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Like classical chemotherapy regimens used to treat cancer, targeted therapies will also rely upon polypharmacology, but tools are still lacking to predict which combinations of molecularly targeted drugs may be most efficacious. In this study, we used image-based proliferation and apoptosis assays in colorectal cancer cell lines to systematically investigate the efficacy of combinations of two to six drugs that target critical oncogenic pathways. Drug pairs targeting key signaling pathways resulted in synergies across a broad spectrum of genetic backgrounds but often yielded only cytostatic responses. Enhanced cytotoxicity was observed when additional processes including apoptosis and cell cycle were targeted as part of the combination. In some cases, where cell lines were resistant to paired and tripled drugs, increased expression of antiapoptotic proteins was observed, requiring a fourth-order combination to induce cytotoxicity. Our results illustrate how high-order drug combinations are needed to kill drug-resistant cancer cells, and they also show how systematic drug combination screening together with a molecular understanding of drug responses may help define optimal cocktails to overcome aggressive cancers. Cancer Res; 76(23); 6950–63. ©2016 AACR.

Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

SummaryActivation of the phosphoinositide 3-kinase (PI3K) pathway occurs frequently in breast cancer. However, clinical results of single-agent PI3K inhibitors have been modest to date. A combinatorial drug screen on multiple PIK3CA mutant cancers with decreased sensitivity to PI3K inhibitors revealed that combined CDK 4/6-PI3K inhibition synergistically reduces cell viability. Laboratory studies revealed that sensitive cancers suppress RB phosphorylation upon treatment with single-agent PI3K inhibitors but cancers with reduced sensitivity fail to do so. Similarly, patients’ tumors that responded to the PI3K inhibitor BYL719 demonstrated suppression of pRB, while nonresponding tumors showed sustained or increased levels of pRB. Importantly, the combination of PI3K and CDK 4/6 inhibitors overcomes intrinsic and adaptive resistance leading to tumor regressions in PIK3CA mutant xenografts.

Tankyrases (TNKS) play roles in Wnt signaling, telomere homeostasis, and mitosis, offering attractive targets for anticancer treatment. Using unbiased combination screening in a large panel of cancer cell lines, we have identified a strong synergy between TNKS and MEK inhibitors (MEKi) in KRAS-mutant cancer cells. Our study uncovers a novel function of TNKS in the relief of a feedback loop induced by MEK inhibition on FGFR2 signaling pathway. Moreover, dual inhibition of TNKS and MEK leads to more robust apoptosis and antitumor activity both in vitro and in vivo than effects observed by previously reported MEKi combinations. Altogether, our results show how a novel combination of TNKS and MEK inhibitors can be highly effective in targeting KRAS-mutant cancers by suppressing a newly discovered resistance mechanism. Cancer Res; 74(12); 3294–305. ©2014 AACR.

Somatic PIK3CA mutations are frequently found in solid tumors, raising the hypothesis that selective inhibition of PI3Kα may have robust efficacy in PIK3CA-mutant cancers while sparing patients the side-effects associated with broader inhibition of the class I phosphoinositide 3-kinase (PI3K) family. Here, we report the biologic properties of the 2-aminothiazole derivative NVP-BYL719, a selective inhibitor of PI3Kα and its most common oncogenic mutant forms. The compound selectivity combined with excellent drug-like properties translates to dose- and time-dependent inhibition of PI3Kα signaling in vivo, resulting in robust therapeutic efficacy and tolerability in PIK3CA-dependent tumors. Novel targeted therapeutics such as NVP-BYL719, designed to modulate aberrant functions elicited by cancer-specific genetic alterations upon which the disease depends, require well-defined patient stratification strategies in order to maximize their therapeutic impact and benefit for the patients. Here, we also describe the application of the Cancer Cell Line Encyclopedia as a preclinical platform to refine the patient stratification strategy for NVP-BYL719 and found that PIK3CA mutation was the foremost positive predictor of sensitivity while revealing additional positive and negative associations such as PIK3CA amplification and PTEN mutation, respectively. These patient selection determinants are being assayed in the ongoing NVP-BYL719 clinical trials. Mol Cancer Ther; 13(5); 1117–29. ©2014 AACR.

Tumors are complex biological systems that often retain proliferative capacity even when challenged with drug treatment. Given this resiliency, drug combinations may provide greater therapeutic benefit, however, which molecules to combine and how many to include in combinations for effective responses is not clear yet. Using image-based proliferation and apoptosis assays in colorectal cancer cell lines we systematically investigated combinations that ranged in number from two to six drugs and targeted critical oncogenic pathways. Drug pairs targeting key signaling pathways resulted in synergies across a broad spectrum of genetic backgrounds, but often yielded only cytostatic responses. Enhanced cytotoxicity was observed when additional processes including apoptosis and cell cycle were targeted as part of the combination. In many cases, where cell lines were resistant to two- and three-way drug combinations, increased expression of anti-apoptotic proteins was observed and induction of cytotoxic responses required up to fourth-order combinations. Our results demonstrate that high-order drug combinations might be needed to kill cancers and show how systematic drug combination screening together with a molecular understanding of drug responses can guide their identification. Citation Format: Thomas Horn, Stephane Ferretti, Nicolas Ebel, Angela Tam, Samuel Ho, Fred Harbinski, Ali Farsidjani, Matt Zubrowski, William R. Sellers, Robert Schlegel, Dale Porter, Erick Morris, Jens Wuerthner, Sebastien Jeay, Joel Greshock, Ensar Halilovic, Levi A. Garraway, Giordano Caponigro, Joseph Lehar. High order drug combinations are required to effectively kill colorectal cancer cells. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1311.

The mammalian target of rapamycin (mTOR) is regulated by oncogenic growth factor signals and plays a pivotal role in controlling cellular metabolism, growth and survival. Everolimus (RAD001) is an allosteric mTOR inhibitor that has shown marked efficacy in certain cancers but is unable to completely inhibit mTOR activity. ATP-competitive mTOR inhibitors such as NVP-BEZ235 can block rapamycin-insensitive mTOR readouts and have entered clinical development as anti-cancer agents. Here, we show the degree to which RAD001 and BEZ235 can be synergistically combined to inhibit mTOR pathway activation, cell proliferation and tumor growth, both in vitro and in vivo. RAD001 and BEZ235 synergized in cancer lines representing different lineages and genetic backgrounds. Strong synergy is seen in neuronal, renal, breast, lung, and haematopoietic cancer cells harboring abnormalities in PTEN, VHL, LKB1, Her2, or KRAS. Critically, in the presence of RAD001, the mTOR-4EBP1 pathway and tumorigenesis can be fully inhibited using lower doses of BEZ235. This is relevant since RAD001 is relatively well tolerated in patients while the toxicity profiles of ATP-competitive mTOR inhibitors are currently unknown.

Tumors are complex and robust biological systems that harbor the potential to proliferate against various drug treatments. Drug combinations provide a promising therapeutic strategy, but it is not clear which and how many drugs are required to overcome cancers. Using image-based proliferation and apoptosis assays in colorectal cancer cells we systematically investigated complex treatments composed of two to six drugs targeting critical oncogenic pathways. Drug pairs targeting growth signaling resulted in synergies across a broad spectrum of genetic backgrounds, but often yielded cytostatic responses and failed to induce apoptosis. Enhanced, and sometimes genotype-specific cytotoxicity was seen after targeting additional mechanisms including apoptosis or cell cycle. Cells that resisted all tested drug pairs and drug triples were protected by a mechanism that prevented apoptosis. Targeted inhibition of this mechanism using combinations of up to four compounds induced cytotoxic responses in cells in vitro and in vivo. Our results demonstrate that complex combinations of targeted drugs might be required to induce killing in cancers and show how the cells' genetic alterations and a molecular understanding of drug responses can guide their identification. The identification of resistance mechanisms that are pre-existing in subpopulations of tumor cells also opens the exciting avenue of sequenced treatments, with each drug or drug combination targeting and eradicating a specific cell population. Citation Format: Thomas Horn, Stéphane Ferretti, Nicolas Ebel, Angela Tam, Samuel Ho, Fred Harbinski, Ali Farsidjani, Matthew Zubrowski, Ensar Halilovic, Erick Morris, William R. Sellers, Robert Schlegel, Jens Wuerthner, Levi A. Garraway, Sébastien Jeay, Joel Greshock, Giordano Caponigro, Joseph Lehár. Complex drug combinations can induce apoptotic killing in robust colorectal cancer cells. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr LB-B04.

Introduction KRAS mutant tumors represent a high unmet need for effective therapy. Even though there is no targeted therapy for KRAS, inhibitors of downstream targets like MEK have shown efficacy in preclinical studies. However, clinical trials of MEK inhibitors in KRAS-mutant tumors have not been successful, although there have been some responders. We sought to understand resistance to MEK inhibition in KRAS-mutant tumors and to develop biomarkers for patient stratification. Results Using DNA and RNA sequencing data from cell lines, we found that high variant allele frequency (>50%) of KRAS functional mutations (G12, G13, Q61) was associated with baseline resistance to MEK inhibitors AZD6244 and MEK162 across multiple tumor types. Low allele frequency of KRAS mutation was found to be necessary but not sufficient for sensitivity to MEK inhibitors, suggesting the existence of additional resistance mechanisms. Comparison of copy number and variant allele frequency of KRAS showed that MEK inhibitor-resistant cell lines have either amplified mutant alleles or lost WT alleles. This prediction of high KRAS mutant allele frequency being associated with MEKi resistance was successfully validated in vivo in 19 primary colorectal tumor xenograft models treated with MEK162. High KRAS mutant allele frequency and high KRAS expression were also associated with acquired resistance to BKM120+MEK162 combination treatment in an in vivo model of HeyA8, an ovarian cell line with a KRAS G12D mutation. This prediction was also supported by the observation in a Phase II clinical trial with MEK inhibitor refametinib + gemcitabine in pancreatic cancer [1]. Patients with disease progression had a significantly higher KRAS mutant allele frequency compared to partial responders and stable disease. Conclusion High allele frequency of KRAS functional mutations is associated with resistance to MEK inhibitors. Excluding patients with high KRAS mutant allele frequency has the potential to improve clinical trial outcomes and benefit patients, with the added benefit that it may not require any additional screening or novel biomarkers. References [1] Reiss et al., J Clin Oncol 32:5s, 2014 (suppl; abstr 4129) Citation Format: Anupama Reddy, Maria Pinzon-Ortiz, Adnan Derti, Joshua Korn, David Ruddy, Guizhi Yang, John Green, Hui Gao, Joseph Lehar, Giordano Caponigro, Z. Alexander Cao. High allele frequency of KRAS functional mutations predicts resistance to MEK inhibitors: Evidence from cell lines and human tumor xenograft models. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2692. doi:10.1158/1538-7445.AM2015-2692

Recent advances in targeted chemotherapy have dramatically improved the survival odds of patients suffering from specific molecular subtypes of cancer. Nonetheless, while a fortunate few enjoy a durable and lasting response to treatment, the majority will eventually relapse after some grace period in remission due to the emergence of drug-resistant cancer cells. Acquired resistance is a complex phenomenon that likely involves intra-tumoral heterogeneity, tumor-stromal interactions, and natural selection due to the pressure of chemotherapy. One promising approach to overcome resistance involves utilization of two or more drug compounds in combination, where each compound targets a different member of a signaling pathway important in the disease. By targeting more proteins, the hope is that the probability of any particular cancer cell failing to respond will decrease as the barrier to survival increases. While drug combinations are an exciting and promising direction for chemotherapeutic research, such studies present additional challenges and opportunities. Interpretation of synergistic and efficacious drug combinations is not always straightforward and pairs of drugs may interact in surprising ways that deviate from expectation based on the molecular pathway relationships between their targets. Moreover, the question of which drugs to prioritize for testing in combination is an important one to which there is currently no standard solution. The brute force approach is not cost-effective due to the factorial growth in the number of possible higher-order drug combinations. In this work, we present and describe an algorithmic approach to (1) predict synergistic and efficacious drug combinations based on pathway relationships between their targets and (2) classify drug combination response surfaces into canonical archetypes of greatest pharmaceutical interest. Our approach borrows from classical work in image analysis and recognition. We trained and tested our algorithm on two publicly available datasets of drug combinations. We further trained on a private dataset of cancer drug combination data and made predictions that were later experimentally validated in vitro. To our knowledge, our approach is the first proof-of-concept algorithm that utilizes the actual shapes of response surfaces to categorize and prioritize drug combinations for further testing. Citation Format: Manway Liu, Thomas Horn, Matthew Greene, Joseph Lehar. Image-based classification of cancer drug combination response surfaces. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Drug Sensitivity and Resistance: Improving Cancer Therapy; Jun 18-21, 2014; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(4 Suppl): Abstract nr B51.

Tankyrases (TNKS) play roles in Wnt signaling, telomere homeostasis and mitosis, and are therefore considered as attractive targets for anti-cancer treatment. Using unbiased combination screens in a large panel of cancer cell lines, we have identified a strong synergy between TNKS and MEK inhibitors in KRAS mutant cancer cells. Our study uncovers a novel function of TNKS in the relief of a feedback loop induced by MEK inhibition on FGFR2 signaling pathway. Moreover, dual inhibition of TNKS and MEK leads to more robust apoptosis and anti-tumor activity both in vitro and in vivo than effects observed by previously reported MEK inhibitor combinations. Altogether, our data provides a strong rationale for combined targeting of TNKS and MEK in KRAS mutant cancers. Citation Format: Wenlin Shao, Marie Schoumacher, Kristen Hurov, Joseph Lehar, Yan Yan-Neale, Yuji Mishina, Dmitriy Sonkin, Joshua Korn, Daisy Flemming, Michael Jones, Brandon Antonakos, Vessilina Cooke, Mark Stump, Nika Danial, William Sellers. Inhibiting TNKS sensitizes KRAS mutant cancer cells to MEK inhibitors by suppressing FGFR2 feedback signaling. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-107. doi:10.1158/1538-7445.AM2014-LB-107

In an attempt to combat the resistance of tumors to chemotherapy, we have already described at this year's AACR, the systematic evaluation of over 11,000 different compound combinations using the human cancer cell line encyclopedia. Correlations of synergy with genetic features were identified and some novel synergies discovered. Here, we describe the efficacy, tolerability and PK-PD of 23 different in vivo combinations selected from the screens to determine the predictability of in vitro screening, including two examples from the combination of a MEK1/2-inhibitor (MEK162) with the taxane, docetaxel. In vitro screens were conducted as previously described using 3-day viability assays, and inhibition of proliferation determined relative to untreated samples, and the degree of synergy scored using different types of analyses: Gaddum, Bliss, Loewe. Only those combinations showing synergy over a wide range of concentrations were chosen for in vivo study. For in vivo studies, cells were injected s.c. in the flank of athymic nude mice, and once tumors reached a mean size of at least 100 mm3 were treated for 2-4 weeks with the appropriate dose and schedule of the compounds either as monotherapy, or in combination. Efficacy and tolerability were determined at the endpoint using the T/CTVol and T/CBW respectively to derive a combination-index as previously described by Clarke (1997), where a negative-value (-CCI) indicated synergy. In most cases, PK-PD was also measured in plasma and tumour either at steady-state and/or the endpoint to study the mechanism of the interaction and to check for drug-drug interactions and their eventual impact on PD and efficacy. Thus far, we have studied in vivo 13 different molecular targets across 6 different histotypes to give 23 different combinations. No antagonism was seen in vivo (+CCI), and 19/23 were deemed synergistic (CCI ≤-0.1), of which 8 showed regression which was not seen with the individual monotherapies. Of the 4 combinations showing no interaction, 2 were predicted by the in vitro score and the other 2 showed negative drug-drug interactions. There were no significant correlations between the CCI and the different types of in vitro score (p>0.35), but perhaps more importantly, cut-offs could be identified suggesting synergy could be predicted (p≤0.02) although not the extent of the interaction. Several novel combinations were identified for clinical investigation, including MEK162 combined with docetaxel in KRAS-mutant NSCLC, which in two different models in vivo had a CCI≤ -0.1, with PD-analyses showing that cytotoxic doses of the taxane activated the MAPK-pathway which was blocked by the combination. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A140. Citation Format: Paul Martin J. Mcsheehy, Alex Cao, Giordi Caponigro, David Duhl, Brant Firestone, Tom Gesner, Daniel Guthy, Jocelyn Holash, Fred King, Joseph Lehar, Christopher Leroy, Manway Liu, Lilli Petruzzelli, Dale Porter, Daniel Menezes, Anupama Reddy, Johannes Roesel, Christian Schnell, Timothy Smith, Mark Stump, Markus Wartmann, Marion Wiesmann. Evaluation of prediction of in vivo activity from in vitro combinations: Examples using a MEK1/2 inhibitor combined with docetaxel in NSCLC models. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A140.

Novartis is undertaking a large-scale effort to comprehensively describe cancer through the lens of cell cultures and tissue samples. In collaboration with academic and industrial partners, we have generated mutation status, gene copy number, and gene expression data for a library of 1,000 cancer cell lines, representing most cancer lineages and common genetic backgrounds. Most of these cell lines have been tested for chemosensitivity against ~1,200 cancer-relevant compounds, and we are systematically exploring drug combinations for synergy against ~100 prioritized CCLE lines. We expect this large-scale campaign to enable efficient patient selection for clinical trials on existing cancer drugs, reveal many therapeutically promising drug synergies or anti-resistance combinations, and provide unprecedented detail on functional interactions between cancer signaling pathways. I will discuss early highlights of this work and describe our plans to make use of this resource. Citation Format: Joseph Lehar. Large-scale systematic explorations of single and combined drug responses to suggest anticancer drugs and overcome resistance. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr IA16.

Cancers are inherently robust biological systems with many compensation mechanisms and genetic redundancies allowing them to escape treatments and become resistant. Theoretically, such complex systems can become fragile when perturbed by multiple attacks. Here, we systematically tested whether pairwise combinations of compounds targeting critical oncogenic pathways in colorectal cancer cells can combine to inhibit their growth or even kill them. Some combinations targeting growth pathways showed synergies in a broad spectrum of genetic backgrounds even when the single agents had more specific effects. Guided by the pairwise combination screening, we identified higher-dimensional combinations that not only showed broad synergy but could also drive cytostatic combination responses into synthetic lethality. Citation Format: Thomas Horn, Xiaobin Zhang, Samuel Ho, Levi Garraway, Robert Schlegel, Giordano Caponigro, Joseph Lehar. High-dimensional combinations of cancer drugs to induce synthetic lethality in colorectal cancer. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr B18.

In recent years great advances have been made in developing targeted cancer therapeutics that produce dramatic responses in a subset of rationally selected patients. The initial breakthrough of the targeted design concept was established by the treatment of chronic myelogenous leukemia with Abl inhibitors and has been expanded to other cancer indications. As a consequence of this early success in CML, the identification and targeting of genetic lesions that confer cancer dependence has become an established strategy for drug discovery efforts. However, this approach has met with mixed degrees of success as confounding factors, such as tumor heterogeneity, have often resulted in partial responses and/or the emergence of resistance when targeted therapies were employed as single agents. To improve the therapeutic benefit in cancer, rationally-devised novel combinations of two or more agents are being explored clinically. To discover combinations that may be more effective therapies, an unbiased, systematic approach was used to identify drug combinations in vitro, using a panel of genetically diverse, and well characterized cell lines from the cancer cell line encyclopedia (CCLE: Barretina et al. Nature 2012). For three cancer indications, all pairwise combinations of 18 selected drugs (both novel inhibitors and standards of care) were tested as dose matrices in a proliferation assay. Synergistic interactions were scored using isobologram/Loewe's excess inhibition and synergistic concentration ranges for each agent were identified. However, the clinical translation of positive combinations from in vitro matrix-based screens into clinically-relevant doses and schedules are challenging, due to host biology, tumor-stroma interactions, and the pharmacokinetic and pharmacodynamics of drug delivery. To explore this higher complexity, we evaluated the in vitro to in vivo translation of drug synergies in immune-compromised mouse tumor xenograft models. To recapitulate the pharmacological combination effects in vivo, mouse pharmacokinetic data and simulation was used to determine single agent doses that would result in the desired compound plasma concentration range and duration of action. Pharmakokinetics, pharmacodynamics, antitumor activity and tolerability of the combinations were then tested in tumor-bearing mice. Observed combination effects in vivo could in some cases be explained by either the expected biological pathway interactions or partially by physiological effects relating to drug-drug interactions. Citation Format: Marion Wiesmann, Mark Stump, Giordano Caponigro, David Duhl, Brant Firestone, Tom Gesner, Bjoern Gruenenfelder, Daniel Alexander Guthy, Jocelyn Holash, Fred King, Joseph Lehar, Christophe Leroy, Manway Liu, Lilli Petruzelli, Dale Porter, Paul McSheehy, Daniel Menezes, Anupama Reddy, Johannes Roesel, Christian Schnell, Timothy R. Smith, Markus Wartmann. Systematic evaluation of drug combinations in vitro and in vivo. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2058. doi:10.1158/1538-7445.AM2013-2058

Defining predictive biomarkers and deriving appropriate patient populations for treatment are key areas of interest in the clinical development of phosphatidyl-inositol-3-kinase (PI3K) inhibitors, but currently most clinical trials do not pre-select patients using defined genetic markers. Pre-clinical studies indicated previously that in vitro sensitivity to BYL719, a potent and selective small molecule inhibitor of the α-isoform of PI3K, was increased in cell lines derived from a subset of indications, such as HER2-positive or luminal breast cancer. Moreover, BYL719 sensitivity in vitro was positively correlated with PIK3CA mutation or amplification and HER2 amplification. PTEN or BRAF mutation, on the other hand, was associated with BYL719 insensitivity. Consequently, presence of an activating PIK3CA alteration in the tumor was a pre-requisite for patients to enroll into the BYL719 first-in-human (FIM) clinical trial. In this study, we applied massive parallel sequencing to tumor specimens from patients enrolled in the BYL719 FIM trial to comprehensively assess the genetic determinants of clinical sensitivity to BYL719. Paraffin-embedded formalin-fixed archival or pre-treatment tumor specimens were obtained from 44 patients with advanced solid tumors upon enrolment into the clinical trial. DNA was isolated and then assayed by massive parallel sequencing covering either a panel of 182 cancer-related genes at Foundation Medicine or the whole exome at the Broad Institute. Mutations were identified by focusing on genes with alterations in the COSMIC data base and excluding germline variants and were subsequently correlated with clinical benefit as defined by stable disease ≥ 4 months, tumor shrinkage ≥ 20% or objective tumor response to BYL719 using a two-tailed Fisher's exact test. Focusing on a panel of 51 genes with known relevance to PI3K signaling, we observed that all pre-treatment tumor samples harbored at least one genetic abnormality and that each of the genes was altered in one or several tumor samples. TP53 (59%), APC (25%), KRAS (23%) and PTEN (18%) were the most frequently altered in addition to PIK3CA. Alterations in TP53 and KRAS were identified as statistically significant negative predictors of BYL719 sensitivity (p < 0.05), while mutations in the APC and PTEN genes were associated with a trend towards lack of benefit from treatment with BYL719. Our results suggest that definition of the genetic biomarkers that govern sensitivity to treatment with BYL719 and–most likely–other PI3Kα inhibitors in the clinic is possible upon analysis of patient's tumors by massive parallel sequencing. We also demonstrate that it is feasible to routinely obtain this information in the clinical trial setting. This implies that exploration of the genetic determinants driving sensitivity or primary resistance to targeted agents in general can now be undertaken clinically. Citation Format: Jordi Rodon, Dejan Juric, Ana-Maria Gonzalez-Angulo, Johanna Bendell, Jordan Berlin, Douglas Bootle, Kathlene Gravelin, Alan Huang, Adnan Derti, Joseph Lehar, Jens Würthner, Markus Boehm, Eliezer van Allen, Nikhil Wagle, Levi A. Garraway, Roman Yelensky, Philip J. Stephens, Vincent A. Miller, Robert Schlegel, Cornelia Quadt, José Baselga. Towards defining the genetic framework for clinical response to treatment with BYL719, a PI3Kalpha-specific inhibitor. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-65. doi:10.1158/1538-7445.AM2013-LB-65

The Cancer Cell Line Encyclopedia (CCLE) represents a collaborative effort to assemble a comprehensive resource of human cancer models for basic and translational research. It contains a detailed genetic profiling of approximately 1,000 human cancer cell lines spanning many tumor types. Thus far, high-density SNP array data, gene expression microarray data and mutation data from hybrid capture sequencing of 1,650 cancer genes has been obtained. Additionally, we have assessed the sensitivity of these same cell lines using a series of pharmacological compounds that represent both conventional cytotoxic and targeted agents. On major goal of the CCLE effort involves systematic integration of the genomic and pharmacologic datasets in order to identify putative targets of prevalent genetic alterations as well as predictors and modifiers of pharmacologic sensitivity and resistance. The availability of high-quality data generated by uniform criteria across hundreds of cell lines markedly enhances the statistical power to discover genetic alterations involved in carcinogenesis and molecular predictors of pharmacologic vulnerability. We developed a framework based on an elastic net machine-learning regression algorithm, and combined with a bootstrapping procedure, to derive predictive models of the sensitivity to each compound, using all genetic features of the cell lines in the collection. Through this computational prediction approach, we have both rediscovered molecular features predicting response to most drugs in our set but also uncovered novel potential biomarkers of sensitivity and resistance to targeted agents and chemotherapy drugs. For instance, we have found that response to topoisomerase 1 inhibitors seems to be linked to the expression of a single gene. We have also observed that tissue lineage is a key predictor for sensitivity to certain compounds, providing rationale for clinical trials of these drugs in particular cancer types and we identified potential stratifiers for existing EGFR targeted therapies. Finally, we have found an additional target for a certain chemotype of MEK inhibitors, and shown that this interaction was responsible for growth suppression, which might be a new indication for this kind of drug. Our cell line-based platform provides a valuable tool for the development of personalized cancer medicine, revealing critical tumor dependencies and helping to stratify patients for clinical trials. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5114. doi:1538-7445.AM2012-5114

NVP-BYL719 is a p110α isoform-specific small molecule inhibitor that is currently in a Phase I clinical trial. To identify cancer populations that most likely to respond to BYL719 treatment, we initiated a comprehensive in vitro pharmacologic profiling screen across a large panel of cancer cell lines that have been previously characterized at the molecular level as part of the Cancer Cell Line Encyclopedia (CCLE) effort. We found that BYL719 responsive cell lines are enriched in indications such as Her2 positive and luminal breast cancer, while lacking in other indications such as Glioblastoma and Melanoma. Further exploration of the underlying genetic and pathway aberrations revealed that BYL719 sensitivity is positively associated with PIK3CA mutation, ERBB2 amplification and PIK3CA amplification/copy number gain. PTEN and BRAF mutations on the other hand, are associated with BYL719 insensitivities. KRAS mutation alone is neither associated with enhanced sensitivity nor insensitivity, however, co-commitant PIK3CA and KRAS mutants are more likely to be insensitive to BYL719 treatment. These findings will help to guide our clinical development plan. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3749. doi:1538-7445.AM2012-3749

We describe an integrative analysis approach leveraging the publicly available Cancer Cell Line Encyclopedia to uncover regulatory pathways in cancer. Six different lines of evidence are considered in identifying candidate factors associated with disease: 1) gene mutation status, 2) copy number variations, 3) RNAi growth inhibitions, 4) pharmacological growth inhibitions, 5) baseline gene expression data, and 6) physical protein-protein interactions. As each of these evidence sources measure a different aspect of cellular regulation, they may be considered in combination to uncover regulatory mechanisms associated with tumorigenesis. For example, gene mutation status or copy number variation data may be combined with baseline gene expressions to identify genes that are differentially expressed in cell lines with aberrant or deactivated versions of specific tumor suppressors and oncogenes. To assess our approach, we applied it to identification of genes involved in the well-known RAS-RAF regulatory signaling pathway in both tissuespecific and nonspecific contexts. The RAS-RAF signaling pathway plays a key role in cellular differentiation and proliferation and is frequently associated with tumorigenesis. Specifically, we identified differentially expressed genes in BRAF mutant vs. wild-type cell lines, genes associated with high and low RAF copy number alteration cell lines, genes correlating in expression to growth inhibition by RAF-targeting compounds, and genes that either rescue or sensitize cell lines to BRAF knockdown by RNA interference. We further considered these genes within the overall context of physical interactions between their protein products. Highlighting the significant, research potential of our approach, our RAF-focused analysis identified both well-known and novel genes, supported by different lines of evidence, that mediate RAS-RAF signaling across multiple tissues. Positive control genes include members of the classical pathway, such as MAPK1, MAP2K1, and PI3K. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the Second AACR International Conference on Frontiers in Basic Cancer Research; 2011 Sep 14-18; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2011;71(18 Suppl):Abstract nr C70.

Constitutive activation of the PI3K pathway is driven by a variety of genetic and epigenetic abnormalities, such as PTEN deletions, mutations, genesilencing, PIK3CA mutations and/or enhanced engagement through genetic abnormalities in upstream RTKs. Targeted therapies against specific components of the PI3K pathway are therefore expected to produce positive outcome as single agents or in combination in cancers presenting these abnormalities. Two molecular entities are in full clinical development: NVP-BEZ235, a dual PI3K/mTOR inhibitor and NVP-BKM120, a pan-class I PI3K inhibitor without mTOR activity. In order to best position these agents clinically, we investigated their efficacy in models bearing PIK3CA, PTEN, HER2, or KRAS genetic alterations. We previously demonstrated that NVP-BEZ235 in in vitro antiproferative activity was not predictive of preclinical antitumor response, in vivo. However, breast cancer models presenting either HER2 amplification or PIK3CA mutations or both were shown to be very sensitive to the compound and the use of cell death induction as an end-point was found to be predictive of strong antitumor activity in vivo. To further validate this finding, dual PI3K/mTOR targeting was phenocopied using an mTOR shRNA inducible system or an mTOR specific inhibitor (AZD8055) in combination with a pan-PI3K inhibitor, in various tumor models. Strong cell death signals were observed in 2 HER2 amplified / PIK3CA mutant breast cancer models but not in KRAS mutant cells or as PTEN silenced breast cancer cell lines, when subjected of PI3K/mTOR targeting. Moreover, in vivo, combination of a pure pan-PI3K with a pure mTOR inhibitor resulted in strong regression of HER2 amplified /PIK3CA mutant MDA-MB361 tumors.On the basis of these observations, NVP-BEZ235 was retested in a set of more then 150 tumor lines across various lineages, and cell death induction was determined. As expected, HER2 amplified /PIK3CA mutant breast cancer lines scored as sensitive. Other models from different indications and genetic background were also found to be sensitive to NVP-BEZ235. Validation studies of these new models and potential indications will be presented, and implications for further clinical trials of dual PI3K/mTOR or pure PI3K inhibitors discussed. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 579. doi:10.1158/1538-7445.AM2011-579

Accurate prediction of patient drug response is required for successful personalized medicine. Cancer is a disease resulting from myriad genetic alterations. Accordingly, it should be possible to predict drug response of specific cancers using genetic and molecular signatures. To aid this effort, an ongoing collaboration established between Novartis and the Broad Institute called the Cancer Cell Line Encyclopedia (CCLE) has (i) comprehensively characterized genome-scale mRNA expression, copy number alteration and mutation profiles for nearly 1000 cancer cell line models spanning many tumor types and (ii) profiled ~500 of these cell lines against ~1000 anticancer compounds. Using these data, we are developing a scalable, extensible predictive modeling framework based on various supervised learning methods that include both categorical classification and linear regression approaches to predict compound sensitivity. By cross-validation of the generated models against test data sets, we quantitatively estimated model performance. Our initial results validate several preexisting genetic predictors of sensitivity. In addition, our models reported 70% or higher sensitivity and specificity for a number of compounds and yielded interesting potentially novel predictive features that, in some cases, outperform previously existing genetic predictors of sensitivity. Our integrative approach demonstrates that pharmacological profiling of large, genomically annotated cancer model systems, coupled with systematic predictive modeling, can uncover novel predictors of drug response and may ultimately aid patient stratification. This talk is also presented as Poster A4.