Journal
CLINICAL CANCER RESEARCH
Volume 25, Issue 10, Pages 2996-3005Publisher
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1078-0432.CCR-18-3309
Keywords
-
Categories
Funding
- NIH [AR069111]
- F30 fellowship [CA206255]
- Carver College of Medicine, Holden Comprehensive Cancer Center
- Iowa City Veteran's Administration Medical Center
- University of Iowa Carver College of Medicine
- Holden Comprehensive Cancer Center (NCI of the NIH) [P30CA086862]
- Carver College of Medicine/Holden Comprehensive Cancer Center core research facility at the University of Iowa
- [R01 CA200673]
- [R01 CA203834]
Ask authors/readers for more resources
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. Sezary syndrome is an aggressive form of CTCL characterized by significant presence of malignant cells in both the blood and skin. The guarded prognosis for Sezary syndrome reflects a lack of reliably effective therapy, due, in part, to an incomplete understanding of disease pathogenesis. Experimental Design: 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 Sezary syndrome. Gene expression used to differentiate the unique transcriptional states were further used 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 CTCL, along with another 19 genes used to predict CTCL stage. Conclusions: This work offers insight into the heterogeneity of Sezary syndrome, providing better understanding of the transcriptomic diversities within a clonal tumor. This transcriptional heterogeneity can predict tumor stage and thereby offer guidance for therapy.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available