CD8+ T cells are critical players in anti-tumor immunity against solid tumors, targeted by immunotherapies. Emerging evidence suggests that CD8+ T cells also play a crucial role in anti-tumor responses and determining treatment outcomes in hematologic malignancies like myelodysplastic neoplasms (MDS) and acute myeloid leukemia (AML). In this review, we focus on the implication of CD8+ T cells in the treatment response of patients with MDS and AML. First, we review reported studies of aberrant functionality and clonality of CD8+ T cells in MDS and AML, often driven by the immunosuppressive bone marrow microenvironment, which can hinder effective antitumor immunity. Additionally, we discuss the potential use of CD8+ T cell subpopulations, including memory and senescent-like subsets, as predictive biomarkers for treatment response to a variety of treatment regimens, such as hypomethylating agents, which is the standard of care for patients with higher-risk MDS, and chemotherapy which is the main treatment of patients with AML. Understanding the multifaceted role of CD8+ T cells and their interaction with malignant cells in MDS and AML will provide useful insights into their potential as prognostic/predictive biomarkers, but also uncover alternative approaches to novel treatment strategies that could reshape the therapeutic landscape, thus improving treatment efficacy, aiding in overcoming treatment resistance and improving patient survival in these challenging myeloid neoplasms.

Association between the frequency of CD57+CXCR3+CD8+ T cells and outcome in patients with HR-MDS and AML under treatment with AZA. A, Box plots displaying the percentage of the CD57+CXCR3+ cells within CD8+ T cells, assessed by flow cytometry in responders and nonresponders (HR-MDS, n = 12 responders and 9 nonresponders; AML, n = 9 responders and 11 nonresponders; CMML, n = 5 responders and 5 nonresponders). B, After stratification of patients with HR-MDS and AML to responders (n = 12) and nonresponders (n = 19), FlowSOM analysis was performed on BM CD8+ T cells, which generated six metaclusters that are projected onto the viSNE plots. Representative viSNE plots (one for each group) are shown. C, Box plots showing the proportion of all metaclusters, expressed as the frequency within CD8+ T cells. D, Heatmap depicting the expression levels of all T-related markers. E, Kaplan–Meier curves for OS in patients which received AZA treatment, with ≤29% (n = 51) and >29% (n = 26) CD57+CXCR3+ CD8+ T cells before treatment initiation. The survival curves were compared by the log-rank (Mantel–Cox) test, and the P value is shown. The median OS of the ≤29% group was 20.98 months, whereas the median OS of the >29% group was 12.05 months. F, Survival curves for each disease subgroup. Increased (%) CD57+CXCR3+ correlates significantly with worse survival in patients with HR-MDS and AML, whereas no association is observed in patients with CMML. G, Patients with HR-MDS and AML with ≤29% CD57+CXCR3+ exhibited higher response rates. No association between the frequency of CD57+CXCR3+CD8+ T cells and response to therapy was observed in patients with CMML. An unpaired Student t test was used in A. A Mann–Whitney U test was used in D. **, P < 0.01; ***, P < 0.001.

Untargeted analysis of CD45+ immune cells in patients with MDS, AML, and CMML by CyTOF. A, Multidimensional scale plot depicting the relationship between BM samples of patients with LR-MDS (n = 12), HR-MDS (n = 15), AML (n = 16), and CMML (n = 5). B, Heatmap showing the expression of the markers used for the characterization of each cell cluster. C, UMAP displaying the major immune cell clusters. D, Box charts displaying the frequency of each cell cluster. E, Violin plots showing the expression level of CXCR3 in the CD8 T1, CD8 T2, and CD4 T2 clusters, respectively. Kruskal–Wallis followed by the “two-stage” Benjamini, Krieger, and Yekutieli multiple comparison test was used in D. One-way ANOVA followed by the “two-stage” Benjamini, Krieger, and Yekutieli multiple comparison test was used in E. *, P < 0.05; **, P < 0.01.

Identification of a CD8+ subpopulation (CD57+CXCR3+) which distinguishes patients with MDS from patients with AML and CMML. A, Representative viSNE plots, derived from the FlowSOM analysis of BM CD8+ T cells from patients with LR-MDS (n = 12), HR-MDS (n = 15), AML (n = 16), and CMML (n = 5). B, Bar plots displaying the proportion of the metaclusters between the groups, expressed as percentage within CD8+ T cells. C, Heatmap depicting the expression level of the T-related markers between the metaclusters. D, Violin plots showing the expression level of CXCR3 in metacluster 1. E, Representative flow cytometry plots for the identification of the CD57+CXCR3+CD8+ T cell subpopulation in a cohort of patients with LR-MDS (n = 7), HR-MDS (n = 27), AML (n = 20), and CMML (n = 10). F, Percentage of CD57+CXCR3+ cells within CD8+ T cells. Kruskal–Wallis was used in B and D. One-way ANOVA followed by the “two-stage” Benjamini, Krieger, and Yekutieli multiple comparison test was used in F. *, P < 0.05; ***, P < 0.001.

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with accelerated atherosclerosis and a 40% increased risk of cardiovascular disease (CVD), linked to increased innate immune cell activation (Jaiswal et al., 2017). Monocytes are critical players in CVD, due to their infiltration in atherosclerotic plaques and their differentiation to foam cells, whereas dyslipidemia is linked to long-term inflammatory activity of monocytes (Mitroulis et al., 2023). Herein, we sought to investigate whether lipid profile is associated with the molecular signature of peripheral monocytes in patients with lower-risk MDS (LR-MDS; i.e., IPSS-R <3.5). To do so, peripheral blood samples were collected from 11 patients with LR-MDS (MDS with low blasts n=9, MDS with SF3B1 n=2). RNA sequencing performed in isolated monocytes. Differential expression analysis was performed utilizing the edgeR Bioconductor R package. Statistically significant differentially expressed genes (DEGs) were identified based on a FDR threshold of <0.1 and a logFC threshold of >0.58. Gene set enrichment analysis (GSEA) was performed, using well-annotated Hallmark gene sets from the Molecular Signatures Database (MSigDB) as input. Principal Component Analysis (PCA) demonstrated a clear segregation of samples into two distinct subgroups (Subgroup_1 and _2). Differential expression analysis identified 483 DEGs upregulated in Subgroup_1 and 1035 DEGs upregulated in Subgroup_2. Pathway enrichment analysis revealed an enrichment of genes associated with oxidative phosphorylation (OXPHOS) and TNF-α signaling in Subgroup_1. Of note, Subgroup_1 exhibited an upregulation of the cholesterol homeostasis pathway, which was in line with the significant increase in total and non-HDL cholesterol and triglycerides in this group. Moreover, unsupervised clustering based on the expression levels of genes involved in inflammasome-related signatures (Basiorka et al., 2016), led to clear separation of the two groups, with Subgroup_1 displaying significantly higher expression levels of the inflammasome-related genes NRLP3 and PYCARD. Herein, we show that lower lipid levels in LR-MDS patients is associated with a suppressed inflammatory signature in peripheral monocytes and a down-regulation in the expression of genes related to inflammasome. Considering the link between CVD and CHIP, our findings show that early administration of lipid-lowering regimens can be beneficial in patients with LR-MDS, suppressing the inflammatory potential of monocytes, a critical cell population in CVD. Acknowledgements: Supported by the UAE-NIH Collaborative Research grant AJF-NIH-25-KU

IntroductionImmune checkpoint blockade (ICB) immunotherapy has revolutionized cancer treatment, demonstrating exceptional clinical responses in a wide range of cancers. Despite the success, a significant proportion of patients still fail to respond, highlighting the existence of unappreciated mechanisms of immunotherapy resistance. Delineating such mechanisms is paramount to minimize immunotherapy failures and optimize the clinical benefit.MethodsIn this study, we treated tumour-bearing mice with PD-L1 blockage antibody (aPD-L1) immunotherapy, to investigate its effects on cancer-induced emergency myelopoiesis, focusing on bone marrow (BM) hematopoietic stem and progenitor cells (HSPCs). We examined the impact of aPD-L1 treatment on HSPC quiescence, proliferation, transcriptomic profile, and functionality.ResultsHerein, we reveal that aPD-L1 in tumour-bearing mice targets the HSPCs in the BM, mediating their exit from quiescence and promoting their proliferation. Notably, disruption of the PDL1/PD1 axis induces transcriptomic reprogramming in HSPCs, observed in both individuals with Hodgkin lymphoma (HL) and tumour-bearing mice, shifting towards an inflammatory state. Furthermore, HSPCs from aPDL1-treated mice demonstrated resistance to cancer-induced emergency myelopoiesis, evidenced by a lower generation of MDSCs compared to control-treated mice.DiscussionOur findings shed light on unrecognized mechanisms of action of ICB immunotherapy in cancer, which involves targeting of BM-driven HSPCs and reprogramming of cancer-induced emergency myelopoiesis.

TF regulatory network analysis in BM-derived CD8+ T cells. A, UMAP depicting the clustering of CD8+ T cells based on regulons. B, Pie charts illustrating the representation of cells from CR and FAIL patients within each regulon. C, Comparison of cell distribution in regulons between the groups using separate UMAPs for each group. D, Heatmap showing the top differentially activated TFs of each regulon cluster. E and F, Violin plots depicting the activity score of selected TFs per sample type in regulons 5 and 7, respectively.

BM-derived CD8+ T cells from responders (CR) to AZA show an enhanced ISG molecular signature compared with nonresponders (FAIL) in scRNA-seq analysis. A, Comparison of separate UMAPs for CR (a total of 13,667 cells, including 7,571 from patients with MDS and 6,096 cells from patients with secondary AML, respectively) and FAIL patients (a total of 14,782 cells, including 8,026 cells from patients with MDS and 6,756 cells from patients with secondary AML, respectively). B, Stacked bar chart showing the average distribution of clusters between the two groups. The percentage of cluster 10 (proliferative) was increased in CR compared with FAIL patients (unpaired Student t test, P = 0.0418). C, Dot plot representing MSigDB (Hallmark 2020) enrichment analysis of positively enriched pathways in CR patients. Enriched pathways with a q-value <0.05 (Benjamini–Hochberg correction) are shown. D, Gradient expression of representative selected genes involved in IFN-related pathways, as they are projected onto UMAPs. E, ISG score of cluster 0 (GZMK) and violin plots showing the expression levels of the top differentially expressed IFN-stimulated genes of cluster 0 (GZMK) between CR and FAIL. F, ISG score of cluster 1 (EOMES) and violin plots showing the expression levels of the top differentially expressed IFN-stimulated genes of cluster 1 (EOMES) between CR and FAIL. G, ISG score of cluster 2 (CTL_1) and violin plots displaying the expression levels of the top differentially expressed IFN-stimulated genes of cluster 2 (CTL_1) between CR and FAIL. H, ISG score of cluster 4 (CTL_2) and violin plots displaying the expression levels of the top differentially expressed IFN-related genes of cluster 4 (CTL_2) between CR and FAIL.

• Aging leads to chronic inflammation and immune dysfunction, heightening the risk of myeloid malignancies like MDS and CMML. • Both aging and MDS show alterations in monocyte subtypes and function. Aging boosts inflammatory genes upregulation, whereas MDS favors antigen presentation, reflecting distinct immune and disease-specific adaptations. • MDS shows reduced inflammatory activity in CD14+ cells, whereas CMML exhibits heightened inflammation, highlighting distinct disease mechanisms.

Profiling of BM-derived CD8+ T cells of patients with HR-MDS and secondary AML with scRNA-seq. A, UMAP of CD8+ T cells identified 11 clusters. A total of 28,449 CD8+ T cells were pooled from four patients with HR-MDS (15,597 cells) and five patients with secondary AML (12,852 cells). B, Bubble plot depicting the average expression of genes used to characterize the clusters. C, Expression of selected genes projected onto UMAPs. D, Heatmap showing selected top DEGs for each cell cluster. E, Ridgeline plots displaying the cytotoxic signature score for each cell cluster, as defined by the expression of key-related genes. F, Ridgeline plots displaying the cell-cycle signature score for each cell cluster.

BM-derived CD8+ T cells of nonresponders (FAIL) displayed a suppressed cytotoxic molecular signature at the single-cell level. A, Dot plot representing MSigDB (Hallmark 2020) enrichment analysis of positively enriched pathways in FAIL patients. Enriched pathways with a q-value <0.05 (Benjamini–Hochberg correction) are shown. B, TGF-β signaling score of cluster 2 (CTL_1) and violin plots displaying the expression levels of the top DEGs of cluster 2 (CTL_1), involved in the enrichment of the TGF-β signaling pathway between CR and FAIL. C, TGF-β signaling score of cluster 4 (CTL_2) and violin plots displaying the expression levels of the top DEGs of cluster 4 (CTL_2), involved in the enrichment of the TGF-β signaling pathway between CR and FAIL. D, Comparison of the cytotoxic score of each group, as it is projected onto the respective UMAPs. E, Cytotoxic score of cluster 2 (CTL_1) and violin plots exhibiting the expression levels of the top differentially expressed cytotoxicity-related genes of cluster 2 (CTL_1) between CR and FAIL. F, Cytotoxic score of cluster 4 (CTL_2) and violin plots exhibiting the expression levels of the top differentially expressed cytotoxicity-related genes of cluster 4 (CTL_2) between CR and FAIL.

ABSTRACT:CD8+ T cells are crucial for antitumor immunity. However, their functionality is often altered in higher-risk myelodysplastic neoplasms (MDS) and acute myeloid leukemia (AML). To understand their role in disease progression, we conducted a comprehensive immunophenotypic analysis of 104 pretreatment bone marrow (BM) samples using mass and flow cytometry. Our findings revealed an increased frequency of CD57+CXCR3+ subset of CD8+ T cells in patients who did not respond to azacitidine (AZA) therapy. Furthermore, an increased baseline frequency (>29%) of the CD57+CXCR3+CD8+ T-cell subset was correlated with poor overall survival. We performed single-cell RNA sequencing to assess the transcriptional profile of BM CD8+ T cells from treatment-naïve patients. The response to AZA was linked to an enrichment of IFN-mediated pathways, whereas nonresponders exhibited a heightened TGF-β signaling signature. These findings suggest that combining AZA with TGF-β signaling inhibitors targeting CD8+ T cells could be a promising therapeutic strategy for patients with higher-risk MDS and AML. SIGNIFICANCE:Immunophenotypic analysis identified a BM CD57+CXCR3+ subset of CD8+ T cells associated with response to AZA in patients with MDS and AML. Single-cell RNA sequencing analysis revealed that IFN signaling is linked to the response to treatment, whereas TGF-β signaling is associated with treatment failure, providing insights into new therapeutic approaches.

Background and aims Myeloid malignancies encompass a diverse group of conditions, including myelodysplastic neoplasms (MDS) and chronic myelomonocytic leukemia (CMML). There is evidence suggesting the role of bone marrow (BM) environment and immune cells in the development and progression of MDS and CMML. Azacytidine (AZA) remains the backbone therapy for higher-risk MDS and non-proliferative CMML. To date, molecular predictors of response to AZA are not yet well-defined. Herein, we sought to investigate the immune landscape of BM samples from patients with lower-risk MDS (LR-MDS; i.e., IPSS-R <3.5), CMML, higher-risk MDS (HR-MDS; i.e., IPSS-R ≥3.5), and acute myeloid leukemia (AML). Methods Samples were collected before treatment initiation. Mass cytometry (CyTOF) was performed on isolated BM mononuclear cells from 48 patients, using the Maxpar® Direct™ Immune Profiling Assay. Flow cytometry was performed on a secondary cohort of 56 patients. scRNA-sequencing was performed on sorted BM CD8+ T cells from patients with HR-MDS (n=4) and AML (n=5), using a 10x Genomics pipeline. Mann-Whitney U test and the Kruskal-Wallis test were used as appropriate. Kaplan-Meier analysis was utilized for survival analysis, and the log-rank test was employed to assess the significance. The level of significance was established at P < 0.05. Results The immunophenotypic analysis using CyTOF demonstrated increased expression of CXCR3 on cytotoxic CD8+ T-cells in CMML and AML samples compared to samples from patients with MDS. Increased frequency of the CD57 +CXCR3 +CD8 + T-cell subset was further observed in patients with HR-MDS and AML that were non-responders to AZA, compared to responders, as observed by both CyTOF and flow cytometry. After determining the optimal cut-off for the frequency of CD8 +CD57 +CXCR3 + T cells at 29%, we observed that HR-MDS and AML patients (Figure 1A) with a frequency of CD57 +CXCR3 +CD8 + of <29% had better overall survival (OS).Multivariate analysisshowed that a frequency of CD57 +CXCR3 +CD8 + of >29% was independently associated with decreased survival in patients with TET2 mutations (HR=2.603; 1.275-5.317). scRNA-seq analysis of isolated BM CD8 + T cells was further performed to characterize the molecular signature that favors response to AZA, which resulted in the identification of several cell clusters based on gene expression. The cluster characterized by the expression of genes associated with cytotoxicity showed significant differences between responders and non-responders. Specifically, within the cytotoxic cluster, genes associated with IFN-related pathways were upregulated in cells from responders to AZA. In contrast, genes upregulated in non-responders displayed an enrichment for the TGF-β signaling pathway, which is known to be associated with the suppression of the killing capacity of T cells (Figure 1B). The aforementioned molecular signatures were further associated with a higher cytotoxic molecular signature in the cytotoxic cell cluster from the group of responders. Conclusion In this study, we focused on BM CD8 + T cells in samples from patients with myeloid neoplasias and performed deep phenotypic analysis using CyTOF coupled with scRNA, in order to characterize the BM immune environment. We demonstrate for the first time that BM CD8 + T cells show different phenotypic and transcriptomic characteristics in responders to AZA. Specifically, we show that the frequency of the effector CD57 +CXCR3 +CD8 + subset is decreased in responders before treatment initiation, and this is associated with better survival. Despite the decreased proportion of this cytotoxic cell population in responders, there are specific signatures associated with effector T cell functionality that can explain the improved outcomes in these patients. These findings underscore the significance of the crosstalk between leukemic and immune cells in the progression of the disease and the effect of treatment. Figure legend A. Increased (%) CD57 +CXCR3 + correlates significantly with worse survival of HR-MDS and AML patients. Kaplan Meier curves for Overall Survival in HR-MDS and AML patients respectively. B. Gene signature derived from the scRNAseq analysis. Interferon-stimulated gene (ISG) signature is increased in the cytotoxic cell cluster in responders and and TGF-β signature in non-responders.

Treatment with hypomethylating agents (HMA), and specifically azacitidine (AZA), is the standard of care for patients with higher-risk myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) that are not eligible to receive intensive chemotherapy. Despite research efforts, it is not possible to predict response to treatment with HMA. In this study, we aimed to identify immune cell signatures in the bone marrow (BM) associated with treatment outcomes. By employing mass cytometry, we performed an in-depth immunophenotypic analysis in BM samples deriving from patients with myeloid neoplasms prior to treatment initiation. We identified an increased pre-treatment frequency of a CD8+ T cell subset, characterized as CD57+CXCR3+CCR7-CD45RA+, in patients with MDS and AML who did not respond to treatment with AZA, compared to responders. Furthermore, an increased baseline frequency (>29%) of CD57+CXCR3+CD8+ T cells was correlated with poor overall survival. We further engaged scRNA-seq to assess the transcriptional profile of BM CD8+ T cells from treatment-naive patients with MDS and AML, to identify molecular signatures in CD8+ T subpopulations associated with favorable outcomes. Response to treatment was positively associated with enrichment of IFN-mediated pathways coupled with enhanced cytotoxic signature, whereas enrichment of the TGF-b signaling pathway was observed in cell clusters from non-responders. Together, this study identified a specific CD57+CXCR3+ CD8+ T cell population with predictive value in patients with MDS and AML treated with AZA and characterized molecular signatures in CD8+ T cells linked to cytokine signaling that were associated with treatment outcomes.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThis study was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI Research Projects to Support Faculty Members & Researchers and Procure High-Value Research Equipment grant (HFRI FM17 452) and the General Secretariat for Research and Technology Management and Implementation Authority for Research, Technological Development, and Innovation Actions (MIA RTDI) (grant T2EDK 02288, MDS TARGET). TA and NEP are supported by the ERC under the European Union Horizon 2020 research and innovation program (grant agreement no 947975 to T. Alissafi) and from the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the 2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers (Project Number: 166 to T. Alissafi) and the Sub-action 1. Funding New Researchers RRF: Basic Research Financing (Horizontal support for all Sciences) (Project number: 15014 to T. Alissafi). IPK is funded by the Academy of Medical Sciences (R2429101) and Rosetrees Trust (R2449101). TC is supported by the Deutsche Forschungsgemeinschaft (TRR332, project B4).### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The study was approved by the Ethics Committee of the University Hospital of Alexandroupolis, under the reference number (877/23-10-2019).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesThe authors state that all data supporting this study are available in the main text or the supplementary material. The raw scRNA-sequencing data for this study have been deposited in the NCBI Gene Expression Omnibus repository and are accessible through accession number GSE250077. All other raw data can be provided by the authors upon reasonable request.

Brucellosis is a common zoonotic disease caused by intracellular pathogens of the genus Brucella . Brucella infects macrophages and evades clearance mechanisms, thus resulting in chronic parasitism. Herein, we studied the molecular changes that take place in human brucellosis both in vitro and ex vivo. RNA sequencing was performed in primary human macrophages (Mφ) and polymorphonuclear neutrophils (PMNs) infected with a clinical strain of Brucella spp. We observed a downregulation in the expression of genes involved in host response, such as TNF signaling, IL-1β production, and phagosome formation in Mφ, and phosphatidylinositol signaling and TNF signaling in PMNs, being in line with the ability of the pathogen to survive within phagocytes. Further transcriptomic analysis of isolated peripheral blood mononuclear cells (PBMCs) and PMNs from patients with acute brucellosis before treatment initiation and after successful treatment revealed a positive correlation of the molecular signature of active disease with pathways associated with response to interferons (IFN). We identified 24 common genes that were significantly altered in both PMNs and PBMCs, including genes involved in IFN signaling that were downregulated after treatment in both cell populations, and IL1R1 that was upregulated. The concentration of several inflammatory mediators was measured in the serum of these patients, and levels of IFN-γ, IL-1β and IL-6 were found significantly increased before the treatment of acute brucellosis. An independent cohort of patients with chronic brucellosis also revealed increased levels of IFN-γ during relapse compared to remissions. Taken together, this study provides for the first time an in-depth analysis of the transcriptomic alterations that take place in human phagocytes upon infection, and in peripheral blood immune populations during active disease.