Comprehensive cross-population analysis of high-grade serous ovarian cancer supports no more than three subtypes

Gregory P. Way, James Rudd, Chen Wang, Habib Hamidi, Brooke L. Fridley, Gottfried E. Konecny, Ellen L. Goode, Casey S. Greene, Jennifer A. Doherty

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Four gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these early studies, a fraction of samples that did not fit well into the four subtype classifications were excluded. Therefore, we sought to systematically determine the concordance of transcriptomic HGSC subtypes across populations without removing any samples. We created a bioinformatics pipeline to independently cluster the five largest mRNA expression datasets using k-means and nonnegative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four cause these results contrast with previous reports, we attempted to reproduce analyses performed in those studies. Our results suggest that early results favoring four subtypes may have been driven by the inclusion of serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.

Original languageEnglish (US)
Pages (from-to)4097-4103
Number of pages7
JournalG3: Genes, Genomes, Genetics
Volume6
Issue number12
DOIs
StatePublished - 2016

Keywords

  • Molecular subtypes
  • Ovarian cancer
  • Reproducibility
  • Unsupervised clustering

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Genetics(clinical)

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