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High throughput sequencing of B and T cells in checkpoint inhibitor treated patients

ABSTRACT: This is a prospective longitudinal study designed to identify biomarkers predictive of the onset of irAEs associated with ICI therapy and determine whether these biomarkers are further related to treatment efficacy. We obtained serial blood samples in patients who will be receiving anti-PD-1/PD-L1 either as a single agent or in combination with other therapy.

ORGANISM(S): Homo sapiens

PROVIDER: GSE313597 | GEO | 2026/05/01

REPOSITORIES: GEO

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High throughput sequencing of T cells from a patient undergoing immune checkpoint inhibitor therapy

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Anti-PD-L1 antibodies exacerbate arthritis and pain via increased production of CXCL8/IL-8 and FGF9 in synovial fibroblasts

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Project description:Immune checkpoint inhibitors (ICIs) have revolutionized by offering remarkable clinical benefits and durable responses for patients with advanced non-small cell lung cancer (NSCLC). However, a very small percentage of patients responded to ICI treatment, and immune-related adverse events (irAEs) leading to treatment discontinuation remain challenges. Despite the recognized need for biomarkers that can predict both the efficacy of ICIs and the risk of irAEs, such biomarkers have yet to be clearly identified. In this study, we performed single-cell RNA sequencing on peripheral blood mononuclear cells (PBMC) from NSCLC patients prior to treatment with ICIs. We found that the immune response is generally reduced in patients with poor prognosis. However, the hypoxic environment is particularly more prominent in patients with primary resistance compared to those with acquired resistance. Meanwhile, granzyme and perforin are closely associated with favorable prognosis. We also identified IL1B and CXCL8 as key predictors of irAEs, with their activation linked to inflammation. Moreover, we found that PRF1 in CD8+ T cells and NK cells plays a critical mediator of complete responses and reduced irAE to ICI therapy. These findings deepen our understanding of the mechanisms of ICI efficacy and provide valuable information for optimizing immunotherapy strategies. By identifying biomarkers for both better prognosis and irAEs, this research lays the foundation for personalized immunotherapy approaches that aim to improve clinical outcomes while minimizing the risks associated with ICI treatment.

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Project description:Background: Checkpoint inhibitor-associated autoimmune diabetes (CIADM) is a rare but life-altering complication of immune checkpoint inhibitor (ICI) therapy. Biomarkers that predict type 1 diabetes (T1D) are unreliable for CIADM. Aim To identify biomarkers for prediction of CIADM. Methods: From our prospective biobank, 14 CIADM patients who had metastatic melanoma treated with anti-PD-1±anti CTLA4 were identified. Controls were selected from the same biobank, matched 2:1. Pre-treatment, on-ICI and post-CIADM serum and peripheral blood mononuclear cells (PBMCs) were analysed. Serum was analysed for T1D autoantibodies, C-peptide, glucose and cytokines. PBMCs were profiled using flow cytometry. Pancreatic volume was measured using CT volumetry. Results: Before treatment, CIADM patients had smaller pancreatic volume (27% reduction, p=0.044) and higher anti-GAD antibody titres (median 2.9 versus 0, p=0.01). They had significantly higher baseline proportions of Th17 helper cells (p=0.03), higher CD4+ central memory cells (p=0.04) and lower naïve CD4+ cells (p=0.01). With ICI treatment, greater declines in pancreatic volume were seen in CIADM patients (p<0.0001). Activated CD4+ subsets increased significantly in CIADM and controls with immune-related adverse effects (IRAE) but not controls without IRAE. Using only pre-treatment results, pancreatic volume, anti-GAD antibody titre and baseline immune flow profile were highly predictive of CIADM development, with an area under the curve (AUC) of >0.96. Conclusions: People who develop CIADM are immunologically predisposed and have antecedent pancreatic and immunological changes that accurately predict disease with excellent sensitivity. These biomarkers could be used to guide ICI use, particularly when planning treatment for low-risk tumours.

2025-12-26 | GSE314698 | GEO

Single cell RNA sequencing reveals distinct T cell populations in immune-related adverse events of checkpoint inhibitors

Project description:PD-1 is an essential inhibitory receptor in T cells, and antibodies that block its interaction with its ligands can augment anti-tumor immune responses. The clinical potential of these agents is limited by the fact that approximately half of all patients who receive anti-PD-1 treatment subsequently develop immune-related Adverse Events (irAEs). To generate insights into the cellular and molecular changes that occur during anti-PD-1 treatment, and to discover predictive biomarkers of irAEs, we performed single cell RNA sequencing of circulating T cells collected from cancer patients who did and did not develop irAEs following PD-1 blockade. Using the K-nearest-neighbor-based network graph drawing layout, we show the involvement of unique gene expression signatures and subpopulations of T cells. We identified that at baseline, patients with arthritis had significantly less cells with features of CD8 TCM cells, patients with pneumonitis had more CD4 TH2 cells, and patients with thyroiditis had more CD4 TH17 cells, when compared to patients who did not develop irAEs. Moreover, T cell receptor sequencing revealed distinctive clonal expansion, and suggested involvement of organ-specific and potentially pathogenic T cell clones among patients with irAEs. Altogether, these data support the hypothesis that different populations of T cells are associated with different irAEs and that quantification and characterization of these populations of T cells pre-treatment could serve as a toxicity-specific, predictive biomarker.

2022-12-14 | GSE216329 | GEO

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