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Unravelling NK cell subset dynamics and specific gene signatures post-ibrutinib therapy in chronic lymphocytic leukaemia via single-cell transcriptomics

BMC Cancer volume 25, Article number: 745 (2025) Cite this article

Abstract

Background

As part of the innate immune system, NK cells contribute to optimizing cancer immunotherapy strategies and are becoming a focal point in cancer research. However, limited research has been conducted to further investigate changes in NK cell subsets and their critical genes following ibrutinib treatment in CLL patients.

Methods

Peripheral blood samples from patients clinically and pathologically diagnosed with monoclonal B-cell lymphocytosis (MBL), newly diagnosed with CLL (ND-CLL), postibrutinib-treated patients who achieved a complete response (CR) or partial response (PR), and those with Richter's syndrome (RS) were collected. Single-cell transcriptome sequencing was performed, followed by pseudotemporal analysis and functional enrichment to characterize the NK cell subsets. Mendelian randomization analysis and colocalization analysis were employed to identify key genes. Multiple algorithms were used for immune infiltration analysis, and drug sensitivity analysis was conducted to pinpoint potential therapeutic agents.

Results

Three distinct NK cell subsets were identified: CD56bright_NK cells, CD56dim_NK cells, and a highly cytotoxic CLL_NK subset. The core genes of the CLL_NK subset were elucidated through Mendelian randomization and colocalization analyses. A cell subset-specific novel index (CNI) was constructed based on these core genes and was shown to be capable of predicting responses to immunotherapy. Oncopredictive algorithms and molecular docking screenings further identified semaxanib and ulixertinib as potential therapeutic candidates for CLL.

Conclusion

The CLL_NK subset plays a crucial role in the development and progression of CLL. The CNI, derived from its key genes, holds promise as a predictor of immune therapeutic responses, highlighting the significance of CLL_NK subset dynamics and their genetic underpinnings in CLL management.

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Introduction

Chronic lymphocytic leukemia (CLL) is a chronic lymphoproliferative disorder characterized by the progressive accumulation of dysfunctional, typically monoclonal lymphocytes, exhibiting high heterogeneity [1,2,3]. Compared with other treatments, ibrutinib administered as monotherapy or in combination significantly improved progression-free survival (PFS) and partial overall survival (OS) in CLL patients, demonstrating benefits in patients with del(17p) and/or del(11q) abnormalities, independent of IGHV mutation status [4,5,6,7,8,9,10,11]. However, prolonged use of ibrutinib can lead to resistance and inevitable disease progression [12]. Therefore, elucidating changes in cellular subsets before and after ibrutinib therapy may facilitate the prediction of immunotherapeutic outcomes and prevent the development of resistance.

Natural killer (NK) cells, which are vital components of the immune system and the tumor microenvironment, are implicated in antitumour defense. An 11-year study revealed that individuals with reduced peripheral NK cell cytotoxicity had a greater incidence of cancer development [13]. In solid tumors, increased infiltration and activation of NK cells correlate with prolonged overall survival among patients with various malignancies [14]. Given that they exist mainly in peripheral blood, NK cells have demonstrated substantial efficacy against hematological malignancies, including acute myeloid leukemia (AML), multiple myeloma (MM), non-Hodgkin lymphoma (NHL), and CLL [15,16,17,18]. Various strategies targeting NK cells for cancer therapy, including drugs that enhance NK cell function, cytokines that stimulate NK cells, antibodies that augment NK cell activity, and adoptive NK cell transfer, have emerged [19, 20]. Nevertheless, the evolution and role of NK cells, particularly their involvement in immune therapy responses, in CLL patients receiving ibrutinib treatment remain poorly understood.

This study aimed to conduct single-cell transcriptomics on patients with monoclonal B-cell lymphocytosis (MBL), newly diagnosed CLL (ND), those who achieved complete response (CR) or partial response (PR) posttreatment, and those with Richter's syndrome (RS) to investigate changes in NK cell subsets before and after ibrutinib therapy and their correlation with immune therapeutic outcomes.

Materials and methods

This study elucidated dynamic evolution and regulatory mechanisms of NK cell subsets in CLL patients undergoing ibrutinib treatment by integrating single-cell multi-omics and computational biology. Key methods included ​10X Genomics single-cell RNA-seq​ on peripheral blood from CLL patients, followed by ​Seurat-based quality control, batch correction, and clustering​ (t-SNE/UMAP visualization) to identify NK cell subsets. Healthy controls from GSE213516 [21](n = 3, age-matched). ​Monocle2​ reconstructed pseudotime trajectories to reveal treatment-associated differentiation pathways. Functional analysis integrated ​GSEA, CellChat, and SCENIC​ to dissect molecular pathways, cell–cell communication, and transcriptional regulation in CLL-NK subsets. ​GEO datasets (GSE161610 [22])​ validated CLL-NK-specific gene signatures, while ​GSE22762 [23]/GSE50006​ cohorts verified the CLL-NK cell index (CNI) for predicting immunotherapy response. Immune microenvironment assessment via ​ESTIMATE​ and clinical validation in the ​IMvigor210 cohort​ further corroborated CNI’s predictive utility, with ​OncoPredict​ and molecular docking identifying potential therapeutics. The experimental workflow is illustrated in Fig. 1.

Fig. 1
figure 1

Flowchart Schematic

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Sources of materials and data

Peripheral blood samples were collected from five patients diagnosed with CLL/SLL by the Hematology Department of the First Affiliated Hospital of Xiamen University. These patients were classified based on clinical and pathological features into MBL, ND, PR, CR, or RS. It was imperative to ensure cell quality and obtain written informed consent from each patient. Sample handling adhered to protocols approved by the hospital's Ethics Committee (Ethics Approval Number: SL- 2023 KY083 - 01). The recruitment period for this study was from June 21, 2023, to June 21, 2024; participants provided informed consent, which was obtained in writing. The clinical information of the samples can be found in Additional File 5.

The NCI-BL2009 cell line (ATCC CRL- 5961), an EBV-positive lymphoblastoid B-cell line derived from a 68-year-old healthy donor, was purchased from Wuhan Pricella Biotechnology Co., Ltd. (Wuhan, China). The MEC- 1 cell line (DSMZ ACC- 497) was kindly provided by the Cell Biology Laboratory, Shanghai Jiao Tong University (Shanghai, China).

Single-cell transcriptome library construction

Cell suspensions were adjusted to a density of 700–1500 cells/μL. Using the 10X Genomics single-cell library preparation technology, gel beads with 10X barcodes, single cells, and reaction reagents were encapsulated in droplets within an oil-in-water emulsion on a microfluidic chip. Within each droplet, mRNA was reverse-transcribed into cDNA, incorporating the 10X barcode. The subsequent steps included cDNA amplification, fragmentation, end repair, and A-tailing. CDNA fragments were then subjected to bidirectional SPRIselect size selection and adapter ligation, followed by sample index PCR (SI-PCR). PCR products underwent another round of bidirectional SPRIselect size selection. After quality control, libraries were ready for sequencing.

Single-cell sequencing

Sequencing was performed using the Illumina NovaSeq 6000 platform with paired-end 150 base pair reads (PE150) on qualified libraries. The resulting.fastq files were processed using 10X Genomics Cell Ranger software for single-cell data analysis, generating a cell expression matrix.

Single-cell RNA-seq data analysis

All scRNA-seq data processing and analysis were conducted using the Seurat package. Quality control (QC) filtered out cells expressing fewer than 1000 genes or more than 20% mitochondrial genes, as well as those with over 30% ribosomal gene expression. A total of 40,768 CLL cells passed QC and were used for downstream analysis. Batch effects between datasets were harmonized using Harmony [24]. After normalization, principal component analysis (PCA) was performed on the top 2000 highly variable genes (dimensionality = 50). Subpopulations were identified through graph-based clustering using the FindNeighbors and FindClusters functions (resolution = 0.5), and visualized with t-distributed stochastic neighbor embedding (tSNE) plots. Differential expression analysis was carried out using the FindAllMarkers function to identify cluster-specific marker genes. For re-clustering of subpopulations, the same approach was applied. All clusters were annotated based on established marker genes from previous studies.

Functional analysis of NK cell subpopulations

Gene set enrichment analysis (GSEA) using cancer hallmark pathways was conducted with the clusterProfiler package for functional exploration of NK cell subsets. Trajectory analysis of CLL-NK cell subsets was carried out using the Monocle2 R package [25] and CytoTRACE package [26] to elucidate differentiation capabilities and functions. Cell-to-cell communication within the tumor microenvironment was investigated among CLL-NK subsets utilizing the CellChat package. Transcription factor (TF) regulation in NK cell subsets was studied under default parameters using SCENIC analysis [27].

Bulk transcriptome data analysis

Microarray data and clinical information from 327 CLL patients were retrieved from the GEO, comprising 107 patients from the GSE22762 dataset and 188 from the GSE50006 dataset, both of which were generated using the Human Genome U133 Plus 2.0 Array platform. The data were normalized with normalizeBetweenArrays, and batch effects were corrected using the combat function from the sva package, followed by log transformation before analysis.

eQTL and Mendelian randomization analysis of key marker genes in CLL

Key marker genes for CLL-NK subsets were identified, followed by eQTL analysis. Gene symbols were converted to ENSEMBL IDs for data uniformity, low-quality SNPs were removed, and genotype imputation was performed. A stringent eQTL p value threshold of 5 × 10^− 8 was used. SNPs associated with key marker genes were extracted from the"finn-b-C3_CLL_EXALLC"dataset as instrumental variables for MR analysis. After calculating R2 and F statistics for each SNP, high-quality SNPs were retained for bidirectional MR executed in two independent datasets. This two-step MR approach assessed the regulatory role of key genes in CLL, with SNPs serving as genetic instruments sourced from comprehensive European GWAS (https://gwas.mrcieu.ac.uk/).

Colocalization analysis of key genes

The eQTL data for gene expression were processed using the vcfR package. After data curation and consolidation, the coloc.abf function was used to perform colocalization analysis under various hypotheses, interpreting results based on Bayesian factor sizes and distributions. A significance threshold of posterior probability of hypothesis 4 (PPH4) > 0.80 was set, with genes colocalizing with CLL considered core key genes. eQTLs linked to target genes were extracted and organized for regional association plotting.

Biological function and pathway enrichment analysis

Critical marker genes of the CLL-NK subsets were organized to construct a CNI signature using single-sample GSEA (ssGSEA), which identified genes that were significantly differentially expressed between the high and low CNI groups according to the following criteria: FDR < 0.05 and log2-fold change (FC) > 1. To investigate the biological functions and pathways associated with CNI, GO and KEGG analyses were conducted using the clusterProfiler package. Differential gene expression analysis between varying CNI groups was enriched through gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) with the GSVA package, which explored pathway differences between high and low CNI groups.

Assessment of the immune microenvironment and immunotherapy response

The immune infiltration abundance in CLL samples was evaluated using diverse algorithms, analysing the correlations of 37 immune regulatory genes [28], including those involved in antigen presentation, cell adhesion, checkpoint inhibitors, costimulators, ligands, and receptors, with CNIs. Additionally, we used the ESTIMATE algorithm to generate overall immunological scores related to the CNI. The IMvigor210 dataset (n = 298) was used to assess the ability of the CNI to predict immunotherapy benefit.

Role of CNI in predicting drug sensitivity

The oncoPredict package [29] was used to determine the IC50 values for chemotherapy drugs commonly used for CLL treatment in the high- and low-CNI groups. The protein structures of NKG7 (− 6.74) and ASCL2 (− 3.12) were obtained from the PDB database (http://www.rcsb.org/) and subjected to hydrogen addition and charge assignment using AutoDock tools. The chemical structures of the active pharmaceutical ingredients were downloaded from the PubChem database (https://www.ncbi.nlm.nih.gov/pccompound/) for docking simulations run by AutoDock Vina to generate binding energies, with complexes visualized in PyMol.

RNA extraction and quantitative real-time reverse transcription polymerase chain reaction

Total RNA was extracted from transfected cells using Trizol (15,596–026, Ambion) according to the provided instructions. Gene expression relative to GAPDH was measured by qRT-PCR using the ABI ViiA™ 7 Real-Time PCR System. The PCR protocol included denaturation at 95 °C for 10 s and annealing/extension at 60 °C for 60 s, repeated for a total of 40 cycles. The primer sequences for human NKG7 were: forward 5’-GCCTGATGTTCTGCCTGATT- 3’ and reverse 5’-TGGGGACAAGGACAAGAGAG- 3’. For human ASCL2, the primer sequences were: forward 5’-TGAGCAAGGTGGAGACGC- 3’ and reverse 5’-TCAGTAGCCCCCTAACCA- 3’. The primer sequences for human GAPDH were: forward 5’-TCAAGAAGGTGGTGAAGCAGG- 3’ and reverse 5’-TCAAAGGTGGAGGAGTGGGT- 3’. Relative expression levels were determined using the 2^-ΔΔCT method.

Statistical analyses

Statistical analyses were performed using R version 4.3.1, with continuous variable differences assessed by the Wilcoxon rank-sum test or Student's t test, as appropriate. Pearson correlation analysis was employed to examine relationships between two continuous variables.

Results

Clustering of cellular subpopulations

Following quality control and batch effect correction, our five samples collectively retained 40,768 eligible cells for dimensionality reduction and unsupervised graph-based clustering analysis. Based on cellular heterogeneity, we identified populations of tumor cells, with clusters defined as T cells and NK cells by the markers CD3D, CD3E, and CD3G; granulocytes defined as S100 A8, S100 A9, and S100 A12; mononuclear phagocytes (mononuclear phagocytes, MPs) defined as CD14, CSF1R, and LYZ; and platelets defined as PPBP, PF4, and GP9 (Fig. 2A-B). Subsequently, we calculated the proportions of each cell type within each sample (Fig. 2C). We observed that posttreatment samples showed a significant increase in T and NK cell counts compared to pretreatment samples. In comparison between primary CLL diagnosis and monoclonal B-cell lymphocytosis (MBL), the majority of patients had tumor cells with fewer other cell types. In the PR group, there was a decrease in the proportion of tumor cells relative to that in the pretreatment group, accompanied by an increase in the proportions of T cells, NK cells, and mononuclear phagocytes. Complete response (CR) samples lacked tumor cells, with mononuclear phagocytes being most prevalent, and an increase in the proportions of T and NK granulocytes. Consequently, we performed reclustering on the T/NK cells and identified three clusters: CD4 + T cells, CD8 + T cells, and NK cells (Fig. 2D). Representative marker genes for each cell type are depicted in the bubble plot (Fig. 2E).

Fig. 2
figure 2

Clustering of Cellular Subpopulations. A Clustering diagram of single cells from chronic lymphocytic leukemia (CLL) patients, illustrating distinct cell type classifications. B Heatmap displaying differential gene expression across five cell types, highlighting unique transcriptional profiles. C Schematic representation of the cell distribution across different samples, revealing cellular heterogeneity. D Clustering diagram focusing on T/natural killer (NK) cells, further categorizing these immune cell subsets. E Bubble plot depicting marker genes for T/NK cells, illustrating their expression levels and importance

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NK cell subset clustering and immune infiltration analysis

We conducted a more detailed clustering analysis on NK cells, identifying three distinct clusters based on prototypical marker genes: CLL-NK, CD56bright_NK, and CD56 dim_NK (Fig. 3A). Using t-distributed stochastic neighbor embedding (t-SNE) analysis, we visualized these three clusters (Fig. 3B) and pinpointed marker genes characterizing each cell population. As illustrated in Fig. 3C, the typical marker genes for NK cells were differentially expressed across the respective subgroups. Notably, compared with CD56 dim_NK cells, CLL-NK cells showed high GNLY expression but lacked KLRG1 expression. Compared to CD56bright NK cells, CLL-NK cells demonstrated greater FCGFR3 A expression but not NCAM1 or SELL expression. These differential expression patterns allowed for the clear distinction of CLL-NK cells, a unique NK cell subgroup in CLL, from CD56bright_NK cells and CD56 dim_NK cells.

Fig. 3
figure 3

NK cell subset clustering and immune infiltration analysis. A Clustering of NK cell subsets, distinguishing different functional or phenotypic variations. B Heatmap of differential gene expression within NK cell subsets showing variation among subgroups. C Violin plots for classical marker genes in NK cell subsets, visualizing expression distribution and variability. D Distribution diagrams of NK cell subsets across various samples, indicating their prevalence. E Cell clustering plot of 5 self-collected samples and 3 healthy controls. F Enrichment of CLL_NK Cells in Peripheral Blood T_NK Cells of the GSE161610 Dataset. G Gene set enrichment analysis (GSEA) results for NK cell subsets revealing enriched pathways

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Visual representation through t-SNE revealed that CLL-NK cells were more prevalent in patients who achieved complete remission (CR) or partial remission (PR) compared with newly diagnosed patients or 3 healthy controls (From GSE213516), and some CLL-NK cells were also detected in monoclonal B-cell lymphocytosis (MBL) patients (Fig. 3D-E). By applying the AddModuleScore to the 10 marker genes of the CLL_NK cell subset in the 5 peripheral blood T_NK cells of the GSE161610 dataset, we found that the CLL_NK cells were still enriched in this dataset (Fig. 3F). Gene set enrichment analysis (GSEA) further revealed pathway enrichment specific to each NK cell subtype. CLL-NK cells were primarily enriched in pathways such as prostaglandin synthesis and regulation, Myc repression, IL6/7 signalling pathway, and natural killer cell-mediated cytotoxicity. CD56 dim_NK cells were enriched in the IL12Rβ2 pathway, hematopoietic cell lineage development, and B-cell receptor signalling, while CD56bright_NK cells were enriched in cell cycle checkpoints, innate immune system processes, and cell cycle regulation (Fig. 3G). These findings align with previous findings that CD56bright NK cells possess a stronger proliferative nature and that CD56 dim NK cells have varied functions, suggesting that CLL-NK cells may play a pivotal role following ibrutinib treatment, contributing to disease dynamics and potentially reflecting their response to therapy.

Functional characterization of Different NK cell subsets in CLL

To investigate the relationships among NK cell subsets, we employed Monocle2 for trajectory analysis to elucidate the differentiation paths of NK cell subsets in CLL. The trajectory analysis revealed that CLL_NK cells reside at an early stage of differentiation and are capable of generating CD56 dim NK and CD56bright NK cells. Along this pseudotemporal ordering, we explored transcriptomic and functional changes. Differential gene enrichment analysis along the trajectory indicated that natural killer cell-mediated cytotoxicity, apoptosis, PD-L1 expression and PD- 1 checkpoint pathways, and IL- 17-mediated TNF signalling pathways were activated in the early and middle stages, whereas pathways such as antigen processing and presentation, cell receptor signalling, Th1 and Th2 cell differentiation, cytokine‒cytokine receptor interaction, and NF-kB signalling pathways were highly enriched in the later stages. These pathways are integral to the formation of the tumor microenvironment and the progression of the disease (Fig. 4A-B), thus validating that CLL_NK is a precursor to the distinct effector subsets of CD56bright_NK and CD56 dim_NK cells.

Fig. 4
figure 4

Functional characterization of different NK cell subsets in CLL. A-B Trajectories and heatmap of CLL-NK cells differentiating into CD56 dimNK and CD56brightNK cells, along with differential gene expression over pseudotime and enriched pathways. C Illustration of cell‒cell communication networks. D Recognition pairs of cell receptor‒ligand interactions. E Heatmap of cancer-related pathways enriched in NK cell subsets. F TF regulatory activity network inferred by SCENIC for different cell subsets

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We found enhanced cell communication between CLL_NK cells and CD8 + T cells, as well as between CD56 dim NK cells and CD56bright NK cells (Fig. 4C). Moreover, we examined the differential expression of intercellular communication receptors during the transition, revealing that CLL_NK might modulate CD56bright NK cells via HLA-A-CD8B, HLA-B-CD8B, HLA-C-CD8B interactions and CD56 dim NK cells through HLA-E-CD94:NKG2 A engagement (Fig. 4D). Pathway enrichment analysis highlighted the significant relationship of NK cell subsets with tumor progression, with each subset exerting unique roles. CLL_NK was particularly enriched in pathways such as downregulation of KRAS signalling, epithelial–mesenchymal transition, hedgehog signalling, and apical surface processes, while CD56 dimNK was enriched in pathways related to TGFβ signalling, the TNFA-induced NFKB pathway, IL2-STAT5 signalling, CD56brightNK in peroxisomal function, MTORC1 signalling, and the G2M checkpoint (Fig. 4E).

To identify key transcription factors (TFs) defining the status of each NK subset, we used the SCENIC method to correlate TFs with different transcriptional programs in NK cell subsets and revealed diverse TF activity profiles (Fig. 4F). In CD56 dimNK cells, TFs such as ETS1, CEBPZ, POLR2 A, KLF6, ELK4, RBBP5, PML, FLI1, SREBF2, and KLF12 are active and are mostly involved in proliferation, differentiation, and lipid metabolism. Moreover, in CD56bright NK cells, TFs such as TCF7, IRF8, SPI1, NFKB2, PHF8, GTF2 F1, JUND, JUN, and FOS were activated and were found to be primarily involved in proliferation, differentiation, and inflammatory responses. Finally, in CLL_NK cells, active TFs, including NR2 C2, SMARCA4, SMARCC2, SP4, NFYB, ELK3, ZNF143, FOXO3, DEAF1, GMEB1, and CUX1, are mainly related to cell growth regulation and other processes.

Construction of the CLL-NK cell index (CNI)

Our investigation of single-cell RNA sequencing (scRNA-seq) data from chronic lymphocytic leukemia (CLL) patients included Mendelian randomization (MR) analysis to identify potential genes influencing CLL susceptibility. Initially, leveraging the Seurat toolkit, we pinpointed 190 pivotal genes distinguishing CLL_NK cells from other T/NK cells and cellular subsets following differential expression analysis(Additional File2,Additional File3), resulting in the identification of 190 key genes (Fig. 5A, Additional File4). Through MR analysis, we genetically confirmed 13 genes significantly associated with CLL risk (Fig. 5B). A forest plot was then constructed to visually represent the odds ratios (ORs) and their 95% confidence intervals (CIs) for each of these impactful genes (Fig. 5C). This analysis highlighted that elevated expression of SLAMF7, CST7, NKG7, CD63, EFHD2, DOK2, FCGR3 A, and TRBC1 was correlated with an increased risk of CLL, whereas heightened expression of ASCL2, IL2RG, GNPTAB, CD81, and GZMB served as a protective factor against CLL.

Fig. 5
figure 5

Construction of the CLL-NK cell index (CNI). A Venn diagram showing key genes in CLL-NK cells. B Volcano plot depicting the association between key genes and CLL risk. C Forest plot from Mendelian randomization (MR) analysis illustrating associations between key genes and CLL risk. D Reverse MR analysis forest plot for key genes associated with CLL risk

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In pursuit of result robustness, we also executed a reverse MR analysis. When CLL was considered the exposure and 13 key genes were considered the outcomes, GNPTAB demonstrated a notable correlation (Fig. 5D). Consequently, GNPTAB was excluded, narrowing our findings to 12 genes significantly related to CLL. These genes formed the foundation of the CLL_NK cell index (CNI), which aggregates their collective influence.

Colocalization analysis

Mendelian randomization (MR) analysis revealed associations between 12 key genes and CLL, allowing us to gauge the strength of their correlations. Notably, specific single nucleotide polymorphisms (SNPs) related to these genes, particularly those associated with NKG7 and ASCL2 through expression quantitative trait loci (eQTLs), demonstrated remarkable correlations in chronic lymphocytic leukemia genome-wide association studies (GWASs) (Fig. 6A-B). This finding provides preliminary evidence suggesting that a potential link exists between these two genes and the development of CLL.

Fig. 6
figure 6

Colocalization analysis. A Regional association plot for eQTLs of the NKG7 gene. B Regional association plot for eQTLs of the ASCL2 gene. C The expression levels of NKG7 in NCI-BL2009 and MEC- 1 cells. D The expression levels of ASCL2 in NCI-BL2009 and MEC- 1 cells

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To delve deeper into the broader implications of these SNPs, we employed a phenoscanner tool to explore their associations with other traits. The findings revealed connections to various traits, suggesting that these SNPs are involved in diverse biological processes. To discern the causal direction in the MR analysis, we employed the Steiger test, which discriminates which phenotype is closer to the gene and is therefore more likely to be the true exposure. Our findings indicate that altered expression of NKG7 and ASCL2 serves as a prominent indicator of CLL progression. The preliminary qPCR experimental results show that there is a significant difference in the expression of NKG7 and ASCL2 between the CLL cell line (MEC- 1) and the normal B lymphocyte cell line (NCI-BL2009) (Fig. 6C, D).

In summary, our study suggested that variations in the expression of NKG7 and ASCL2, as identified through rigorous statistical assessments and genetic correlation analyses, are significantly indicative of chronic lymphocytic leukemia development. These insights contribute to a deeper understanding of CLL pathogenesis and may inform future diagnostic and therapeutic strategies.

Functional identification of genes associated with the CNI

Gene Ontology (GO) analysis revealed that the CLL_NK cell index (CNI) was strongly associated with immune receptor activation, lymphocyte-mediated immunity, T-cell-mediated immunity, cytotoxicity, and cell killing, highlighting its vital role in immune modulation (Fig. 7A). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that CNI potentially regulates natural killer (NK) cell-mediated cytotoxicity (Fig. 7B), reinforcing its involvement in immune response mechanisms.

Fig. 7
figure 7

Functional identification of genes associated with the CNI. A Gene Ontology (GO) analysis. B Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. C GSEA pathways enriched in upregulated and downregulated genes in CLL-NK cells. D High- and low-CNI risk groups based on mutation profiles

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Gene set enrichment analysis (GSEA) further revealed that upregulated CNI was mainly involved in apoptosis, while downregulated CNI was closely tied to antigen presentation (Fig. 7C), underscoring its dual impact on cell fate determination and immune recognition.

By dividing the subjects into high- and low-CNI risk groups, we observed through Fig. 7D that the high-risk group showed enrichment in numerous cancer-related pathways, implicating CNIs in a broader spectrum of oncogenic processes. These findings support the notion that CNI is significantly correlated with immune function and actively participates in tumorigenesis, potentially playing an indispensable role in predicting the immune response against tumors and their progression.

Association of CNI with immunotherapy

To evaluate the role of CNI in the immune microenvironment of CLL patients, we assessed the correlation between CNI and immune cell infiltration using seven infiltration algorithms and revealed that a high CNI was positively associated with extensive immune cell infiltration (Fig. 8A). To explore the connection between CNI and immune regulatory genes, we analysed the expression of various immune modulators involved in antigen presentation, cell adhesion, checkpoint inhibitors, costimulators, ligands, and receptors and found that most were upregulated in the high CNI group (Fig. 8B).

Fig. 8
figure 8

Linkage of CNI with immunotherapy. A Correlation of the CNI with immune cell infiltration. B Association of CNI with immune regulatory molecules. C Assessment of stromal and immune scores, ESTIMATE scores, and tumor purity for CNI. D Correlations of key genes with immune infiltration. E Survival and immune therapy response rates in different CNI groups from the IMvigor210 cohort

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Notably, we observed that CLL patients with high CNI scores had higher stromal scores, immune scores, and ESTIMATE immune scores (ESTIMAEs) and lower tumor purity, suggesting more favourable immune infiltration in the tumor microenvironment of high CNI patients, which is conducive to tumor suppression (Fig. 8C).

To validate these findings, we leveraged immune therapy-related datasets to gauge the predictive value of the CNI for immunotherapy efficacy. We discovered that a high CNI was associated with better overall survival (OS) in the IMvigor210 cohort (Fig. 8E). Moreover, CNI scores were greater in CR and PR patients than in those with progressive disease (PD)/stable disease (SD), with a greater proportion of patients achieving CR or PR belonging to the high CNI group (Fig. 7E).

Collectively, these observations imply that a high CNI predicts a superior immune response rate, leading to better therapeutic outcomes, underscoring its potential as a predictive indicator for immunotherapy response in patients with CLL.

Drug sensitivity analysis

In addition to immunotherapy, chemotherapy and targeted therapy remain treatment options for CLL. Leveraging the OncoPredict tool, we assessed the performance of the CNI in predicting drug sensitivity to CLL treatments and found that 5-fluorouracil, cisplatin, dasatinib, danusertib, and fludarabine displayed greater sensitivity in patients with high CNI scores (Fig. 9A). Given the previously identified associations of NKG7 and ASCL2 expression with CLL progression, we separately analysed drug sensitivity in patients with high expression of NKG7 (Fig. 9B) and ASCL2 (Fig. 9C). The analysis revealed that NKG7-high patients were more sensitive to simotesinib, trametinib, and ERK_6604, while ASCL2-high patients were more responsive to ibrutinib, fludarabine, and AZ960_1250.

Fig. 9
figure 9

Drug Sensitivity Analysis. A Chemotherapy sensitivity analysis across CNI groups. B Chemosensitivity analysis based on NKG7 expression levels. C Antitumour drug sensitivity analysis based on ASCL2 expression levels. D-E Simulated molecular docking illustrations

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To further elucidate the mechanisms underlying the enhanced drug sensitivity associated with NKG7 and ASCL2 expression, molecular docking simulations were conducted (Fig. 9 D-E). The results indicated successful docking of the NKG7 protein with parts of the simotesinib drug molecule and of the ASCL2 protein with parts of the ibrutinib molecule. These findings suggest that simotesinib and ibrutinib may hold promise as potential therapeutic agents for CLL treatment, potentially by targeting the molecular interactions implicated in disease progression.

Discussion

Impact of tyrosine kinase inhibitors on CLL Patient NK cell subpopulations and prognosis

In this study, we found significant differences in NK cell subsets among CLL patients at different pathological stages following treatment with TKIs. Notably, in patients achieving CR and PR, an increase was observed in a novel subset of NK cells termed CLL_NK. The changes in NK subpopulations reflect both direct differences in NK cell control over tumor cells and may serve as biomarkers of immunosuppression states. Functional enrichment analysis revealed that these CLL_NK cells are primarily involved in biological processes including prostaglandin synthesis and regulation, Myc inhibition pathways, IL6/7 signaling pathways, and NK cell-mediated cytotoxicity. Specifically, prostaglandins can influence the immune system, including altering NK cell activity [30, 31]; members of the Myc family, such as c-Myc, are critical for lymphocyte development, impacting both the quantity and quality of NK cells, and Myc can also regulate gene expression involved in NK cell activation [32, 33]. Additionally, IL- 6 and IL- 7 directly affect NK cell function: IL- 7 is essential for NK cell development and maintenance, while IL- 6 not only enhances the cytotoxicity and cytokine production capabilities of NK cells but also promotes mature NK cells to secrete more IFN-γ, thereby increasing their efficiency in killing target cells [34,35,36,37]. NK cell-mediated cytotoxicity is one of the core functions of NK cells. The enrichment of the aforementioned pathways in CLL_NK cells may support NK cell proliferation and functionality, thereby contributing to improved outcomes for CLL patients. Previous studies have indicated functional impairment and reduced proliferation of NK cells in CLL [38,39,40,41], possibly related to the loss of mature cells expressing inhibitory killer-cell immunoglobulin-like receptors (KIRs) [15], which contributes to disease progression. This observation aligns with our finding that reductions in NK cell subsets and their functional impairments could lead to inferior responses to immunotherapy or disease deterioration. Thus, we speculate that an increase in the CLL_NK subset is associated with favorable outcomes in immunotherapy, highlighting its importance as a positive prognostic indicator and potential therapeutic intervention target.

Genes linked to CLL_NK subpopulations and their role in disease and treatment response

Further Mendelian randomization analysis identified 12 genes associated with the CLL_NK subset: SLAMF7, CST7, NKG7, CD63, EFHD2, DOK2, FCGR3 A, TRBC1, ASCL2, IL2RG, CD81, and GZMB. Each of these genes has distinct roles. SLAMF7 serves as an important modulator of B cell receptor (BCR) signaling; it can enhance immune control in CLL by boosting NK cell function, typically associated with a more indolent clinical course [42]. CD63 is present in exosomes from the plasma of CLL patients, participating in BCR regulation and potentially being inhibited by tyrosine kinase inhibitors, indicating its possible role in disease progression [43]. CD81 acts as a critical discriminator for CLL and minimal residual disease, aiding in monitoring therapeutic responses [44, 45]. GZMB plays a central role in inducing apoptosis of target cells to clear tumor cells, serving as a key component of NK cell-mediated cytotoxicity [46].

Additionally, colocalization analysis confirmed a significant correlation between changes in the expression of NKG7 and ASCL2 and the onset and development of CLL. NKG7 is an intracellular membrane protein associated with cytolytic granules within NK cells, highly expressed in cells with cytotoxic functions [47]. Upon stimulation, NKG7 translocates to the plasma membrane, facilitating the cytotoxic activities of both NK and T cells [48], enhancing their ability to form immunological synapses with tumor targets, thus increasing the efficiency of triggering cell death [49]. NKG7 plays a pivotal role in antitumor immunity, with its deficiency impairing the antitumor functions of T cells and NK cells [50,51,52]. Therefore, enhanced NKG7 expression in CLL_NK cells of CR patients exhibits heightened cytotoxicity and potentially better assists T cells in antitumor activities.ASCL2 is a basic helix-loop-helix transcription factor predominantly expressed in T follicular helper (Tfh) cells, initiating their development [53, 54]. It contributes to epithelial-mesenchymal transition [55] and is generally regarded as a poor prognostic marker in various malignancies such as colorectal, breast, gastric, glioma, and esophageal cancers [55,56,57,58,59]. In our analysis, ASCL2 also emerged as a risk factor for CLL progression, although the precise mechanisms remain unclear; downregulating ASCL2 expression might confer a more favorable prognosis.

In summary, the CLL_NK cell subset and its specific markers, such as NKG7, hold promise as useful indicators for assessing patient sensitivity to tyrosine kinase inhibitors. Given the high cytotoxic potential of CLL_NK cells, future research should explore whether specific interventions can further increase the quantity and functionality of these cells, thereby achieving better therapeutic outcomes.

CNI: optimizing immune therapy outcomes and personalized treatment strategies

Based on the aforementioned 12 key genes, this study constructed a CLL_NK cell index (CNI) that positively correlates with immune cell infiltration and immune checkpoint activity, demonstrating its potential to predict the efficacy of immunotherapy. For instance, patients with higher CNI scores may respond more effectively to tyrosine kinase inhibitors due to enhanced NK cell functionality, which can better assist the drug in killing tumor cells. Conversely, for patients with lower CNI scores, it might be necessary to consider combining other types of immunotherapy or adopting alternative treatment strategies. Moreover, the CNI can serve as a target for drug development, guiding the creation of new CLL therapies, especially those aimed at enhancing NK cell activity or improving their function. Using the CNI, we have identified several potential candidate drugs for CLL treatment, with molecular docking analysis suggesting that simotesinib could be a novel and promising therapeutic option for CLL.

Despite our findings, limitations such as a small sample size and a lack of experimental validation for the identified genes'roles in CLL pathogenesis exist. Future research should include larger cohort studies and animal model experiments to fully understand the functions and regulatory networks of CLL_NK cells. Additionally, further investigation into interactions between CLL_NK cells and other immune cells is needed to better comprehend the overall immune system's dynamic balance.

Conclusion

Our study revealed the existence of a novel CLL_NK cell subset in patients exhibiting favourable responses to ibrutinib treatment, underscoring its pivotal role in the therapeutic efficacy of ibrutinib for CLL. By identifying NKG7 and ASCL2 as genes significantly associated with the development and progression of chronic lymphocytic leukemia, we highlight a new avenue for investigations in which targeting these molecules could represent innovative strategies for CLL management. Furthermore, the CLL_NK cell index (CNI) has emerged as a valuable predictor of immunotherapy outcomes, increasing our ability to predict treatment responses and guide personalized therapeutic decisions in patients with CLL. Overall, our findings not only deepen our understanding of CLL biology but also offer translational insights with the potential to enhance treatment strategies and improve patient outcomes.

Data availability

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA010997) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human.

Abbreviations

CLL:

Chronic Lymphocytic Leukemia

NK cell:

Natural Killer cell

CLL-NK :

Chronic Lymphocytic Leukemia-associated NK cell subset

CNI :

CLL-NK Cell Index

RS:

Richter’s syndrome

eQTL :

Expression Quantitative Trait Loci

SCENIC:

Single-Cell Regulatory Network Inference and Clustering

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

MBL:

Monoclonal B-cell Lymphocytosis

GSEA:

Gene Set Enrichment Analysis

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Acknowledgements

We are grateful to the First Affiliated Hospital of Xiamen University for providing access to their facilities and resources. Lastly, we extend our appreciation to all the participants who contributed to this study, without whom this work would not have been possible.

Funding

This work was supported by the Natural Science Foundation of Fujian Province (2021 J011368).

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Author notes

  1. Chunlan Liu, Tianjian Ding and Rong Zou contributed equally to this work.

Authors and Affiliations

  1. Department of Hematology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China

    Chunlan Liu & Zhengzhuo Zhi

  2. Department of Thoracic Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China

    Tianjian Ding

  3. Xiamen Hong Ai Hospital, Xiamen, Fujian, China

    Rong Zou

  4. Longyan Hospital of Fujian Province, Fujian, Longyan, China

    Aili Zhang

  5. Department of Hematology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China

    Sili Wang

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Contributions

C.L., T.D. and R.Z.made equal contributions to this work.C.L. and T.D. primarily contributed to the conception, methodology, and data analysis of this article. R.Z. was responsible for sample collection and associated cellular assays. A.Z., and Z.S. mainly contributed to data acquisition and organization. D.L. was responsible for drafting the manuscript. S.W. primarily provided the funding source and contributed to the revision of the paper.All authors have reviewed the manuscript and agree to its publication.

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Correspondence to Sili Wang.

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This study was conducted in accordance with the ethical standards outlined in the Declaration of Helsinki and received ethical approval from the Medical Ethics Committee of The First Affiliated Hospital of Xiamen University, with the assigned protocol number SL- 2023 KY083 - 01. All participants provided written informed consent prior to their inclusion in the study, ensuring that they were fully aware of the study objectives, procedures, potential risks, and benefits.

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Liu, C., Ding, T., Zou, R. et al. Unravelling NK cell subset dynamics and specific gene signatures post-ibrutinib therapy in chronic lymphocytic leukaemia via single-cell transcriptomics. BMC Cancer 25, 745 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-025-14166-0

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BMC Cancer

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