Predicting the impact of cancer somatic mutations on protein-protein interactions

Abstract

Cancer is a complex disease with one of the highest mortality rates. An existence challenge in cancer research is to identify the cancer driver mutation that causes cancer progression. Many biological processes are governed by protein-protein interactions. As such understanding which somatic mutations lead to a disruption in these interactions is critical for cancer research. A drawback common to most of the existing models is the assumption that a mutation in a protein causes the same effect on all of its interactions. Predicting how each interaction is affected by a mutation of interest is crucial for understanding disease development. In this thesis, we propose a model named Predator. Predator provides better prediction accuracy than the alternatives. The developed model is applied on the cancer data in the TCGA. Proposed approach is a novel study to analyze the effects of cancer mutations from TCGA on protein-protein interactions by classifying the impacts into disruptive/nondisruptive classes and having analyses specific to cancer. Such analyses may be beneficial for providing new insights regarding the mutations that are predicted to have a disruptive effect on cancer groups.