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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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,andMED1 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.

Original languageEnglish (US)
Pages (from-to)6785-6794
Number of pages10
JournalCancer Research
Issue number23
StatePublished - Dec 1 2016

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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