| Autor: |
Thomsen LCV; 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., Kleinmanns K; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway., Anandan S; 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., Gullaksen SE; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway., Abdelaal T; Delft Bioinformatics Laboratory, Delft University of Technology, 2628XE Delft, The Netherlands.; Department of Radiology, Leiden University Medical Center, 2333ZA Leiden, The Netherlands., Iversen GA; Department of Obstetrics and Gynecology, Haukeland University Hospital, 5021 Bergen, Norway., Akslen LA; Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, 5021 Bergen, Norway.; Department of Pathology, Haukeland University Hospital, 5021 Bergen, Norway., McCormack E; 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., Bjørge L; 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. |
| Abstrakt: |
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. |
