Abstract 3989: Identification of biomarkers for UM-164 in triple-negative breast cancer

Abstract

Abstract Triple negative breast cancer (TNBC) is one of the deadliest forms of breast cancer due to limited treatment options beyond conventional chemotherapy. While making up approximately 10-20% of total breast cancer cases in the US, TNBC carries a serious burden of disease as it tends to be more aggressive, higher grade, and have a poorer prognosis than other forms of breast cancer. Though new therapies, such as PARP inhibitors, have shown promise in clinical trials in very select patients (e.g. BRCA1 mutation carriers), there is a general lack of predictive markers of therapeutic efficacy to identify subsets of patients most likely to positively respond to a given targeted or conventional therapy. Thus, there is a critical need for biomarkers of drug efficacy in TNBC to help design precision medicine clinical trials based on systematically derived predictive biomarkers. To fill this gap, we devised a strategy to identify biomarkers of drug efficacy in TNBC. As a paradigm of our approach, we treated a panel of 23 TNBC cell lines with our potent c-Src/p38 inhibitor, UM-164, and determined a drug sensitivity score (DSS) in each line. We then calculated correlations of DSS with a variety of molecular readouts, including RNA sequencing, reverse-phase protein array, DNA sequencing array, and Nanostring RNA and miRNA arrays. From these correlations, we have identified expression DJ1, MAPK11, and PCM1 as strong predictors of UM-164 efficacy in TNBC. Building on this, we used our results to identify companion drugs that lead to synergy or test others that could instead result in antagonism when used in combination. To verify our results, independently targeting the DJ1 pathway, we show that AKT inhibition results in synergy when combined with UM-164. The outcome of this research yields a robust and generalizable methodology for the identification of biomarkers in cancer to design Phase I-III trials and for generating the mechanistic evidence of effective rational combinations. Citation Format: Nathan M. Merrill, Nathalie M. Vandecan, John P. Lloyd, Eric J. Lachacz, Peter J. Ulintz, Sofia D. Merajver, Matthew B. Soellner. Identification of biomarkers for UM-164 in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3989.