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摘要:Identifying causative somatic genome alterations(SGAs) driving an individual tumor could both provide insight into disease mechanisms and support personalized modeling for precision oncology.Here,we present a Tumor-specific Causal Inference(TCI) framework that infers causal relationships between SGAs and molecular phenotypes(e.g.,transcriptomic,proteomic,or metabolomic changes) within a specific tumor.We applied the TCI algorithm to 4,468 tumors across 16 cancer types from The Cancer Genome Atlas(TCGA) and identified those SGAs that causally regulate the differentially expressed genes(DEGs)within each tumor.TCI identified 424 SGAs that had a significant functional impact on transcription in tumors,including most(86%) of the previously reported drivers as well as many novel candidate drivers.Our computational evaluation of these SGAs and DEGs support that the causal relationships inferred by TCI are statistically robust and biologically sensible,and preliminary experimental results support the predicted novel functional impact of previously understudied SGAs.
会议名称:

全球华人生物学家大会暨第十六届美洲华人生物科学学会学术研讨会

会议时间:

2017-06-29

会议地点:

中国浙江杭州

  • 专辑:

    医药卫生

  • 专题:

    肿瘤学

  • 分类号:

    R730.2

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