e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks
Abstract
Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 19, 2020
- Source ID
- 10.1093/nar/gkaa1015
Entities
People
- Brandon Burgman
- Daniel J. Mcgrail
- Erxi Wu
- Ishaani S Khatri
- Nidhi Sahni
- S. Gail Eckhardt
- Sairahul R Pentaparthi
- Song Yi
- Yang Li
- Yongsheng Li
- Zhe Su
Organizations
- American Association for the Study of Liver Diseases
- Baylor College of Medicine
- Baylor Scott & White Health
- Cancer Prevention and Research Institute of Texas
- National Cancer Institute
- National Institutes of Health
- Susan G. Komen for the Cure
- Texas A&M University
- United States Department of Defense
- University of Texas at Austin