Date of Award

January 2016

Document Type

Open Access Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Hongyu Zhao

Abstract

In the past decades, targeted cancer therapies have made considerable achievements in inhibiting cancer progression by modulating specific molecular targets. However, targeted cancer therapies have reached a plateau of efficacy as the primary therapy since tumor cells can achieve adaptability through functional redundancies and activation of compensatory signaling pathways. Therapies using drug combinations have been developed to overcome the bottleneck. Accurate predictions of synergies effect can help prioritize biological experiments to identify effective combination therapies. Data integration can give us a deeper insight into the mechanism of cancer and drug synergies and help to address the challenge in prediction of drug combinations. In this thesis, we illustrate that integrative analysis of multiple types of omics data and pharmacological data can more effectively identify drug synergies, hence improve the prediction accuracy. As part of the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, we showed that multiple data integration methods could identify multiple oncogenes and tumor suppressor genes as signature genes. We showed that several models built through data integration outperformed benchmark models without data integration methods.

Comments

This is an Open Access Thesis.

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