Severe immune-related adverse events (irAEs) occur in up to 60% of patients with melanoma treated with immune checkpoint inhibitors (ICIs). However, it is unknown whether a common baseline immunological state precedes irAE development. Here we applied mass cytometry by time of flight, single-cell RNA sequencing, single-cell V(D)J sequencing, bulk RNA sequencing and bulk T cell receptor (TCR) sequencing to study peripheral blood samples from patients with melanoma treated with anti-PD-1 monotherapy or anti-PD-1 and anti-CTLA-4 combination ICIs. By analyzing 93 pre- and early on-ICI blood samples and 3 patient cohorts (n = 27, 26 and 18), we found that 2 pretreatment factors in circulation—activated CD4 memory T cell abundance and TCR diversity—are associated with severe irAE development regardless of organ system involvement. We also explored on-treatment changes in TCR clonality among patients receiving combination therapy and linked our findings to the severity and timing of irAE onset. These results demonstrate circulating T cell characteristics associated with ICI-induced toxicity, with implications for improved diagnostics and clinical management.
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Bulk and single-cell expression data generated in this work have been deposited in GEO under accession no. GSE186144. All requests for raw data will be promptly reviewed by the corresponding authors to determine whether the request is subject to any confidentiality obligations. Any data that can be shared will be released via a material transfer agreement. The published expression datasets analyzed in this work (Supplementary Table 20) are available from GEO with accession nos. GSE50772, GSE61635, GSE72509, GSE126124, GSE3365 and GSE100833. Processed CyTOF, MiXCR and immunoSEQ data are publicly available from https://doi.org/10.25936/f3np-k536. Additional data supporting the findings in this work are available in the main text, figures, extended data and supplementary files.
Custom scripts for the training and validation of composite models, evaluating freedom from severe toxicity and generating related figures are publicly available from https://doi.org/10.25936/f3np-k536.
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We thank the patients and families involved in this study. We thank A. Nassar and L. Chen for technical assistance with the CyTOF experiments, including provision of resources, staining and processing of samples. We thank G. Ansstas and C. Kaufman for clinical samples. We also thank T. Ley, S. Devarakonda and M. Tal for providing critical feedback on the manuscript. This work was supported by grants from the National Cancer Institute (no. K08CA238711 to A.A.C., no. K08CA237727 to D.Y.C., no. R01CA238471 to K.D., no. R21CA218950 to R.H. and no. R00CA187192 to A.M.N.), National Heart, Lung, and Blood Institute (no. T35HL007649 to A.N.), National Institute of Arthritis and Musculoskeletal and Skin Diseases (no. R01AR077926 to K.D.), a fellowship from the Natural Sciences and Engineering Research Council of Canada (A.X.L.), the Cancer Research Foundation Young Investigator Award (A.A.C.), V Foundation for Cancer Research V Scholar Award (A.A.C.), Washington University Alvin J. Siteman Cancer Research Fund (A.A.C.), Yale Cancer Center Meyers Award (M.S. and R.H.), a 10x Genomics Pilot Program Award (R.H.), the Melanoma Research Alliance (no. 137453 and no. 828544 to R.H.), the Virginia and D.K. Ludwig Fund for Cancer Research (A.M.N.), Stinehart-Reed Foundation (A.M.N.), Stanford Bio-X Interdisciplinary Initiatives Seed Grants Program (IIP) (A.M.N.) and Donald E. and Delia B. Baxter Foundation (A.M.N.).
A.A.C. has patent filings related to cancer biomarkers and digital cytometry and has served as an advisor/consultant to Roche, AstraZeneca, Daiichi Sankyo, Tempus, Geneoscopy, NuProbe, Fenix Group International and Guidepoint. A.A.C. has stock options in Geneoscopy, research support from Roche and ownership interests in Droplet Biosciences. M.S. has consulted for Idera Pharmaceuticals, Regeneron Pharmaceuticals, Apexigen, Alligator Bioscience, Verastem Oncology, Agenus, Rubius Therapeutics, Bristol Myers Squibb, Genentech-Roche, Boston Pharmaceuticals, Servier Laboratories, Adaptimmune Therapeutics, Immunocore, Dragonfly Therapeutics, Pierre Fabre Pharmaceuticals, Molecular Partners, Boehringer Ingelheim, Innate Pharma, Nektar Therapeutics, Pieris Pharmaceuticals, Numab Therapeutics, Abbvie, Zelluna Immunotherapy, Seattle Genetics/Seagen, Genocea Biosciences, GI Innovation, Chugai-Roche, BioNTech, Eli Lilly, Modulate Therapeutics, Array Biopharma, AstraZeneca and Genmab. M.S. has stock options in EvolveImmune, NextCure, Repertoire Immune Medicines, Adaptive Biotechnologies, Actym Therapeutics and Amphivena Therapeutics and has stock ownership in GlaxoSmithKline and Johnson & Johnson. A.M.N. has patent filings related to expression deconvolution, digital cytometry and cancer biomarkers and has served as an advisor/consultant to Roche, Merck and CiberMed. The other authors declare no conflicts of interest.
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Extended Data Fig. 1 Quality control and extended characterization of cell states identified by unsupervised clustering of scRNA-seq data.
a, UMAP representation of pretreatment peripheral blood leukocytes profiled by droplet-based scRNA-seq (10x Genomics) from 13 patients with metastatic melanoma, colored by major cell lineages, severe irAE status, TCR expression by scV(D)J-seq, and BCR expression by scV(D)J-seq (related to Fig. 3a). b, Unsupervised hierarchical clustering (average linkage) of the mean log2 transcriptome per CD4 T cell cluster identified from scRNA-seq data. c, Dot plot showing the average expression of key activation (HLA-DX, MKI67) and lineage markers (SELL, CCR7) in CD4 T cell clusters. d, Same as Fig. 3b but showing all pairwise combinations of scRNA-seq clusters within each of the major cell types analyzed (B cells, CD4 T cells, CD8 T cells, NK cells, monocytes). Across 82 possible pairwise combinations, CD4 T 5 + 3 achieved the highest Spearman correlation against CD4 TEM levels enumerated by CyTOF and the strongest association with severe irAE development. Cells annotated as ‘T/NKT’ were collapsed into CD8 T cells. e, Same as panel d but showing all pairwise combinations ranked by the mean of each feature following unit variance normalization (mean of 0 and standard deviation of 1). In this analysis, the –log10 P-value for the association with severe irAE (two-sided, unpaired Wilcoxon rank sum test) was normalized to unit variance without considering the direction of the association.
a, UMAP projections of scRNA-seq data generated in this work, embedded and labeled by Azimuth using a reference PBMC atlas of 162k cells profiled by scRNA-seq and 228 antibodies (Methods). b, Confusion matrix showing the agreement between phenotypic labels determined by marker genes and unsupervised clustering (rows; related to Fig. 3a and Extended Data Fig. 1a) versus reference-guided annotation with Azimuth (columns). In total, 85% of single cells assigned to a major lineage group by Azimuth (B cells, CD4 T, CD8 T, NK cells, monocytes) were assigned to the same identity by canonical marker gene assessment. Given the absence of NKT cells in the reference atlas used for Azimuth, the T/NKT cluster defined by unsupervised analysis was relabeled as CD8 T cells. c, Same analysis as in Fig. 3b but shown for all 27 phenotypic states identified by Azimuth. Among these states, CD4 TEM was most associated with severe irAE and CyTOF-enumerated CD4 TEM. A population combining CD4 TEM and CD4 Proliferating states was also strongly associated with severe irAE. The latter showed the highest expression of HLA-DX and lowest expression of SELL (panel d), consistent with an activated CD4 TEM phenotype. d, Dot plot depicting key activation and lineage markers among CD4 T cell states annotated by Azimuth. e, Violin plots showing protein expression levels imputed by Azimuth using antibody-derived tag (ADT) data, supporting the combination of CD4 TEM and CD4 Proliferating states in panels c and f. f, Performance of top-ranking cell subsets identified by Azimuth and unsupervised clustering for prediction of severe irAEs. The combined CD4 T 5 + 3 clusters (Fig. 3b) were more associated with severe irAE and CyTOF than the top-ranking reference-guided population (panel c). Statistical significance was calculated using a two-sided, unpaired Wilcoxon rank sum test. Data in all panels shown are from the 13 samples profiled by scRNA-seq in Fig. 3.
Extended Data Fig. 3 Analysis of activated, resting, and parental T cell subsets in relation to severe irAE development.
a, Association between severe irAE development and pretreatment levels of T cell states identified by unsupervised clustering (left) and memory-like T cell states identified by Azimuth (right) in 13 PBMC samples profiled by scRNA-seq (Figs. 1 and 3a). Activated cells were defined as those expressing HLA-DX or MKI67 (CPM > 0); resting cells were defined by the absence of HLA-DX and MKI67 expression (CPM = 0). b, Left: Association between severe irAE development and pretreatment levels of memory T cell subsets, total CD4 and CD8 T cells, and total T cells quantified by CyTOF, for all 18 patients analyzed in the single-cell discovery cohort (Figs. 1 and 2a). Activated phenotypes were defined as CD38+ or HLA-DR+ or Ki67+. Resting phenotypes were defined as CD38–HLA-DR–Ki67–. Right: ROC plot showing the performance of activated and resting CD4 TEM subsets (left panel) for predicting severe irAE development. Cell fractions were assessed relative to total PBMC content. Statistical significance in a, b was determined by a two-sided, unpaired Wilcoxon rank sum test and nominal –log10 P-values are displayed. –log10 P-values were further multiplied by –1 for associations with no severe irAE. See also Supplementary Table 6.
Extended Data Fig. 4 Extended characterization of immune repertoire diversity from single-cell V(D)J sequencing data.
a, Key TCR diversity measures and the impact of cell abundance, TCR richness, and distinct clonal repertoires on such measures. Hypothetical CD4 naïve and TEM cell subsets are shown as examples. Triangles depicting differences in magnitude are not drawn to scale. b, Mean Shannon entropy versus mean clonality (1 – Pielou’s evenness) for each CD4 T cell state identified by unsupervised clustering of scRNA-seq data. CD4 T 5 + 3 (Fig. 3b,c), a TEM state enriched for activated cells, shows elevated clonality relative to other CD4 states, as expected for this phenotype77, while also showing higher diversity (Shannon entropy), indicating elevated richness. c, Distribution of EM-like CD4 T cell states (from Fig. 3f) with available scTCR clonotype data. d, Association between severe irAE development and TCR diversity (Shannon entropy) in pseudo-bulk T cells from pretreatment blood, shown for all T cell states identified by scRNA-seq (left) and after the removal of the EM-like states indicated in panel c (no severe irAE, n = 5 patients; severe irAE, n = 4 patients). e, Same as d but shown for EM-like states alone. f, Area under the curve (AUC) for the association between pretreatment peripheral TCR diversity (Shannon entropy) and severe irAE development, shown for all combinations of the constituent cell states in e, including the combined CD4 T 5 + 3 cluster after restricting to activated cells (CPM > 0 for HLA-DX or MKI67). Of note, no other combination of activated EM-like states achieved an AUC > 0.85 in this analysis. g, BCR clonotype diversity (Shannon entropy), shown for each B cell state identified by unsupervised clustering (Fig. 3a). In b, d–f, only patients with at least 100 TCR clones were analyzed (n = 9; Methods). The same patients were analyzed in g for consistency. In panels d, e, and g, center lines, bounds of the box, and whiskers indicate medians, 1st and 3rd quartiles, and minimum and maximum values, respectively.
a, Expression of developmentally-regulated marker genes in major CD4 T cell subsets from the LM22 signature matrix (MAS5 normalized), showing that the LM22 reference signature for activated CD4 memory T cells has a TEM profile. b, CIBERSORTx versus mass cytometry for enumeration of activated CD4 memory T cells in the pretreatment peripheral blood of 17 metastatic melanoma patients (Supplementary Table 1). A linear regression line with 95% confidence band is shown. Concordance and significance were determined by Pearson r and a two-sided t test, respectively. While activated CD4 memory T cells quantitated by CyTOF were defined by CD38 expression in this plot, other activated CD4 TEM subsets were also significantly correlated with CIBERSORTx (panel c). c, Cross correlation plot of lymphocyte subset frequencies determined by CyTOF and CIBERSORTx. Act., Activated. d, Correlation between activated CD4 memory T cell levels inferred by CIBERSORTx and 14 memory T cell states profiled by CyTOF, including CD38+ activated subsets manually gated within each population, in PBMCs from 17 metastatic melanoma patients (Supplementary Table 1). e, Scatter plot depicting the global correlation of lymphocyte subsets enumerated by CIBERSORTx and flow cytometry in peripheral blood samples from five healthy subjects profiled by bulk RNA-seq (Supplementary Table 1). A linear regression line with 95% confidence band is shown. Concordance and significance were determined by Pearson r and a two-sided t test, respectively. As monocytes were variably underestimated by cytometry compared to complete blood counts, all results in b–e are expressed as a function of total lymphocytes. f, Distribution of activated CD4 memory T cell levels quantitated by CyTOF (CD38+, HLA-DR+ or Ki67+ CD4 TEM cells, n = 28 patients), scRNA-seq (HLA-DX+ or MKI67+ cells within CD4 T clusters 5 and 3, n = 13 patients), and CIBERSORTx (n = 60 patients) across all irAE-evaluable samples profiled by each modality in this work (Supplementary Table 1). Box center lines, bounds of the box, and whiskers indicate medians, 1st and 3rd quartiles, and minimum and maximum values, respectively. Statistical significance was determined by a Kruskal-Wallis test. n.s., not significant (P > 0.05).
Extended Data Fig. 6 Extended analysis of TCR diversity from pretreatment peripheral blood expression profiles.
a–d, Association between baseline bulk TCR diversity and the highest irAE grade observed for each patient in bulk cohorts 1 and 2 (Supplementary Tables 7 and 9), shown for two diversity measures (a and c, Shannon entropy; b and d, Gini-Simpson index) and stratified by therapy type. In a and b, patients treated with combination therapy are stratified by future irAE status: no severe irAE (n = 10) versus severe irAE (n = 14 patients) (left) and irAE grade (right): 0/1 (n = 3), 2 (n = 7), 3 (n = 12), and 4 (n = 2). In c and d, patients treated with PD1 monotherapy are stratified by future irAE status: no severe irAE (n = 26) versus severe irAE (n = 3 patients) (left) and irAE grade (right): 0/1 (n = 19), 2 (n = 7), 3 (n = 2), and 4 (n = 1). Two-group comparisons were assessed by a two-sided, unpaired Wilcoxon rank sum test. n.s., not significant (P > 0.05). Linear regression was applied to evaluate the median value of each measure grouped by irAE grade (insets). The significance of linear concordance was determined by a two-sided t test. Grades 0 and 1 reflect no toxicity and asymptomatic toxicity, respectively, and were combined. In all panels, the box center lines, bounds of the box, and whiskers denote medians, 1st and 3rd quartiles, and minimum and maximum values within 1.5 × IQR (interquartile range) of the box limits, respectively.
Extended Data Fig. 7 Composite model performance across patients, key patient subgroups, the number of symptomatic irAEs per patient, and organ system involvement.
a, Same as Fig. 4d, but applied to both bulk cohorts (n = 53 patients) using leave-one-out cross-validation (LOOCV) (Methods). b, Same as Fig. 4c, but shown for model scores determined by LOOCV. c, Performance of the composite model versus other candidate pretreatment factors for predicting severe irAE development (Methods). The composite model was trained in bulk cohort 1 (BC1) and validated in bulk cohort 2 (BC2) or vice versa, as indicated. d, Performance of the composite model trained on bulk cohort 1 for predicting severe irAEs in different patient subgroups from bulk cohort 2. DCB, durable clinical benefit; NDB, no durable clinical benefit; GI, gastrointestinal. e, Composite model scores determined by LOOCV for all bulk cohort patients treated with combination therapy (n = 24), stratified by future irAE grade: 0/1 (n = 3), 2 (n = 7), 3 (n = 12), and 4 (n = 2). f, Model performance for predicting grade 2 + , 3 + , or 4 irAE development in combination therapy patients using the scores in e. g,h, Composite model scores determined by LOOCV in both bulk cohorts (n = 53 patients) versus the number of symptomatic irAEs (grade 2 + ) per patient (g) and the number of organ system toxicities per patient (h). i, Distribution of irAEs across patients and organ systems (Supplementary Table 15). Patients from bulk cohorts 1 and 2 are organized by decreasing composite model scores determined via LOOCV (Methods). The line distinguishing high/low scores was optimized using LOOCV (Methods). j, Fraction of patients in both bulk cohorts that developed irAEs in at least 2 organ systems versus those that did not, stratified by the threshold in panel i (Methods). Significance was determined by a two-sided Fisher’s exact test. In e, g, and h, center lines, bounds of the box, and whiskers indicate medians, 1st and 3rd quartiles, and minimum and maximum values within 1.5 × IQR (interquartile range) of the box limits, respectively. Statistical significance in e, g, and h was determined by a Kruskal-Wallis test.
Extended Data Fig. 8 Composite model performance for predicting time to severe irAE in validation bulk cohort 2.
a–c, Kaplan-Meier analysis for freedom from severe irAE in bulk cohort 2 for patients treated with combination or PD1 immune checkpoint blockade (a), combination therapy (b), or PD1 monotherapy (c), stratified by the composite model score (Methods). Statistical significance was calculated by a two-sided log-rank test. In all panels, training was performed in bulk cohort 1 and the cut-point predicting severe irAE was optimized for bulk cohort 1 using Youden’s J statistic (Supplementary Table 10; Methods). Notably, the analyses in a–c were landmarked between treatment initiation and three months following treatment initiation, with all severe irAEs occurring within this period. The Kaplan-Meier plots are shown out to four months given the extended follow-up of patients that did not develop any severe irAE (Supplementary Table 9).
a, Evenness (Pielou’s index) of TCR repertoires assembled by MiXCR (bulk RNA-seq) and immunoSEQ® (genomic DNA) from paired pretreatment PBMC samples (n = 15 combination therapy patients) (Supplementary Tables 1 and 18). Concordance and significance were determined by Spearman ρ and a two-sided t test, respectively. b, Similar to Fig. 5b but showing clonality for each pre- and on-treatment PBMC sample (Supplementary Table 18). Statistical significance was determined by a two-sided, paired Wilcoxon rank sum test. ns, not significant (P > 0.05). c, Fraction of pretreatment peripheral blood TCR clonotypes detected on-treatment in 15 combination therapy patients (Supplementary Table 18), stratified by no severe (n = 6) and severe (n = 9) irAE status. Clonotypes with matching productive CDR3 β-chain nucleotide sequences were considered identical. Center lines, bounds of the box, and whiskers indicate medians, 1st and 3rd quartiles, and minimum and maximum values, respectively. Significance was determined by a two-sided, unpaired Wilcoxon rank sum test. d–g, Clonal dynamics in circulating T cells following combination therapy initiation. d, Persistent T cell clones identified by immunoSEQ® were cross-referenced with scTCR-seq and scRNA-seq data of pretreatment PBMCs from the same three patients (YUALOE, YUNANCY, YUHONEY), all of whom received combination therapy and developed severe ICI-induced toxicity (Supplementary Table 18; Methods). e, Log2 expression of key lineage and activation markers across major T cell states annotated by Azimuth along with persistent clones classified into CD4 and CD8 T cells (Methods). f, Aggregate change from baseline in the productive frequencies of persistent clonotypes, stratified by lineage (n = 2 cell types) and patient (n = 3). The sum of the difference in productive frequencies (on-treatment % – pretreatment %) was calculated from immunoSEQ® data. Bars denote mean + /- SD. g, Top: Change in bulk TCR clonality from baseline (Fig. 5b). Bottom: Same as f but showing the underlying clonotypes, where circle size is proportional to pretreatment clone frequency (immunoSEQ®). h, Same as Fig. 5d but restricted to blood draws taken cycle 1 day 1 of combination therapy and <1 month later (n = 7 patients; Supplementary Table 18).
Extended Data Fig. 10 Schema of large-scale assessment of peripheral blood leukocytes in autoimmune disorders versus healthy controls.
Schema describing the workflow and statistical meta-analysis for evaluating the enrichment of individual circulating leukocyte subsets in autoimmune disorders relative to healthy controls (Fig. 6; Methods). In brief, CIBERSORTx was applied to enumerate 15 leukocyte subsets in bulk RNA-seq or microarray profiles of peripheral blood samples from patients with either systemic lupus erythematosus57,58,59 (SLE; n = 239) or inflammatory bowel disease56,60,61 (IBD; n = 348) compared to healthy controls (Supplementary Table 20). For each dataset and cell subset, a two-sided, unpaired Wilcoxon rank sum test was applied to assess the difference in relative abundance between healthy and disease phenotypes. Results were subsequently combined across studies by meta-z statistics (Methods).
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Lozano, A.X., Chaudhuri, A.A., Nene, A. et al. T cell characteristics associated with toxicity to immune checkpoint blockade in patients with melanoma. Nat Med 28, 353–362 (2022). https://doi.org/10.1038/s41591-021-01623-z
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