Department of Obstetrics and Gynaecology, Division of Gynaecological Oncology, University Hospitals Leuven, Leuven, Belgium.
Department of Oncology, Laboratory of Gynaecologic Oncology, KU Leuven, Leuven, Belgium.
Genome Med. 2021 Jul 9;13(1):111. doi: 10.1186/s13073-021-00922-x.
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.
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.
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.
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.
高级别浆液性卵巢癌（HGSTOC）的特点是广泛的肿瘤内和肿瘤间异质性，导致持续的治疗抵抗和疾病预后不良。基于批量 RNA 测序的分子亚型分类有助于更准确地描述这种异质性，但目前缺乏与这些亚型的强预后或预测相关性，阻碍了它们的临床应用。基质混合物对分子亚型的预后影响深远，但基质细胞对每个亚型的贡献尚未得到充分描述。基于单细胞 RNA 测序（scRNA-seq）提高分子亚型的转录组分辨率，可能为这些亚型的预后和预测相关性提供新的认识。
我们对 7 例未经治疗的 HGSTOC 肿瘤中随机收集的 18403 个细胞进行了 scRNA-seq。对于每个肿瘤或基质细胞的表型簇，我们确定了特定的转录组标记物。我们通过分析这些转录组标记物在 1467 个肿瘤的微阵列数据中的表达，探索了哪些表型簇与总生存相关。通过评估单细胞中的分子亚型特征，我们评估了肿瘤或基质细胞的表型簇对每个分子亚型的贡献程度。
我们在 HGSTOC 肿瘤中鉴定出 11 种癌症和 32 种基质细胞表型。其中，肌成纤维细胞、TGF-β 驱动的癌症相关成纤维细胞、间皮细胞和淋巴管内皮细胞的相对频率预测预后不良，而浆细胞与更有利的预后相关。此外，我们在癌细胞中鉴定出一种清晰的类似透明细胞的转录组特征，与 HGSTOC 患者的总生存不良相关。基质细胞表型在分子亚型之间有很大差异。例如，间质、免疫反应和分化特征分别由特定的成纤维细胞、免疫细胞和肌成纤维细胞/间皮细胞表型来描述。与不良预后相关的细胞表型在与不良预后相关的分子亚型中富集。
我们使用 scRNA-seq 鉴定了与 HGSTOC 患者总生存相关的基质细胞表型。这些基质特征解释了分子亚型与预后的相关性，但也解释了后者在临床应用中的弱点。基于特定于这些表型的标记基因对患者进行分层是预测预后或对治疗反应的一种很有前途的方法。
