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. 2020 May 21;4(3):e371.
doi: 10.1097/HS9.0000000000000371. eCollection 2020 Jun.

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

Nina F Øbro  1   2 , Jacob Grinfeld  1   2   3 , Miriam Belmonte  1   2   4 , Melissa Irvine  1   2 , Mairi S Shepherd  1   2 , Tata Nageswara Rao  5 , Axel Karow  5   6 , Lisa M Riedel  1   2 , Oliva B Harris  1   2 , E Joanna Baxter  3 , Jyoti Nangalia  7 , Anna Godfrey  3 , Claire N Harrison  8 , Juan Li  1   2 , Radek C Skoda  5 , Peter J Campbell  7 , Anthony R Green  1   2   3 , David G Kent  1   2   4
Affiliations

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

Nina F Øbro et al. Hemasphere. .

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.

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Figures

Figure 1
Figure 1
Serum cytokine profiling identifies distinct cytokine networks in MPNs. (A) Overview of serum cytokine screen. Initially levels of 38 cytokines were assessed in serum samples from MPN patients by Luminex-based multiplexed ELISA. Following data analysis, 10 cytokines were selected for their ability to track with disease subtypes, disease severity, or overall survival. (B) Summary information for patient groups. Median follow-up time is displayed in years with minimum/maximum follow-up indicated in parenthesis. Median values are displayed for Hb, WBC, and Plt. (C) Principal components analysis plot displaying the serum samples from 291 MPN patients (PC1 = 31.9%, PC2 = 16.3%) with ET (blue circles) and MF (green circles) patient samples positioned in distinct areas. Data are from the initial Cambridge cohort where all disease subtypes were collected in an unbiased fashion (ET n = 146, PV n = 94, PMF n = 51) and normal controls (n = 14). The right panel shows the loadings plot identifying cytokines accounting for the differences. ET = essential thrombocythemia, PV = polycythemia vera, MF = primary myelofibrosis, Hb = hemoglobin, WBC = white blood cell count, Plt = platelet count, sMF = secondary MF, sAML = secondary AML.
Figure 2
Figure 2
Elevated levels of EGF, eotaxin, and GRO-α in patients with essential thrombocythemia. (A) Serum levels of individual cytokines that are increased in patients with essential thrombocythemia compared to other MPN subtypes. Initial Cambridge cohort (ET n = 146, PV n = 94, MF (primary myelofibrosis) n = 51) and normal controls (n = 14). Boxes show medians with interquartile range (IQR). Mann-Whitney U test ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001. (B) Peripheral blood MNCs from MPN patients were stimulated with LPS for 4 hours and GRO-α production measured by flow cytometry (% positive of total MNCs) (Normal controls n = 3, ET = 5, PV = 10). (C) Cell types producing GRO-α after LPS stimulation was evaluated using surface markers for T-cells, NK-cells, NKT-cells, CD56− monocytes, and CD56+ monocytes (Normal controls n = 3, ET n = 5, PV n = 6). The proportion of cellular sources of GRO-α in patients are displayed in a stacked bar graph, showing a higher frequency of GRO-α+CD56+CD14+ monocytes observed in ET patients compared to normal controls (p = 0.008).
Figure 3
Figure 3
Cytokine measurements add prognostic value beyond genomics data alone. (A) Kaplan-Meier analysis of progression-free survival according to pre-transformation levels of GRO-α in ET and PV patients with transformation follow-up data in initial Cam cohort (n = 151). High levels of GRO-α correlate with increased risk of transformation from chronic phase to secondary MF (Cox proportional hazards modeling including age, sex and diagnosis. p = 0.004). B) Kaplan-Meier analysis of progression-free survival in PT-1 cohort. High levels of GRO-α correlate with increased risk of transformation from ET to secondary MF (Cox proportional hazards modeling including age and sex. p = 0.01). (C) Variables considered in the prognostic model included age, sex, levels of 10 cytokines, and presence/absence of 11 driver mutations. (D) The predictive yield (as assessed by model concordance, equivalent to the area under the curve for the receiver-operator characteristic) of adding cytokine quantification to prognostic models utilizing demographic and clinical data alone, and those additionally incorporating genomic variables. Genomic characterization improves the prediction for disease transformation, and inclusion of cytokine measurements further improves predictive power. OS = overall survival; MF-PFS = myelofibrosis progression-free survival; and AML-PFS = AML progression-free survival with transformation follow-up data (n = 122). E) Kaplan-Meier curve of progression-free survival where patients have been stratified into equally sized groups (quartiles of 30 patients each) according to their predicted risk as defined by a multivariate Cox proportional hazards model using age, GRO-α levels, IL-8 levels, IP-10 levels, and presence/absence of an SRSF2/U2AF1 mutation (p < 0.001).
Figure 4
Figure 4
Decreases in EGF levels over time associate with MF transformation. (A) EGF levels were assessed at multiple time points during disease and patients were classified as having increasing, decreasing, or stable levels of EGF. EGF stability was defined as an absolute rate of change of <8pg/mL per year. The 46% (18/39) of patients with decreased levels of EGF over time transformed to MF or AML, while only 24% (4/17) and 8% (2/25) of patients with stable and increasing levels of EGF respectively transformed to MF or AML. Dark lines indicate the point up until which sampling occurred and light lines indicate non-sampled follow-up. Longitudinal samples were taken pre- or post-transformation and are separated accordingly. (B) MF progression-free survival of patient cohorts defined by increasing, stable or decreasing EGF levels over time. Patients in whom levels of EGF decreased were 4.3-fold (95% confidence interval 1.7–10.9) more likely to transform to MF or AML (p value = 0.008).
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