Computational Prediction of Driver Genes in Cancer Genome Sequencing Studies


主講人:劉鵬淵  浙江大學教授




主講人介紹:劉鵬淵博士,2016年加盟浙江大學轉化醫學研究院以及醫學院附屬邵逸夫醫院?;貒叭瓮箍敌玲t學院的副教授和擔任其醫學院系統分子醫學中心的計算生物學負責人。長期從事生物信息學、基因組學和癌癥遺傳的研究。已在生物信息學等領域發表了118篇包括Nature Genetics、JNCI、AJHG、PNAS、Nucleic Acids Research、Cancer Research、Oncogene和Bioinformatics等SCI論文。2010年獲教育部自然科學獎一等獎(第5完成人)。擔任Physiological Genomics等雜志的編委、威斯康星醫學院兼職教授、全國衛生產業企業管理協會精準醫療分會常務理事、浙江省生物信息學學會精準醫學專業委員會副主委、浙江省數理醫學會甲狀腺疾病專委會副主委、浙江省數理醫學會生物醫學大數據專委會常務委員。

內容介紹:Cancer is a genetic disease with somatically acquired genomic aberrations. Driver mutations are required for the cancer phenotype, whereas passenger mutations are irrelevant to tumor development and accumulate through DNA replication. Several major cancer sequencing projects, such as The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) have created a comprehensive catalog of somatic mutations across all major cancer types. A major goal of these sequencing projects is to identify cancer genes with mutations that drive the cancer phenotype. Better identification of cancer driver genes would inform potential therapies targeted against the products of these aberrant genomic alterations in addition to fundamentally advancing the knowledge of tumor initiation, promotion and progression. In my presentation, I will briefly review several computational tools for prioritizing cancer driver genes from cancer genome sequencing projects. In particular, I will focus on two computational tools (DrGaP and DriverML) developed in my laboratory to identify cancer driving genes.