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dc.contributor.authorLiu, J
dc.contributor.authorLichtenberg, T
dc.contributor.authorHoadley, KA
dc.contributor.authorPoisson, LM
dc.contributor.authorLazar, AJ
dc.contributor.authorCherniack, AD
dc.contributor.authorKovatich, AJ
dc.contributor.authorBenz, CC
dc.contributor.authorLevine, DA
dc.contributor.authorLee, AV
dc.contributor.authorOmberg, L
dc.contributor.authorWolf, DM
dc.contributor.authorShriver, CD
dc.contributor.authorThorsson, V
dc.contributor.authorHu, H
dc.date.accessioned2020-12-17T03:09:42Z
dc.date.available2020-12-17T03:09:42Z
dc.date.issued2018-04-05
dc.identifierpii: S0092-8674(18)30229-0
dc.identifier.citationLiu, J., Lichtenberg, T., Hoadley, K. A., Poisson, L. M., Lazar, A. J., Cherniack, A. D., Kovatich, A. J., Benz, C. C., Levine, D. A., Lee, A. V., Omberg, L., Wolf, D. M., Shriver, C. D., Thorsson, V. & Hu, H. (2018). An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. CELL, 173 (2), pp.400-+. https://doi.org/10.1016/j.cell.2018.02.052.
dc.identifier.issn0092-8674
dc.identifier.urihttp://hdl.handle.net/11343/254770
dc.description.abstractFor a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.
dc.languageEnglish
dc.publisherCELL PRESS
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.titleAn Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics
dc.typeJournal Article
dc.identifier.doi10.1016/j.cell.2018.02.052
melbourne.affiliation.departmentSir Peter MacCallum Department of Oncology
melbourne.affiliation.departmentSurgery (RMH)
melbourne.affiliation.facultyMedicine, Dentistry & Health Sciences
melbourne.source.titleCell
melbourne.source.volume173
melbourne.source.issue2
melbourne.source.pages400-+
dc.rights.licenseCC BY-NC-ND
melbourne.elementsid1323386
melbourne.contributor.authorBoussioutas, Alex
melbourne.contributor.authorCorcoran, Niall
melbourne.contributor.authorHovens, Christopher
melbourne.contributor.authorCostello, Anthony
dc.identifier.eissn1097-4172
melbourne.accessrightsOpen Access


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