Innate and adaptive immune features associated with immune-related adverse events

Abstract

Background: While highly efficacious for numerous cancers, immune checkpoint inhibitors (ICIs) can cause unpredictable and potentially severe immune-related adverse events (irAEs), underscoring the need to understand irAE biology.

Methods: We used a multidimensional approach incorporating single-cell RNA sequencing, mass cytometry, multiplex cytokine assay, and antinuclear antibody (ANA) profiling to characterize the peripheral immune landscape of patients receiving ICI therapy according to irAE development.

Results: Analysis of 162 patients revealed that individuals who developed clinically significant irAEs exhibited a baseline proinflammatory, autoimmune-like state characterized by a significantly higher abundance of CD57⁺ T and natural killer (NK) T cells, plasmablasts, proliferating and activated CXCR3⁺ lymphocytes, CD8⁺ effector and terminal effector memory T cells, along with reduced NK cells and elevated plasma ANA levels. In irAE cases, we identified distinct baseline proinflammatory gene signatures, including markedly higher expression of IL1B and CXCL8 in monocytes and CXCR3, TNF, and IFNG in T/NK cells. TNF signaling was the most enriched pathway, while immunosuppressive genes SIGLEC7 and CXCR4 were downregulated. Following ICI initiation, these patients exhibited an enhanced shift toward an activated and inflammatory immune phenotype, including monocyte reprogramming characterized by upregulation of IL18 and elevated gene expression levels of CXCL10. Conversely, post-treatment levels of CXCL8 were decreased in irAE patients. Notably, in patients who did not develop clinically significant irAE, we identified increased baseline abundance of a TGFBI^high myeloid cluster enriched in immunosuppressive markers such as STAB1. In addition, patients without irAE exhibited upregulation of TNF and AIRE, accompanied by distinct myeloid protumorigenic reprogramming.

Conclusions: A pre-existing activated, autoimmune-like proinflammatory state drives the development of irAE during ICI therapy through three key axes: increased plasmablast/ANA, heightened interferon-gamma/CXCL10/CXCR3 axis, and amplified TNF signaling. These findings may serve as potential peripheral immune biomarkers for predicting irAE and provide biological insights into the mechanisms governing and mitigating irAE.

Keywords: Antibody; Autoimmune; Immune Checkpoint Inhibitor; Immune related adverse event - irAE; Immunotherapy.

Figure 1. Study design. The study included 162 patients with cancer receiving ICI therapy (either anti-PD1, anti-PDL1 or combination anti-PD1 plus anti-CTLA4). Blood was collected at baseline before the initiation of ICI therapy and post-immunotherapy (2–3 weeks and 6–8 weeks). PBMCs were used for single-cell RNA sequencing (scRNA-seq) and mass cytometry assays. Plasma was used to assess levels of antinuclear antibodies (ANA) and 40 cytokines. The number of patients used for each assay is shown in the brackets. BL, baseline; CTLA4, cytotoxic T lymphocyte antigen 4; ICI, immune checkpoint inhibitor; PBMC, peripheral blood mononuclear cell; PD1, programmed death 1; PDL1, programmed death ligand 1; t-SNE, t-distributed stochastic neighbor embedding.

Figure 2. Single-cell RNA sequencing (scRNA-seq) analysis of PBMCs at baseline and post-ICI therapy. (A) scRNA-seq experimental design for 72 blood samples (36 pairs) from patients with cancer at baseline (BL) before the initiation of ICI therapy and 6–8 weeks after ICI initiation. (B) UMAP of 23 cell states identified by unsupervised clustering shown according to cell type. (C) Frequency plot showing the proportion of 23 clusters identified by unsupervised clustering in irAE versus no-irAE group at BL and 6–8 weeks post-ICI therapy. (D) Dot plot showing canonical marker gene expression for 23 unsupervised clusters. Arrows point to cluster 2 (NK) and cluster 18 (Mki67+). (E) The abundance of cluster 18 (Mki67+), shown as a percentage of total PBMCs in two groups at baseline (BL) identified by unsupervised clustering. (F) UMAP of PBMCs annotated by Azimuth Reference shown by major immune cell types and 30 immune cell subsets. The abundance of NK (CD56dim) cells (G), CD4 proliferating T cells (H), plasmablasts (I), and combined proliferating lymphocyte cluster (CD4, CD8 and NK) (J) shown as a percentage of total PBMCs in No irAE versus irAE group at BL guided by Azimuth Reference. ASDC, AXL+SIGLEC6+ dendritic cell; cDC, conventional dendritic cell; CTLA4, cytotoxic T lymphocyte antigen 4; DC, dendritic cell; dnT, double-negative T cell; gdT, gamma delta T cell; HSPC, hematopoietic stem and progenitor cell; ICI, immune checkpoint inhibitor; irAE, immune-related adverse event; MAIT, mucosal-associated invariant T cell; NK, natural killer cell; PBMC, peripheral blood mononuclear cell; PD1, programmed death 1; pDC, plasmacytoid dendritic cell; PDL1, programmed death ligand 1; TCM, central memory T cell; TEM, effector memory T cell; Treg, T regulatory cell; UMAP, uniform manifold approximation and projection.

Figure 3. Mass cytometry (CyTOF) analysis of PBMC samples at baseline and post-ICI therapy. (A) CyTOF experimental design of 110 blood samples (55 pairs) from 55 patients with cancer (n=12 no-irAE and n=43 irAE) at baseline (BL) before the initiation of ICI therapy and 6–8 weeks post immunotherapy. Opt-SNE of 30 cell clusters identified by unsupervised consensus meta-clustering of CYTOF data using Euclidean as a distance metric is shown according to major immune cell types (B) and 30 immune cell subsets (C). (D) Heatmap showing expression of 35 cell surface immune markers used in the CyTOF panel in 30 metaclusters. (E) Volcano plot of CyTOF data showing significant metaclusters (aqua circles) differentially abundant in irAE versus no-irAE at baseline with (p<0.05). The abundance of significant clusters; K10, plasmablast (F); K2, NK (G); K4, CD8 terminal effector (H); K8, TCRγδT (I) and K13, CD8 terminal effector (J), identified by unsupervised clustering of CyTOF data using Edge R (p<0.05) shown as a percentage of total PBMCs in no-irAE versus irAE cases at BL. The abundance of significant clusters, including NKT (K) and CD57 positive clusters (L) identified by manual gating strategy in irAE versus no-irAE cases. (M) The abundance of CD8+ KLRG1+ CCR7 CD8 effector memory cluster identified by unsupervised T/NK subclustering analysis of scRNA-seq data. Significant increase in abundance of cluster K21 (CD4 memory) in irAE and no-irAE cases postimmunotherapy in all cases (N) and in melanoma cases (O) by Wilcoxon matched-pairs signed-rank test of CyTOF data. CTLA4, cytotoxic T lymphocyte antigen 4; FC, fold change; FDR, false discovery rate; ICI, immune checkpoint inhibitor; irAE, immune-related adverse event; NK, natural killer cell; PBMC, peripheral blood mononuclear cell; PD1, programmed death 1; pDC, plasmacytoid dendritic cell; PDL1, programmed death ligand 1; TCM, central memory T cell; TCR, T cell receptor; scRNA-seq, single-cell RNA sequencing.

Figure 4. Baseline autoimmune-like, proliferative and inflammatory state associated with irAE development. (A) Abundance of plasmablasts as a percentage of total PBMCs at baseline (BL) according to the grade of irAE. (B) Comparison of plasma concentration of antinuclear antibodies (ANA) (N=65 total; n=22 no-irAE and n=43 irAE) at BL and postimmunotherapy (at 2–4 weeks and 6–8 weeks). (C) Comparison of plasma concentration of ANA in 65 patients based on irAE grade at BL and postimmunotherapy. G0-1, Grade 0–1; G2, Grade 2; G3, Grade 3. (D) Abundance of CXCR3+Mki67+ cell cluster as a percentage of total PBMCs at BL based on the grade of irAE. (E) Pseudobulk scRNA-seq differential gene expression analysis of T and NK cells in the irAE group compared with the no-irAE group at baseline is shown as a volcano plot. Significantly upregulated genes are shown in blue dots, significantly downregulated genes in red dots, and genes with no significant change are shown in gray (F) Heat map depicting logFC and p values for the 3-gene signature in T and NK cells comparing irAE versus No irAE group at baseline (adjusted p<0.05 and ≥2 fold change). (G) MsigDB pathway enrichment analysis for T/NK cells at baseline. Red arrow shows the TNFα signaling as the highly enriched pathway in irAEs at baseline. (H) GSEA enrichment analysis for T/NK cells showing TNF signaling as the most enriched pathway at baseline. Abundance of CXCR3+Mki67+ cluster (I) and CXCR3+CD4+ memory cluster (J) identified by high-resolution unsupervised clustering of scRNA-seq data. (K) Abundance of CXCR3+CD8+ memory clusters in the scRNA-seq data at baseline. (L) CXCR3 gene expression in T and NK cells at baseline. Abundance of CXCR3+CD8+ clusters (M) and CXCR3+CD4+ (N) memory clusters at baseline identified in the CyTOF data. FC, fold change; GSEA, gene set enrichment analysis; irAE, immune-related adverse event; NK, natural killer cell; PBMC, peripheral blood mononuclear cell; scRNA-seq, single-cell RNA sequencing.

Figure 5. Single-cell RNA sequencing analysis of myeloid cells in patients with and without irAE. (A) Pseudobulk differential expression analysis of unsupervised myeloid cell clusters of scRNA-seq data in irAE versus no-irAE group at baseline is shown as a volcano plot (adjusted p<0.05 and fold change ≥1.5). Significantly upregulated genes are shown in blue dots, significantly downregulated genes in red dots, and genes with no change are shown in gray. Gene set enrichment analysis (GSEA) in panel B and MsigDB pathway enrichment in panel C of myeloid cells at baseline in irAE patients. The red arrow in panel C shows the TNF signaling and inflammatory response pathways as highly enriched in myeloid cells in the irAE group at baseline. (D) Heatmap of selected upregulated genes in myeloid cells in irAE versus no-irAE at baseline (adjusted p<0.05 and fold change ≥1.5). (E) Gene expression changes of CXCL10, CCL3, CCL4, SPARC and SIGLEC1 in myeloid cells in irAE versus no-irAE cases post-therapy (adjusted p<0.05 and fold change ≥1.5). (F) Gene expression changes of TNF and IRF4 in monocytes in no-irAE cases post-therapy compared with baseline (adjusted p<0.05 and fold change ≥1.5). (G) Heatmap of selected upregulated and downregulated genes in Azimuth-guided CD14+ classical monocytes in irAE versus no-irAE at baseline (adjusted p<0.05 and fold change ≥1.5). (H) Gene Ontology analysis in CD14+ classical monocytes in irAE versus no-irAE at baseline. (I) Heatmap of selected upregulated genes in Azimuth-guided CD14+ and CD16+ classical monocytes in irAE versus no-irAE at post-therapy (adjusted p<0.05 and fold change ≥1.5). (J) Heatmap of selected upregulated genes in Azimuth-guided CD14+ and CD16+ classical monocytes in no-irAE at post-therapy compared with baseline (adjusted p<0.05 and fold change ≥1.5). (K) Unsupervised subclustering analysis of all myeloid cells (without pDCs). (L) Dot plot showing gene expression markers for 16 unsupervised Louvain clusters for myeloid cells. (M) Significantly upregulated cytokines/chemokine ligands and their receptors at baseline in irAE patients compared with no-irAE patients at baseline (adjusted p<0.05 and fold change ≥1.5). (N) Trajectory analysis of myeloid cells using Monocle 3. (O) Continuous embedding depicting the fraction of myeloid cells expressing CXCL8 and IL1B genes and their corresponding gene expression changes between irAE and no-irAE patients at baseline in myeloid cells. (P) Abundance of TGFBIhigh cluster 13 in irAE versus no-irAE patients at baseline shown as a percentage of total myeloid cells. BL, baseline; FC, fold change; IFN, interferon; irAE, immune-related adverse event; pDC, plasmacytoid dendritic cell; scRNA-seq, single-cell RNA sequencing; UMAP, uniform manifold approximation and projection.

Figure 6. Multiplex cytokine and chemokine analysis in patients with and without irAEs. Comparison of plasma concentrations of chemokines at baseline (BL) and postimmunotherapy (6–8 weeks). CXCL10 (A) and CXCL9 (B) in all patients. CXCL10 (C) and CXCL9 (D) in PD1/PDL1-treated patients. CXCL10 (E) and CXCL9 (F) in combination (CTLA4+PD1)-treated patients. CXCL8 in all patients (G), in PD1/PDL1-treated patients (H) and in combination (CTLA4+PD1)-treated patients (I). Dot plot showing comparison of relative gene expression values of chemokines CXCL10, CXCL9 and CXCL8 at baseline (BL) and postimmunotherapy (6–8 weeks) in myeloid cells in scRNA-seq data (J). Number of patients for comparison of plasma concentration of chemokines: All patients (N=146 total: n=79 irAE and n=67 no-irAE); PD1/PDL1-treated patients (N=122 total: n=59 No irAE and n=63 irAE); patients treated with combination CTLA4+PD1 therapy (N=18 total: n=6 No irAE and n=12 irAE). CTLA4, cytotoxic T lymphocyte antigen 4; irAE, immune-related adverse event; PD1, programmed death 1; PDL1, programmed death ligand 1; scRNAseq, -seq, single-cell RNA sequencing.