Applying Expression Profile Similarity for Discovery of Patient-Specific Functional Mutations

The progress of cancer genome sequencing projects yields unprecedented information of mutations for numerous patients. However, the complexity of mutation profiles of cancer patients hinders the further understanding to mechanisms of oncogenesis. One basic question is how to find mutations with functional impacts. In this work, we introduce a computational method to predict functional somatic mutations of each patient by integrating mutation recurrence with expression profile similarity. With this method, the functional mutations are determined by checking the mutation enrichment among a group of patients with similar expression profiles. We applied this method to three cancer types and identified the functional mutations. Comparison of the predictions for three cancer types suggested that most of the functional mutations were cancer-type-specific with one exception to p53. By checking predicted results, we found that our method effectively filtered non-functional mutations resulting from large protein sizes. In addition, this method can also perform functional annotation to each patient to describe their association with signalling pathways or biological processes. In breast cancer, we predicted "cell adhesion" and other terms to be significantly associated with oncogenesis.