The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling
Open accessAbstract
Kinase inhibitors constitute an important new class of cancer drugs, whose selective efficacy is largely determined by underlying tumor cell genetics. We established a high-throughput platform to profile 500 cell lines derived from diverse epithelial cancers for sensitivity to 14 kinase inhibitors. Most inhibitors were ineffective against unselected cell lines but exhibited dramatic cell killing of small nonoverlapping subsets. Cells with exquisite sensitivity to EGFR, HER2, MET, or BRAF kinase inhibitors were marked by activating mutations or amplification of the drug target. Although most cell lines recapitulated known tumor-associated genotypes, the screen revealed low-frequency drug-sensitizing genotypes in tumor types not previously associated with drug susceptibility. Furthermore, comparing drugs thought to target the same kinase revealed striking differences, predictive of clinical efficacy. Genetically defined cancer subsets, irrespective of tissue type, predict response to kinase inhibitors, and provide an important preclinical model to guide early clinical applications of novel targeted inhibitors.
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Cites
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Mutations of the BRAF gene in human cancer
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MET Amplification Leads to Gefitinib Resistance in Lung Cancer by Activating ERBB3 Signaling
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Cited by
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950
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Self-citationA Chromatin-Mediated Reversible Drug-Tolerant State in Cancer Cell Subpopulations
Sharma, S. V., Lee, D. Y., Li, B., Quinlan, M. P., Takahashi, F., Maheswaran, S., McDermott, U., Azizian, N., Zou, L., Fischbach, M. A., et al. (2010) · Cell · doi:10.1016/j.cell.2010.02.027 · Cited by 2,770
Self-citationSystematic identification of genomic markers of drug sensitivity in cancer cells
Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., et al. (2012) · Nature · doi:10.1038/nature11005 · Cited by 2,456
Self-citationSubtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy
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Metadata
- Authors
- McDermott, U., Sharma, S. V., Dowell, L., Greninger, P., Montagut, C., Lamb, J., Archibald, H., Raudales, R., Tam, A., Lee, D., Rothenberg, S. M., Supko, J. G., Sordella, R., Ulkus, L. E., Iafrate, A. J., Maheswaran, S., Njauw, C. N., Tsao, H., Drew, L., Hanke, J. H., Ma, X.-J., Erlander, M. G., Gray, N. S., Haber, D. A., & Settleman, J.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2007-12-11
- Volume
- 104
- Issue
- 50
- Pages
- 19936-19941
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2008345619
- Cited by
- 356
- References
- 3
- FWCI
- 22.39
- Type
- journal article
- Language
- en
- Open access
- green
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- McDermott, U., Sharma, S. V., Dowell, L., Greninger, P., Montagut, C., Lamb, J., Archibald, H., Raudales, R., Tam, A., Lee, D., Rothenberg, S. M., Supko, J. G., Sordella, R., Ulkus, L. E., Iafrate, A. J., Maheswaran, S., Njauw, C. N., Tsao, H., Drew, L., . . . Settleman, J. (2007). Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0707498104
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
