 eIF4E-binding proteins (4E-BPs) amino acid starvation/insulin, effects, 182–183 ATP-dependent RNA helicases eIF4AI/II , role, 182 eIF4E/eIF4G/eIF4A complex (eIF4F), 182 eIF4G, mRNA translation initiation, 182 human 4E-BP family members, phosphorylation sites in, 182 phosphorylation of Thr37/46, 183 eIF4E/4E-BP1, role in cell transformation ‘constitutively active’ eIF4F complexes, 183 4E-T, nucleocytoplasmic shuttling factor for eIF4E, 184 impaired mTORC1 signaling, results, 184
Index insensitivity of eIF4E/4E-BP1 to rapamycin, reasons, 184–185 mRNA translation by eIF4E, 183–184 rapamycin, effects on eIF4E, 184 HIF-1α, 188–189 inhibition of VEGF expreesion by rapamycin, 188 mTORC1, pro-angiogenic effects, 188 mTORC1 and control of autophagy Atg1 kinase activity, role, 191 DNA damage prevention/genomic stability maintenance, 190 macroautophagy, 190 PI(3) kinase Vps34, role, 191 S6 kinases, role of autophagy control in Drosophila, 191 stimulatory effect of rapamycin, 191 mTORC1 and control of transcription, 192 mTORC1 and Lipin 1 mTORC1 and CLIP-170, 192 proteins regulated via TOS motifs Lobe, role in eye development and cell survival, 188 PRAS40, role in apoptosis, 187–188 raptor-mediated phosphorylation of proteins, 181 regulation of translation elongation by mTORC1, 189–190 inactivation of eEF2 kinase, 190 signaling events downstream of mTORC1, 189f ribosomal protein S6 kinases knockout of S6K genes in Drosophila/ mice, findings, 185–186 phosphorylation of Thr389, 181f, 185 PI 3-kinase/Akt signaling, impairment of, 186 S6K1 recruitment to SKAR in cell cycle, 181f, 186 substrates for S6 kinases, 186 SGK1, mTORC1, and mTORC2, 193 signaling downstream of mTORC1 inhibition of mTORC1 kinase activity by CCI-779, 180 mTORC1, components/characteristics, 180 mTORC2, components/characteristics, 180 through proteins with TOS motifs, 181f Downstream targets of mTOR/role in cancer AKT regulation by mTORC2 in cancer, 211
289 4E-BPs eIF4E, target for anti-cancer therapies, 210 inactivation by phosphorylation, cancer development, 209 over-expression of eIF4E, cause of oncogenesis, 210 prognostic indicator in human cancer, 209 as tumour suppressors, 209 eIF4G, 210–211 S6 kinase EMT of ovarian cancer cells, role, 210 mTORC1-mediated phosphorylation, effects, 210 DRAM, see Damage-regulated autophagy modulator (DRAM) Drosophila, 6–7, 9–10, 14, 81, 86, 185, 188, 191, 204–205, 270 Drug combinations, therapeutic approach for mTORC1 inhibitors combination with RTK inhibitors combination with cytotoxic agents, 162–164 combination with E2 antagonists, 160–162 combination with ErbB receptor tyrosine kinase inhibitors, 153–156 inhibiting autophagy, 165–166 interfering with tumor cell metabolism, 164–165 synergistic inhibition of PI3K/AKT/mTOR pathway, 156–158 targeting multiple kinases, 158–160 mTORC1 pathway as target in cancer, 150–152 application of mTORC1 inhibitors, treatment of cancer, 151 combination therapies, 152 connection with kinases for cell cycle regulation, 151 inhibitors of mTORC1 kinase, role, 151 rationale for combination therapy, 150f regulation of mRNA translation, role, 150–151 perspectives, 166–167 Drug combinations with RTK inhibitors antibody- and small molecule-based treatment strategies, 152 EGFR-directed monoclonal antibody-based therapies, 152–153
290 Drug combinations with RTK (cont.) autophagy inhibition, 165–166 combinations of PI3K and mTORC1 inhibitors, 166 pro-autophagic chemotherapy, example, 166 combination with cytotoxic agents combination with DNA-damaging agents, 162–163 microtubule-targeted agents, 163–164 combination with E2 antagonists endocrine deprivation therapy, 161 estrogen/ER pathway, 160 mTORC1/ER signaling inhibition, effects on breast cancer, 161 RAD001/CCI-779 in breast cancer, clinical trials, 161–162 reduction in breast cancer mortality, 160 combination with ErbB receptor tyrosine kinase inhibitors, 153–156 analysis of patients with invasive breast cancers, 153 IGF-1 receptor tyrosine kinase, 154–156 mTORC1 inhibitors with anticancer drugs, pre-clinical studies, 154 PI3K/AKT/mTORC1 pathway inhibition, effect on AKT phosphorylation, 154–155, 155f PI3K/AKT/mTORC1 pathway, mediator, 150f, 153 RAD001 with EGFR/ErbB2 inhibitor in cancer models, 153 rapamycin in combination with EKI-785, antiproliferative/ pro-apoptotic effects, 153 rapamycin treatment, results, 153 therapeutic potential of mTORC1 inhibitors, study in cancer models, 154 interfering with tumor cell metabolism AMPK pathway, suppression of tumorigenesis, 165 CCI-779-induced HIF-1α downregulation, 165 tumor cell killing, mTORC1 and glycolysis inhibition, 165 synergistic inhibition of PI3K/AKT/mTOR pathway, 155f, 156–158 targeting multiple kinases, 158–160 Drug resistance by mTOR signaling, 124–126
Index E 4E-BP1, 2–4, 6, 8, 10, 12, 15, 16–17, 16f, 19, 56–57, 77, 78f, 101, 120, 151, 181–185, 187, 203–205, 204f, 209, 211, 243, 246, 264, 266, 268 4E-BPs, inhibitory-binding proteins, 15 eEF2, see Eukaryotic elongation factor 2 (eEF2) EGF, see Epidermal growth factor (EGF) 4EGI-1, inhibitor of eIF4E:eIF4G interaction, 246, 268 eIF2, see Eukaryotic initiation factor 2 (eIF2) eIF4AIII (DDX48), 259–260 eIF4E-binding proteins (4E-BPs) amino acid starvation/insulin, effects, 182–183 ATP-dependent RNA helicases eIF4AI/II , role, 182 eIF4E/eIF4G/eIF4A complex (eIF4F), 182 eIF4G, mRNA translation initiation, 182 human 4E-BP family members, phosphorylation sites in, 182 phosphorylation of Thr37/46, 183 eIF4E/4E-BP1, role in cell transformation ‘constitutively active’ eIF4F complexes, 183 4E-T, nucleocytoplasmic shuttling factor for eIF4E, 184 impaired mTORC1 signaling, results, 184 insensitivity of eIF4E/4E-BP1 to rapamycin, reasons, 184–185 mRNA translation by eIF4E, 183–184 rapamycin, effects on eIF4E, 184 eIF4F activity as target in cancer therapy blocking eIF4E–m7 G cap recognition with cap analogues ribavirin, inhibition of eIF4E-mediated processes, 268 structure–activity relationships, 267–268 treatment of cells with digitonin, effects, 268 disrupting the eIF4F complex treatment with 4EGI-1, 268 reducing eIF4F activity by targeting eIF4A, 268–272 CBFs, 271–272 hippuristanol, 270–271 pateamine, 269–270 eIF4F complex formation, 202, 202f eIF4B, role in ATPase and helicase activity, 202–203 eIF4G binding to 40S ribosome, 202
Index eIF4F complex, signal transduction pathways, 264–265 4E-BP1, phosphorylation events, 264 eIF4E-binding motifs of 4E-BPs, 264 eIF4F–mRNA interaction, 265 mTOR-mediated phosphorylation of PDCD4, effects, 264 phosphorylation of eIF4E by MNKs, effects, 265 prevention by CGP57380, 265 eIF4F, composition, 258f eIF4A, 259 eIF4E, 259 eIF4G, 259 Electrochemical proton gradient, 76 EMT, see Epithelial to mesenchymal transition (EMT) Endocrine deprivation therapy, 161 Endocrine therapy, 160–161 Endometrial cancers, 139–140 everolimus treatment, results, 140 temsirolimus treatment of 6 months, reports, 139–140 Endothelial cells, 51–52, 54–60, 68–69, 122–123, 134, 137, 143, 159–160, 163, 273 Energy metabolism, 39f, 40, 43–45, 165, 188 Epidermal growth factor (EGF), 8, 51–52, 61, 152 Epithelial to mesenchymal transition (EMT), 210 Etoposide, topoisomerase II inhibitor, 85, 124 Eukaryotic elongation factor 2 (eEF2), 88, 186, 190, 204, 206f Eukaryotic initiation factor 2 (eIF2), 88 Everolimus (RAD-001, Novartis), 69 F Faivre, S., 133–145 Feng, Z., 37–46 FGF, see Fibroblast growth factor (FGF) Fibroblast growth factor (FGF), 19, 51–52, 67 FKBP12, see Immunophilin (FKBP12) 18 flurodeoxyglucose, 44 Fonseca, B. D., 179–193 FOXO transcription factors, 40 ‘FRAP’(mTOR), 192 G GAP, see GTPase activating protein (GAP) GEF, see Guanine nucleotide exchange factor (GEF)
291 Gene expression, 21–22, 86, 192, 218, 226–227, 244–249, 270 Genes encoding α-subunits in humans, 76 Genome-wide translational analysis advancement from integrative translatomics to mechanisms de novo identification approach, 231–232 identification of RNA regulatory elements, 232–233, 233f post-transcriptional operon hypothesis (Jack Keene), 231 insights global translational regulation downstream of eIF4E, 224–226 other cancer-related systems, 226–229 integrative translatomics, 229–231 exploratory microarray analysis, 230 extended microarray study, 229–230 hypothesis-driven meta analysis, 230 summarizing microarray analysis, 230 polysome microarray data analysis transcriptional/translational regulation, issues, 220–222, 223f proteomics, shortcomings in transcriptional analysis, 218 ribosome recruitment, assessment methods, 218–220 antibody-mediated pull-down of mRNAs in translation, 220 large-scale sample preparation (Wang), shortcomings, 220 polysome microarray approach, 219–220, 219f problems with tissue samples, 220 See also Microarray Glioblastoma, 63, 64t–66t, 99–107, 115, 117t, 118, 122, 142, 156–157, 159, 166, 227 Gliomas, 142 Graff, J. R., 257–275 Growth factor receptors, 115–116 in cancer, examples c-erb-B1, 115 c-MET, 115 Her2/neu, 115 αPDGFR, 115 GTPase activating protein (GAP), 53, 77, 78f, 119 Guanine nucleotide exchange factor (GEF), 19, 79f, 88, 150f Guba, M., 49–70
292 H HEK293 cells, see Human embryonic kidney 293 (HEK293) cells Hepatocellular carcinoma (HCC) activation of AKT in specific groups, study, 139 activation of mTOR pathways in, 138 activation of PI3K/AKT/mTOR pathway in human HCC, study, 138 clinical trials in HCC patients, reports, 139 effect of rapamycin on BEL-7402 and HepG-2, study, 139, 140f Hippuristanol, 258f, 270–271 Homo sapiens, 6 Hormone therapy, 161–162 Houghton, P. J., 113–126 HSP proteins, 40 Human embryonic kidney 293 (HEK293) cells, 77, 80, 83, 89 Human umbilical cord vein endothelial cell (HUVEC), 54, 57, 273 HUVEC, see Human umbilical cord vein endothelial cell (HUVEC) ‘Hyper-activated mode’ of translation, 244 Hypoxia, 5f, 12, 14, 37, 39f, 43, 51, 54–55, 75–90, 120–124, 228, 248 Hypoxia-inducible factor (HIF-1α), 14, 44, 76, 78f, 82, 84f, 88–90, 122, 160, 165, 188–189, 192 inhibition of VEGF expreesion by rapamycin, 188 mTORC1, pro-angiogenic effects, 188 I IGF-1, see Insulin-like growth factor 1 (IGF-1) Imatinib, 113–114 Immunophilin (FKBP12), 3, 69, 151 Initiation step of translation, stages, 240 Insulin-like growth factor 1 (IGF-1), 4, 6, 37–46, 40, 122, 125, 152, 154–156, 159 Integrative translatomics, 229–231 Internal ribosome entry sites (IRES), 88, 242, 268–271 IRES, see Internal ribosome entry sites (IRES) K Kaposi’s sarcoma (KS), 54, 68f KS, see Kaposi’s sarcoma (KS) Kubica, N., 1–23 L LAM, see Lymphangioleiomyomatosis (LAM) Lane, H. A., 149–167
Index Larsson, O., 217–234 LC-MS/MS, see Liquid chromatography tandem mass spectrometry (LC-MS/MS) Levine, A. J., 37–46 Liquid chromatography tandem mass spectrometry (LC-MS/MS), 8 LKB1 (liver kinase B1), 2, 12–13, 63t, 78f, 79–81, 87, 106–107, 118–119, 209 Lobe, Drosophila protein, 188 LPA, see Lysophosphatic acid (LPA) Lung cancer, 66t, 87, 115–116, 118–119, 141–142, 154, 159, 272 Lymphangiogenesis, 55, 67–68, 68f See also MTOR and lymphangiogenesis “Lymphangiogenic switch,” 67 Lymphangioleiomyomatosis (LAM), 2, 63t Lysophosphatic acid (LPA), 52 M Macroautophagy, see Autophagy Major disturbances in mTOR pathway, classification, 66t Malignant disease, 61–66, 113, 122 Mammalian target of rapamycin complex 1 (mTORC1), 1–23 Mammalian target of rapamycin (mTOR), 49–70, 75–90, 99–107, 257–275 Mantle cell lymphomas, 142–143 comparison of two temsirolimus regimen, phase III study, 143 mTOR inhibitors, effects, 142–143 temsirolimus treatment, phase II trial, 143 MAP4K3, 11 Mead, H., 49–70 Melphalan, 124 Metabolism, cellular, 150, 150f, 152 Methyl methane sulfonate (MMS), 85, 124 Microarray, 84, 86, 217, 219–225, 219f, 229–230, 233f, 244, 247 variations of microarray methodology exon arrays, 218 tiling arrays, 218 Microtubule-targeted agents, 163–164 Mischel, P. S., 99–107 mLST8, 1, 3–4, 10, 78f, 101, 180, 203 MMS, see Methyl methane sulfonate (MMS) mRNA translation, 5f, 15–20, 16f, 89, 151, 182, 186, 201–203, 205, 206f, 208–210, 240, 262 mSIN1 (mammalian stress-activated protein kinase (SAPK)-interacting protein), 4, 56, 101, 180, 203
Index mTOR, see Mammalian target of rapamycin (mTOR) mTOR and cancer therapy activation of PI3K/mTOR pathway in cancer activation of PI3K catalytic subunit p110α, 116–117 AKT amplification, 118 amplification/overexpression of growth factor receptors, 115–116 PTEN mutation/deletion/silencing, 117–118 TSC/LKB mutations, 118–119 chronic myeloid leukemia/gastrointestinal stromal tumors treatment with imatinib, 113–114 mTORC1 signaling in solid tumors mTORC1 signaling in survival, 121 regulation of mTORC1 by cellular stress, 120–121 role of mTORC1/C2 signaling in motility and invasion, 122 mTOR in tumor stem cells, 123 mTOR kinase inhibitors, role, 115 mTOR signaling in angiogenesis, 122–123 mTOR signaling in drug resistance resistance to cancer chemotherapeutic agents, 124–125 resistance to molecularly targeted agents, 125 PI3K/mTORC1 pathway and other oncogenes in tumorigenesis, 120 rapamycin treatment of cancer, 114 rapamycin analogs, antitumor action of, 114 Rheb amplification/overexpression alterations downstream of mTORC1 in cancer, 119–120 signaling pathways that activate mTOR complexes, 115f mTOR and cancer therapy, clinical development/novel prospects cell signaling of mTOR, 133–134 mTOR inhibitors in clinical trials duration of drug exposure, factor, 135 rapamycin/rapalogs, 134 non-rapalog mTOR kinase inhibitors, 144 potential novel indications beyond RCC breast cancer, 141 colon cancers, 142 endometrial cancers, 139–140 gliomas, 142
293 HCC, 138–139 mantle cell lymphomas, 142–143 neuroendocrine tumors, 141 NSCLC, 141–142 rapalogs antitumor effects, imaging of, 134, 136–137 biological activity of, 137–138 optimizing activity of, 143–144 pharmacokinetics of, 136 toxicity of, dose and schedule impact, 135–136 mTOR and lymphangiogenesis, 66–68 “lymphangiogenic switch,” 67 lymphatic vessel formation/growth, factors, 67 mTOR inhibition, antilymphangiogenic effects, 67–68 reduction in malignancies after organ transplantation, clinical evidence, 68, 68f VEGF-C/VEGF-D-VEGF-R3 pathway, 67 mTORC1, see Mammalian target of rapamycin complex 1 (mTORC1) mTORC1 and control of autophagy Atg1 kinase activity, role, 191 DNA damage prevention/genomic stability maintenance, 190 macroautophagy, 190 PI(3) kinase Vps34, role, 191 S6 kinases, role of autophagy control in Drosophila, 191 stimulatory effect of rapamycin, 191 mTORC1 and control of transcription, 192 mTORC1 and Lipin 1, 192 mTORC1, components, 203 mTORC2, components, 203 mTORC1 inhibition (hypothesis), clinical examples rapamycin treatment of RCC, 54–55 treatment of KS tumors (Stallone), 54 mTORC1 inhibitors, 151 AP23573 (deforolimus), 151 CCI-779 (temsirolimus), 151 preclinical/clinical application in treatment of cancer, 151–152 RAD001 (everolimus), 151 mTORC1, role in cell growth cellular signaling upstream of, 5f growth factor signaling, 4–9 nutrients, 10–12 stress signals, 12–14
294 mTORC1, role in cell growth (cont.) See also Cellular signaling pathways upstream of mTORC1 domain structure and protein complex assembly 4E-BP1/S6K1 binding to raptor in mTOR–/– mice, 3–4 mLST8 overexpression/knockdown, effects, 4 mTOR protein complexes, cellular functions of, 3 PIKK superfamily, features, 2 protein complexes of yeast, 3 raptor overexpression/knockdown, effects, 3 downstream targets mRNA translational control, 15–20 ribosomal biogenesis, 20–22 4E-BP1 and S6K1, 15 importance in cancer biology, 2 mRNA translational control translation initiation, 15 mTOR, serine/threonine kinase, 2 oncogenes/tumor suppressors, members of, 2 ribosomal biogenesis chromatin-based mechanisms on RP gene expression, 21 regulation of ribosomal proteins, 21 repression of Pol III transcription, 22 repression of Pol I transcription, causative factors/rescue, 20 rRNA synthesis, rate-limiting process, 20 tumor pre-disposition syndromes, 2 mTORC1 signaling and hypoxia energy requirements, cell function ATP generation, 76 genes encoding α-subunits in humans, 76 HIF activity regulation by oxygen, 76 hypoxia effector pathways BNIP3, 88 inactivation of eEF2, 89 mTORC1 inhibition in Tsc2-deficient fibroblasts, 88 PML, 88 mTORC1 inhibition by hypoxia, AMPK, 80–81 negative feedback loop: HIF-1 regulation by mTORC1, 89–90 HIF in RCC development, role, 90 REDD1 protein
Index in cancer, 86–87 hypoxia-independent regulation of REDD1, 84–86 identification of REDD1 orthologues, 81 mTORC1 inhibition, 82–83 regulation of mTORC1 by oxygen mTORC1 activity in HEK293 cells, study, 77 mTORC1 inhibition, study in primary rat hepatocytes, 77 Tinton and Buc-Calderon, 77 TSC1/TSC2 complex activation of receptor tyrosine kinases, 77–79 cell proliferation inhibition by hypoxia, 80 nutrient signaling to mTORC1, 79 regulation of protein translation by hypoxia, 78f, 79–80 Rheb regulation by, as GAP, 77 role in signal relay, 79 mTORC1 signaling in solid tumors mTORC1 signaling in survival, 121 regulation of mTORC1 by cellular stress, 120–121 role of mTORC1/C2 signaling in motility and invasion, 122 mTOR inhibitors of angiogenesis clinical development of non-cytotoxic combination therapies, 70 rapamycin analogs, treatment of human tumor AP23574, 69 temsirolimus, 69 rapamycin, antiproliferative/antiangiogenic properties, 69 mTOR in tumor stem cells, 123 mTOR kinase therapeutic target in tumor angiogenesis antiangiogenic properties, 60 rapamycin analogs, treatment of tumors, 60 VEGF-mediated PI3K/Akt/mTOR signaling pathway, role, 60 mTOR kinase inhibitors, 57, 102, 107, 115, 134, 144, 183 mTOR protein complexes (mTORC1/mTORC2), 3 mTOR signaling in angiogenesis, 122–123 downstream endothelial cell signaling
Index PI3K/Akt/mTOR pathway in tumor angiogenesis, 58–60 PI3K/Akt/mTOR pathway, regulation/ function of, 56–58 VEGF/VEGF-R-mediated signaling in endothelial cells, 55 integrating inflammation and tumor angiogenesis, 66 malignant diseases associated with activated angiogenesis classification of the major disturbances in mTOR pathway, 66t mTOR upstream signaling pathway, 62f PI3K/Akt/mTOR exogenous or endogenous activation, 65t PI3K signaling disturbance in human malignancies, 64t tumor-prone syndromes with disturbances in mTOR pathway, 63t mTOR and lymphangiogenesis, 66–68 “lymphangiogenic switch,” 67 lymphatic vessel formation/growth, factors, 67 mTOR inhibition, antilymphangiogenic effects, 67–68 VEGF-C/VEGF-D-VEGF-R3 pathway, 67 mTOR kinase, therapeutic target in tumor angiogenesis, 60 targeting angiogenesis by mTOR inhibitors FKBP 12–rapamycin inhibition of mTORC1 function, 69 suppression of VEGF-mediated tumor angiogenesis, 69 See also mTOR inhibitors of angiogenesis tumor angiogenesis ‘angiogenic switch,’ 51 embryonic vasculogenic process, 51–52 process of angiogenesis, 51 scheme of vascular development, 52f tumor blood vessels, unique feature, 51 tumor-driven vasculature, architecture/function, 51 VEGF, overproduction of, 51 upstream activation of angiogenesis cellular proliferation/survival/migration by VEGF-R activation, 53 downstream targets for Akt, 53 hypothesis of mTORC1 inhibition, examples, see mTORC1 inhibition (hypothesis), clinical examples
295 hypoxia, stimulus of tumor angiogenesis, 53 PI3K/Akt/mTOR pathway, 52, 53f PI(3)K pathway signaling, 52–53 rapamycin, anticancer effects, 54 mTOR signaling in drug resistance resistance to cancer chemotherapeutic agents AKT activation, 124 mTORC1 regulation of DNA damage by rapamycin/S. cerevisiae, 124–125 rapamycin, in vitro/in vivo effects, 124 temsirolimus/cisplatin, apoptosis activity, 125 vincristine, role, 125 resistance to molecularly targeted agents combination of erlotinib with rapamycin, 125 gefitinib/erlotinib, resistance to, 125 trastuzumab, resistance to, 125 mTOR signaling in glioblastoma dual PI3K/mTOR inhibitors “biopsy-treat-biopsy” strategy, 105–106 NVP-BEZ235, clinical trials of, 105 EGFR/PI3K/mTOR signaling pathway efficacy of targeted agents, assessment, 103–104 mechanism of EGFR inhibitor resistance, 103 rapamycin, lack of efficacy/clinical failure, 103–105 salvage surgical resection, clinical management, 104 treatment with EGFR tyrosine kinase inhibitors, results, 103 mTOR at interface of signal transduction and cellular metabolism, 106–107 LKB1/AMPK signaling, 106–107 mTOR, therapeutic target in GBM 4E-BP1/p70S6K, regulation of protein synthesis, 101 mTORC1 and mTORC2 activity, 101 mTORC2 signaling in glioblastoma, 102–103 mTORC1 signaling through S6K1 in transformation model, 102 rapamycin-based treatments, dual role of mTORC1, 102 pathway cross talk and feedback loops in patients, 105 PI3K pathway activation
296 mTOR signaling in glioblastoma (cont.) amplification/activation of mutations of PI3K subunits, 100 class IA PI3Ks, activation of, 100 EGFR-amplified glioblastomas, 100 and glioma formation in mouse genetic models, 101 loss of PTEN tumor suppressor, role, 100 NF1 loss, role, 100–101 PI3K-activating mutations, 100 N Neoplastic lymphangiogenesis, 68f NET, see Neuroendocrine tumors (NET) Neuroendocrine tumors (NET), 141 Neurofibromatosis (NF), 2, 63t NF, see Neurofibromatosis (NF) “Non-competitive” endogenous mRNAs, 19 Non-rapalog mTOR kinase inhibitors, 144 Non-small cell lung carcinoma (NSCLC), 141–142, 154, 156, 163 NSCLC, see Non-small cell lung carcinoma (NSCLC) Nutrient sensing, 10 NVP-BEZ235, 105, 125, 158 O Oncogenesis, 52, 186, 210, 238, 241–242 Ornithine decarboxylase (ODC), 19, 242 Oxygen, electron acceptor, 76 P p97, 260, 269 PA, see Phosphatidic acid (PA) p110α, catalytic subunit of PI3K, 41f, 63, 65t, 116–117, 157–158 Pateamine, chemical inducer of dimerization, 246, 258f, 269–270 PBMC, see Peripheral blood mononuclear cells (PBMC) PDGF, see Platelet-derived growth factor (PDGF) p53 DNA consensus sequence, 38 Pelletier, J., 257–275 Pentose phosphate pathway (PPP), 44 Peripheral blood mononuclear cells (PBMC), 137–138 PET, see Positron emission tomography (PET) Peutz–Jeghers syndrome (PJS), 2, 13, 63t, 87 loss of LKB1 function, cause of, 13 Phosphatase and tensin homolog (PTEN), 87, 100, 117, 150f, 152, 209 Phosphatidic acid (PA), 7, 192
Index Phosphatidylinositol 3-kinase (PI3K), 64f, 77, 99–100, 150 Phosphatidylinositol 3,4,5 triphosphate (PI3P), 56, 62 Phospholipase D1 (PLD1), 7 PI3K/Akt/mTOR pathway regulation/function of, 56–58 Akt functions upon activation, 56 Akt/mTOR positive/negative feedback loops, 58f in tumor angiogenesis, 58–60 mechanisms of mTOR inhibition, 60f “vascular normalization,” 59 PIKK, see PI3K-related kinase (PIKK) PIKK superfamily, features of HEAT repeats of mTOR, 2–3 FAT/FRB/FATC domain, 3 TOR and FKBP12–rapamycin complex, interactions, 3 PI3K, see Phosphatidylinositol 3-kinase (PI3K) PI3K/mTOR pathway in cancer activation of PI3K catalytic subunit p110α, 116–117 frequency of PIK3CA amplification in human cancer, 117t frequency of PIK3CA mutations in human carcinoma, 116t PIK3CA mutations, 116 AKT amplification, 118 amplification/overexpression of growth factor receptors oncogene addiction, 115 RTK activation of PI3K, 116 See also Growth factor receptors PTEN mutation/deletion/silencing, 117–118 alterations in PTEN in human cancer, 117t drug hypersensitivity of PTEN-deficient cells, 118 negative regulator of the PI3K/AKT signaling pathway, 117 temsirolimus treatment of cancer, 118 TSC/LKB mutations, 118–119 PI3K-related kinase (PIKK), 2–3 PI3K signaling disturbance in human malignancies, 64t PJS, see Peutz-Jeghers syndrome (PJS) Platelet-derived growth factor (PDGF), 51, 134, 158 PLD1, see Phospholipase D1 (PLD1) PML, see Promyelocytic leukemia (PML)
Index Polunovsky, V. A., 237–249 Polyribosome, 219 Polysome, 219–220 See also Polysome microarray approach Polysome microarray approach, 219–220, 219f Positron emission tomography (PET), 43 p53 pathway, regulation of IGF-1/mTOR, 37–40, 39f genomic stability control auto-polyubiquitination/degradation of MDM-2, 38 p53 DNA consensus sequence, 38 stress signals, role in, 37–38 incidence of tumors in humans, cause, 39 outcomes p48/p53R2/sestrins, role in DNA repair, 39 p53, role in energy metabolism, 40 p53, role in maternal reproduction, 40 PPP, see Pentose phosphate pathway (PPP) p48/p53R2 proteins, 39 PRAS40, 5f, 8, 101, 181f, 186–188, 203 “Premature senescence,” 243 Pro-autophagic chemotherapy, 166 Promyelocytic leukemia (PML), 88, 239 Protein kinase, 180, 186, 193 Protein metabolism, 3 Proteins regulated via TOS motifs Lobe, role in eye development and cell survival, 188 PRAS40, role in apoptosis, 187–188 Protein synthesis, 3, 14, 18–19, 22, 56–57, 77, 101, 114, 133, 180, 188, 191, 206–207, 210, 219, 257, 261, 265, 269, 271–273 Proud, C. G., 179–193 PTEN, see Phosphatase and tensin homolog (PTEN) PTEN mutation/deletion/silencing, 117–118 PTEN-negative tumors, 118 p53, tumor suppressor, 37–46 p27, tumor suppressor protein, 40, 54, 57, 151, 242 Pyruvate dehydrogenase, 44, 61, 155f R RAIP motif, 183 Rapalogs, 180 antitumor effects, imaging of, 134, 136–137 computerized tomography scans, 137 18-FDG PET scanning, 137 mTOR inhibitors in RCC, effects, 137
297 biological activity of dose-dependent effect of rapalogs in PBMC, 137–138 S6K dephosphorylation in PBMC by everolimus, study, 138 S6K phosphorylation in skin/tumor tissue biopsies by everolimus, study, 138 optimizing activity of, 143–144 combination of rapalogs with conventional chemotherapy, 143 combination of rapalogs with drugs targeting angiogenesis, 144 combination of rapalogs with EGFR inhibitors, study, 143–144 key factors for primary resistance, 143 pharmacokinetics of bioconversion of temsirolimus into sirolimus, 136 dose-proportional increased exposure, 136 half-life of everolimus/deforolimus, 136 half-life of temsirolimus-derived sirolimus, 136 rapalogs modified by concomitant administration of drugs, 136 toxicity of, dose and schedule impact intravenous and oral schedules, 135 long-term effects of rapalogs on pneumonitis, reports, 135–136 MTD, phase I trials, 135 side effects at recommended doses, 135 Rapamycin, 2–3, 7–11, 13, 17–22, 54–60, 67–69, 77, 87–90, 100, 102–105, 114, 114f, 118, 120–125, 134–137, 139, 140f, 143, 151, 154–166, 155f, 180, 182–192, 208, 211, 226–227, 232, 264, 271 Raptor, 3, 4, 5f, 8–10, 12, 46, 56, 69, 78f, 79, 101, 106, 115, 151, 165, 180–183, 181f, 187–190, 192, 203, 208, 211 Raptor-mediated phosphorylation of proteins, 181 Ras homolog enriched in brain (RHEB), 53 Raymond, E., 133–145 RCC, see Renal cell carcinoma (RCC) Receptor tyrosine kinases (RTKs), 152–166 REDD1 orthologues, 81
298 REDD1 protein, 81–87 in cancer association with tumor predisposition syndromes, 87 mTORC1 inhibitors, clinical study, 87 REDD1, role in tumor suppression, 87 hypoxia-independent regulation of REDD1, 84–86 REDD1, role in cell differentiation, 86 transcriptional upregulation of REDD1, proposed mechanisms, 84f identification of REDD1 orthologues overexpression/deficient scylla and charybdis, effects, 81 study in Drosophila, 81 in mTORC1 inhibition, 82–83 REDD1 expression, upregulation of, 82 REDD1 in hypoxia signaling, analysis of Redd1-deficient MEFs, 83 REDD1 overexpression, 82–83 REDD2 protein, 14, 82–83 Regulation of IGF-1/mTOR pathway by p53 coordinate regulation glucose deprivation sensed by activated oncogene, 43 p53-PTEN-AKT-1-MDM-2 loop, role, 43 PTEN/TSC2/beta-subunit of AMPK/IGF-BP3, target genes, 41–42 removal of FOXO proteins from nucleus, effects, 40–41 in response to a stress signal, 41, 41f Ser-15 phosphorylation during nutrient deprivation, 42–43 p53 pathway, see P53 pathway, regulation of IGF-1/mTOR p53 regulation of energy metabolism activation of oncogenes, effects on glycolysis, 44 cancer cells and glycolytic pathway, dependence, 45 glycolysis in tumor cells, 43 loss of p53, decreased mitochondrial respiration, 44 SCO2, role in mitochondrial respiration, 44 TIGAR, role, 44 tumor detection by PET, 43–44 Warburg effect, 43 Renal cell carcinoma (RCC), 54, 63, 64t, 69–70, 80, 87, 114, 118, 134, 138–143, 160, 180
Index Repositioning (shunting) of ribosomes, 258 RHEB, see Ras homolog enriched in brain (RHEB) Ribi-regulons, 21 Ribosomal biogenesis, 3, 15, 20–22 Ribosomal protein S6 kinases knockout of S6K genes in Drosophila/ mice, findings, 185–186 phosphorylation of Thr389, 181f, 185 PI 3-kinase/Akt signaling, impairment of, 186 S6K1 recruitment to SKAR in cell cycle, 181f, 186 substrates for S6 kinases, 186 Ribosomal proteins (RPs), 18, 20–21, 225–227, 262 Ribosome, 15 Ribosome recruitment, cis-acting mRNA elements m7 G cap structure eIF4E and 7-methyl guanosine, interaction, 261–262 translation of uncapped mRNAs, 262 mRNA secondary structure, 263 poly(A) tail, 263 protein–mRNA interactions, 263 5 -terminal oligopyrimidine (5 -TOP) tracts, 262 Ribosome recruitment, trans-acting factors in eIF4A (DDX2) DDX3, translation inhibition, 260 Ded1, inhibition of BMV RNA replication, 260 DHX29, 48S complex formation, 260 eIF4AIII (DDX48), role in ribosome recruitment, 259–260 gene products of, 259 member of DEAD-box family of helicases, 259 structural analysis, 259 eIF4B and eIF4H downregulation of eIF4H, anti-proliferative effects in cancer, 261 eIF4B phosphorylation by PI3K/mTOR and MAPK pathway, 261 eIF4H interaction with Vhs, 261 RRMs in, 260–261 sequestration of eIF4B by 14-3-3σ, 261 eIF4E, 259
Index eIF4G isoforms in mammals, 260 poly(A)-binding protein, 261 Rictor, 4, 40–41, 41f, 56–57, 101–102, 115, 135, 157, 180, 203, 208 RNA recognition motifs (RRMs), 260 RPs, see Ribosomal proteins (RPs) RRMs, see RNA recognition motifs (RRMs) Rsc9, 21 RTKs, see Receptor tyrosine kinases (RTKs) RTP801, see REDD1 protein RTP801L, see REDD2 protein S Scanning process, 258 Schizosaccharomyces pombe, 7 SCO2 (the synthesis of cytochrome c oxidase-2), 40, 44–45 Scylla and Charybdis genes, 14, 81–82, 86 Senescence, 38–40, 39f, 41f, 238, 242–243 Sestrins, 39 Signaling downstream of mTORC1 inhibition of mTORC1 kinase activity by CCI-779, 180 mTORC1, components/characteristics, 180 mTORC2, components/characteristics, 180 through proteins with TOS motifs, 181f SILAC, see Stable isotope labeling by amino acids in cell culture (SILAC) S6K1, 2–4, 6, 8–20, 16f, 56, 65t, 66t, 78f, 101–102, 104, 117–118, 120, 122, 135, 138, 142, 151, 154, 156, 163–164, 185–186, 193, 203–207, 210, 264 S6 kinase (S6K), 17, 185–186, 205–207, 210 Sonenberg, N., 201–211 Stable isotope labeling by amino acids in cell culture (SILAC), 8 Stress signals in p53 pathway, 37–38 Structure of rapamycin, 114f T TAMs, see Tumor-associated macrophages (TAMs) Targeted anticancer agents, 150–152 Temsirolimus (CCI-779, Wyeth Pharmaceuticals), 69 5 -terminal oligopyrimidine (5 -TOP) tracts, 18, 205, 231, 262 Therapeutic approaches, eIF4F complex downstream from mTOR cis-acting mRNA elements m7 G cap structure, 261–262 mRNA secondary structure, 263
299 poly(A) tail, 263 protein–mRNA interactions, 263 5 -terminal oligopyrimidine (5 -TOP) tracts, 262 eIF4F complex, signal transduction pathways, 264–265 elevated eIF4F activity in cancer progression, impact eIF4F complex and mRNA discrimination, 265–266 eIF4F in cancer, 266, 267f reducing eIF4F activity by decreasing eIF4E expression apoptosis by caspase 3 activation and TUNEL staining, 272 eIF4E, impact on potent angiogenic factors, 272–273 eIF4E, mRNA trafficking into ribosomes, 273 HUVECs transfected with eIF4E ASO, results, 273 preclinical xenograft tumor studies, 273–274 second-generation ASOs, 272 ribosome recruitment phase of translation initiation, 258f cap structure (m7 GpppX), role, 258–259 rate-limiting step of protein synthesis, 257 43S pre-initiation complex, 257 targeting eIF4F activity for cancer therapy blocking eIF4E–m7 G cap recognition with cap analogues, 267–268 disrupting the eIF4F complex, 268 reducing eIF4F activity by targeting eIF4A, 268–272 trans-acting factors in ribosome recruitment eIF4A, 259–260 eIF4B and eIF4H, 260–261 eIF4E, 259 eIF4G, 260 poly(A)-binding protein, 261 TIGAR, 40, see TP53-induced glycolysis and apoptosis regulator (TIGAR) TOR complex formation, 203 TOR genes in yeast (yTOR1/yTOR2), 2 TP53-induced glycolysis and apoptosis regulator (TIGAR), 40, 44–45 Transcriptomics, 217 Translation, 217–234 See also MRNA translation
300 Translational addiction of cancer increased expression of eIF4E in tumorigenesis, 243 overexpression of eIF4E in mouse models of cancer, findings, 243 switch over to hyper-activated mode of translation, 243–244 Translational control of antitumor defense systems eIF4F collaboration with oncogenes, effects, 242–243 paradigm of oncogene cooperation, 242 “premature senescence,” 243 senescence, 243 Translational control of cancer anticipated therapeutic limitations, 249 antitranslational therapeutics targeting cancer stem cells, 248 targeting proliferating tumor cell populations, 247–248 targeting quiescent tumor cells, 248 cancer drug discovery, challenges, 237–238 antineoplastic therapies, limiting factors, 238 downstream targets of mTOR and their role in cancer AKT regulation by mTORC2 in cancer, 211 4E-BPs, 209–210 eIF4G, 210–211 S6 kinase, 210 eIF4F inhibition, therapeutic margin between normal/cancer cells, 247 molecular biomarkers, successful translational therapy, 246–247 transcriptional gene expression profiling, drawbacks, 247 “translational signature,” 247 PI3K/AKT/mTOR signalling pathway in cellular processes, 201 pro-oncogenic recruitment of ribosomes to mRNA, 244–245 ectopic expression of eIF4F in breast carcinoma cells, 244f receptor signaling networks cap recognition process, primary target, 240 initiation step of translation, stages, 240 tumor progression by translational complex eIF4F, 240–241, 241f regulation of translation initiation by mTOR signaling 4E-BPs, 204–208
Index rise of targeted cancer therapy/limitations “Achilles heel” in cancer, 238–239 advantage of targeted cancer therapeutics, 239 paradigm of oncogenesis/its consequence, 238 single-target treatments, 239 strategy to target eIF4F, 245f antagonizing eIF4E-to-cap interaction, 246 decreasing abundance of eIF4E, 246 disrupting eIF4A/eIF4G association, 246 disrupting eIF4E/eIF4G association, 246 normalizing eIF4F function, steps, 245 targeting a nexus of cancer pathways, 239–240 convergence principle of oncogenic pathways, 240 mTORC1-regulated eIF4F mediated translation, 240 PI3K/Akt/mTOR/translation, 240 TOR complex formation mTORC1/mTORC2, role in, 203 translational addiction of cancer increased expression of eIF4E in tumorigenesis, 243 overexpression of eIF4E in mouse models of cancer, findings, 243 switch over to hyper-activated mode of translation, 243–244 translational control of antitumor defense systems eIF4F collaboration with oncogenes, effects, 242–243 paradigm of oncogene cooperation, 242 “premature senescence,” 243 senescence, 243 translational control of cell cycle machinery cellular quiescence, 242 IRES-mediated translation of mRNA, 242 molecular mechanism, 242 PI3K/Akt/mTOR pathway, role, 242 translation and cancer, 208–209 efficient translation of mRNAs, requirements, 208–209 increased mTOR signalling, cause of cancer, 209 loss/inactivation of tumour suppressors, effects, 209
Index translation initiation eIF4F complex formation, rate-limiting step, 202, 202f translation process, stages, 202 Translational control of cell cycle machinery cellular quiescence, 242 IRES-mediated translation of mRNA, 242 molecular mechanism, 242 PI3K/Akt/mTOR pathway, role, 242 Translational elongation, 15 “Translational signature,” 247 Translational termination, 15 Translation elongation by mTORC1, 189–190 inactivation of eEF2 kinase, 190 signaling events downstream of mTORC1, 189f Translation initiation, 15, 16f, 257–275 4E-BP1, role of, 15–17 structural model of the 4E-BP1–eIF4E interaction, 16–17 mTORC1 regulation of specific RNA species, 18–19 eIF4E-overexpressing model systems, 19 eIF4F complex formation and eIF4A/eIF4B activation, 19 S6K isoforms, activation of, 17 S6K1, role of mTORC1/S6K1, role as “pioneer” round of translation, 18 phosphorylation of eIF4B by S6K1, 17 phosphorylation of PDCD4 on Ser-67 by S6K1, 17 spatial and temporal nature of mTORC1 regulation, 19–20 Translation initiation by mTOR signaling 4E-BPs, 204–205 eIF4G, 207–208 mTORC2 regulation of AKT, 208 other targets of mTORC1, 208 regulation of eIF4E-BP1 by phosphorylation, 204f S6 kinase, role in mRNA translation, 205–207, 206f Translatomics, 218
301 See also Genome-wide translational analysis TSAP-6 gene, 39 TSC, see Tuberous sclerosis complex (TSC) TSC1 (hamartin), 6, 53 TSC2 (tuberin), 6 Tuberous sclerosis complex (TSC), 2, 87 Tuberous sclerosis complex 1 (TSC1), 77–80 Tuberous sclerosis complex 2 (TSC2), 77–80 Tumor-associated macrophages (TAMs), 66 Tumor cell metabolism, 164–165 Tumor pre-disposition syndromes, 2, 5f Tumor-promoting phorbol esters, 8 Tumor-prone syndromes with disturbances in mTOR pathway, 63t V Vascular endothelial growth factor (VEGF), 19, 51, 122, 134, 150f, 151, 188 “Vascular normalization,” 59 Vascular smooth muscle cells (VSMCs), 52, 59, 66 Vasculogenesis, 51 VEGF, see Vascular endothelial growth factor (VEGF) VEGF-C/VEGF-D-VEGF-R3 pathway, 67 VEGF-R, see VEGF/VEGF receptor (VEGF-R) VEGF-specific anticancer therapy, 159–160 VEGF/VEGF receptor (VEGF-R), 53–55, 59, 67–70 VEGF/VEGF-R-mediated signaling in endothelial cells, 55 Vincristine, 124–125 Virion host shutoff (Vhs) protein, 261 Von Willebrand’s factor, 273 Vps34, lipid kinase, 10–11, 106, 150f, 166, 191 VSMCs, see Vascular smooth muscle cells (VSMCs) W Warburg effect, 43–45 Z Zeremski, M., 49–70
