Immune deconvolution from whole transcriptomic data suggests expansion of exhausted T-cells and natural killer cells following tyrosine kinase inhibitor therapy in chronic myeloid leukemia patients Free

Introduction Tyrosine kinase inhibitor (TKI) therapy, while targeting leukemic cells, also alters the immune landscape in chronic myeloid leukemia (CML). Notably, expansion of immune cell populations such as T- or NK-cells has been associated with achieving and maintaining treatment-free remission (TFR). Moreover, distinct exhausted T-cell (Tex) and NK cell (NKex) may affect TKI response and TFR durability. While multicolor flow-cytometry and single cell RNAseq (scRNAseq) are promising tools for exploring immune dynamics, these techniques are costly and require fresh samples limiting scalability. In contrast, immune cell deconvolution using bulk RNA sequencing (RNAseq) data offers a cost-effective alternative requiring less specialized expertise. This study aimed to apply immune cell deconvolution using bulk RNA sequencing data to characterize immune cell populations, particularly T-, NK-, Tex and NKex cells, and to evaluate correlation of these cell subpopulations with clinical outcomes of CML pts. Additionally, it assessed the feasibility of immune deconvolution in this context.

Patients and method Paired bone marrow samples from diagnosis (dx) and follow up (FU) from 72 pts were sequenced using the Illumina TruSeq mRNA panel. ScRNAseq was performed on 3 bone marrow samples (CML diagnosis, post-TKI follow-up and healthy donor) using Chromium x10 scRNAseq. SingleR with the Monaco Immune Data reference was used to annotate data. Exhausted T and NK cells were identified based on expression of exhaustion gene markers (i.e. PDCD1, LAG3, TIM3, and TIGIT) within the total T cell and NK cell population. After validation of the Monaco Immune Data reference with the scRNAseq data, cell type proportions in bulk RNAseq samples were estimated using BisqueRNA deconvolution. Analysis focused on quantifying 10 immune cell types and 30 different subtypes across patient samples. Immune cell populations were compared pairwise (Dx vs. FU) between patient subgroups with optimal response (n=41) and those with resistance or disease progression (n=30; resistance: n=19, progression: n=13).

Results The study included 72 CML pts (43% female, median age 58 years). At diagnosis, 64 pts (91%) were in chronic phase, with Sokal risk scores classified as high (n=11), intermediate (n=28), or low (n=33). Most pts received imatinib as first-line therapy (n=58, 83%), while 14 pts received (2G-TKIs). With a median follow-up of 1,740 days, the pts were classified into optimal response (n=40), resistance (n=19), or progression (n=13) according to their clinical outcomes. Analysis of T-cell abundance in CML pts at dx revealed a significant inverse correlation between proportion of T cells and Sokal risk group (Kruskal-Wallis p = 0.0094). Pts classified as high-risk had a lower mean T cell proportion (17.8%) compared to those in the intermediate-risk (19.6%) and low-risk (21.0%) groups. Deconvolution of the bulk RNAseq data revealed distinct immune cell composition patterns with elevated proportions of NK and T cells in Dx samples compared to FU, consistent with the immune profiles inferred from matched scRNAseq data. Specifically, Dx samples showed a marked increase in T cells (19.3% to 26.1%, p = 2.12 ×10⁻¹⁴), NK cells (1.7% to 5.1%, p = 1.36×10⁻⁷), central memory CD8⁺ T cells (0.38% to 1.29%, p = 5.67×10⁻⁵), follicular helper T cells (0.28% to 1.98%, p = 1.02×10⁻⁹), regulatory T cells (0.03% to 0.31%, p = 1.86×10⁻⁶), Th1 cells (0.004% to 2.3%, p = 4.5× 10⁻²⁰), and Th17 cells (0.23% to 1.29%, p = 1.74×10⁻⁹). Of interest, FU samples exhibited an increase in the populations of exhausted T- and NK-cells with expansion of Tex (1.79% vs. 0.51%, p=3.46 x 10-5) and NKex (0.48% vs. 0.24%, p=1.94x10-2). Interestingly, comparison of immune profiles between optimal responders and pts with resistance/ progression revealed no significant differences.

Conclusion Immune deconvolution from bulk RNAseq is feasible to evaluate immune cell composition in CML pts. An inverse correlation between risk group and proportion of T cells was detected, suggesting enhanced T-cell immunity in lower disease risk. Notably, in contrast to previous studies, the proportion of Tex-cells increased significantly post-TKI therapy, supporting TKI may induce T-cell exhaustion rather than recover T-cell functions. Future studies using this method may clarify mechanisms of TKI-induced T- and NK-cell exhaustion and their impact on TFR.