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The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
Open accessAbstract
The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA–RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the ‘CNA-devoid’ subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.
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“Currently, numerous research efforts are focused on utilizing genetic data and artificial intelligence algorithms to develop diagnostic models to enhance the clinical efficiency and accuracy of breast cancer diagnosis [109,110,111].”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [111]
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“This framework was also employed for a landmark study which used genomic and transcriptomic data from 2,000 breast tumours and discovered novel subgroups amongst them (Curtis et al., 2012).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Curtis et al., 2012
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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
Self-citationNishimura, 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
Sørlie, T., Perou, C. M., Tibshirani, R. J., Aas, T., Geisler, S. B., Johnsen, H., Hastie, T. J., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., et al. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.191367098 · Cited by 11,506
Self-citationSupervised 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
Li, J., Yen, C., Liaw, D., Podsypanina, K., Bose, S., Wang, S. I., Puc, J., Miliaresis, C., Rodgers, L., McCombie, R., et al. (1997) · Science · doi:10.1126/science.275.5308.1943 · Cited by 4,785
Ki67 expression in breast cancer
Elkablawy, M. A., Albasri, A. M., Mohammed, R. A., Hussainy, A. S., Nouh, M. M., & Alhujaily, A. S. (2016) · Saudi Medical Journal · doi:10.15537/smj.2016.2.12285 · Cited by 4,382
Diagnosis of multiple cancer types by shrunken centroids of gene expression
Tibshirani, R. J., Hastie, T. J., Narasimhan, B., & Chu, G. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082099299 · Cited by 2,931
Tumor-Infiltrating CD8+Lymphocytes Predict Clinical Outcome in Breast Cancer
Mahmoud, S., Paish, E. C., Powe, D. G., Macmillan, R. D., Grainge, M. J., Lee, A. H. S., Ellis, I. O., & Green, A. R. (2011) · Journal of Clinical Oncology · doi:10.1200/jco.2010.30.5037 · Cited by 1,411
Self-citationGenomic and transcriptional aberrations linked to breast cancer pathophysiologies
Chin, K., DeVries, S., Fridlyand, J., Spellman, P. T., Roydasgupta, R., Kuo, W.-L., Lapuk, A. V., Neve, R. M., Qian, Z., Ryder, T., et al. (2006) · Cancer Cell · doi:10.1016/j.ccr.2006.10.009 · Cited by 1,309
Frequent Mutations of Chromatin Remodeling Gene ARID1A in Ovarian Clear Cell Carcinoma
Jones, S., Wang, T., Shih, I., Mao, T., Nakayama, K., Roden, R. B. S., Glas, R., Slamon, D. J., Diaz, L. A., Vogelstein, B., et al. (2010) · Science · doi:10.1126/science.1196333 · Cited by 1,218
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Items 301–400 of 1,028
Mérino, D., Whittle, J. R., Vaillant, F., Serrano, A., Gong, J.-N., Giner, G., Maragno, A. L., Chanrion, M., Schneider, E., Pal, B., et al. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aam7049 · Cited by 195
Modeling Breast Cancer Using CRISPR-Cas9–Mediated Engineering of Human Breast Organoids
Dekkers, J. F., Whittle, J. R., Vaillant, F., Chen, H., Dawson, C. A., Liu, K., Geurts, M. H., Herold, M. J., Clevers, H. C., Lindeman, G. J., et al. (2019) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djz196 · Cited by 194
Hubert, P., Roncarati, P., Demoulin, S., Pilard, C., Ancion, M., Reynders, C., Lerho, T., Bruyère, D., Lebeau, A., Radermecker, C., et al. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-001966 · Cited by 191
Functionalized liposomes for targeted breast cancer drug delivery
Nel, J., Elkhoury, K., Velot, É., Bianchi, A., Acherar, S., Francius, G., Tamayol, A., Grandemange, S., & Arab‐Tehrany, E. (2023) · Bioactive Materials · doi:10.1016/j.bioactmat.2022.12.027 · Cited by 193
Kennedy, J. J., Abbatiello, S. E., Kim, K., Yan, P., Whiteaker, J. R., Lin, C., Kim, J. S., Zhang, Y., Wang, X., Ivey, R. G., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2763 · Cited by 184
In vivomodels in breast cancer research: progress, challenges and future directions
Holen, I., Speirs, V., Morrissey, B., & Blyth, K. (2017) · Disease Models & Mechanisms · doi:10.1242/dmm.028274 · Cited by 183
Blasco-Benito, S., Seijo-Vila, M., Caro-Villalobos, M., Tundidor, I., Andradas, C., García-Taboada, E., Wade, J., Smith, S., Guzmán, M., Pérez-Gómez, E., et al. (2018) · Biochemical Pharmacology · doi:10.1016/j.bcp.2018.06.025 · Cited by 183
FOXM1 (Forkhead box M1) in Tumorigenesis
Wierstra, I. (2013) · Advances in cancer research · doi:10.1016/b978-0-12-407190-2.00016-2 · Cited by 190
Absolute Assignment of Breast Cancer Intrinsic Molecular Subtype
Paquet, É. R., & Hallett, M. T. (2014) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/dju357 · Cited by 186
Aberrant DNA methylation impacts gene expression and prognosis in breast cancer subtypes
Győrffy, B., Bottai, G., Fleischer, T., Munkácsy, G., Budczies, J., Paladini, L., Børresen‐Dale, A., Kristensen, V. N., & Santarpia, L. (2015) · International Journal of Cancer · doi:10.1002/ijc.29684 · Cited by 182
Self-citationSynthetic lethal combination targeting BET uncovered intrinsic susceptibility of TNBC to ferroptosis
Verma, N., Vinik, Y., Saroha, A., Nair, N. U., Ruppin, E., Mills, G. B., Karn, T., Dubey, V., Khera, L., Raj, H., et al. (2020) · Science Advances · doi:10.1126/sciadv.aba8968 · Cited by 184
Nagarajan, S., Rao, S. V., Sutton, J., Cheeseman, D., Dunn, S., Papachristou, E. K., Prada, J.-E. G., Couturier, D., Kumar, S. S., Kishore, K., et al. (2020) · Nature Genetics · doi:10.1038/s41588-019-0541-5 · Cited by 183
Self-citationIntegrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer
Siegel, M. B., He, X., Hoadley, K. A., Hoyle, A. P., Pearce, J. B., Garrett, A., Kumar, S., Moylan, V. J., Brady, C., Swearingen, A. E. D. V., et al. (2018) · Journal of Clinical Investigation · doi:10.1172/jci96153 · Cited by 186
Aftimos, P. G., Oliveira, M., Irrthum, A., Fumagalli, D., Sotiriou, C. P., Gal‐Yam, E. N., Robson, M. E., Ndozeng, J., Leo, A. D., Ciruelos, E., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1647 · Cited by 180
A stemness-related ZEB1–MSRB3 axis governs cellular pliancy and breast cancer genome stability
Morel, A., Ginestier, C., Pommier, R. M., Cabaud, O., Ruiz, E. M., Wicinski, J., Devouassoux‐Shisheboran, M., Combaret, V., Finetti, P., Chassot, C., et al. (2017) · Nature Medicine · doi:10.1038/nm.4323 · Cited by 189
The Spectrum of Triple-Negative Breast Disease
Geyer, F. C., Pareja, F. G., Weigelt, B., Rakha, E. A., Ellis, I. O., Schnitt, S. J., & Reis-Filho, J. S. (2017) · American Journal Of Pathology · doi:10.1016/j.ajpath.2017.03.016 · Cited by 189
Self-citationKay, E. J., Paterson, K., Riera‐Domingo, C., Sumpton, D. P., Däbritz, J. H. M., Tardito, S., Boldrini, C., Hernandez-Fernaud, J. R., Athineos, D., Dhayade, S., et al. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00582-0 · Cited by 197
Triple-Negative Breast Cancer and the Need for New Therapeutic Targets
Engebraaten, O., Vollan, H. K. M., & Børresen‐Dale, A. (2013) · American Journal Of Pathology · doi:10.1016/j.ajpath.2013.05.033 · Cited by 178
Self-citationLu, K., Alcivar, A. L., Ma, J., Foo, T. K., Zywea, S., Mahdi, A., Huo, Y., Kensler, T. W., Gatza, M. L., & Xia, B. (2017) · Cancer Research · doi:10.1158/0008-5472.can-16-2204 · Cited by 181
Therapeutically Active RIG-I Agonist Induces Immunogenic Tumor Cell Killing in Breast Cancers
Elion, D. L., Jacobson, M. E., Hicks, D. J., Rahman, B., Sanchez, V. M., Ericsson, P. I. G., Fedorova, O., Pyle, A. M., Wilson, J. T., & Cook, R. S. (2018) · Cancer Research · doi:10.1158/0008-5472.can-18-0730 · Cited by 178
A novel approach for data integration and disease subtypingPreprint
Nguyen, T., Tagett, R., Diaz, D., & Drăghici, S. (2017) · Genome Research · doi:10.1101/gr.215129.116 · Cited by 183
Triple-Negative Breast Cancer: Molecular Subtypes and New Targets for Therapy
Lehmann, B. D., Pietenpol, J. A., & Tan, A. R. (2015) · American Society of Clinical Oncology Educational Book · doi:10.14694/edbook_am.2015.35.e31 · Cited by 181
Tan, V. Y., Lewis, S. J., Adams, J. C., & Martin, R. M. (2013) · BMC Medicine · doi:10.1186/1741-7015-11-52 · Cited by 179
Costa, R. L. B., Shah, A. N., Santa-Maria, C. A., Cruz, M. R. D. S., Mahalingam, D., Carneiro, B. A., Chae, Y. K., Cristofanilli, M., Gradishar, W. J., & Giles, F. J. (2017) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2016.12.010 · Cited by 177
Circulating tumour cells and cell-free DNA as tools for managing breast cancer
Mattos‐Arruda, L. D., Cortés, J., Santarpia, L., Vivancos, A. P., Tabernero, J. M., Reis-Filho, J. S., & Seoane, J. (2013) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2013.80 · Cited by 176
Re-definition of claudin-low as a breast cancer phenotype
Fougner, C., Bergholtz, H., Norum, J. H., & Sørlie, T. (2020) · Nature Communications · doi:10.1038/s41467-020-15574-5 · Cited by 175
Inferring biological tasks using Pareto analysis of high-dimensional data
Hart, Y., Sheftel, H., Hausser, J., Székely, P., Moshe, N. B. B., Kohanim, Y. K., Tendler, A., Mayo, A., & Alon, U. S. (2015) · Nature Methods · doi:10.1038/nmeth.3254 · Cited by 174
Rare Breast Cancer Subtypes: Histological, Molecular, and Clinical Peculiarities
Dieci, M. V., Orvieto, E., Dominici, M., Conte, P., & Guarneri, V. (2014) · The Oncologist · doi:10.1634/theoncologist.2014-0108 · Cited by 174
Kumar, S., Wilkes, D. W., Samuel, N., Blanco, M. A., Nayak, A., Alicea-Torres, K. M., Gluck, C. B., Sinha, S., Gabrilovich, D. I., & Chakrabarti, R. (2018) · Journal of Clinical Investigation · doi:10.1172/jci99673 · Cited by 174
Ahmad, A., Mubarak, N. M., Jannat, F. T., Ashfaq, T., Santulli, C., Rizwan, M., Najda, A., Bin-Jumah, M. N., Abdel‐Daim, M. M., Hussain, S., et al. (2021) · Processes · doi:10.3390/pr9010137 · Cited by 175
Tackling the Diversity of Triple-Negative Breast Cancer
Turner, N. C., & Reis-Filho, J. S. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-0915 · Cited by 175
Rios, A. C., Capaldo, B. D., Vaillant, F., Pal, B., van Ineveld, R. L., Dawson, C. A., Chen, Y., Nolan, E., Fu, N. Y., Jackling, F. C., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.02.010 · Cited by 175
Michaut, M., Chin, S., Majewski, I. J., Severson, T., Bismeijer, T., de Koning, L., Peeters, J. K., Schouten, P. C., Rueda, O. M., Bosma, A. J., et al. (2016) · Scientific Reports · doi:10.1038/srep18517 · Cited by 174
Self-citationNagpal, C., Li, X., & Dubrawski, A. W. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3052441 · Cited by 190
Li, H., Liang, J.-L., Kuo, Y., Lee, H.-H., Calkins, M. J., Chang, H.-T., Lin, F., Chen, Y., Hsu, T., Hsiao, M., et al. (2017) · Breast Cancer Research · doi:10.1186/s13058-017-0918-2 · Cited by 169
RNA sequencing of cancer reveals novel splicing alterations
Eswaran, J., Horvath, A., Godbole, S., Reddy, S. D. N., Mudvari, P., Ohshiro, K., Cyanam, D., Nair, S. S., Fuqua, S. A. W., Polyák, K., et al. (2013) · Scientific Reports · doi:10.1038/srep01689 · Cited by 176
Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes
Collaborators, G. S., Fachal, L., Collaborators, E., KConFab Investigators, HEBON Investigators, ABCTB Investigators, Aschard, H., Beesley, J., Barnes, D. R., Allen, J., et al. (2020) · Nature Genetics · doi:10.1038/s41588-019-0537-1 · Cited by 168
Genomics to select treatment for patients with metastatic breast cancer
André, F., Filleron, T., Kamal, M. T., Mosele, F., Arnedos, M., Dalenc, F., Sablin, M., Campone, M., Bonnefoi, H. R., Lefeuvre‐Plesse, C., et al. (2022) · Nature · doi:10.1038/s41586-022-05068-3 · Cited by 167
Wu, T.-C., Xu, K., Martínek, J., Young, R. R., Banchereau, R., George, J., Turner, J., Kim, K. I., Zurawski, S., Wang, X., et al. (2018) · Cancer Research · doi:10.1158/0008-5472.can-18-0413 · Cited by 168
Age-specific changes in intrinsic breast cancer subtypes: a focus on older women
Jenkins, E. O., Deal, A. M., Anders, C. K., Prat, A., Perou, C. M., Carey, L. A., & Muss, H. B. (2014) · The Oncologist · doi:10.1634/theoncologist.2014-0184 · Cited by 165
Zhu, Y., Li, H., Guo, W., Drukker, K., Lan, L., Giger, M. L., & Ji, Y. (2015) · Scientific Reports · doi:10.1038/srep17787 · Cited by 165
Minin, G. D., Bellazzo, A., Ferro, M. D., Chiaruttini, G., Nuzzo, S., Bicciato, S., Piazza, S., Rami, D., Bulla, R., Sommaggio, R., et al. (2014) · Molecular Cell · doi:10.1016/j.molcel.2014.10.013 · Cited by 165
Petrocca, F., Altschuler, G. M., Tan, S. M., Mendillo, M. L., Yan, H., Jerry, D. J., Kung, A. L., Hide, W. A., Ince, T. A., & Lieberman, J. (2013) · Cancer Cell · doi:10.1016/j.ccr.2013.07.008 · Cited by 165
CDK12 regulates alternative last exon mRNA splicing and promotes breast cancer cell invasion
Tien, J. F., Mazloomian, A., Cheng, S., Hughes, C. S., Chow, C. C. T., Canapi, L. T., Oloumi, A., Trigo‐Gonzalez, G., Bashashati, A., Xu, J., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx187 · Cited by 169
Self-citationLiu, W., Xie, L., He, Y.-H., Wu, Z.-Y., Liu, L.-X., Bai, X., Deng, D.-X., Xu, X., Liao, L.-D., Lin, W., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25202-5 · Cited by 178
Matrix metalloproteinases as drivers and therapeutic targets in breast cancer
Radisky, E. S. (2015) · Frontiers in bioscience · doi:10.2741/4364 · Cited by 168
FOXA1 Directs H3K4 Monomethylation at Enhancers via Recruitment of the Methyltransferase MLL3
Jozwik, K. M., Chernukhin, I. V., Sérandour, A. A., Nagarajan, S., & Carroll, J. S. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.11.028 · Cited by 173
Chae, Y. K., Hong, F., Vaklavas, C., Cheng, H. H., Hammerman, P. S., Mitchell, E. P., Zwiebel, J. A., Ivy, S. P., Gray, R. J., Li, S., et al. (2020) · Journal of Clinical Oncology · doi:10.1200/jco.19.02630 · Cited by 164
Liu, Y., Ge, J., Chen, Y., Liu, T., Chen, L., Liu, C., Ma, D., Chen, Y., Cai, Y., Xu, Y., et al. (2023) · Advanced Science · doi:10.1002/advs.202205395 · Cited by 167
Jadaliha, M., Zong, X., Malakar, P., Ray, T., Singh, D. K., Freier, S. M., Jensen, T., Prasanth, S. G., Karni, R., Ray, P., et al. (2016) · Oncotarget · doi:10.18632/oncotarget.9622 · Cited by 163
Craven, K. E., Gökmen‐Polar, Y., & Badve, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-83913-7 · Cited by 163
Targeting BCL-2 to enhance vulnerability to therapy in estrogen receptor-positive breast cancer
Mérino, D., Lok, S. W., Visvader, J. E., & Lindeman, G. J. (2015) · Oncogene · doi:10.1038/onc.2015.287 · Cited by 164
Memmi, E. M., Sanarico, A. G., Giacobbe, A., Peschiaroli, A., Frezza, V., Cicalese, A., Pisati, F., Tosoni, D., Zhou, H., Tonon, G., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1500762112 · Cited by 162
Changes of N6-methyladenosine modulators promote breast cancer progression
Wu, L., Wu, D., Ning, J., Liu, W., & Zhang, D. (2019) · BMC Cancer · doi:10.1186/s12885-019-5538-z · Cited by 162
EMBRACE, Dunning, A. M., Collaborators, G. S., HEBON, kConFab Investigators, Michailidou, K., Kuchenbaecker, K. B., Thompson, D., French, J. D., Beesley, J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3521 · Cited by 161
Self-citationThe complex genetic landscape of familial breast cancer
Melchor, L., & Benitez, J. J. (2013) · Human Genetics · doi:10.1007/s00439-013-1299-y · Cited by 174
Malorni, L., Piazza, S., Ciani, Y., Guarducci, C., Bonechi, M., Biagioni, C., Hart, C. D., Verardo, R., Leo, A. D., & Migliaccio, I. (2016) · Oncotarget · doi:10.18632/oncotarget.12010 · Cited by 163
Detecting repeated cancer evolution from multi-region tumor sequencing data
Caravagna, G., Giarratano, Y., Ramazzotti, D., Tomlinson, I. P. M., Graham, T. A., Sanguinetti, G., & Sottoriva, A. (2018) · Nature Methods · doi:10.1038/s41592-018-0108-x · Cited by 162
Heterogeneity within molecular subtypes of breast cancer
Turner, K. M., Yeo, S. K., Holm, T. M., Shaughnessy, E., & Guan, J. (2021) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00109.2021 · Cited by 171
Guenther, S., Muirhead, L. J., Speller, A. V. M., Golf, O., Strittmatter, N., Ramakrishnan, R., Goldin, R. D., Jones, E. A., Veselkov, K., Nicholson, J. K., et al. (2015) · Cancer Research · doi:10.1158/0008-5472.can-14-2258 · Cited by 159
The mutational landscape of phosphorylation signaling in cancer
Reimand, J., Wagih, O., & Bader, G. D. (2013) · Scientific Reports · doi:10.1038/srep02651 · Cited by 163
The Warburg effect version 2.0: Metabolic reprogramming of cancer stem cells
Menéndez, J. A., Joven, J., Cufí, S., Corominas-Faja, B., Oliveras‐Ferraros, C., Cuyàs, E., Martı́n-Castillo, B., López‐Bonet, E., Alarcón, T., & Vázquez‐Martín, A. (2013) · Cell Cycle · doi:10.4161/cc.24479 · Cited by 165
Goh, J. Y., Feng, M., Wang, W., Oguz, G., Yatim, S. M. J. M., Lee, P. L., Bao, Y., Lim, T. H., Wang, P., Tam, W. L., et al. (2017) · Nature Medicine · doi:10.1038/nm.4405 · Cited by 158
Overexpression of CENPF correlates with poor prognosis and tumor bone metastasis in breast cancer
Sun, J., Huang, J., Lan, J., Zhou, K., Gao, Y., Song, Z., Deng, Y., Liu, L., Dong, Y., & Liu, X. (2019) · Cancer Cell International · doi:10.1186/s12935-019-0986-8 · Cited by 158
Hoang, D.-T., Dinstag, G., Shulman, E. D., Hermida, L. C., Ben-Zvi, D. S., Elis, E., Caley, K., Sammut, S.-J., Sinha, S., Sinha, N., et al. (2024) · Nature Cancer · doi:10.1038/s43018-024-00793-2 · Cited by 157
Self-citationIntratumor Heterogeneity in Breast Cancer
de Beça, F. F., & Polyák, K. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-22909-6_7 · Cited by 160
Kirilovsky, A., Marliot, F., Sissy, C. E., Haicheur, N., Galon, J., & Pagès, F. (2016) · International Immunology · doi:10.1093/intimm/dxw021 · Cited by 159
Heindl, A., Šestak, I., Naidoo, K., Cuzick, J. M., Dowsett, M., & Yuan, Y. (2017) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djx137 · Cited by 162
Self-citationTET1-Mediated Hypomethylation Activates Oncogenic Signaling in Triple-Negative Breast Cancer
Good, C. R., Panjarian, S. B., Kelly, A. D., Madžo, J., Patel, B. G., Jelı́nek, J., & Issa, J. J. (2018) · Cancer Research · doi:10.1158/0008-5472.can-17-2082 · Cited by 157
Prat, A., Guarneri, V., Pascual, T., Brasó‐Maristany, F., Sanfeliu, E., Paré, L., Schettini, F., Martínez, D., Jares, P., Griguolo, G., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2021.103801 · Cited by 157
Mazzu, Y. Z., Armenia, J., Chakraborty, G., Yoshikawa, Y., Coggins, S. A., Nandakumar, S., Gerke, T. A., Pomerantz, M. M., Qiu, X., Zhao, H., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-4046 · Cited by 156
Thrombospondin-4 mediates TGF-β-induced angiogenesis
Muppala, S., Xiao, R., Krukovets, I., Verbovetskiy, D., Yendamuri, R. S., Habib, N. A., Raman, P., Plow, E. F., & Stenina‐Adognravi, O. (2017) · Oncogene · doi:10.1038/onc.2017.140 · Cited by 155
Etiologic, Phenomenologic, and Endophenotypic Overlap of Schizophrenia and Bipolar Disorder
Pearlson, G. D. (2015) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032814-112915 · Cited by 154
Lipid phosphate phosphatases and their roles in mammalian physiology and pathology
Tang, X., Benesch, M. G. K., & Brindley, D. N. (2015) · Journal of Lipid Research · doi:10.1194/jlr.r058362 · Cited by 154
BCL11A is a triple-negative breast cancer gene with critical functions in stem and progenitor cells
Khaled, W. T., Lee, S. C., Stingl, J., Chen, X., Ali, H. R., Rueda, O. M., Hadi, F., Wang, J., Yu, Y., Chin, S., et al. (2015) · Nature Communications · doi:10.1038/ncomms6987 · Cited by 158
Self-citationTargeting Akt3 Signaling in Triple-Negative Breast Cancer
Chin, Y. R., Yoshida, T., Marusyk, A., Beck, A. H., Polyák, K., & Toker, A. (2013) · Cancer Research · doi:10.1158/0008-5472.can-13-2175 · Cited by 157
Decision support systems for personalized and participative radiation oncology
Lambin, P., Zindler, J. D., Vanneste, B. G. L., Voorde, L. V. D., Eekers, D. B. P., Compter, I., Panth, K. M., Peerlings, J., Larue, R. T. H. M., Deist, T. M., et al. (2016) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2016.01.006 · Cited by 162
Altea‐Manzano, P., Doglioni, G., Liu, Y., Cuadros, A. M., Nolan, E., Fernández-García, J., Wu, Q., Planque, M., Laue, K., Cidre‐Aranaz, F., et al. (2023) · Nature Cancer · doi:10.1038/s43018-023-00513-2 · Cited by 158
Mechanisms of resistance of chemotherapy in early-stage triple negative breast cancer (TNBC)
Wein, L., & Loi, S. (2017) · The Breast · doi:10.1016/j.breast.2017.06.023 · Cited by 153
Association Between Genomic Metrics and Immune Infiltration in Triple-Negative Breast Cancer
Karn, T., Jiang, T., Hatzis, C., Sänger, N., El‐Balat, A., Rody, A., Holtrich, U., Becker, S., Bianchini, G., & Pusztai, L. (2017) · JAMA Oncology · doi:10.1001/jamaoncol.2017.2140 · Cited by 156
Oliveri, S., Ferrari, F., Manfrinati, A., & Pravettoni, G. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00624 · Cited by 153
CXCR3/CCR5 pathways in metastatic melanoma patients treated with adoptive therapy and interleukin-2
Bedognetti, D., Spivey, T., Zhao, Y., Uccellini, L., Tomei, S., Dudley, M. E., Ascierto, M. L., Giorgi, V. D., Liu, Q., Delogu, L. G., et al. (2013) · British Journal of Cancer · doi:10.1038/bjc.2013.557 · Cited by 153
Tan, J., Ung, M. H., Cheng, C., & Greene, C. S. (2014) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814644730_0014 · Cited by 152
Fey, D., Halász, M., Dreidax, D., Kennedy, S. P., Hastings, J. F., Rauch, N., Muñoz, A. G., Pilkington, R., Fischer, M., Westermann, F., et al. (2015) · Science Signaling · doi:10.1126/scisignal.aab0990 · Cited by 152
Targeting immune checkpoints in breast cancer: an update of early results
Solinas, C., Gombos, A., Latifyan, S., Piccart, M. J., Kok, M., & Buisseret, L. (2017) · ESMO Open · doi:10.1136/esmoopen-2017-000255 · Cited by 157
The molecular diversity of Luminal A breast tumors
Ciriello, G., Sinha, R., Hoadley, K. A., Skanderup, A. J., Reva, B. A., Perou, C. M., Sander, C., & Schultz, N. D. (2013) · Breast Cancer Research and Treatment · doi:10.1007/s10549-013-2699-3 · Cited by 151
Cook, D. E., Bayless, A. M., Wang, K., Guo, X., Song, Q., Jiang, J., & Bent, A. F. (2014) · PLANT PHYSIOLOGY · doi:10.1104/pp.114.235952 · Cited by 155
Therapeutic Potential of Matrix Metalloproteinase Inhibition in Breast Cancer
Radisky, E. S., Raeeszadeh‐Sarmazdeh, M., & Radisky, D. C. (2017) · Journal of Cellular Biochemistry · doi:10.1002/jcb.26185 · Cited by 152
ALIX Regulates Tumor-Mediated Immunosuppression by Controlling EGFR Activity and PD-L1 Presentation
Monypenny, J., Milewicz, H., Flores‐Borja, F., Weitsman, G. E., Cheung, A., Chowdhury, R., Burgoyne, T., Arulappu, A., Lawler, K., Barber, P. R., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.06.066 · Cited by 151
Precise targeting of POLR2A as a therapeutic strategy for human triple negative breast cancer
Xu, J., Liu, Y., Li, Y., Wang, H., Stewart, S., der Jeught, K. V., Agarwal, P., Zhang, Y., Liu, S., Zhao, G., et al. (2019) · Nature Nanotechnology · doi:10.1038/s41565-019-0381-6 · Cited by 153
Androgen Receptor in Breast Cancer—Clinical and Preclinical Research Insights
Anestis, A., Zoi, I., Papavassiliou, A. G., & Karamouzis, M. V. (2020) · Molecules · doi:10.3390/molecules25020358 · Cited by 157
A human breast atlas integrating single-cell proteomics and transcriptomics
Gray, G. K., Li, C. M.-C., Rosenbluth, J. M., Selfors, L. M., Girnius, N., Lin, J., Schackmann, R. C. J., Goh, W. L., Moore, K., Shapiro, H., et al. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.05.003 · Cited by 156
A whole-genome sequence and transcriptome perspective on HER2-positive breast cancers
Ferrari, A., Vincent-Salomon, A., Pivot, X., Sertier, A.-S., Thomas, E., Tonon, L., Boyault, S., Mulugeta, E., Treilleux, I., MacGrogan, G., et al. (2016) · Nature Communications · doi:10.1038/ncomms12222 · Cited by 150
FOXC1 Activates Smoothened-Independent Hedgehog Signaling in Basal-like Breast Cancer
Han, B., Qu, Y., Jin, Y., Yu, Y., Deng, N., Wawrowsky, K., Zhang, X., Li, N., Bose, S., Wang, Q., et al. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.09.063 · Cited by 150
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Singhal, H., Greene, M. E., Tarulli, G. A., Zarnke, A. L., Bourgo, R. J., Lainé, M., Chang, Y.-F., Ma, S., Dembo, A. G., Raj, G. V., et al. (2016) · Science Advances · doi:10.1126/sciadv.1501924 · Cited by 150
Park, J. H., Ahn, J. H., & Kim, S. (2018) · ESMO Open · doi:10.1136/esmoopen-2018-000357 · Cited by 158
Molecular subtyping of cancer: current status and moving toward clinical applications
Zhao, L., Lee, V. H.-F., Ng, M. K., Yan, H., & Bijlsma, M. F. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby026 · Cited by 156
Molecular stratification within triple-negative breast cancer subtypes
Wang, D., Jiang, Z., Ben‐David, Y., Woodgett, J. R., & Zacksenhaus, E. (2019) · Scientific Reports · doi:10.1038/s41598-019-55710-w · Cited by 159
Truong, D. D., Kratz, A., Park, J. G., Barrientos, E. S., Saini, H., Nguyen, T., Pockaj, B., Mouneimne, G., LaBaer, J., & Nikkhah, M. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-2293 · Cited by 149
Metadata
- Authors
- 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., Yuan, Y., Gräf, S., Ha, G., Haffari, G., Bashashati, A., Russell, R., McKinney, S., Langerød, A., Green, A., Provenzano, E., Wishart, G., Pinder, S., Watson, P., Markowetz, F., Murphy, L., Ellis, I., Purushotham, A., Børresen-Dale, A.-L., Brenton, J. D., Tavaré, S., Caldas, C., & Aparicio, S.
- Affiliations
- University of Cambridge (GB)
- University of British Columbia (CA)
- Oslo University Hospital (NO)
- University of Nottingham (GB)
- Cambridge University Hospitals NHS Foundation Trust (GB)
- King's College London (GB)
- University of Southern California (US)
- Corresponding author
- Caldas, Carlos
- Aparicio, Samuel
- ORCID
- Curtis, Christina
- Shah, Sohrab P.
- Chin, Suet-Feung
- Turashvili, Gulisa
- Rueda, Oscar M.
- Dunning, Mark J.
- Speed, Doug
- Lynch, Andy G.
- Samarajiwa, Shamith
- Yuan, Yinyin
- Gräf, Stefan
- Ha, Gavin
- Haffari, Gholamreza
- Bashashati, Ali
- Green, Andrew
- Provenzano, Elena
- Wishart, Gordon
- Pinder, Sarah
- Watson, Peter
- Markowetz, Florian
- Murphy, Leigh
- Ellis, Ian
- Purushotham, Arnie
- Brenton, James D.
- Tavaré, Simon
- Caldas, Carlos
- Aparicio, Samuel
- Venue
- Nature
- Published
- 2012-04-18
- Volume
- 486
- Issue
- 7403
- Pages
- 346-352
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3440846
- OpenAlex
- W2005895632
- Cited by
- 6,097
- References
- 32
- Citations by year
- 2026: 341 · 2025: 411 · 2024: 458 · 2023: 497 · 2022: 496 · 2021: 615 · 2020: 533 · 2019: 449 · 2018: 426 · 2017: 361 · 2016: 373 · 2015: 378 · 2014: 339 · 2013: 291 · 2012: 119
- FWCI
- 119.67
- 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)
- RePEc: Research Papers in Economics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- University of Southern California
- Michael Smith Health Research BC
- Cancer Research UK (A7199, 15601, 19556)
- National Institute for Health and Care Research (ACF-2012-14-006, NF-SI-0611-10154)
- University of Cambridge
- King's College London
- National Institutes of Health
- BC Cancer Agency
- NIHR Cambridge Biomedical Research Centre
- National Human Genome Research Institute (P50-HG02790, P50HG002790)
- Division of Mathematical Sciences
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
- Keywords
- breast cancer, copy number alterations, somatic copy number alterations, gene expression, single nucleotide polymorphisms, PPP2R2A, MTAP, MAP2K4, estrogen receptor-positive breast cancer, adaptive immune response, basal-like breast cancer, chromosome 5 deletion, molecular stratification, prognosis
- MeSH terms
- Breast Neoplasms, Female, Humans, Prognosis, Genome, Human, Gene Expression Regulation, Neoplastic, Treatment Outcome, Polymorphism, Single Nucleotide, Gene Expression Profiling, Genomics, MAP Kinase Kinase 4, Genes, Neoplasm, Kaplan-Meier Estimate, Gene Regulatory Networks, Protein Phosphatase 2, DNA Copy Number Variations
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