CD4⁺ T-cell reserve predicts bispecific antibody efficacy and can be enhanced by BTK inhibition in B-cell lymphoma

CD4⁺ T-cell reserve predicts bispecific antibody efficacy and can be enhanced by BTK inhibition in B-cell lymphoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article CD4⁺ T-cell reserve predicts bispecific antibody efficacy and can be enhanced by BTK inhibition in B-cell lymphoma Hui Yu, Jin Chai, Jiaowu Cao, Dingyao Hu, Zhao Jin, Yanxiang Li, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8819442/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Bispecific antibodies (BsAbs) targeting CD20×CD3 have achieved remarkable efficacy in relapsed or refractory (R/R) B-cell lymphoma, yet clinical responses remain heterogeneous and predictive biomarkers are lacking. In this study, we identified peripheral CD4⁺ T-cell abundance as a robust predictor of BsAb efficacy. Among 40 patients treated with CD20×CD3 BsAbs, higher baseline CD4⁺ T-cell counts correlated with superior response rates and prolonged progression-free survival, whereas CD8⁺ counts showed no association. Elevated CD4⁺ T-cell levels were linked to increased activation of effector CD8⁺CD28⁺ T cells and greater induction of IL-2 and IFN-γ following treatment, indicating that CD4⁺ help potentiates cytotoxic T-cell function. Mechanistic analyses revealed that Bruton’s tyrosine kinase (BTK) inhibitors expanded CD4⁺ T cells and reprogrammed T/NK compartments toward proliferative (MKI67⁺) and cytotoxic (GZMB⁺, IFNG⁺) phenotypes, with upregulation of T-cell activation and interferon signaling pathways. In clinical observation, sequential BTK inhibition before BsAb therapy was associated with durable remissions in most patients. Collectively, these findings establish CD4⁺ T-cell reserve as a key determinant of BsAb responsiveness and support BTK inhibition as a feasible immune-priming strategy to enhance BsAb efficacy in B-cell lymphoma. Biological sciences/Cancer Biological sciences/Immunology B-cell lymphoma bispecific antibody CD4⁺ T cell BTK inhibitor immune priming Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Relapsed or refractory (R/R) B-cell lymphoma remains a major therapeutic challenge, with limited curative options and poor long-term outcomes despite recent advances [ 1 , 2 ] . The introduction of CD20×CD3 bispecific antibodies (BsAbs), such as glofitamab and epcoritamab, has transformed the treatment landscape by redirecting endogenous T cells to lyse malignant B cells [ 3 – 5 ] . These agents have achieved meaningful responses in heavily pretreated patients; however, clinical experience consistently reveals substantial heterogeneity, with many patients exhibiting primary resistance or early relapse [ 6 – 9 ] . Understanding the immunologic determinants that underlie such variability is critical for optimizing BsAb-based therapy and patient selection. Previous attempts to identify predictive biomarkers of BsAb efficacy have largely relied on tumor characteristics or high-dimensional molecular profiling [ 10 , 11 ] . Clinical factors such as tumor burden, performance status, and prior therapy provide pragmatic but nonspecific indicators [ 9 – 12 ] . Molecular and immunomic signatures offer deeper biological insight but are constrained by technical complexity, high cost, and limited reproducibility across real-world settings [ 10 ] . Consequently, there remains a need for simple, reliable, and clinically applicable immune biomarkers. Peripheral blood T-cell subset profiling is already widely used in lymphoma management, yet its value in predicting BsAb response has not been systematically established [ 13 , 14 ] . Although exploratory studies have suggested that T-cell abundance or activation status may influence BsAb activity, most existing evidence has been indirect—focusing on treatment-induced immune activation or intratumoral T-cell composition rather than baseline peripheral immune competence [ 8 , 14 ] . In particular, the predictive relevance of baseline circulating CD4⁺ T-cell counts remains poorly defined, and their mechanistic contribution to BsAb-driven cytotoxicity has not been elucidated. Moreover, it is unknown whether pharmacologic modulation of T-cell fitness could further enhance BsAb efficacy in resistant disease. Addressing these limitations is essential to translate immune monitoring into actionable clinical tools. In our exploratory multivariate analysis of real-world data, we unexpectedly identified baseline peripheral CD4⁺ T-cell counts as a strong and independent predictor of response and progression-free survival in patients treated with CD20×CD3 BsAbs. This observation prompted us to investigate the biological basis underlying this association. Building on our previous work demonstrating that Bruton tyrosine kinase (BTK) inhibition modulates T-cell proliferation and activation, we hypothesized that BTK inhibitors could enhance BsAb efficacy by improving CD4⁺ T-cell fitness. Accordingly, the present study integrates clinical validation with functional and single-cell transcriptomic analyses to delineate the predictive and mechanistic roles of CD4⁺ T cells in BsAb therapy and to evaluate the potential of BTK inhibition as a combinatorial strategy. Together, these findings establish a feasible immune biomarker and provide a mechanistic rationale for enhancing BsAb responsiveness in relapsed or refractory B-cell lymphoma. Methods Patient cohort and study design This study retrospectively analyzed patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who received glofitamab therapy at Peking University Cancer Hospital between January 2021 and September 2025. Additional patients who received other CD20×CD3 bispecific antibodies (BsAbs) were included to validate immune correlates, resulting in a total of 40 patients with B-cell lymphoma. Clinical data were extracted from institutional databases, and response was assessed according to the Lugano 2014 criteria. All patients provided written informed consent, and the study was approved by the institutional review board in accordance with the Declaration of Helsinki. Treatment and response assessment Patients received glofitamab either as part of a registered clinical trial or in the post-approval real-world setting. In both contexts, obinutuzumab pre-treatment was administered according to the standard step-up protocol before the first glofitamab dose. For patients enrolled in clinical trials, treatment was continued until disease progression, unacceptable toxicity, or withdrawal of consent, in accordance with the trial protocol. In the real-world setting, treatment discontinuation occurred for a broader range of reasons, including disease progression, initiation of subsequent bridging or consolidative therapy (such as hematopoietic stem-cell transplantation or CAR-T therapy), or patient preference. For other bispecific antibodies included in the extended cohort, dosing schedules and discontinuation criteria followed the respective clinical trial protocols. Radiologic responses were assessed by PET-CT or contrast-enhanced CT every 2–3 cycles, and evaluated according to the Lugano 2014 criteria. Progression-free survival (PFS) was defined as the time from BsAb initiation to documented progression or death from any cause, and overall survival (OS) as the time from treatment initiation to death or last follow-up. Peripheral blood immune profiling Peripheral blood immune parameters, including lymphocyte subset counts and plasma cytokine levels, were retrospectively collected from routine clinical laboratory records. For patients who received bispecific antibody (BsAb) therapy, lymphocyte subset data (CD3⁺, CD4⁺, CD8⁺, CD19⁺, CD16⁺/CD56⁺ populations) were obtained from flow cytometric analyses performed as part of standard-of-care immune monitoring in the institutional clinical laboratory. Activated and inhibitory T-cell subsets were defined as CD8⁺CD28⁺ and CD8⁺CD28⁻, respectively. Absolute counts were calculated by the clinical laboratory from total lymphocyte counts and subset percentages. Plasma cytokine concentrations, including IL-2, IFN-γ, and IL-6, were measured by the hospital’s central laboratory using routine multiplex bead-based immunoassays as part of standard post-treatment safety and immune monitoring procedures. Both lymphocyte subset and cytokine data were extracted from medical records at two time points—prior to therapy initiation and after the first cycle of BsAb treatment. All data were reviewed and verified by two investigators for completeness and consistency. BTK inhibitor cohort and sample collection To evaluate the immunomodulatory effects of Bruton’s tyrosine kinase (BTK) inhibitors, we analyzed paired peripheral blood samples from 30 patients with B-cell non-Hodgkin lymphoma (B-NHL) treated with BTK inhibitors (ibrutinib, zanubrutinib, or orelabrutinib) for at least 4 weeks. Blood samples were obtained before treatment initiation and at follow-up visits. Absolute lymphocyte subsets were measured using the same flow cytometry protocol described above. Single-cell RNA sequencing and analysis Single-cell RNA sequencing (scRNA-seq) was performed on peripheral blood collected from three patients before and after BTK inhibitor therapy. Raw sequencing reads were processed with Cell Ranger (v7.0.1, 10x Genomics) and aligned to the GRCh38-2020-A human reference genome. Downstream analyses were performed in R (v4.3.1) using Seurat (v4.3.0). Cells with 200–5,000 detected genes and 0.2) or identified as doublets (DoubletFinder) were removed, yielding 34,525 high-quality cells. Data were normalized (“LogNormalize”), and the top 2,000 highly variable genes were selected. After scaling, PCA and Harmony integration were performed to reduce dimensionality and correct batch effects. The top 20 dimensions were used for clustering and UMAP visualization. Clusters were annotated into eight immune populations using canonical markers: CD8⁺ T (CD8A, GZMB), CD4⁺ T (CD4, IL7R), NK (NKG7, GNLY), B (MS4A1, CD79A), monocytes (CD14, FCGR3A), neutrophils (S100A8, FCGR3B), proliferating T (MKI67), and platelets (PPBP). Differentially expressed genes (DEGs) between pre- and post-treatment samples were identified with the Wilcoxon test (adjusted p 0.25), and Gene Ontology enrichment was analyzed using clusterProfiler. Preclinical experiments To investigate the mechanistic basis of BTK inhibition on T-cell activation, both in vitro and in vivo models were established under protocols approved by the Ethics Committee of Peking University Cancer Hospital & Institute. Peripheral blood samples from patients were collected in EDTA anticoagulant tubes (BD). Peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll density gradient centrifugation, and CD3⁺ T cells were purified using the RosetteSep™ Human T Cell Enrichment Cocktail (STEMCELL Technologies, Cat#15061). For proliferation assays, T cells were labeled with CellTrace™ CFSE (Thermo Fisher Scientific, Cat#C34554), cultured in IL-2 and ImmunoCult™ Human CD3/CD28 T Cell Activator (STEMCELL Technologies, Cat#10971), and treated with ibrutinib (0.1–1 μM) for 5 days. Flow cytometry was performed to assess CD4⁺ T-cell activation (CD69, CD25; BioLegend), and cytokine production of IL-2 (Proteintech, Cat#KE00017) and IFN-γ (Proteintech, Cat#KE00063) was quantified by ELISA after 72 hours. Six- to eight-week-old female BALB/c mice (Charles River, Beijing, China) were maintained under specific pathogen-free (SPF) conditions in compliance with institutional animal welfare guidelines. For xenograft establishment, A20 murine B-cell lymphoma cells (2 × 10⁶) were suspended in PBS mixed 1:1 with Matrigel (Corning, Cat#356237) and subcutaneously inoculated into the right flank. When tumors reached 100–150 mm³, mice were randomized to receive vehicle, ibrutinib (20 mg/kg, oral gavage), or zanubrutinib (5 mg/kg, oral gavage) daily. Tumor-infiltrating lymphocytes (TILs) were analyzed by flow cytometry using the following antibodies: anti-mouse CD3 (PerCP/Cy5.5, clone 17A2, BioLegend), CD4 (Alexa Fluor 700, clone GK1.5, BioLegend), CD8a (APC/Cy7, clone 53-6.7, BioLegend), and granzyme B (PE/Cy7, clone QA16A02, BioLegend). All animal experiments were approved by the Institutional Animal Care and Use Committee of Beijing Cancer Hospital. Statistical analysis Continuous variables were summarized as medians (ranges). Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test. PFS and other survival outcomes were estimated via the Kaplan–Meier method, with between-group comparisons using the log-rank test. Cox proportional hazards models identified independent PFS predictors; proportional hazards assumptions were verified via Schoenfeld residuals to ensure model robustness, with potential confounders included. Optimal cutoff for key predictive variable was determined by ROC curve analysis, using the Youden’s index to maximize diagnostic efficiency. All analyses were performed with GraphPad Prism (v10.0; GraphPad). Two-sided P < 0.05 was considered statistically significant; no multiple comparison adjustment was applied unless stated. Results Patient characteristics Between January 2021 and September 2025, a total of 18 patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who received glofitamab at Peking University Cancer Hospital were retrospectively analyzed. Twelve patients received glofitamab in the post-approval setting, and six were treated within a clinical trial prior to regulatory approval. Patient characteristics are summarized in Table 1. The median age at treatment was 65 years (range, 35–75), with a slight female predominance (55.6%). The cohort comprised 11 cases of DLBCL, not otherwise specified (NOS), 2 primary mediastinal large B-cell lymphoma (PMBL), and 5 transformed large B-cell lymphoma. Most patients presented with advanced-stage disease (Ann Arbor stage III or IV, 94.4%) and had refractory disease (primary refractory in 16.7% and refractory to last therapy in 33.3%). Patients were heavily pretreated, with a median of 3 prior lines of therapy (range, 2–7). A total of 72.2% of patients had received at least three prior therapies, and 10 (55.6%) had undergone prior chimeric antigen receptor (CAR) T-cell therapy, of whom 50% was refractory. The median number of glofitamab cycles administered was 7 (range, 2–12). At the data cutoff date (September 10, 2025), three patients remained on active glofitamab treatment . Clinical response and survival outcomes Among the 18 patients, one was not evaluable for response. Of the 17 evaluable patients, the best overall response rate (ORR) was 76.4%, comprising 10 complete response (CR, 58.8%) and 3 partial response (PR, 17.6%) (Figure 1 A and 1B). At a median follow-up of 9.6 months, 7 patients (41.2%) remained in CR at data cutoff. Disease progression occurred in 7 patients, 5 of whom died. The median progression-free survival (mPFS) and the median overall survival (mOS) was not reached (Figure 1 C and 1D). These findings confirmed that real-world outcomes with glofitamab were consistent with clinical trial data, even among heavily pretreated and transformed histologies. Baseline immune profiles and their prognostic significance Given that bispecific antibody (BsAb) efficacy relies on T-cell engagement, we next explored whether baseline lymphocyte subsets could predict clinical outcomes. Univariable Cox regression identified the absolute CD4⁺ T-cell count—but not CD8⁺ count—as significantly associated with longer PFS (HR 0.994; P = 0.046) (Table S1). Each 1-cell/μL increase in CD4⁺ count reduced progression risk by 0.6%. To further validate this observation, we expanded the study cohort by including 22 additional patients with B-cell lymphoma who received other CD20/CD3 bispecific antibodies at our center, resulting in a total of 40 patients (Table S2). In Cox proportional hazards models, the CD3+, CD4+and CD8+ absolute count was analyzed as a continuous variable per 100-cell/μL increase to facilitate clinical interpretation. In the pooled analysis, higher baseline CD4⁺ counts remained an independent predictor of prolonged PFS (HR 0.602; 95% CI 0.444–0.815) (Table 2). Additionally, baseline %CD19 + B cells was also significantly correlated with PFS (HR 0.883; 95% CI 0.785–0.994). Receiver operating characteristic (ROC) curve analysis determined the optimal cutoff for %CD19+ as 0.1. Survival analysis demonstrated that patients with %CD19+ ≥ 0.1 had significantly longer PFS (p < 0.001, Figure S1). Together, these findings suggested that peripheral immune composition—particularly CD4⁺ T-cell abundance—reflects the host’s capacity to respond to BsAb therapy. Association between CD4⁺ T-cell levels and clinical response To further delineate the predictive role of CD4⁺ T cells, we compared baseline CD4⁺ T cell counts between responders and non-responders. Patients achieving CR/PR exhibited significantly higher CD4⁺ T cell counts than those with SD/PD (Figure 2A). ROC analysis identified 470 cells/μL as the optimal cutoff (AUC = 0.798, 95% CI 0.631–0.965, P = 0.003) (Figure 2B). Kaplan–Meier analysis confirmed that patients with CD4⁺ T cells ≥ 470/μL had significantly prolonged PFS compared to those below this threshold (P < 0.01) (Figure 2C). These results established baseline CD4⁺T cell count as a robust, clinically measurable predictor of BsAb efficacy. CD4⁺ T cells support effector activation and cytokine response during BsAb therapy We next investigated the immunologic basis linking baseline CD4⁺ T cells to BsAb efficacy. Among 33 patients (from 40 patients with BsAb treatment) with paired lymphocyte‐subset and cytokine data, the pre-treatment absolute CD4⁺ T-cell count showed a significant positive correlation with the post-treatment absolute CD8⁺ T-cell count (Figure 3A), and with the proportion of activated CD8⁺ CD28⁺ T cells (Figure 3B), while inversely correlating with inhibitory CD8⁺ CD28⁻ T cells (Figure 3C). Furthermore, higher baseline CD4⁺ counts were positively correlated with key effector cytokines, including IL-2 (Figure 3D) and IFN-γ (Figure 3E) after one BsAb cycle, but not with IL-6 (Figure 3F). These findings indicate that patients with higher pre-treatment CD4⁺ T-cell reserves exhibit stronger T-cell activation and cytokine production upon BsAb stimulation, supporting a mechanistic role for CD4⁺ help in shaping effector cytotoxic responses. BTK inhibitors expand peripheral CD4⁺ T cells and reprogram cytotoxic and proliferative programs. Because higher CD4⁺ T-cell levels predicted improved BsAb outcomes, we sought to identify clinically actionable approaches to enhance this immune compartment. Our preclinical studies demonstrated that Bruton’s tyrosine kinase (BTK) inhibitors promote CD4⁺ T-cell proliferation (Figure 4A), activation (Figure 4B) and cytotoxic cytokine release (Figure 4C&D) in a dose-dependent manner, and increase the intratumoral infiltration of cytotoxic T cells in lymphoma mouse models (Figure 4E&F), suggesting that BTK blockade may improve the functional readiness of the T-cell compartment. Therefore, we explored whether BTK inhibitors could restore or augment CD4⁺ T-cell immunity in patients. We thus collected 30 B-NHL patients treated with BTK inhibitors (Detailed information shown in Table S3), and it was observed that BTK inhibition significantly increased absolute CD4⁺ T-cell counts in peripheral blood (Figure 4G). To delineate functional consequences, we performed single-cell RNA sequencing on three patient samples collected before and after BTK-inhibitor therapy (Figure 4H-J). Uniform Manifold Approximation and Projection (UMAP) revealed expansion of proliferating T (MKI67⁺) and cytotoxic T/NK clusters (GZMB⁺, IFNG⁺) after treatment (Figure S2A-C). Differential expression and GO enrichment analyses demonstrated upregulation of pathways involved in T-cell activation, leukocyte-mediated cytotoxicity, and interferon signaling (Figure 4K). Building on these mechanistic findings, we next examined whether BTK inhibition–induced immune remodeling could translate into clinical benefit when used sequentially with BsAb therapy. In real-world observations, three patients with relapsed/refractory lymphoma received BTK inhibitor therapy as immune priming before the initiation of glofitamab (Figure 4L). Two patients have maintained durable complete remissions to date, whereas the third achieved a complete response but relapsed 8.4 months later. Notably, this relapsed patient had both the lowest baseline and on-treatment CD4⁺ T-cell counts (from 282 to 410 cells/μL) (Figure 4M) among the three and discontinued BTK inhibition approximately one month after initiation. Although limited by the small sample size, these observations are consistent with our proposed model that the magnitude of CD4⁺ T-cell augmentation may correlate with the depth and durability of BsAb response. Collectively, these data support a clinically actionable paradigm in which BTK inhibition functions as an immune-priming strategy that enhances CD4⁺ T-cell–dependent antitumor immunity and improves responsiveness to subsequent bispecific antibody therapy. Discussion In this study, we identified peripheral CD4⁺ T-cell abundance as a robust and clinically measurable biomarker predicting outcomes of CD20×CD3 bispecific antibody (BsAb) therapy in patients with relapsed or refractory B-cell lymphoma. Higher baseline CD4⁺ T-cell counts were associated with improved response rates and prolonged progression-free survival. Mechanistically, patients with higher pre-treatment CD4⁺ T-cell levels exhibited stronger activation of effector CD8⁺ T cells and greater induction of IL-2 and IFN-γ following BsAb exposure, suggesting that CD4⁺ T-cell help is critical for optimal T-cell engagement and durable antitumor immunity. Furthermore, we demonstrated that BTK inhibition can expand CD4⁺ T cells and enhance cytotoxic and proliferative T/NK-cell programs, providing a potential immune-priming strategy to improve BsAb efficacy. Although BsAbs directly link CD3⁺ T cells to malignant B cells, their therapeutic activity depends on the pre-existing immune landscape [ 3 ] . Our results indicate that CD4⁺ T-cell reserve represents a key determinant of the magnitude of BsAb-induced cytotoxic response. This finding is biologically plausible: CD4⁺ T cells are essential for licensing antigen-presenting cells [ 15 , 16 ] , producing cytokines such as IL-2, and sustaining CD8⁺ T-cell proliferation and memory formation [ 17 , 18 ] . Prior studies in CAR-T therapy and checkpoint blockade have also shown that impaired CD4⁺ T-cell recovery correlates with poor expansion and inferior outcomes [ 19 – 22 ] . Our data extend this paradigm to BsAb therapy, emphasizing that not all T cells are functionally equivalent—patients with adequate CD4⁺ “helper” capacity are better equipped to mount a coordinated and durable antitumor response. Our observation that baseline CD4⁺ T-cell reserve strongly correlated with bispecific antibody efficacy prompted us to explore potential pharmacologic strategies capable of restoring or augmenting T-cell fitness. Among these, BTK inhibition emerged as a promising approach. Beyond its canonical role in B-cell receptor signaling blockade, BTK inhibition has been increasingly recognized to exert broad immunomodulatory effects on the tumor microenvironment. Consistent with prior mechanistic and translational reports—showing that BTK inhibitors enhance T-cell activation and proliferation [ 23 , 24 ] , suppress myeloid-derived immunosuppressive pathways [ 25 ] [ 13 ] , and induce interferon-related gene programs [ 26 ] [ 27 ] —we found that BTK inhibition expanded CD4⁺ T-cell populations, upregulated cytotoxic and proliferative signatures in T/NK cells, and activated interferon signaling, as confirmed by single-cell RNA sequencing of patient samples. Collectively, these findings suggest that BTK inhibition can “recondition” the immune landscape, generating a pool of functionally competent effector T cells that are more effectively engaged by bispecific antibodies, thereby providing a mechanistic basis for the observed synergistic activity. The observation that sequential BTK inhibition followed by glofitamab yielded durable remissions in two of three patients, with the sole relapse occurring in the case with minimal CD4⁺ expansion, provides preliminary clinical support for this hypothesis. Although anecdotal, these results highlight a rational combinatorial strategy: short-term BTK inhibitor exposure to prime CD4⁺ and effector T cells before BsAb initiation. Such an approach may be particularly valuable for heavily pretreated or immunologically exhausted patients, where the T-cell compartment is quantitatively and functionally compromised. Prospective clinical trials evaluating optimal sequencing, dosing, and timing of BTK inhibitor “immune priming” are warranted. Biomarker-guided selection based on baseline CD4⁺ counts could further refine patient stratification and personalize BsAb therapy. This study has several limitations. The cohort size was modest and derived from a single center, which may limit generalizability. The correlative analyses, although supported by mechanistic data, remain exploratory and require validation in larger, prospective cohorts. Single-cell profiling was performed on a limited number of patients, and the direct quantification of CD4⁺ subsets by scRNA-seq is constrained by sampling and platform biases. Finally, while the sequential BTK inhibitor–BsAb cases are hypothesis-generating, causality cannot be established without controlled clinical testing. Conclusions In summary, our findings define CD4⁺ T-cell reserve as a central determinant of BsAb responsiveness and propose BTK inhibition as a feasible immune-priming strategy to enhance T-cell readiness. This integrative clinical and translational evidence underscores the importance of host immune fitness in shaping BsAb efficacy and provides a mechanistic framework for rational combination and sequencing strategies aimed at improving outcomes in patients with relapsed or refractory B-cell lymphoma. Declarations Disclosures No conflicts of interests to disclosure. Contributions H.Y., Y.S., and J.Z. designed this study, H.Y., and J.C. wrote the manuscript. H.Y., J.C., D.H., performed in vitro and in vivo experiments. J.C., J.L., Y.W., J.C., and W.Z. provided human B cell lymphoma samples. Y.H., J.C., Z.J., and Y.L. analysed scRNA sequencing data. All authors read the final manuscript and approved the final submitted version. Data-sharing statement Any data in this study are available from the corresponding author on reasonable request. Ethics Approval All procedures were approved by the Institutional Review Board and Ethical Committee of the Peking University Cancer Hospital and Institute (Approval No. 2023KT87). Funding Statement This research was supported by the National Nature Science Foundation of China (No. 82300214, 82270195) and Science Foundation of Peking University Cancer Hospital (No. PY202312). Author Contribution H.Y., Y.S., and J.Z. designed this study, H.Y., and J.C. wrote the manuscript. H.Y., J.C., D.H., performed in vitro and in vivo experiments. J.C., J.L., Y.W., J.C., and W.Z. provided human B cell lymphoma samples. Y.H., J.C., Z.J., and Y.L. analysed scRNA sequencing data. All authors read the final manuscript and approved the final submitted version. Data Availability Any data in this study are available from the corresponding author on reasonable request. References CRUMP M, NEELAPU S S, FAROOQ U, et al. 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Ibrutinib is an irreversible molecular inhibitor of ITK driving a Th1-selective pressure in T lymphocytes [J]. Blood, 2013, 122(15): 2539-49. LONG M, BECKWITH K, DO P, et al. Ibrutinib treatment improves T cell number and function in CLL patients [J]. The Journal of clinical investigation, 2017, 127(8): 3052-64. CORZO C A, VARFOLOMEEV E, SETIADI A F, et al. The kinase IRAK4 promotes endosomal TLR and immune complex signaling in B cells and plasmacytoid dendritic cells [J]. Sci Signal, 2020, 13(634). YU H, MI L, ZHANG W, et al. Ibrutinib combined with low-dose histone deacetylases inhibitor chidamide synergistically enhances the anti-tumor effect in B-cell lymphoma [J]. Hematol Oncol, 2022, 40(5): 894-905. PAPAZOGLOU D, WANG X V, SHANAFELT T D, et al. Ibrutinib-based therapy reinvigorates CD8+ T cells compared to chemoimmunotherapy: immune monitoring from the E1912 trial [J]. Blood, 2024, 143(1): 57-63. Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files TableS1.pdf TableS2.pdf TableS3.pdf Table1.pdf Table2.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8819442","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":598875936,"identity":"67fd5e42-5f47-4fcc-a37d-c687637eac0b","order_by":0,"name":"Hui Yu","email":"","orcid":"","institution":"Peking University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Yu","suffix":""},{"id":598875937,"identity":"f511504e-7c50-4d85-a5e4-494565617514","order_by":1,"name":"Jin Chai","email":"","orcid":"","institution":"Peking University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Chai","suffix":""},{"id":598875938,"identity":"49dcbd73-1353-4a2f-99d4-a4c4dcd29a68","order_by":2,"name":"Jiaowu Cao","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Jiaowu","middleName":"","lastName":"Cao","suffix":""},{"id":598875939,"identity":"0d6dee00-3670-44ca-a516-61b2733a129e","order_by":3,"name":"Dingyao Hu","email":"","orcid":"","institution":"Beijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dingyao","middleName":"","lastName":"Hu","suffix":""},{"id":598875940,"identity":"952a2462-03b9-49a0-a155-707d0a42c66d","order_by":4,"name":"Zhao Jin","email":"","orcid":"","institution":"Peking University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Jin","suffix":""},{"id":598875941,"identity":"9c6f246e-0a2e-42ac-b12e-f528e355ebb4","order_by":5,"name":"Yanxiang Li","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Yanxiang","middleName":"","lastName":"Li","suffix":""},{"id":598875942,"identity":"1cc0cecb-6471-4a10-9355-c74787577188","order_by":6,"name":"Jie Lv","email":"","orcid":"","institution":"Peking University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Lv","suffix":""},{"id":598875943,"identity":"e6738207-6b9f-448f-a33b-7047b8ff424c","order_by":7,"name":"Jie Chen","email":"","orcid":"","institution":"Peking University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Chen","suffix":""},{"id":598875944,"identity":"70089ba8-2622-43d0-af59-9a85ded68581","order_by":8,"name":"Yue Wang","email":"","orcid":"","institution":"Peking University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Wang","suffix":""},{"id":598875945,"identity":"0cf42c19-a446-4d85-b3b6-f8723cb0f184","order_by":9,"name":"Wenhui Zhang","email":"","orcid":"","institution":"Peking University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenhui","middleName":"","lastName":"Zhang","suffix":""},{"id":598875946,"identity":"a53eeee1-b6f8-4b32-b6f7-507ec3954ec3","order_by":10,"name":"Jun Zhu","email":"","orcid":"","institution":"Peking University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zhu","suffix":""},{"id":598875947,"identity":"819fb317-cdab-47cb-8e14-298995e936f0","order_by":11,"name":"Yuqin Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBADHgb25oMPPhjY2JGghedYsuGMgrRkEuyR8DGT5vlwiLGBkEL59t7Dr3nbrGXMJRgMpG0MDjAzsB8+ugGfFoMz59KsedvSeSxnNyQY5xjc4WPgSUu7gVeLRI6ZMW/bYR6DOwcOJOcYPGNmkOAxw6tFfgZMy43EhsMWBocZGwhpYbiRY/wYoiWZsZmBGC0GZ86YMc45l85jcOYYM2OPQVoyGyG/yLf3GH94U2Ztb3C8//uPH39s7PjZDx/D7zAGBjYpHgZmJC4B5SDA/PEHspZRMApGwSgYBegAAOBGSuQr+7r7AAAAAElFTkSuQmCC","orcid":"","institution":"Peking University Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yuqin","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2026-02-08 06:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8819442/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8819442/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103839344,"identity":"6768bd8e-33b9-41ab-b01e-9c12dbdc23ad","added_by":"auto","created_at":"2026-03-03 14:31:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79263,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical responses and survival outcomes in patients treated with glofitmab.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Swimmer plot showing showing the clinical response trajectory of each patient during glofitamab therapy. Changes in disease status (CR, PR, PD) are indicated over the treatment course.\u003c/p\u003e\n\u003cp\u003e(B) Best overall response, including complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD).\u003c/p\u003e\n\u003cp\u003e(C\u0026amp;D) Kaplan–Meier curve of progression-free survival (PFS) (C) and overall survival (OS) (D) for patients post glofitmab treatment.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8819442/v1/9416ed613b3b8d7a217ab58a.jpg"},{"id":104400872,"identity":"553a27b6-02a4-4473-b9cc-9583c58cd973","added_by":"auto","created_at":"2026-03-11 12:11:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":170285,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBaseline CD4⁺ T-cell counts predict response and survival in patients treated with CD20×CD3 bispecific antibodies.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Comparison of baseline absolute CD4⁺ T-cell counts between responders (CR/PR) and non-responders (SD/PD) treated with CD20×CD3 bispecific antibodies.\u003c/p\u003e\n\u003cp\u003e(B) Receiver operating characteristic (ROC) curve identifying 470 cells/μL as the optimal cutoff for predicting response (AUC=0.798, P=0.003).\u003c/p\u003e\n\u003cp\u003e(C) Kaplan–Meier analysis of PFS stratified by baseline CD4⁺ T-cell counts (≥470 vs. \u0026lt;470 cells/μL).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8819442/v1/efe229d18433d44b074eaf53.jpg"},{"id":103839348,"identity":"a47de683-90e9-4ca1-bdf9-908aee5b24f6","added_by":"auto","created_at":"2026-03-03 14:31:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":299437,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between baseline CD4⁺ T-cell levels and effector T-cell activation during bispecific antibody therapy.\u003c/p\u003e\n\u003cp\u003e(A–C) Correlations between baseline CD4⁺ T-cell counts and (A) post-treatment absolute CD8⁺ T-cell counts, (B) proportions of activated CD8⁺CD28⁺ T cells, and (C) inhibitory CD8⁺CD28⁻ T cells.\u003c/p\u003e\n\u003cp\u003e(D–F) Correlations between baseline CD4⁺ T-cell counts and plasma cytokines after one treatment cycle: (D) IL-2, (E) IFN-γ, and (F) IL-6.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8819442/v1/a51995dc80921a924233a181.jpg"},{"id":103839351,"identity":"fce8927d-d9aa-4822-9c0b-62e98a978d21","added_by":"auto","created_at":"2026-03-03 14:31:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":514445,"visible":true,"origin":"","legend":"\u003cp\u003eBTK inhibition promotes CD4⁺ T-cell expansion and reshapes immune programs in vitro, in vivo, and at the single-cell level.\u003c/p\u003e\n\u003cp\u003e(A–D) In vitro assays showing BTK inhibitor–induced CD4⁺ T-cell proliferation, activation, and cytokine production (IL-2, IFN-γ).\u003c/p\u003e\n\u003cp\u003e(E–F) Flow cytometry of A20 mouse models showing increased tumor-infiltrating CD4⁺ T cells and granzyme B⁺ CD8⁺ T cells after BTK inhibition.\u003c/p\u003e\n\u003cp\u003e(G) Paired absolute CD4⁺ T-cell counts in 30 patients before and after BTK inhibitor therapy.\u003c/p\u003e\n\u003cp\u003e(H–J) Single-cell RNA-seq profiling of peripheral blood immune cells before and after BTK inhibition.\u003c/p\u003e\n\u003cp\u003e(H) UMAP projection of all cells annotated into major immune populations based on canonical markers.\u003c/p\u003e\n\u003cp\u003e(I) Feature plots showing gene-expression patterns of representative markers across cell populations.\u003c/p\u003e\n\u003cp\u003e(J) Heatmap of cluster-defining genes used for annotation of immune subsets.\u003c/p\u003e\n\u003cp\u003e(K) Differentially expressed genes and enriched pathways highlighting T-cell activation, cytotoxicity, and interferon signaling post BTK inhibitor treatment.\u003c/p\u003e\n\u003cp\u003e(L–M) Clinical cases receiving sequential BTK inhibitor followed by glofitamab therapy: two patients achieved durable CR, whereas the patient with minimal CD4⁺ expansion relapsed.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8819442/v1/be16ef468fac59288161478a.jpg"},{"id":107705217,"identity":"6b81f566-f098-4f60-82cb-ffddc98750ee","added_by":"auto","created_at":"2026-04-24 09:09:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1277910,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8819442/v1/93958fc2-165a-4395-b0fd-72bbf103ae61.pdf"},{"id":103839346,"identity":"304c93aa-672b-4eb4-875a-3739d4b347e1","added_by":"auto","created_at":"2026-03-03 14:31:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":131768,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8819442/v1/b0e6817841a1a44e334e6ae4.pdf"},{"id":103839347,"identity":"20273afd-c873-469b-a830-2ceeb44e094c","added_by":"auto","created_at":"2026-03-03 14:31:47","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":111970,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8819442/v1/b127797c0d608cbb8edc1947.pdf"},{"id":103839349,"identity":"6d6c425a-defa-47dd-b1b7-a61b8be6cc64","added_by":"auto","created_at":"2026-03-03 14:31:47","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":116993,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8819442/v1/1288e23ae99dd667f4e43813.pdf"},{"id":103839352,"identity":"67710f9e-aa80-4366-867d-aebf15b0de4f","added_by":"auto","created_at":"2026-03-03 14:31:48","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":110390,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8819442/v1/6dab9cf95c4aa242bb04f721.pdf"},{"id":103839350,"identity":"7c5756a8-7407-473c-bf60-c4ea493782cc","added_by":"auto","created_at":"2026-03-03 14:31:48","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":147637,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8819442/v1/7ababc77f14fb17efbc5d416.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CD4⁺ T-cell reserve predicts bispecific antibody efficacy and can be enhanced by BTK inhibition in B-cell lymphoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRelapsed or refractory (R/R) B-cell lymphoma remains a major therapeutic challenge, with limited curative options and poor long-term outcomes despite recent advances\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The introduction of CD20\u0026times;CD3 bispecific antibodies (BsAbs), such as glofitamab and epcoritamab, has transformed the treatment landscape by redirecting endogenous T cells to lyse malignant B cells\u003csup\u003e[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. These agents have achieved meaningful responses in heavily pretreated patients; however, clinical experience consistently reveals substantial heterogeneity, with many patients exhibiting primary resistance or early relapse\u003csup\u003e[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Understanding the immunologic determinants that underlie such variability is critical for optimizing BsAb-based therapy and patient selection.\u003c/p\u003e \u003cp\u003ePrevious attempts to identify predictive biomarkers of BsAb efficacy have largely relied on tumor characteristics or high-dimensional molecular profiling\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Clinical factors such as tumor burden, performance status, and prior therapy provide pragmatic but nonspecific indicators\u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Molecular and immunomic signatures offer deeper biological insight but are constrained by technical complexity, high cost, and limited reproducibility across real-world settings\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Consequently, there remains a need for simple, reliable, and clinically applicable immune biomarkers. Peripheral blood T-cell subset profiling is already widely used in lymphoma management, yet its value in predicting BsAb response has not been systematically established\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough exploratory studies have suggested that T-cell abundance or activation status may influence BsAb activity, most existing evidence has been indirect\u0026mdash;focusing on treatment-induced immune activation or intratumoral T-cell composition rather than baseline peripheral immune competence\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. In particular, the predictive relevance of baseline circulating CD4⁺ T-cell counts remains poorly defined, and their mechanistic contribution to BsAb-driven cytotoxicity has not been elucidated. Moreover, it is unknown whether pharmacologic modulation of T-cell fitness could further enhance BsAb efficacy in resistant disease. Addressing these limitations is essential to translate immune monitoring into actionable clinical tools.\u003c/p\u003e \u003cp\u003eIn our exploratory multivariate analysis of real-world data, we unexpectedly identified baseline peripheral CD4⁺ T-cell counts as a strong and independent predictor of response and progression-free survival in patients treated with CD20\u0026times;CD3 BsAbs. This observation prompted us to investigate the biological basis underlying this association. Building on our previous work demonstrating that Bruton tyrosine kinase (BTK) inhibition modulates T-cell proliferation and activation, we hypothesized that BTK inhibitors could enhance BsAb efficacy by improving CD4⁺ T-cell fitness. Accordingly, the present study integrates clinical validation with functional and single-cell transcriptomic analyses to delineate the predictive and mechanistic roles of CD4⁺ T cells in BsAb therapy and to evaluate the potential of BTK inhibition as a combinatorial strategy. Together, these findings establish a feasible immune biomarker and provide a mechanistic rationale for enhancing BsAb responsiveness in relapsed or refractory B-cell lymphoma.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePatient cohort and study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study retrospectively analyzed patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who received glofitamab therapy at Peking University Cancer Hospital between January 2021 and September 2025. Additional patients who received other CD20\u0026times;CD3 bispecific antibodies (BsAbs) were included to validate immune correlates, resulting in a total of 40 patients with B-cell lymphoma. Clinical data were extracted from institutional databases, and response was assessed according to the Lugano 2014 criteria. All patients provided written informed consent, and the study was approved by the institutional review board in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment and response assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients received glofitamab either as part of a registered clinical trial or in the post-approval real-world setting. In both contexts, obinutuzumab pre-treatment was administered according to the standard step-up protocol before the first glofitamab dose. For patients enrolled in clinical trials, treatment was continued until disease progression, unacceptable toxicity, or withdrawal of consent, in accordance with the trial protocol. In the real-world setting, treatment discontinuation occurred for a broader range of reasons, including disease progression, initiation of subsequent bridging or consolidative therapy (such as hematopoietic stem-cell transplantation or CAR-T therapy), or patient preference. For other bispecific antibodies included in the extended cohort, dosing schedules and discontinuation criteria followed the respective clinical trial protocols. Radiologic responses were assessed by PET-CT or contrast-enhanced CT every 2\u0026ndash;3 cycles, and evaluated according to the Lugano 2014 criteria. Progression-free survival (PFS) was defined as the time from BsAb initiation to documented progression or death from any cause, and overall survival (OS) as the time from treatment initiation to death or last follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePeripheral blood immune profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeripheral blood immune parameters, including lymphocyte subset counts and plasma cytokine levels, were retrospectively collected from routine clinical laboratory records. For patients who received bispecific antibody (BsAb) therapy, lymphocyte subset data (CD3⁺, CD4⁺, CD8⁺, CD19⁺, CD16⁺/CD56⁺ populations) were obtained from flow cytometric analyses performed as part of standard-of-care immune monitoring in the institutional clinical laboratory. Activated and inhibitory T-cell subsets were defined as CD8⁺CD28⁺ and CD8⁺CD28⁻, respectively. Absolute counts were calculated by the clinical laboratory from total lymphocyte counts and subset percentages.\u003c/p\u003e\n\u003cp\u003ePlasma cytokine concentrations, including IL-2, IFN-\u0026gamma;, and IL-6, were measured by the hospital\u0026rsquo;s central laboratory using routine multiplex bead-based immunoassays as part of standard post-treatment safety and immune monitoring procedures. Both lymphocyte subset and cytokine data were extracted from medical records at two time points\u0026mdash;prior to therapy initiation and after the first cycle of BsAb treatment. All data were reviewed and verified by two investigators for completeness and consistency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBTK inhibitor cohort and sample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the immunomodulatory effects of Bruton\u0026rsquo;s tyrosine kinase (BTK) inhibitors, we analyzed paired peripheral blood samples from 30 patients with B-cell non-Hodgkin lymphoma (B-NHL) treated with BTK inhibitors (ibrutinib, zanubrutinib, or orelabrutinib) for at least 4 weeks. Blood samples were obtained before treatment initiation and at follow-up visits. Absolute lymphocyte subsets were measured using the same flow cytometry protocol described above.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell RNA sequencing and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) was performed on peripheral blood collected from three patients before and after BTK inhibitor therapy. Raw sequencing reads were processed with Cell Ranger (v7.0.1, 10x Genomics) and aligned to the GRCh38-2020-A human reference genome. Downstream analyses were performed in R (v4.3.1) using Seurat (v4.3.0). Cells with 200\u0026ndash;5,000 detected genes and \u0026lt;20% mitochondrial transcripts were retained, while those with high ambient RNA contamination (decontX score \u0026gt;0.2) or identified as doublets (DoubletFinder) were removed, yielding 34,525 high-quality cells. Data were normalized (\u0026ldquo;LogNormalize\u0026rdquo;), and the top 2,000 highly variable genes were selected. After scaling, PCA and Harmony integration were performed to reduce dimensionality and correct batch effects. The top 20 dimensions were used for clustering and UMAP visualization. Clusters were annotated into eight immune populations using canonical markers: CD8⁺ T (CD8A, GZMB), CD4⁺ T (CD4, IL7R), NK (NKG7, GNLY), B (MS4A1, CD79A), monocytes (CD14, FCGR3A), neutrophils (S100A8, FCGR3B), proliferating T (MKI67), and platelets (PPBP). Differentially expressed genes (DEGs) between pre- and post-treatment samples were identified with the Wilcoxon test (adjusted p \u0026lt; 0.05, |log₂FC| \u0026gt; 0.25), and Gene Ontology enrichment was analyzed using clusterProfiler.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreclinical experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the mechanistic basis of BTK inhibition on T-cell activation, both in vitro and in vivo models were established under protocols approved by the Ethics Committee of Peking University Cancer Hospital \u0026amp; Institute.\u003c/p\u003e\n\u003cp\u003ePeripheral blood samples from patients were collected in EDTA anticoagulant tubes (BD). Peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll density gradient centrifugation, and CD3⁺ T cells were purified using the RosetteSep\u0026trade; Human T Cell Enrichment Cocktail (STEMCELL Technologies, Cat#15061). For proliferation assays, T cells were labeled with CellTrace\u0026trade; CFSE (Thermo Fisher Scientific, Cat#C34554), cultured in IL-2 and ImmunoCult\u0026trade; Human CD3/CD28 T Cell Activator (STEMCELL Technologies, Cat#10971), and treated with ibrutinib (0.1\u0026ndash;1 \u0026mu;M) for 5 days. Flow cytometry was performed to assess CD4⁺ T-cell activation (CD69, CD25; BioLegend), and cytokine production of IL-2 (Proteintech, Cat#KE00017) and IFN-\u0026gamma; (Proteintech, Cat#KE00063) was quantified by ELISA after 72 hours.\u003c/p\u003e\n\u003cp\u003eSix- to eight-week-old female BALB/c mice (Charles River, Beijing, China) were maintained under specific pathogen-free (SPF) conditions in compliance with institutional animal welfare guidelines. For xenograft establishment, A20 murine B-cell lymphoma cells (2 \u0026times; 10⁶) were suspended in PBS mixed 1:1 with Matrigel (Corning, Cat#356237) and subcutaneously inoculated into the right flank. When tumors reached 100\u0026ndash;150 mm\u0026sup3;, mice were randomized to receive vehicle, ibrutinib (20 mg/kg, oral gavage), or zanubrutinib (5 mg/kg, oral gavage) daily.\u003c/p\u003e\n\u003cp\u003eTumor-infiltrating lymphocytes (TILs) were analyzed by flow cytometry using the following antibodies: anti-mouse CD3 (PerCP/Cy5.5, clone 17A2, BioLegend), CD4 (Alexa Fluor 700, clone GK1.5, BioLegend), CD8a (APC/Cy7, clone 53-6.7, BioLegend), and granzyme B (PE/Cy7, clone QA16A02, BioLegend). All animal experiments were approved by the Institutional Animal Care and Use Committee of Beijing Cancer Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables were summarized as medians (ranges). Categorical variables were compared using Pearson\u0026rsquo;s chi-square test or Fisher\u0026rsquo;s exact test. PFS and other survival outcomes were estimated via the Kaplan\u0026ndash;Meier method, with between-group comparisons using the log-rank test. Cox proportional hazards models identified independent PFS predictors; proportional hazards assumptions were verified via Schoenfeld residuals to ensure model robustness, with potential confounders included. Optimal cutoff for key predictive variable was determined by ROC curve analysis, using the Youden\u0026rsquo;s index to maximize diagnostic efficiency. All analyses were performed with GraphPad Prism (v10.0; GraphPad). Two-sided P \u0026lt; 0.05 was considered statistically significant; no multiple comparison adjustment was applied unless stated.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween January 2021 and September 2025, a total of 18 patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who received glofitamab at Peking University Cancer Hospital were\u0026nbsp;retrospectively analyzed. Twelve patients received glofitamab in the post-approval setting, and six were treated within a clinical trial prior to regulatory approval. Patient characteristics are summarized in Table 1. The median age at treatment was 65 years (range, 35\u0026ndash;75), with a slight female predominance (55.6%). The cohort comprised 11 cases of DLBCL, not otherwise specified (NOS), 2 primary mediastinal large B-cell lymphoma (PMBL), and 5 transformed large B-cell lymphoma. Most patients presented with advanced-stage disease (Ann Arbor stage III or IV, 94.4%) and had refractory disease (primary refractory in 16.7% and refractory to last therapy in 33.3%). Patients were heavily pretreated, with a median of 3 prior lines of therapy (range, 2\u0026ndash;7). A total of 72.2% of patients had received at least three prior therapies, and 10 (55.6%) had undergone prior chimeric antigen receptor (CAR) T-cell therapy, of whom 50% was refractory. The median number of glofitamab cycles administered was 7 (range, 2\u0026ndash;12). At the data cutoff date (September 10, 2025), three patients remained on active glofitamab treatment\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical response and survival outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 18 patients, one was not evaluable for response. Of the 17 evaluable patients, the best overall response rate (ORR) was 76.4%, comprising 10 complete response (CR, 58.8%) and 3 partial response (PR, 17.6%) (Figure 1 A and 1B). At a median follow-up of 9.6 months, 7 patients (41.2%) remained in CR at data cutoff. Disease progression occurred in 7 patients, 5 of whom died. The median progression-free survival (mPFS) and the median overall survival (mOS) was not reached (Figure 1 C and 1D). These findings confirmed that real-world outcomes with glofitamab were consistent with clinical trial data, even among heavily pretreated and transformed histologies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline immune profiles and their prognostic significance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven that bispecific antibody (BsAb) efficacy relies on T-cell engagement, we next explored whether baseline lymphocyte subsets could predict clinical outcomes. Univariable Cox regression identified the absolute CD4⁺ T-cell count\u0026mdash;but not CD8⁺ count\u0026mdash;as significantly associated with longer PFS (HR 0.994; P = 0.046) (Table S1). Each 1-cell/\u0026mu;L increase in CD4⁺ count reduced progression risk by 0.6%. To further validate this observation, we expanded the study cohort by including 22 additional patients with B-cell lymphoma who received other CD20/CD3 bispecific antibodies at our center, resulting in a total of 40 patients (Table S2).\u0026nbsp;In Cox proportional hazards models, the CD3+, CD4+and CD8+ absolute count was analyzed as a continuous variable per 100-cell/\u0026mu;L increase to facilitate clinical interpretation. In the pooled analysis, higher baseline CD4⁺ counts remained an independent predictor of prolonged PFS (HR 0.602; 95% CI 0.444\u0026ndash;0.815) (Table 2).\u0026nbsp;Additionally, baseline %CD19\u003csup\u003e+\u003c/sup\u003e B cells was also significantly correlated with PFS (HR 0.883; 95% CI 0.785\u0026ndash;0.994). Receiver operating characteristic (ROC) curve analysis determined the optimal cutoff for %CD19+ as 0.1. Survival analysis demonstrated that patients with %CD19+ \u0026ge; 0.1 had significantly longer PFS (p \u0026lt; 0.001, Figure S1). Together, these findings suggested that peripheral immune composition\u0026mdash;particularly CD4⁺ T-cell abundance\u0026mdash;reflects the host\u0026rsquo;s capacity to respond to BsAb therapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between CD4⁺ T-cell levels and clinical response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further delineate the predictive role of CD4⁺ T cells, we compared baseline CD4⁺ T cell counts between responders and non-responders. Patients achieving CR/PR exhibited significantly higher CD4⁺ T cell counts than those with SD/PD (Figure 2A). ROC analysis identified 470 cells/\u0026mu;L as the optimal cutoff (AUC = 0.798, 95% CI 0.631\u0026ndash;0.965, P = 0.003) (Figure 2B). Kaplan\u0026ndash;Meier analysis confirmed that patients with CD4⁺ T cells \u0026ge; 470/\u0026mu;L had significantly prolonged PFS compared to those below this threshold (P \u0026lt; 0.01) (Figure 2C). These results established baseline CD4⁺T cell count as a robust, clinically measurable predictor of BsAb efficacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCD4⁺ T cells support effector activation and cytokine response during BsAb therapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe next investigated the immunologic basis linking baseline CD4⁺ T cells to BsAb efficacy. Among 33 patients (from 40 patients with BsAb treatment) with paired lymphocyte‐subset and cytokine data, the pre-treatment absolute CD4⁺ T-cell count showed a significant positive correlation with the post-treatment absolute CD8⁺ T-cell count (Figure 3A), and with the proportion of activated CD8⁺ CD28⁺ T cells (Figure 3B), while inversely correlating with inhibitory CD8⁺ CD28⁻ T cells (Figure 3C). Furthermore, higher baseline CD4⁺ counts were positively correlated with key effector cytokines, including IL-2 (Figure 3D) and IFN-\u0026gamma; (Figure 3E) after one BsAb cycle, but not with IL-6 (Figure 3F). These findings indicate that patients with higher pre-treatment CD4⁺ T-cell reserves exhibit stronger T-cell activation and cytokine production upon BsAb stimulation, supporting a mechanistic role for CD4⁺ help in shaping effector cytotoxic responses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBTK inhibitors expand peripheral CD4⁺ T cells and reprogram cytotoxic and proliferative programs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause higher CD4⁺ T-cell levels predicted improved BsAb outcomes, we sought to identify clinically actionable approaches to enhance this immune compartment. Our preclinical studies demonstrated that Bruton\u0026rsquo;s tyrosine kinase (BTK) inhibitors promote CD4⁺ T-cell proliferation (Figure 4A), activation (Figure 4B) and cytotoxic cytokine release (Figure 4C\u0026amp;D) in a dose-dependent manner, and increase the intratumoral infiltration of cytotoxic T cells in lymphoma mouse models (Figure 4E\u0026amp;F), suggesting that BTK blockade may improve the functional readiness of the T-cell compartment. Therefore, we explored whether BTK inhibitors could restore or augment CD4⁺ T-cell immunity in patients. We thus collected 30 B-NHL patients treated with BTK inhibitors (Detailed information shown in Table S3), and it was observed that BTK inhibition significantly increased absolute CD4⁺ T-cell counts in peripheral blood (Figure 4G).\u003c/p\u003e\n\u003cp\u003eTo delineate functional consequences, we performed single-cell RNA sequencing on three patient samples collected before and after BTK-inhibitor therapy (Figure 4H-J). Uniform Manifold Approximation and Projection (UMAP) revealed expansion of proliferating T (MKI67⁺) and cytotoxic T/NK clusters (GZMB⁺, IFNG⁺) after treatment (Figure S2A-C). Differential expression and GO enrichment analyses demonstrated upregulation of pathways involved in T-cell activation, leukocyte-mediated cytotoxicity, and interferon signaling (Figure 4K).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBuilding on these mechanistic findings, we next examined whether BTK inhibition\u0026ndash;induced immune remodeling could translate into clinical benefit when used sequentially with BsAb therapy. In real-world observations, three patients with relapsed/refractory lymphoma received BTK inhibitor therapy as immune priming before the initiation of glofitamab (Figure 4L). Two patients have maintained durable complete remissions to date, whereas the third achieved a complete response but relapsed 8.4 months later. Notably, this relapsed patient had both the lowest baseline and on-treatment CD4⁺ T-cell counts (from 282 to 410 cells/\u0026mu;L) (Figure 4M) among the three and discontinued BTK inhibition approximately one month after initiation.\u003c/p\u003e\n\u003cp\u003eAlthough limited by the small sample size, these observations are consistent with our proposed model that the magnitude of CD4⁺ T-cell augmentation may correlate with the depth and durability of BsAb response. Collectively, these data support a clinically actionable paradigm in which BTK inhibition functions as an immune-priming strategy that enhances CD4⁺ T-cell\u0026ndash;dependent antitumor immunity and improves responsiveness to subsequent bispecific antibody therapy.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identified peripheral CD4⁺ T-cell abundance as a robust and clinically measurable biomarker predicting outcomes of CD20\u0026times;CD3 bispecific antibody (BsAb) therapy in patients with relapsed or refractory B-cell lymphoma. Higher baseline CD4⁺ T-cell counts were associated with improved response rates and prolonged progression-free survival. Mechanistically, patients with higher pre-treatment CD4⁺ T-cell levels exhibited stronger activation of effector CD8⁺ T cells and greater induction of IL-2 and IFN-γ following BsAb exposure, suggesting that CD4⁺ T-cell help is critical for optimal T-cell engagement and durable antitumor immunity. Furthermore, we demonstrated that BTK inhibition can expand CD4⁺ T cells and enhance cytotoxic and proliferative T/NK-cell programs, providing a potential immune-priming strategy to improve BsAb efficacy.\u003c/p\u003e \u003cp\u003eAlthough BsAbs directly link CD3⁺ T cells to malignant B cells, their therapeutic activity depends on the pre-existing immune landscape\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Our results indicate that CD4⁺ T-cell reserve represents a key determinant of the magnitude of BsAb-induced cytotoxic response. This finding is biologically plausible: CD4⁺ T cells are essential for licensing antigen-presenting cells\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, producing cytokines such as IL-2, and sustaining CD8⁺ T-cell proliferation and memory formation\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Prior studies in CAR-T therapy and checkpoint blockade have also shown that impaired CD4⁺ T-cell recovery correlates with poor expansion and inferior outcomes\u003csup\u003e[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Our data extend this paradigm to BsAb therapy, emphasizing that not all T cells are functionally equivalent\u0026mdash;patients with adequate CD4⁺ \u0026ldquo;helper\u0026rdquo; capacity are better equipped to mount a coordinated and durable antitumor response.\u003c/p\u003e \u003cp\u003eOur observation that baseline CD4⁺ T-cell reserve strongly correlated with bispecific antibody efficacy prompted us to explore potential pharmacologic strategies capable of restoring or augmenting T-cell fitness. Among these, BTK inhibition emerged as a promising approach. Beyond its canonical role in B-cell receptor signaling blockade, BTK inhibition has been increasingly recognized to exert broad immunomodulatory effects on the tumor microenvironment. Consistent with prior mechanistic and translational reports\u0026mdash;showing that BTK inhibitors enhance T-cell activation and proliferation \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, suppress myeloid-derived immunosuppressive pathways \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, and induce interferon-related gene programs \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;we found that BTK inhibition expanded CD4⁺ T-cell populations, upregulated cytotoxic and proliferative signatures in T/NK cells, and activated interferon signaling, as confirmed by single-cell RNA sequencing of patient samples. Collectively, these findings suggest that BTK inhibition can \u0026ldquo;recondition\u0026rdquo; the immune landscape, generating a pool of functionally competent effector T cells that are more effectively engaged by bispecific antibodies, thereby providing a mechanistic basis for the observed synergistic activity.\u003c/p\u003e \u003cp\u003eThe observation that sequential BTK inhibition followed by glofitamab yielded durable remissions in two of three patients, with the sole relapse occurring in the case with minimal CD4⁺ expansion, provides preliminary clinical support for this hypothesis. Although anecdotal, these results highlight a rational combinatorial strategy: short-term BTK inhibitor exposure to prime CD4⁺ and effector T cells before BsAb initiation. Such an approach may be particularly valuable for heavily pretreated or immunologically exhausted patients, where the T-cell compartment is quantitatively and functionally compromised. Prospective clinical trials evaluating optimal sequencing, dosing, and timing of BTK inhibitor \u0026ldquo;immune priming\u0026rdquo; are warranted. Biomarker-guided selection based on baseline CD4⁺ counts could further refine patient stratification and personalize BsAb therapy.\u003c/p\u003e \u003cp\u003eThis study has several limitations. The cohort size was modest and derived from a single center, which may limit generalizability. The correlative analyses, although supported by mechanistic data, remain exploratory and require validation in larger, prospective cohorts. Single-cell profiling was performed on a limited number of patients, and the direct quantification of CD4⁺ subsets by scRNA-seq is constrained by sampling and platform biases. Finally, while the sequential BTK inhibitor\u0026ndash;BsAb cases are hypothesis-generating, causality cannot be established without controlled clinical testing.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our findings define CD4⁺ T-cell reserve as a central determinant of BsAb responsiveness and propose BTK inhibition as a feasible immune-priming strategy to enhance T-cell readiness. This integrative clinical and translational evidence underscores the importance of host immune fitness in shaping BsAb efficacy and provides a mechanistic framework for rational combination and sequencing strategies aimed at improving outcomes in patients with relapsed or refractory B-cell lymphoma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDisclosures\u003c/h2\u003e \u003cp\u003eNo conflicts of interests to disclosure.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eContributions\u003c/h2\u003e \u003cp\u003eH.Y., Y.S., and J.Z. designed this study, H.Y., and J.C. wrote the manuscript. H.Y., J.C., D.H., performed in vitro and in vivo experiments. J.C., J.L., Y.W., J.C., and W.Z. provided human B cell lymphoma samples. Y.H., J.C., Z.J., and Y.L. analysed scRNA sequencing data. All authors read the final manuscript and approved the final submitted version.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eData-sharing statement\u003c/h2\u003e \u003cp\u003eAny data in this study are available from the corresponding author on reasonable request.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Approval\u003c/h2\u003e \u003cp\u003e All procedures were approved by the Institutional Review Board and Ethical Committee of the Peking University Cancer Hospital and Institute (Approval No. 2023KT87).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e \u003cp\u003eThis research was supported by the National Nature Science Foundation of China (No. 82300214, 82270195) and Science Foundation of Peking University Cancer Hospital (No. PY202312).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.Y., Y.S., and J.Z. designed this study, H.Y., and J.C. wrote the manuscript. H.Y., J.C., D.H., performed in vitro and in vivo experiments. J.C., J.L., Y.W., J.C., and W.Z. provided human B cell lymphoma samples. Y.H., J.C., Z.J., and Y.L. analysed scRNA sequencing data. All authors read the final manuscript and approved the final submitted version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAny data in this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCRUMP M, NEELAPU S S, FAROOQ U, et al. Outcomes in refractory diffuse large B-cell lymphoma: results from the international SCHOLAR-1 study [J]. Blood, 2017, 130(16): 1800-8.\u003c/li\u003e\n\u003cli\u003ePENNINGS E R A, DURMAZ M, VISSER O, et al. Treatment and outcomes for patients with relapsed or refractory diffuse large B-cell lymphoma: a contemporary, nationwide, population-based study in the Netherlands [J]. 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Nature, 2017, 545(7652): 60-5.\u003c/li\u003e\n\u003cli\u003eZHANG H, WU W, WANG M, et al. Integrated peripheral blood multi-omics profiling identifies immune signatures predictive of neoadjuvant PD-1 blockade efficacy in head and neck squamous cell carcinoma [J]. J Transl Med, 2025, 23(1): 693.\u003c/li\u003e\n\u003cli\u003eDUBOVSKY J A, BECKWITH K A, NATARAJAN G, et al. Ibrutinib is an irreversible molecular inhibitor of ITK driving a Th1-selective pressure in T lymphocytes [J]. Blood, 2013, 122(15): 2539-49.\u003c/li\u003e\n\u003cli\u003eLONG M, BECKWITH K, DO P, et al. Ibrutinib treatment improves T cell number and function in CLL patients [J]. The Journal of clinical investigation, 2017, 127(8): 3052-64.\u003c/li\u003e\n\u003cli\u003eCORZO C A, VARFOLOMEEV E, SETIADI A F, et al. The kinase IRAK4 promotes endosomal TLR and immune complex signaling in B cells and plasmacytoid dendritic cells [J]. Sci Signal, 2020, 13(634).\u003c/li\u003e\n\u003cli\u003eYU H, MI L, ZHANG W, et al. 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Blood, 2024, 143(1): 57-63.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"B-cell lymphoma, bispecific antibody, CD4⁺ T cell, BTK inhibitor, immune priming","lastPublishedDoi":"10.21203/rs.3.rs-8819442/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8819442/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBispecific antibodies (BsAbs) targeting CD20\u0026times;CD3 have achieved remarkable efficacy in relapsed or refractory (R/R) B-cell lymphoma, yet clinical responses remain heterogeneous and predictive biomarkers are lacking. In this study, we identified peripheral CD4⁺ T-cell abundance as a robust predictor of BsAb efficacy. Among 40 patients treated with CD20\u0026times;CD3 BsAbs, higher baseline CD4⁺ T-cell counts correlated with superior response rates and prolonged progression-free survival, whereas CD8⁺ counts showed no association. Elevated CD4⁺ T-cell levels were linked to increased activation of effector CD8⁺CD28⁺ T cells and greater induction of IL-2 and IFN-γ following treatment, indicating that CD4⁺ help potentiates cytotoxic T-cell function. Mechanistic analyses revealed that Bruton\u0026rsquo;s tyrosine kinase (BTK) inhibitors expanded CD4⁺ T cells and reprogrammed T/NK compartments toward proliferative (MKI67⁺) and cytotoxic (GZMB⁺, IFNG⁺) phenotypes, with upregulation of T-cell activation and interferon signaling pathways. In clinical observation, sequential BTK inhibition before BsAb therapy was associated with durable remissions in most patients. Collectively, these findings establish CD4⁺ T-cell reserve as a key determinant of BsAb responsiveness and support BTK inhibition as a feasible immune-priming strategy to enhance BsAb efficacy in B-cell lymphoma.\u003c/p\u003e","manuscriptTitle":"CD4⁺ T-cell reserve predicts bispecific antibody efficacy and can be enhanced by BTK inhibition in B-cell lymphoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 14:31:42","doi":"10.21203/rs.3.rs-8819442/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17e56829-f781-4df1-93f0-2ae91b082199","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63726602,"name":"Biological sciences/Cancer"},{"id":63726603,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2026-04-17T00:38:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 14:31:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8819442","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8819442","identity":"rs-8819442","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}