【摘 要】
：
Cancer is mainly caused by heterogeneous somatic genome alterations (SGAs).Genome-scale data from individual patients are now readily available,and it is an
【出 处】
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第七届全国生物信息学与系统生物学学术大会
论文部分内容阅读
Cancer is mainly caused by heterogeneous somatic genome alterations (SGAs).Genome-scale data from individual patients are now readily available,and it is anticipated that precisely targeting specific genomic alterations of individual tumors will bring in more effective therapies.However,there are 3 major gaps that hinder the translation of the genome data of a tumor to personalized therapy.First,a tumor often hosts hundreds to a thousand SGAs,with a few being drivers and the majority being passengers,and the methods for determining the drivers (thereby the targets of therapy) of a given tumor remain to be fully developed.Second,majority of driver genes are "undruggable".This problem can potentially addressed by mapping undruggable drivers to targetable pathways,but it remains a largely unsolved problem to map driver SGAs to such 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 single agent or combination therapy for a specific patient.In this presentation,I will discuss novel computational approaches for bridging the above gaps developed in the Center of Causal Discovery (www.ccd.pitt.edu) at the University of Pittsburgh,and I will report our recent progresses towards the goal of translating big data of cancer genomics to precision medicine for cancer.
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