Kahuku Oades

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

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 LB-224: Development of a novel breast cancer segregation panel assay for multiple genetic subtyping based on gene expression modules developed from Oncomine™

Breast cancer is a highly heterogeneous disease as evidenced by comprehensive genetic studies which have revealed multiple subtypes using gene expression profiling and cell lineage classifier analyses. Previous studies have characterized different subtypes including normal breast-like, luminal epithelial A, luminal epithelial B, Her 2 over-expression and basal type carcinoma. However, the genetic variation within breast cancer is far more diverse than these core subtypes, and it is necessary to fully characterize this diversity in order to move beyond simple prognosis and to specifically predict drug sensitivity. In a review of global gene expression and SNP-based cytogenetic data of more than 5,000 breast cancer patients in the Oncomine™ database, we have been able to characterize approximately 30 different genetic variations that are shared by 1% or more of the breast cancer population. These core, independent variables reflect diverse elements of the disease at a molecular level including cell lineage, dysregulated core biological functions, factors of cell growth, and importantly, the tumor microenvironment. Further genetic subtypes are characterized within the various large and focal genomic amplifications, such as Her2 and Myc, as well as focal expression events present subpopulations of patients. In aggregate these genetic variables represent all of the major genetic factors that present within breast cancer. Currently biomarker/diagnostic approaches have tended to be over-tailored to specific clinical questions and therefore have lacked broad applicability, with every diagnostic test requiring a custom gene set and tailored signature and in some cases, requiring separate validated assays using multiple technologies and consequent splitting of clinical samples. To overcome these limitations, we have developed a single, 96-gene qRT-PCR test for rapid breast cancer companion diagnostics development using FFPE tumor tissue. All 30 of the core variables or “modules” are represented by this test which reports on both gene expression and chromosomal amplification events. We demonstrate in this study that this single test, with its multiple modules, can report on standard histopathological parameters, such as ER, PR and Her2, and reproduce existing prognostic and predictive genomic signatures. Data will be presented on prediction of overall survival, neoadjuvant chemotherapy response, and in-vitro sensitivity to MEK and PI3K inhibitors. 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 LB-224. doi:10.1158/1538-7445.AM2011-LB-224