Hameed Sodiq A, Kolch Walter, Brennan Donal J, Zhernovkov Vadim
Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland.
Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland.
Sci Rep. 2025 Apr 25;15(1):14484. doi: 10.1038/s41598-025-98463-5.
The tumour microenvironment is composed of a complex cellular network involving cancer, stromal and immune cells in dynamic interactions. A large proportion of this network relies on direct physical interactions between cells, which may impact patient responses to clinical therapy. Doublets in scRNA-seq are usually excluded from analysis. However, they may represent directly interacting cells. To decipher the physical interaction landscape in relation to clinical prognosis, we inferred a physical cell-cell interaction (PCI) network from 'biological' doublets in a scRNA-seq dataset of approximately 18,000 cells, obtained from 7 treatment-naive ovarian cancer patients. Focusing on cancer-stromal PCIs, we uncovered molecular interaction networks and transcriptional landscapes that stratified patients in respect to their clinical responses to standard therapy. Good responders featured PCIs involving immune cells interacting with other cell types including cancer cells. Poor responders lacked immune cell interactions, but showed a high enrichment of cancer-stromal PCIs. To explore the molecular differences between cancer-stromal PCIs between responders and non-responders, we identified correlating gene signatures. We constructed ligand-receptor interaction networks and identified associated downstream pathways. The reconstruction of gene regulatory networks and trajectory analysis revealed distinct transcription factor (TF) clusters and gene modules that separated doublet cells by clinical outcomes. Our results indicate (i) that transcriptional changes resulting from PCIs predict the response of ovarian cancer patients to standard therapy, (ii) that immune reactivity of the host against the tumour enhances the efficacy of therapy, and (iii) that cancer-stromal cell interaction can have a dual effect either supporting or inhibiting therapy responses.
肿瘤微环境由一个复杂的细胞网络组成，该网络涉及癌症细胞、基质细胞和免疫细胞之间的动态相互作用。这个网络的很大一部分依赖于细胞之间的直接物理相互作用，这可能会影响患者对临床治疗的反应。单细胞RNA测序（scRNA-seq）中的双峰通常会被排除在分析之外。然而，它们可能代表直接相互作用的细胞。为了解析与临床预后相关的物理相互作用格局，我们从7例未经治疗的卵巢癌患者的约18000个细胞的scRNA-seq数据集中的“生物学”双峰推断出一个物理细胞-细胞相互作用（PCI）网络。聚焦于癌症-基质PCI，我们发现了分子相互作用网络和转录格局，这些格局根据患者对标准治疗的临床反应对其进行了分层。良好反应者的PCI特征是免疫细胞与包括癌细胞在内的其他细胞类型相互作用。反应不佳者缺乏免疫细胞相互作用，但显示出癌症-基质PCI的高度富集。为了探索反应者和非反应者之间癌症-基质PCI的分子差异，我们确定了相关的基因特征。我们构建了配体-受体相互作用网络，并确定了相关的下游途径。基因调控网络的重建和轨迹分析揭示了不同的转录因子（TF）簇和基因模块，这些簇和模块根据临床结果将双峰细胞分开。我们的结果表明：（i）PCI导致的转录变化可预测卵巢癌患者对标准治疗的反应；（ii）宿主对肿瘤的免疫反应性可增强治疗效果；（iii）癌症-基质细胞相互作用可产生支持或抑制治疗反应的双重作用。
