High-grade serous tubo-ovarian cancer refined with single-cell RNA sequencing: specific cell subtypes influence survival and determine molecular subtype classification

Abstract

Background High-grade serous tubo-ovarian cancer (HGSTOC) is characterised by extensive inter- and intratumour heterogeneity, resulting in persistent therapeutic resistance and poor disease outcome. Molecular subtype classification based on bulk RNA sequencing facilitates a more accurate characterisation of this heterogeneity, but the lack of strong prognostic or predictive correlations with these subtypes currently hinders their clinical implementation. Stromal admixture profoundly affects the prognostic impact of the molecular subtypes, but the contribution of stromal cells to each subtype has poorly been characterised. Increasing the transcriptomic resolution of the molecular subtypes based on single-cell RNA sequencing (scRNA-seq) may provide insights in the prognostic and predictive relevance of these subtypes. Methods We performed scRNA-seq of 18,403 cells unbiasedly collected from 7 treatment-naive HGSTOC tumours. For each phenotypic cluster of tumour or stromal cells, we identified specific transcriptomic markers. We explored which phenotypic clusters correlated with overall survival based on expression of these transcriptomic markers in microarray data of 1467 tumours. By evaluating molecular subtype signatures in single cells, we assessed to what extent a phenotypic cluster of tumour or stromal cells contributes to each molecular subtype. Results We identified 11 cancer and 32 stromal cell phenotypes in HGSTOC tumours. Of these, the relative frequency of myofibroblasts, TGF-β-driven cancer-associated fibroblasts, mesothelial cells and lymphatic endothelial cells predicted poor outcome, while plasma cells correlated with more favourable outcome. Moreover, we identified a clear cell-like transcriptomic signature in cancer cells, which correlated with worse overall survival in HGSTOC patients. Stromal cell phenotypes differed substantially between molecular subtypes. For instance, the mesenchymal, immunoreactive and differentiated signatures were characterised by specific fibroblast, immune cell and myofibroblast/mesothelial cell phenotypes, respectively. Cell phenotypes correlating with poor outcome were enriched in molecular subtypes associated with poor outcome. Conclusions We used scRNA-seq to identify stromal cell phenotypes predicting overall survival in HGSTOC patients. These stromal features explain the association of the molecular subtypes with outcome but also the latter’s weakness of clinical implementation. Stratifying patients based on marker genes specific for these phenotypes represents a promising approach to predict prognosis or response to therapy.

DNA methylation and transcriptomic features are preserved throughout disease recurrence and chemoresistance in high grade serous ovarian cancers

“…Our study describes the first comprehensive analysis of paired methylation landscapes generated by WGBS in HGSOCs and their recurrences after chemotherapy, and the first such comparison in patients with and without germline BRCA1 and BRCA2 mutations. This study highlights the molecular heterogeneity that exists in HGSOCs between patients, consistent with single-cell analyses [ 80 , 81 ], and provides evidence that this heterogeneity extends to chemoresistant, recurrent disease. We have shown that there is an absence of common methylation signatures or specific methylation biomarkers that would indicate common mechanisms and underlying biology associated with disease recurrence or chemoresistance.…”

The multicellular signalling network of ovarian cancer metastases

“…Very recently, data for cells from seven untreated HGSC tumours were published, and for each cluster of tumour and host cells, specific transcriptomic markers were identified. 26 We could verify in the scRNA‐Seq dataset the cell‐type specificity of the majority of TU‐selective (7/12), immune‐cell‐selective (32/43) and CAF‐selective (8/13) cytokine genes identified in the present study (Figure S16 ), supporting the conclusions drawn from our data.…”

“…Despite considerable progress over the past years, our knowledge of the intercellular signalling network operating in HGSC metastases remains fragmentary. Published systematic transcriptomic studies suitable for the development of signalling networks are limited to ascites cells, 17 , 25 and were partly performed by scRNA‐Seq, 19 , 22 , 23 , 26 which is not informative for a subset of weakly expressed genes and possesses a limited power for differential expression studies compared to bulk analyses. 27 , 28 , 29 , 30 , 31 , 32 It is also currently unclear, to which extent ascites‐derived cell types resemble their counterparts in solid tumour lesions, since unbiased omics analysis have not been described for tumour‐associated non‐immune cells from HGSC patients.…”

Stanniocalcin 1 promotes metastasis, lipid metabolism and cisplatin chemoresistance via the FOXC2/ITGB6 signaling axis in ovarian cancer

“…We used marker genes CD45 and EPCAM to separate the epithelial cells from stromal cells. Cells were negative for CD45 and strongly expressed EPCAM were identified as epithelial cells [ 34 , 35 ]. Cells were filtered through a seventy-millimeter cell-Strainer (BD Biosciences, San Jose, CA, USA) and centrifuged at 1200 rpm for five minutes.…”