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

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          SUMMARY

          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.

          Graphical abstract

          In Brief 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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          Most cited references18

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          Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.

          Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies. Copyright © 2014 Elsevier Inc. All rights reserved.
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            REporting recommendations for tumour MARKer prognostic studies (REMARK)

            Despite years of research and hundreds of reports on tumour markers in oncology, the number of markers that have emerged as clinically useful is pitifully small. Often initially reported studies of a marker show great promise, but subsequent studies on the same or related markers yield inconsistent conclusions or stand in direct contradiction to the promising results. It is imperative that we attempt to understand the reasons that multiple studies of the same marker lead to differing conclusions. A variety of methodological problems have been cited to explain these discrepancies. Unfortunately, many tumour marker studies have not been reported in a rigorous fashion, and published articles often lack sufficient information to allow adequate assessment of the quality of the study or the generalisability of the study results. The development of guidelines for the reporting of tumour marker studies was a major recommendation of the US National Cancer Institute and the European Organisation for Research and Treatment of Cancer (NCI-EORTC) First International Meeting on Cancer Diagnostics in 2000. Similar to the successful CONSORT initiative for randomised trials and the STARD statement for diagnostic studies, we suggest guidelines to provide relevant information about the study design, preplanned hypotheses, patient and specimen characteristics, assay methods, and statistical analysis methods. In addition, the guidelines suggest helpful presentations of data and important elements to include in discussions. The goal of these guidelines is to encourage transparent and complete reporting so that the relevant information will be available to others to help them to judge the usefulness of the data and understand the context in which the conclusions apply.
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              Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration

              The REMARK “elaboration and explanation” guideline, by Doug Altman and colleagues, provides a detailed reference for authors on important issues to consider when designing, conducting, and analyzing tumor marker prognostic studies.
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                Author and article information

                Journal
                0413066
                2830
                Cell
                Cell
                Cell
                0092-8674
                1097-4172
                2 July 2018
                05 April 2018
                05 April 2019
                : 173
                : 2
                : 400-416.e11
                Affiliations
                [1 ]Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA 15963, USA
                [2 ]Nationwide Children’s Hospital, Columbus, OH 43205, USA
                [3 ]Department of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
                [4 ]Henry Ford Cancer Institute and Hermelin Brain Tumor Center, Henry Ford Health System, Detroit, MI 48202, USA
                [5 ]Departments of Pathology, Genomic Medicine, and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
                [6 ]The Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
                [7 ]Clinical Breast Care Project, Murtha Cancer Center, Uniformed Services University/Walter Reed National Military Medical Center, Bethesda, MD 20889, USA
                [8 ]Buck Institute for Research on Aging, Novato, CA 94945, USA
                [9 ]Division of Gynecologic Oncology, Department of OB/GYN, NYU Langone Medical Center, New York, NY 10016, USA
                [10 ]Department of Pharmacology and Chemical Biology and Human Genetics, University of Pittsburgh, Women’s Cancer Research Center, UPMC Hillman Cancer Center and Magee-Womens Research Institute, Pittsburgh, PA 15213, USA
                [11 ]Sage Bionetworks, Seattle, WA 98109, USA
                [12 ]Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
                [13 ]Murtha Cancer Center, Uniformed Services University/Walter Reed National Military Medical Center, Bethesda, MD 20889, USA
                [14 ]Institute for Systems Biology, Seattle, WA 98109, USA
                Author notes
                [* ]Correspondence: h.hu@ 123456wriwindber.org
                [15]

                Lead Contact

                Article
                NIHMS978596
                10.1016/j.cell.2018.02.052
                6066282
                29625055
                491feee9-e757-436f-9c9f-c0f7a22d0e61

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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