ITK overexpression enhances T cell cytotoxicity against DLBCL through the TCR-Ca²⁺-Calcineurin-NFAT-IFN-γ pathway

Abstract

Diffuse Large B-Cell Lymphoma (DLBCL) is the most prevalent pathological subtype of non-Hodgkin lymphoma (NHL). Patients with DLBCL often experience extremely poor prognoses due to drug resistance or relapse, and the limitations of existing treatment regimens are evident. This study focuses on the mechanistic role and clinical significance of interleukin-2-inducible T-cell kinase (ITK) in DLBCL. Through bioinformatics analysis, it was found that ITK, as a key molecule in the T Cell Receptor (TCR) signaling pathway, is significantly underexpressed in DLBCL. This underexpression is associated with a poorer overall survival (OS) of patients and exhibits a negative correlation with the expression of the immune checkpoint molecule Programmed Death Ligand 1 (PD-L1), suggesting its potential involvement in the regulation of T cell exhaustion. In vitro experiments demonstrated that overexpression of Itk significantly enhanced the immunological activity of mouse T lymphocytes (CTLL-2), activating the TCR-Ca²⁺-Calcineurin-NFAT-IFN-γ signaling pathway. This led to improved calcium ion responsiveness in T cells, increased calcineurin activity, and nuclear translocation of Nuclear Factor of Activated T Cells (NFAT). It also enhanced cytokine secretion levels and proliferative capacity of cytotoxic T lymphocytes (CD8⁺ T cells), thereby driving an anti-tumor immune response. In a co-culture model, overexpression of Itk significantly augmented the killing effect of CTLL-2 cells on DLBCL, inducing apoptosis in DLBCL cells. Furthermore, T cells overexpressing Itk exhibited a synergistic effect when combined with Rituximab, significantly enhancing the inhibition of DLBCL cell proliferation. This provides a clinically feasible approach to address the challenge of drug resistance in DLBCL treatment. This study unveils the molecular mechanism by which ITK enhances anti-tumor immunity through remodeling the T cell receptor signaling pathway, offering new theoretical grounds for the development of T cell function-activating combined therapeutic strategies for DLBCL.

Introduction

Diffuse Large B-Cell Lymphoma (DLBCL) is the most prevalent aggressive subtype of non-Hodgkin lymphoma (NHL)¹. Although the R-CHOP regimen (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) has significantly improved patient survival rates, 30–40% of patients still experience extremely poor prognosis due to drug resistance or relapse, with a 5-year overall survival (OS) rate of less than 50%^2–6. This heterogeneity is partly attributed to the complexity of the tumor microenvironment (TME)^7–10, where T cell dysfunction is considered a core mechanism of immune evasion^11–13. In recent years, T cell-activating immunotherapies, such as chimeric antigen receptor T-cell immunotherapy (CAR-T), have demonstrated therapeutic potential in relapsed or refractory DLBCL; however, their efficacy is limited by issues such as T cell exhaustion and antigen escape^14,15. There is an urgent need to explore new molecular targets to optimize existing treatment strategies¹⁶.

The T cell receptor (TCR) signaling pathway plays a central role in anti-tumor immune responses, and its downstream molecule, ITK, is a key regulator of calcium ion signaling, T cell activation, and cytokine secretion¹⁷. In the TCR signaling pathway, after TCR binds to the major histocompatibility complex (MHC)-antigen complex¹⁸, it activates lymphocyte-specific protein tyrosine kinase (LCK), Fyn protein tyrosine kinase, and other kinases, leading to the phosphorylation of the protein complex CD3 molecules and immunoreceptor tyrosine-based activation motifs (ITAMs), and activating ZAP70 kinase¹⁹. ZAP70 further phosphorylates downstream substrates, recruits various signaling molecules, and triggers a series of reactions, such as the activation of Rho family guanosine triphosphate (GTP) enzymes by the guanine nucleotide exchange factor (GEF) VAV, the activation of the Ras-MAPK pathway by growth factor receptor-bound protein 2 (GRB2) activating Ras, and the hydrolysis of PIP₂ by PLC-γ1 to generate DAG and IP₃. IP₃ promotes the release of Ca²⁺ from the endoplasmic reticulum²⁰. Ca²⁺ activates calcineurin (CaN), which dephosphorylates NFAT and translocates it to the cell nucleus, regulating the transcription of cytokines such as IFN-γ²¹. Meanwhile, previous studies have suggested that low ITK expression in the TME may be associated with T cell exhaustion, but its specific mechanism in DLBCL remains unclear²². Additionally, while Rituximab exhibits anti-tumor effects, its efficacy is limited by the functional suppression of effector cells, such as CD8⁺ T cells and NK cells^23–25. Therefore, it is of great significance to elucidate how ITK reshapes the immune microenvironment through the TCR signaling pathway²⁶, and to explore its synergistic effects with existing therapies^27–30.

This study systematically investigates the mechanistic role of ITK in DLBCL based on bioinformatics analysis and functional experiments. Initially, through transcriptome data mining, we found that ITK is significantly underexpressed in DLBCL and is associated with poor patient prognosis and an immunosuppressive microenvironment. Subsequently, using cell models, we verified that ITK overexpression promotes IFN-γ secretion by enhancing the TCR-Ca²⁺-Calcineurin-NFAT signaling pathway and enhances the function of CD8⁺ T cells. Finally, through co-culture models and drug synergistic experiments, we confirmed that ITK overexpression exhibits a synergistic effect with rituximab. This study aims to elucidate the molecular mechanism by which ITK regulates the immune microenvironment in DLBCL and to provide a theoretical basis for the development of combined treatment strategies centered on T cell function remodeling.

Materials and methods

Bioinformatics analysis

In this study, the GSE32018 dataset was retrieved from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), which includes a total of 114 B-cell non-Hodgkin lymphoma (B-NHL) samples, of which 22 are DLBCL samples. Additionally, the dataset contains 13 healthy control samples, consisting of 7 normal fresh-frozen lymph node samples and 6 normal fresh-frozen reactive tonsil samples. Differentially expressed genes (DEGs) were analyzed using the R package Limma (adjusted threshold p < 0.05, |Log₂ Foldchange|> 2). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed using Metascape (https://metascape.org/). The correlation between ITK expression and prognostic survival in patients receiving immunotherapy was assessed using Kaplan–Meier Plotter (https://kmplot.com/analysis/). The correlation between ITK expression and tumor purity as well as immune cell infiltration was analyzed using Timer2.0. To further explore the function of ITK in DLBCL, a CD8²⁺ T cell-specific ITK network module in DLBCL, containing 41 genes, was obtained from the CellNetdb database (http://www.bioailab.com:3838/CellNetdb/). GO function and KEGG enrichment analyses of module genes were performed using the R package clusterProfiler. The R package GSVA was employed to calculate the ITK network module scores for the samples, utilizing the GSE32918 dataset, which includes 172 DLBCL patients, 140 of whom were treated with R-CHOP and 32 who did not receive treatment with curative intent. Samples were divided into high and low ITK module score groups based on the median score. The prognostic value of module scores for patient survival was assessed using Kaplan–Meier survival analysis, ROC curves, and multivariate Cox regression. To investigate the transcriptional heterogeneity between high and low ITK module score groups, DEGs analysis was performed using limma (adjusted p < 0.05, |Log₂ Foldchange|> 2), and functional enrichment analysis of module DEGs was conducted. To reveal the inter-group heterogeneity of the tumor immune microenvironment, the R package CIBERSORT was used to calculate the proportion of immune cell infiltration. Immune cytolytic activity (CYT) was calculated using granzyme A (GzmA) and perforin 1 (PRF1). The association between ITK module scores and immunotherapy response was explored. Group comparisons were performed using the Wilcoxon rank-sum test (p < 0.05 was considered statistically significant).

Cell culture

The mouse cytotoxic T lymphocyte line CTLL-2 was purchased from the American Type Culture Collection (ATCC, USA, TIB-214), and the mouse B-cell lymphoma cell line A20 was purchased from ATCC (USA, TIB-208). CTLL-2 cells were cultured in RPMI 1640 medium (Gibco, USA, 11875093) supplemented with 10% fetal bovine serum (FBS, Gibco, 10099141), 100 U/ml recombinant mouse IL-2 (PeproTech, USA, 212–12), and 1% penicillin–streptomycin (Gibco, USA, 15140122). A20 cells were cultured in RPMI 1640 medium supplemented with 10% FBS. All cells were maintained in a humidified incubator at 37 °C with 5% CO₂ and were passaged every 2–3 days. Cells were continuously grown in the logarithmic growth phase to maintain good cell viability.

ITK overexpression and knockdown

For Itk overexpression, the full-length cDNA of mouse Itk (GenBank: NM_001081143.1) was cloned into the lentiviral vector pLVX-IRES-Puro (Clontech, USA, 632,183). Prior to transfection, HEK293T cells were seeded in 6-well plates and transfected when the cell confluence reached 70–80%. The above vector was co-transfected with packaging plasmids (psPAX2, pMD2.G) into HEK293T cells (ATCC, USA, CRL-3216). Viral supernatants were collected 48–72 h post-transfection and used to infect CTLL-2 cells in the logarithmic growth phase. During infection, polybrene (8 μg/ml, Sigma, USA, TR-1003) was added to facilitate viral infection. Stable ITK-overexpressing cell lines were selected using puromycin (2 μg/mL, Gibco, USA, A1113803) for 2–3 weeks until stable cell lines were obtained.

To construct the Itk knockdown vector, three shRNAs targeting the Itk mRNA sequence (shRNA-1: 5′-GCAAGATGTACTTCAAGAT-3′; shRNA-2: 5′-GCCTGTAATGTGCTCATTA-3′) were designed and cloned into the pLKO.1 vector (Addgene, USA, 10,878). A scrambled vector (pLKO.1-scramble) was used as a negative control. Prior to transfection, CTLL-2 cells were seeded in 24-well plates and transfected when the cell confluence reached 70–80%. Lipofectamine RNAiMAX (Thermo Fisher Scientific, USA, 13,778,150) transfection reagent was used. Cells were collected 48 h post-transfection, and the mRNA and protein expression levels of Itk were detected by qPCR and Western Blot to confirm the knockdown effect.

qRT-PCR analysis

Total RNA was extracted using Trizol reagent (Thermo Fisher Scientific, USA, A33252), and RNA concentration and purity were detected using a Nanodrop 2000. cDNA was synthesized using the Revert Aid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, USA, K1622). qRT-PCR analysis was performed using the SYBR Green Supermix Kit (Thermo Fisher Scientific, USA, 11,762,100). The primer sequences are shown in Table 1. A 20 μl reaction system was prepared in a 96-well plate, including 10 μL SYBR Green Supermix, 0.5 μL each of the forward and reverse primers (10 μM, primers were PAGE-purified to ensure purity), 2 μL cDNA template, and 7 μL ddH₂O. The reaction conditions were as follows: 95 °C for 30 s for pre-denaturation, followed by 40 cycles of 95 °C for 5 s for denaturation and 60 °C for 30 s for annealing and extension. Melt curve analysis was also performed to verify the specificity of the amplification products. Each sample was analyzed in three biological replicates, with β-actin used as an internal reference gene. The relative expression levels of target genes were calculated using the 2^−ΔΔCt method.

Table 1.

Primer sequence.

Species	Primer name	Amplicon size	F/R	Sequence 5’-3’	Length	Location
Mus musculus	Itk	116	Forward	GGAAGAAGCGCACGTTGAAG	20	137–156
			Reverse	ATGCACGACCTGAAAAGGGTA	21	252–232
	TNF-α	127	Forward	GAAAAGCAAGCAGCCAACCA	20	85–104
			Reverse	TCTTCTGCCAGTTCCACGTC	20	211–192
	CD69	102	Forward	CCCTTGGGCTGTGTTAATAGTG	22	123–144
			Reverse	AACTTCTCGTACAAGCCTGGG	21	224–204

ELISA

ELISA kits (R&D Systems, USA, DY485 for IFN-γ detection and DY410 for TNF-α detection) were used to measure the concentrations of IFN-γ and TNF-α in cell culture supernatants. Ninety-six-well ELISA plates were coated with the corresponding capture antibodies and incubated overnight at 4 °C. The next day, the coating solution was discarded, and the plates were washed three times with washing buffer (provided in the kit) for 3 min each time. Blocking buffer (provided in the kit) was added, and the plates were incubated at room temperature for 1 h. After discarding the blocking buffer and washing three times, cell culture supernatants were added (with three biological replicates for each sample), and the plates were incubated at room temperature for 2 h. Following three washes, biotinylated detection antibodies were added and incubated at room temperature for 1 h. After three washes, avidin-HRP was added and incubated at room temperature for 30 min. After three washes, TMB substrate solution was added, and the color was developed in the dark for 15–30 min. Stop solution (provided in the kit) was added to terminate the reaction, and the absorbance at 450 nm was read on a microplate reader (BioTek, Synergy H1). The kit provided standard substances, which were diluted and used to plot standard curves according to the instructions. The cytokine concentrations were calculated based on the standard curves.

Western blot

Cells were collected and lysed on ice for 30 min with RIPA lysis buffer (Beyotime, China, P0013B) containing protease inhibitors (Roche, Switzerland, 04,693,132,001) and phosphatase inhibitors (Thermo Fisher Scientific, USA, 78,420). The lysates were centrifuged at 12,000 rpm and 4 °C for 15 min to obtain total proteins. The protein concentrations were measured using a BCA protein quantification kit (Thermo Fisher Scientific, USA, 23,225). After adjusting the protein concentrations, 5 × SDS loading buffer (Beyotime, China, P0015L) was added, and the samples were boiled at 100 °C for denaturation. SDS-PAGE gels of appropriate concentrations (using a Tris–HCl buffer system) were prepared for electrophoresis to separate the proteins. The proteins were then transferred to PVDF membranes (Millipore, USA, IPVH00010). The membranes were blocked with 5% non-fat milk (BD, USA, 232,100) in TBST buffer (20 mM Tris–HCl, 150 mM NaCl, 0.1% Tween 20, pH 7.6) for 1–2 h. The membranes were incubated overnight at 4 °C with rabbit anti-mouse ITK antibody (Cell Signaling Technology, USA, #3769) diluted 1:1000 and rabbit anti-mouse β-actin antibody (Cell Signaling Technology, USA, #4970) diluted 1:2000. HRP-labeled goat anti-rabbit secondary antibody (Cell Signaling Technology, USA, #7074) diluted 1:5000 was added and incubated at room temperature for 1–2 h. After washing with TBST, the membranes were developed using a chemiluminescent substrate (Thermo Fisher Scientific, USA, 34,580) and exposed on a chemiluminescent imaging system (Bio-Rad, USA, ChemiDoc MP). The band gray values were analyzed using ImageJ software. The relative expression of ITK was calculated using β-actin as an internal reference to analyze the differences in ITK protein expression among different treatment groups.

Immunofluorescence assay

Cells were collected and digested into a single-cell suspension using 0.25% trypsin–EDTA (Gibco, USA, 25,200,072). The cell density was adjusted to 1 × 10⁵ cells/mL, and the cells were seeded on poly-L-lysine-coated slides (Thermo Fisher Scientific, USA, 15,040–010), cell slides were formed after 24 h of culture. The cells were fixed with 4% paraformaldehyde (Sigma, USA, P6148), permeabilized with 0.1% Triton X-100 (Sigma, USA, T8787), and blocked with 5% bovine serum albumin (BSA, Sigma, USA, A9647). Diluted anti-NFATc1 antibody (Cell Signaling, USA, #8032, 1:200) was added and incubated overnight at 4 °C. The next day, after washing with PBS (Gibco, USA, 10,010,023), Alexa Fluor 488-labeled goat anti-rabbit secondary antibody (Invitrogen, USA, A-11008, 1:500) was added and incubated at room temperature in the dark. After washing with PBS, the cell nuclei were stained with 4’,6-diamidino-2-phenylindole (DAPI, Sigma, USA, D9542, 1 μg/mL). Finally, the slides were mounted with fluorescence mounting medium (Vector Laboratories, USA, H-1000) and observed under a confocal microscope (Zeiss LSM 880). The fluorescence intensity differences were analyzed using ImageJ software.

Dynamic monitoring of calcium ions

CTLL-2 cells were seeded in confocal dishes (Ibidi, Germany, 81,218–200) at a density of 1 × 10⁵ cells per dish 24 h before the experiment to allow cell attachment. On the day of the experiment, the cells were washed twice with PBS and incubated with Fluo-4 AM (Thermo Fisher Scientific, USA, F14201)-containing serum-free RPMI-1640 medium at a concentration of 5 μM at 37 °C for 30 min to allow sufficient entry of Fluo-4 AM into the cells. After incubation, the cells were washed three times with PBS to remove unincorporated Fluo-4 AM. The Fluo-4 AM-loaded CTLL-2 cells were placed in a culture medium containing anti-CD3/CD28 magnetic beads (Thermo Fisher Scientific, USA, 11452D) (with a bead-to-cell ratio of 1:1) and observed under a fluorescence microscope. Fluorescence signals were captured every 10 s for 5 min. The peak calcium ion intensity was analyzed using ImageJ software.

Calcineurin phosphatase activity assay

Calcineurin phosphatase activity was measured using a Calcineurin phosphatase activity assay kit (Abcam, USA, ab138910). The cells were washed twice with pre-cooled PBS, collected into centrifuge tubes, and lysed on ice for 30 min with an appropriate amount of cell lysis buffer (provided in the kit), with occasional vortexing during the process. After lysis, the samples were centrifuged at 12,000 rpm and 4 °C for 15 min, and the supernatants were collected as cell lysates. According to the kit instructions, the reaction was carried out in a 96-well plate. Fifty μL of cell lysate was added to each reaction well, followed by 50 μL of reaction buffer (provided in the kit) and 50 μL of substrate solution (provided in the kit), and gently mixed. The 96-well plate was placed in a microplate reader and incubated at 37 °C for 30 min, with the absorbance at 405 nm read every 5 min. Calcineurin phosphatase activity was expressed as the amount of substrate hydrolysis per unit time (calculated based on the absorbance change) using the following formula: Calcineurin phosphatase activity = (A405 final-405 initial)/reaction time (minutes) × dilution factor, where A405 final and A405 initial are the absorbance values at 405 nm at the end and start of the reaction, respectively.

TCR activation experiment

Anti-mouse CD3 antibody (BioLegend, USA, 100,302, 1 μg/mL) and anti-mouse CD28 antibody (BioLegend, USA, 102,102, 1 μg/mL) were mixed at a 1:1 ratio and added to a centrifuge tube containing Dynabeads™ M-450 magnetic beads (Thermo Fisher Scientific, USA, 14013D) to achieve a final bead concentration of 1 × 10⁷/mL. The mixture was incubated at 37 °C for 2 h, with gentle vortexing every 20 min to ensure sufficient binding of the antibodies to the beads. After incubation, the beads were washed three times with PBS. During each wash, the beads were placed on a magnetic stand, the supernatant was discarded, and fresh PBS was added to resuspend the beads to remove unbound antibodies. CTLL-2 cells were adjusted to a density of 1 × 10⁶/mL and added to a 24-well plate at a bead-to-cell ratio of 1:1, with a volume of 1 mL per well. The plate was co-incubated for 24 h at 37 °C in a 5% CO₂ incubator to ensure an appropriate growth environment for the cells. After incubation, the cells were collected for subsequent analysis to investigate the cellular responses after TCR activation.

Co-culture model of A20 cells and CTLL-2 cells

Before the experiment, a 96-well plate was washed twice with sterile PBS and placed in a super clean bench to air-dry. A20 cells in the logarithmic growth phase were digested with 0.25% trypsin–EDTA, and the digestion was terminated with RPMI 1640 medium containing 10% FBS. The cells were collected by centrifugation at 1000 rpm for 5 min. The cells were resuspended in fresh medium, and the cell density was adjusted to 3 × 10³ cells/mL. The cells were seeded in a 96-well plate at a volume of 100 μL per well and cultured in a 37 °C, 5% CO₂ incubator to allow cell attachment for 2–4 h. After A20 cell attachment, CTLL-2 cells were processed in the same way, and the cell density was adjusted to 1 × 10³ cells/mL. One hundred μL of CTLL-2 cell suspension was added to each well of the 96-well plate already seeded with A20 cells for co-culture. The co-culture time was set to 24 h. During the co-culture period, the cell state was closely monitored to ensure a stable culture environment and avoid contamination.

Cell viability assay

After 24 h of co-culture, 10 μL of CCK-8 reagent (Dojindo, USA, CK04) was added to each well and gently mixed to avoid bubbles. The 96-well plate was placed in a 37 °C, 5% CO₂ incubator and incubated for another 2 h. After incubation, the 96-well plate was taken out of the incubator and placed on a microplate reader to measure the absorbance at 450 nm. The cell viability was calculated as follows: Cell viability (%) = (number of viable cells/total number of cells) × 100%. Each experimental condition had six biological replicates, and the average values were used for statistical analysis to reduce experimental errors.

Apoptosis assay

Apoptosis was detected using a TUNEL kit (Roche, USA, 11,684,795,910). The co-cultured cells were gently washed twice with PBS for 5 min each time. The cells were fixed with 4% paraformaldehyde at room temperature for 30 min and then washed three times with PBS for 5 min each time. The cells were permeabilized with 0.1% Triton X-100 at room temperature for 10 min and then washed three times with PBS for 5 min each time. 50 μL of TUNEL reaction mixture (prepared according to the kit instructions) was added to each well and gently mixed to avoid bubbles. The 96-well plate was placed in a wet box and incubated at 37 °C in the dark for 60 min. After incubation, the cells were washed three times with PBS for 5 min each time. 50 μL of DAPI staining solution was added to each well and incubated at room temperature in the dark for 5 min to stain the cell nuclei. The cells were washed three times with PBS for 5 min each time. The cells were observed under a fluorescence microscope, with TUNEL-positive cells (green fluorescence) observed at a wavelength of 488 nm and DAPI-stained cell nuclei (blue fluorescence) observed at a wavelength of 358 nm. Each group had six biological replicates. The cells were observed under a fluorescence microscope, and the fluorescence intensity was analyzed using ImageJ software.

Rituximab synergy experiment

A20 cells were seeded in a 96-well plate at a density of 3 × 10³ cells per well. After cell attachment, different concentrations (0, 0.1, 1, 10 μg/mL) of rituximab (Roche, USA, 04,718,616,001) and CTLL-2 cells (1 × 10³ cells per well) were added for co-culture. The co-culture time was 24 h. After co-culture, cell viability was measured using the CCK-8 method. According to the above cell viability assay method, the absorbance at 450 nm was measured on a microplate reader, and the cell viability of different treatment groups was calculated. Based on the cell viability data, reaction curves were plotted using GraphPad Prism 9.0 software, and the IC₅₀ value (half-maximal inhibitory concentration), which is the drug concentration required to inhibit 50% of cell growth, was calculated through non-linear regression analysis. Each concentration had six biological replicates, and the experiment was repeated three times to ensure the reliability and repeatability of the experimental results.

Statistical analysis

All experimental data were presented as mean ± standard deviation (mean ± SD). SPSS 26.0 software (IBM Corporation) and GraphPad Prism 9.0 software were used for statistical analysis. First, normality tests (such as the Shapiro–Wilk test) were performed on the data. If the data followed a normal distribution, independent sample t tests were used for comparisons between two groups, and one-way analysis of variance (ANOVA) with Tukey’s test for multiple comparisons was used for more than two groups. If the data did not follow a normal distribution, non-parametric tests (such as the Kruskal–Wallis test) with Dunn’s test for multiple comparisons were used. The significance level was indicated as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. p < 0.05 was considered statistically significant, and ns = not significant.

Results

Analysis of DEGs between DLBCL tissues and normal lymph node tissues and the correlation between ITK expression and prognosis of immune checkpoint therapy

The GSE32018 dataset includes 114 B-NHL samples, of which 22 correspond to DLBCL. Focusing on the gene expression profiles associated with DLBCL, this study specifically selected the 22 DLBCL samples and compared them to 7 healthy lymph node samples, leading to the identification of 382 DEGs. Among these, 68 genes were upregulated and 314 genes were downregulated in DLBCL tissues compared to normal tissues. The results demonstrated that ITK was significantly underexpressed in DLBCL tissues (Fig. 1a). KEGG pathway and GO functional enrichment analyses of the DEGs were conducted using Metascape (Fig. 1b). The results indicated that the DEGs were mainly enriched in pathways and functions such as cytokine-cytokine receptor interaction, T cell receptor signaling pathway, adaptive immune response, and T cell-mediated immunity, suggesting that these pathways and functions may play crucial roles in the pathogenesis of DLBCL.

Fig. 1.

Analysis of DEGs between DLBCL tissues and normal lymph node tissues and the correlation between ITK expression and prognosis of immune checkpoint therapy. (a) Volcano plot of DEGs between DLBCL tissues and normal lymph node tissues. (b) GO functional enrichment and KEGG pathway enrichment of DEGs.^31–33 GO enrichment includes biological processes (BP), cellular components (CC), and molecular functions (MF). (c) Impact of high and low ITK expression on the overall survival of patients treated with anti-CTLA-1. (d) Impact of high and low ITK expression on the overall survival of patients treated with anti-PD-1. (e) Impact of high and low ITK expression on the overall survival of patients treated with anti-PD-L1. f. Correlation between tumor purity (Purity), CD8⁺ T cell, CD4⁺ T cell, and regulatory T cell immune infiltration levels calculated by TIMER and ITK expression.

The Kaplan–Meier Plotter online website was employed to analyze the prognostic impact of ITK expression specifically in DLBCL patients treated with immune checkpoint inhibitors. It was found that the overall survival (OS) of DLBCL patients in the high ITK-expression group was significantly better than that in the low ITK-expression group after receiving treatment with cytotoxic T lymphocyte-associated protein 4 (Tremelimumab, anti-CTLA-4), programmed cell death 1 antibody (Cemiplimab, anti-PD-1), or programmed cell death 1 ligand 1 inhibitor (Durvalumab, anti-PD-L1) (Fig. 1c–e). TIMER 2.0 analysis revealed a significant negative correlation between ITK expression and tumor purity, while a positive correlation was observed between ITK expression and the infiltration levels of CD8⁺ T cells, CD4⁺ T cells, and regulatory T cells (Fig. 1f). This suggests that ITK may be involved in regulating immune cell infiltration in the TME, thereby affecting treatment response.

Construction and functional enrichment of the ITK network module in CD8⁺ T cells

To investigate the function of ITK in CD8⁺ T cells, this study obtained a CD8⁺ T cell-specific ITK network module in DLBCL from the CellNetdb database (Fig. 2a). GO functional enrichment analysis of the ITK network module showed (Fig. 2b) that the module was mainly involved in multiple BP such as antigen receptor-mediated signaling pathways, T cell activation regulation, and thymic T cell selection. KEGG pathway analysis further revealed that the module network was significantly enriched in pathways including the T cell receptor signaling pathway, PD-L1 expression and PD-1 cancer checkpoint pathway, and natural killer cell-mediated cytotoxicity (Fig. 2c).

Fig. 2.

Construction of the ITK network module in CD8⁺ T cells and its correlation analysis with the survival of DLBCL patients. (a) CD8⁺ T cell-specific ITK network module obtained from CellNetdb. The size of the dots represents the degree of the module. (b) GO (BP) functions enriched in the network module. The size of the dots represents the number of genes overlapping with the functions. (c) KEGG pathways enriched in the network module.^31–33 The size of the dots represents the number of genes overlapping with the pathways. (d) Scatter plot of ITK module scores in DLBCL samples, with samples arranged from low to high scores. (e) Kaplan–Meier survival curves of the high- and low-ITK-module-score groups. (f) ROC curve for predicting 3-year survival outcomes based on ITK module scores. (g) Forest plot of multivariate Cox proportional hazards regression analysis.

Survival predictive value of the ITK network module score

To assess the overall activity of the ITK network module in patients, the GSE32918 dataset (containing 172 DLBCL patients) was used. The ITK module score was calculated using the R package GSVA, and samples were divided into high- and low-module-score groups based on the median score (Fig. 2d). Kaplan–Meier survival analysis demonstrated that DLBCL patients in the low-module-score group had a worse 3-year OS (Fig. 2e). The ROC curve showed that the area under the curve (AUC) for predicting 1-3-year survival in DLBCL patients was 0.65, 0.64, and 0.63, respectively (Fig. 2f), indicating that the ITK module score could accurately predict the 3-year survival outcome of patients. Further multivariate Cox proportional hazards regression analysis was conducted. The forest plot showed that the ITK module score was an independent protective factor for patient survival, independent of gender, age, and molecular subtype (Germinal Center B-cell, GCB/non-GCB) (HR = 0.374, P value = 0.016) (Fig. 2g). This suggests that ITK may play a central regulatory role in the immunotherapy of DLBCL by participating in various BP.

Transcriptional heterogeneity analysis of high- and low-ITK-module-score groups

DEGs analysis was performed on DLBCL patients in the high- and low-ITK-module-score groups (adjusted p value < 0.05 and |log₂ FC|> 1.5). A total of 405 DEGs were identified, including 33 upregulated genes and 372 downregulated genes (Fig. 3a, b). GO functional enrichment analysis showed that the ITK-module-related genes were significantly enriched in functions such as T cell activation, immune synapse formation, and cytotoxic effects (Fig. 3c). KEGG pathway enrichment analysis revealed that the genes were enriched in pathways such as the T cell receptor signaling pathway and cytotoxic molecule pathway (Fig. 3d), suggesting that the activity of the ITK module may be closely related to T cell exhaustion.

Fig. 3.

Transcriptional heterogeneity analysis and functional enrichment results of high- and low-ITK-module-score groups. (a) Volcano plot of high- and low-ITK-module-score groups. The genes labeled in the figure are the five genes with the highest/lowest log₂ (Foldchange). (b). Heatmap of DEGs in high- and low-ITK-module-score groups. The color of the heatmap represents the gene expression levels in samples. Higher expression is indicated by a deeper color (red for high expression, green for low expression). (c) GO (BP) enrichment results of DEGs. (d) KEGG pathway enrichment results of DEGs.^31–33.

Inter-group heterogeneity of the tumor immune microenvironment

CIBERSORT was used to analyze the infiltration of 22 types of immune cells. It was found that the proportion of memory B cells and plasma cells was significantly increased in the low-score group, while the proportion of CD8⁺ T cells, follicular helper T cells (Tfh), and activated memory CD4⁺ T cells was significantly decreased (Fig. 4a, b). ITK module scores were positively correlated with CD8⁺ T cell infiltration and negatively correlated with memory B cell infiltration (Fig. 4c). CYT analysis was conducted using GZMA and PRF1. Patients were divided into immune-therapy-responsive and immune-therapy-non-responsive groups. The proportion of immune-therapy-responsive patients was significantly higher in the high-ITK-module-score group, and the ITK module scores of responsive patients were significantly higher than those of non-responsive patients (Fig. 4d–e). Correlation analysis showed a positive correlation between ITK module scores and CYT values (R = 0.45) (Fig. 4f), suggesting that ITK may enhance the immune-therapy response of patients by affecting CD8⁺ T cell immune infiltration.

Fig. 4.

Heterogeneity of the tumor immune microenvironment among groups in DLBCL and the correlation between ITK expression and response to immunotherapy. (a) Box plots of the distribution of the proportions of differential immune cells. (b) Heatmap showing the expression levels of differential immune cells (blue represents low levels, and red represents high levels). (c) Lollipop plot displaying the correlation between the ITK module score and the content of differential immune cells. (d) Bar chart illustrating the distribution of CYT scores in samples, with annotation information for the high and low ITK module score groups above. (e) Box plot depicting the distribution of ITK module scores between the immune-responsive and non-responsive groups. (f) Scatter plot showing the correlation between the ITK module score and the CYT score. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Overexpression of Itk activates the TCR signaling pathway and enhances T cell function

To investigate the regulatory role of ITK in the immune capacity of CTLL-2 cells, CTLL-2 cell models with Itk overexpression (Itk OE) and Itk knockdown (Itk KD) were constructed, using an empty vector group and a blank group as controls. To validate the successful construction of the model, we performed qPCR and Western blot analyses to assess the expression levels of Itk in the different experimental groups. The results demonstrated a significant increase in both mRNA and protein expression of Itk in the Itk OE group, while a substantial decrease in expression was observed in the Itk KD group, confirming the successful establishment of the gene overexpression and knockdown models (Fig. S1a). The corresponding uncropped Western Blot membranes are shown in Fig. S2a. Further investigation revealed that, compared with the blank group and the empty vector group, the mRNA expression levels of tumor necrosis factor-α (TNF-α), ITK, and cluster of differentiation 69 (CD69) were significantly downregulated in the Itk KD group and significantly upregulated in the Itk OE group (Fig. 5a–c), indicating that Itk overexpression positively regulates the ability of CTLL-2 cells to secrete antitumor factors. The CCK-8 cell proliferation assay demonstrated that the cell proliferation capacity was significantly enhanced in the Itk OE group and significantly reduced in the Itk KD group (Fig. 5d), suggesting a positive correlation between Itk expression levels and the proliferation capacity of CTLL-2 cells.

Fig. 5.

Effects of Itk on the immune capacity of CTLL-2 cells. (a–c) Changes in the mRNA expression levels of TNF-α, ITK, and CD69 in CTLL-2 cells with different Itk expression levels. (d) Detection of the proliferation capacity of CTLL-2 cells with different Itk expression levels. (e, f). Overexpression of the Itk gene increases the intracellular calcium ion concentration in CTLL-2 cells. Scale bar, 50 µm. The green fluorescence in Fig. 5e indicates Fluo-4 staining, reflecting intracellular calcium levels. (g) Overexpression of Itk promotes an increase in calcineurin activity. (h, i) Both TCR activation and Itk overexpression promote NFAT nuclear translocation. Scale bar, 100 µm. (j) Overexpression of Itk promotes an increase in IFN-γ concentrations. n = 6, **p < 0.01, ***p < 0.001, ****p < 0.0001, two-tailed t test.

Further investigation into the role of ITK in the TCR signaling pathway was conducted. CTLL-2 cells were co-incubated with anti-CD3/CD28 antibody-coated magnetic beads to simulate antigen presentation and activate the TCR in CTLL-2 cells. Compared to the untreated control group and the anti-IgG (negative control) group, the total level of the key signaling molecule PLCγ1 remained unchanged after Anti-CD3/CD28 treatment, while its phosphorylation level significantly increased, indicating successful TCR activation (Fig. S1b). The corresponding uncropped Western Blot membranes are presented in Fig. S2b and c. For the calcium ion flow monitoring experiment, the CTLL-2 cells were divided into the following four groups: untreated CTLL-2 cells as the control group (Control), TCR-activated CTLL-2 cells as the TCR activation group (TCR activation), TCR-activated CTLL-2 cells transfected with an empty vector as the empty vector group (Empty vector), and TCR-activated CTLL-2 cells with Itk overexpression as the Itk OE group. The experimental results revealed that Itk overexpression significantly enhanced the calcium signal intensity after TCR activation. The calcium ion concentration showed a clear dynamic change trend from 0 to 60 s, with a significantly higher calcium ion peak compared with the EV group and the TCR activation group (Fig. 5e, f). Additionally, calcineurin (CaN) was continuously activated, and its activity assay showed a significant increase in the enzymatic reaction rate (Fig. 5i), suggesting a positive regulatory role of ITK in the TCR signaling pathway. Immunofluorescence confocal microscopy observations indicated that the nuclear translocation efficiency of the transcription factor NFAT was significantly higher in the Itk OE group than in the EV group (Fig. 5g, h), indicating that Itk overexpression significantly enhanced NFAT nuclear translocation after TCR activation, further supporting the positive regulatory role of ITK in the TCR signaling pathway. ELISA was further used to verify the changes in the secretion level of IFN-γ after TCR activation. The results showed that compared with the control group and the EV group, the IFN-γ concentration was significantly higher in the Itk OE group (Fig. 5j), suggesting that Itk overexpression enhances the calcium signal-Calcineurin-NFAT pathway, activates NFAT nuclear translocation to drive IFN-γ gene transcription, and increases its secretion level through signal amplification by ITK.

Overexpression of Itk enhances the killing effect of CTLL-2 cells on DLBCL cells and its synergistic effect with rituximab

Considering that the A20 cell line is a mouse-derived B cell lymphoma cell line with certain biological features resembling those of DLBCL, we established an in vitro model using this cell line in the present study to assess the impact of Itk overexpression in CTLL-2 cells on DLBCL. A20 cells were co-cultured with Itk-overexpressing CTLL-2 cells. The experimental groups included a blank control group with only A20 cells (A20), a co-culture group of A20 cells and CTLL-2 cells (A20 + CTLL-2), and a group of A20 cells and Itk-overexpressing CTLL-2 cells (A20 + Itk OE CTLL-2). The results showed that compared with the blank control group, the number of A20 cells was significantly reduced after 24 h of co-culture in the A20 + Itk OE CTLL-2 group (Fig. 6a–c), indicating that Itk overexpression significantly reduced the survival rate of A20 cells in the CTLL-2 and A20 cell co-culture model. TUNEL apoptosis assay results further confirmed that Itk-overexpressing CTLL-2 cells could significantly induce apoptosis in A20 cells (Fig. 6e, f). These results suggest that Itk overexpression enhances the killing and pro-apoptotic effects of CTLL-2 cells on A20 cells. In addition, in the synergistic experiment with rituximab, the cell survival rate was significantly lower in the A20 + Itk OE CTLL-2 + Rituximab group compared with the A20 + Rituximab group and the A20 + CTLL-2 + Rituximab group. The IC50 value was 179.8 μg/mL in the A20 + Rituximab group, 109.7 μg/mL in the A20 + CTLL-2 + Rituximab group, and 53.53 μg/mL in the A20 + Itk OE CTLL-2 + Rituximab group. A decrease in the IC50 concentration of rituximab was observed after the combined treatment, suggesting a synergistic killing effect of Itk-overexpressing CTLL-2 cells and rituximab on A20 cells (Fig. 6d, g). The above results indicate that Itk overexpression not only enhances the killing ability of CTLL-2 cells against A20 cells but also has a synergistic effect when used in combination with rituximab, providing new potential strategies and theoretical bases for DLBCL immunotherapy.

Fig. 6.

Effects of Itk-overexpressing CTLL-2 cells on A20. (a) Schematic diagram of the direct co-culture model of CTLL-2 cells (Itk OE) and A20 mouse B-cell lymphoma cells. (b, c). Effects of Itk overexpression in CTLL-2 cells on the proliferation of A20 cells. Scale bar, 50 µm. (d, e). Effects of Itk-overexpressing CTLL-2 cells on the apoptosis of A20 cells. Scale bar, 50 µm. (f) Fitted curve of cell survival rates in the rituximab synergistic experiment. (g) Bar chart of cell survival rates in each group in the rituximab synergistic experiment. n = 6, **p < 0.01, ***p < 0.001, ****p < 0.0001, two-tailed t test.

Discussion

This study systematically elucidated the function of ITK in the TCR signaling pathway, revealing its molecular mechanism of enhancing the anti-tumor activity of CD8⁺ T cells through the TCR-Ca²⁺-Calcineurin-NFAT-IFN-γ signaling pathway (Fig. 7). It also proposed ITK as a potential target for improving immunotherapy in DLBCL. Below, we discuss the study’s findings in terms of mechanistic innovation, clinical translational significance, and research limitations, integrating our experimental results with existing literature.

Fig. 7.

Overexpression of ITK promotes TCR signaling pathway activation in CTLL-2 cells, inhibits DLBCL growth, and has a synergistic effect with traditional chemotherapeutic drugs.

Our study found that overexpression of Itk significantly enhanced the calcium ion response capability of CTLL-2 cells, activated the Calcineurin-NFAT signaling pathway, and ultimately drove IFN-γ secretion. This result aligns with the TCR signaling cascade model proposed by Vincent Guichard et al. which suggests that ITK promotes IP₃ generation by phosphorylating PLCγ1, thereby mediating calcium ion release^34–36. However, our study further revealed that ITK’s regulatory role extends beyond the initiation phase of calcium signaling; it can also enhance NFAT nuclear translocation by increasing Calcineurin activity, thus improving the function and proliferative capacity of CD8⁺ T cells and enhancing their cytotoxic activity against lymphoma cells (Fig. 7). This discovery supplements previous understanding of ITK’s function, unveiling its new role in the dynamic regulation of signal transduction.

Bioinformatics analysis indicated that low ITK expression is closely associated with poor prognosis and an immunosuppressive microenvironment in DLBCL patients. Specifically, samples with low ITK expression exhibited reduced CD8⁺ T cell infiltration and increased expression of immune checkpoint molecules such as PD-L1, suggesting that tumors may induce T cell exhaustion by inhibiting ITK to achieve immune evasion. This finding is consistent with Wang et al.'s research on T cell dysfunction in DLBCL^8,37,38. However, our study directly linked ITK deficiency with the upregulation of immune checkpoints, providing new insights for developing combination therapies targeting the TME.

Our co-culture experiments demonstrated that CTLL-2 cells overexpressing Itk significantly inhibited A20 cell proliferation and induced apoptosis, and when combined with rituximab, the IC₅₀ value was significantly reduced. This synergistic effect may stem from two mechanisms: on one hand, ITK activates and upregulates CD16 expression, promoting the synergistic killing of tumors by NK cells and T cells^39–41; on the other hand, cytokine-mediated immune activation, such as IFN-γ, enhances tumor antigen presentation by upregulating MHC-I expression, thereby amplifying the targeted effect of rituximab⁴². Based on this, the development of small-molecule ITK agonists or combination with immune checkpoint inhibitors (such as PD-1 blockers) may represent an effective strategy to overcome R-CHOP resistance.⁴³

Despite revealing the critical role of ITK, our study has certain limitations. In terms of models, the experiments primarily utilized the mouse T cell lines (CTLL-2) and lymphoma cell lines (A20). Given the distinct genetic and immune backgrounds of these two cell lines, incorporating additional in vitro models in future research would enhance the generalizability and reliability of the findings. To further improve clinical applicability, validation experiments should also be conducted using patient-derived primary T cells and PDX models, which would help address the differences in immune microenvironments between mouse models and human DLBCL. Furthermore, the current study employed an effector-to-target (E:T) ratio of 1:3. Although this ratio is representative to some extent, presenting experimental results across a range of E:T ratios in future studies would further strengthen the persuasiveness of the data and the reliability of the conclusions.

In terms of mechanistic depth, although we confirmed NFAT nuclear translocation, the regulatory role of ITK on other transcription factors (such as AP-1 or NF-κB) remains to be explored. Our study focused solely on NFAT nuclear translocation without delving into the interactions between ITK and other transcription factors or epigenetic regulatory factors (such as histone-modifying enzymes), which may influence the comprehensive remodeling of T cell function. In terms of clinical relevance, the bioinformatics analysis was based on public databases (such as GSE32018, GSE32918), lacking validation from independent clinical cohorts, particularly insufficient data on the direct correlation between ITK expression and response to rituximab treatment. The analysis of the immune microenvironment did not utilize single-cell technology to dissect the heterogeneity of cell subpopulations and dynamic temporal effects. Future research needs to further explore model expansion, mechanistic deepening, clinical translation, and strategy optimization to enhance our understanding.

Future research can proceed in the following directions: first, screening highly selective ITK agonists and evaluating their efficacy and safety in preclinical models; second, combining single-cell sequencing technology to dissect the heterogeneity of T cell subpopulations regulated by ITK; third, exploring the synergistic effects of ITK with other immune regulatory pathways, such as the Stimulator of interferon genes (STING) pathway or the T cell costimulatory receptor pathway (Tumor necrosis factor receptor superfamily member 4, OX40), to open new avenues for DLBCL treatment.

Conclusion

This study systematically elucidated the immune regulatory mechanism and clinical significance of ITK in DLBCL through bioinformatics analysis and functional experiments. ITK is underexpressed in DLBCL tissues, and its expression level is positively correlated with patients’ overall survival (OS) and negatively correlated with the infiltration degree of CD8⁺ T cells and the expression of immune checkpoint molecules such as PD-L1. In vitro experiments clarified the molecular mechanism by which ITK enhances the anti-DLBCL activity of CD8⁺ T cells through the TCR-Ca²⁺-Calcineurin-NFAT-IFN-γ signaling pathway and revealed its synergistic therapeutic potential with rituximab. These findings provide an important theoretical basis for developing combination therapies for DLBCL based on T cell function remodeling, holding significant translational medicine value.

Abbreviations

DLBCL

Diffuse large B-cell lymphoma

NHL

Non-Hodgkin lymphoma

TME

Tumor microenvironment

CAR-T

Chimeric antigen receptor T-cell immunotherapy

DEGs

Differentially expressed genes

GO

Gene ontology

KEGG

Kyoto encyclopedia of genes and genomes

CYT

Immune cytolytic activity

GzmA

Granzyme A

PRF

1Perforin 1

BP

Biological processes

CC

Cellular components

MF

Molecular functions

Tfh

Follicular helper T cells

Itk OE

Itk overexpression

Itk KD

Itk knockdown

TNF-α

Tumor necrosis factor-α

CD69

Cluster of differentiation 69

MHC

Major histocompatibility complex

LCK

Lymphocyte-specific protein tyrosine kinase

ITAMs

Immunoreceptor tyrosine-based activation motifs

GTP

Guanosine triphosphate

GEF

Guanine nucleotide exchange factor

GRB2

Growth factor receptor-bound protein 2

ADCC

Antibody-dependent cell-mediated cytotoxicity

STING

Stimulator of interferon genes

B-NHL

B-cell non-Hodgkin lymphoma

Author contributions

Huifang Liu: Data curation, Software, Writing—original draft. Mengyang Zhang: Data curation, Methodology. Xianguang Zhu: Methodology. Guanghui Chen: Writing—review & editing. All authors reviewed the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

Data is provided within the manuscript or supplementary information files.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-16112-3.