Predicting Breast Cancer Chemotherapeutic Response Using a Novel Tool for Microarray Data Analysis

摘要：

We developed a novel tool for microarray data analysis that can parsimoniously discover highly predictive genes by finding the optimal trade off between fold change and t-test p value through rigorous cross validation, hi addition to find a small set of highly predictive genes, the tool also has a procedure that recursively discovers and removes predictive genes from the dataset until no such genes can be found. We applied our tool to a public breast cancer dataset with the goal to discover genes that can predict patients response to a preoperative chemotherapy. The results show that estrogen receptor (ER) gene is the most important gene to predict chemotherapeutic response and no gene signatures can add much clinical benefit for the whole patient population. We further identified a clinically homogenous subgroup of patients (ERnegative, PR-negative and HER2-negative) whose response to the chemotherapy can be reasonably predicted. Many of the discovered predictive markers for this subgroup of patients were successfully validated using a blinded validation set.

作者: Jie Cheng Joel Greshock Jeffery Painter Xiwu Lin Kwan Lee Shu Zheng Alan Menius

作者单位: Quantitative Sciences, GlaxoSmithKline, Collegeville, PA 19426, USA Cancer Research, GlaxoSmithKline, Collegeville, PA 19426, USA Cancer Institute, Zhejiang University, Hangzhou, 310009, China

会议类型: 国际会议

会议名称: International Symposium on Integrative Bioinformatics the 8th Annual Meeting(2012年整合生物信息学国际会议暨第八届年度会议 IB 2012)

会议地点: 杭州

会议语种:英文

页码: 111-117

在线出版日期: 2012-04-02（万方平台首次上网日期，不代表论文的发表时间）