Identification association of drug-disease by using functional gene module for breast cancer.

Lida Zhu,Fuxi Zhu

doi:10.1186/1755-8794-8-s2-s3

Abstract

In oncology drug development, it is important to develop low risk drugs efficiently. Meanwhile, computational methods have been paid more and more attention in drug discovery. However, few studies attempt to discover the mutual gene modules shared by the drug and disease association. Here we introduce a novel method to identify repositioned drug for breast cancer by integrating the breast cancer survival data with the drug sensitivity information. Among the 140 drug candidates, we are able to filter 4 FDA approved drugs and identify 2 breast cancer drugs among 4 known breast cancer therapeutic drug in total.

Highlights

The goal of drug repositioning is to discover new association between indications of diseases and known drugs based on known associations [1]
Drug repositioning provides a possible way to speed up the drug development and avoids the development cost and time consuming in the drug discovery
Since the prognosis studies were sampled by high throughput gene expression profiles, in this work we present how to make use of this kind of clinical prognosis data and initiate the mutual gene modules to investigate drug repositioning for the treatment of breast cancer

Summary

Introduction

The goal of drug repositioning is to discover new association between indications of diseases and known drugs based on known associations [1]. Data description The tumor progression associated information is collected from three breast cancer datasets (GSE2034 [15], GSE7390 [16] and GSE11121 [17]) These data are used to select the differential expressed gene modules associated to the prognosis outcome among all the patient samples. Identification of prognosis gene modules For each disease dataset, we used the t-test (as shown in formula (4)) to find whether the gene set has significant differential enrichment scores between the good outcome and bad outcome. According to the ES, we estimated our performance by counting on the number of positive drug in the ranked drug list

Result

Discussion and conclusion

Aronson JK

18. Shoemaker RH

Findings

25. Fox EJ