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I. Veletic et al.
ing sodium heparin (BD, Franklin Lakes, NJ, USA). Low-density cells were fractionated using Ficoll-Histopaque 1077 (Sigma- Aldrich, St. Louis, MO, USA), washed in phosphate-buffered saline (Invitrogen, Carlsbad, CA, USA), spun down, dissolved in 10% dimethylsulfoxide solution (Sigma-Aldrich, St. Louis, MO, USA) supplemented with fetal bovine serum (Invitrogen, Carlsbad, CA, USA), and frozen in liquid nitrogen. Prior to flow cytometry analysis, cells were thawed, washed and re-suspend- ed in fetal bovine serum. After trypan blue viability assessment, live cells (106) were incubated with the appropriate antibodies or their corresponding isotype controls, and their cell surface pro- tein expression was assessed using the Gallios multichannel flow cytometer (Beckman Coulter, Brea, CA, USA). The anti- bodies used and their isotype controls are listed in Online Supplementary Table S1.
Flow cytometry analysis of T cells
A universal gating strategy was applied to identify individual T-cell subsets. Singlet lymphocytes in the CD45+ cell population were identified based on size and lack of granularity (Figure 1A- C). Subsequently, T cells were gated by using anti-CD3 and anti- HLA-DR antibodies and further subdivided using anti-TCR γ/δ antibodies (Figure 1D and E). The CD4+ and CD8+ subpopula- tions of the α/β + T cells were further separated into naïve (TN), central memory (TCM), effector (TEFF),and effector memory (TEM) subsets, using anti-CD62L and anti-CD45RO antibodies (Figure 1F and G). The percent of PD1+ T cells was assessed in each sub- set (Figure 1H). All flow cytometry data were analyzed using FlowJo software v10.5 (Treestar, San Carlos, CA, USA).
Statistical analyses
The Student t-test was used to assess whether T-cell subsets of normal individuals were significantly different from those of patients with MF. A paired t-test was used to determine whether ruxolitinib treatment significantly affected T-cell subset distribu- tion. A linear mixed-effects model with repeated measures was developed to determine whether there were differences in T-cell subsets at sequential time-points. In order to correct for clinical response or progression over time, several model specifications that included clinical variables obtained at the time of sample collection were compared using mean Akaike and Bayesian information criteria and R-squared values, and the best perform- ing model was selected for use in the longitudinal analyses. Significance of overall change in time and each predictor were assessed using the Kenward-Roger adjusted F-test. Correlations between continuous clinical variables and T-cell subsets were assessed using the Pearson coefficient and between-group differ- ences were calculated using the Welch t-test. The percentage of each T-cell subset was dichotomized into high and low groups using the optimal cutoff value of maximally selected rank statis- tics. The patients’ overall survival was estimated by the Kaplan- Meier method and a log-rank test was used to compare the sur- vival probabilities. A univariate Cox proportional hazard regres- sion model was fitted to assess the association between clinical variables and overall survival. To assess the predictive value of T-cell subsets, a multivariate Cox proportional hazard model was applied, adjusted for the clinical variables that were found to be significant in the univariate analyses. The Wald test was used to assess the significance of each covariate in Cox models. Statistical analysis was performed using Stata/SE v15.1 (Stata Corp, College Station, TX, USA) and R v3.6.3 (R Foundation for Statistical Computing, Vienna, Austria) statistical software with tidyverse v1.3.0, lme4 v1.1-23, pbkrtest v0.4-8.6, and survival v3.1-8 packages. Graphs were created using GraphPad Prism v7.03 (GraphPad Software, La Jolla, CA, USA) and R packages
corrplot v0.84, ggplot2 v3.3.0, ggpubr v0.2.5, ggeffects v0.14.3, and survminer v0.4.6.
Data sharing statement
De-identified original data are available from the corresponding author(zestrov@mdanderson.org).
Results
Clinical features of myelofibrosis patients and out- come of ruxolitinib treatment
To evaluate the distribution and characteristics of T-cell sub- sets in patients with MF, we analyzed PB and BM specimens of 47 MF patients (27 with PMF, 13 with post-polycythemia vera MF, and 7 with post-essential thrombocythemia MF) and 28 age- matched healthy controls (Online Supplementary Table S2). The median daily dose of ruxolitinib was 50 mg (range, 20-200 mg) at the start of the clinical trial. The dose was reduced because of anemia and/or thrombocytopenia in seven patients. The median duration of treatment was 38.9 months. Among the patients who had their specimens analyzed in this study, two discontin- ued treatment because of myelosuppression and four because of transformation to acute myeloid leukemia. Infection (pneumo- nia and/or sepsis) was the cause of death in five of the 16 (31.3%) patients who died while on trial.
Analysis of myelofibrosis patients’ T-cell subsets
The percentages of CD4+ and CD8+ cells and their TN,TCM,TEFF, and TEM subsets were assessed in BM or PB specimens from 41 MF patients and 28 healthy individuals. Because analyses of T-cell sub- sets using PB (n=35) and BM (n=16) specimens from the same MF patients revealed similar results (Online Supplementary Figure S3), we have not presented the data separately. Whereas CD4+ and CD8+ cell distributions in MF patients were not different from those in healthy individuals (Figure 2Ai and Bi), marked differences were found in both CD4-derived (Figure 2Aii and Bii) and CD8-derived (Figure 2Aiii and Biii) T-cell subsets. We detected a 2.93-fold and a 3.45-fold (P<0.001 for both) reduction in the number of TN cells, and a 3.45-fold and a 4.03-fold (P<0.001 for both) reduction in TCM cells within the CD4+ and CD8+ cell subsets, respectively, in MF-derived T cells as compared to normal controls. Conversely, we detected an increase in the number of TEFF cells within both CD4+ and CD8+ cell subsets (mean fold changes, 2.75 and 1.86, respectively; P<0.001 for both), and in the number of TEM cells within the CD4+ cell fraction (mean fold change, 1.51; P=0.005) but not within the CD8+ cell frac- tion. Whereas CD4+ and CD8+ resting subsets (TN and TCM) in MF patients correlated significantly and positively with one another, two effector subsets (TEM and TEFF) exhibited negative correlation between both one another and the resting subsets (Figure 2C), indi- cating that one effector population prevails within each patient’s CD4 or CD8 subset. Overall, the increase in effector T-cell pheno- type suggests that in patients with MF T cells shift from a quiescent to an activated state. Compared to CD4+, MF CD8+ T cells shift more towards a terminally activated state, suggesting a predomi- nant effector-mediated cytotoxic response in MF.
Long-term effects of ruxolitinib treatment on T-cell subsets
Because treatment with ruxolitinib reduces plasma levels of cytokines and chemokines and significantly reduces spleen size in most MF patients,24,26,27 we sought to assess the effect of rux- olitinib treatment on the distribution of T-cell subsets. Analysis of the corresponding PB or BM specimens obtained from 25 MF patients before and during ruxolitinib treatment demonstrated an overall shift towards a CD8+ phenotype over the course of
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haematologica | 2021; 106(9)