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 VermeerPublished 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.
Keyphrases
- single cell
- induced apoptosis
- end stage renal disease
- cell cycle arrest
- newly diagnosed
- peritoneal dialysis
- prognostic factors
- endoplasmic reticulum stress
- flow cytometry
- cell therapy
- case report
- gene expression
- acute myeloid leukemia
- cell proliferation
- transcription factor
- mass spectrometry
- optical coherence tomography
- immune response
- dna methylation
- high resolution
- bone marrow
- machine learning
- rectal cancer
- artificial intelligence