1. High-grade serous tubo-ovarian cancer refined with single-cell RNA sequencing: specific cell subtypes influence survival and determine molecular subtype classification
- Author
-
Ignace Vergote, Pieter Busschaert, Adriaan Vanderstichele, Annick Van den Broeck, Junbin Qian, Toon Van Gorp, Liselore Loverix, Diether Lambrechts, Siel Olbrecht, Sileny Han, An Coosemans, Els Van Nieuwenhuysen, and Anne-Sophie Van Rompuy
- Subjects
0301 basic medicine ,TO-MESENCHYMAL TRANSITION ,Cell Communication ,QH426-470 ,Transcriptome ,Single-cell RNA sequencing ,0302 clinical medicine ,Tumor Microenvironment ,Overall survival ,Gene Regulatory Networks ,TUMOR-INFILTRATING LYMPHOCYTES ,High-grade serous tubo-ovarian cancer ,Genetics (clinical) ,GENE-EXPRESSION ,Genetics & Heredity ,Ovarian Neoplasms ,CLASS-II ,High-Throughput Nucleotide Sequencing ,Transcriptomic markers ,Prognosis ,Gene Expression Regulation, Neoplastic ,Phenotype ,Organ Specificity ,030220 oncology & carcinogenesis ,Medicine ,Molecular Medicine ,Cytokines ,Female ,Single-Cell Analysis ,Life Sciences & Biomedicine ,Stromal cell ,DNA Copy Number Variations ,Molecular subtypes ,Plasma Cells ,Biology ,OVARIAN-CANCER ,03 medical and health sciences ,Meta-Analysis as Topic ,Genetics ,medicine ,Biomarkers, Tumor ,Humans ,Molecular Biology ,SIGNATURES ,COPY NUMBER ANALYSIS ,Neoplasm Staging ,Tumour microenvironment ,Science & Technology ,PLATINUM-BASED CHEMOTHERAPY ,Whole Genome Sequencing ,Tumor-infiltrating lymphocytes ,Research ,Gene Expression Profiling ,Mesenchymal stem cell ,Cancer ,Computational Biology ,Stromal heterogeneity ,Molecular Sequence Annotation ,medicine.disease ,FALLOPIAN-TUBE ,030104 developmental biology ,Immunoglobulin G ,Cancer cell ,Cancer research ,CLEAR-CELL ,Neoplasm Grading ,Stromal Cells ,Ovarian cancer ,Clear cell - 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.
- Published
- 2021