Signal-oriented pathway analyses reveal a signaling complex as a synthetic lethal target for p53 mutations

Lu S, Cai C, Yan G, Zhou Z, Wan Y, Chen V, Chen L, Cooper GF, Obeid LM, Hannun YA, Lee AV, Lu X. Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations. Cancer Res. 206 Dec 1:76(23):6785-6794. doi: 10.1158/0008-5472.CAN-16-1740. Epub 2016 Oct 10. PMID: 27758891. PMCID: PMC5165695.

The multi-omics data from The Cancer Genome Atlas (TCGA) provide an unprecedented opportunity to investigate cancer pathways and therapeutic targets through computational analyses. In this study, we developed a signal-oriented computational framework for cancer pathway discovery. First, we identify transcriptomic modules that are abnormally expressed in multiple tumors, such that genes in a module are most likely regulated by a common aberrant signal. Then, for each transcriptomic module, we search for a set of somatic genome alterations (SGAs) that perturbs the signal regulating the transcriptomic module.  Computational evaluations indicate that our methods can identify pathways perturbed by SGAs. In particular, our analyses revealed that SGAs affecting TP53, PTK2, YWHAZ, and MED1 perturb a set of signals that promote cell proliferation, anchor-free colony formation, and epithelial-mesenchymal transition (EMT). We further demonstrate that these proteins form a signaling complex that mediates these oncogenic processes in a coordinated fashion. These findings lead the hypothesis that disrupting the complex could be a novel therapeutic strategy for treating tumors with these genomic alterations. Finally, we show that disrupting the signaling complex by knocking down PTK2, YWHAZ, or MED1 attenuates and reverses oncogenic phenotypes caused by mutant p53 in a “synthetic lethal” fashion. This signal-oriented framework for searching pathways and therapeutic targets is applicable to all cancer types, and thus potentially could have a broad impact on precision medicine in cancer.

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Songjian Lu, Chunhui Cai, Gonghong Yan, Zhuan Zhou, Yong Wan, Lujia Chen, Vicky Chen, Gregory F Cooper, Lina M. Obeid, Yusuf A Hannun, Adrian V Lee and Xinghua Lu
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