EuroFlow standardized multiparameter flow cytometry improves identification, quantitation, and characterization of circulating Sézary cells.
Immunophenotypically distinct Sézary subsets are clonally related and share a transcriptomic signature indicating impaired T-cell function.
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 patients with SS using standardized and sensitive EuroFlow-based multiparameter flow cytometry. 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 intrapatient heterogeneity and phenotypic changes over time. Sézary cells exhibited phenotypes corresponding with classical and nonclassical T helper subsets with different maturation phenotypes. We combined multiparameter flow cytometry analyses with fluorescence-activated 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 monoclonality in Sézary subsets, compared transcriptomes of phenotypically distinct Sézary subsets, and identified novel downregulated genes, most remarkably 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.