P1-07-04: Gene Expression Module Biomarkers To Stratify Multiple Clinical and Therapeutic Endpoints for Universal Breast Cancer Companion Diagnostic.

Abstract

Abstract 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 run many tests, 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 meta-analysis approach that identifies novel biomarker modules that results in multiple clinical and therapeutic read-outs. 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. These module based biomarkers were tested for their ability to associate with prognostic and predictive targets in published datasets. In addition, each module was reduced from 10 - 1,000 genes to the top performing 2–3 genes based on the degree of co-expression across the meta-analysis and validation by quantitative PCR in an independent panel of FFPE tumor samples. This study demonstrates that a single 96 gene qPCR test utilizing multiple module biomarkers is not only capable of stratifying patients by standard histopathological parameters (ER, PR and Her2), but also stratifies by other diverse elements of the disease (cell lineage, dysregulated core biological functions, factors of cell growth, underlying genomic aberrations and the tumor microenvironment). Taken together, these biological variables represent the major biological diversity present within the breast cancer population. A series of retrospective analyses demonstrated that different single module and combinations of modules were capable of predicting a variety of clinical endpoints, including 5-year survival, neoadjuvant chemotherapy response in ER- patients and targeted therapy response in model systems.The molecular heterogeneity of breast cancer can be summarized by discrete gene expression modules that individually represent distinct biological pathways, and that collectively can be represented by as few as 96 genes. These breast cancer 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. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-07-04.