Combining Mass Cytometry Data by CyTOFmerge Reveals Additional Cell Phenotypes in the Heterogeneous Ovarian Cancer Tumor Microenvironment: A Pilot Study
Cancers, vol. 15, pp. 5106
Abstract
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.
Authors 9
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Liv Cecilie Vestrheim Thomsen corresponding
Norwegian Institute of Public Health · Haukeland University Hospital · University of Bergen
Affiliation as printed
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
Norwegian Institute of Public Health, 5015 Bergen, Norway
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Affiliation as printed
Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
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Haukeland University Hospital · University of Bergen
Affiliation as printed
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
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Affiliation as printed
Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
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Leiden University Medical Center · Delft University of Technology
Affiliation as printed
Delft Bioinformatics Laboratory, Delft University of Technology, 2628XE Delft, The Netherlands
Department of Radiology, Leiden University Medical Center, 2333ZA Leiden, The Netherlands
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Affiliation as printed
Department of Obstetrics and Gynecology, Haukeland University Hospital, 5021 Bergen, Norway
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Haukeland University Hospital · University of Bergen
Affiliation as printed
Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, 5021 Bergen, Norway
Department of Pathology, Haukeland University Hospital, 5021 Bergen, Norway
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Affiliation as printed
Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
Centre for Pharmacy, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
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Haukeland University Hospital · University of Bergen
Affiliation as printed
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
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