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