Details

Autor(en) / Beteiligte

• Reinhold, William C
• Varma, Sudhir
• Sunshine, Margot
• Rajapakse, Vinodh
• Luna, Augustin
• Kohn, Kurt W
• Stevenson, Holly
• Wang, Yonghong
• Heyn, Holger
• Nogales, Vanesa
• Moran, Sebastian
• Goldstein, David J
• Doroshow, James H
• Meltzer, Paul S
• Esteller, Manel
• Pommier, Yves

Titel

The NCI-60 Methylome and Its Integration into CellMiner

Ist Teil von

• Cancer research (Chicago, Ill.), 2017-02, Vol.77 (3), p.601-612

Ort / Verlag

United States: American Association for Cancer Research, Inc

Erscheinungsjahr

2017

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• A unique resource for systems pharmacology and genomic studies is the NCI-60 cancer cell line panel, which provides data for the largest publicly available library of compounds with cytotoxic activity (∼21,000 compounds), including 108 FDA-approved and 70 clinical trial drugs as well as genomic data, including whole-exome sequencing, gene and miRNA transcripts, DNA copy number, and protein levels. Here, we provide the first readily usable genome-wide DNA methylation database for the NCI-60, including 485,577 probes from the Infinium HumanMethylation450k BeadChip array, which yielded DNA methylation signatures for 17,559 genes integrated into our open access CellMiner version 2.0 (https://discover.nci.nih.gov/cellminer). Among new insights, transcript versus DNA methylation correlations revealed the epithelial/mesenchymal gene functional category as being influenced most heavily by methylation. DNA methylation and copy number integration with transcript levels yielded an assessment of their relative influence for 15,798 genes, including tumor suppressor, mitochondrial, and mismatch repair genes. Four forms of molecular data were combined, providing rationale for microsatellite instability for 8 of the 9 cell lines in which it occurred. Individual cell line analyses showed global methylome patterns with overall methylation levels ranging from 17% to 84%. A six-gene model, including PARP1, EP300, KDM5C, SMARCB1, and UHRF1 matched this pattern. In addition, promoter methylation of two translationally relevant genes, Schlafen 11 (SLFN11) and methylguanine methyltransferase (MGMT), served as indicators of therapeutic resistance or susceptibility, respectively. Overall, our database provides a resource of pharmacologic data that can reinforce known therapeutic strategies and identify novel drugs and drug targets across multiple cancer types. Cancer Res; 77(3); 601-12. ©2016 AACR.