Genomic analysis using regularized regression in high-grade serous ovarian cancer

Yanina Natanzon, Madalene Earp, Julie M. Cunningham, Kimberly R. Kalli, Chen Wang, Sebastian M. Armasu, Melissa C. Larson, David D.L. Bowtell, Dale W. Garsed, Brooke L. Fridley, Stacey J. Winham, Ellen L. Goode

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


High-grade serous ovarian cancer (HGSOC) is a complex disease in which initiation and progression have been associated with copy number alterations, epigenetic processes, and, to a lesser extent, germline variation. We hypothesized that, when summarized at the gene level, tumor methylation and germline genetic variation, alone or in combination, influence tumor gene expression in HGSOC. We used Elastic Net (ENET) penalized regression method to evaluate these associations and adjust for somatic copy number in 3 independent data sets comprising tumors from more than 470 patients. Penalized regression models of germline variation, with or without methylation, did not reveal a role in HGSOC gene expression. However, we observed significant association between regional methylation and expression of 5 genes (WDPCP, KRT6C, BRCA2, EFCAB13, and ZNF283). CpGs retained in ENET model for BRCA2 and ZNF283 appeared enriched in several regulatory elements, suggesting that regularized regression may provide a novel utility for integrative genomic analysis.

Original languageEnglish (US)
JournalCancer Informatics
StatePublished - 2018


  • Elastic net penalized regression
  • High-grade serous ovarian cancer
  • Tumor DNA methylation

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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