We propose to operate a Genome Characterization Center for The Cancer Genome Atlas (TCGA) of the National Cancer Institute. Our goal is to characterize the genomes and transcriptomes of 10,000 human cancers over 5 years, together with the collaborators of TCGA Research Network. Given the ongoing revolution in genome analysis, our Center will transition from current-generation microarray technologies to next-generation sequencing technologies for cancer genome characterization. We will accomplish this by benchmarking the next-generation technologies against state-of-the-art array technologies used in the pilot project of TCGA. Our Center is uniquely qualified for this effort, because of our expertise in next-generation sequencing as well as current-generation microarray production and technology development, our knowledge and demonstrated accomplishments in cancer genomics, and our commitment to and experience with multi-institutional collaborations including the pilot project of TCGA. Specifically, we intend to accomplish the following aims for TCGA:
Aim 1. Using microarray technologies: Characterize DNA and RNA from 2000 cancer samples and appropriate controls during year 1.
Aim 2. Using next-generation sequencing: (i) Perform direct comparison and validation of the three alternative platforms during year 1, by characterizing DNA from 300 cancer/normal sample pairs and RNA from 100 cancer samples. (ii) Select the most cost-effective sequencing platform at the end of year 1, based on the results and in conjunction with NCI staff. (iii) Implement this sequencing platform to characterize DNA and RNA from 2000 cancer samples and appropriate controls in Years 2-5. (iv) Continue to decrease cost and increase resolution of genomic analysis.

Public Health Relevance

Cancer is a genomic disease, caused by somatic and germ-line alterations in DNA and RNA. By discovering and enumerating these alterations, we can improve the diagnosis and treatment of cancer: this is the goal of The Cancer Genome Atlas. Our proposed Genome Characterization Center could contribute significantly to TCGA by advancing genomic technology and by rapidly accomplishing cancer genome discoveries.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
5U24CA143867-02
Application #
7942757
Study Section
Special Emphasis Panel (ZCA1-SRLB-U (O1))
Program Officer
Lee, Jerry S
Project Start
2009-09-29
Project End
2014-07-31
Budget Start
2010-08-01
Budget End
2011-07-31
Support Year
2
Fiscal Year
2010
Total Cost
$2,754,287
Indirect Cost
Name
Broad Institute, Inc.
Department
Type
DUNS #
623544785
City
Cambridge
State
MA
Country
United States
Zip Code
02142
Ricketts, Christopher J; De Cubas, Aguirre A; Fan, Huihui et al. (2018) The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma. Cell Rep 23:313-326.e5
Knijnenburg, Theo A; Wang, Linghua; Zimmermann, Michael T et al. (2018) Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Rep 23:239-254.e6
Peng, Xinxin; Chen, Zhongyuan; Farshidfar, Farshad et al. (2018) Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers. Cell Rep 23:255-269.e4
Huang, Kuan-Lin; Mashl, R Jay; Wu, Yige et al. (2018) Pathogenic Germline Variants in 10,389 Adult Cancers. Cell 173:355-370.e14
Ding, Li; Bailey, Matthew H; Porta-Pardo, Eduard et al. (2018) Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics. Cell 173:305-320.e10
Seiler, Michael; Peng, Shouyong; Agrawal, Anant A et al. (2018) Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types. Cell Rep 23:282-296.e4
Liu, Yang; Sethi, Nilay S; Hinoue, Toshinori et al. (2018) Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas. Cancer Cell 33:721-735.e8
Jayasinghe, Reyka G; Cao, Song; Gao, Qingsong et al. (2018) Systematic Analysis of Splice-Site-Creating Mutations in Cancer. Cell Rep 23:270-281.e3
Saltz, Joel; Gupta, Rajarsi; Hou, Le et al. (2018) Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep 23:181-193.e7
Ellrott, Kyle; Bailey, Matthew H; Saksena, Gordon et al. (2018) Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. Cell Syst 6:271-281.e7

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