Dataset Information

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

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.

SUBMITTER: Thomsen LCV

PROVIDER: S-EPMC10605295 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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

Thomsen Liv Cecilie Vestrheim LCV Kleinmanns Katrin K Anandan Shamundeeswari S Gullaksen Stein-Erik SE Abdelaal Tamim T Iversen Grete Alrek GA Akslen Lars Andreas LA McCormack Emmet E Bjørge Line L

Cancers 20231023 20

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 Cy ...[more]

PMID: 37894472

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Project description:BackgroundOvarian cancer is one of the leading causes of cancer-related death in women. The incidence of ovarian cancer is insidious, and the recurrence rate is high. The survival rate of ovarian cancer has not significantly improved over the past decade. Recently, immune checkpoint inhibitors such as those targeting CTLA-4, PD-1, or PD-L1 have been used to treat ovarian cancer. Therefore, a full analysis of the immune biomarkers associated with this malignancy is necessary.MethodsIn this study, we used data from The Cancer Genome Atlas (TCGA) database to analyze the infiltration patterns of specific immune cell types in tumor samples. Data from the Gene Expression Omnibus (GEO) database was used for external validation. According to the invasion patterns of immune cells, we divided the ovarian cancer microenvironment into two clusters: A and B. These tumor microenvironment (TME) subtypes were associated with genomic and clinicopathological characteristics. Subsequently, a random forest classification model was established. Differential genomic features, functional enrichment, and DNA methylation were analyzed between the two clusters. The characteristics of immune cell infiltration and the expression of immune-related cytokines or markers were analyzed. Somatic mutation analysis was also performed between clusters A and B. Finally, multivariate Cox analysis was used to analyze independent prognostic factors.ResultsThe ovarian cancer TME cluster A was characterized by less infiltration of immune cells and sparse distribution and low expression of immunomodulators. In contrast, cytotoxic T cells and immunosuppressive cells were significantly increased in the ovarian cancer TME cluster B. Additionally, immune-related cytokines or markers, including IFN-γ and TNF-β, were also expressed in large quantities. In total, 35 differentially methylated and expressed genes (DMEGs) were identified. Functional enrichment analyses revealed that the DMEGs in cluster B participated in important biological processes and immune-related pathways. The mutation load in cluster B was insignificantly higher than that of cluster A (p = 0.076). Multivariate Cox analysis showed that TME was an independent prognostic factor for ovarian cancer (hazard ratio: 1.33, 95% confidence interval: 1.01-1.75, p = 0.041).ConclusionThis study described and classified basic information about the immune invasion pattern of ovarian cancer and integrated biomarkers related to different immunophenotypes to reveal interactions between ovarian cancer and the immune system.

Dataset Information

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

Publications

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

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