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High-grade serous tubo-ovarian cancer refined with single-cell RNA sequencing: specific cell subtypes influence survival and determine molecular subtype classification.
- Source :
-
Genome medicine [Genome Med] 2021 Jul 09; Vol. 13 (1), pp. 111. Date of Electronic Publication: 2021 Jul 09. - Publication Year :
- 2021
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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.<br />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.<br />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.<br />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.
- Subjects :
- Biomarkers, Tumor
Cell Communication
Computational Biology methods
Cytokines metabolism
DNA Copy Number Variations
Female
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
High-Throughput Nucleotide Sequencing
Humans
Immunoglobulin G biosynthesis
Immunoglobulin G immunology
Meta-Analysis as Topic
Molecular Sequence Annotation
Neoplasm Grading
Neoplasm Staging
Organ Specificity
Ovarian Neoplasms diagnosis
Phenotype
Plasma Cells immunology
Plasma Cells metabolism
Prognosis
Stromal Cells metabolism
Stromal Cells pathology
Tumor Microenvironment genetics
Whole Genome Sequencing
Gene Expression Profiling
Ovarian Neoplasms genetics
Ovarian Neoplasms mortality
Single-Cell Analysis
Transcriptome
Subjects
Details
- Language :
- English
- ISSN :
- 1756-994X
- Volume :
- 13
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Genome medicine
- Publication Type :
- Academic Journal
- Accession number :
- 34238352
- Full Text :
- https://doi.org/10.1186/s13073-021-00922-x