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Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations.


ABSTRACT: Defining processes that are synthetic lethal with p53 mutations in cancer cells may reveal possible therapeutic strategies. In this study, we report the development of a signal-oriented computational framework for cancer pathway discovery in this context. We applied our bipartite graph-based functional module discovery algorithm to identify transcriptomic modules abnormally expressed in multiple tumors, such that the genes in a module were likely regulated by a common, perturbed signal. For each transcriptomic module, we applied our weighted k-path merge algorithm to search for a set of somatic genome alterations (SGA) that likely perturbed the signal, that is, the candidate members of the pathway that regulate the transcriptomic module. Computational evaluations indicated that our methods-identified pathways were perturbed by SGA. In particular, our analyses revealed that SGA affecting TP53, PTK2, YWHAZ, and MED1 perturbed a set of signals that promote cell proliferation, anchor-free colony formation, and epithelial-mesenchymal transition (EMT). These proteins formed a signaling complex that mediates these oncogenic processes in a coordinated fashion. Disruption of this signaling complex by knocking down PTK2, YWHAZ, or MED1 attenuated and reversed oncogenic phenotypes caused by mutant p53 in a synthetic lethal manner. This signal-oriented framework for searching pathways and therapeutic targets is applicable to all cancer types, thus potentially impacting precision medicine in cancer. Cancer Res; 76(23); 6785-94. ©2016 AACR.

SUBMITTER: Lu S 

PROVIDER: S-EPMC5165695 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations.

Lu Songjian S   Cai Chunhui C   Yan Gonghong G   Zhou Zhuan Z   Wan Yong Y   Chen Vicky V   Chen Lujia L   Cooper Gregory F GF   Obeid Lina M LM   Hannun Yusuf A YA   Lee Adrian V AV   Lu Xinghua X  

Cancer research 20161010 23


Defining processes that are synthetic lethal with p53 mutations in cancer cells may reveal possible therapeutic strategies. In this study, we report the development of a signal-oriented computational framework for cancer pathway discovery in this context. We applied our bipartite graph-based functional module discovery algorithm to identify transcriptomic modules abnormally expressed in multiple tumors, such that the genes in a module were likely regulated by a common, perturbed signal. For each  ...[more]

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