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Clin Cancer Res. 2019 Feb 4. pii: clincanres.3309.2018. doi: 10.1158/1078-0432.CCR-18-3309. [Epub ahead of print]

Single-cell profiling of cutaneous T-cell lymphoma reveals underlying heterogeneity associated with disease progression.

Author information

1
Department of Pathology, University of Iowa.
2
University of Iowa.
3
Dermatology, University of Iowa.
4
Internal Medicine - Heme-Onc, University of Iowa College of Medicine.
5
Dermatology, University of Iowa ali-jabbari@uiowa.edu.

Abstract

PURPOSE:

Cutaneous T cell lymphomas (CTCL), encompassing a spectrum of T-cell lymphoproliferative disorders involving the skin, have collectively increased in incidence over the last 40 years. Sézary syndrome (SS) is an aggressive form of CTCL characterized by significant presence of malignant cells in both the blood and skin. The guarded prognosis for SS reflects a lack of reliably effective therapy, due in part to an incomplete understanding of disease pathogenesis.

METHODS:

Using single-cell sequencing of RNA and the machine-learning reverse graph embedding approach in the Monocle package, we defined a model featuring distinct transcriptomic states within SS. Gene expression used to differentiate the unique transcriptional states were further utilized to develop a boosted tree classification for early versus late CTCL disease.

RESULTS:

Our analysis showed the involvement of FOXP3 + malignant T cells during clonal evolution, transitioning from FOXP3 + T cells to GATA3 + or IKZF2 + (HELIOS) tumor cells. Transcriptomic diversities in a clonal tumor can be used to predict disease stage, and we were able to characterize a gene signature that predicts disease stage with close to 80% accuracy. FOXP3 was found to be the most important factor to predict early disease in SS, along with another 19 genes used to predict CTCL stage.

CONCLUSIONS:

This work offers insight into the heterogeneity of SS, providing better understanding of the transcriptomic diversities within a clonal tumor. This transcriptional heterogeneity can predict tumor stage and thereby offer guidance for therapy.

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