Date of Award
Open Access Thesis
Master of Public Health (MPH)
School of Public Health
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.
Gao, Xiaoting, "Data Integration And Targeted Anticancer Drug Synergies Prediction" (2016). Public Health Theses. 1098.