Abstract 3287: An integrated TCGA pan-cancer clinical data resource to drive high quality survival outcome analytics
Authors: Jianfang Liu, Tara Lichtenberg, Katherine A. Hoadley, Laila M. Poisson, Alexander J. Lazar, Andrew D. Cherniack, Albert J. Kovatich, Christopher C. Benz, Douglas A. Levine, Adrian V. Lee, Larsson Omberg, Denise M. Wolf, Craig D. Shriver, Vesteinn Thorsson, The Cancer Genome Atlas Research Network, Hai Hu
Journal: Cancer Research (2018)

Abstract Background The Cancer Genome Atlas (TCGA) program collected detailed clinicopathologic annotation data along with multi-platform molecular profiles across 33 cancer types (&amp;gt;11,000 tumors). These data contain features unique to the democratized nature of this largest ever clinical-genomic dataset, but questions have been raised about the quality of its outcome measures. To address these questions, we produced and quality checked a standardized compendium named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR). Method Clinical data were downloaded from TCGA data portal. Commonly used data elements were quality assessed (QA), integrated and processed with the help from disease experts and TCGA Biospecimen Core Resource. Clinical outcome endpoints of overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) were derived. Each qualifying end point was subjected to multi-step assessment including evaluation for sufficient follow-up (Maller and Zhou, 1994; Shen, 2000), event number &amp;gt;20 and rate ≥ 30%, plateaued cumulative event plot, and median event time &amp;lt; median censored time. Validation and application examples used standard statistical methods including Cox proportional hazards regression. Results Thirty-three enrollment and 97 follow-up data files were processed for 11,160 patients. Over 1000 QA issues were identified and resolved for 21 commonly used clinical data elements during derivation of the 4 outcome endpoints. Each endpoint was quality scored with tumor-specific use recommendations provided. Despite perceived short follow-up times, PFI was derived with high confidence and recommended as a clinical endpoint choice for 27 of the 33 pan-cancer types, but with caution for 5 others. OS is recommended as an appropriate endpoint for 23 cancer types, and with caution for 6 others. Guidance for using DSS and DFI is also provided. The value of TCGA-CDR was demonstrated through validation and application examples including, 1) patients with ER(-) breast tumors showing worse outcomes compared to those with ER(+) tumors; 2) patients with higher-stage (III, IV) tumors showing worse outcomes than those with lower-stage (I, II) tumors; and 3) patients achieving disease-free status after diagnosis with better outcomes than those never achieving disease-free status. Conclusion We compiled and quality assessed a new TCGA-CDR containing 4 standardized outcomes for 11,160 cancer patients across 33 tumor types. This resource, generating endpoint results consistent with independent non-TCGA findings, provides new research opportunities to produce biologically insightful observations at unprecedented clinical scale. Disclaimer The views expressed in this article are those of the authors and do not reflect the official policy of the department of Army/Navy/Air Force, Department of Defense, or U.S. government. Citation Format: Jianfang Liu, Tara Lichtenberg, Katherine A. Hoadley, Laila M. Poisson, Alexander J. Lazar, Andrew D. Cherniack, Albert J. Kovatich, Christopher C. Benz, Douglas A. Levine, Adrian V. Lee, Larsson Omberg, Denise M. Wolf, Craig D. Shriver, Vesteinn Thorsson, The Cancer Genome Atlas Research Network, Hai Hu. An integrated TCGA pan-cancer clinical data resource to drive high quality survival outcome analytics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3287.