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An Information-Intensive Approach to the Molecular Pharmacology of Cancer
Abstract
Since 1990, the National Cancer Institute (NCI) has screened more than 60,000 compounds against a panel of 60 human cancer cell lines. The 50-percent growth-inhibitory concentration (GI 50 ) for any single cell line is simply an index of cytotoxicity or cytostasis, but the patterns of 60 such GI 50 values encode unexpectedly rich, detailed information on mechanisms of drug action and drug resistance. Each compound's pattern is like a fingerprint, essentially unique among the many billions of distinguishable possibilities. These activity patterns are being used in conjunction with molecular structural features of the tested agents to explore the NCI's database of more than 460,000 compounds, and they are providing insight into potential target molecules and modulators of activity in the 60 cell lines. For example, the information is being used to search for candidate anticancer drugs that are not dependent on intact p53 suppressor gene function for their activity. It remains to be seen how effective this information-intensive strategy will be at generating new clinically active agents.
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p53 Mutations in Human Cancers
Hollstein, M., Sidransky, D., Vogelstein, B., & Harris, C. C. (1991) · Science · doi:10.1126/science.1905840 · Cited by 8,018
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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
Systematic 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
Systematic variation in gene expression patterns in human cancer cell lines
Ross, D. T., Scherf, U., Eisen, M. B., Perou, C. M., Rees, C., Spellman, P., Iyer, V., Jeffrey, S. S., Van de Rijn, M., Waltham, M., et al. (2000) · Nature Genetics · doi:10.1038/73432 · Cited by 2,088
Self-citationBiclustering algorithms for biological data analysis: a survey
Madeira, S. C., & Oliveira, A. L. (2004) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2004.2 · Cited by 2,049
Multidimensional Drug Profiling By Automated Microscopy
Perlman, Z. E., Slack, M. D., Feng, Y., Mitchison, T. J., Wu, L. F., & Altschuler, S. J. (2004) · Science · doi:10.1126/science.1100709 · Cited by 660
'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns
Hastie, T., Tibshirani, R., Eisen, M. B., Alizadeh, A., Levy, R., Staudt, L., Chan, W. C., Botstein, D., & Brown, P. (2000) · Genome Biology · doi:10.1186/gb-2000-1-2-research0003 · Cited by 547
Discovery of multi-dimensional modules by integrative analysis of cancer genomic data
Zhang, S., Liu, C.-C., Li, W., Shen, H., Laird, P. W., & Zhou, X. J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks725 · Cited by 374
Comprehensive characterization of the Published Kinase Inhibitor Set
Elkins, J. M., Fedele, V., Szklarz, M., Abdul Azeez, K. R., Salah, E., Mikolajczyk, J., Romanov, S., Sepetov, N., Huang, X.-P., Roth, B. L., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3374 · Cited by 323
van der Vos, K. E., Abels, E. R., Zhang, X., Lai, C., Carrizosa, E., Oakley, D., Prabhakar, S., Mardini, O., Crommentuijn, M. H. W., Skog, J., et al. (2016) · Neuro-Oncology · doi:10.1093/neuonc/nov244 · Cited by 315
Gadd45 in Stress Signaling, Cell Cycle Control, and Apoptosis
Salvador, J. M., Brown-Clay, J. D., & Fornace, A. J. (2013) · Advances in Experimental Medicine and Biology · doi:10.1007/978-1-4614-8289-5_1 · Cited by 308
Self-citationPRIMA-1MET synergizes with cisplatin to induce tumor cell apoptosis
Bykov, V. J. N., Zache, N., Stridh, H., Westman, J., Bergman, J., Selivanova, G., & Wiman, K. G. (2005) · Oncogene · doi:10.1038/sj.onc.1208419 · Cited by 252
Advances in the preclinical testing of cancer therapeutic hypotheses
Caponigro, G., & Sellers, W. R. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3385 · Cited by 165
In Vitro Human Cell Line Models to Predict Clinical Response to Anticancer Drugs
Niu, N., & Wang, L. (2015) · Pharmacogenomics · doi:10.2217/pgs.14.170 · Cited by 162
Liscovitch, M., & Ravid, D. (2007) · Cancer Letters · doi:10.1016/j.canlet.2006.01.013 · Cited by 152
Modulatory profiling identifies mechanisms of small molecule-induced cell death
Wolpaw, A. J., Shimada, K., Skouta, R., Welsch, M. E., Akavia, U. D., Pe'er, D., Shaik, F., Bulinski, J. C., & Stockwell, B. R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1106149108 · Cited by 133
van Staveren, W. C. G., Solís, D. W., Delys, L., Duprez, L., Andry, G., Franc, B., Thomas, G., Libert, F., Dumont, J. E., Detours, V., et al. (2007) · Cancer Research · doi:10.1158/0008-5472.can-06-4026 · Cited by 123
Bender, A., Jenkins, J. L., Glick, M., Deng, Z., Nettles, J. H., & Davies, J. W. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci600197y · Cited by 110
Identification of cancer-cytotoxic modulators of PDE3A by predictive chemogenomics
de Waal, L., Lewis, T. A., Rees, M. G., Tsherniak, A., Wu, X., Choi, P. S., Gechijian, L., Hartigan, C., Faloon, P. W., Hickey, M. J., et al. (2016) · Nature Chemical Biology · doi:10.1038/nchembio.1984 · Cited by 96
Biological mechanism profiling using an annotated compound library
Root, D. E., Flaherty, S. P., Kelley, B. P., & Stockwell, B. R. (2003) · Chemistry & Biology · doi:10.1016/j.chembiol.2003.08.009 · Cited by 95
van Pel, D. M., Barrett, I. J., Shimizu, Y., Sajesh, B. V., Guppy, B. J., Pfeifer, T., McManus, K. J., & Hieter, P. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003254 · Cited by 89
HLA class I and II genotype of the NCI-60 cell lines
Adams, S., Robbins, F.-M., Chen, D., Wagage, D., Holbeck, S. L., Morse, H. C., Stroncek, D., & Marincola, F. M. (2005) · Journal of Translational Medicine · doi:10.1186/1479-5876-3-11 · Cited by 68
Fowles, J. S., Dailey, D. D., Gustafson, D. L., Thamm, D. H., & Duval, D. L. (2017) · Veterinary and Comparative Oncology · doi:10.1111/vco.12192 · Cited by 49
Compound Classification Using Image‐Based Cellular Phenotypes
Adams, C. L., Kutsyy, V., Coleman, D. A., Cong, G., Crompton, A. M., Elias, K. A., Oestreicher, D. R., Trautman, J. K., & Vaisberg, E. (2006) · Methods in Enzymology · doi:10.1016/s0076-6879(06)14024-0 · Cited by 51
Chen, P.-H., Cheng, C.-H., Shih, C.-M., Ho, K.-H., Lin, C.-W., Lee, C.-C., Liu, A.-J., Chang, C.-K., & Chen, K.-C. (2016) · PLOS ONE · doi:10.1371/journal.pone.0167096 · Cited by 41
The opportunities of mining historical and collective data in drug discovery
Wassermann, A. M., Lounkine, E., Davies, J. W., Glick, M., & Camargo, L. M. (2015) · Drug Discovery Today · doi:10.1016/j.drudis.2014.11.004 · Cited by 34
Naïve Bayesian Models for Vero Cell Cytotoxicity
Perryman, A. L., Patel, J. S., Russo, R., Singleton, E., Connell, N., Ekins, S., & Freundlich, J. S. (2018) · Pharmaceutical Research · doi:10.1007/s11095-018-2439-9 · Cited by 33
Multidimensional profiling in the investigation of small-molecule-induced cell death
Wolpaw, A. J., & Stockwell, B. R. (2014) · Methods in Enzymology · doi:10.1016/b978-0-12-801430-1.00011-1 · Cited by 10
Metadata
- Authors
- Weinstein, J. N., Myers, T. G., O'Connor, P. M., Friend, S. H., Fornace, A. J., Kohn, K. W., Fojo, T., Bates, S. E., Rubinstein, L. V., Anderson, N. L., Buolamwini, J. K., van Osdol, W. W., Monks, A. P., Scudiero, D. A., Sausville, E. A., Zaharevitz, D. W., Bunow, B., Viswanadhan, V. N., Johnson, G. S., Wittes, R. E., & Paull, K. D.
- Venue
- Science
- Published
- 1997-01-17
- Volume
- 275
- Issue
- 5298
- Pages
- 343-349
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2167395325
- Cited by
- 1,183
- References
- 1
- FWCI
- 82.03
- Type
- journal article
- Language
- en
- Open access
- closed
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