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.
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| Bailey, Matthew H; Tokheim, Collin; Porta-Pardo, Eduard et al. (2018) Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell 173:371-385.e18 |
| Hmeljak, Julija; Sanchez-Vega, Francisco; Hoadley, Katherine A et al. (2018) Integrative Molecular Characterization of Malignant Pleural Mesothelioma. Cancer Discov 8:1548-1565 |
| Sanchez-Vega, Francisco; Mina, Marco; Armenia, Joshua et al. (2018) Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell 173:321-337.e10 |
| Way, Gregory P; Sanchez-Vega, Francisco; La, Konnor et al. (2018) Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. Cell Rep 23:172-180.e3 |
| 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 |
| 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 |
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