Comprehensive Cross-Population Analysis of High-Grade Serous Ovarian Cancer Supports No More Than Three Subtypes

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 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 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.

Pooled Clustering of High-Grade Serous Ovarian Cancer Gene Expression Leads to Novel Consensus Subtypes Associated with Survival and Surgical Outcomes

“…This large-scale analysis confirmed the previously described mesenchymal (s1.MES), immunoreactive (s2.IMM), and proliferative (s3.PRO) subtypes as distinct entities. While our work was in progress, a cross-population analysis has also confirmed at least mesenchymal and proliferative subtypes were stable through clustering comparisons across individual expression studies (31). In the present study, a new subtype “anti-mesenchymal” (s5.ANM) was derived from TCGA-differentiated/Tothill-C4 subtype, with noticeable down-regulation of mesenchymal signature genes and better survival outcomes in both the public and Mayo Clinic HGSOC cohorts.…”

Challenges and Opportunities in Studying the Epidemiology of Ovarian Cancer Subtypes

“…A recent analysis of genotyping accuracy has raised questions about the quality of sequencing-based variant calls in TCGA’s HGSC samples [76] which may affect the findings in Hofree et al [75]. Given the exploratory nature of molecular clustering and limitations of the approaches used, more research is needed about how many underlying molecular subtypes exist [77–79] and if they are consistent across populations [80]. …”

Functional network community detection can disaggregate and filter multiple underlying pathways in enrichment analyses

“…While previous reports have described four HGSC subtypes, the multi-population study suggested that the number was three or fewer 26 . Given these conflicting results, we applied community detection to HGSC subtype-specific gene lists previously derived from results classifying 2, 3, and 4 subtypes 26 . Because this is an analysis of cancer genomics data, we used cancer pathways from the Pathway Interaction Database (PID) 5 .…”

“…We previously derived these gene lists from a differential expression analysis across HGSC subtypes that were concordant across different populations 26 . We selected only the top performing algorithms from our simulation study, Walktrap and Multilevel.…”

“…The gene lists were previously identified by a one cluster versus all differential expression analysis 26 of cluster specific genes in common to four HGSC datasets 27–30 . While previous reports have described four HGSC subtypes, the multi-population study suggested that the number was three or fewer 26 . Given these conflicting results, we applied community detection to HGSC subtype-specific gene lists previously derived from results classifying 2, 3, and 4 subtypes 26 .…”