Assessing the clinical utility of cancer genomic and proteomic data across tumor types

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Assessing the clinical utility of cancer genomic and proteomic data across tumor types

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Title: Assessing the clinical utility of cancer genomic and proteomic data across tumor types
Author: Yuan, Yuan; Van Allen, Eliezer M.; Omberg, Larsson; Wagle, Nikhil; Amin-Mansour, Ali; Sokolov, Artem; Byers, Lauren A.; Xu, Yanxun; Hess, Kenneth R.; Diao, Lixia; Han, Leng; Huang, Xuelin; Lawrence, Michael S.; Weinstein, John N.; Stuart, Josh M.; Mills, Gordon B.; Garraway, Levi A.; Margolin, Adam A.; Getz, Gad; Liang, Han

Note: Order does not necessarily reflect citation order of authors.

Citation: Yuan, Y., E. M. Van Allen, L. Omberg, N. Wagle, A. Amin-Mansour, A. Sokolov, L. A. Byers, et al. 2014. “Assessing the clinical utility of cancer genomic and proteomic data across tumor types.” Nature biotechnology 32 (7): 644-652. doi:10.1038/nbt.2940. http://dx.doi.org/10.1038/nbt.2940.
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Abstract: Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, miRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We found that incorporating molecular data with clinical variables yielded statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2–23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data.
Published Version: doi:10.1038/nbt.2940
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4102885/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:13890706
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