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Computational Drug Repurposing for Breast Cancer Subtypes

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  • Thu, 09/13/2018 - 1:00pm - 3:00pm

Computational Drug Repurposing for Breast Cancer Subtypes

MSc Thesis Proposal by:

Roopesh Dhara

Date:  Thursday, September 13th, 2018
Time:  1: 00 pm – 3:00 pm
Location: 3105, Lambton Tower

Abstract: Breast cancer makes up 25 percent of all new cancer diagnosis globally according to the American Cancer Society (ACS). Developing a highly effective drug can be a time consuming and an expensive ordeal. Drug repurposing is a tremendous approach which takes away some disadvantages of traditional drug development procedures making it both time and cost effective. In this thesis, we are interested in finding good drugs for each of the ten subtypes of breast cancer. Repurposing incorporates identifying unique indications of pre-approved drugs and utilizing them to observe the anti-correlation between the perturbation data and disease data. If anti-correlation, whether it is upregulation or downregulation, is detected, it indicates that those drugs cause an effect making them a suitable candidate for drug repurposing. The gene expression data and the discrete copy number variation data will be used to compute z-scores and normalize the data for ten sets of disease subtypes. Gene expression data for ten subtypes was extracted from the METABRIC dataset. The values corresponding to seven cell lines were extracted from the pharmacogenomics perturbation data which is the National Institute of Health’s (NIH) Library of Integrated Network-Based Cellular Signatures (LINCS) dataset. We expect to obtain the suitable drugs displaying anti-correlation for all ten subtypes and show that those drugs are suitable candidates for drug repurposing.

Thesis Committee:
Internal Reader: Dr. Luis Rueda
External Reader: Dr. Huapeng Wu
Advisor: Dr. Alioune Ngom

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