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TCPA: a resource for cancer functional proteomics data
Open accessAbstract
Functional proteomics represents a powerful approach to understand the pathophysiology and therapy of cancer. However, comprehensive cancer proteomic data have been relatively limited. As a part of The Cancer Genome Atlas (TCGA) Project and other efforts, we have generated protein expression data over a large number of tumor and cell line samples using reverse-phase protein arrays (RPPAs). RPPA is a quantitative, antibody-based technology that can assess multiple protein markers in many samples in a cost-effective, sensitive and high-throughput manner 1 , 2 . This technology has been extensively validated for both cell line and patient samples 3 , 4 , 5 , and its applications range from building reproducible prognostic models 6 to generating experimentally verified mechanistic insights 7 .
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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
Human Protein Reference Database--2009 update
Keshava Prasad, T. S., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, B., Venugopal, A., et al. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn892 · Cited by 3,234
Towards a knowledge-based Human Protein Atlas
Uhlen, M., Oksvold, P., Fagerberg, L., Lundberg, E., Jonasson, K., Forsberg, M., Zwahlen, M., Kampf, C., Wester, K., Hober, S., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt1210-1248 · Cited by 2,440
Tibes, R., Qiu, Y., Lu, Y., Hennessy, B., Andreeff, M., Mills, G. B., & Kornblau, S. M. (2006) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-06-0334 · Cited by 718
Self-citationNishizuka, S., Charboneau, L., Young, L., Major, S., Reinhold, W. C., Waltham, M., Kouros-Mehr, H., Bussey, K. J., Lee, J. K., Espina, V., et al. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2331323100 · Cited by 474
Self-citationSheehan, K. M., Calvert, V. S., Kay, E. W., Lu, Y., Fishman, D., Espina, V., Aquino, J., Speer, R., Araujo, R., Mills, G. B., et al. (2005) · Molecular & Cellular Proteomics · doi:10.1074/mcp.t500003-mcp200 · Cited by 306
Self-citationHennessy, B. T., Lu, Y., Gonzalez-Angulo, A. M., Carey, M. S., Myhre, S., Ju, Z., Davies, M. A., Liu, W., Coombes, K., Meric-Bernstam, F., et al. (2010) · Clinical Proteomics · doi:10.1007/s12014-010-9055-y · Cited by 241
Self-citationLiang, H., Cheung, L. W. T., Li, J., Ju, Z., Yu, S., Stemke-Hale, K., Dogruluk, T., Lu, Y., Liu, X., Gu, C., et al. (2012) · Genome Research · doi:10.1101/gr.137596.112 · Cited by 241
Self-citationReverse-phase protein lysate microarrays for cell signaling analysis
Spurrier, B., Ramalingam, S., & Nishizuka, S. (2008) · Nature Protocols · doi:10.1038/nprot.2008.179 · Cited by 181
Predicting time to ovarian carcinoma recurrence using protein markers
Yang, J.-Y., Yoshihara, K., Tanaka, K., Hatae, M., Masuzaki, H., Itamochi, H., Takano, M., Ushijima, K., Tanyi, J. L., Coukos, G., et al. (2013) · Journal of Clinical Investigation · doi:10.1172/jci68509 · Cited by 68
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Akbani, R., Ng, P. K. S., Werner, H. M. J., Shahmoradgoli, M., Zhang, F., Ju, Z., Liu, W., Yang, J.-Y., Yoshihara, K., Li, J., et al. (2014) · Nature Communications · doi:10.1038/ncomms4887 · Cited by 607
Self-citationThe landscape and therapeutic relevance of cancer-associated transcript fusions
Yoshihara, K., Wang, Q., Torres-Garcia, W., Zheng, S., Vegesna, R., Kim, H., & Verhaak, R. G. W. (2015) · Oncogene · doi:10.1038/onc.2014.406 · Cited by 450
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Yuan, Y., Liu, L., Chen, H., Wang, Y., Xu, Y., Mao, H., Li, J., Mills, G. B., Shu, Y., Li, L., et al. (2016) · Cancer Cell · doi:10.1016/j.ccell.2016.04.001 · Cited by 331
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Giampazolias, E., Zunino, B., Dhayade, S., Bock, F., Cloix, C., Cao, K., Roca, A., Lopez, J., Ichim, G., Proïcs, E., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3596 · Cited by 258
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Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254
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Metadata
- Authors
- Li, J., Lu, Y., Akbani, R., Ju, Z., Roebuck, P. L., Liu, W., Yang, J.-Y., Broom, B. M., Verhaak, R. G. W., Kane, D. W., Wakefield, C., Weinstein, J. N., Mills, G. B., & Liang, H.
- Venue
- Nature Methods
- Published
- 2013-09-15
- Volume
- 10
- Issue
- 11
- Pages
- 1046-1047
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W1979340580
- Cited by
- 512
- References
- 10
- FWCI
- 9.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
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