Prognostic and Therapeutic Relevance of Molecular Subtypes in High-Grade Serous Ovarian Cancer

Abstract

Molecular classification of high-grade serous ovarian cancer (HGSOC) using transcriptional profiling has proven to be complex and difficult to validate across studies. We determined gene expression profiles of 174 well-annotated HGSOCs and demonstrate prognostic significance of the prespecified TCGA Network gene signatures. Furthermore, we confirm the presence of four HGSOC transcriptional subtypes using a de novo classification. Survival differed statistically significantly between de novo subtypes (log rank, P = .006) and was the best for the immunoreactive-like subtype, but statistically significantly worse for the proliferative- or mesenchymal-like subtypes (adjusted hazard ratio = 1.89, 95% confidence interval = 1.18 to 3.02, P = .008, and adjusted hazard ratio = 2.45, 95% confidence interval = 1.43 to 4.18, P = .001, respectively). More prognostic information was provided by the de novo than the TCGA classification (Likelihood Ratio tests, P = .003 and P = .04, respectively). All statistical tests were two-sided. These findings were replicated in an external data set of 185 HGSOCs and confirm the presence of four prognostically relevant molecular subtypes that have the potential to guide therapy decisions.

Recurrent ubiquitin B silencing in gynecological cancers establishes dependence on ubiquitin C

“…To understand the relevance of this specific silencing event in the context of previously defined disease subtypes within the HGSOC data set (20,21), UBB expression was examined in each of the 4 established subtypes: immunoreactive, mesenchymal, differentiated, and proliferative ( Figure 1B). Our results indicate that low UBB expression based on RNASeq by expectation-maximization (RSEM) values (log 2 RSEM values ≤ 12) occurs preferentially in the proliferative and differentiated subtypes.…”

Gene-expression signatures in ovarian cancer: Promise and challenges for patient stratification

“…Unsupervised clustering confirmed stable clustering of high grade serous ovarian cancer into four molecular subgroups. Moreover, we were able to show that the distinct gene expression patterns were each associated with statistically significantly different clinical outcomes where patients whose tumors expressed the immunoreactive signature had the best and those patients whose tumors expressed the mesenchymal signature had the worst overall survival [25]. In our study using the Mayo Clinic samples the unsupervised clustering by consensus NMF also demonstrated stable clustering of high grade serous ovarian cancers into three or two subgroups.…”

“…In our study using the Mayo Clinic samples the unsupervised clustering by consensus NMF also demonstrated stable clustering of high grade serous ovarian cancers into three or two subgroups. However, unlike clustering based on four subgroups, neither classification into three or two subgroups had prognostic relevance [25]. A comparison of group assignments by cross tabulation suggested that the expression matrix of the immunoreactive and mesenchymal groups were merged when three clusters were depicted [20].…”

“…Many early generation ovarian cancer gene expression profiling studies focused primarily on the prognostic value of gene expression signatures. Results of these early prognostic gene expression studies [6][7][8][9][10][11][12][13][14] and those following [15][16][17][18][19][20][21][22][23][24][25] are summarized in Table 1 (Table 1). Most of these studies have identified a group of prognostically relevant genes in relatively small training sets but did, to their credit, validate the prognostic relevance of the respective gene signatures in independent cohorts.…”

“…For the TCGA data set gene set activation scores for each individual sample were calculated and 82% of the 489 tumor samples could be assigned to at least two subtypes and 44% to at least three subtypes when dichotomous cut offs were used for the gene set activation scores. In our own independent studies using the same method of single sample gene set enrichment analysis (ssGSEA) 40% of high grade serous ovarian cancers samples could be assigned to two subtypes and 2% to three subtypes [25]. Although most gene expression studies have been performed on the samples with high tumor content, present host cells such as immune cells, fibroblasts, endothelial cells or vascular pericytes may nevertheless contribute to the TCGA signatures such as those seen in the immunoreactive and mesenchymal (stromal) subgroups and thus be a reason that individual samples can harbor gene expression profiles that would allow an assignment to two different subgroups at the same time.…”