HemaSphere (Jun 2020)

Longitudinal Cytokine Profiling Identifies GRO-α and EGF as Potential Biomarkers of Disease Progression in Essential Thrombocythemia

  • Nina F. Øbro,
  • Jacob Grinfeld,
  • Miriam Belmonte,
  • Melissa Irvine,
  • Mairi S. Shepherd,
  • Tata Nageswara Rao,
  • Axel Karow,
  • Lisa M. Riedel,
  • Oliva B. Harris,
  • E. Joanna Baxter,
  • Jyoti Nangalia,
  • Anna Godfrey,
  • Claire N. Harrison,
  • Juan Li,
  • Radek C. Skoda,
  • Peter J. Campbell,
  • Anthony R. Green,
  • David G. Kent

DOI
https://doi.org/10.1097/HS9.0000000000000371
Journal volume & issue
Vol. 4, no. 3
p. e371

Abstract

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Abstract. Myeloproliferative neoplasms (MPNs) are characterized by deregulation of mature blood cell production and increased risk of myelofibrosis (MF) and leukemic transformation. Numerous driver mutations have been identified but substantial disease heterogeneity remains unexplained, implying the involvement of additional as yet unidentified factors. The inflammatory microenvironment has recently attracted attention as a crucial factor in MPN biology, in particular whether inflammatory cytokines and chemokines contribute to disease establishment or progression. Here we present a large-scale study of serum cytokine profiles in more than 400 MPN patients and identify an essential thrombocythemia (ET)-specific inflammatory cytokine signature consisting of Eotaxin, GRO-α, and EGF. Levels of 2 of these markers (GRO-α and EGF) in ET patients were associated with disease transformation in initial sample collection (GRO-α) or longitudinal sampling (EGF). In ET patients with extensive genomic profiling data (n = 183) cytokine levels added significant prognostic value for predicting transformation from ET to MF. Furthermore, CD56+CD14+ pro-inflammatory monocytes were identified as a novel source of increased GRO-α levels. These data implicate the immune cell microenvironment as a significant player in ET disease evolution and illustrate the utility of cytokines as potential biomarkers for reaching beyond genomic classification for disease stratification and monitoring.