Abstract B26: Identification of genes that predict response to paclitaxel in breast cancer using an in vivo genome-wide knockdown screen

Abstract

Abstract Treatment decisions for breast cancer are based upon stage, tumor grade and hormone receptor status, and can include surgical resection, hormone receptor antagonists, radiation, and chemotherapy (e.g. paclitaxel). Breast cancer treatment success depends upon avoidance of chemotherapy resistance (i.e. achieving complete response) and prevention of both over- and under-treatment. Increased understanding of the genes which cause resistance and sensitivity to currently used drugs would lead to development of more effective therapeutic strategies that are specifically tailored to patient groups based on molecular profiling of their tumors (i.e. personalized medicine). Being able to identify the genes which when expressed in a tumor predict sensitivity or resistance to treatment prior to administration of paclitaxel would improve treatment efficacy and patient survival. We performed an in vivo shRNA genome-wide screen with MDA-MB-231 tumors treated with paclitaxel for the purpose of identifying genes which determine breast cancer response to paclitaxel. Completion of 6 replicates of the in vivo screen identified 26 putative paclitaxel sensitivity genes and 14 putative paclitaxel resistance genes (e.g. BCL6) for breast cancer. Screen-identified putative paclitaxel resistance were verified by individual knockdown clone generation and comparison of their sensitivity to paclitaxel-induced decreased cell proliferation, cell-cycle arrest, and apoptosis to a shRNA scramble control clone. Upon individual knockdown of the putative resistance genes (e.g. BCL6), MDA-MB-231 cells were more sensitive to paclitaxel and demonstrated increased apoptosis and decreased paclitaxel IC50 concentrations. Finally, expression of a preliminary gene signature generated from the screen-identified hits was tested for its ability to predict response to paclitaxel in two archived patient data sets. The preliminary gene signature predicted response to paclitaxel in the datasets with an accuracy ranging from 70 to 100%. Further confirmation experiments of the remaining potential resistance and sensitivity genes will help to generate a more robust genetic profile which can be used to identify candidate breast cancer patients who would most benefit from paclitaxel treatment as opposed to treatment with other drugs. Citation Format: Mohammad Sultan, Thomas Tan Huynh, Margaret Lois Thomas, Krysta Mila Coyle, Carman A. Giacomantonio, Paola Marcato. Identification of genes that predict response to paclitaxel in breast cancer using an in vivo genome-wide knockdown screen. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr B26.