An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

• Type of Material: Article
• Authors: Keith Delman, Emory University; Dong Shin, Emory University; Taofeek Owonikoko, Emory University; Erwin Van Meir, Emory University; Suresh Ramalingam, Emory University; Jeffrey Olson, Emory University; Daniel Brat, Emory University; Amy Chen, Emory University; Shishir Maithel, Emory University; Jianfang Liu, Chan Soon-Shiong Institute of Molecular Medicine at Windber; Tara Lichtenberg, Children's Hospital Columbus; Katherine A. Hoadley, University of North Carolina at Chapel Hill; Laila M. Poisson, Henry Ford Health System; Alexander J. Lazar, University of Texas MD Anderson Cancer Center; Andrew D. Cherniack, Broad Institute; Albert J. Kovatich, Walter Reed National Military Medical Center; Christopher C. Benz, Buck Institute for Age Research; Douglas A. Levine, NYU Langone Medical Center; Adrian V. Lee, Magee-Womens Research Institute; Larsson Omberg, Sage Bionetworks; Denise M. Wolf, University of California, San Francisco; Craig D. Shriver, Walter Reed National Military Medical Center; Vesteinn Thorsson, Institute for Systems Biology; Hai Hu, Chan Soon-Shiong Institute of Molecular Medicine at Windber
• Language: English
• Date: 2018-04-05
• Publisher: IOS Press
• Publication Version: Final Published Version
• Copyright Statement: © 2018 Elsevier Inc.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.cell.2018.02.052
• Title of Journal or Parent Work: Analytical Cellular Pathology / Cellular Oncology
• ISSN: 1570-5870
• Volume: 173
• Issue: 2
• Start Page: 400
• End Page: 416.e11
• Grant/Funding Information: The study was supported by W81XWH-12-2-0050, HU0001-16-2-0004 from the U.S. Department of Defense through the Henry M. Jackson Foundation for the Advancement of Military Medicine.; The study was also supported by the TCGA grants U54 HG003273, U54 HG003067, U54 HG003079, U24 CA143799, U24 CA143835, U24 CA143840, U24 CA143843, U24 CA143845, U24 CA143848, U24 CA143858, U24 CA143866, U24 CA143867, U24 CA143882, U24 CA143883, U24 CA144025, P30 CA016672.
• Keywords: follow-up time; Humans; The Cancer Genome Atlas; progression-free interval; Cox proportional hazards regression model; translational research; Proportional Hazards Models; Kaplan-Meier Estimate; disease-specific survival; disease-free interval; Neoplasms; clinical data resource; overall survival; Databases, Genetic; TCGA; Genomics
• Research Categories: Health Sciences, Oncology; Health Sciences, Pathology

Abstract

For 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. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types.