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Humoral determinants of checkpoint immunotherapy: anti-TL1A treatment has a synergistic effect with anti-PD-1 treatment

ABSTRACT: While the role of cellular immunity in checkpoint immunotherapy (CPI) for cancer is well established, the impact of antibody-mediated humoral immunity is comparably underexplored. Here we used rapid extracellular antigen profiling (REAP) to map the autoantibody reactome within a cohort of 374 cancer patients treated with CPIs and 131 healthy controls for autoantibodies against 6,172 extracellular and secreted proteins (the ‘exoproteome’). Globally, CPI-treated cancer patients harbored an exquisite diversity of autoreactivities that were elevated relative to controls, but changed minimally with treatment. Autoantibody signatures within CPI-treated patients strikingly distinguished them from controls. While associations of specific autoantibodies with immune-related adverse events (irAE) were sparse, we detected numerous individual autoantibodies that were associated with dramatically altered odds ratios (ORs) for response to therapy. These included autoantibodies against immunomodulatory proteins, such as cytokines, growth factors, and immunoreceptors, as well as tumor surface proteins. Functional evaluation of several autoantibody responses indicated that they neutralized the activity of their target proteins including type-I IFN (IFN-I), IL-6, OSM, TL1A, and BMPR1A/BMPR2. Modeling the effects of autoantibodies against IFN-I and TL1A in preclinical mouse tumor models resulted in enhanced CPI efficacy, consistent with their effects in patients. This single-cell RNA sequencing dataset evaluates the synergistic effect between anti-PD-1 and anti-TL1A treatments.

ORGANISM(S): Mus musculus

PROVIDER: GSE294482 | GEO | 2025/04/15

REPOSITORIES: GEO

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Project description:Introduction: Pediatric systemic lupus erythematosus (pSLE) patients often initially present with more active and severe disease than adults, including a higher frequency of lupus nephritis. Specific autoantibodies, including anti-C1q, anti-DNA and anti-alpha-actinin, have been associated with kidney involvement in SLE, and DNA antibodies are capable of initiating early stage lupus nephritis in severe combined immunodeficiency (SCID) mice. Over 100 different autoantibodies have been described in SLE patients, highlighting the need for comprehensive autoantibody profiling. Knowledge of the antibodies associated with pSLE and proliferative nephritis will increase the understanding of SLE pathogenesis, and may aid in monitoring patients for renal flare. Methods: We used autoantigen microarrays composed of 140 recombinant or purified antigens to compare the serum autoantibody profiles of new-onset pSLE patients (n=45) to healthy controls (n=17). We also compared pSLE patients with biopsy-confirmed class III or IV proliferative nephritis (n=23) and without significant renal involvement (n=18). We performed ELISA with selected autoantigens to validate the microarray findings. We created a multiple logistic regression model, based on the ELISA and clinical information, to predict whether a patient had proliferative nephritis, and used a validation cohort (n=23) and longitudinal samples (88 patient visits) to test its accuracy. Results: Fifty autoantibodies were at significantly higher levels in the sera of pSLE patients compared to healthy controls, including anti-B-cell activating factor (BAFF). High levels of anti-BAFF were associated with active disease. Thirteen serum autoantibodies were present at significantly higher levels in pSLE patients with proliferative nephritis than those without, and we confirmed five autoantigens (dsDNA, C1q, collagens IV & X and aggrecan) by ELISA. Our model, based on ELISA measurements and clinical variables, correctly identified patients with proliferative nephritis with 91% accuracy. Conclusions: Autoantigen microarrays are an ideal platform for identifying autoantibodies associated with both pSLE and specific clinical manifestations of pSLE. Using multiple regression analysis to integrate autoantibody and clinical data permits accurate prediction of clinical manifestations with complex etiologies in pSLE.

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Humoral determinants of checkpoint immunotherapy: anti-TL1A treatment has a synergistic effect with anti-PD-1 treatment

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