The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
CDK7-Dependent Transcriptional Addiction in Triple-Negative Breast Cancer
Open accessAbstract
SUMMARY Triple-negative breast cancer (TNBC) is a highly aggressive form of breast cancer that exhibits extremely high levels of genetic complexity and yet a relatively uniform transcriptional program. We postulate that TNBC might be highly dependent on uninterrupted transcription of a key set of genes within this gene expression program and might therefore be exceptionally sensitive to inhibitors of transcription. Utilizing kinase inhibitors and CRISPR/Cas9-mediated gene editing, we show here that triple-negative but not hormone receptor-positive breast cancer cells are exceptionally dependent on CDK7, a transcriptional cyclin-dependent kinase. TNBC cells are unique in their dependence on this transcriptional CDK and suffer apoptotic cell death upon CDK7 inhibition. An “Achilles cluster” of TNBC-specific genes is especially sensitive to CDK7 inhibition and frequently associated with super-enhancers. We conclude that CDK7 mediates transcriptional addiction to a vital cluster of genes in TNBC and CDK7 inhibition may be a useful therapy for this challenging cancer.
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PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F.-A., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E.-M., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,845
Molecular portraits of human breast tumours
Perou, C. M., Sørlie, T., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., Rees, C. A., Pollack, J. R., Ross, D. T., Johnsen, H., Akslen, L. A., et al. (2000) · Nature · doi:10.1038/35021093 · Cited by 16,261
Nishimura, R., Osako, T., Nishiyama, Y., Tashima, R., Nakano, M., Fujisue, M., Toyozumi, Y., & Arima, N. (2013) · Oncology · doi:10.1159/000353099 · Cited by 13,166
Comprehensive molecular portraits of human breast tumours
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129
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 7,925
Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes
Parker, J. S., Mullins, M., Cheang, M. C. U., Leung, S., Voduc, D., Vickery, T., Davies, S., Fauron, C., He, X., Hu, Z., et al. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2008.18.1370 · Cited by 4,721
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
Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes
Whyte, W. A., Orlando, D. A., Hnisz, D., Abraham, B. J., Lin, C. Y., Kagey, M. H., Rahl, P. B., Lee, T. I., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.03.035 · Cited by 4,143
Self-citationComprehensive genomic characterization of squamous cell lung cancers
The Cancer Genome Atlas Research Network (2012) · Nature · doi:10.1038/nature11404 · Cited by 3,994
Twist, a Master Regulator of Morphogenesis, Plays an Essential Role in Tumor Metastasis
Yang, J., Mani, S. A., Donaher, J. L., Ramaswamy, S., Itzykson, R. A., Come, C., Savagner, P., Gitelman, I., Richardson, A., & Weinberg, R. A. (2004) · Cell · doi:10.1016/j.cell.2004.06.006 · Cited by 3,855
Cited by
Coactivator condensation at super-enhancers links phase separation and gene control
Sabari, B. R., Dall’Agnese, A., Boija, A., Klein, I. A., Coffey, E. L., Shrinivas, K., Abraham, B. J., Hannett, N. M., Zamudio, A. V., Manteiga, J. C., et al. (2018) · Science · doi:10.1126/science.aar3958 · Cited by 2,595
Self-citationA Phase Separation Model for Transcriptional Control
Hnisz, D., Shrinivas, K., Young, R. A., Chakraborty, A. K., & Sharp, P. A. (2017) · Cell · doi:10.1016/j.cell.2017.02.007 · Cited by 1,753
Self-citationCDK4/6 inhibition triggers anti-tumour immunity
Goel, S., DeCristo, M. J., Watt, A. C., BrinJones, H., Sceneay, J., Li, B. B., Khan, N., Ubellacker, J. M., Xie, S., Metzger-Filho, O., et al. (2017) · Nature · doi:10.1038/nature23465 · Cited by 1,513
Self-citationGarrido-Castro, A. C., Lin, N. U., & Polyak, K. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-18-1177 · Cited by 1,451
PARPs and ADP-ribosylation: recent advances linking molecular functions to biological outcomes
Gupte, R., Liu, Z., & Kraus, W. L. (2017) · Genes & Development · doi:10.1101/gad.291518.116 · Cited by 681
“Furthermore, given the important role of nuclear PARPs in gene regulation, PARPis may be useful in targeting transcriptionally addicted cancers (Franco and Kraus 2015; Wang et al. 2015).”
Background
Metadata
- Authors
- Wang, Y., Zhang, T., Kwiatkowski, N., Abraham, B. J., Lee, T. I., Xie, S., Yuzugullu, H., Von, T., Li, H., Lin, Z., Stover, D. G., Lim, E., Wang, Z. C., Iglehart, J. D., Young, R. A., Gray, N. S., & Zhao, J. J.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2015-09
- Volume
- 163
- Issue
- 1
- Pages
- 174-186
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W2149765709
- Cited by
- 465
- References
- 69
- FWCI
- 10.21
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Wang, Y., Zhang, T., Kwiatkowski, N., Abraham, B. J., Lee, T. I., Xie, S., Yuzugullu, H., Von, T., Li, H., Lin, Z., Stover, D. G., Lim, E., Wang, Z. C., Iglehart, J. D., Young, R. A., Gray, N. S., & Zhao, J. J. (2015). CDK7-Dependent Transcriptional Addiction in Triple-Negative Breast Cancer. Cell. https://doi.org/10.1016/j.cell.2015.08.063
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