ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2015
Identifying mutation specific cancer pathways using a structurally resolved protein interaction network.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- ASCARIS: Positional feature annotation and protein structure-based representation of single amino acid variations.Computational and structural biotechnology journal · 2023Article
- Predicting functional consequences of mutations using molecular interaction network features.Human genetics · 2022Article
- A full-proteome, interaction-specific characterization of mutational hotspots across human cancers.Genome research · 2022Article
- Computational methods for detecting cancer hotspots.Computational and structural biotechnology journal · 2020Review
- 3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients.PLoS computational biology · 2019Article
- Integrating molecular networks with genetic variant interpretation for precision medicine.Wiley interdisciplinary reviews. Systems biology and medicine · 2019Review
- The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine.Journal of molecular biology · 2018Review
- Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces.PloS one · 2016Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Here we present a method for extracting candidate cancer pathways from tumor 'omics data while explicitly accounting for diverse consequences of mutations for protein interactions. Disease-causing mutations are frequently observed at either core or interface residues mediating protein interactions. Mutations at core residues frequently destabilize protein structure while mutations at interface residues can specifically affect the binding energies of protein-protein interactions. As a result, mutations in a protein may result in distinct interaction profiles and thus have different phenotypic consequences. We describe a protein structure-guided pipeline for extracting interacting protein sets specific to a particular mutation. Of 59 cancer genes with 3D co-complexed structures in the Protein Data Bank, 43 showed evidence of mutations with different functional consequences. Literature survey reciprocated functional predictions specific to distinct mutations on APC, ATRX, BRCA1, CBL and HRAS. Our analysis suggests that accounting for mutation-specific perturbations to cancer pathways will be essential for personalized cancer therapy.
Indexed as
Identifiers
25592571PMC4299875W1988589657What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.