TY - JOUR T1 - Comprehensive Cross-Population Analysis of High-Grade Serous Ovarian Cancer Supports No More Than Three Subtypes JF - bioRxiv DO - 10.1101/030239 SP - 030239 AU - Gregory P Way AU - James Rudd AU - Chen Wang AU - Habib Hamidi AU - Fridley Fridley AU - Gottfried Konecny AU - Ellen L Goode AU - Casey S Greene AU - Jennifer A Doherty Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/06/05/030239.abstract N2 - Four gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these 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 non-negative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four subtypes, our cross-population comparison does not support four. Because 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 including serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.CONFLICTS OF INTEREST The authors do not declare any conflicts of interestOTHER PRESENTATIONS Aspects of this study were presented at the 2015 AACR Conference and the 2015 Rocky Mountain Bioinformatics Conference.AUTHORS’ CONTRIBUTIONS Study concept and design: GW, JR, CG, JD. Original data collection and processing: CW, HH, BF, GK, EG. Data analysis: GW, JR, CG, JD. Manuscript drafting and editing: GW, JR, CG, JD. All authors read, commented on, and approved the final manuscript. ER -