In the past two decades, it has become clear that cancer arises not only as a consequence of genetic events, such as mutations, copy number alterations, and sequence rearrangements, but also as a result of extensive changes to the epigenome. Epigenetic alterations contribute causally to the origin and malignant progression of cancer, and affect clinical outcome. The USC-JHU Cancer Epigenome Characterization Center specializes in the production and analysis of genome-scale cancer epigenetic data. During the pilot phase of The Cancer Genome Atlas, our center has been responsible for the production and deposition of all high-throughput epigenomic data within TCGA. A key outcome of the TCGA pilot was our finding of a link in glioblastoma multiforme between MGMT promoter methylation, treatment with alkylating agents, and a hypermutator phenotype associated with mismatch repair deficiency, an observation with potential clinical implications. For the next phase of TCGA, we propose the following three objectives. Objective 1 is to characterize the DNA methylation status of 27,578 CpG dinucleotides located in 14,495 gene promoters in at least 10,000 human cancer samples and 1,000 control samples using the lllumina Infinium DNA Methylation analysis platform. Objective 2 is to transition epigenomic data production in TCGA to whole genome shotgun bisulfite sequence analysis to provide single-base-pair resolution DNA methylation data for TCGA cancer samples. Objective 3 is to implement quality control and quality assurance measures to ensure that epigenomic data deposited for public dissemination meets rigorous standards. Our group has considerable expertise in cancer epigenomics, with two founders of the field of cancer epigenetics and with members who collectively have pioneered the majority of widely used DNA methylation analysis techniques, and who have provided many of the biological insights. Epigenomic data are a necessary component for a full understanding of the relationship between alterations in the cancer genome and the origin and clinical diversity of individual tumors.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
3U24CA143882-05S3
Application #
9214434
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Tarnuzzer, Roy W
Project Start
2009-09-29
Project End
2016-12-31
Budget Start
2013-08-01
Budget End
2016-12-31
Support Year
5
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Southern California
Department
Surgery
Type
Schools of Medicine
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90032
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
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 23:3698
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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