Abstract 2382: Development of predictive biomarker gene expression signatures that associates with drug sensitivity and kinase activation in breast cancer.
Authors: O'Brien Carol Lynn , Xiao Yuanyuan , Spoerke Jill , Huw Ling , Hegde Priti , Kowanetz Marcin , Lackner Mark , Wilson Timothy 
Published: August 2014

Abstract
Breast cancer is a heterogeneous disease that is commonly divided into 3 specific subtypes. Hormone receptor positive breast cancers that express either the estrogen or progesterone receptors are commonly treated with anti-estrogen therapies and have a favorable clinical outcome. HER2 positive breast cancers that overexpress the HER2 oncogene, are treated with HER2-directed therapies, including Herceptin and Pertuzumab, which has dramatically improved the clinical outcome in this group. Triple negative breast cancers lack expression of estrogen receptor, progesterone receptor and HER2, are treated with chemotherapeutic regimens and have the worst prognosis in this disease. Recent studies indicates that key kinase driving events are common within specific subtypes of breast cancer, for example, the PIK3CA gene encoding p110-alpha subunit of PI3-kinase, which drives downstream pro-survival signals, is mutated in ∼35-40% of hormone receptor positive breast cancers, implicating potential new treatment strategies. Moreover, as emerging clinical evidence suggests that anti-tumor activity to targeted anti-cancer medicines is usually limited to specific patient subsets, the identification of predictive biomarkers is essential for the clinical development of new therapeutic agents.
Clinical validation of in vitro derived predictive biomarkers has been largely hampered due to commonly used formalin fixed, paraffin embedded procedures that results in low yield and often degraded nucleic acids. In this study, we use the NanoString nCounter gene expression system to characterize and validate candidate biomarkers using an in-house derived custom panel of 400 genes, which includes key breast cancer-related genes, immune-related genes and pro-survival genes that encompass the PI3-kinse and MAP-kinase pathways. As a proof-of-concept, we compared the NanoString nCounter to commonly used platforms including Affymetrix microarray, RNA sequencing and BioMark in a panel of FFPE extracted breast cancer cell lines. Firstly, we applied the 400-gene signature to a panel of breast cancer cell lines to derive gene signatures that correlate with response to anti-cancer agents such as PI3-kinase, MAP-kinase inhibitors and chemotherapy agents. Next, we applied our in vitro derived sensitivity signature to a panel of 150 breast cancer FFPE tumors and demonstrate that the sensitivity signatures derived in vitro are observed, thereby potentially identifying clinically actionable patient subsets. Finally, we correlated our gene signature with mutations and copy number alterations commonly altered in breast cancer. In summary, we show that an in-house derived custom gene panel identifies biomarkers of response to anti-cancer medicines and associates with key kinase-driving events, a crucial study for the future development of targeted anti-cancer medicines.
Citation Format: Carol Lynn O'Brien, Yuanyuan Xiao, Jill Spoerke, Ling Huw, Priti Hegde, Marcin Kowanetz, Mark Lackner, Timothy Wilson. Development of predictive biomarker gene expression signatures that associates with drug sensitivity and kinase activation in breast cancer. [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 2382. doi:10.1158/1538-7445.AM2013-2382