The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Comprehensive molecular portraits of human breast tumours
Open accessAbstract
We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at >10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer. The Cancer Genome Atlas Network describe their multifaceted analyses of primary breast cancers, shedding light on breast cancer heterogeneity; although only three genes (TP53, PIK3CA and GATA3) are mutated at a frequency greater than 10% across all breast cancers, numerous subtype-associated and novel mutations were identified. This Article from the Cancer Genome Atlas consortium describes a multifaceted analysis of primary breast cancers in 825 people. Exome sequencing, copy number variation, DNA methylation, messenger RNA arrays, microRNA sequencing and proteomic analyses were performed and integrated to shed light on breast-cancer heterogeneity. Just three genes — TP53, PIK3CA and GATA3 — are mutated at greater than 10% frequency across all breast cancers. Many subtype-associated and novel mutations were identified, as well as two breast-cancer subgroups with specific signalling-pathway signatures. The analyses also suggest that much of the clinically observable plasticity and heterogeneity occurs within, and not across, the major subtypes of breast cancer.
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“In breast cancer, hypoxia is more evident in TNBC than in other breast cancer subtypes11,12.”
From Hypoxia induces HIF1α-dependent epigenetic vulnerability in triple negative breast cancer to confer immune effector dysfunction and resistance to anti-PD-1 immunotherapy (2022) · cites this paper as [11]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Hypoxia-inducible factor 1α (HIF-1α) is a master transcription factor that is highly expressed in cancer tissues, including breast cancer, and is particularly enriched in triple-negative breast cancer (TNBC)2.”
From Liposomal Formulation of HIF-1α Inhibitor Echinomycin Eliminates Established Metastases of Triple-negative Breast Cancer (2020) · cites this paper as [2]
Experimental — accuracy not yet measuredIn the citing paper
“A recent analysis, combining mutation, mRNA expression and protein levels, determined that the PI3K/Akt pathway was elevated in basal-like cancers as compared with luminal or HER2+ (47).”
From Mislocalization of the Cell Polarity Protein Scribble Promotes Mammary Tumorigenesis and Is Associated with Basal Breast Cancer (2014) · cites this paper as [47]
In the citing paper
“data analysis of The Cancer Genome Atlas (TCGA) dataset11 identified that hypoxic gene signature, which includes commonly documented hypoxia-responsive genes26, was inversely correlated with immune effector genes (IFNG, TNF, GZMB, PRF1), IFNγ-responsive genes, as well as genes associated with tumor-infiltrating lymphocytes (TILs) in TNBC (n = 99) (also referred as a basal subtype of breast cancer)”
In the citing paper
“Kaplan–Meier analysis using public breast cancer meta-analysis database11,30 reveals that TNBC patients with higher hypoxia gene signature are associated with worse disease outcomes, including both overall survival (OS) and distant metastasis-free survival (DMFS), compared to those with lower expression of hypoxia gene signature (Fig. 1h).”
In the citing paper
“Analysis of hundreds of triple-negative breast cancers revealed a wide diversity of somatic mutations none of which were found at high frequency [4].”
From The hypoxic tumor microenvironment: A driving force for breast cancer progression (2016) · cites this paper as [4]
In the citing paper
“Given the remarkable overexpression of HIF target genes in triple-negative breast cancer [4, 21], the master regulatory role of HIFs in metastasis (Fig. 5), and their involvement in chemotherapy resistance, HIF inhibitors may represent a targeted therapy for these breast cancers.”
From The hypoxic tumor microenvironment: A driving force for breast cancer progression (2016) · cites this paper as [4]
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Molecular portraits of human breast tumours
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Gene expression profiling predicts clinical outcome of breast cancer
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Integrated genomic analyses of ovarian carcinoma
Perou, C. M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/hvp3-wg08 · Cited by 7,927
Comprehensive genomic characterization defines human glioblastoma genes and core pathways
The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735
Self-citationA Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer
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The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
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Repeated observation of breast tumor subtypes in independent gene expression data sets
Sørlie, T., Tibshirani, R. J., Parker, J. S., Hastie, T. J., Marron, J. S., Nobel, A. B., Deng, S., Johnsen, H., Pesich, R., Geisler, S. B., et al. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0932692100 · Cited by 5,508
Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes
Parker, J. S., Mullins, M., Cheang, M. C. U., Leung, S. C. Y., Voduc, D. K., Vickery, T. L., Davies, S. R., Fauron, C. M. ‐., He, X., Hu, Z., et al. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2008.18.1370 · Cited by 4,818
Mitogen-Activated Protein Kinase Pathways Mediated by ERK, JNK, and p38 Protein Kinases
Johnson, G. L., & Lapadat, R. (2002) · Science · doi:10.1126/science.1072682 · Cited by 4,295
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PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal
Gao, J., Aksoy, B. A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S. O., Sun, Y., Jacobsen, A., Sinha, R., Larsson, E., et al. (2013) · Science Signaling · doi:10.1126/scisignal.2004088 · Cited by 15,160
Nishimura, R., Osako, T., Nishiyama, Y., Tashima, R., Nakano, M., Fujisue, M., Toyozumi, Y., & Arima, N. (2013) · Oncology · doi:10.1159/000353099 · Cited by 13,239
Inferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778
The cancer genome atlas pan-cancer analysis project
The Cancer Genome Atlas Research Network, Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., & Stuart, J. M. (2013) · Nature Genetics · doi:10.1038/ng.2764 · Cited by 9,092
Metadata
- Authors
- The Cancer Genome Atlas Network
- Corresponding author
- The Cancer Genome Atlas Network
- Venue
- Nature
- Published
- 2012-09-23
- Volume
- 490
- Issue
- 7418
- Pages
- 61-70
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3465532
- OpenAlex
- W4301159569
- Cited by
- 12,129
- References
- 56
- Citations by year
- 2026: 552 · 2025: 690 · 2024: 761 · 2023: 781 · 2022: 918 · 2021: 1164 · 2020: 1005 · 2019: 961 · 2018: 942 · 2017: 949 · 2016: 979 · 2015: 909 · 2014: 879 · 2013: 590 · 2012: 40
- FWCI
- 256.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- U.S. Department of Defense
- Breast Cancer Research Foundation (P50CA116201)
- Henry M. Jackson Foundation
- National Institutes of Health
- National Human Genome Research Institute (U54HG003067, U54HG003079, U54HG004028)
- National Cancer Institute (R01CA094143, U24CA143882, U01CA084955, U24CA143840, U24CA143858, U24CA144025, K08CA148912, U24CA143866, U24CA143883, U24CA143845, U24CA143867, U24CA143848, U24CA143799, U24CA143835, P50 CA 58223, P30CA016672, P30CA016058, P50CA058223, P50CA116201, P30CA016086)
- U.S. National Library of Medicine (R01LM009722)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Pathology and Forensic Medicine › Breast Lesions and Carcinomas
- Keywords
- breast cancer, molecular subtypes, tumor heterogeneity, somatic mutations, TP53, PIK3CA, GATA3, MAP3K1, luminal A, HER2-enriched breast cancer, basal-like breast cancer, high-grade serous ovarian cancer, DNA methylation, copy number variation, gene expression signatures, signaling pathways, tumor microenvironment, multi-omics integration
- MeSH terms
- Breast Neoplasms, DNA Mutational Analysis, Female, Humans, Mutation, Ovarian Neoplasms, Receptors, Estrogen, RNA, Messenger, RNA, Neoplasm, Genome, Human, Gene Expression Regulation, Neoplastic, Genes, p53, Retinoblastoma Protein, Genes, erbB-2, Genetic Heterogeneity, DNA Methylation, Genes, BRCA1, Phosphatidylinositol 3-Kinases, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Genomics, MicroRNAs, Protein Array Analysis, Proteomics, MAP Kinase Kinase Kinase 1, GATA3 Transcription Factor, Genes, Neoplasm, DNA Copy Number Variations, Class I Phosphatidylinositol 3-Kinases, Exome
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