Department of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, People's Republic of China.
Henan Key Laboratory of Molecular Pathology, Zhengzhou, People's Republic of China.
Sci Rep. 2024 Nov 4;14(1):26617. doi: 10.1038/s41598-024-77630-0.
Tumor heterogeneity is associated with poor prognosis and drug resistance, leading to therapeutic failure. Here, we used tumor evolution analysis to determine the intra- and intertumoral heterogeneity of high-grade serous ovarian cancer (HGSOC) and analyze the correlation between tumor heterogeneity and prognosis, as well as chemotherapy response, through single-cell and spatial transcriptomic analysis. We collected and curated 28 HGSOC patients' single-cell transcriptomic data from five datasets. Then, we developed a novel text-mining-based machine-learning approach to deconstruct the evolutionary patterns of tumor cell functions. We then identified key tumor-related genes within different evolutionary branches, characterized the microenvironmental cell compositions that various functional tumor cells depend on, and analyzed the intra- and intertumoral heterogeneity as well as the tumor microenvironments. These analyses were conducted in relation to the prognosis and chemotherapy response in HGSOC patients. We validated our findings in two spatial and seven bulk transcriptomic datasets (total: 1,030 patients). Using transcriptomic clusters as proxies for functional clonality, we identified a significant increase in tumor cell state heterogeneity that was strongly correlated with patient prognosis and treatment response. Furthermore, increased intra- and intertumoral functional clonality was associated with the characteristics of cancer-associated fibroblasts (CAFs). The spatial proximity between CXCL12-positive CAFs and tumor cells, mediated through the CXCL12/CXCR4 interaction, was highly positively correlated with poor prognosis and chemotherapy resistance in HGSOC. Finally, we constructed a panel of 24 genes through statistical modeling that correlate with CXCL12-positive fibroblasts and can predict both prognosis and the response to chemotherapy in HGSOC patients. Our study offers insights into the collective behavior of tumor cell communities in HGSOC, as well as potential drivers of tumor evolution in response to therapy. There was a strong association between CXCL12-positive fibroblasts and tumor progression, as well as treatment outcomes.
肿瘤异质性与预后不良和耐药性相关,导致治疗失败。在这里,我们使用肿瘤进化分析来确定高级别浆液性卵巢癌(HGSOC)的肿瘤内和肿瘤间异质性,并通过单细胞和空间转录组学分析来分析肿瘤异质性与预后以及化疗反应之间的相关性。我们收集并整理了来自五个数据集的 28 名 HGSOC 患者的单细胞转录组学数据。然后,我们开发了一种新的基于文本挖掘的机器学习方法来解构肿瘤细胞功能的进化模式。然后,我们确定了不同进化分支中的关键肿瘤相关基因,描述了各种功能肿瘤细胞所依赖的微环境细胞组成,并分析了肿瘤内和肿瘤间的异质性以及肿瘤微环境。这些分析与 HGSOC 患者的预后和化疗反应有关。我们在两个空间和七个批量转录组数据集(总计:1030 名患者)中验证了我们的发现。使用转录组聚类作为功能克隆性的代表,我们发现肿瘤细胞状态异质性显著增加,与患者的预后和治疗反应密切相关。此外,肿瘤内和肿瘤间功能克隆性的增加与癌症相关成纤维细胞(CAF)的特征相关。CXCL12 阳性 CAF 与肿瘤细胞之间的空间接近度,通过 CXCL12/CXCR4 相互作用介导,与 HGSOC 中的不良预后和化疗耐药性高度正相关。最后,我们通过统计建模构建了一个包含 24 个基因的面板,这些基因与 CXCL12 阳性成纤维细胞相关,可以预测 HGSOC 患者的预后和对化疗的反应。我们的研究深入了解了 HGSOC 中肿瘤细胞群体的集体行为,以及肿瘤进化对治疗的潜在驱动因素。CXCL12 阳性成纤维细胞与肿瘤进展以及治疗结果之间存在很强的关联。