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Abstract Details

Activity Number: 351
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract - #305560
Title: Supervised Predictive Gene Signatures Selection for Cancer Treatment with Survival Endpoint
Author(s): Keyue Ding*+
Companies: Queen's University
Address: 10 stuart st., NCIC CTG, Kingston, ON, K7M 3N6, Canada
Keywords: Microarray data ; Clinical trials ; predictive biomarker ; Cancer therapy ; Survival analysis
Abstract:

Many cancer therapies benefit only part of patients who are treated. Tumor samples are now routinely collected in cancer clinical trials. It is hypothesized that the gene expression profile of pretreatment cancer tissue possesses the information about patient's disease prognosis and the sensitivity to cancer therapy. The identification of gene signatures predicting patients' response to targeted and cytotoxic therapies will make it possible to individualize and improve the effectiveness of anticancer therapies. Various algorithms for developing predictive gene signatures based on predictive genes had been proposed. The key issue is the method used to synthesize information across the multiple predictive genes to derive a robust classifier that differentiates patients who are benefit from the study therapy. In this paper, simulation studies are performed to compare a few most often used methods for various situations, and identify most appropriate approaches. Finally, we apply the chosen algorithm to a NCIC CTG cancer clinical trial, and obtain a five-gene signature that predicts Stage IB and II NSCLC patients who would be benefit from adjuvant chemotherapy.


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