Tumor cells have evolved complex strategies to escape immune surveillance, a process which involves NK cells and T lymphocytes, and various immunological factors. Indeed, tumor cells recruit immunosuppressive cells [including regulatory T-cells (Treg), myeloid-derived suppressor cells (MDSC)] and express factors such as PD-L1. Molecularly targeted therapies, such as imatinib, have off-target effects that may influence immune function. Imatinib has been shown to modulate multiple cell types involved in anti-cancer immune surveillance, with potentially detrimental or favorable outcomes. Imatinib and other tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) have dramatically changed disease course. Our study aimed to characterize the different populations of the immune system in patients with CML affected by their treatment. Methods
Forty-one patients with CML [33 treated with TKIs and 8 with TKIs plus interferon (IFN)-α] and 20 controls were enrolled in the present study. Peripheral blood populations of the immune system [referred to as the overview of immune system (OVIS) panel, Treg cells and MDSCs] and PD-1 expression were evaluated by flow cytometry. The immunological profile was assessed using the mRNA Pan-Cancer Immune Profiling Panel and a NanoString nCounter FLEX platform. Results
Patients receiving combination therapy (TKIs + IFN-α) had lower numbers of lymphocytes, particularly T cells [838/µL (95% CI 594–1182)] compared with healthy controls [1500/µL (95% CI 1207 – 1865), p = 0.017]. These patients also had a higher percentage of Treg (9.1%) and CD4 + PD-1 + cells (1.65%) compared with controls [Treg (6.1%) and CD4 + /PD-1 + (0.8%); p ≤ 0.05]. Moreover, patients treated with TKIs had more Mo-MDSCs (12.7%) whereas those treated with TKIs + IFN-α had more Gr-MDSC (21.3%) compared to controls [Mo-MDSC (11.4%) and Gr-MDSC (8.48%); p ≤ 0.05]. CD56 bright NK cells, a cell subset endowed with immune-regulatory properties, were increased in patients receiving TKIs plus IFN-α compared with those treated with TKIs alone. Interestingly, serum IL-21 was significantly lower in the TKIs plus IFN-α cohort. Within the group of patients treated with TKI monotherapy, we observed that individuals receiving 2nd generation TKIs had lower percentages of CD4 + Treg (3.63%) and Gr-MDSC (4.2%) compared to patients under imatinib treatment (CD4 + Treg 6.18% and Gr-MDSC 8.2%), but higher levels of PD-1-co-expressing CD4 + cells (1.92%). Conclusions
Our results suggest that TKIs in combination with IFN-α may promote an enhanced immune suppressive state. Background
Chronic myeloid leukemia (CML) is a clonal myeloproliferative disorder characterized by the presence of the oncogenic BCR - ABL1 fusion gene derived from the reciprocal translocation of the long arms of chromosome 9 and chromosome 22 [ 1 ]. Disease course is typically triphasic, with the majority of patients presenting in the relatively stable chronic phase. However, if left untreated, patients with chronic-phase CML progress to accelerated phase and ultimately to blast crisis, which is invariably fatal [ 2 ].
The discovery of the unique molecular aberration of CML allowed the development of targeted therapies with tyrosine kinase inhibitors (TKIs), which revolutionized the management of CML in the late 1990s, offering the prospect of long-term disease control and near-normal life expectancy [ 3 , 4 ]. Outside of clinical trials, three TKIs have been approved as front-line treatment for chronic-phase CML, i.e., imatinib, nilotinib, and dasatinib [ 1 ]. Although response rates are excellent, between 10 and 15% of CML patients fail to achieve adequate responses to multiple TKIs, due to the development of either resistance or intolerance. Patients with the deepest responses might be eligible for treatment interruption, given the observation that up to 40% of them remain in remission following TKI cessation [ 5 ]. Until the advent of TKIs, interferon (IFN)-α was used as standard therapy for chronic-phase CML. Interestingly, the upfront administration of TKIs and IFN-α, followed by low-dose IFN-α maintenance, enabled a high rate of imatinib discontinuation in CML patients in major molecular response (MMR) [ 6 ].
During tumor development, cancer cells evolve complex strategies to elude immune surveillance, a process aimed at restraining cancer cell proliferation and involving multiple cell types, such as natural killer (NK) cells and T lymphocytes, and numerous immune factors, such as IL-2, tumor necrosis factor (TNF)-α and IFN-γ [ 7 ]. Furthermore, cancer cells can recruit immunosuppressive cells, such as tumor-associated macrophages (TAM), regulatory T cells (Treg) and myeloid-derived suppressor cells (MDSCs) [ 8 ], and express or secrete immunosuppressive factors such as indoleamine 2,3-dioxygenase-1 (IDO1), and programmed death-ligand 1 (PD-L1) [ 9 ], all of which promote dysfunctional immune responses and shape a highly suppressive tumor microenvironment, ultimatey leading to exhaustion and/or apoptosis of PD-1-expressing cells via the activation of the PD-L1 signalling pathway [ 8 , 10 , 11 ]. CML promotes a highly immune-suppressive tumor microenvironment, by favoring lymphocyte anergy or exhaustion, and inducing the expansion of Treg cells and MDSCs [ 12 , 13 ]. It has been shown that targeted anti-cancer therapies with TKIs may also have off-target or immune-mediated effects. For instance, imatinib modulates the function of multiple cell types involved in anti-cancer immune responses, with potentially detrimental as well as favorable outcomes [ 14 ]. The immunological effects of TKIs thus far described are diverse and include M2 reprogramming of TAMs [ 15 ]; inhibition of dendritic cell (DC) recovery [ 16 ] and effector cytokine production by CD4 + T cells [ 17 ]; reduction of IgM-producing memory B cells [ 18 ]; T helper 1 (Th1) polarization [ 19 ]; triggering of NK function [ 20 , 21 ]; down-regulation of IDO1 [ 22 ]; normalization of MDSC numbers [ 23 ] and impairment of Treg function [ 24 ].
The immune changes induced by TKIs and IFN-α in patients with CML have not been investigated previously and have important translational implications to optimize clinical trials of TKI discontinuation. Herein, we profiled the peripheral immunome of CML patients treated with TKIs alone or in combination with IFN-α. We used the Overall Immune System (OVIS) staining panel for the flow cytometric assessment of key immune modulatory cell subsets, including Treg cells and MDSCs, and to quantify PD1 expression on T cells [ 25 ]. Additionally, we evaluated the blood immune transcriptome and we identified changes in immune gene expression profiles in patients treated with TKIs either alone or in combination with IFN-α. Taken together, our results suggest that TKIs in combination with IFN-α may promote an enhanced immune suppressive state in patients with CML. Methods Study population
Sixty-one subjects were enrolled in the present study (41 patients with CML and 20 healthy controls). The participants were recruited at Centro Hospitalar Universitário de Coimbra (CHUC) and Hospital Distrital da Figueira da Foz (HDFF, EPE), Portugal. Patients were grouped according to the specific treatment allocated (TKIs alone or TKIs plus IFN-α). Clinical and biological characteristics are summarized in Table 1 . Treatment response criteria were defined according to the European Leukemia-Net (ELN) guidelines [ 1 ]. In the TKI group, 26 patients were classified as optimal responders and seven as a warning or failure. In the TKI plus IFN-α group, seven patients were classified as optimal responders and one patient as a warning. The study was conducted in accordance with the Helsinki Declaration, and all participants provided informed consent for participation prior to enrolment. The Ethics Committee of the Faculty of Medicine (University of Coimbra, Portugal) approved all research procedures. Table 1 Biodemographic and clinical characteristics of patients and controls Full size table Overview of immune system (OVIS) flow cytometry panel
Peripheral blood was collected into EDTA Vacutainers. We transferred 100 µL of whole blood into Trucount™ tubes (BD Biosciences) using reverse pipetting. Cells were stained using a 10-color panel, containing fluorescently labeled monoclonal antibodies (mAbs) specific for the major immune cell populations. The OVIS panel included the following: anti-CD8 (FITC), anti-CD19 (PE), anti-CD28 (ECD), anti-CD56 (PE-Cy5), anti-CD3 (PE-Cy7), anti-CD45RA (APC), anti-CD14 (Alexa Fluor-700), anti-CD27 (APC eFluor-780), anti-CD45 (Pacific Blue), and anti-CD4 (Krome Orange) mAbs. After a 15-min incubation at room temperature, erythrocytes were lysed by BD Pharm Lyse™ reagent. Cells were run through a Gallios™ flow cytometer (Beckman Coulter), and data were analysed with the Kaluza Software (Beckman Coulter). The number of cells per microliter of whole blood was calculated as described by the manufacturer. For the Trucount method, 50 µL of mouse WB were added into Trucount tubes and processed as per the manufacturer’s protocol, except for the lysis buffer used. Isolation of peripheral blood mononuclear cells (PBMCs)
Peripheral blood mononuclear cells (PBMCs) were used for Treg and MDSC evaluation. PBMCs were separated from whole blood using density gradient centrifugation on Ficoll-Hypaque (GE Healthcare) according to the manufacturer’s protocol. After isolation, one aliquot of cells was used immediately, and the remaining aliquot was frozen (10 × 10 6 cells/vial) for Treg studies. Regulatory T cell (Treg) assessment
Frozen PBMCs were thawed following the Cellular Technology Limited protocol (available online at http://www.immunospot.com ). PBMCs were rested in RPMI-1640 supplemented with CTL-Wash™ for 2 h at 37 °C before staining with the following mAbs in the Treg panel: anti-PD-1 (FITC), anti-ICOS (PE), anti-CD3 (ECD), anti-CD25 (PE-Cy5), anti-CD39 (PE-Cy7), anti-CD8 (Alexa Fluor 700), anti-CD127 (APC eFluor 780), anti-CD4 mAbs (Krome Orange) and anti-FoxP3 (eFluor 660). A LIVE/DEAD™ Fixable Violet solution was used to exclude dead cells from the analysis. Briefly, 1 × 10 6 cells were incubated for 10 min at 4 °C with FcR blocking reagent. After washing with PBS, PBMCs were stained for cell surface markers at room temperature for 10 min. The LIVE/DEAD™ Fixable Violet solution dye was then added, and cells were incubated for 30 min at room temperature. The FoxP3 Fix/Perm Kit was used for intracellular staining of FoxP3 according to the manufacturer’s protocol. Myeloid-derived suppressor cell (MDSC) evaluation
Immediately after isolation, 1 × 10 6 PBMCs were stained with the MDSC antibody panel, which included anti-CD11b (PE), anti-CD33 (PE-Cy5), anti-CD15 (PE-Cy7), anti-arginase-1 (Alexa Fluor 700), and anti-CD45 (Pacific Blue) mAbs. Briefly, cells were incubated for 10 min at 4 °C with FcR blocking reagent. After washing with PBS, PBMCs were stained for cell surface markers at room temperature for 15 min in the dark. Cells were then fixed and permeabilized with the Fix/Perm solution for 30 min at room temperature in the dark. After a further washing step, cells were stained with anti-arginase-1 mAbs for 15 min at room temperature in the dark. Targeted immune gene expression profiling
We used the nCounter™ FLEX platform (NanoString Technologies Inc., Seattle, WA) to assess immune transcriptomic profiles in patient PBMCs [ 26 ]. The nCounter™ analysis system is a robust and highly reproducible method for detecting the expression of up to 800 genes in a single reaction with high sensitivity and linearity across a broad range of expression levels [ 27 ]. It is based on digital detection and direct molecular barcoding of individual target molecules through the use of a unique probe pair carrying 35- to 50-base target-specific sequences. This technology allows for direct multiplexed measurements of gene expression from a low amount of mRNA (25 to 300 ng) without the need for amplification by PCR. The RNA Pan-Cancer Immune Profiling Panel™, which includes 770 genes (109 cell surface markers for 24 immune cell types, 30 cancer-testis antigens, > 500 immune response genes, and 40 reference genes), was used in our experiments. Digital images were processed within the nCounter Digital Analyzer™ instrument, and the reporter probe counts, i.e., the number of times the color-coded barcode for that gene is detected, were tabulated in a comma-separated value (CSV) format for data analysis with the nSolver™ software package. The analysis software automatically performs quality controls, normalization, data analysis and creates reports with the options of performing advanced analyses, including pathway applications [ 28 ]. The nCounter Advanced Analysis module (version 2.0.115) was used to calculate the relative abundance of immune cell types. The total lymphocyte score was defined as the average of the B cell, T cell, CD45, macrophage and cytotoxic T-cell scores. The other relative abundance scores were calculated by subtracting the total lymphocyte score from each cell type score. For instance, a NK-cell score will measure the relative abundance of NK cells within the total immune population. Each score will increase by 1 when NK cells double their frequency relative to the 5 immune populations defining the total lymphocyte score. Measurement of serum IL-21
Serum was harvested after the commencement of treatment with either TKIs alone (n = 20 patients) or with TKIs and IFN-α (n = 8 patients) and from 12 healthy controls. IL-21 was quantitated using commercially available reagents (IL-21 LEGEND MAX™ Human ELISA kit; BioLegend, San Diego, CA; sensitivity: 4.2 pg/mL). Statistical analyses
Dependent variables were logarithmically transformed to achieve an approximation to a normal distribution and to reduce heterogeneity. We tested the effect of the independent variables on the measured parameters using linear models (LM). For each dependent variable, multiple pairwise comparisons were performed using sequential Bonferroni correction. Model validation was performed, for each LM, on the residuals by checking heteroscedasticity, normality, and influential observations. The results are expressed as estimated mean and 95% confidence intervals (CI) unless otherwise stated. For correlation analysis, the nonparametric Spearman rank test was used. All statistical comparisons were considered significant when p values were 4 or
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