Proteogenomics connects somatic mutations to signalling in breast cancer
Open accessAbstract
Somatic mutations have been extensively characterized in breast cancer, but the effects of these genetic alterations on the proteomic landscape remain poorly understood. Here we describe quantitative mass-spectrometry-based proteomic and phosphoproteomic analyses of 105 genomically annotated breast cancers, of which 77 provided high-quality data. Integrated analyses provided insights into the somatic cancer genome including the consequences of chromosomal loss, such as the 5q deletion characteristic of basal-like breast cancer. Interrogation of the 5q trans-effects against the Library of Integrated Network-based Cellular Signatures, connected loss of CETN3 and SKP1 to elevated expression of epidermal growth factor receptor (EGFR), and SKP1 loss also to increased SRC tyrosine kinase. Global proteomic data confirmed a stromal-enriched group of proteins in addition to basal and luminal clusters, and pathway analysis of the phosphoproteome identified a G-protein-coupled receptor cluster that was not readily identified at the mRNA level. In addition to ERBB2, other amplicon-associated highly phosphorylated kinases were identified, including CDK12, PAK1, PTK2, RIPK2 and TLK2. We demonstrate that proteogenomic analysis of breast cancer elucidates the functional consequences of somatic mutations, narrows candidate nominations for driver genes within large deletions and amplified regions, and identifies therapeutic targets.
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Contemporary proteomic research relies heavily on high-resolution mass spectrometry (HR-MS) platforms including Orbitrap and time-of-flight (TOF) systems.
“Contemporary proteomic research relies heavily on high-resolution mass spectrometry (HR-MS) platforms including Orbitrap and time-of-flight (TOF) systems.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [169]
The ‘Proteogenomic analysis of TCGA samples’ section says the study used high-resolution, accurate-mass tandem mass spectrometry for breast-tumor proteomics. The Introduction also describes mass spectrometry as a means of analyzing tumor proteomes. Our copy does not identify Orbitrap or time-of-flight systems or establish how heavily proteomic research generally relies on them. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAI-based analytical methods are increasingly used for enrichment, site identification, and functional network modeling of PTMs.
“Given the complexity and low abundance of many PTMs, AI-based analytical methods are increasingly used for enrichment, site identification, and functional network modeling139,169,178,179.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [169]
The Clustering and Network analysis section describes a Joint Random Forest method used to identify network modules, supporting the network-modeling part of the claim. The Proteogenomic analysis of TCGA samples section instead describes experimental phosphopeptide enrichment and mass-spectrometry identification of phosphosites; it does not attribute those steps to AI-based methods. The paper also does not establish that use of such methods is increasing (page numbers unavailable in our copy). (LLM, conf=0.85)
Experimental — accuracy not yet measuredHR-MS platforms quantify thousands of proteins across a wide dynamic range.
“These tools quantify thousands of proteins across a wide dynamic range168,169.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [169]
The ‘Proteogenomic analysis of TCGA samples’ section reports that high-resolution mass spectrometry quantified relative abundances for 12,553 proteins across breast tumors, supporting the ‘thousands of proteins’ part. Our copy does not establish that the measurements spanned a wide dynamic range. (LLM, conf=0.84)
Experimental — accuracy not yet measuredModern mass spectrometry can profile more than 12,000 proteins (>10,000 genes) from mammalian tissue samples.
“MS can profile more than 12,000 proteins (> 10,000 genes) from mammalian tissue samples [16–18].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [16]
Proteogenomic analysis of functional consequences of cancer somatic mutations has assisted in narrowing down candidate driver genes within large deletions and amplified regions.
“The value of a proteogenomic analysis of functional consequences of cancer somatic mutations has assisted in narrowing down candidate driver genes within large deletions and amplified regions96.”
From A high-stringency blueprint of the human proteome (2020) · cites this paper as [96]
CPTAC applied standards and workflows to tumours previously genomically characterized by The Cancer Genome Atlas, pioneered the integration of proteomics with genomics to produce a more unified and comprehensive understanding of cancer biology, and implemented its transition into cancer clinical research studies.
“CPTAC applied these standards and workflows to tumours previously genomically characterized by The Cancer Genome Atlas. In doing so, CPTAC pioneered the integration of proteomics with genomics (i.e., proteogenomics) to produce a more unified and comprehensive understanding of cancer biology and implemented its transition into cancer clinical research studies96,104,105.”
From A high-stringency blueprint of the human proteome (2020) · cites this paper as [96]
The flagship efforts of the Cancer Proteomics Consortium, CPTAC, have brought targeted proteomics and proteogenomics into regular use and produced major studies on ovarian, breast, and colorectal cancer.
“The flagship efforts of the Cancer Proteomics Consortium, CPTAC, have brought targeted proteomics and proteogenomics into regular use and produced major studies on ovarian ( 10 ), breast ( 11 ), and colorectal cancer ( 12 ).”
From The Human Proteoform Project: Defining the human proteome (2021) · cites this paper as [11]
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- Authors
- CPTAC, N., Mertins, P., Mani, D. R., Ruggles, K. V., Gillette, M. A., Clauser, K. R., Wang, P., Wang, X., Qiao, J. W., Cao, S., Petralia, F., Kawaler, E., Mundt, F., Krug, K., Tu, Z., Lei, J. T., Gatza, M. L., Wilkerson, M., Perou, C. M., Yellapantula, V., Huang, K.-L., Lin, C., McLellan, M. D., Yan, P., Davies, S. R., Townsend, R. R., Skates, S. J., Wang, J., Zhang, B., Kinsinger, C. R., Mesri, M., Rodriguez, H., Ding, L., Paulovich, A. G., Fenyö, D., Ellis, M. J., & Carr, S. A.
- Affiliations
- Broad Institute (US)
- Fred Hutch Cancer Center (US)
- Washington University in St. Louis (US)
- NYU Langone Health (US)
- Baylor College of Medicine (US)
- University of North Carolina at Chapel Hill (US)
- Vanderbilt University (US)
- National Institutes of Health (US)
- Corresponding author
- Mertins, Philipp
- Ellis, Matthew J.
- Carr, Steven A.
- ORCID
- Mertins, Philipp
- Mani, D. R.
- Ruggles, Kelly V.
- Gillette, Michael A.
- Clauser, Karl R.
- Wang, Pei
- Wang, Xianlong
- Cao, Song
- Petralia, Francesca
- Kawaler, Emily
- Mundt, Filip
- Krug, Karsten
- Tu, Zhidong
- Lei, Jonathan T.
- Gatza, Michael L.
- Wilkerson, Matthew
- Perou, Charles M.
- Yellapantula, Venkata
- Huang, Kuan-lin
- Lin, Chenwei
- McLellan, Michael D.
- Yan, Ping
- Davies, Sherri R.
- Townsend, R. Reid
- Skates, Steven J.
- Wang, Jing
- Zhang, Bing
- Kinsinger, Christopher R.
- Mesri, Mehdi
- Rodriguez, Henry
- Ding, Li
- Paulovich, Amanda G.
- Fenyö, David
- Ellis, Matthew J.
- Carr, Steven A.
- Venue
- Nature
- Published
- 2016-05-25
- Volume
- 534
- Issue
- 7605
- Pages
- 55-62
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC5102256
- OpenAlex
- W2395887422
- Cited by
- 1,676
- References
- 44
- Citations by year
- 2026: 125 · 2025: 119 · 2024: 131 · 2023: 146 · 2022: 181 · 2021: 229 · 2020: 213 · 2019: 172 · 2018: 182 · 2017: 137 · 2016: 37
- FWCI
- 87.70
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (5u24ca160035-05, 5p41rr000954-34, UL1TR000448)
- National Cancer Institute (P30 CA 91842, U24CA159988, R00CA166228, U24-CA-160035, U24CA160036, U24CA160019, U24CA160034, P30CA016087, P30CA091842)
- National Institute of General Medical Sciences (R01GM108711, T32GM088129, P41GM103422)
- National Center for Research Resources (P41-RR000954, UL1RR024992, RR140033)
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- breast cancer, proteogenomics, somatic mutations, phosphoproteomics, proteomics, 5q deletion, basal-like breast cancer, chromosome loss, trans-acting effects, EGFR, Src kinase, stromal proteins, G protein-coupled receptors, ERBB2, amplicons, driver genes, therapeutic target, PAM50 molecular subtypes
- MeSH terms
- Breast Neoplasms, Calcium-Binding Proteins, Chromosome Deletion, Chromosomes, Human, Pair 5, Female, Humans, Mutation, Phosphoproteins, Protein Kinases, Mass Spectrometry, Signal Transduction, Gene Expression Regulation, Neoplastic, Tumor Suppressor Protein p53, Receptor, ErbB-2, Erb-b2 Receptor Tyrosine Kinases, Cyclin-Dependent Kinases, src-Family Kinases, Phosphatidylinositol 3-Kinases, Genomics, Proteomics, Receptors, G-Protein-Coupled, S-Phase Kinase-Associated Proteins, Focal Adhesion Kinase 1, Receptor-Interacting Protein Serine-Threonine Kinase 2, p21-Activated Kinases, Class I Phosphatidylinositol 3-Kinases, Molecular Sequence Annotation, ErbB Receptors
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