Tumor-infiltrating and circulating granulocytic myeloid-derived suppressor cells correlate with disease activity and adverse clinical outcomes in mycosis fungoides

Abstract

Purpose: Cutaneous T cell lymphomas (CTCL) are rare and histologically diverse lymphoproliferative neoplasms, with mycosis fungoides (MF) representing the most common disease subset. Given the emerging role of myeloid-derived suppressor cells (MDSC) as a clinically applicable biomarker in solid tumors, we sought to investigate the presence of tumor-infiltrating and circulating MDSC in early- and advanced-stage MF patients and evaluate their prognostic significance in patient overall survival. Methods: Tumor-infiltrating MDSC were assessed immunohistochemically with Arginase-1 in 31 MF and 14 non-MF skin punch biopsies. Circulating MDSC were assessed with flow cytometry in freshly isolated PBMC from 29 MF patients. Granulocytic MDSC (G-MDSC) were defined as CD11b+CD14−CD15+ and monocytic MDSC (M-MDSC) were defined as CD11b+CD14+HLA-DRlow/-. Results: MDSC infiltration occurred in approximately one-third (35.5%) of CTCL lesions, with a predilection for non-MF lesions (p < 0.05). The predominant morphology of MDSC was granulocytic. Although in MF lesions the presence of MDSC infiltrates did not correlate with clinical stage, it conferred significantly worse overall survival outcomes (p < 0.05). Circulating G-MDSC were significantly higher in MF patients compared to healthy donor controls (p < 0.0001), while M-MDSC did not show any statistically significant difference. G-MDSC were significantly higher in patients with active disease compared to patients who were in partial remission (p < 0.01). As with tumor-infiltrating MDSC, clinical stage did not correlate with circulating G-MDSC levels, while prospective overall survival analysis showed that patients with high levels of circulating G-MDSC have significantly inferior outcomes (p < 0.01). Conclusions: This study shows that G-MDSC could represent a novel and easily assessable biomarker in MF, which mirrors disease activity and can predict patient subgroups with aggressive clinical features.