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Improved Sézary cell detection and novel insights into immunophenotypic and molecular heterogeneity in Sézary syndrome.

Safa NajidhCornelis P TensenAlita J van der Sluijs-GellingCristina TeodosioDavy CatsHailiang MeiThomas B KuipersJacoba J Out-LuitingWillem H ZoutmanThorbald van HallAlberto OrfaoJulia AlmeidaJacques J M van DongenMaarten H Vermeer
Published in: Blood (2021)
Sézary syndrome (SS) is an aggressive leukemic form of Cutaneous T-cell Lymphoma with neoplastic CD4+ T cells present in skin, lymph nodes, and blood. Despite advances in therapy, prognosis remains poor with a 5-year overall survival of 30%. The immunophenotype of Sézary cells is diverse, which hampers efficient diagnosis, sensitive disease monitoring, and accurate assessment of treatment response. Comprehensive immunophenotypic profiling of Sézary cells with an in-depth analysis of maturation and functional subsets has not been performed thus far. We immunophenotypically profiled 24 SS patients employing standardized and sensitive EuroFlow-based multiparameter flow cytometry (MFC). We accurately identified and quantified Sézary cells in blood and performed an in-depth assessment of their phenotypic characteristics in comparison with their normal counterparts in the blood CD4+ T-cell compartment. We observed inter-and intra-patient heterogeneity and phenotypic changes over time. Sézary cells exhibited phenotypes corresponding with classical and non-classical T helper subsets with different maturation phenotypes. We combined MFC analyses with FACS cell sorting and performed RNA-sequencing studies on purified subsets of malignant Sézary cells and normal CD4+ T cells of the same patients. We confirmed pure mono-clonality in Sézary subsets, we compared transcriptomes of phenotypically distinct Sézary subsets and identified novel down-regulated genes, most remarkable THEMIS and LAIR1 which discriminate Sézary cells from normal residual CD4+ T cells. Together, these findings further unravel the heterogeneity of Sézary cell subpopulations within and between patients. These new data will support improved blood staging and more accurate disease monitoring.
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