Touching millions of life: Translating Cancer Genome Big Data to Personalized Cancer Therapy
Cancer is a disease resulting from genome alterations. Contemporary biotechnologies make genome-scale data from individual patients readily available, and personalized precision medicine based on specific genomic alterations of individual tumors presents promises of more effective therapy. However, there are 3 major gaps hinder the translation from genome data to personalized precision therapy. First, a cancer tumor often hosts hundreds to a thousand somatic genome alterations (SGAs), with some being drivers and the majority being passengers, and the methods for determining the drivers (thereby the targets of therapy) of a given tumor remains missing. Second, majority of genes contributing to cancer development are “undruggable”, and it remains a largely unsolved problem to map driver SGAs to targetable pathways. Third, cancer results from orchestrated perturbation of multiple pathways, it is a challenge to find combinatorial patterns of pathway perturbations and then design efficient combination therapy for a specific patient. In this seminar, I will discuss novel computational approaches and preliminary results for filling the above gaps through mining comprehensive cancer genome big data from the TCGA.