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Whole-genome analysis informs breast cancer response to aromatase inhibition
Open accessAbstract
To correlate the variable clinical features of oestrogen-receptor-positive breast cancer with somatic alterations, we studied pretreatment tumour biopsies accrued from patients in two studies of neoadjuvant aromatase inhibitor therapy by massively parallel sequencing and analysis. Eighteen significantly mutated genes were identified, including five genes (RUNX1, CBFB, MYH9, MLL3 and SF3B1) previously linked to haematopoietic disorders. Mutant MAP3K1 was associated with luminal A status, low-grade histology and low proliferation rates, whereas mutant TP53 was associated with the opposite pattern. Moreover, mutant GATA3 correlated with suppression of proliferation upon aromatase inhibitor treatment. Pathway analysis demonstrated that mutations in MAP2K4, a MAP3K1 substrate, produced similar perturbations as MAP3K1 loss. Distinct phenotypes in oestrogen-receptor-positive breast cancer are associated with specific patterns of somatic mutations that map into cellular pathways linked to tumour biology, but most recurrent mutations are relatively infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing. Whole-genome analysis of oestrogen-receptor-positive tumours in patients treated with aromatase inhibitors show that distinct phenotypes are associated with specific patterns of somatic mutations; however, most recurrent mutations are relatively infrequent so prospective clinical trials will require comprehensive sequencing and large study populations. Elaine Mardis and colleagues use whole-genome sequencing to gain insight into the mutational landscape of tissue samples from patients with oestrogen-receptor-positive (ER+) breast cancer treated with a neoadjuvant aromatase inhibitor. They identify a number of disease-linked mutations that specifically correlate with tumour-cell histology, proliferation rates and response to treatment. Such information could be used to determine which patients will benefit from aromatase-inhibitor therapy.
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Cites
Lynch, T. J., Bell, D. W., Sordella, R., Gurubhagavatula, S., Okimoto, R. A., Brannigan, B. W., Harris, P. L., Haserlat, S. M., Supko, J. G., Haluska, F. G., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040938 · Cited by 11,486
Recurring mutations found by sequencing an acute myeloid leukemia genome
Mardis, E. R., Ding, L., Dooling, D. J., Larson, D. E., McLellan, M. D., Chen, K., Koboldt, D. C., Fulton, R. S., Delehaunty, K. D., McGrath, S. D., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0903840 · Cited by 2,219
Self-citationA comprehensive catalogue of somatic mutations from a human cancer genome
Pleasance, E. D., Cheetham, R. K., Stephens, P. J., McBride, D. J., Humphray, S. J., Greenman, C. D., Varela, I., Lin, M.-L., Ordóñez, G. R., Bignell, G. R., et al. (2010) · Nature · doi:10.1038/nature08658 · Cited by 1,696
Somatic SF3B1 Mutation in Myelodysplasia with Ring Sideroblasts
Papaemmanuil, E., Cazzola, M., Boultwood, J., Malcovati, L., Vyas, P., Bowen, D., Pellagatti, A., Wainscoat, J. S., Hellstrom-Lindberg, E., Gambacorti-Passerini, C., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1103283 · Cited by 1,222
Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D., Szeto, C., Zhu, J., Haussler, D., & Stuart, J. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq182 · Cited by 786
Self-citation
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Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., & Kinzler, K. W. (2013) · Science · doi:10.1126/science.1235122 · Cited by 7,863
Coming of age: ten years of next-generation sequencing technologies
Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.49 · Cited by 4,352
Mutational landscape and significance across 12 major cancer types
Kandoth, C., McLellan, M. D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., Zhang, Q., McMichael, J. F., Wyczalkowski, M. A., et al. (2013) · Nature · doi:10.1038/nature12634 · Cited by 4,419
Self-citationHarbeck, N., Penault-Llorca, F., Cortes, J., Gnant, M., Houssami, N., Poortmans, P., Ruddy, K., Tsang, J., & Cardoso, F. (2019) · Nature Reviews Disease Primers · doi:10.1038/s41572-019-0111-2 · Cited by 3,024
Landscape of somatic mutations in 560 breast cancer whole-genome sequences
Nik-Zainal, S., Davies, H., Staaf, J., Ramakrishna, M., Glodzik, D., Zou, X., Martincorena, I., Alexandrov, L. B., Martin, S., Wedge, D. C., et al. (2016) · Nature · doi:10.1038/nature17676 · Cited by 2,218
Analysis of Circulating Tumor DNA to Monitor Metastatic Breast Cancer
Dawson, S.-J., Tsui, D. W. Y., Murtaza, M., Biggs, H., Rueda, O. M., Chin, S.-F., Dunning, M. J., Gale, D., Forshew, T., Mahler-Araujo, B., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1213261 · Cited by 2,207
Activating ESR1 mutations in hormone-resistant metastatic breast cancer
Robinson, D. R., Wu, Y.-M., Vats, P., Su, F., Lonigro, R. J., Cao, X., Kalyana-Sundaram, S., Wang, R., Ning, Y., Hodges, L., et al. (2013) · Nature Genetics · doi:10.1038/ng.2823 · Cited by 1,145
Clonal evolution in breast cancer revealed by single nucleus genome sequencing
Wang, Y., Waters, J., Leung, M. L., Unruh, A., Roh, W., Shi, X., Chen, K., Scheet, P., Vattathil, S., Liang, H., et al. (2014) · Nature · doi:10.1038/nature13600 · Cited by 1,029
Self-citationJeselsohn, R., Yelensky, R., Buchwalter, G., Frampton, G., Meric-Bernstam, F., Gonzalez-Angulo, A. M., Ferrer-Lozano, J., Perez-Fidalgo, J. A., Cristofanilli, M., Gómez, H., et al. (2014) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-2332 · Cited by 674
Mutational landscape of aggressive cutaneous squamous cell carcinoma
Pickering, C. R., Zhou, J. H., Lee, J. J., Drummond, J. A., Peng, S. A., Saade, R. E., Tsai, K. Y., Curry, J. L., Tetzlaff, M. T., Lai, S. Y., et al. (2014) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-14-1768 · Cited by 612
Lee, J.-E., Wang, C., Xu, S., Cho, Y.-W., Wang, L., Feng, X., Baldridge, A., Sartorelli, V., Zhuang, L., Peng, W., et al. (2013) · eLife · doi:10.7554/elife.01503 · Cited by 522
Mertins, P., Yang, F., Liu, T., Mani, D. R., Petyuk, V. A., Gillette, M. A., Clauser, K. R., Qiao, J. W., Gritsenko, M. A., Moore, R. J., et al. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.036392 · Cited by 400
Self-citationThe RUNX family: developmental regulators in cancer
Ito, Y., Bae, S.-C., & Chuang, L. S. H. (2015) · Nature Reviews Cancer · doi:10.1038/nrc3877 · Cited by 399
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
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
MLL3/MLL4/COMPASS Family on Epigenetic Regulation of Enhancer Function and Cancer
Sze, C. C., & Shilatifard, A. (2016) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a026427 · Cited by 164
Splicing factor mutations and cancer
Yoshida, K., & Ogawa, S. (2014) · WIREs RNA · doi:10.1002/wrna.1222 · Cited by 155
Effect of ASCO/CAP guidelines for determining ER status on molecular subtype
Deyarmin, B., Kane, J. L., Valente, A. L., van Laar, R., Gallagher, C., Shriver, C. D., & Ellsworth, R. E. (2013) · Annals of Surgical Oncology · doi:10.1245/s10434-012-2588-8 · Cited by 149
Arruabarrena-Aristorena, A., Maag, J. L. V., Kittane, S., Cai, Y., Karthaus, W. R., Ladewig, E., Park, J., Kannan, S., Ferrando, L., Cocco, E., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.08.003 · Cited by 130
Ghoreyshi, N., Heidari, R., Farhadi, A., Chamanara, M., Farahani, N., Vahidi, M., & Behroozi, J. (2025) · Discover Oncology · doi:10.1007/s12672-025-01816-9 · Cited by 91
Zhang, G., Wang, Y., Chen, B., Guo, L., Cao, L., Ren, C., Wen, L., Li, K., Jia, M., Li, C., et al. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2019.04.23 · Cited by 66
The MLL3/4 H3K4 methyltransferase complex in establishing an active enhancer landscape
Wang, L.-H., Aberin, M. A. E., Wu, S., & Wang, S.-P. (2021) · Biochemical Society Transactions · doi:10.1042/bst20191164 · Cited by 53
Upreti, M., Jyoti, A., Johnson, S. E., Swindell, E. P., Napier, D., Sethi, P., Chan, R., Feddock, J. M., Weiss, H. L., O’Halloran, T. V., et al. (2106) · Oncotarget · doi:10.18632/oncotarget.9490 · Cited by 21
“Of those diagnosed with breast cancer, 15–20% are classified as triple negative breast cancer (TNBC) [1, 2] prevalent in women with African American ethnicity and younger age [3–5].”
BackgroundKunasegaran, K., Ho, V., Chang, T. H. T., De Silva, D., Bakker, M. L., Christoffels, V. M., & Pietersen, A. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0110191 · Cited by 18
Reply to J. Perlmutter et al and D. Yee et al
Schott, A. F., & Hayes, D. F. (2012) · Journal of Clinical Oncology · doi:10.1200/jco.2012.45.3589
Metadata
- Authors
- Ellis, M. J., Ding, L., Shen, D., Luo, J., Suman, V. J., Wallis, J. W., Van Tine, B. A., Hoog, J., Goiffon, R. J., Goldstein, T. C., Ng, S., Lin, L., Crowder, R., Snider, J., Ballman, K., Weber, J., Chen, K., Koboldt, D. C., Kandoth, C., Schierding, W. S., McMichael, J. F., Miller, C. A., Lu, C., Harris, C. C., McLellan, M. D., Wendl, M. C., DeSchryver, K., Allred, D. C., Esserman, L., Unzeitig, G., Margenthaler, J., Babiera, G. V., Marcom, P. K., Guenther, J. M., Leitch, M., Hunt, K., Olson, J., Tao, Y., Maher, C. A., Fulton, L. L., Fulton, R. S., Harrison, M., Oberkfell, B., Du, F., Demeter, R., Vickery, T. L., Elhammali, A., Piwnica-Worms, H., McDonald, S., Watson, M., Dooling, D. J., Ota, D., Chang, L.-W., Bose, R., Ley, T. J., Piwnica-Worms, D., Stuart, J. M., Wilson, R. K., & Mardis, E. R.
- Venue
- Nature
- Published
- 2012-06
- Volume
- 486
- Issue
- 7403
- Pages
- 353-360
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2150757282
- Cited by
- 1,023
- References
- 5
- FWCI
- 38.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
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