Peter Wyngaard

Identification of targetable FGFR gene fusions in diverse cancers.

Through a prospective clinical sequencing program for advanced cancers, four index cases were identified which harbor gene rearrangements of FGFR2, including patients with cholangiocarcinoma, breast cancer, and prostate cancer. After extending our assessment of FGFR rearrangements across multiple tumor cohorts, we identified additional FGFR fusions with intact kinase domains in lung squamous cell cancer, bladder cancer, thyroid cancer, oral cancer, glioblastoma, and head and neck squamous cell cancer. All FGFR fusion partners tested exhibit oligomerization capability, suggesting a shared mode of kinase activation. Overexpression of FGFR fusion proteins induced cell proliferation. Two bladder cancer cell lines that harbor FGFR3 fusion proteins exhibited enhanced susceptibility to pharmacologic inhibition in vitro and in vivo. Because of the combinatorial possibilities of FGFR family fusion to a variety of oligomerization partners, clinical sequencing efforts, which incorporate transcriptome analysis for gene fusions, are poised to identify rare, targetable FGFR fusions across diverse cancer types.

Development and Validation of a Scalable Next-Generation Sequencing System for Assessing Relevant Somatic Variants in Solid Tumors

Next-generation sequencing (NGS) has enabled genome-wide personalized oncology efforts at centers and companies with the specialty expertise and infrastructure required to identify and prioritize actionable variants. Such approaches are not scalable, preventing widespread adoption. Likewise, most targeted NGS approaches fail to assess key relevant genomic alteration classes. To address these challenges, we predefined the catalog of relevant solid tumor somatic genome variants (gain-of-function or loss-of-function mutations, high-level copy number alterations, and gene fusions) through comprehensive bioinformatics analysis of >700,000 samples. To detect these variants, we developed the Oncomine Comprehensive Panel (OCP), an integrative NGS-based assay [compatible with < 20 ng of DNA/RNA from formalin-fixed paraffin-embedded (FFPE) tissues], coupled with an informatics pipeline to specifically identify relevant predefined variants and created a knowledge base of related potential treatments, current practice guidelines, and open clinical trials. We validated OCP using molecular standards and more than 300 FFPE tumor samples, achieving >95% accuracy for KRAS, epidermal growth factor receptor, and BRAF mutation detection as well as for ALK and TMPRSS2:ERG gene fusions. Associating positive variants with potential targeted treatments demonstrated that 6% to 42% of profiled samples (depending on cancer type) harbored alterations beyond routine molecular testing that were associated with approved or guideline-referenced therapies. As a translational research tool, OCP identified adaptive CTNNB1 amplifications/mutations in treated prostate cancers. Through predefining somatic variants in solid tumors and compiling associated potential treatment strategies, OCP represents a simplified, broadly applicable targeted NGS system with the potential to advance precision oncology efforts.

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 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 3664: Breast cancer companion diagnostic platform based on objectively defined tumor co-expression patterns stratifies multiple clinical and therapeutic endpoints comparison to existing molecular subtyping definitions

Gene expression profiles of human breast tumors have greatly expanded our understanding of the genes and pathways that underlie breast cancer. Profiling studies have also supported a molecular classification of breast cancer. The resulting molecular subtypes Luminal, Basal-like, ERBB2+, and Normal-like were shown to have different prognostic and predictive characteristics. Related studies have led to a proliferation of multigene prognostic and predictive diagnostic tests. Two independent multigene tests, OncoType Dx and MammaPrint, have been shown to be helpful in predicting the risk of recurrence of patients with early stage breast cancer. Current multigene tests consistently prioritize the proliferation, estrogen receptor (ER), and ERBB2 pathways. An alternative approach to identifying key molecular variables within breast cancer is based on a definition of objectively defined tumor co-expression patterns. To this end, we defined co-expression patterns within 56 independent breast cancer molecular profiling datasets representing >5,000 unique patients. We then performed a meta-analysis across datasets to define the most robust, consistently occurring co-expression patterns. These patterns, termed modules, recapitulate the proliferation, ER, and ERBB2 pathways, but also monitor expression of other important variables including core cancer cell growth pathways, immune signaling and microenvironment, and hallmark genomic aberrations. An important feature of co-expression patterns is that a small number of genes serve as an effective surrogate for each module. Thus, we created a single multigene qPCR test that measures the expression of 18 distinct breast cancer modules and validated the test for use with formalin-fixed paraffin-embedded (FFPE) tumor samples. In retrospective microarray scoring analyses with key clinical datasets, and with analysis of FFPE specimens from breast cancer cohorts, we demonstrate that breast cancer modules can be used to recapitulate the molecular subtypes of breast cancer and to have prognostic and predictive properties similar to the current multigene tests. Because they recapitulate existing molecular tests, while also reading out many additional axes of molecular variability, breast cancer modules provide a universal assay with broad application to companion diagnostics development. 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 3664. doi:1538-7445.AM2012-3664

Abstract CN07-02: Target discovery with Oncomine: Discovering new targets and positioning old targets by integrative analysis of 10,000s of tumor genomes and transcriptomes.

The identification of oncogenic protein kinases in cancer has prompted the development of several novel targeted therapies. In many cases, such therapies have elicited dramatic clinical responses in patients harboring genetic activation of the target. To identify novel targets and additional opportunities to apply existing targeted therapies, we undertook a systematic analysis of somatic mutations and DNA copy number alterations across 10,000+ of cancer patients from The Cancer Genome Atlas (TCGA), COSMIC and Oncomine™ databases. We applied multiple analysis strategies to filter genetic alterations for those most likely to exert a ‘driver’ oncogenic effect. For somatic mutation data, we prioritized likely oncogenes as those with frequent hotspot mutations and infrequent nonsense mutations. For copy number data, we prioritized likely oncogenes as those undergoing frequent, high-level focal amplifications resulting in concomitant over-expression in amplified cases. To identify additional opportunities to apply existing targeted therapies, we characterized 22 specific kinases that are targeted by approved or investigational therapies. Among the 22 kinases, nearly all showed evidence for recurrent hotspot mutations or high-level focal amplifications in at least a small fraction of tumors. This objective analysis confirmed well known associations between genetic alterations and particular cancer types, but also identified several novel associations, usually in small sub-populations, suggesting additional indications for existing therapies. In addition to this focused analysis, we also conducted a genome-wide survey of all genes with evidence for oncogenic alterations and the cancer types with most frequent alterations. While most of the top hits correspond to well-known oncogenes, several novel candidate oncogenes were prioritized. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr CN07-02.

Identification of breast cancer genomic subtypes that associate with prognosis and response to therapy in retrospective analyses.

e21151 Background: Gene expression patterns in breast cancer can be used to stratify patients based on prognosis and response to therapy, but are impractical clinically because each test requires its own patient tumor sample. We sought to develop a method for identifying all major breast cancer subtypes in a single formalin fixed paraffin embedded (FFPE) reverse transcriptase polymerase chain reaction (RT-PCR) assay for use in the development of gene expression companion diagnostic assays. METHODS A co-expression meta-analysis on 5,339 breast cancer samples from Oncomine identified highly co-expressed sets of genes (modules) across multiple breast cancer microarray datasets, with each module consisting on average of 450 genes (range 11 - 962). These modules represented expected subclasses (e.g., basal, luminal A, luminal B), as well as additional subclasses (e.g., immune response, proliferation). Restriction of each module to fewer genes (3-5) was accomplished by initially characterizing 384 candidates in an FFPE RT-PCR assay, from which 96 genes were selected. The approach was tested on 65 FFPE samples with known histological parameters such as ER, PR, and HER2. Finally, we asked whether the patterns of module expression in retrospective studies match in expected ways with prognosis and drug response. RESULTS We demonstrate that this single test of 96 genes accurately identifies each of the individual modules, that different module combinations define molecular subtypes with greatly increased resolution over previous approaches, and that standard parameters such as ER, PR and HER2 are accurately identified using this approach. Specific data on the association of modules with prediction of overall survival, neoadjuvant chemotherapy response, and in-vitro sensitivity to MEK and PI3K inhibitors will be presented. CONCLUSIONS A broad range of breast cancer heterogeneity on both gene expression and chromosomal amplification events can be summarized by combinations of core modules represented by 96 gene expression measurements. Multiplex RT-PCR assays capable of measuring these modules are expected to have broad application in the development of companion diagnostics.

Association of gene expression module biomarkers with clinical and therapeutic endpoints and their use with a universal companion diagnostic assay.

228 Background: Gene expression patterns are increasingly capable of stratifying patients based on prognosis and response to therapy. Given the limited availability of sample tissue, however, it is not feasible to utilize every test for every patient, suggesting the need for a universal companion diagnostic assay that is informative with respect to multiple clinical and therapeutic endpoints. Key challenges are identification of appropriate gene expression biomarkers, translation of biomarkers to clinical assays, and development of reliable gene expression profiling of formalin-fixed clinical specimens. Here we describe a novel RT-PCR biomarker assay optimized for FFPE clinical samples that has broad prognostic and predictive potential. METHODS A co-expression meta-analysis of 5,339 breast tumors from 56 microarray datasets identified highly co-expressed sets of genes (modules) across multiple datasets. Module biomarkers were tested for their ability to associate with prognostic and predictive targets in published datasets. In addition, each module was reduced from 10-1000 genes to 2-3 genes for use in companion diagnostic assays based on degree of co-expression across the meta-analysis, and validated against an independent panel of tumor samples. RESULTS This study demonstrates that a single test utilizing multiple module biomarkers is informative with respect to standard parameters such as ER, PR and Her2, and in addition reproduces existing prognostic and predictive genomic signatures. Furthermore, we show that modules of 10-1000 genes can be represented by 2-3 genes for direct use in companion diagnostics development. CONCLUSIONS The molecular heterogeneity of breast cancer can be summarized by discrete gene expression modules that individually represent distinct biological programs, and that collectively can be represented by as few as 96 genes. Modules, together with outlier genes, allow for summation of the entire transcriptional program and provide a universal assay with broad application to companion diagnostics development.

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