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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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“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
“In breast cancer, hypoxia is more evident in TNBC than in other breast cancer subtypes11,12.”
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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Cites
Molecular portraits of human breast tumours
Perou, C., Sørlie, T., Eisen, M., van de Rijn, M., Jeffrey, S., Rees, C. A., Pollack, J., Ross, D., Johnsen, H., Akslen, L., et al. (2000) · Nature · doi:10.1038/35021093 · Cited by 16,551
Slamon, D. J., Clark, G. M., Wong, S. G., Levin, W. J., Ullrich, A., & McGuire, W. L. (1987) · Science · doi:10.1126/science.3798106 · Cited by 11,760
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
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
Paik, S., Shak, S., Tang, G., Kim, C., Baker, J. B., Cronin, M. T., Baehner, F. L., Walker, M. G., Watson, D., Park, T., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041588 · Cited by 6,321
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
METABRIC Group, Curtis, C., Shah, S. P., Chin, S.-F., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., et al. (2012) · Nature · doi:10.1038/nature10983 · Cited by 6,097
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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Items 401–500 of 1,085
Weigelt, B., Ghajar, C. M., & Bissell, M. J. (2014) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2014.01.001 · Cited by 347
Advanced Heat Map and Clustering Analysis Using Heatmap3
Zhao, S., Guo, Y. P., Sheng, Q., & Shyr, Y. (2014) · BioMed Research International · doi:10.1155/2014/986048 · Cited by 333
Inflammatory Bowel Disease as a Model for Translating the Microbiome
Huttenhower, C., Kostic, A. D., & Xavier, R. J. (2014) · Immunity · doi:10.1016/j.immuni.2014.05.013 · Cited by 332
GenVisR: Genomic Visualizations in R
Skidmore, Z. L., Wagner, A. H., Lesurf, R., Campbell, K. M., Kunisaki, J., Griffith, O. L., & Griffith, M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw325 · Cited by 338
Rondinelli, B., Gogola, E., Yücel, H., Duarte, A. A., van de Ven, M., van der Sluijs, R., Konstantinopoulos, P. A., Jonkers, J., Ceccaldi, R., Rottenberg, S., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3626 · Cited by 347
Signaling Pathways in Cancer: Therapeutic Targets, Combinatorial Treatments, and New Developments
Yip, H. Y. K., & Papa, A. (2021) · Cells · doi:10.3390/cells10030659 · Cited by 341
Intron retention is a widespread mechanism of tumor-suppressor inactivation
Jung, H., Lee, D., Lee, J., Park, D., Kim, Y. J., Park, W., Hong, D., Park, P. J., & Lee, E. (2015) · Nature Genetics · doi:10.1038/ng.3414 · Cited by 344
Targeting the PI3-kinase pathway in triple-negative breast cancer
Pascual, J., & Turner, N. C. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz133 · Cited by 339
Subtyping of triple‐negative breast cancer: Implications for therapy
Abramson, V. G., Lehmann, B. D., Ballinger, T. J., & Pietenpol, J. A. (2014) · Cancer · doi:10.1002/cncr.28914 · Cited by 329
Zhang, Y., He, Q., Hu, Z., Feng, Y., Fan, L., Tang, Z., Yuan, J., Shan, W., Li, C., Hu, Z., et al. (2016) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3211 · Cited by 340
MET-dependent solid tumours — molecular diagnosis and targeted therapy
Guo, R., Luo, J., Chang, J. C., Rekhtman, N., Arcila, M. E., & Drilon, A. E. D. C. (2020) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-0377-z · Cited by 333
Comprehensive Analysis of Genetic Ancestry and Its Molecular Correlates in Cancer
Carrot‐Zhang, J., Chambwe, N., Damrauer, J. S., Knijnenburg, T., Robertson, A. G., Yau, C. S. F., Zhou, W., Berger, A. C., Huang, K., Newberg, J. Y., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.04.012 · Cited by 331
Proteomic maps of breast cancer subtypes
Tyanova, S., Albrechtsen, R., Kronqvist, P., Cox, J., Mann, M., & Geiger, T. (2016) · Nature Communications · doi:10.1038/ncomms10259 · Cited by 330
PIK3CAH1047R induces multipotency and multi-lineage mammary tumours
Koren, S., Reavie, L. B., Couto, J. P., Silva, D. D., Stadler, M. B., Roloff, T. C., Britschgi, A., Eichlisberger, T., Kohler, H., Aina, O. H., et al. (2015) · Nature · doi:10.1038/nature14669 · Cited by 335
Gupta, G. K., Collier, A. L., Lee, D. C., Hoefer, R. A., Zheleva, V. P., van Reesema, L. L. S., Tang-Tan, A. M., Guye, M. L., Chang, D. Z., Winston, J. S., et al. (2020) · Cancers · doi:10.3390/cancers12092392 · Cited by 336
Diverse Mechanisms of Somatic Structural Variations in Human Cancer Genomes
Yang, L., Luquette, L. J., Gehlenborg, N., Xi, R., Haseley, P. S., Hsieh, C.-H., Zhang, C., Ren, X., Protopopov, A. I., Chin, L., et al. (2013) · Cell · doi:10.1016/j.cell.2013.04.010 · Cited by 329
Loss of NF1 in Cutaneous Melanoma Is Associated with RAS Activation and MEK Dependence
Nissan, M. H., Pratilas, C. A., Jones, A. M., Ramirez, R. J., Won, H. H., Liu, C., Tiwari, S., Kong, L., Hanrahan, A. J., Yao, Z., et al. (2014) · Cancer Research · doi:10.1158/0008-5472.can-13-2625 · Cited by 345
The genomic landscape of small intestine neuroendocrine tumors
Banck, M. S., Kanwar, R., Kulkarni, A. A., Boora, G. K., Metge, F., Kipp, B. R., Zhang, L., Thorland, E. C., Minn, K. T., Tentu, R., et al. (2013) · Journal of Clinical Investigation · doi:10.1172/jci67963 · Cited by 325
Zhao, W., He, X., Hoadley, K. A., Parker, J. S., Hayes, D. N., & Perou, C. M. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-419 · Cited by 338
A multivariate approach to the integration of multi-omics datasets
Meng, C., Küster, B., Culhane, A. C., & Gholami, A. M. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-162 · Cited by 336
Eliyatkın, N., Yalcin, E., Zengel, B., Aktaş, S., & Vardar, E. (2015) · Meme sağlığı dergisi/Meme sağlığı dergisi · doi:10.5152/tjbh.2015.1669 · Cited by 343
Systemic Therapy for Estrogen Receptor–Positive, HER2-Negative Breast Cancer
Burstein, H. J. (2020) · New England Journal of Medicine · doi:10.1056/nejmra1307118 · Cited by 316
Genomic Characterization of Primary Invasive Lobular Breast Cancer
Desmedt, C., Zoppoli, G. M., Gundem, G., Pruneri, G., Larsimont, D., Fornili, M., Fumagalli, D., Brown, D. N., Rothé, F., Vincent, D., et al. (2016) · Journal of Clinical Oncology · doi:10.1200/jco.2015.64.0334 · Cited by 316
Marra, A., Trapani, D., Viale, G., Criscitiello, C., & Curigliano, G. (2020) · npj Breast Cancer · doi:10.1038/s41523-020-00197-2 · Cited by 332
Precision medicine for metastatic breast cancer—limitations and solutions
Arnedos, M., Vicier, C., Loi, S., Lefèbvre, C., Michiels, S., Bonnefoi, H. R., & André, F. (2015) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2015.123 · Cited by 322
SHP2 inhibition prevents adaptive resistance to MEK inhibitors in multiple cancer models
Fedele, C., Ran, H., Diskin, B., Wei, W., Jen, J., Geer, M. J., Araki, K., Ozerdem, U., Simeone, D. M., Miller, G., et al. (2018) · Cancer Discovery · doi:10.1158/2159-8290.cd-18-0444 · Cited by 315
Quiescent cancer cells resist T cell attack by forming an immunosuppressive niche
Baldominos, P., Barbera-Mourelle, A., Barreiro, O., Huang, Y., Wight, A. E., Cho, J.-W., Zhao, X., Estivill, G., Adam, I., Sanchez, X., et al. (2022) · Cell · doi:10.1016/j.cell.2022.03.033 · Cited by 318
Ellis, M. J., Gillette, M. A., Carr, S. A., Paulovich, A. G., Smith, R. D., Rodland, K., Townsend, R. R., Kinsinger, C. R., Mesri, M., Rodriguez, H., et al. (2013) · Cancer Discovery · doi:10.1158/2159-8290.cd-13-0219 · Cited by 321
Mechanisms of resistance to trastuzumab emtansine (T-DM1) in HER2-positive breast cancer
Hunter, F. W., Barker, H. R., Lipert, B. A., Rothé, F., Gebhart, G., Piccart, M. J., Sotiriou, C. P., & Jamieson, S. M. F. (2019) · British Journal of Cancer · doi:10.1038/s41416-019-0635-y · Cited by 311
Identification of differentially methylated cell types in epigenome-wide association studies
Zheng, S., Breeze, C. E., Beck, S., & Teschendorff, A. E. (2018) · Nature Methods · doi:10.1038/s41592-018-0213-x · Cited by 318
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge
Veta, M., Heng, Y. J., Stathonikos, N., Bejnordi, B. E., de Beça, F. F., Wollmann, T., Rohr, K., Shah, M., Wang, D., Rousson, M., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.02.012 · Cited by 309
Lock, E. F., & Dunson, D. B. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt425 · Cited by 320
–Omic and Electronic Health Record Big Data Analytics for Precision Medicine
Wu, P.-Y., Cheng, C., Kaddi, C. D., Venugopalan, J., Hoffman, R., & Wang, M. D. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2573285 · Cited by 317
Angus, L., Smid, M., Wilting, S. M., van Riet, J., van Hoeck, A., Nguyen, L., Nik‐Zainal, S., Steenbruggen, T. G., Tjan‐Heijnen, V. C. G., Labots, M., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0507-7 · Cited by 315
Landscape of tumor-infiltrating T cell repertoire of human cancers
Li, B., Li, T., Pignon, J.-C., Wang, B., Wang, J., Shukla, S. A., Dou, R., Chen, Q., Hodi, F. S., Choueiri, T. K., et al. (2016) · Nature Genetics · doi:10.1038/ng.3581 · Cited by 306
Zhao, S. G., Chang, S. L., Erho, N., Yu, M., Lehrer, J. M., Alshalalfa, M., Speers, C. W., Cooperberg, M. R., Kim, W. B., Ryan, C. J., et al. (2017) · JAMA Oncology · doi:10.1001/jamaoncol.2017.0751 · Cited by 306
Xu, J., Yang, P., Xue, S., Sharma, B., Sanchez-Martin, M., Wang, F., Beaty, K. A., Dehan, E., & Parikh, B. (2019) · Human Genetics · doi:10.1007/s00439-019-01970-5 · Cited by 305
The NF1 somatic mutational landscape in sporadic human cancers
Philpott, C., Tovell, H., Frayling, I. M., Cooper, D. N., & Upadhyaya, M. (2017) · Human Genomics · doi:10.1186/s40246-017-0109-3 · Cited by 318
GLUT1 inhibition blocks growth of RB1-positive triple negative breast cancer
Wu, Q., Ba-Alawi, W., Deblois, G., Cruickshank, J., Duan, S., Lima‐Fernandes, E., Haight, J., Tonekaboni, S. A. M., Fortier, A.-M., Kuasne, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18020-8 · Cited by 305
ClearCode34: A Prognostic Risk Predictor for Localized Clear Cell Renal Cell Carcinoma
Brooks, S. A., Brannon, A. R., Parker, J. S., Fisher, J. C., Sen, O., Kattan, M. W., Hakimi, A. A., Hsieh, J. J.-D., Choueiri, T. K., Tamboli, P., et al. (2014) · European Urology · doi:10.1016/j.eururo.2014.02.035 · Cited by 307
TP53 Mutation Spectrum in Breast Cancer Is Subtype Specific and Has Distinct Prognostic Relevance
Silwal‐Pandit, L., Vollan, H. K. M., Chin, S., Rueda, O. M., McKinney, S., Osako, T., Quigley, D. A., Kristensen, V. N., Aparicio, S. A. J. R., Børresen‐Dale, A., et al. (2014) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-2943 · Cited by 304
PKM2 methylation by CARM1 activates aerobic glycolysis to promote tumorigenesis
Liu, F., Ma, F., Wang, Y., Hao, L., Zeng, H., Jia, C., Wang, Y., Liu, P., Ong, I. M., Li, B., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3630 · Cited by 306
Koch, A., Jeschke, J., Criekinge, W. V., van Engeland, M., & Meyer, T. D. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz445 · Cited by 304
Jiang, Y.-Z., Liu, Y., Xiao, Y., Hu, X., Jiang, L., Zuo, W., Ma, D., Ding, J.-H., Zhu, X., Zou, J., et al. (2020) · Cell Research · doi:10.1038/s41422-020-0375-9 · Cited by 304
Samanta, D., Park, Y., Ni, X., Li, H., Zahnow, C. A., Gabrielson, E., Pan, F., & Semenza, G. L. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1718197115 · Cited by 302
Intrinsic basal and luminal subtypes of muscle-invasive bladder cancer
Choi, W., Czerniak, B. A., Ochoa, A. E., Su, X., Siefker-Radtke, A. O., Dinney, C. P., & McConkey, D. J. (2014) · Nature Reviews Urology · doi:10.1038/nrurol.2014.129 · Cited by 306
Current approaches to the treatment of metastatic brain tumours
Owonikoko, T. K., Arbiser, J. L., Zelnak, A. B., Shu, H. G., Shim, H., Robin, A. M., Kalkanis, S. N., Whitsett, T. G., Salhia, B., Tran, N. L., et al. (2014) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2014.25 · Cited by 302
A new genome‐driven integrated classification of breast cancer and its implications
Dawson, S., Rueda, O. M., Aparicio, S. A. J. R., & Caldas, C. (2013) · The EMBO Journal · doi:10.1038/emboj.2013.19 · Cited by 314
Recurrent and functional regulatory mutations in breast cancer
Rheinbay, E., Parasuraman, P., Grimsby, J., Tiao, G., Engreitz, J., Kim, J., Lawrence, M. S., Taylor-Weiner, A. N., Rodriguez-Cuevas, S. A., Rosenberg, M. W., et al. (2017) · Nature · doi:10.1038/nature22992 · Cited by 311
Urea Cycle Dysregulation Generates Clinically Relevant Genomic and Biochemical Signatures
Lee, J. S., Adler, L. N., Karathia, H., Carmel, N. N., Rabinovich, S., Auslander, N., Keshet, R., Stettner, N., Silberman, A., Agemy, L., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.019 · Cited by 304
DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer
Bashashati, A., Haffari, G., Ding, J., Ha, G., Lui, K., Rosner, J., Huntsman, D. D., Caldas, C., Aparicio, S. A. J. R., & Shah, S. P. (2012) · Genome biology · doi:10.1186/gb-2012-13-12-r124 · Cited by 301
RETRACTED: Collagen Prolyl Hydroxylases Are Essential for Breast Cancer Metastasis
Gilkes, D. M., Chaturvedi, P., Bajpai, S., Wong, C. C., Wei, H., Pitcairn, S., Hubbi, M. E., Wirtz, D., & Semenza, G. L. (2013) · Cancer Research · doi:10.1158/0008-5472.can-12-3963 · Cited by 296
RetractedIntegrated analysis of germline and somatic variants in ovarian cancer
Kanchi, K., Johnson, K. J., Lu, C., McLellan, M. D., Leiserson, M. D. M., Wendl, M. C., Zhang, Q., Koboldt, D. C., Xie, M., Kandoth, C., et al. (2014) · Nature Communications · doi:10.1038/ncomms4156 · Cited by 308
Targeting DNA damage response in cancer therapy
Hosoya, N., & Miyagawa, K. (2014) · Cancer Science · doi:10.1111/cas.12366 · Cited by 309
TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages
Silva, T. C., Colaprico, A., Olsen, C., D’Angelo, F., Bontempi, G., Ceccarelli, M., & Noushmehr, H. (2016) · F1000Research · doi:10.12688/f1000research.8923.1 · Cited by 303
Targeting AKT for cancer therapy
Shariati, M., & Meric‐Bernstam, F. (2019) · Expert Opinion on Investigational Drugs · doi:10.1080/13543784.2019.1676726 · Cited by 295
Biology and Management of Patients With Triple-Negative Breast Cancer
Sharma, P. (2016) · The Oncologist · doi:10.1634/theoncologist.2016-0067 · Cited by 294
Proteogenomic Characterization of Human Early-Onset Gastric Cancer
Mun, D.-G., Bhin, J., Kim, S., Kim, H., Jung, J. H., Jung, Y., Jang, Y. E., Park, J. M., Kim, H., Jung, Y., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2018.12.003 · Cited by 294
Integrative multi-omics approaches in cancer research: from biological networks to clinical subtypes
Heo, Y. J., Hwa, C., Lee, G.-H., Park, J.-M., & An, J.-Y. (2021) · Molecules and Cells · doi:10.14348/molcells.2021.0042 · Cited by 294
The Molecular Balancing Act of p16INK4a in Cancer and Aging
LaPak, K. M., & Burd, C. E. (2013) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-13-0350 · Cited by 304
Patterns and functional implications of rare germline variants across 12 cancer types
Lu, C., Xie, M., Wendl, M. C., Wang, J., McLellan, M. D., Leiserson, M. D. M., Huang, K., Wyczalkowski, M. A., Jayasinghe, R. G., Banerjee, T., et al. (2015) · Nature Communications · doi:10.1038/ncomms10086 · Cited by 298
Statistical and integrative system-level analysis of DNA methylation data
Teschendorff, A. E., & Relton, C. L. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.86 · Cited by 297
Comprehensive omic characterization of breast cancer in Mexican-Hispanic women
Romero-Cordoba, S. L., Salido-Guadarrama, I., Rebollar‐Vega, R., Bautista‐Piña, V., Domínguez-Reyes, C. A., Tenorio‐Torres, A., Villegas‐Carlos, F., Fernández-López, J. C., Uribe-Figueroa, L., Alfaro-Ruíz, L. A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22478-5 · Cited by 293
An update on vitamin D signaling and cancer
Carlberg, C., & Múñoz, A. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.05.018 · Cited by 298
Targeted Therapies for Triple-Negative Breast Cancer: Combating a Stubborn Disease
Kalimutho, M., Parsons, K., Mittal, D., López, J. A., Srihari, S., & Khanna, K. K. (2015) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2015.08.009 · Cited by 298
Alsuliman, A., Çolak, D., Al‐Harazi, O., Fitwi, H., Tulbah, A. M., Al‐Tweigeri, T., Al-Alwan, M. M., & Ghebeh, H. (2015) · Molecular Cancer · doi:10.1186/s12943-015-0421-2 · Cited by 292
Modeling precision treatment of breast cancer
Daemen, A., Griffith, O. L., Heiser, L. M., Wang, N. J., Enache, O. M., Sanborn, Z., Pepin, F., Durinck, S., Korkola, J. E., Griffith, M., et al. (2013) · Genome biology · doi:10.1186/gb-2013-14-10-r110 · Cited by 291
Ganly, I., Makarov, V., Vasudeva, S. D., Dong, Y., Reznik, E., Seshan, V., Nanjangud, G. J., Eng, S., Bose, P., Kuo, F., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.07.002 · Cited by 289
Small Molecule Kinase Inhibitor Drugs (1995–2021): Medical Indication, Pharmacology, and Synthesis
Ayala-Aguilera, C. C., Valero, T., Lorente‐Macías, Á., Baillache, D. J., Croke, S., & Unciti‐Broceta, A. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c00963 · Cited by 294
Logue, S. E., McGrath, E. P., Cleary, P., Greene, S., Mnich, K., Almanza, A., Chevet, É., Dwyer, R. M., Oommen, A. M., Legembre, P., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05763-8 · Cited by 300
Jenkins, L., Jungwirth, U., Avgustinova, A., Iravani, M., Mills, A., Haider, S. A., Harper, J. A., & Isacke, C. M. (2022) · Cancer Research · doi:10.1158/0008-5472.can-21-4141 · Cited by 293
Priorities for the primary prevention of breast cancer
Colditz, G. A., & Bohlke, K. (2014) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21225 · Cited by 296
Exosomes Increase the Therapeutic Index of Doxorubicin in Breast and Ovarian Cancer Mouse Models
Hadla, M., Palazzolo, S., Corona, G., Caligiuri, I., Canzonieri, V., Toffoli, G., & Rizzolio, F. (2016) · Nanomedicine · doi:10.2217/nnm-2016-0154 · Cited by 289
Double PIK3CA mutations in cis increase oncogenicity and sensitivity to PI3Kα inhibitors
Vasan, N., Razavi, P., Johnson, J. L., Shao, H., Shah, H., Antoine, A., Ladewig, E., Gorelick, A. N., Lin, T.-Y., Toska, E., et al. (2019) · Science · doi:10.1126/science.aaw9032 · Cited by 296
Distinct structural classes of activating FOXA1 alterations in advanced prostate cancer
Parolia, A., Cieslik, M., Chu, S.-C., Xiao, L., Ouchi, T., Zhang, Y., Wang, X., Vats, P., Cao, X., Pitchiaya, S., et al. (2019) · Nature · doi:10.1038/s41586-019-1347-4 · Cited by 284
Meyer, A. S., Miller, M. A., Gertler, F. B., & Lauffenburger, D. A. (2013) · Science Signaling · doi:10.1126/scisignal.2004155 · Cited by 283
Oncogenic PI3K and K-Ras stimulate de novo lipid synthesis through mTORC1 and SREBP
Ricoult, S. J. H., Yecies, J. L., Ben‐Sahra, I., & Manning, B. D. (2015) · Oncogene · doi:10.1038/onc.2015.179 · Cited by 288
Lehmann, B. D., Colaprico, A., Silva, T. C., Chen, J., An, H., Ban, Y. J., Huang, H., Wang, L. H.-C., James, J. L., Balko, J. M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26502-6 · Cited by 298
Huo, D., Hu, H., Rhie, S. K., Gamazon, E. R., Cherniack, A. D., Liu, J., Yoshimatsu, T. F., Pitt, J. J., Hoadley, K. A., Troester, M. A., et al. (2017) · JAMA Oncology · doi:10.1001/jamaoncol.2017.0595 · Cited by 290
The CDK4/6 inhibitor revolution — a game-changing era for breast cancer treatment
Morrison, L., Loibl, S., & Turner, N. C. (2023) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-023-00840-4 · Cited by 290
Therapeutic advances of targeting receptor tyrosine kinases in cancer
Tomuleasa, C., Tigu, A.-B., Munteanu, R. A., Moldovan, C. S., Kegyes, D., Onaciu, A., Gulei, D., Ghiaur, G., Einsele, H., & Croce, C. M. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01899-w · Cited by 307
Cancer-specific defects in DNA repair pathways as targets for personalized therapeutic approaches
Dietlein, F., Thelen, L., & Reinhardt, H. C. (2014) · Trends in Genetics · doi:10.1016/j.tig.2014.06.003 · Cited by 290
Patient-derived xenograft models of breast cancer and their predictive power
Whittle, J. R., Lewis, M. T., Lindeman, G. J., & Visvader, J. E. (2015) · Breast Cancer Research · doi:10.1186/s13058-015-0523-1 · Cited by 293
Targeting the NFκB Signaling Pathways for Breast Cancer Prevention and Therapy
Wang, W., Nag, S. A., & Zhang, R. (2014) · Current Medicinal Chemistry · doi:10.2174/0929867321666141106124315 · Cited by 287
Breast Tumor Heterogeneity: Source of Fitness, Hurdle for Therapy
Koren, S., & Bentires‐Alj, M. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.10.031 · Cited by 289
Signaling through cyclin D-dependent kinases
Choi, Y., & Anders, L. (2013) · Oncogene · doi:10.1038/onc.2013.137 · Cited by 289
Li-Fraumeni syndrome: cancer risk assessment and clinical management
McBride, K. A., Ballinger, M. L., Killick, E., Kirk, J., Tattersall, M. H. N., Eeles, R. A., Thomas, D. M., & Mitchell, G. (2014) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2014.41 · Cited by 286
Past, Present, and Future Challenges in Breast Cancer Treatment
Sledge, G. W., Mamounas, E. P., Hortobágyi, G. N., Burstein, H. J., Goodwin, P. J., & Wolff, A. C. (2014) · Journal of Clinical Oncology · doi:10.1200/jco.2014.55.4139 · Cited by 285
Breast Cancer Molecular Stratification
Russnes, H. G., Lingjærde, O. C., Børresen‐Dale, A., & Caldas, C. (2017) · American Journal Of Pathology · doi:10.1016/j.ajpath.2017.04.022 · Cited by 277
Collagen prolyl 4-hydroxylase 1 is essential for HIF-1α stabilization and TNBC chemoresistance
Xiong, G., Stewart, R. L., Chen, J., Gao, T., Scott, T. L., Samayoa, L. M., O’Connor, K. L., Lane, A. N., & Xu, R. (2018) · Nature Communications · doi:10.1038/s41467-018-06893-9 · Cited by 283
Sebestyén, E., Singh, B., Miñana, B., Pagès, A., Mateo, F., Pujana, M. A., Valcárcel, J., & Eyras, E. (2016) · Genome Research · doi:10.1101/gr.199935.115 · Cited by 276
Iglesia, M. D., Vincent, B. G., Parker, J. S., Hoadley, K. A., Carey, L., Perou, C. M., & Serody, J. S. (2014) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-3368 · Cited by 282
Luo, N., Nixon, M. J., Gonzalez-Ericsson, P. I., Sanchez, V., Opalenik, S. R., Li, H., Zahnow, C. A., Nickels, M. L., Liu, F., Tantawy, M. N., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02630-w · Cited by 275
A Comprehensive Immunologic Portrait of Triple-Negative Breast Cancer
Liu, Z., Li, M., Jiang, Z., & Wang, X. (2018) · Translational Oncology · doi:10.1016/j.tranon.2018.01.011 · Cited by 275
Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment
Pinker, K., Chin, J., Melsaether, A. N., Morris, E. A., & Moy, L. (2018) · Radiology · doi:10.1148/radiol.2018172171 · Cited by 275
Targeting BCL-2 with the BH3 Mimetic ABT-199 in Estrogen Receptor-Positive Breast Cancer
Vaillant, F., Mérino, D., Lee, L., Breslin, K. A., Pal, B., Ritchie, M. E., Smyth, G. K., Christie, M., Phillipson, L. J., Burns, C. J., et al. (2013) · Cancer Cell · doi:10.1016/j.ccr.2013.06.002 · Cited by 275
Precision oncology for acute myeloid leukemia using a knowledge bank approach
Gerstung, M., Papaemmanuil, E., Martin, S., Bullinger, L., Gaidzik, V. I., Paschka, P., Heuser, M., Thol, F. R., Bolli, N., Ganly, P. S., et al. (2017) · Nature Genetics · doi:10.1038/ng.3756 · Cited by 286
Translational AI and deep learning in diagnostic pathology
Serag, A., Ion-Margineanu, A., Qureshi, H., McMillan, R., Saint Martin, M.-J., Diamond, J., O'Reilly, P., & Hamilton, P. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00185 · Cited by 274
Role of hypoxia-inducible factors in breast cancer metastasis
Gilkes, D. M., & Semenza, G. L. (2013) · Future Oncology · doi:10.2217/fon.13.92 · Cited by 274
Molecular classification of breast cancer
Vuong, D., Simpson, P. T., Green, B., Cummings, M. C., & Lakhani, S. R. (2014) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-014-1593-7 · Cited by 299
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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