Interpretation of cancer mutations using a multiscale map of protein systems
Open accessAbstract
A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges—how to comprehensively map such systems and how to identify which are under mutational selection—have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales.
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Understanding how a cellular subsystem or protein complex is disrupted in a diseased tissue can aid development of therapeutic interventions.
“Every AIVC prediction could be substantiated with the corresponding multi-scale interactions that determine resulting states, e.g., understanding how a cellular subsystem or protein complex is disrupted in a diseased tissue can aid development of therapeutic interventions.118,119”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [118]
The Results section, “Identifying Recurrently Mutated Systems at Multiple Scales” (page unavailable), links disruption of the Extended BRCC complex in bladder cancer cells to sensitivity to olaparib or cisplatin. This supports the idea that understanding a disrupted protein system can reveal treatment-relevant vulnerabilities, but the paper does not show that the finding aided development of a therapeutic intervention in diseased tissue. (LLM, conf=0.81)
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Self-citationThe Functional Proximal Proteome of Oncogenic Ras Includes mTORC2
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Flagged first, then by citations
Research works printed but not linked: 2
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Cited by
Sorted by citations
How to build the virtual cell with artificial intelligence: Priorities and opportunities
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“118,119 The modular structure of the AIVC will enable researchers to pinpoint specific genes, proteins, or molecular processes involved in each predicted behavior.”
Big data in basic and translational cancer research
Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254 · see citing works
Evaluation of large language models for discovery of gene set function
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Self-citationAI-readiness Criteria for Biomedical Data
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Self-citation
- Authors
- Zheng, F., Kelly, M. R., Ramms, D. J., Heintschel, M. L., Tao, K., Tutuncuoglu, B., Lee, J. J., Ono, K., Foussard, H., Chen, M., Herrington, K. A., Silva, E., Liu, S. N., Chen, J., Churas, C., Wilson, N., Kratz, A., Pillich, R. T., Patel, D. N., Park, J., Kuenzi, B., Yu, M. K., Licon, K., Pratt, D., Kreisberg, J. F., Kim, M., Swaney, D. L., Nan, X., Fraley, S. I., Gutkind, J. S., Krogan, N. J., & Ideker, T.
- Affiliations
- University of California San Diego (US)
- Oregon Health & Science University (US)
- Gladstone Institutes (US)
- University of California, San Francisco (US)
- Corresponding author
- Zheng, Fan
- Kelly, Marcus R.
- Krogan, Nevan J.
- Ideker, Trey
- ORCID
- Zheng, Fan
- Kelly, Marcus R.
- Ramms, Dana J.
- Heintschel, Marissa L.
- Tao, Kai
- Tutuncuoglu, Beril
- Lee, John J.
- Ono, Keiichiro
- Foussard, Helene
- Chen, Michael
- Herrington, Kari A.
- Silva, Erica
- Liu, Sophie N.
- Chen, Jing
- Churas, Christopher
- Kratz, Anton
- Pillich, Rudolf T.
- Patel, Devin N.
- Park, Jisoo
- Kuenzi, Brent
- Yu, Michael K.
- Pratt, Dexter
- Kreisberg, Jason F.
- Kim, Minkyu
- Swaney, Danielle L.
- Nan, Xiaolin
- Fraley, Stephanie I.
- Gutkind, J. Silvio
- Krogan, Nevan J.
- Ideker, Trey
- Venue
- Science
- Published
- 2021-10
- Volume
- 374
- Issue
- 6563
- Pages
- eabf3067-eabf3067
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC9126298
- OpenAlex
- W3204152948
- Cited by
- 81
- References
- 144
- Citations by year
- 2026: 8 · 2025: 25 · 2024: 27 · 2023: 8 · 2022: 10 · 2021: 3
- FWCI
- 3.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (1s10od026929-01)
- National Human Genome Research Institute (R01HG009979)
- National Cancer Institute (F99CA212456, U54CA209891, U54CA209988, K00CA212456, F30CA236404, F32CA239336, R50CA243885, T32CA067754)
- National Institute of General Medical Sciences (R01GM132322, P41GM103504)
- National Institute of Dental and Craniofacial Research (R01DE026870, U01DE028227)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- cancer mutations, protein complexes, phosphatidylinositol 3-kinase signaling, collagen mutations, tumor cell proliferation, tumor evolution, tumor progression, clinical biomarkers, nests
- MeSH terms
- Humans, Mutation, Neoplasm Proteins, Neoplasms, Protein Interaction Mapping, Genes, Neoplasm, Protein Interaction Maps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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