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dc.contributor.authorYuan, Yuanen_US
dc.contributor.authorVan Allen, Eliezer M.en_US
dc.contributor.authorOmberg, Larssonen_US
dc.contributor.authorWagle, Nikhilen_US
dc.contributor.authorAmin-Mansour, Alien_US
dc.contributor.authorSokolov, Artemen_US
dc.contributor.authorByers, Lauren A.en_US
dc.contributor.authorXu, Yanxunen_US
dc.contributor.authorHess, Kenneth R.en_US
dc.contributor.authorDiao, Lixiaen_US
dc.contributor.authorHan, Lengen_US
dc.contributor.authorHuang, Xuelinen_US
dc.contributor.authorLawrence, Michael S.en_US
dc.contributor.authorWeinstein, John N.en_US
dc.contributor.authorStuart, Josh M.en_US
dc.contributor.authorMills, Gordon B.en_US
dc.contributor.authorGarraway, Levi A.en_US
dc.contributor.authorMargolin, Adam A.en_US
dc.contributor.authorGetz, Gaden_US
dc.contributor.authorLiang, Hanen_US
dc.date.accessioned2015-02-02T15:32:55Z
dc.date.issued2014en_US
dc.identifier.citationYuan, 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.en
dc.identifier.issn1087-0156en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:13890706
dc.description.abstractMolecular 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.en
dc.language.isoen_USen
dc.relation.isversionofdoi:10.1038/nbt.2940en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4102885/pdf/en
dash.licenseLAAen_US
dc.titleAssessing the clinical utility of cancer genomic and proteomic data across tumor typesen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalNature biotechnologyen
dash.depositing.authorVan Allen, Eliezer M.en_US
dc.date.available2015-02-02T15:32:55Z
dc.identifier.doi10.1038/nbt.2940*
dash.authorsorderedfalse
dash.contributor.affiliatedVan Allen, Eliezer
dash.contributor.affiliatedGetz, Gad
dash.contributor.affiliatedGarraway, Levi


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