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An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics
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 co...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6066282/ https://www.ncbi.nlm.nih.gov/pubmed/29625055 http://dx.doi.org/10.1016/j.cell.2018.02.052 |
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author | Liu, Jianfang Lichtenberg, Tara Hoadley, Katherine A. Poisson, Laila M. Lazar, Alexander J. Cherniack, Andrew D. Kovatich, Albert J. Benz, Christopher C. Levine, Douglas A. Lee, Adrian V. Omberg, Larsson Wolf, Denise M. Shriver, Craig D. Thorsson, Vesteinn Hu, Hai |
author_facet | Liu, Jianfang Lichtenberg, Tara Hoadley, Katherine A. Poisson, Laila M. Lazar, Alexander J. Cherniack, Andrew D. Kovatich, Albert J. Benz, Christopher C. Levine, Douglas A. Lee, Adrian V. Omberg, Larsson Wolf, Denise M. Shriver, Craig D. Thorsson, Vesteinn Hu, Hai |
author_sort | Liu, Jianfang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6066282 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
record_format | MEDLINE/PubMed |
spelling | pubmed-60662822019-04-05 An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics Liu, Jianfang Lichtenberg, Tara Hoadley, Katherine A. Poisson, Laila M. Lazar, Alexander J. Cherniack, Andrew D. Kovatich, Albert J. Benz, Christopher C. Levine, Douglas A. Lee, Adrian V. Omberg, Larsson Wolf, Denise M. Shriver, Craig D. Thorsson, Vesteinn Hu, Hai Cell Article 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. 2018-04-05 /pmc/articles/PMC6066282/ /pubmed/29625055 http://dx.doi.org/10.1016/j.cell.2018.02.052 Text en This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (http://http://creativecommons.org/licenses/by-nc-nd/4.0/) ). |
spellingShingle | Article Liu, Jianfang Lichtenberg, Tara Hoadley, Katherine A. Poisson, Laila M. Lazar, Alexander J. Cherniack, Andrew D. Kovatich, Albert J. Benz, Christopher C. Levine, Douglas A. Lee, Adrian V. Omberg, Larsson Wolf, Denise M. Shriver, Craig D. Thorsson, Vesteinn Hu, Hai An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics |
title | An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics |
title_full | An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics |
title_fullStr | An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics |
title_full_unstemmed | An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics |
title_short | An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics |
title_sort | integrated tcga pan-cancer clinical data resource to drive high-quality survival outcome analytics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6066282/ https://www.ncbi.nlm.nih.gov/pubmed/29625055 http://dx.doi.org/10.1016/j.cell.2018.02.052 |
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