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

The Cancer Genome Atlas Research Network

Research output: Contribution to journalArticle

111 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)400-416.e11
JournalCell
Volume173
Issue number2
DOIs
Publication statusPublished - 5 Apr 2018

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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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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

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 - Pihl, Todd

AU - Sun, Qiang

AU - Wan, Yunhu

AU - Wu, Ye

AU - Cho, Juok

AU - DeFreitas, Timothy

AU - Frazer, Scott

AU - Gehlenborg, Nils

AU - Getz, Gad

AU - Heiman, David I.

AU - Kim, Jaegil

AU - Lawrence, Michael S.

AU - Lin, Pei

AU - Meier, Sam

AU - Noble, Michael S.

AU - Saksena, Gordon

AU - Voet, Doug

AU - Zhang, Hailei

AU - Bernard, Brady

AU - Chambwe, Nyasha

AU - Dhankani, Varsha

AU - Knijnenburg, Theo

AU - Kramer, Roger

AU - Leinonen, Kalle

AU - Liu, Yuexin

AU - Miller, Michael

AU - Reynolds, Sheila

AU - Shmulevich, Ilya

AU - Thorsson, Vesteinn

AU - Zhang, Wei

AU - Akbani, Rehan

AU - Broom, Bradley M.

AU - Hegde, Apurva M.

AU - Ju, Zhenlin

AU - Kanchi, Rupa S.

AU - Korkut, Anil

AU - Li, Jun

AU - Liang, Han

AU - Ling, Shiyun

AU - Liu, Wenbin

AU - Lu, Yiling

AU - Mills, Gordon B.

AU - Ng, Kwok Shing

AU - Rao, Arvind

AU - Ryan, Michael

AU - Wang, Jing

AU - Weinstein, John N.

AU - Zhang, Jiexin

AU - Abeshouse, Adam

AU - Armenia, Joshua

AU - Chakravarty, Debyani

AU - Chatila, Walid K.

AU - de Bruijn, Ino

AU - Gao, Jianjiong

AU - Gross, Benjamin E.

AU - Heins, Zachary J.

AU - Kundra, Ritika

AU - La, Konnor

AU - Ladanyi, Marc

AU - Luna, Augustin

AU - Nissan, Moriah G.

AU - Ochoa, Angelica

AU - Phillips, Sarah M.

AU - Reznik, Ed

AU - Sanchez-Vega, Francisco

AU - Sander, Chris

AU - Schultz, Nikolaus

AU - Sheridan, Robert

AU - Sumer, S. Onur

AU - Sun, Yichao

AU - Taylor, Barry S.

AU - Wang, Jioajiao

AU - Zhang, Hongxin

AU - Anur, Pavana

AU - Peto, Myron

AU - Spellman, Paul

AU - Benz, Christopher

AU - Stuart, Joshua M.

AU - Wong, Christopher K.

AU - Yau, Christina

AU - Hayes, D. Neil

AU - Parker, Joel S.

AU - Wilkerson, Matthew D.

AU - Ally, Adrian

AU - Balasundaram, Miruna

AU - Bowlby, Reanne

AU - Brooks, Denise

AU - Carlsen, Rebecca

AU - Chuah, Eric

AU - Dhalla, Noreen

AU - Holt, Robert

AU - Jones, Steven J.M.

AU - Kasaian, Katayoon

AU - Lee, Darlene

AU - Ma, Yussanne

AU - Marra, Marco A.

AU - Mayo, Michael

AU - Moore, Richard A.

AU - Mungall, Andrew J.

AU - Mungall, Karen

AU - Robertson, A. Gordon

AU - Sadeghi, Sara

AU - Schein, Jacqueline E.

AU - Sipahimalani, Payal

AU - Tam, Angela

AU - Thiessen, Nina

AU - Tse, Kane

AU - Wong, Tina

AU - Berger, Ashton C.

AU - Beroukhim, Rameen

AU - Cherniack, Andrew D.

AU - Cibulskis, Carrie

AU - Gabriel, Stacey B.

AU - Gao, Galen F.

AU - Ha, Gavin

AU - Meyerson, Matthew

AU - Schumacher, Steven E.

AU - Shih, Juliann

AU - Kucherlapati, Melanie H.

AU - Kucherlapati, Raju S.

AU - Baylin, Stephen

AU - Cope, Leslie

AU - Danilova, Ludmila

AU - Bootwalla, Moiz S.

AU - Lai, Phillip H.

AU - Maglinte, Dennis T.

AU - Van Den Berg, David J.

AU - Weisenberger, Daniel J.

AU - Auman, J. Todd

AU - Balu, Saianand

AU - Bodenheimer, Tom

AU - Fan, Cheng

AU - Hoadley, Katherine A.

AU - Hoyle, Alan P.

AU - Jefferys, Stuart R.

AU - Jones, Corbin D.

AU - Meng, Shaowu

AU - Mieczkowski, Piotr A.

AU - Mose, Lisle E.

AU - Perou, Amy H.

AU - Perou, Charles M.

AU - Roach, Jeffrey

AU - Shi, Yan

AU - Simons, Janae V.

AU - Skelly, Tara

AU - Soloway, Matthew G.

AU - Tan, Donghui

AU - Veluvolu, Umadevi

AU - Fan, Huihui

AU - Hinoue, Toshinori

AU - Laird, Peter W.

AU - Shen, Hui

AU - Zhou, Wanding

AU - Bellair, Michelle

AU - Chang, Kyle

AU - Covington, Kyle

AU - Creighton, Chad J.

AU - Dinh, Huyen

AU - Doddapaneni, Harsha Vardhan

AU - Donehower, Lawrence A.

AU - Drummond, Jennifer

AU - Gibbs, Richard A.

AU - Glenn, Robert

AU - Hale, Walker

AU - Han, Yi

AU - Hu, Jianhong

AU - Korchina, Viktoriya

AU - Lee, Sandra

AU - Lewis, Lora

AU - Li, Wei

AU - Liu, Xiuping

AU - Morgan, Margaret

AU - Morton, Donna

AU - Muzny, Donna

AU - Santibanez, Jireh

AU - Sheth, Margi

AU - Shinbro, Eve

AU - Wang, Linghua

AU - Wang, Min

AU - Wheeler, David A.

AU - Xi, Liu

AU - Zhao, Fengmei

AU - Hess, Julian

AU - Appelbaum, Elizabeth L.

AU - Bailey, Matthew

AU - Cordes, Matthew G.

AU - Ding, Li

AU - Fronick, Catrina C.

AU - Fulton, Lucinda A.

AU - Fulton, Robert S.

AU - Kandoth, Cyriac

AU - Mardis, Elaine R.

AU - McLellan, Michael D.

AU - Miller, Christopher A.

AU - Schmidt, Heather K.

AU - Wilson, Richard K.

AU - Crain, Daniel

AU - Curley, Erin

AU - Gardner, Johanna

AU - Lau, Kevin

AU - Mallery, David

AU - Morris, Scott

AU - Paulauskis, Joseph

AU - Penny, Robert

AU - Shelton, Candace

AU - Shelton, Troy

AU - Sherman, Mark

AU - Thompson, Eric

AU - Yena, Peggy

AU - Bowen, Jay

AU - Gastier-Foster, Julie M.

AU - Gerken, Mark

AU - Leraas, Kristen M.

AU - Lichtenberg, Tara M.

AU - Ramirez, Nilsa C.

AU - Wise, Lisa

AU - Zmuda, Erik

AU - Corcoran, Niall

AU - Costello, Tony

AU - Creaney, Jenette

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 - clinical data resource

KW - Cox proportional hazards regression model

KW - disease-free interval

KW - disease-specific survival

KW - follow-up time

KW - overall survival

KW - progression-free interval

KW - TCGA

KW - The Cancer Genome Atlas

KW - translational research

UR - http://www.scopus.com/inward/record.url?scp=85044905247&partnerID=8YFLogxK

U2 - 10.1016/j.cell.2018.02.052

DO - 10.1016/j.cell.2018.02.052

M3 - Article

VL - 173

SP - 400-416.e11

JO - Cell

JF - Cell

SN - 0092-8674

IS - 2

ER -