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Association of circulating transcriptomic profiles with mortality in sickle cell disease

Ankit A. Desai, Zhengdeng Lei, Neil Bahroos, Mark Maienschein-Cline, Santosh L. Saraf, Xu Zhang, Binal N. Shah, Seyed M. Nouraie, Taimur Abbasi, Amit R. Patel, Roberto M. Lang, Yves Lussier, Joe G. N. Garcia, Victor R. Gordeuk and Roberto F. Machado

Key Points

  • We validated the association of a circulating genome-wide gene expression profile with poor outcomes in 3 cohorts of SCD.

  • A composite risk score using this genomic biomarker with clinical risk factors exhibited improved prediction than clinical factors alone.

Publisher's Note: There is an Inside Blood Commentary on this article in this issue.

Abstract

Sickle cell disease (SCD) complications are associated with increased morbidity and risk of mortality. We sought to identify a circulating transcriptomic profile predictive of these poor outcomes in SCD. Training and testing cohorts consisting of adult patients with SCD were recruited and prospectively followed. A pathway-based signature derived from grouping peripheral blood mononuclear cell transcriptomes distinguished 2 patient clusters with differences in survival in the training cohort. These findings were validated in a testing cohort in which the association between cluster 1 molecular profiling and mortality remained significant in a fully adjusted model. In a third cohort of West African children with SCD, cluster 1 differentiated SCD severity using a published scoring index. Finally, a risk score composed of assigning weights to cluster 1 profiling, along with established clinical risk factors using tricuspid regurgitation velocity, white blood cell count, history of acute chest syndrome, and hemoglobin levels, demonstrated a higher hazard ratio for mortality in both the training and testing cohorts compared with clinical risk factors or cluster 1 data alone. Circulating transcriptomic profiles are a powerful method to risk-stratify severity of disease and poor outcomes in both children and adults, respectively, with SCD and highlight potential associated molecular pathways.

  • Submitted November 16, 2016.
  • Accepted March 27, 2017.
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