The prognosis of high-grade serous ovarian carcinoma (HGSOC) is poor, and treatment selection is challenging. A heterogeneous tumor microenvironment (TME) characterizes HGSOC and influences tumor growth, progression, and therapy response. Better characterization with multidimensional approaches for simultaneous identification and categorization of the various cell populations is needed to map the TME complexity. While mass cytometry allows the simultaneous detection of around 40 proteins, the CyTOFmerge MATLAB algorithm integrates data sets and extends the phenotyping. This pilot study explored the potential of combining two datasets for improved TME phenotyping by profiling single-cell suspensions from ten chemo-naïve HGSOC tumors by mass cytometry. A 35-marker pan-tumor dataset and a 34-marker pan-immune dataset were analyzed separately and combined with the CyTOFmerge, merging 18 shared markers. While the merged analysis confirmed heterogeneity across patients, it also identified a main tumor cell subset, additionally to the nine identified by the pan-tumor panel. Furthermore, the expression of traditional immune cell markers on tumor and stromal cells was revealed, as were marker combinations that have rarely been examined on individual cells. This study demonstrates the potential of merging mass cytometry data to generate new hypotheses on tumor biology and predictive biomarker research in HGSOC that could improve treatment effectiveness.
高级别浆液性卵巢癌（HGSOC）预后较差，治疗选择具有挑战性。其肿瘤微环境（TME）具有高度异质性，影响着肿瘤的生长、进展及治疗反应。为解析TME的复杂性，需要通过多维方法对多种细胞群进行同步识别与分类，以实现更精准的表征。质谱流式技术可同时检测约40种蛋白质，而CyTOFmerge MATLAB算法能整合数据集并扩展表型分析范围。本项探索性研究通过对十例未经化疗的HGSOC肿瘤单细胞悬液进行质谱流式检测，尝试结合两个数据集以优化TME表型分析。研究分别分析了包含35个标志物的泛肿瘤数据集和包含34个标志物的泛免疫数据集，并通过CyTOFmerge算法整合了18个共有标志物。合并分析在确认患者间异质性的同时，除泛肿瘤组识别的九个亚群外，还鉴定出一个主要肿瘤细胞亚群。此外，研究揭示了肿瘤细胞与基质细胞上传统免疫细胞标志物的表达情况，以及鲜少在单细胞层面检测的标志物组合。本研究证明了整合质谱流式数据在HGSOC肿瘤生物学研究和预测性生物标志物探索方面的潜力，有望为提升治疗效果提供新思路。
肿瘤(癌症)患者之家