Primary resistance to ATP-competitive mtor inhibitors for the treatment of solid tumors. Gregory Stuart Ducker

Transcription

1 Primary resistance to ATP-competitive mtor inhibitors for the treatment of solid tumors By Gregory Stuart Ducker A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Chemistry in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Kevan M. Shokat, Chair Professor Christopher J. Chang Professor Karsten Weis Spring 2013

2 Primary resistance to ATP-competitive mtor inhibitors for the treatment of solid tumors Copyright 2013 by Gregory Stuart Ducker

3 Abstract Primary resistance to ATP-competitive mtor inhibitors for the treatment of solid tumors by Gregory Stuart Ducker Doctor of Philosophy in Chemistry University of California, Berkeley Professor Kevan M. Shokat, Chair The mammalian target of rapamycin (mtor) functions to integrate nutrient and energy availability with extracellular growth factor signals to regulate macromolecular biosynthesis including protein translation and lipogenesis. This essential gene in conserved from yeast to humans and is a core metabolic regulatory element. Aberrant regulation of metabolism is a phenotype recognized as a hallmark of cancer, and it has been shown that increases in translation alone can be oncogenic. Additionally, many of the most common oncogenes in cancer alter the growth factor signaling network upstream of mtor and these lesions likely activate mtor in many cancers. Thus, repression of mtor activity is an emerging therapeutic strategy for human cancer and there is both strong mechanistic and epidemiological evidence to suggest that attenuation of mtor signaling may be broadly applicable. As a kinase with a defined smallmolecule binding pocket, mtor presents an attractive target for therapeutic intervention because of its conserved role in integrating many different oncogenic lesions and its central requirement for metabolic regulation. The successful application of mtor inhibitors to clinical oncology will require the development of potent and selective inhibitors of this kinase and equally importantly, an understanding of which oncogenic lesions mark tumors as specifically sensitive to mtor inhibition as well as independent biomarkers for in vivo efficacy. Fortuitously, mtor is inhibited with near perfect selectivity by the natural product rapamycin, and analogs of this compound have been approved for the treatment of solid tumors. Rapamycin inhibits mtor through a non-conserved non-competitive mechanism of action and only blocks the phosphorylation of certain substrates. ATP-competitive inhibitors of mtor have recently been invented that occupy the kinase active site and block all substrate phosphorylation. In many preclinical models they are significantly more potent than rapamycin and as they enter clinical trials, questions about how to maximize their therapeutic index naturally arise. This may be challenging to ask for mtor because it is not mutated in cancer and what genetic lesions mark cancers as susceptible or not to mtor inhibitors has not been determined. To address the question of how best to apply ATP-competitive mtor inhibitors to human cancer, I performed a large (~650) unbiased cell screen to identify markers for sensitivity and resistance to PP242, a tool compound that has been developed into the phase I clinical drug, MLN0128. In comparison to rapamycin, PP242 was a more effective compound and more cell 1

4 lines were inhibited. Colon origin was significantly associated with resistance to both drugs. For PP242, mutations in the gene PIK3CA were a marker of sensitivity. I subsequently focused on colon cancer because it gave the strongest mtor drug dependent signature, and one of resistance. A panel of mtor and PI3K pathway inhibitors differentiated colon cancer cell lines based on RAS and PIK3CA genotypes. Ordering of colon cancer cell lines by sensitivity to PP242 revealed a striking resistance to mutations in KRAS within the already resistant colon cancer set. I identified that this KRAS specific resistance was due to a specific failure to inhibit phosphorylation of the translational repressor 4E-BP1 even when other mtor substrates were inhibited. Resistance correlated with the amount of KRAS in the active GTP-bound form and was independent of canonical mitogen activated protein kinase signaling. Finally, introduction of mutant PIK3CA can sensitize even KRAS mutant colon cancer cells to PP242 and the mechanism is correlated with 4E-PB1 phosphorylation. In colon cancer cell lines I identified predictive markers of sensitivity and resistance and a biomarker that reported upon functional inhibition of mtor in vivo. Cell lines have well documented shortcomings, and I worked to characterize a colon cancer patient-derived xenograft (PDX) model and apply it to early drug discovery to validate these findings. The PDX model uses metastatic colon cancer removed from patients that is then propagated in nude mice. Each patient tumor can be expanded into a cohort of identical tumors, and a drug trial can be conducted on an individual s tumor. In the colon cancer PDX model, PP242 was orally bioavailable and acutely inhibited mtor signaling in many tumors. In a continuous dosing trial however, sensitivity to the drug paralleled what was observed in cell lines. Single KRAS mutant tumors were refractory to treatment. Overall, failure to durably inhibit 4E-BP1 phosphorylation correlated with failure to respond to treatment. I discovered a set of cell lines that were resistant to the ATP-competitive mtor inhibitor PP242 and identified a defect in inhibition of 4E-BP1 phosphorylation as giving rise to this phenotype. This mechanism of resistance appears to be common in KRAS mutant colon cancer cell lines as well as patients. The differential inhibition of distinct mtor substrates I discovered reveals an additional layer of as yet uncharacterized biological control in this kinase signaling pathway. 4E-BP1 is a robust biomarker for ATP-competitive drugs and should be strongly considered for clinical use in trials of these agents. 2

5 Table of Contents Preliminary Pages Table of Contents List of Figures List of Tables List of Abbreviations Acknowledgments i iii iv v vi Chapter 1: Introduction- Inhibition of mtor for the treatment of malignant neoplasms The mammalian target of rapamycin (mtor) signaling complex Nutrient sensing functions of mtor The role of mtor in oncogenic growth factor signaling pathways Pharmacological inhibition of mtor ATP-competitive inhibitors in the clinic and patient selection Summary and significance 8 Chapter 2: Cell screening for the identification of markers of sensitivity and resistance to mtor inhibitors Abstract Introduction-High throughput cell screening to identify resistance and sensitivity to kinase inhibitors PP242 is more potent inhibitor of cell growth than rapamycin Colon and pancreas are mtor inhibitor resistant organ types PIK3CA and RAS mutations are markers for response to PP242 but not rapamycin PIK3CA mutations are predictors of sensitivity to PP242 in multiple tumor types Cluster analysis reveals that PI3K/mTOR inhibitor segregate colon cancer cell lines by genotype Discussion Data and Methods 23 Chapter 3: Patient-derived xenografts in preclinical drug development Abstract Introduction- Xenografts in colon cancer Establishment of human metastatic colorectal cancers xenografts Synthesis of ATP-competitive mtor inhibitor PP Oral dosing of PP242 is effective at inhibiting mtor phosphorylation in vivo Discussion Materials and Methods 33 Chapter 4: Incomplete inhibition of phosphorylation of 4E-BP1 as a mechanism of primary resistance to ATP-competitive mtor inhibitors 36 i

6 4.1 Abstract Introduction Screening of cancer cell lines mtorc1 substrates 4E-BP1 and rps6 are differentially inhibited by PP MAPK signaling differences do not alter mtorc1 substrate specificity Mutant PIK3CA but not PTEN loss leads to mtor inhibitor sensitization KRAS mutation status predicts response to PP242 in human primary xenografts Inhibition of 4E-BP1 and not rps6 correlates with anti-tumor effect of PP Discussion Materials and Methods 52 Chapter 5: Conclusions and future perspectives The genetic landscape of cancer and targeted therapies Combining anti-mtor therapy with other agents Conclusion 57 References 59 Appendix 1: A cell growth screen of mtor inhibitors rapamycin and PP ii

7 List of Figures Figure 1.1 The mtor signaling pathway 3 Figure 1.2 Clinical mtor inhibitors 6 Figure 1.3 mtorc1 inhibition induced feedback activation of Akt 7 Figure 2.1 Distribution of cell growth responses to treatment with mtor inhibitors 13 Figure 2.2 Distribution of cell growth responses by organ type 15 Figure 2.3 Residuals from linear regression models of genotype and mtor inhibitor response 19 Figure 2.4 Effect of genotypes in PP242 sensitivity in select organ types 20 Figure 2.5 A panel of PI3K/mTOR inhibitors distinguishes colon cancer cell lines by genotype 21 Figure 3.1 Patient-derived xenografts maintain key morphological features of human colon cancer 27 Figure 3.2 Synthetic scheme for PP242 and MLN Figure 3.3 PP242 inhibits mtor outputs in different colorectal cancer genetic backgrounds 31 Figure 3.4 Tumor morphology of PP242 treated tumors 32 Figure 4.1 An unbiased cell screen reveals factors leading to resistance and sensitivity to the ATP-competitive mtor inhibitor PP Figure 4.2 mtorc1 substrates are differentially inhibited in PP242 resistant versus sensitive cell lines 41 Figure 4.3 PP242, but not rapamycin inhibits mtorc1 substrates in colon cancer cell lines 42 Figure 4.4 Quantification of phosphorylated mtorc1 substrates upon PP242 treatment 44 Figure 4.5 Inhibition of MAPK signaling does not alter mtorc1 substrate phosphorylation 46 Figure 4.6 PIK3CA mutation but not PTEN loss sensitizes KRAS mutant cells to PP Figure 4.7 KRAS mutant patient-derived xenografts are resistant to PP242 by incomplete inhibition of 4E-BP1 phosphorylation 49 iii

8 List of Tables Table 2.1 Linear regression models for genotypes and PP Table 2.2 Linear regression models for genotypes and rapamycin 18 Table 3.1 Genotypes and patient characteristics of tumors used in this study 28 Table 4.1 KRAS and PIK3CA mutations modulate sensitivity to mtor inhibition 40 iv

9 List of Abbreviations 4E-BP1 eif4e binding protein 1 AMPK 5 adenosine monophosphate-activated protein kinase ATP adenosine triphosphate CML chronic mylogenous leukemia EGFR epidermal growth factor receptor ER estrogen receptor GST glutathione S-transferase IC 50 inhibitory constant 50 IRS insulin receptor substrate LKB1 liver kinase B1 MAPK mitogen activated protein kinase mlst8 mammalian lethal with SEC13 protein 8 mtor mammalian target of rapamycin mtorc1 mtor complex 1 mtorc2 mtor complex 2 msin1 mammalian stress-activated map kinase-interacting protein 1 NSCLC non-small cell lung cancer OLS ordinary least squares PARP poly (ADP-robose) polymerase PDX patient-dervied xenograft PH pleckstrin homology PKCα protein kinase C alpha PIP3 phosphatidylinositol (3,4,5)-triphosphate PTEN phosphatase and tensin homolog raptor regulatory-associated protein of mtor rictor raptor independent companion of mtor complex 2 rps6 ribosomal protein S6 RTK receptor tyrosine kinase S6K p70s6 Kinase S or Ser serine SGK1 serum and glucocorticoid protein kinase 1 SREBP Sterol regulatory element-binding protein T or Thr threonine TSC tuberous sclerosis protein v

10 Acknowledgements Completing my doctorate required the support and assistance of a great many people, for help both scientific and moral. Here I would like to name them and their contributions to my efforts, without which I would surely not have made it to completion. I cannot overstate how important their faith in me, belief in me, and friendship to me has been and how these have given me the strength and tools necessary to realize my academic pursuits. First I need to acknowledge the huge debt of gratitude I owe my advisor, Professor Kevan Shokat. I have had an amazing experience in graduate school, and so much of it is due to the fact that I was able to complete my research in the Shokat lab. And it was not a straightforward path for me- I wasn t even enrolled at the right school. I met Kevan on the Berkeley admitted students day in spring 2007, not even realizing how unusual it was for him to meet with a prospective Cal student. It was at that initial meeting that I first saw the qualities of Kevan that I have sought to emulate and I knew then that he was my first choice of advisor from everyone I had met. Kevan has an ability to spin the complex threads of experimental science into a whole narrative cloth that reveals a greater truth. His clear presentation of his uniquely creative science gave me much to aspire to and I am forever grateful that I made the commitment to come across the bay and accept his invitation to work at the University of California, San Francisco. It has been a pleasure and an honor to work for Kevan and I am proud to have been trained by him. At Berkeley, I have to acknowledge the chemical biology graduate program for admitting me. I thank former chemistry department chair Professor Michael Marletta for helping to convince me to go to Berkeley and sell me on chemical biology at Cal. I thank my rotation advisors at Berkeley during my first year. I learned an immense amount that year and it was due in large part to them. Professor Christopher Chang was not only my first rotation advisor, but also my qualifying exam committee chair and served on my thesis committee. I thank Professors Rebecca Heald and Karsten Weis of molecular and cell biology for my third rotation. Furthermore, Professor Weis served as the outside member of my qualifying exam committee and thesis committees. I also thank Professors Carolyn Bertozzi and John Kuriyan for serving on my qualifying exam committee. My thesis work touched on many different fields and I was very dependent upon collaborators for the success of my project. In particular, I have had a deep multi-year collaboration with the surgical oncology group at the University of California, San Francisco. Led by Dr. Robert Warren, a fellow Saint Paul native, this group developed the mouse xenograft models and asked the patient driven questions that drove my research. I d like to thank Dr. Warren for his support and assistance. David Donner Ph.D. has been a constant supporter of my work and been extremely generous in his time to help edit and improve my manuscripts. Mary Matli located patient samples, performed staining and coordinated much of the essential logistics. Medical oncologist Dr. Emily Bergsland met with us frequently during the gestation of my project and made key insights into the importance of treating KRAS mutant colon cancer patients that really drove the research forward. Dr. Jeffry Simko performed pathology on the weekends for my mouse samples. And lastly, I need to thank Dr. Byron Hann of the UCSF preclinical therapeutic core facility for managing, caring for and treating the mice used in my experiments. My ability to easily interface with Byron, and his staff scientist, Don Hom Ph.D., allowed me to execute my collaboration with the surgical oncology group with a minimum of difficulty. They both showed interest in the project and provided valuable help in shaping experimental design and ensuring a successful outcome. vi

11 What made graduate school so enjoyable and successful for me was the immense amount of support I received from my fellow students and post docs in the Shokat lab. For this I need to first thank our lab administrator, Valerie Ohman. She made this process so smooth for me and took on all sorts of work on my behalf so that I could concentrate only on research. Her organizational skills and leadership make this lab work as well as it does. I owe much to the senior graduate students who worked on mtor and PI3K before me and provided me with so much. I thank Eli Zunder Ph.D. and Morri Feldman Ph.D. for their great assistance. Eli and Morri are great biologists and they provided me with an amazing start. My closest collaborator in the Shokat lab has been a clinical fellow and now assistant faculty member, Dr. Chloe Atreya. Chloe trained as a medical oncologist in gastro-intestinal cancers (GI), and her expertise was invaluable for me. She gave me the medical knowledge I needed, told me about conferences to attend including the ASCO GI conference in San Francisco and went with me to the AACR conference on molecular targeted therapies in San Diego in She has been a major supporter of my research and I am very happy to have worked so closely with her. My rotation in the Shokat lab was overseen by the most talented synthetic chemist I have ever met, Tatsuya Okuzumi Ph.D. I had virtually no experience in medicinal chemistry, and still am by no means a skilled organic chemist, but nearly everything I know I learned from him in 10 short weeks that were bisected by Christmas and New Years. He was extremely generous and understanding of my poor skills, and a patient teacher. I joined Kevan s lab in the same class as three other graduate students with chemistry backgrounds, Joseph Kliegman, Michael Lopez and Nicholas Hertz. Suffice it to say, graduate school would have not have been the same without them and our collective friendship is one of the core defining features of this entire experience for me. Somehow we all ended up in the same bay and have been a constant presence together on the bench for 5 years. I have learned more about the day-to-day work of being a scientist and more importantly how to think like a scientist from them than any other single source. Each of them knows how to combine serious scholarship and questioning with an infectious joie-de-vie that makes science a joy. I liked coming into lab because I liked talking with Nick, Mike and Joe and I knew that they would always have my back if things weren t going well. Research is hard, but it need not be painful or solitary and one can have a full life in and out of lab. The greatest sorrow in leaving the Shokat lab is the dissolution of this group of researchers but I m confident I will remain friends with them for the rest of my career. My family has always valued education and scholarship and their support of my work has been invaluable. I would like to thank my father and mother, Dr. Thomas Ducker and Suzanne Ducker. I cannot easily summarize what they ve given me, short of saying everything. They have always challenged me with high expectations and their support has been extraordinary. I want to thank my best friends, my brothers Michael, Laurence and Erik Ducker, for their support and friendship. Lastly, I want to thank my friends outside of lab for putting up with my trials and tribulations and remaining close while I was in California. I would like to thank old friends Meg Pain, Kaisa Taipale, Ethan Mooar, and Mark Huberty who have been with me for many years now despite now being far apart. I would like to thank my undergraduate advisor Professor Joseph Chihade for turning me on to biological chemistry and being my greatest advocate. I thank Emily Johnson for her support during my first years here and the transition to the west coast and Austin Pitcher for helping me to find my footing after moving to the city. Thanks to Katyn Chmielewski for perspective and moral support in gritting out the degree. And thanks to vii

12 Squaw Valley and Alpine Meadows for providing much needed relief in the long dark winter months and reminding me that I am from the north country and truly at home in the snow. viii

13 CHAPTER 1: Introduction- Inhibition of mtor for the treatment of malignant neoplasms 1

14 1.1 The mammalian target of rapamycin (mtor) signaling complex The mammalian target of rapamycin (mtor) is a serine/threonine protein kinase member of the phosphoinositide 3-kinase (PI3K) related kinase (PIKK) family. An evolutionarily conserved regulator of cell metabolism, mtor integrates external growth factor signaling with nutrient availability to control protein translation and influence cell growth and proliferation (1,2). mtor function is essential and conserved in eukaryotes; homozygous mtor knockouts in mice are embryonic lethal (3). In humans mtor is active in the catalytic core of two related heteromeric protein complexes, mtorc1 and mtorc2 (4-6). mtorc1 regulates protein translation by phosphorylating critical regulatory proteins, of which the best described are p70s6 kinase (S6K) which controls ribosome biogenesis through ribosomal protein S6 (rps6) and eif4e binding protein 1 (4E-BP1), a repressor of cap-dependent translation (7,8). The kinase activity of mtor is tightly regulated by the protein complexes in which it exclusively functions. These two defined signaling complexes are multimeric, related and evolutionarily conserved (Figure 1.1a) (9). Common components of the two complexes include the proteins mammalian lethal with SEC13 protein 8 (mlst8) and deptor (1,2,10). mtor complex 1 (mtorc1) is defined by the additional presence of regulatory-associated protein of mtor (raptor) and PRAS40 (3-5). In mtor complex 2 (mtorc2) the proteins mammalian stress-activated map kinase-interacting protein 1 (msin1), raptor independent companion of mtor complex 2 (rictor) define the complex while protor has been recently identified as a component (4-6,11). These distinct sets of component proteins shape the substrate specificity of the two mtor complexes. These differences also lead to distinct effects of pharmacological agents. Most notably, mtorc1 is acutely inhibited by the natural product macrolide rapamycin via a unique allosteric mechanism that selectively inhibits substrate access to mtor only when found as a member of mtorc1. Rapamycin forms a ternary complex between the FRB domain of mtor, itself and the prolyl isomerase FKB12 (Figure 1.1b) (7,8,12,13). When mtor is bound with rictor in mtorc2, the FRB domain of mtor is occluded and rapamycin is ineffective. However, in some cell types and likely in humans, chronic rapamycin treatment may be able to titrate out mtor from mtorc2 (9,14). The two complexes have distinct and non-overlapping sets of substrates. The best characterized phosphorylation targets of mtorc1 control protein synthesis via the protein kinase p70s6 (S6K) and the translational repressor protein 4E-BP (15). mtorc1 mediated control of translation is specific and certain genes are selectivity inhibited (16). One pathway notably affected by mtor-regulated translation is lipid synthesis. The biosynthetic genes in this pathway are controlled by the transcription factors sterol regulatory element binding protein1/2 (SREBP1/2) which themselves are regulated by mtorc1 (17,18). Independent of translation, mtorc1 critically regulates autophagy and acute inhibition induces this cell scavenger program. mtorc1 phosphorylates regulatory components ATG13 and ULK1 to regulate this activity (19-21). In contrast mtorc2 targets are less well described and all seem to be members of the AGC kinase family. Serine (S) 473 of AKT is the best-described target in mammalian systems (22), but serum and glucocorticoid protein kinase 1 (SGK1) and protein kinase C alpha (PKCα) have also been described at mtorc2 specific targets. SGK1 enhances sodium ion channel expression through phosphorylation that is mtorc2 activity specific (23). Phosphorylation of PKCα mediates a cytoskeleton specific effect of mtorc2 activity in yeast and perhaps certain mammalian cell lineages (11). 2

15 a mtorc1 mtor Pras40 Raptor mlst8 deptor mtorc2 mtor Sin1 Rictor mlst8 deptor Protor b FKBP12 RAP mtor Pras40 Raptor mlst8 deptor c RAS S O S Raf P P R T K P P I R S P P P P p85 O HO OH PI3Kα PTEN P O HO P P OH P T308 Akt P S473 P TSC1 TSC2 mtorc2 AMPK ATP AAs Mek P S6K P mtorc1 RAGs Erk P P S6 P P P P P 4E-BP1 PROLIFERATION TRANSLATION eif4e Figure 1.1 The mtor signaling pathway. (a) The mtorc1 and mtorc2 complexes are independent conserved signaling complexes. (b) Rapamycin engages FKBP12 to inhibit mtor in a non-competitive manner only when it is a member of mtorc1. (c) Human growth factor signaling pathways that impact mtor. Frequently mutated oncogenes and tumor suppressors are colored in red. 3

16 The x-ray crystal structure of mtor solved in complex with mlst8 provides mechanistic insight into how these different complexes regulate access to the active site (24). Previous work had identified a conserved TOR signaling motif (TOS) present in mtorc1 substrates that was shown to interact with raptor (25). But work with rapamycin and PP242 has shown that different substrates of mtorc1 are treated differently and additional regulation was necessary. mtor was crystalized in the active confirmation suggesting that regulation of phosphorylation occurs entirely via substrate accessibility. The rapamycin binding FRB domain of mtor is an insert in the N-lobe of the kinase domain and creates a crowded active site. This domain acts as a second mtorc1 substrate recognition site (after raptor) and also binds FKB12 with rapamycin to alter access to the active site as was seen earlier in a cryogenic electron microscopy structure of the complete complex (26). The difficult accessibility of the constitutively active mtor active site, coupled with the prime positioning of the FRB domain to interact with substrates allow for a mechanistic understanding of how rapamycin inhibits some but not all mtorc1 substrates and mtorc2 functions completely differently. 1.2 Nutrient sensing functions of mtor The conserved role of mtor in eukaryotes is that of an integrator of different nutrient states to regulate cell growth and metabolic homeostasis (27). These nutrient sensing pathways persist in humans and have recently been elucidated in great detail. mtor senses adenosine triphosphate (ATP) levels via liver kinase B1 (LKB1) which signals through 5 adenosine monophosphate-activated protein kinase (AMPK) (28,29). Amino acid levels appear to be sensed via a set of Rag GTPases that bring the entire mtorc1 complex to the lysosome and are able to sense amino acid levels in the lysosomal lumen (30,31). An unexpected role for these amino acid sensors was uncovered in mice homozygous for a constitutively active mutant of RagA. Mice developed normally but died immediately after birth, in an effect traced to impaired glucose sensing by mtorc1 (32). These results demonstrate that mtor senses levels of both available and potential cellular energy (ATP and glucose) as well as core metabolic building blocks (amino acids) and that by integrating these signals it is a master regulator of cell growth. 1.3 The role of mtor in oncogenic growth factor signaling pathways mtorc1 is regulated by growth factor signaling via PI3K signaling through AKT (Figure 1.1c) (33,34). Activated receptor tyrosine kinases (RTKs) recruit PI3K to the cell membrane via phospho-tyrosine binding SH2 domains contained in the regulatory subunits of PI3K (35). PI3K is made up of a catalytic and regulatory subunit. Each catalytic unit has preferred regulatory partners. These regulatory units repress the catalytic function and mutations impair this control mechanism (36). There are 4 isoforms of catalytic PI3K subunits (3 class 1Aα, β, δ and 1 class IB, γ). PI3K α and β are universally expressed whereas γ and δ are restricted to myeloid lineages. All PI3K perform the same chemical reaction, the phosphorylation of the 3- position of phosphatidylinositol (4,5)-bisphosphate to form phosphatidylinositol (3,4,5)- triphosphate (PIP3). PIP3 recruits AKT via a pleckstrin homology (PH) domain to the membrane where it can be phosphorylated by PDK1 at threonine (T) 308 and mtorc2 at S473. AKT phosphorylates many substrates critical for cellular metabolism and transcription. It also controls mtor via phosphorylation-induced repression of tuberin, a member of the tuberous sclerosis complex (TSC1/2), and the repressor of the mtorc1 activator Rheb. 4

17 Dysregulated mtorc1 signaling is present in human diseases that alter metabolism, including both diabetes and cancer (37,38). In cancer, tumor suppressor loss leading to elevated mtor protein levels has been observed with some frequency, and functional mtor mutations are observed in 1% of tumors but no conserved mutations have been identified (39,40). Instead, mtor is more commonly subject to many variations of aberrant activation as part of the mtorc1 complex (41). mtorc1 is activated by mutations in upstream signaling networks, and in cancer the best described alterations are in the PI3K/AKT/TSC pathway (33). Many of the most commonly mutated oncogenes and tumor suppressors in cancer signal to mtor including diverse receptor tyrosine kinases, PI3K, phosphatase and tensin homolog (PTEN) and AKT. Ras, the most mutated oncogene in cancer can activate the p110α catalytic subunit of PI3K and it may be considered to signal upstream of mtor as well (42). It has been well-documented that sporadically occurring solid tumors display mtor activation as evidenced by immunohistochemical analysis of the most common mtor substrates S6K and 4E-BP1 (43). The activation of aberrant mtor signaling by a specific upstream mutation is most clearly seen in tuberous sclerosis. This disease is characterized by inherited mutations in tuberin that constitutively activate mtor signaling and drive tumor growth; treatment with rapamycin analogs that block mtorc1 signaling is now clinically approved for these patients (44). Rare sporadic TSC1 mutations in cancer are also highly rapamycin responsive (45). However, whether mutations in genes further upstream, including PIK3CA and AKT are also predicative of sensitivity to mtor targeted therapy and which downstream biomarkers of mtor activity are most correlated with clinical response is not yet clear. One recent study assessing the efficacy of rapamycin in pre-clinical models used both upstream and downstream signals (p-akt and p- rps6) as biomarkers to predict sensitivity to the drug suggesting that both signals are independently important in predicting response (46). 1.4 Pharmacological inhibition of mtor Rapamycin (clinically known as sirolimus) first found clinical efficacy as a immunosuppressant (47). Through T-cell specific mechanisms, rapamycin is able to impair T- cell mediated responses without affected the humoral response (48). Long-term treatment with rapamycin is generally well tolerated, although hyperglycemia is commonly seen leading to the onset of diabetes (49-51). Provocatively, this insulin resistance phenotype was shown to be due to inhibition of the rapamycin insensitive mtorc2 complex in vivo, and that this on-target sensitivity was separable from rapamycin s life extension benefits (52). Rapamycin s low toxicity, high selectivity, and history of successful clinical application make it an attractive drug for several indications, but it may have only limited efficacy in the oncology setting. There are currently only two mtor inhibitors, the rapamycin derivatives temsirolimus and everolimus, approved for the treatment of solid tumors (Figure 1.2a). Both are indicated for advanced renal cancer although the activity of these agents is somewhat limited and the mechanism of action is debated (53,54). New phase III data suggest that everolimus also delays pancreatic neuroendocrine tumor progression, and it recently gained approval for this indication, although overt tumor shrinkage is rare (55,56). Recent data suggest a role for everolimus in the treatment of estrogen receptor (ER) positive breast cancer due to selective mtor pathway activation downstream of this amplification(57). And yet both clinical and cell line data strongly support that notion that rapamycin is only a partial mtor inhibitor and as such is unable to 5

18 inhibit hyper-activated mtor signaling in such a way as to cause complete growth arrest or cell death. A new and potentially more efficacious class of mtor inhibitors has been developed specifically to target cancer (58-60). These small molecule drugs are not natural products and directly bind the ATP binding pocket of the mtor kinase domain. The discovery of these molecules allowed for the division of canonical mtorc1 substrates into classes: those sensitive to inhibition by rapamycin (S6K) and those that were relatively insensitive to rapamycin treatment (4E-BP1). This discovery has provided mechanistic insight into the poor success of rapamycin derivatives in clinical trials and how more complete inhibitors of mtor signaling may be significantly more effective in inhibiting neoplastic growth. a b R= Rapamycin (Sirolimus) Everolimus Temsirolimus BEZ-235 c MLN0128 WYE OSI-027 AZD8055 Figure 1.2 Clinical mtor inhibitors. (a) Rapamycin (marketed as sirolimus) its derivatives everolimus and temsirolimus. These pharmacokinetic (PK) optimized derivatives of the natural product are FDA approved for the treatment of certain human solid tumors. (b) BEZ-235 is a dual mtor/ PI3K inhibitor that reached phase II clinical trials. (c) Selective ATP-competitive mtor inhibitors now in phase I clinical trials for the treatment of human solid tumors. Of special note is the differing effects of rapamycin and ATP-competitive mtor inhibitors on AKT phosphorylation. Acute inhibition of mtor with rapamycin leads to a time dependent increase in phosphorylation of T308 phosphorylation on AKT in many cell lines, including colon (Figure 1.3a). This effect is due to a negative feedback loop involving S6K and the insulin receptor substrate (IRS) (Figure 1.3b) (61). Inhibition with an ATP-competitive 6

19 mtor inhibitor such as PP242 abrogates this phenomenon in a dose-dependent fashion (Figure 1.3c). This data supports a theory that this S473 mtorc2 dependent phosphorylation stabilizes T308 phosphorylation and without active mtorc2, AKT cannot be in the active state (62). This may have clinical impact as this feedback loop was associated with poor response to rapamycin in a glioblastoma trial (63). a Time (hrs) p-akt T308 p-rps6 (240/244) β-actin c p-akt T308 p-akt S473 p-rps6 (240/244) β-actin Rapamycin (20nM) Rapamycin 0 0 b PP242 S O S P P R T K P P I R S P P P P p85 O HO OH S6K P P S6 PI3Kα PTEN P O HO P P OH P T308 Akt P S473 P TSC1 TSC2 mtorc1 P P P P P 4E-BP1 mtorc2 Figure 1.3 mtorc1 inhibition induced feedback activation of Akt. (a) Rapamycin treatment induces a profound hyper-phosphorylation of Akt T308. HCT 15 cells were treated with 20 nm rapamycin for the indicated time, lysed and western blotted with anti-p-t308 antibodies. (b) A negative feedback loop exists between mtorc1 signaling and the insulin receptor substrate (IRS). (c) Inhibition of both mtorc1 and mtorc2 abrogates feedback induced Akt activation. The ATP-competitive inhibitor PP242 blocks phosphorylation of Akt completely 24 hours after initiation of treatment. 1.5 ATP-competitive inhibitors in the clinic and patient selection ATP-competitive inhibitors designed to be anti-cancer agents have entered clinical trials for a variety of malignant neoplasms (64,65). Their advancement to human trials was based on pre-clinical data showing significant effects in both solid and hematologic malignancies. But as discussed earlier, mtor is not mutated in cancer, and common upstream mutations are actually several mechanistic steps away from mtor itself and for what tumors inhibition will be most successful remains an open question. This difference distinguishes ATP-competitive mtor inhibitors from other clinical kinase inhibitors developed to treat cancer. Drugs such as imatinib, vemurafenib, and lapatinib have been developed specifically for patients with mutations in their target kinases and represent classic cases of what is now termed oncogene addiction (66-69). This model posits that mutated oncogenes alter the signaling pathways of the cell and that tumors become addicted to the aberrant growth for continued survival and proliferation. However, without a clear molecular signature, mtor inhibitors are 7

20 being tested in a diverse patient population without pre-selection on genetic markers. As mtor is such a central kinase in control of cell growth, changes to multiple inputs, independently or in concert, may increase dependence upon mtor signaling. This challenges the paradigm of oncogene addiction and calls for new techniques that take into account the entire signaling network. ATP-competitive mtor inhibitors have now been in clinical trials for over 4 years (Figure 1.2b-c). The first inhibitors to enter trials were dual-pi3k/mtor inhibitors. BEZ-235 was the most potent of this first class of inhibitors and reached clinical trials first, but it has not advanced to FDA approval (70). To both lower off-targets effects and to better validate inhibition of mtor as a target in human oncology, more selective kinase inhibitors were needed (71). This second class of more selective mtor inhibitors without PI3K inhibitory activity are earlier in development and include the pyrazolopyrimidines MLN0128 and WYE , the imidazo[1,5-a]pyrazine OSI-127 and the pyridopyrimidine AZD8055 (Figure 1.2b) (72-75). As of yet, no results from clinical trials of these second-generation more selective inhibitors have been published. Pre-clinical data from these inhibitors and preclinical tool compounds suggest various markers for efficacy. Apoptosis has been observed in multiple different hematological malignancies upon treatment with selective ATP-competitive inhibitors. OSI-027 treatment lead to apoptosis in acute lymphoblastic leukemia, mantle cell lymphoma and marginal zone lymphoma (74). PP242, the precursor to MLN0128 induced apoptosis in Philadelphia chromosome harboring acute leukemia cell lines as well as multiple myeloma (76,77). In many cases these effects were specific to malignant cell lines, but the general observation is that white blood derived cell lines may be especially sensitive to mtor inhibition. Observations from solid tumor models is less impressive with regards to apoptosis, but growth arrest is profound and dependent upon upstream aberrations in activated oncogenes (72,78). These studies differed however in the role of the PI3K activator and tumor suppressor PTEN. In a PTEN null prostate mouse model, inhibition with MLN0128 was highly effective whereas in breast cancer cell lines, PTEN loss was not correlated with response to PP242. This contradiction highlights the context dependence of mutations in predicting sensitivity and that large data sets covering many different tissue types will be necessary to understand how upstream network changes will effect mtor inhibition. 1.6 Summary and significance The mtor kinase functions within two key signaling complexes that are critical for regulating cell growth via control of macromolecule biosynthesis. The mtorc1 complex lies downstream of many of the most common mutations identified in cancer and as such has generated significant interest as a drug target. Existing approved small molecule inhibitors are based off an unusual natural product, rapamycin, that acts through a unique mechanism of action. This mechanism results in only incomplete inhibition and is insufficient for strong anti-tumor responses. New inhibitors of mtor that are ATP-competitive have now been developed. They are superior to rapamycin in pre-clinical trials and have advanced to patient trials. To maximize chances that these drugs can be approved and useful for cancer, careful patient selection will be necessary. However, there is not an understanding of which patients may be sensitive or resistant to these agents. Identifying mutations in signaling pathways that are associated with resistance and sensitivity, as well as the mechanism underlying these effects, will be critical to successfully 8

21 developing ATP-competitive mtor inhibitors. This knowledge will allow both the selection of patients likely to respond as well as the characterization of biomarkers to track response in individuals in real-time. 9

22 CHAPTER 2: Cell line screening for the identification of markers of sensitivity and resistance to mtor inhibitors 10

23 2.1 Abstract The identification of select patient sub-populations that will benefit from new targeted therapies for cancer is essential to maximizing their therapeutic potential. The large degree of patient heterogeneity makes preclinical predictions of efficacy very difficult. While targeted drugs are often developed with specific mutations in mind, unexpected resistance and sensitivity can emerge from other signaling nodes. Large format cell screening can be of use in overcoming these obstacles. We screened representatives (PP242 and rapamycin) of 2 independent classes of inhibitor of the mammalian target of rapamycin (mtor) against a greater than 650 cell set of human solid tumor cell lines. The conserved kinase mtor is not itself mutated in cancer and it has yet to be validated what markers for sensitivity and resistance exist. We discovered significant differences in efficacy between the two inhibitors and little correlation in cellular response between the drugs. PP242 was more effective and we analyzed the effect of several mutations in predicting response to PP242. Screening a subset of colon cancer cells with an expanded set of PI3K/mTOR inhibitors revealed significant similarities in response to these agents and allowed for the clustering of cell lines with similar activating mutations validating their role in predicting efficacy to these agents. 2.2 Introduction-High throughput cell screening to identify resistance and sensitivity to kinase inhibitors The discovery of oncogenes (genes that when hyperactive lead to a cancer phenotype) and tumor suppressors (genes the when deleted or inactivated lead to a cancer phenotype) has allowed for the development of small molecule and antibody drugs that target specific mutated proteins found in tumors and not normal tissue. The development of imatinib, a small molecule kinase inhibitor against the oncogenic fusion protein BCR-ABL, has most clearly demonstrated the utility of this advance, transforming a once fatal diagnosis of chronic mylogenous leukemia (CML) into a manageable chronic condition with greater than 90% survival (66). Drugs have been approved or are in development against dozens of oncogenes, but no combination of target and drug have shown to be as successful as BCR-ABL and imatinib (79,80). Unlike CML, a hematological malignancy defined by a single diagnostic mutation, solid tumors have complex etiologies and are characterized by dozens of non-overlapping mutations. While certain oncogenes are thought to be driver mutations, the impact of inhibiting the same mutant gene can vary widely from one tumor to the next (81). Coincident with this problem, the number of newly discovered frequently mutated kinases in cancer is decreasing, even as cancer genome sequencing accelerates, making it important to target non-mutated kinases that exist in critical common pathways (82,83). In response to this, interest has recently been renewed in targeting key cellular pathways that are deregulated in common among many cancers, chief among them the cellular growth and nutrient sensing pathway regulated by mammalian target of rapamycin (mtor) (1). mtor is a protein kinase conserved throughout eukaryotic life and is critical for integrating nutrient availability with growth factor signaling to make cell growth decisions (reviewed in chapter 1) (2). Many of the most common oncogenes are components of growth factor signaling and direct biological connections can be drawn between aberrant signaling in these proteins and mtor. Upstream of mtor lies the epidermal growth factor receptor (EGFR), and its intracellular partners, RAS and phosphoinositide 3-kinase catalytic subunit alpha 11

24 (PIK3CA). These three genes are commonly activated in solid tumors. RAS (the most mutated oncogene in solid tumors) activity is further mediated by braf, the most common mutated gene in melanoma. RAS and braf are thought to carry signals in a pathway parallel to mtor and their activation may bypass mtor but significant cross-talk exists. PIK3CA activity is opposed by the tumor suppressor phosphatase and tensin homolog (PTEN), and both types of mutations lead to directly to the activation of mtor. With this biological framework, new small-molecule inhibitors of mtor have been developed and have recently entered phase I clinical trials for the treatment of diverse solid tumors(58,60,84). Evidence from other agents that target proteins in the mtor pathway and preclinical models of mtor inhibitors suggests that there will be a large variety of responses, and successful approval of mtor inhibitors may depend upon the identification of a selected patient population that will benefit from these agents. Identifying the characteristics of a tumor that will render it sensitive to a specific therapy is one of the greatest challenges in cancer drug discovery. Data driven methods such as SNP arrays, copy number arrays and complete genome sequencing provide the most complete analysis of a tumor s variability, but their clinical application remains a long way off (85). Instead, clinically actionable criteria for patient selection are limited to either tissue of origin or mutations and/or expression changes in one or maybe two genes. Importantly, these can be both positive and negative markers for selection. EGFR inhibitors are indicated for patients with mutations in EGFR in the context of lung cancer, while in colorectal cancer, EGFR antibodies are excluded from patients with RAS mutations (86). The proceeding EGFR examples of selection criteria were discovered and validated in patients and experimental models after the drug had entered the clinic (87). To prospectively identify criteria to select patients for mtor inhibitors is the goal of this analysis. The prospective identification of criteria predicting sensitivity and resistance to a specific therapy requires a large screening set that can capture the variability seen in actual patient populations. A suitably large panel of human cancer cell lines may contain enough diversity to predict sensitivity and resistance to a therapeutic agent. The research group of Jeffrey Settleman has collected a large (>700) set of solid tumor human cell lines and developed the robotic handling equipment necessary to screen these cell lines for growth inhibition against new drugs (88). This method was validated by testing several inhibitors and showing that the cell lines most potently inhibited contained the mutant oncogene targeted by the drug of interest (e.g. EGFR and the EGFR inhibitor lapatinib) (89). We sought to expand this method to the analysis of two representative mtor inhibitors, the natural product rapamycin and the ATP-competitive inhibitor PP242. PP242 was a more effective agent and there was a stronger genetic signature or resistance and sensitivity. Rapamycin was a weak inhibitor and little trend was observed. Interestingly, there was almost no correlation between rapamycin and PP242 response, showing that the mechanism and completeness of mtor inhibition strongly differentiate these drugs. 2.3 PP242 is more potent inhibitor of cell growth than rapamycin PP242 and rapamycin were screened against a panel of human solid tumor cell lines. For PP242 there were 666 cell lines with data for three concentrations tested. PP242 was screened at 5000, 500 and 50 nm for 72 hrs (complete data available in appendix 1). For rapamycin, there were 707 cell lines with growth information at three concentration of drug: 10, 1 and 0.1 µm. Although data was collected for both drugs at all concentrations, the analysis was conducted at 12

25 the medium concentration for both drugs to maximize the ability to discover both resistance and sensitivity in the same analysis. a Percent PP nm Proportion of ctrl cell growth b Percent Rapamycin 50 nm Proportion of ctrl growth c d Rapamycin vs PP242 Growth PP242 Rapamycin Obs Mean Std. Dev Percentiles PP242 cell growth R 2 = e 500 nm PP Rapamycin cell growth R 2 = nm PP242 Figure 2.1 Distribution of cell growth responses to treatment with mtor inhibitors. (a) A histogram of the growth of 666 human solid tumor cell lines treated with the 500 nm of the ATP-competitive mtor inhibitor PP242 for 72 hrs. (b) A histogram of the same cell line set treated with 1 µm rapamycin. (c) Summary statistics for PP242 and rapamycin datasets. (d) A scatterplot of PP242 cell growth vs rapamycin cell growth shows no correlation. (e) A scatterplot between two different concentrations of PP242 shows a moderate level of correlation. Cell growth was scaled to a no treatment control, so that a value of 1 was equivalent to no treatment, and 0 would be the value if all cells were killed. Values of greater than 1 were 13

26 possible due to both measurement error and the chance that some cells could actually grow better with the drug, but those cases were rare with PP242, and more common with rapamycin (1.2% vs. 8.1%). The values of cell growth in response to 500 nm PP242 (PP242_med) were normally distributed about with a standard deviation of (Figure 2.1a and c). The Shapiro- Wilk test of normality was not significant for PP242_med (p =.150) and we could safely proceed with the normal curve assumption for the outcome data. Cell growth in response to rapamycin was less affected by the treatment and the distribution more skewed (Figure 2.1b and c). The distribution of cell growth was not normal (Shapiro-Wilk test, p=.045), and the average response was only (±0.182). While we proceeded with our analysis, the interpretation of the rapamycin dataset must necessarily be done carefully. There was no correlation between cellular responses to rapamycin and PP242 (Figure 2.1d). A scatter plot of the cell growth effects of 1 µm rapamycin vs 500 nm PP242 gave no trend and the R 2 for the linear regression was only This lack of correlation highlights the significant differences in mechanism between these two inhibitors. For PP242, there was a strong correlation between the responses of cell lines to different concentrations of the drug (Figure 2.1e). A plot of 500 nm PP242 against 50 nm PP242 shows a clear relationship and the R 2 for the linear regression was We believe that this significant but still moderate level of correlation between doses of the same drug can be understood in terms of varying difference in drug sensitivity. Cell growth responses to changes in drug concentration are not linear but bounded by the design of the assay, the shape of the dose-response curve, and where on this curve for a specific cell line the tested drug concentration lay. 2.4 Colon and pancreas are mtor inhibitor resistant organ types The organ of origin is a critical variable in describing the outcome of drug treatment on a particular tumor or cell lines. Each organ has its own set of common mutations and inherent properties, and the organ type captures large amounts of information. A box plot of the response of each organ type to rapamycin and PP242 shows significant variance among types (Figure 2.2a and b). A one-way ANOVA was very significant for both rapamycin (F=5.36, p<0.0005) and PP242 (F=2.89, p<0.0005). Several organ types were significantly resistant or sensitive to PP242 and rapamycin treatment. Figure 2.2c lists the treatment average of each organ type as a standard deviation from the complete screen treatment mean and the p-value associated with the comparison of means (Student s T-test). To correct for multiple comparisons, an adjusted p- value of was used for the significance level of α=0.05 using the conservative Bonferroni adjustment. The organ types that had the significant p-values for mtor inhibitor response were colon and pancreas and both were resistant to inhibitor treatment. For rapamycin treatment, average cell growth for colon and pancreas cell lines were and standard deviations above the entire treatment set and both comparisons of meeting had a p-value of < For PP242, colon cell lines were standard deviations above the group mean and again this was very significant (p value <0.0005). Several cell types showed sensitivity to mtor inhibitors but the magnitude of the effect was smaller than observed in the resistant cases with the exception of uterus and rapamycin (p value <0.0005). 14

27 a Rapamycin by Organ b PP242 by Organ c Rapamycin cell growth Bladder Bone Brain Breast Cervix Esophagus Head & Neck Intestine Kidney Liver Lung:NSCLC Miscellaneous Muscle Nervous System Rapamycin Stdev mean p value Ovary Pancreas Prostate Skin Stomach Testes Thyroid Uterus PP242 cell growth PP242 Stdev mean p value bladder bone brain breast cervix esophogus head and neck colon kidney liver lung nslc misc muscle nervous ovary pancreas prostate skin stomach thyroid uterus Bladder Bone Brain Breast Cervix Esophagus Head & Neck Intestine Kidney Liver Lung:NSCLC Miscellaneous Std. dev. mean x> >x> >x> <x< <x<-0.25 x<-.5 Muscle Nervous System Ovary Pancreas Prostate Skin Stomach Testes Thyroid Uterus Figure 2.2 Distribution of cell growth responses by organ type. (a) A boxplot of cell growth distributions by organ type after treatment with rapamycin. (b) A boxplot of cell growth distributions by organ type after treatment with PP242. (c) Standard deviations (Stdev mean) and p values for each organ type. P values less than are highlighted as being significant at the level of α=0.05 using the Bonferroni adjustment for multiple comparisons. 15

28 2.5 PIK3CA and RAS mutations are markers for response to PP242 but not rapamycin Upstream mutations in signaling pathways are thought to sensitize cells to mtor inhibition. To analyze the relative contribution of common oncogenic mutations to the observed sensitivity and resistance to mtor inhibitors, multiple regression using ordinary least squares (OLS) was used. For both mtor treatments, the resulting models fit the data very poorly with R 2 values of less than 0.1. For PP242 treatment, the model using all 15 gene indicator variables fit the complete 357 observation data set very poorly (Model 1, Table 2.1). The R 2 was and adjusted R The genetic data gathered for the cell lines only explained 7% of the variance in growth effect due to treatment with PP242. Analysis of the residuals from the model showed that the poor model performance was not due to errors in functional form or to extreme outliers (Figure 2.3a and b). The plot of standardized residuals versus fitted values showed no bias in residuals as fitted value increased and standardizes residuals were evenly distributed both above and below zero (Figure 2.3a). There were 4 outliers (std. dev. residual> 2.5, but that would be expected for a data set of this size. These outliers were all cell lines that were particularly insensitive to PP242, and one in particular, SW620, had a growth value of significantly greater than 1. A qnorm plot of the standardized residuals versus a normal distribution showed similar behavior with the only deviation from normality observed at the upper end of the fitted values, consistent with the known outlier data (Figure 2.3b). As would be expected with such a low R 2, only two of the genes were significant, PIK3CA and RAS. For PIK3CA the coefficient was negative with a p-value.017 and for RAS it was positive with a p-value of However, given 15 variables, it would be expected that one would be significant at a p-value of.05 just by chance. With that in mind, it is difficult to be confident in the significance of these results other that they suggest that these variables might be significant but it warrants further analysis to be definitive. To further test whether these variables might be significant, we constructed additional models using restricted genotype dataset information. We employed both a biased and unbiased approaches to reduce the number of genotype variables analyzed. A stepwise function employs an algorithm to eliminate variables that are not significant until a consistent set of significant variables remains. Using information about the signaling pathways in cancer, we tested the 4 genes whose mutations we believed would be most likely to affect mtor (BRAF, RAS, PTEN and PIK3CA). The stepwise backwards elimination model was constructed using a generous significance cutoff of p=0.15 knowing that few of the genes had been significant in the full model. The model reduced the number of gene variables from 15 to 5 (Model 2, Table 2.1). These genes were RAS, PIK3CA, RB1, CTNNB and APC. The R 2 was lower than in the full model, , but the adjusted R 2 was nearly the same,.033. In this model, 2 genes were again significant at the p=.05 level, but they were PIK3CA and RB1, and not RAS as in the full model. The coefficient on PIK3CA was again negative and very similar to the full model (-.079 restrict versus full) and the p-value decreased to The newly significant gene RB1 had a positive coefficient of 0.08 and an associated p-value of Finally, a model testing 4 known mtor effector genes was constructed (Model 3, Table 2.1). The fit of the model using only BRAF, PTEN, RAS and PIK3CA was the worst of the three tested. The R 2 was and the adjusted R However, these differences were not statistically different from the full model. A F-test comparison of this restricted model 3 versus 16

29 Table 2.1 Linear regression models for genotypes and PP242 Variables Model 1 Model 2 Model 3 Full Model Stepwise (backwards) p=.15 Targeted Genotypes r2 adjusted r2 root MSE Coeff 95% CI (lo) 95% CI (hi) P value Coeff 95% CI (lo) 95% CI (hi) P value Coeff 95% CI (lo) 95% CI (hi) P value RAS PTEN PIK3CA BRAF P CDKN2A RB APC MAP2K VHL NF CTNNB STK EFGR SMAD Constant < < <

30 Table 2.2 Linear regression models for genotypes and rapamycin Variables Model 4 Model 5 Model 6 Full Model Stepwise (backwards) p=.15 Targeted Genotypes r2 adjusted r2 root MSE Coeff 95% CI (lo) 95% CI (hi) P value Coeff 95% CI (lo) 95% CI (hi) P value Coeff 95% CI (lo) 95% CI (hi) P value RAS PTEN PIK3CA BRAF P CDKN2A RB APC MAP2K VHL NF CTNNB STK EFGR SMAD Constant < < <

31 a PP242 b Rapamycin Standardized residuals Standardized residuals Fitted values Fitted values c d Standardized residuals Inverse Normal Figure 2.3 Residuals from linear regression models of genotype and mtor inhibitor response. (a) Standardized residuals vs fitted values for the linear regression of PP242 against genotype. (b) A q-norm plot of the standardized residuals for PP242. (c) Standardized residuals vs fitted values for the linear regression of rapamycin against genotype. (d) A q-norm plot of the standardized residuals for rapamycin. Standardized residuals Inverse Normal the complete model 1 was not significant (F= 1.49 p=0.1318). While the F test for the full model was significant (p=.0345), implying that a least one variable is better than chance alone, the restricted model describes the data as well (or equally poorly in the context) as the full model. Only the RAS variable was significant in model 3 with a coefficient of 0.06 and a p-value of.011. For rapamycin, there was even less of a relationship between genotype and cellular response to inhibitor. In the complete 15 gene regression, only one genotype was significant, APC (Model 4, Table 2.2). The R 2 for the model was and adjusted R APC mutations are highly coincident with colon cancer and so the fact that both colon and APC were resistance markers in rapamycin is not surprising. The lack of significance for APC mutations in PP242 treatment is more puzzling. The residuals were not as evenly distributed about zero as in the PP242 data set and a preponderance of them appeared to be negative, but the qnorm plot showed minimal departure from linearity (Figure 2.3c and d). Simplified models of the rapamycin genotype data did not uncover new genotypes that would be robust predictors of sensitivity and resistance. The stepwise backwards elimination of variables left 5 variables in the model, but only one additional variable was significant (CTNNB) (Model 5, Table 2.2). This variable was also significant in PP242 case in the stepwise model. 19

32 But there are only 7 cell lines mutant for this protein and it is also highly coincident with colon cancer. When the analysis was restricted to the targeted genotypes (RAS, PTEN, PIK3CA and BRAF) only RAS was significant as was the case with PP242 (Model 6, Table 2.2). 2.6 PIK3CA mutations are predictors of sensitivity to PP242 in multiple tumor types Cell signaling pathways are tissue specific and the role of mutations in different tumor backgrounds may not be conserved. The impact of specific mutations can be dependent upon organ. We analyzed the role that mutations would have within tumor types in a subset of tumor types, focusing on those that were most represented as well as select cell lines with high numbers of mutations. Because the numbers were much smaller in these subsets, we did not perform multiple regressions to avoid problems with over fitting, but instead performed unpaired T-tests. In all, 5 genotypes were analyzed in 8 organ backgrounds using both the rapamycin and PP242 datasets. For no combination of organ type and genotype were the results significant with the rapamycin set. But for PP242, several associations were markers of sensitivity and resistance. a PIK3CA Ras Rb1 Cell growth Growth Cell growth Growth Cell growth Growth MUT MUT MUT NSCLC * Colon * Breast ** WT WT WT Cell growth Growth MUT WT Breast Cervix * * Cell growth Growth MUT WT b n RAS PIK3CA BRAF PTEN RB1 Breast (coef) p value n.s Colon (coef) p value n.s. NSCLC (coef) p value n.s n.s. Cervix (coef) p value Uterus 8 n.s. Pancreas 15 n.s. n.s. Skin 50 n.s. n.s. n.s. Brain 22 n.s. n.s. Figure 2.4 Effect of genotypes in PP242 sensitivity in select organ types (a) Cell growth effect of PP242 on mutant vs wildtype PIK3CA, RAS and RB1 in select organ types. All comparisons were made using unpaired Student s T- tests. * p <0.05. (b) Coefficients and p values for comparisons in (a). In 5 genotype organ combinations the presence of a mutation was significant for PP242 response (Figure 2.4a and b). The combinations are PIK3CA with breast, NSCLC and cervix, 20

33 RAS with colon, and RB1 with breast. As expected from the earlier regression data, PIK3CA mutations predict sensitivity to PP242 and RB1 and RAS resistance. For PIK3CA, the coefficient for a mutation was for Breast cancer (p=0.015), for cervix (p=0.048) and for NSCLC (p=0.015). In colon cancer, the coefficient for RAS was large (0.18) and significant as well (p=.035). a mtor Inhibitors PI3K Inhibitors Dual Inhibitors PP30 PP242 Rapa PIK 93 TGX 221 PIK 90 PP102 PI 103 PP121 BEZ 235 Cell Line mtorc1/2 mtorc1/2 mtorc1 p110,,, VPS34 p110 p110,, p110,,, p110,, mtorc1/2 PI3Ks mtorc1/2 RTKs p110,, mtorc1/2 VPS34 HCT 116 > >10 > > HCT 15 > >10 >10 > > DLD >10 > > HT > SW620 >10 11 >10 >10 > > > SW1116 >10 15 >10 >10 > > > LS174T >10 >10 > KM12 > >10 >10 >10 >10 > Hela >10 >10 >10 >10 >10 >10 >10 >10 > COLO 201 > >10 >10 > >10 > COLO > > b PP30 PP242 PIK 90 PI 103 TGX 221 Rapamycin PP102 BA121 PIK 93 BEZ 235 Sorafenib SW13 SW620 SW1116 HCT 116 HCT 15 DLD-1 COLO 201 LS174T KM12 HT-29 COLO 205 HeLa KRAS Mut KRAS Mut/ PI3K Mut MSI unstable BRAF V600E Figure 2.5 A panel of PI3K/mTOR inhibitors distinguishes colon cancer cell lines by genotype. (a) Inhibitory constant 50 (IC 50 ) values for PI3K/mTOR inhibitors against colon cancer cell lines. Values were calculated from 6- point drug dilution series measure with resazurin. Chemical structures of each inhibitor are shown. (b) Clustering analysis of the data from panel (a). Cell lines were clustered by drug response. Mutational similarities among grouped cell lines are shown at right. 2.7 Cluster analysis reveals that PI3K/mTOR inhibitor segregate colon cancer cell lines by genotype 21

34 We next asked if a cell screen starting with multiple inhibitors of the mtor pathway would be able to discriminate cell lines based upon genotype. This would validate our hypothesis that genotypes were important to understanding the efficacy of mtor pathway inhibitors. This pilot cell screen was conducted in only a single tissue background, colon, and with 11 cell lines. Colon was chosen because it was particularly resistant to both mtor inhibitors tested in the large screen and also harbored large numbers of mutations (90). 10 inhibitors of either mtor, the upstream signaling kinase PI3K or dual inhibitors of both proteins were profiled against the cell lines in 6-point growth inhibition assays. IC 50 (inhibitory constant 50%) values were calculated from fitted sigmoidal dose-response equations and reported in micromolar (Figure 2.5a). Unsurprisingly, the most promiscuous inhibitors led to the most severe growth defects. BEZ-235 inhibits all main isoforms of PI3K as well as mtor and average IC 50 values were less than 100 nm. The pan-pi3k kinase inhibitor PIK90 was also particularly effective across cell types. Two compounds stood out as potently inhibiting some cell lines while having little effect on others. PP102 is a pan PI3K inhibitor that was less potent against PI3Kα than PIK90, but inhibited two BRAF V600E cell lines at 600 nm (91). And PP242 was effective against many cell lines, but not Hela cells or SW620 and SW1116 cell lines. With these data in hand, we asked how the profiles of each cell line across the group of similar inhibitors would cluster. We preformed cluster analysis to determine the similarities between cell lines and visualized the results (Figure 2.5b). The analysis divided the cell lines into 5 groups with readily identifiable genotypes. The two closest cell lines were SW1116 and SW620, two PP242 resistance cell lines that are both mutant for KRAS and WT for PIK3CA, PTEN and BRAF. A group of three cell lines (HCT 15, HCT 116 and DLD-1) that are mutant for KRAS and PIK3CA clustered together as well. Two known microsatellite unstable (MSI unstable) cells grouped together as did the BRAF V600E containing cells. Finally, as a control, it was gratifying to see the Hela cells, which are not of colon origin, did not cluster with any colon cells. This analysis strongly supports the hypothesis that PI3K/ mtor pathway inhibitors are sensitive to common mutations in colon cancer and these should be carefully considered in any trial testing their efficacy. 2.8 Discussion We screened two different mtor inhibitors to uncover determinants of resistance and sensitivity to this method of intervention. Rapamycin and PP242 act thought different modes of action and have been shown to have significant differences in vitro (60). These differences were very apparent in the large format cell screen experiments we conducted. Rapamycin was less effective than PP242, responses between the two drugs did not correlate, and markers of resistance and sensitivity were not conserved. These significant differences add to the developing understanding that ATP-competitive mtor inhibitors are fundamentally different from natural product analogs of rapamycin, and their biologic effects are likely to be profoundly different as well. The cell screen analysis uncovered two organ types that were strongly resistance to mtor inhibition by both inhibitors tested. Colon and pancreas were markers of resistance. Interestingly, both tumor types are strongly associated with mutations in KRAS, and yet RAS mutation is not a predictor of resistance in the rapamycin dataset, and only weakly predictive in PP242. This can be explained by the observation that RAS appeared to only be a marker of 22

35 resistance in select cell types. In non-small cell long cancer (NSCLC), the most represented cell type, RAS mutation was not predictive of resistance to PP242 or rapamycin. Brain and uterus were sensitive to rapamycin, but not PP242. Multiple regression by OLS was used in three sets of models to uncover significant markers of resistance and sensitivity by genotype. The first model examined all genotypes and subsequent models focused on a narrow set. The differences between rapamycin and PP242 were profound. The single largest marker for sensitivity and resistance to PP242 (PIK3CA and RAS respectively) were not significant in the rapamycin dataset. In fact, the only significant gene in the rapamycin dataset (APC) was a marker for colon cancer. Unfortunately, even for the PP242 datasets, the significance for any one genotype was very low. Among these common genotypes, we did not uncover a very strong marker, and these results do not appear very robust absent more data. Within specific organ types, the significance of genetic variables varied. For tumors with PIK3CA mutations, which in the entire data set predicted sensitivity to PP242, sensitivity was observed in 3 organ types examined (NSCLC and breast and cervix). For RAS mutations, resistance to PP242 was seen in 1 organ type examined (colon). This suggests that the effect of certain mutations in the whole data set was actually driven by their effect in specific organ subsets, but without greater enrichment for either the mutation or organ of interest, we were unable to address this further. All inhibitors have off-target effects and the validation of mechanisms of action requires multiple inhibitors of the same pathway to identify the common effects that are presumably due to on-target activities. To control for this effect, and to understand the sensitivity of cell lines to groups of common inhibitors, a group of colon cancer cells was treated with a panel of PI3K/mTOR inhibitors. From our cell screen results, their appeared to be a weak but potentially significant correlation between PIK3CA and RAS mutation and response. To further validate this, we sought to determine if cellular responses to inhibitors of these pathways depended upon mutations in these genes. We observed through clustering analysis that cell with common signaling mutations in these pathways responded to the inhibitors in common ways strongly suggesting to us that these mutations are important to understanding the efficacy of mtor inhibitors. The goals of the analysis were to determine if any common gene mutation or organ type are associated with sensitivity or resistance to PP242 treatment. Furthermore, in the organ types that are more represented, we examined whether there are genetic effects specific to that background. The data are not very dense and do not show an obvious genetic signature for either resistance or sensitivity. However, there is evidence that RAS and PIK3CA mutations can alter sensitivity, and that the effects of PP242 are dependent upon organ type. Inclusion of significantly more descriptive data (including gene expression data and SNP arrays) about the specific cell lines tested in the assay may allow for the creation of a more predictive model for response to mtor inhibition (88). 2.9 Data and Methods Cell growth screen. For the high throughput growth screen, cell lines were grown and drug treated as previously described (89). Rapamycin was obtained from commercial sources. PP242 was synthesized according to previously published procedures as detailed in chapter 3 (92). 23

36 For the subsequent colon cancer cell line panel, cells were purchased from the ATCC (Manassas, VA). PI3K and mtor pathway inhibitors were synthesized according to published protocols (PP30, PP242, PP102, PI 103, PP121) (92) or obtained from commercial suppliers (Rapamycin, PIK93, TGX221 BEZ 235). Cell growth was assessed in a 96-well plate format using a resazurin assay 48 to 96 hours after initiation of treatment with inhibitor in 0.1% DMSO final concentration. Drugs were diluted in a 6-point series 3-fold from 10 µm. Analysis and statistics. Each cell line was coded for one of 22 common organ types or miscellaneous. The distribution of cell lines among organs was highly unequal reflecting both the unequal distribution of cancer but also the biases of the cell line set analyzed. A subset of the cell lines (n=357 for PP242, n=355 for Rapamycin) were annotated for common mutations in the Sanger COSMIC database ( For each observation, mutations in 15 common genes from the Sanger 50 set were recorded as indicator variables (1 for mutant, 0 for wild-type). The frequency of these mutations in the data set ranged from 2 (VHL) to 220 (P53). Overall, 733 mutations were recorded for the PP242 data set and each cell line averaged 2.05 mutations. 23 cell lines contained no mutations in the 15 genes analyzed, and 5 cell lines contained mutations in 5 genes. All statistical comparisons were conducted using the software suite Stata release 12 (Statacorp LLC, College Station, TX). Analysis was based on the treatment of growth as a continuous variable. This is in contrast to previous work by the Settleman group which converted the cell growth data from a continuous output to a categorical response. Cell lines were categorized as high, medium, low or non-responders. For agents that targeted specific mutant genes, the pattern of responses was a few responders (high, medium and low) while the majority of cell lines were non-responsive. From this highly skewed distribution of outcomes, simple comparison tests were preformed to assess whether a genotype or tissue of origin were more highly represented among the responders compared to the non-responders. To address the continuous growth data in this analysis, multiple regression using ordinary least squares was used. Clustering analysis was conducted separately using the open source programs Cluster and visualized with TreeView ( 24

37 CHAPTER 3: Patient-derived xenografts in preclinical drug development 25

38 3.1 Abstract Patient-derived xenografts (PDX) are a model of human cancer that is able to recapitulate key features of tumors better than standard cell line models. Sets of these xenografts allow access to groups of human tumors in an organismal setting without having to go through the adaptation process to plastic tissue culture. The use of this model may be particularly useful in preclinical drug development. PDX models allow for the drug to be assayed for both tumor intrinsic and extrinsic mechanisms of action in the same model. Furthermore, the accessibly heterogeneity of different patients allows for class effects to be separated from model specific results and provide a more robust platform than cell lines. We characterize the histological and cell-signaling response of a set colorectal cancer patient derived xenografts to treatment with the ATPcompetitive mtor inhibitor PP242. The xenografts faithfully maintained the patient specific tumor architecture over many subsequent murine passages. Treatment with PP242 resulted in changes to cell signaling and repressed apoptosis but did not alter tumor architecture. 3.2 Introduction- Xenografts in colon cancer Direct xenotransplantation of human tumors into immunocompromised mice allows for the maintenance of these cancers in vivo without undergoing the irreversible changes that occur upon in vitro culture (93). This strategy seeks to avoid many of the common problems encountered in the standard cell lines and cell line xenograft models typically used in preclinical testing which are poorly predicative of clinical response (94,95). The use of a patient-derived xenograft model for preclinical drug testing has been most thoroughly developed in primary pancreatic cancer (96,97). Here, it was shown that the xenografts were stable over multiple murine passages with respect to many different tumor characteristics including gene expression, protein expression, and drug sensitivity. In a separate small study of colorectal cancer, the treatment response of xenografts generated from primary surgical resections closely matched the actual patient response to the same agents (98). A large-scale colon cancer derived xenograft study validated the important clinical predictive features of this model by showing that KRAS mutant tumors were resistant to the anti-egfr antibody cetuximab (87). Colorectal cancer is the fourth most common cancer diagnosis and the second most common cause of cancer death in the United States. Patients who present with local disease can be successfully treated with surgery and adjuvant chemotherapy but for those with distant metastases, prognosis is poor with a 5 year median survival rate of 11% ((99). Treatment options for metastatic disease are limited and significantly improved therapies are needed. However, for a subset of patients with metastatic disease confined to the liver, surgical resection of these tumors is possible and outcomes are dramatically improved (40% median 5 year survival) (100,101). These surgeries provide rare direct access to metastatic tumor; our goal was that such tumors could be used to produce xenografts in nude mice to generate a novel preclinical model in which to study the biology of advanced colorectal cancer. 3.3 Establishment of human metastatic colorectal cancers xenografts Colorectal cancer liver metastases were surgically resected as part of standard treatment with curative intent. Portions of these metastases were directly implanted into female nude athymic mice and subsequently further passaged in mice (Figure 3.1a). The patients whose 26

39 a b CR703 CR708 Patient P1 P5 P10 Patient P1 P3 P5 c Patient-derived xenograft doubling times d Doubling time (days) 30 CR CR 698 CR Passage # CS174T e DLD-1 DAPI Mouse Human Merge Figure 3.1 Patient-derived xenografts maintain key morphological features of human colon cancer. (a) Schematic of patient-derived xenograft generation from surgical specimens (b) Sequential passage of human tumors in mice 27

40 maintains tumor architecture. The first panel in each row is an H&E stain of a patient surgical specimen from a liver resection and the later panels are subsequent passages of the same tumor in nude mice. Patient characteristics are presented in Table 1. Scale bar represents 50 µm. (c) Doubling times of select xenografts across multiple patients. Tumors were measured approximately every week by external calipers and a volume was calculated assuming an ellipsoid form. Doubling time was estimated by fitting an exponential growth function to the measured values. (d) Tumors derived from colorectal cell lines do not resemble resected metastases. CS174T (top) and DLD-1 (bottom) were subcutaneously injected and allowed to develop into tumors that are homogeneous and have little stromal component. (e) Following xenotransplantation, tumor architecture is maintained with murine-derived stroma. Genomic FISH analysis was performed using species-specific labeled probes for the COT-I gene to identify the origin of each cell. Labeling strictly segregates the contribution of each species and pathology confirms the stroma is uniquely labeled with the murine probe. Sample shown was from patient CR703 and is passage 5. tumors were used in this xenotransplantation study presented with advanced (stage III or IV) disease at diagnosis and were treated with surgical resection of the primary tumor followed by systemic chemotherapy (predominantly 5-fluorouracil and oxaliplatin based regimens) (Table 3.1). Liver metastases were removed either when prior treatment made it possible to do, or soon upon relapse. Table 3.1 Genotypes and patient characteristics of tumors used in this study Patient CR 698 CR 702 CR 727 CR 736 CR 739 CR 700 CR 703 CR 708 Sex M F M M F M F F Age Stage at DX IV IV III IV III III IV II Prior FOLFOX FOLFOX FOLFOX FOLFOX FOLFOX FOLFOX FOLFOX therapy bev bev bev none NED 3 TTP 6 TTP 2 TTP 9 TTP 14 NED 2 No F/U No F/U Outcome yrs mos mos mos mos yrs PIK3CA WT WT H1047R H1047L WT WT WT WT KRAS WT G12D G12V G13D WT G12D G12D WT BRAF WT WT WT WT WT WT WT WT p53 WT R248L L130P exon 6 del WT R175H WT R273C Prior treatment before surgical resection of liver metastases is abbreviated as follows: FOLFOX: (5-fluorouracil/ leucovorin/ oxaliplatin), Bev: bevacizumab. Outcomes are defined as: NED: No evidence of disease; TTP: time to progression; No F/U: no follow-up at UCSF. To establish whether the passaged xenografts retained the characteristics of hepatic colorectal metastases, histopathological analysis was performed. Original patient tumor samples removed during surgery were compared with tumors taken from each subsequent murine passage and compared to determine if tumors had undergone gross changes in mice or retained their original pathological features (Figure 3.1b). Comparisons of patient surgical samples to later xenografts of the same tumor were made by a pathologist. Xenograft tumors were pathologically nearly identical to their original patient source material. For example, patient tumor CR 703 was distinguished by containing significant necrotic areas, a high degree of extracellular mucin and 28

41 having pleomorphic nuclei. These same features were noted in similar ratios in the passages P1, P3 and P5 of the same tumor (Figure 3.1b-lower panels). To assess whether xenograft tumors were accumulating changes in gross growth characteristics, tumor-doubling times were calculated (Figure 3.1c). Within tumor lines, average tumor doubling times were roughly consistent and no clear trend of increasing growth rates was observed. In contrast to the primary xenografts, similar implantations of cultured tumor cell lines generated a tumor that was homogeneous and defined by solid growth of epithelial sheets with little stromal network (Figure 3.1d). We observed that the tumors had maintained their overall architecture through multiple murine passages and that stromal cells were consistently supporting the structure. To determine whether murine stromal cells had been recruited to the tumors in order to support its stromal networks through multiple passages and subsequent outgrowths, genomic fluorescence in-situ hybridization (FISH) was performed. Using species-specific probes against the human and murine COT-1 gene, the contribution of each species to the tumor could be ascertained (Figure 3.1e). FISH staining of a P5 xenograft showed unambiguously that while the epithelial cells were human in origin, the stroma in the xenograft tumors was entirely murine in origin. 3.4 Synthesis of ATP-competitive mtor inhibitor PP242 The mtor inhibitor PP242 is representative of a new class of ATP-competitive inhibitors that have shown greater efficacy in preclinical trials than allosteric inhibitors that are derivatives of the natural product rapamycin. It remains unknown for which cancer patients these inhibitors will be most effective for. To identify early biomarkers that may be indicative of response to PP242 in colorectal cancer xenografts, gram quantities of PP242 were synthesized for dosing in mice at 100 mg/ kg according to previous published protocols (60). PP242 and its clinical derivative MLN0128 were synthesized according to the scheme outlined in Figure 3.2 following published procedures (72,92). Oral administration of PP242 was effective at reaching xenografts and inhibiting phosphorylation of mtor substrates within 2 hours of dosing. Quantitative mass spectrometry was performed on serum samples to determine the actual concentration of PP242 achieved in vivo. Two hours after administration, the PP242 serum concentration was 5.8 µm, a very comparable number to efficacious concentrations in cell culture. 3.5 Oral dosing of PP242 is effective at inhibiting mtor phosphorylation in vivo To investigate how different patient backgrounds would affect PP242 s ability to inhibit phosphorylation of downstream mtor targets, multiple patient tumors were expanded into cohorts of nude mice. Of particular interest were the differences in mutations to common colorectal oncogenes, KRAS and PIK3CA. Six different patient-derived tumors representing three different combinations of mutant PIK3CA (p110α) and KRAS were analyzed: WT for KRAS and WT for PIK3CA (CR 698 and CR 739); Mut for KRAS and WT for PIK3CA (CR 702 and CR 649); Mut for KRAS and Mut for PIK3CA (CR 727 and CR 736). Mice were subcutaneous implanted with two tumors, one on each flank, and administered drug at 100 mg/kg orally once daily for three days. Four hours after the final dose, animals were euthanized and tumors harvested for western blot analysis. PP242 effectively inhibited phosphorylation of both mtorc1 and C2 substrates (Figure 3.3a). mtorc2 substrate AKT S473 was inhibited 29

42 significantly in all tumors compared to vehicle controls. The phosphorylation of targets of mtorc1, 4E-BP1 and rps6 was inhibited without any change in protein abundance Figure 3.2 O NH 2 I CN O 180 o C N DMF N + N + N I-N H 2 N N H NH O/N 2 N N 80 o N C O/N N N H H H O 1 2 N NH 2 N I N H N + I K t-butoxide DMF 0 RT O/N N NH 2 N I N N + (HO) 2 B N Boc OMe Tetrakis(Pd), Na 2 OH EtOH, DMF 90 o C 4 hr 2 3 OMe OH NH 2 NH BBr 3, DCM NH 2 NH N N N N -80 o C RT N N N N 4 5 (PP242) O NH 2 N NH 2 N I N N + (HO) 2 B N O NH 2 Pd(OAc) 2, Triphenylphosphine, Na 2 OH, EtOH, DMF 90 o C 2 hr N NH 2 N N N N 3 Figure 3.2 Synthetic scheme for PP242 and MLN (MLN0128) The variance in cell signaling between tumors was much higher between mice than within the same mouse as seen by the comparison of changes between left and right tumors from the same mouse versus the same tumor in different mice. We analyzed the overall effect of 30

43 PP242 in all six tumors by quantifying the phosphorylated forms of rps6 (S240/44), AKT (S473) and 4EBP1 (T37/46) in each treated tumor compared to the vehicle control (Figure 3.3b). PP242 consistently inhibited phosphorylation of all measured mtor substrates. At this early treatment time point, the inhibition of mtor substrates in the three genetic backgrounds was consistent and no correlation existed with either KRAS or PIK3CA mutation status. a Patient CR 698 Vehicle PP242 CR 702 Vehicle PP242 CR 727 Vehicle PP242 p-akt S473 p-rps6 S240/244 rps6 L R L R L R L R L R L R L R L R L R L R L R L R L R L R L R L R L R L R p-4e-bp1 T37/46 4E-BP1 cleaved PARP β-actin p-akt S473 p-rps6 S240/244 rps6 p-4e-bp1 T37/46 CR 739 CR 700 CR 736 Vehicle PP242 L R L R L R L R L R L R Vehicle PP242 Vehicle PP242 L R L R L R L R L R L R L R L R L R L R L R L R 4E-BP1 cleaved PARP β-actin b Treated/Ctrl mtor substrate phosphorylation p-4ebp1 p-akt S473 p-rps6 KRAS PIK3CA CR 698 WT WT CR 739 WT WT CR 702 G12D WT CR 700 G12D WT CR 736 G13D H1047L CR 727 G12V H1047R c Fluorescence Intensity (normalized to β- actin) CR 698 CR 739 Cleaved PARP Vehicle PP242 CR 702 CR 700 CR 736 CR 727 Figure 3.3 PP242 inhibits mtor outputs in different colorectal cancer genetic backgrounds. (a) Western blots of six different patient tumors are shown. For each patient tumor, 6 mice were implanted with two tumors each (left (L) and right (R) flank) and then randomized to receive either PP242 (100 mg/kg) or vehicle (three mice each) for three days before being sacrificed. (b) Quantification of mtor signaling outputs in patient tumors from different KRAS/ PIK3CA genetic backgrounds. Western blots (representative samples shown in Figure 2D) were quantified using fluorescent secondary antibodies and the ratio of treated phosphoprotein to control was plotted for each tumor. (c) Quantification of leaved PARP in PP242 treated tumors. Western blots for cleaved PARP were conducted and imaged as in (b). We analyzed tumors for drug-induced changes in apoptosis using Poly ADP ribose polymerase (PARP) cleavage as a surrogate. Basal levels of cleaved PARP were patient tumor specific and detectable in most tumors (Figure 3.3c). Upon 3 days of PP242 treatment, 31

44 significant changes in PARP cleavage were only observed in one tumor, CR700. Higher PARP cleavage in this KRAS mutant/ PIK3CA WT tumor suggest that PP242 may be efficacious in this tumor despite no major differences in mtor signaling compared to similar tumors. Long-term drug treatment with PP242 did result in significant differences in tumor growth during a drug treatment trial (See chapter 4). Compared to the significant differences observed in tumor growth between the treated and untreated samples, alterations in gross tumor pathology were minor. Only in tumor CR 702 were significant differences noted between treated and untreated tumors. PP242 treatment induced cystic necrosis in the tumor as compared to vehicle controls (Figure 3.4). Surprisingly, the growth inhibited tumors CR 727 and CR 698 (834-3veh, PP) showed no identifiable pathological differences. In the trials conducted, short term treatment with PP242 did not result in identifiable pathological changes while longterm exposure led to increases in necrosis that were not accompanied by a reduction in tumor growth rate. VEHICLE PP242 CR727 CR702 CR698 Figure 3.4 Tumor morphology of PP242 treated tumors. H&E stains of paired treated and untreated PDX tumors. Tumors were treated for a minimum of 21 days once daily. 32