Combining Mass Cytometry Data by CyTOFmerge Reveals Additional Cell Phenotypes in the Heterogeneous Ovarian Cancer Tumor Microenvironment: A Pilot Study

Liv Cecilie Vestrheim Thomsen*, Katrin Kleinmanns, Shamundeeswari Anandan, Stein Erik Gullaksen, Tamim Abdelaal, Grete Alrek Iversen, Lars Andreas Akslen, Emmet McCormack, Line Bjørge

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

11 Downloads (Pure)

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.

Original languageEnglish
Article number5106
Number of pages22
JournalCancers
Volume15
Issue number20
DOIs
Publication statusPublished - 2023

Keywords

  • heterogeneity
  • high-grade serous ovarian cancer (HGSOC)
  • immunophenotyping
  • mass cytometry
  • merging algorithm
  • tumor microenvironment (TME)

Fingerprint

Dive into the research topics of 'Combining Mass Cytometry Data by CyTOFmerge Reveals Additional Cell Phenotypes in the Heterogeneous Ovarian Cancer Tumor Microenvironment: A Pilot Study'. Together they form a unique fingerprint.

Cite this