Mark Tomilo

Abstract 2897: Discovery and characterization of driver MAPK and PI3K pathway mutations in tumors and association with drug response in cell lines.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC The MAPK and PI3K pathways are frequently altered in human cancer and are targeted by dozens of agents in clinical trials. The successful application of these therapies, alone or in combination, may depend on the activation status of both pathways. Next-generation sequencing of cancer exomes provides a unique opportunity to systematically survey pathway alterations in cancer. Using somatic mutation data obtained from The Cancer Genome Atlas, we sought to catalog the members of the MAPK and PI3K pathways with driver mutations, the frequency of occurrence in common cancers and the frequency of co-occurrence. Furthermore, we sought to characterize the association of pathway mutation status with drug response in pre-clinical models. While the MAPK and PI3K pathways were frequently altered, the frequency of single and dual pathway alteration and the altered genes varied substantially across cancer types. The MAPK pathway was most frequently altered in rectal (62%), colon (59%), uterine (31%) and lung adenocarcinoma (45%) but infrequently altered in and breast cancer (4%). KRAS, BRAF and NRAS hotspot mutations were the most common pathway drivers, along with NF1 deleterious mutations in certain cancer types. The PI3K pathway was most frequently altered in uterine (84%), breast (40%) and glioblastoma (41%) but was rarely altered in lung adenocarcinoma (9%). Hotspot mutations in PIK3CA and hotspot and deleterious mutations in PTEN were the most common pathway alterations. In addition, predicted driver mutations occurred in PIK3R1, PIK3R3, MTOR, AKT1 and AKT3. Notably, MAPK and PI3K pathway alterations co-occurred in uterine (30%), colon (17%) and gastric (12%) cancers more so than would be expected by chance (p < 0.02). In contrast, other cancer types favored one pathway almost exclusively and thus had little co-occurrence. For example, breast cancer significantly favored PI3K pathway whereas lung adenocarcinoma favored MAPK pathway. To assess the effect of pathway mutation status on treatment response, we integrated hybrid-capture sequencing data from the Cancer Cell Line Encyclopedia with pharmacological data from over 150 compounds. We found that MAPK and PI3K pathway mutations most significantly associated with sensitivity to MEK and PI3K/AKT/mTOR inhibitors, respectively. Notably though, cell lines with co-occurring MAPK pathway and PIK3CA mutations were insensitive to MEK inhibitors and cell lines with co-occurring PI3K pathway and KRAS mutations were insensitive to PI3K inhibitors. Also, not all pathway mutations conferred equal sensitivity. For example, BRAF mutants were generally sensitive, KRAS mutants were mixed and NF1 mutants were generally insensitive to MEK inhibitors. Taken together, our work highlights the need to consider pathways and co-occurrence in the development of targeted therapies. Citation Format: Mark Tomilo, Paul D. Williams, Emma T. Bowden, Supra R. Gajjala, Santhoshi Bandla, Sean F. Eddy, Seth E. Sadis, Peter J. Wyngaard, Nickolay A. Khazanov, Daniel R. Rhodes. Discovery and characterization of driver MAPK and PI3K pathway mutations in tumors and association with drug response in cell lines. [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 2897. doi:10.1158/1538-7445.AM2013-2897

Abstract 3665: An EMT gene expression diagnostic predicts resistance to EGFR and MEK-targeted therapies in cell lines and patients

Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL The epithelial to mesenchymal transition (EMT) in cancer cells results in the acquisition of metastatic properties and may contribute to chemoresistance. Several studies have shown that transition to a mesenchymal phenotype leads to decreased dependence on EGFR-RAS signaling and insensitivity to EGFR inhibitors. To better understand the importance of EMT as a general predictor of drug response, we defined an EMT gene signature derived from a meta-analysis of differential gene expression signatures representing genes up-regulated following transfection of breast cell lines with various EMT regulators (Taube et al., 2010 Proc Natl Acad Sci USA 107:15449-54). We then determined the expression of the EMT signature across cell line panels and determined whether it predicted sensitivity or resistance to various targeted therapies. Consistent with previous results, expression of EMT signature was significantly associated with resistance to an EGFR inhibitor, lapatinib. Similarly, the EMT signature also predicted resistance to PQIP (IGF1R), GSK1120212 (MEK), GSK690693 (AKT), and perifosine (AKT/PI3K), suggesting that EMT may be a common resistance mechanism to a number of drugs that target growth factor signaling. As more of these targeted agents are entering clinical trials, the ability to characterize the signature may have important implications for drug development. To study the relevance of the EMT signature in clinical tumors, we compared the signature to a collection of tumor co-expression patterns, known as OncoScore modules, which were defined from 40,000+ tumor microarray experiments. Notably, the EMT signature was significantly associated with a major tumor co-expression pattern representing mesenchymal and/or stromal phenotype observed in almost all major solid tumor types. In retrospective microarray scoring analyses of key clinical datasets, the mesenchymal/stromal module predicted resistance to cetuximab. This finding was validated with an independent cohort of colorectal cancer patients treated with cetuximab using the Oncoscore Colon diagnostic. Oncoscore Colon is a qPCR test optimized for formalin-fixed paraffin-embedded tissue that measures the twelve key colon cancer transcriptional modules, including the mesenchymal module. Because the mesenchymal/stromal module monitors a fundamental phenotype of cancer cells important for drug response, this validated qPCR test has broad application to companion diagnostics development and personalized medicine. 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 3665. doi:1538-7445.AM2012-3665

Abstract 2779: Rapid drug target ranking system developed from a systematic analysis of cancer genomic data from the Oncomine™ knowledgebase identifies an oncogenic role for the NFE2L2 pathway in multiple cancer types

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Late stage drug attrition rates in oncology remain higher than other therapeutic areas. To reduce attrition, it is critical to identify appropriate drug targets and pre-clinical models. Next-generation sequencing (NGS) is poised to accelerate the discovery of new drug targets through identification of genomic markers of response and identification of models to characterize candidate drivers. To maximize the value of NGS, it is imperative to develop data analysis/interpretation solutions that accurately assess genomic aberrations, delineate driver alterations from passengers, annotate alterations for clinical relevance and integrate alterations by gene and pathway. Here, we present our framework for the systematic analysis of thousands of clinical NGS samples as well as expertly curated oncology data for the purpose of identifying candidate drug targets. Our methodology was developed through a systematic interrogation of genomic aberrations in a training set of gold standard oncogenes such as EGFR and PIK3CA, and tumor suppressors such as TP53 and PTEN. The resulting platform was used to rank genes through an assessment of driver genomic aberrations, associations with patient survival, and potential clinical actionability. Using this framework, we found supporting evidence implicating NFE2L2 as an oncogene. Recurrent NFE2L2 mutations were found in multiple cancer types and associated with poor outcome in head and neck squamous cell carcinoma. Recurrent mutations were identified in: 14.0% of squamous cell lung carcinoma, 12.5% of hepatocellular carcinoma, 11.1% of infiltrating bladder urothelial carcinoma, 7.7% of cervical squamous carcinoma, and 4% of head and neck squamous cell carcinoma. We also investigated KEAP1, a repressor of NFE2L2 activity. Mutations in KEAP1 tended to localize within the NFE2L2 binding domains and displayed evidence of mutual exclusivity with NFE2L2 recurrent mutations. KEAP1 mutations were identified in 14% of lung adenocarcinoma, 13.5% of squamous cell lung carcinoma, 7.4% of infiltrating bladder urothelial carcinoma, and 4% of hepatocellular carcinoma. Genes up-regulated in patients with mutations in either NFE2L2 or KEAP1 in squamous cell lung carcinoma included many direct NFE2L2 target genes (e.g. GCLC, GCLM, NQO1). Genes up-regulated in NFE2L2 or KEAP1 mutant patients significantly associated with genes up-regulated in cell lines resistant to chemotherapy. Using Oncomine™ database curated data and cell line exome data we were able to identify cell lines that were representative of clinical populations containing these mutations. We have provided proof of concept identification of a potentially clinically relevant candidate driver gene using a novel systematic analysis of cancer genomic data. Citation Format: Sean Eddy, Mary Ellen Urick, Mark Tomilo, Armand Bankhead, Dan Rhodes, Emma T. Bowden. Rapid drug target ranking system developed from a systematic analysis of cancer genomic data from the Oncomine™ knowledgebase identifies an oncogenic role for the NFE2L2 pathway in multiple cancer types. [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 2779. doi:10.1158/1538-7445.AM2014-2779

Abstract C256: Expanded clinical opportunities for crizotinib from an analysis of over 5,000 cancer patient exomes.

Patients with chromosomal rearrangements resulting in fusion proteins are amongst the most responsive to targeted therapy. For example, targeting of the BCR-ABL fusion in chronic myelogenous leukemia (CML) with imatinib and the EML4-ALK fusion in non-small cell lung cancer (NSCLC) with crizotinib has led to dramatic patient responses in these diseases. While crizotinib is approved for use in EML4-ALK positive NSCLC through its inhibition of ALK, the drug also inhibits ROS1, MST1R (RON), MET, and more recently has been shown to inhibit the ALK homolog, LTK. To gain a more comprehensive understanding of the full therapeutic potential of crizotinib, we undertook a genomic survey of ALK, LTK, ROS1, MET and MST1R across thousands of patients subjected to full exome sequencing including patients from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), as well as tens thousands of patients from Oncomine® databases. We confirmed the presence of EML4-ALK fusions in both lung and colorectal cancer (CRC), identified a PRKAR1A-ALK fusion in CRC, and found evidence of novel recurrent ALK fusions in kidney papillary renal cell carcinoma and thyroid gland carcinoma. ALK hotspot mutations and focal amplifications were confined to neuroblastoma, as previously described. We also report the first instance of an LTK fusion, identified in thyroid gland carcinoma. LTK amplifications were also observed in 1.4% of gastric cancers and rarely in medulloblastoma and breast cancer. LTK was prominently over-expressed in leukemia, and in an analysis of over 4,000 PML-RARA fusion positive leukemia patients, LTK was amongst the most significantly over-expressed genes. In addition to confirming previously published ROS1 fusions, our survey of ROS1 identified rare novel fusions in NSCLC and glioblastoma. High-level MET amplifications were observed in 1-5% of papillary renal cell carcinoma, the intestinal subtype of gastric adenocarcinoma, oligodendroglioma, glioblastoma and lung adenocarcinoma. Hotspot mutations in MET were frequently observed in head and neck squamous cell carcinoma (HNSCC) (11%), and observed in a third of metastatic HNSCC samples. Additional hotspot mutations were also observed in lung adenocarcinoma (2%) and small cell lung cancer (2%). Aberrations in MST1R were rare. These results leverage all available genomic profiling data to provide a broadened scope of therapeutic opportunity for inhibitors like crizotinib. With the growing availability of next-generation sequencing data and analyses, such surveys can support hypothesis-driven development of targeted therapies and help expand opportunities for clinical stage therapies. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):C256. Citation Format: Sean Eddy, Mark Tomilo, Mary Ellen Urick, Nickolay A. Khazanov, Paul Williams, Armand Bankhead, Dinesh Cyanam, Supra Gajjala, Peter Wyngaard, Emma Bowden, Dan R. Rhodes. Expanded clinical opportunities for crizotinib from an analysis of over 5,000 cancer patient exomes. [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 C256.

Abstract A19: A qPCR assay, OncoScore Colon, predicts resistance to cetuximab in formalin-fixed, paraffin-embedded colorectal cancer tissue independent of KRAS status

Gene expression modules derived from an unsupervised analysis of 20 independent microarray datasets comprising more than 2,000 colorectal cancer patients were identified. Each module represents a set of highly co-expressed genes related to an important aspect of underlying cancer variability. Modules containing genes related to epithelial and mesenchymal biology associated with sensitivity and resistance to EGFR family targeted inhibitors (gefitinib and lapatinib), respectively. In retrospective analysis of clinical samples, the epithelial-mesenchymal axis associated with cetuximab response in two independent patient cohorts. The first study was a Phase II clinical trial (Khambata-Ford et al., J Clin Oncol, 2007) with accompanying microarray data from pre-treatment metastatic colorectal tumor biopsies. Expression of the modules was determined by normalizing and averaging co-expressed module genes. Patients with a more epithelial and less mesenchymal module expression profile were enriched for cetuximab response. An independent cohort of patients was analyzed using module scores that were generated from a qPCR gene expression module test, OncoScore™ Colon, which quantifies modules by averaging three representative module genes relative to housekeeping genes using formalin-fixed-paraffin-embedded primary tumor samples. In these patients, presence of the mesenchymal module was significantly associated with a decrease in progression free survival. Notably, the status of the mesenchymal module was independent of KRAS mutation status—as KRAS mutations occurred in both mesenchymal module-positive and -negative patients. Further clinical studies are ongoing to continue to support the development of the OncoScore™ Colon assay and to further test the predictive capacity of the module with regards to cetuximab resistance and other MAPK pathway inhibitors. This study demonstrates the value of a gene expression module-based qPCR panel for stratifying colorectal cancer patients for treatment response, and suggests that our approach may have immediate utility for cetuximab treatment response prediction.

Abstract 3149: OncoPredictor: A systematic approach for predicting responsive cancer populations from large scale cell line screening

Investigational new drugs for cancer must demonstrate convincing preclinical efficacy and a compelling strategy to translate preclinical observations to the clinical setting. Personalized medicine approaches are gaining wider acceptance, and large scale cell line databases have demonstrated utility in identifying biomarkers of drug response that can inform clinical development strategies. However, the lack of an integrated platform to translate preclinical biomarker profiles to clinical populations limits the power of this approach. To solve this problem we developed, first, a cell line screening and genomic analysis pipeline that associates drug response across 200+ cell lines with mutation, DNA copy number, and gene expression biomarkers; and second, a parallel database of biomarker frequencies in clinical tumor samples, compiled from all available published genomic data. In the present study, we tested 8 targeted anti-cancer agents and identified cell line biomarkers representing each of the genomic data types – mutation, DNA amplification, and gene over-expression – and then assessed the distribution of these biomarkers across tumor samples. In each case the tumor populations predicted to be responsive by this unsupervised approach were validated by results from clinical trials. We also present an example of biomarker results leading to potential new indications for an approved drug. 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 3149. doi:10.1158/1538-7445.AM2011-3149

Abstract B13: OncoPredictor: An integrative genomics platform to systematically predict responsive tumor populations from in vitro drug response data

A key challenge in drug development is identifying clinically relevant genomic biomarkers from preclinical data. Here, we developed OncoPredictor, a cellular screening and bioinformatics platform to (1) evaluate multiple types of genomic biomarkers for association with in vitro drug response and (2) analyze identified biomarkers in clinical tumor populations, thereby suggesting potential drug development strategies. We examined data for 18 targeted, anticancer agents tested across more than 200 cancer cell lines for which detailed genomic data were collected. To support association analysis of drug response and genomic biomarkers, a cell line biomarker catalog was constructed including gene and pathway mutations, copy number aberrations and gene expression changes, in addition to tumor-derived gene expression molecular subtypes. Similarly, to support the rapid translation of in vitro biomarkers to clinical tumor populations, a tumor biomarker catalog was developed, characterizing the frequency of biomarkers across the Oncomine database, which includes more than 40,000 genomic profiles from tumor specimens. Notably, our analysis uncovered several known and novel drug- biomarker-tumor population associations providing a series of cancer treatment hypotheses. For example, within Ras/Raf mutant cell lines, overexpression of a novel biomarker associated with sensitivity to CI-1040, a MEK inhibitor, and identified a sub-population of Ras/Raf mutant colorectal cancer and melanoma that may be more likely to benefit from MEK inhibitors. Similarly, we showed that a growth factor activation network associated with a dramatic response to sunitinib and identified a fraction of glioblastoma and particular types of sarcoma with activation, suggesting that these indications may benefit from a tumor intrinsic response to sunitinib. Finally, for everolimus, an mTOR inhibitor, we found that a gene expression-based molecular subtype of breast cancer were particularly sensitive and demonstrated that the while subtype-positive breast cancers have a good prognosis, 10% of breast cancers that metastasize are subtype-positive, suggesting a defined clinical population that could benefit from mTOR inhibitors. In summary, our platform provides a comprehensive and clinically relevant framework to discover and apply genomic biomarkers of drug response. Citation Information: Clin Cancer Res 2010;16(14 Suppl):B13.