Combining Mass Cytometry Data by CyTOFmerge Reveals Additional Cell Phenotypes in the Heterogeneous Ovarian Cancer Tumor Microenvironment
A Pilot Study
Liv Cecilie Vestrheim Thomsen (University of Bergen, TU Delft - Pattern Recognition and Bioinformatics, Norwegian Institute of Public Health, Haukeland University Hospital)
Katrin Kleinmanns (University of Bergen)
Shamundeeswari Anandan (University of Bergen, Haukeland University Hospital)
Stein Erik Gullaksen (University of Bergen)
Tamim Abdelaal (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)
Grete Alrek Iversen (Haukeland University Hospital)
Lars Andreas Akslen (Haukeland University Hospital, University of Bergen)
Emmet McCormack (University of Bergen)
Line Bjørge (University of Bergen, Haukeland University Hospital)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.
help_outline