TY - JOUR
T1 - An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics
AU - The Cancer Genome Atlas Research Network
AU - Liu, Jianfang
AU - Lichtenberg, Tara
AU - Hoadley, Katherine A.
AU - Poisson, Laila M.
AU - Lazar, Alexander J.
AU - Cherniack, Andrew D.
AU - Kovatich, Albert J.
AU - Benz, Christopher C.
AU - Levine, Douglas A.
AU - Lee, Adrian V.
AU - Omberg, Larsson
AU - Wolf, Denise M.
AU - Shriver, Craig D.
AU - Thorsson, Vesteinn
AU - Caesar-Johnson, Samantha J.
AU - Demchok, John A.
AU - Felau, Ina
AU - Kasapi, Melpomeni
AU - Ferguson, Martin L.
AU - Hutter, Carolyn M.
AU - Sofia, Heidi J.
AU - Tarnuzzer, Roy
AU - Wang, Zhining
AU - Yang, Liming
AU - Zenklusen, Jean C.
AU - Zhang, Jiashan (Julia)
AU - Chudamani, Sudha
AU - Liu, Jia
AU - Lolla, Laxmi
AU - Naresh, Rashi
AU - Borad, Mitesh
AU - Bryce, Alan H.
AU - Castle, Erik
AU - Chandan, Vishal
AU - Cheville, John
AU - Copland, John A.
AU - Flotte, Thomas
AU - Ho, Thai
AU - Kendrick, Michael
AU - Kocher, Jean Pierre
AU - O'Neill, Brian Patrick
AU - Patel, Tushar
AU - Petersen, Gloria
AU - Que, Florencia
AU - Roberts, Lewis
AU - Smallridge, Robert
AU - Smyrk, Thomas
AU - Stanton, Melissa
AU - Torbenson, Michael
AU - Zhang, Lizhi
N1 - Funding Information:
We thank the patients who participated in this study. We are grateful for the clinical staff of TCGA Tissue Source Sites and the staff of the TCGA Biospecimen Core Resource for collecting, compiling, and processing the clinical data for TCGA that laid the foundation for this study. We thank Ms. Anupama Praveen-Kumar and Drs. Joseph Vockley and Praveen-Kumar Raj-Kumar for helpful discussions and Mr. Peter T. Hu for creating the graphical abstract. Members of the TCGA PanCanAtlas Network and especially those from the Immune Response AWG participated in discussions as the project progressed. 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 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. 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.
PY - 2018/4/5
Y1 - 2018/4/5
N2 - 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.
AB - 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.
KW - Cox proportional hazards regression model
KW - TCGA
KW - The Cancer Genome Atlas
KW - clinical data resource
KW - disease-free interval
KW - disease-specific survival
KW - follow-up time
KW - overall survival
KW - progression-free interval
KW - translational research
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U2 - 10.1016/j.cell.2018.02.052
DO - 10.1016/j.cell.2018.02.052
M3 - Article
C2 - 29625055
AN - SCOPUS:85044905247
VL - 173
SP - 400-416.e11
JO - Cell
JF - Cell
SN - 0092-8674
IS - 2
ER -