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

The Cancer Genome Atlas Research Network, Jianfang Liu, Tara Lichtenberg, Katherine A. Hoadley, Laila M. Poisson, Alexander J. Lazar, Alexander Lazar, Albert J. Kovatich, Christopher C. Benz, Douglas A. Levine, Adrian V. Lee, Larsson Omberg, Denise M. Wolf, Craig D. Shriver, Vesteinn Thorsson, Samantha J. Caesar-Johnson, John A. Demchok, Yuexin Liu, Rehan Akbani, Bradley McIntosh Broom & 31 others Rupa Sridevi Kanchi, Anil Korkut, Jun Li, Han Liang, Shiyun Ling, Yiling Lu, Gordon B Mills, Arvind Rao Uppore Kukkillaya, John N Weinstein, Xiuping Liu, Linghua Wang, André L. Carvalho, José Humberto T. G. Fregnani, Cristovam Neto Scapulatempo, Jaffer A Ajani, Maria del Carmen Behrens, Russell Broaddus, Bogdan A Czerniak, Bita Esmaeli, Junya Fujimoto, Jeffrey E Gershenwald, Christopher J Logothetis, Funda Meric-Bernstam, Cesar Moran, Lois M Ramondetta, Anil K Sood, Pheroze Tamboli, Timothy Thompson, Patricia Troncoso, Anne Tsao, Andreas von Deimling

Research output: Contribution to journalArticle

  • 12 Citations

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.

LanguageEnglish (US)
Pages400-416.e11
JournalCell
Volume173
Issue number2
DOIs
StatePublished - Apr 5 2018

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Atlases
Genes
Genome
Survival
Neoplasms
Tumors
Genomics

Keywords

  • Cox proportional hazards regression model
  • TCGA
  • The Cancer Genome Atlas
  • clinical data resource
  • disease-free interval
  • disease-specific survival
  • follow-up time
  • overall survival
  • progression-free interval
  • translational research

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. / The Cancer Genome Atlas Research Network.

In: Cell, Vol. 173, No. 2, 05.04.2018, p. 400-416.e11.

Research output: Contribution to journalArticle

The Cancer Genome Atlas Research Network. / An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. In: Cell. 2018 ; Vol. 173, No. 2. pp. 400-416.e11.
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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.",
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AU - Ajani, Jaffer A

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