Phenotypic characterization by mass cytometry of the microenvironment in ovarian cancer and impact of tumor dissociation methods

Journal Article (2021)
Author(s)

Shamundeeswari Anandan (University of Bergen, Haukeland University Hospital)

Liv Cecilie V. Thomsen (University of Bergen, Haukeland University Hospital)

Stein Erik Gullaksen (University of Bergen)

Tamim Abdelaal (Delft Bioinformatics Lab, TU Delft - Pattern Recognition and Bioinformatics)

Katrin Kleinmanns (University of Bergen)

Jørn Skavland (University of Bergen)

Geir Bredholt (University of Bergen)

Bjørn Tore Gjertsen (Haukeland University Hospital, University of Bergen)

Emmet McCormack (University of Bergen)

Line Bjørge (Haukeland University Hospital, University of Bergen)

Research Group
Pattern Recognition and Bioinformatics
DOI related publication
https://doi.org/10.3390/cancers13040755 Final published version
More Info
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Publication Year
2021
Language
English
Research Group
Pattern Recognition and Bioinformatics
Journal title
Cancers
Issue number
4
Volume number
13
Article number
755
Pages (from-to)
1-18
Downloads counter
354
Collections
Institutional Repository
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Abstract

Improved molecular dissection of the tumor microenvironment (TME) holds promise for treating high-grade serous ovarian cancer (HGSOC), a gynecological malignancy with high mortality. Reliable disease-related biomarkers are scarce, but single-cell mapping of the TME could identify patient-specific prognostic differences. To avoid technical variation effects, however, tissue dissociation effects on single cells must be considered. We present a novel Cytometry by Time-of-Flight antibody panel for single-cell suspensions to identify individual TME profiles of HGSOC patients and evaluate the effects of dissociation methods on results. The panel was developed utilizing cell lines, healthy donor blood, and stem cells and was applied to HGSOC tissues dissociated by six methods. Data were analyzed using Cytobank and X-shift and illustrated by t-distributed stochastic neighbor embedding plots, heatmaps, and stacked bar and error plots. The panel distinguishes the main cellular subsets and subpopulations, enabling characterization of individual TME profiles. The dissociation method affected some immune (n = 1), stromal (n = 2), and tumor (n = 3) subsets, while functional marker expressions remained comparable. In conclusion, the panel can identify subsets of the HGSOC TME and can be used for in-depth profiling. This panel represents a promising profiling tool for HGSOC when tissue handling is considered.