Treating the undruggables: Mining cancer genome big data to identify molecular targets for blocking p53-mediated cancer development
讲座名称：
Treating the undruggables: Mining cancer genome big data to identify molecular targets for blocking p53-mediated cancer development
讲座时间：
2014-09-22
讲座人：
鲁兴华
形式：
校区：
兴庆校区
实践学分：
讲座内容：
应数学与统计学院的邀请，美国匹兹堡大学生物医学信息学系 (Dept. of Biomedical Informatics，University of Pittsburgh) 鲁兴华教授将访问我校，并作学术报告。
题 目：Treating the undruggables: Mining cancer genome big data to identify molecular targets for blocking p53-mediated cancer development
时 间：9月22日（周一）上午9:00
地 点：中1-2124（交大东校区）
摘 要：Mutations in p53, a tumor suppressor, underlie the development of over half (4 millions) of new cancers cases annually. Molecular therapy targeting at mutated p53 remains elusive because it is infeasible to restore its tumor-suppressor function. We have developed an integrative framework combining graph-theory-based algorithms with semantic analysis tools to systematically mine contemporary cancer genome big data, such as the Cancer Genome Atlas (TCGA). We found that somatic genomic alterations affecting TP53, YWHAZ, PTK2, and MED1 cooperatively perturb the cellular signals that drive cell proliferation and metastasis in breast cancers and other cancers. The analyses indicate that aberrant signals resulting from perturbing one member lead to changed functions of the others, and together they encode a common set of signals driving important cancer processes. The interdependence among the proteins leads to a therapeutic strategy to block the aberrant signals of p53 mutations by targeting at other members, even if the targeted protein is not affected by SGAs. We show that knocking down PTK2, YWHAZ, or MED1 in cell lines with p53 mutations attenuate the cellular phenotypes driven by the aberration of this pathway. If the strategy is proved to be successful in clinic, this approach will significantly impact personalized cancer therapy.
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