Thomsen Liv Cecilie Vestrheim, Kleinmanns Katrin, Anandan Shamundeeswari, Gullaksen Stein-Erik, Abdelaal Tamim, Iversen Grete Alrek, Akslen Lars Andreas, McCormack Emmet, Bjørge Line
Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway.
Department of Obstetrics and Gynecology, Haukeland University Hospital, 5021 Bergen, Norway.
Cancers (Basel). 2023 Oct 23;15(20):5106. doi: 10.3390/cancers15205106.
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）的预后较差，治疗方案的选择具有挑战性。异质性肿瘤微环境（TME）是HGSOC的特征，并影响肿瘤的生长、进展和治疗反应。需要采用多维方法对各种细胞群进行同时识别和分类，以更好地表征TME的复杂性。虽然质谱流式细胞术能够同时检测约40种蛋白质，但CyTOFmerge MATLAB算法可整合数据集并扩展表型分析。这项初步研究通过对来自10例未经化疗的HGSOC肿瘤的单细胞悬液进行质谱流式细胞术分析，探索了合并两个数据集以改善TME表型分析的潜力。分别分析了一个包含35个标记物的泛肿瘤数据集和一个包含34个标记物的泛免疫数据集，并与CyTOFmerge相结合，合并了18个共享标记物。虽然合并分析证实了患者之间的异质性，但除了泛肿瘤检测板识别出的9个亚群外，还确定了一个主要的肿瘤细胞亚群。此外，还揭示了肿瘤细胞和基质细胞上传统免疫细胞标志物的表达情况，以及很少在单个细胞上检测的标志物组合。这项研究证明了合并质谱流式细胞术数据以生成关于HGSOC肿瘤生物学和预测性生物标志物研究的新假设的潜力，这可能会提高治疗效果。
