Abstract 2618: Identification of multidrug response genes by meta-analysis based on ChemoFx® in human breast cancer cell lines

Abstract

Abstract The success of chemotherapeutic agents to treat various forms of cancer has a long history. Unfortunately, a major cause of treatment failure is drug resistance to currently available chemotherapy agents. Understanding the mechanism of resistance to multiple drugs has been a subject of considerable interest. ChemoFx® holds the promise of not only determining which patient will respond to what therapy but also can be used to learn more about drug resistance while acting as a proxy to clinical outcome. In this study, we used the ChemoFx® to identify response vs. non-response to seven different chemotherapeutic agents (paclitaxel, docetaxel, epirubicin, doxorubicin, gemcitabine, fluorouracil, cyclophosphamide) in 28 human breast cancer cell lines. Based on publically available gene expression profiles of these breast cancer cell lines and the ChemoFx® results, differentially expressed (DE) genes related to multi-drug response were identified through meta-analysis method. Five hundred thirty six DE genes were identified to be related to multidrug response at the false discovery rate of 0.05. To segregate the effect of ER status, we also performed meta-analysis within ER positive or ER negative breast cancer cell lines separately. Thirty seven DE genes were identified related to multiple drug response in ER negative breast cancer cell lines and 20 DE genes in ER positive cell lines. Among all these DE genes, only one is in common between ER positive and ER negative groups. These results suggest that multi-drug resistance is the result of not just one gene but the contribution of multiple genes. In addition, gene set enrichment analysis showed that multidrug response genes are involved in different biological processes in ER positive vs. ER negative cell lines. The dissimilarity of multidrug response genes in ER positive vs. ER negative shows the value of integrating multidrug response gene information with clinical factors to identify patients who are unlikely to benefit from current chemotherapeutic drugs. Moreover, understanding the mechanisms of multidrug response has the potential of identifying new therapeutic targets. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2618.