Single-Cell CD4 and CD8 T-Cell Secretome Profiling Reveals Temporal and Niche Differences in Acute Myeloid Leukemia Following Immune Checkpoint Blockade Therapy

UNLABELLED: Acute myeloid leukemia (AML) is a heterogeneous malignancy of the blood primarily treated with intensive chemotherapy. The allogeneic T-cell antileukemic activity via donor lymphocyte infusions and stem cell transplantation suggests a potential role for checkpoint blockade therapy in AML. While clinical trials employing these treatments have fallen short of expected results, a deeper exploration into the functional states of T cells in AML could bridge this knowledge gap. In this study, we analyzed the polyfunctional activity of T cells in a cohort of patients with relapsed/refractory (RelRef) AML treated on the clinical trial (ClinicalTrials.gov identifier: NCT02397720) of combination therapy using azacitidine and nivolumab (Aza/Nivo). We utilized the single-cell polyfunctional multiplexed immune assay IsoPlexis to evaluate the CD4 and CD8 T cells in peripheral blood and bone marrow samples collected before and after immunotherapy. This revealed at a pseudobulk level that the CD4 T cells exhibited higher functional activity post-immunotherapy (post-IO), suggesting that CD4-directed therapies may play a role in RelRef AML. Additional single-cell analysis revealed significant differences in baseline polyfunctionality in bone marrows of responders as compared with nonresponders for both CD4 and CD8 T cells. Overall, this study highlights the impact of polyfunctional assessment in understanding CD4 and CD8 dynamics in contexts of therapy in AML. SIGNIFICANCE: We found T-cell polyfunctionality differs between local and systemic microenvironments. Enhanced variability in proteomic profiles of bone marrow CD4 T cells post-IO suggests their pivotal role in AML treatment response. Single-cell analysis identified novel CD4 and CD8 T-cell functional groups linked to immunotherapy response within the bone marrow

The effect of eltrombopag in managing thrombocytopenia associated with tyrosine kinase therapy in patients with chronic myeloid leukemia and myelofibrosis

Approximately 20-50% patients with chronic phase chronic myeloid leukemia (CML-CP) treated with tyrosine kinase inhibitors (TKI) or with myelofibrosis (MF) treated with ruxolitinib develop grade ≥3 thrombocytopenia needing treatment interruptions and dose reductions. We conducted a non-randomized, phase II, single-arm study to determine the efficacy of eltrombopag for patients with CML or MF with persistent thrombocytopenia while on TKI or ruxolitinib. Eltrombopag was initiated at 50 mg/day, with dose escalation up to 300 mg daily allowed every 2 weeks. Twenty-one patients were enrolled (CML=15, MF=6); with a median age of 60 years (range, 31-97 years). The median platelet count was 44x109/L (range, 3-49x109/L) in CML and 62x109/L (range, 21-75x109/L) in MF. After a median of 18 months (range, 5-77 months), 12 of 15 patients with CML achieved complete platelet response. The median peak platelet count among responders was 154x109/L (range, 74-893x109/L). Among CML patients five could re-escalate the TKI dose and nine improved their response. None of the six patients with MF had a sustained response. Therapy was generally well tolerated. One patient discontinued therapy due to toxicity (elevated transaminases). One patient with CML developed significant thrombocytosis (>1,000x109/L). Another CML patient developed non occlusive deep venous thrombosis in the right upper extremity without thrombocytosis, and one MF patient had myocardial infarction. Eltrombopag may help improve platelet counts in CML patients receiving TKI with recurrent thrombocytopenia. Further studies are warranted (clinicaltrials gov. Identifier: NCT01428635)

Comprehensive Characterization of Ifnγ Signaling in Acute Myeloid Leukemia Reveals Prognostic and Therapeutic Strategies

Interferon gamma (IFNγ) is a critical cytokine known for its diverse roles in immune regulation, inflammation, and tumor surveillance. However, while IFNγ levels were elevated in sera of most newly diagnosed acute myeloid leukemia (AML) patients, its complex interplay in AML remains insufficiently understood. We aim to characterize these complex interactions through comprehensive bulk and single-cell approaches in bone marrow of newly diagnosed AML patients. We identify monocytic AML as having a unique microenvironment characterized by IFNγ producing T and NK cells, high IFNγ signaling, and immunosuppressive features. IFNγ signaling score strongly correlates with venetoclax resistance in primary AML patient cells. Additionally, IFNγ treatment of primary AML patient cells increased venetoclax resistance. Lastly, a parsimonious 47-gene IFNγ score demonstrates robust prognostic value. In summary, our findings suggest that inhibiting IFNγ is a potential treatment strategy to overcoming venetoclax resistance and immune evasion in AML patients

FIGURE 4 from Single-Cell CD4 and CD8 T-Cell Secretome Profiling Reveals Temporal and Niche Differences in Acute Myeloid Leukemia Following Immune Checkpoint Blockade Therapy

Single-cell analysis of IsoPlexis data for CD4 cells (left) and CD8 cells (right). A, UMAP visualization colored by sample location (orange for PB, purple for BM). B, UMAP colored by time point (green for baseline, pink for post-therapy). C, Neighborhood graph depicting differential abundance testing results obtained from miloR. Colors represent the log fold-change between baseline (red) and post-IO (blue) cells. White neighborhoods are nondifferential (FDR 10%). The edges depict links between cells shared by neighborhoods. D, Beeswarm plot of the distribution of neighborhoods by timepoint. E, Beeswarm plot of the distribution of neighborhoods by UMAP-based cluster.</p

FIGURE 3 from Single-Cell CD4 and CD8 T-Cell Secretome Profiling Reveals Temporal and Niche Differences in Acute Myeloid Leukemia Following Immune Checkpoint Blockade Therapy

Exploratory analysis of CR and NR patients at baseline. CRs have significantly higher CD4 effector cytokine expression than NRs in the BM. No significant difference in functional group expression from CD8 cells in either PB and BM between CR and NR.</p

FIGURE 1 from Single-Cell CD4 and CD8 T-Cell Secretome Profiling Reveals Temporal and Niche Differences in Acute Myeloid Leukemia Following Immune Checkpoint Blockade Therapy

Integrative analysis approach and clinical characteristic profile of patient cohort. A, Clinical characteristics of RelRef AML cohort for 21 patients involved in the phase II clinical trial. Among the 21 patients enrolled, 20 had baseline PB samples, 16 had baseline BM samples, 10 had posttreatment PB samples, and 6 had posttreatment BM samples. Four patients had prior allogeneic stem cell transplantation (all-SCT) therapy; 11 patients had prior HMA therapy; 11 patients had Secondary AML; 10 patients had CR to Aza/Nivo immunotherapy in the phase II clinical trial; Karyotype and molecular diagnostics at baseline for all 21 patients are included. B, Venn diagram visualizing the patient cohort.</p

Supplementary Figure 2 from Single-Cell CD4 and CD8 T-Cell Secretome Profiling Reveals Temporal and Niche Differences in Acute Myeloid Leukemia Following Immune Checkpoint Blockade Therapy

Baseline correlation analysis of age with individual cytokines and functional groups.</p

FIGURE 2 from Single-Cell CD4 and CD8 T-Cell Secretome Profiling Reveals Temporal and Niche Differences in Acute Myeloid Leukemia Following Immune Checkpoint Blockade Therapy

Baseline T-cell functional activity in PB and BM. A, Heat map of T-cell cytokine expression values in PB (n = 20) and BM (n = 16) samples at baseline split by cell subset and scaled by row with clinical characteristic and functional group annotations. ComplexHeatmap was used for data analysis and visualization. B, ComplexHeatmap comparing cytokine expression based on functional groups of CD4 and CD8 cells between TMEs at baseline in the PB and BM. C, Bar plot representation of the significant functional groups from B. Pairwise analysis using ggpaired shows differences based on functional group expression compared between TMEs (P < 0.05).</p