Abstract
Immune checkpoint inhibitor (ICI) therapy has revolutionized oncology, but treatments are limited by immune-related adverse events, including checkpoint inhibitor colitis (irColitis). Little is understood about the pathogenic mechanisms driving irColitis, which does not readily occur in model organisms, such as mice. To define molecular drivers of irColitis, we used single-cell multi-omics to profile approximately 300,000 cells from the colon mucosa and blood of 13 patients with cancer who developed irColitis (nine on anti-PD-1 or anti-CTLA-4 monotherapy and four on dual ICI therapy; most patients had skin or lung cancer), eight controls on ICI therapy and eight healthy controls. Patients with irColitis showed expanded mucosal Tregs, ITGAEHi CD8 tissue-resident memory T cells expressing CXCL13 and Th17 gene programs and recirculating ITGB2Hi CD8 T cells. Cytotoxic GNLYHi CD4 T cells, recirculating ITGB2Hi CD8 T cells and endothelial cells expressing hypoxia gene programs were further expanded in colitis associated with anti-PD-1/CTLA-4 therapy compared to anti-PD-1 therapy. Luminal epithelial cells in patients with irColitis expressed PCSK9, PD-L1 and interferon-induced signatures associated with apoptosis, increased cell turnover and malabsorption. Together, these data suggest roles for circulating T cells and epithelial–immune crosstalk critical to PD-1/CTLA-4-dependent tolerance and barrier function and identify potential therapeutic targets for irColitis.
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Data availability
The scRNA-seq and snRNA-seq data and results are available for browsing at https://villani.mgh.harvard.edu/ircolitis. The same processed data files are available on the Gene Expression Omnibus (GSE206301) and Zenodo (https://zenodo.org/doi/10.5281/zenodo.8088435). Raw human sequencing data are available at the database of Genotypes and Phenotypes (dbGaP) (phs003418.v1.p1). The dbGaP provides authorized access to sequencing data that requires a formal request be made to the appropriate National Institutes of Health Data Access Committee.
Further information and requests for resources and reagents should be directed to, and will be fulfilled by, the lead contact, A.-C.V. (avillani@mgh.harvard.edu).
Code availability
Source code for data analysis and the website is available on GitHub (https://github.com/villani-lab/ircolitis) and has been archived at Zenodo (https://zenodo.org/doi/10.5281/zenodo.8088435).
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Acknowledgements
We are deeply grateful to all donors and their families as well as to the Severe Immunotherapy Complications service at Massachusetts General Hospital. We acknowledge the contributions of E. Drokhlyansky, R. Xavier, M. Biton and Hacohen laboratory members for their feedback on experimental design and data interpretation. We thank C. Flayer and K. Gallagher for help with generating Luminex data. This work was supported by several training grants, including National Institute of Allergy and Infectious Diseases grant T32AR007258 (to K.S.); National Heart, Lung, and Blood Institute grant K08HL157725; an American Heart Association Career Development Award (to P.S.); two National Institute of Diabetes and Digestive and Kidney Diseases training grants (1K08DK127246-01A1 and T32DK007191 (to M.F.T)); a Spanish Society of Medical Oncology grant (to L.Z.); 1T32CA207021 (to J.H.C.); 1K08CA273547-01A1 (to J.H.C.); Massachusetts General Hospital Fund for Medical Discovery (to J.H.C. and M.F.T.); Massachusetts General Hospital Krantz Stewardship (to J.H.C.); Society for Immunotherapy of Cancer/AstraZeneca Forward Fund (to J.H.C.); and National Cancer Institute R00CA259511 (to K.P.). This work was made possible by generous support from the National Institutes of Health Director’s New Innovator Award (DP2CA247831 to A.-C.V.); the Massachusetts General Hospital Transformative Scholar in Medicine Award (to A.-C.V.); the Damon Runyon-Rachleff Innovation Award (to A.-C.V.); the Melanoma Research Alliance Young Investigator Award (https://doi.org/10.48050/pc.gr.143739 to A.-C.V.); a Kraft Foundation award (to K.L.R. and A.-C.V.); the Arthur, Sandra, and Sarah Irving Fund for Gastrointestinal Immuno-Oncology (to J.H.C., N.H. and A.-C.V.); Gordon Pugh (to K.L.R.); the Adelson Foundation (to G.M.B.); R01AI169188-01 (to M.D.); the Fariborz Maseeh Award for Innovative Medical Education (to M.D.); the Peter and Ann Lambertus Family Foundation (to M.D. and R.J.S.); Merck (to R.J.S.); and the American College of Gastroenterology Clinical Research Award and R01AG068390 (to H.K.). This work was also made possible by the generous support of an anonymous donor (to K.L.R. and A.-C.V.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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Authors and Affiliations
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M.F.T. and A.-C.V. conceived of and led the study. M.F.T. and A.-C.V. led experimental design. M.F.T. carried out experiments, with assistance from K.M., J.T., P.S., M.N., A.T. and B.Y.A. K.S. designed and performed computational analysis, with input and assistance from B.L., M.N., N.S. and S.R. M.F.T., L.T.N. and J.H.C. designed and performed microscopy experiments, with input and assistance from K.H.X., Y.S. and V.J. T.E. and K.P. provided input for scRNA-seq/snRNA-seq experiments, protocols and data interpretation. M.F.T., L.Z., C.J.P., T.S., R.G., P.Y.C., R.J.S., D.J., G.M.B., H.K., K.L.R. and M.D. provided clinical expertise and coordinated and performed sample acquisition and/or administrative coordination. M.F.T. and M.D. performed endoscopic examinations. M.D. provided additional expertise on study design. N.H. contributed to biological expertise and study design and provided advice. B.L. contributed to computational expertise and provided advice. A.-C.V. managed and supervised the study. A.-C.V. and K.L.R. raised funding for this work. M.F.T., K.S. and A.-C.V. wrote the paper, with input from all authors.
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Competing interests
From 9 August 2021, B.L. is an employee of Genentech. M.D. has received consulting fees from Genentech, Partner Therapeutics, SQZ Biotech, AzurRx, Eli Lilly, Mallinckrodt Pharmaceuticals, Aditum, Foghorn Therapeutics, Palleon, Sorriso Pharmaceuticals, Generate Biomedicines, Asher Bio, Neoleukin Therapeutics, Moderna, Alloy Therapeutics, Third Rock Ventures, DE Shaw Research, Agenus and Curie Bio. M.D. is also a member of the scientific advisory board for Veravas, Monod Bio, Axxis Bio and Cerberus Therapeutics. R.J.S. is a consultant for Bristol Myers Squibb, Marengo, Merck, Novartis, Pfizer and Replimune. H.K. received research funding from Pfizer and Takeda. H.K. received consulting fees from Aditium Bio, AbbVie and Takeda. H.K. serves on the scientific advisory board of Vivante Health. D.J. reports grants and personal fees from Novartis, Genentech, Syros and Eisai. D.J. reports personal fees from Vibliome, PIC Therapeutics, Mapkure and Relay Therapeutics. D.J. reports grants from Pfizer, Amgen, InventisBio, Arvinas, Takeda, Blueprint Medicines, AstraZeneca, Ribon Therapeutics and Infinity that are outside the submitted work. G.M.B. has sponsored research agreements through her institution with Olink Proteomics, Teiko Bio, InterVenn Biosciences and Palleon Pharmaceuticals. G.M.B. served on advisory boards for Iovance, Merck, Nektar Therapeutics, Novartis and Ankyra Therapeutics. G.M.B. consults for Merck, InterVenn Biosciences, Iovance and Ankyra Therapeutics. She holds equity in Ankyra Therapeutics. K.P. is a consultant for Santa Ana Bio. N.H. holds equity in and advises Danger Bio/Related Sciences, is on the scientific advisory board of Repertoire Immune Medicines and CytoReason, owns equity in and has licensed patents to BioNTech and receives research funding from Bristol Myers Squibb and Calico Life Sciences. K.L.R. has received advisory board fees from SAGA Diagnostics and institutional research support from Bristol Myers Squibb. A.-C.V. received consulting fees from Merck and Bristol Myers Squibb. A.-C.V. has a financial interest in 10x Genomics. The company designs and manufactures gene sequencing technology for use in research, and such technology is being used in this research. A-.C.V.’s interests were reviewed by Massachusetts General Hospital and Mass General Brigham in accordance with their institutional policies. M.F.T., K.S., K.M., P.S., N.S., J.T., M.N., L.Z., N.P.S., A.T., S.R., B.Y.A., L.T.N., J.H.C., T.E., Y.S., K.H.X., V.J., C.J.P., T.S., R.G. and P.Y.C. do not have competing interests to declare.
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Extended data
Extended Data Fig. 1 Detailed cell type composition of each donor for immune cells from colon tissue.
(a) Estimated number of cells per biopsy in each CD8 T/GDT/NK cell cluster for cases (n = 5) and controls (n = 7). (b) Detailed composition of each patient across cell clusters for CD8 T/GDT/NK cells. (c) Estimated number of cells per biopsy in each CD4 T cell cluster for cases (n = 5) and controls (n = 7). (d) Detailed composition of each patient across cell clusters for CD4 T cells. Cell clusters depicted with different colors in UMAP embeddings of the (e) eight mononuclear phagocyte subsets and (g) 13 B cell subsets from colon tissue. Detailed composition of each patient across cell clusters for (f) mononuclear phagocytes and (h) B cells from colon tissue. For panels B, D, F, and H, every column represents one individual donor referenced, labeled at the bottom of the heatmap. Boxplots show median and interquartile range. Heatmap color indicates percent of a patient’s cells assigned to each cell cluster. NK natural killer. GDT gamma delta T cells. Related to Figs. 2–4.
Extended Data Fig. 2 Tissue cell type abundance associated with control patients on anti-PD-1 versus no therapy.
(a) Abundance analysis of tissue cell clusters in control patients on anti-PD-1 therapy (n = 4) versus healthy controls not on ICI therapy (n = 8) (that is, those undergoing screening colonoscopy). Each dot represents a patient. Composition of each donor is reported as a percent of cells from each patient in each cluster, and box plots show median and interquartile range. Error-bars indicate logistic regression OR 95% CI for differential abundance of cells from controls on anti-PD-1 versus healthy controls not on ICI therapy for each cell cluster. Unadjusted likelihood ratio test p-values are shown. MNP cell cluster 8 was detected in only one patient in the control anti-PD-1 group, so the logistic regression model was not fit for this cluster. (b) Volcano plots show log fold-change (x-axis) and negative log10 P-value (two-sided) (y-axis) for each gene. Number of differentially expressed genes (FC > 1.5 and FDR < 10%) is shown, and some of the top genes are labeled. ADAR expression is shown for cluster 9 CD4 Tregs. Dots represent individual patients. Feature plots for HLA-DOB and ATP11A expression in MP cells is shown. (c) Number of differentially expressed genes in each cell cluster (FC > 1.5 and FDR < 10%). Related to Fig. 1.
Extended Data Fig. 3 TCR and BCR detection and diversity analysis for immune cells from colon tissue and paired blood specimens.
UMAP embedding with color depicting cells with TCR (A, B, E, F) or BCR (C, D) are shown (left most column) for (a) CD8 T cells from colon tissue, (b) CD4 T cells from colon tissue (c) B cells from colon tissue (d) B cells from blood, (e) CD4 T cells from blood, and (f) CD8 T cells from blood. TCR (A, B, E, F) or BCR (C, D) diversity plots are shown in the remaining three columns including the number of distinct clones per patient (second column from the left), cumulative percent of cells with the top N unique TCR or BCR clones (second column from the right), and Hill diversity index (right most column). Related to Figs. 2 and 4.
Extended Data Fig. 4 Detailed analysis of CD8 T/Gamma Delta T/NK cells and CD4 T cells from blood.
(a) UMAP embedding, (b) normalized gene expression, and (c) cell subset abundance difference between irColitis cases and controls for CD8 T/GDT/NK cells. (d) Percent of alpha beta CD8 T cell TCR clones shared between paired blood and tissue for each individual patient. Dots represent patients. irColitis case versus control two-sided t-test p-value is shown. (e) Volcano plot of pseudobulk differential gene expression with all cells for the contrast of irColitis cases versus controls for CD8 T/GDT/NK cells. The x-axis indicates the fold-change and y-axis indicates negative log10 p-value (two-sided) reported by limma. (f) Spearman correlation between each cluster and each of the reported CD8 T cell subsets from Giles et al, Immunity27. (g) UMAP embedding, (h) normalized gene expression, and (i) cell subset abundance difference between irColitis cases and controls for CD4 T cells. (j) Volcano plot of pseudobulk differential gene expression with all cells for the contrast of irColitis cases versus controls for CD4 T cells. (k) Spearman correlation between each cluster and each of the reported CD4 T cell subsets from Giles et al, Immunity27. Panels B, H show normalized gene expression (mean zero, unit variance) for selected genes, showing relative expression across cell clusters. Panels C, I show cell subset abundance differences between irColitis cases in orange and controls in gray, unadjusted likelihood ratio test p-values. Boxplots show median and interquartile range of patient cell type compositions where each dot represents a patient. The cellular composition of each patient is reported as the percentage of cells from a patient in each cell cluster. Error-bars indicate logistic regression OR 95% CI for differential abundance of cells from irColitis cases for each cell cluster. GDT gamma delta T cell. NK natural killer. Related to Figs. 2 and 4.
Extended Data Fig. 5 Detailed analysis of MP cells from colon tissue.
(a) UMAP embedding of 2,242 MP cells. Colors indicate cell cluster identities, which are listed on the right. (b) Normalized expression (mean zero, unit variance) of selected genes showing relative expression across cell clusters. Heatmap rows are aligned to every cluster defined in the UMAP. (c) Cell subset abundance differences between cases in orange (n = 13) and controls in gray (n = 14) across all MP cell subsets. Boxplots show patient cell type compositions where each dot represents a patient. Mononuclear phagocyte composition of each patient is reported as the percent of cells from a patient in each cell cluster, and box plots show median and interquartile range. Error bars indicate logistic regression 95% CI of OR for differential abundance of case cells for each cell cluster, and unadjusted likelihood ratio test p-values are shown. (d) Volcano of pseudobulk differential gene expression with all cells for the contrast of irColitis cases versus controls. The x-axis indicates the fold-change and y-axis indicates negative log10 p-value reported by limma (two-sided). (e) Bar plots showing differentially expressed (DE) genes per MP cluster (fold-change > 1.5 and FDR < 5%). (f) MP cell gene expression fold-change and log2CPM for cases (orange) and controls (black) is reported for selected genes across T cells (left and middle columns). Heatmap color indicates FC differences between irColitis cases and controls (right column). White dot indicates FDR < 5%. Panels show representative genes across multiple biological themes. (g) Gene expression for PD-1 ligands PD-L1 (CD274) and PD-L2 (PDCD1LG2) in each respective UMAP embedding (left panels). Estimated fold-changes between case and control for each myeloid cell cluster (middle). Gene expression values for the cells from each patient in each myeloid cell cluster where dots represent individual patients (right). Error bars show 95% CI and box plots show median and interquartile range. Related to Fig. 1.
Extended Data Fig. 6 Detailed analysis of B cells from colon tissue.
(a) UMAP embedding of 40,352 B cells. Colors indicate cell cluster identities, which are listed on the right. (b) Normalized expression (mean zero, unit variance) of selected genes showing relative expression across cell clusters. Heatmap rows are aligned to every cluster defined in the UMAP. (c) Cell subset abundance differences between cases in orange (n = 13) and controls in gray (n = 14) across all B cell subsets. Boxplots show patient cell type compositions where each dot represents a patient. B cell composition of each patient is reported as the percent of cells from a patient in each cell cluster. Error-bars indicate 95% CI of logistic regression OR for differential abundance of case cells for each cell cluster, and unadjusted likelihood ratio test p-values are shown. (d) Volcano of pseudobulk differential gene expression with all B cells for the contrast of irColitis cases versus controls. The x-axis indicates the fold-change and y-axis indicates negative log10 p-value reported by limma. (e) Bar plots showing differentially expressed (DE) genes per B cluster (fold-change greater than 1.5 and FDR less than 5%). (f) Ratio of IgG to IgA plasma cells across individual irColitis cases and controls. Each dot represents an individual patient. P-value for Wilcoxon rank sum test is shown. (g–h) irColitis in a B cell-depleted patient receiving ICI therapy for lymphoma. (G) Multispectral immunofluorescence staining of fixed colon mucosal tissue from patient C14* with a 7-color panel: DAPI (blue), panCK (gray), CD8A (aqua), PD-1 (orange), FOXP3 (yellow), CD68 (pink), and PD-L1 (green). (H) tSNE-embedding of 3,295 CD45+-sorted cells from a patient depleted of B cells. Cell cluster identity and top three AUC genes in parentheses are shown. MP: mononuclear phagocyte. Box plots show median and interquartile range. Related to Fig. 1.
Extended Data Fig. 7 Detailed analysis of epithelial and mesenchymal cells from tissue.
(a) Detailed composition of each patient across cell clusters for epithelial and mesenchymal nuclei from colon tissue. Heatmap color indicates percent of a patient’s cells assigned to each cell cluster. Every column represents one individual donor, referenced at the bottom of the heatmap. Upper right panel shows unique cell clusters depicted with different colors in UMAP embedding, which match the color scheme of the cell subset identities listed on the right side of the heatmap. (b) Schematic representing computationally-predicted cell-cell interactions between T cells expressing PD-1 (PDCD1) and cells expressing the PD-1 receptors PD-L1 (CD274) and PD-L2 (PDCD1LG2) (top cartoon). Three middle panels show differential expression analysis of means of gene pairs in indicated tissue cell types with cases (orange) and controls (black). Dots represent individual patients. Box plots show median and interquartile range. Limma fold-changes (FC) and two-sided p-values are shown. Bottom panels show feature plots of gene expression (Log2CPM) level in the UMAP embedding for MP cells from Extended Data Fig. 5a (left two panels) and CD8/GDT/NK cells from Fig. 2a (right two panels) presented separately for cells from cases and controls. Number and percentage of cells (from cases or from controls) with detected expression are reported at the bottom of each feature plot. (c) Feature plots use color to indicate gene expression (Log2CPM) level for selected epithelial and mesenchymal genes in the UMAP embedding in panel A. Number and percentage of nuclei with detected expression of each candidate gene are reported at the bottom of each feature plot. (d) FC, 95% CI, and gene expression (Log2CPM) (two left columns) for cases (orange) (n = 12) and controls (black) (n = 14) is reported for a set of genes organized across 7 biological themes. Heatmap (five right columns) indicates FC differences between cases and controls. White dot indicates FDR < 5%. (e) Feature plots show gene expression in nuclei from cases (left) and controls (right) using UMAP embedding in panel A. All feature plots shown in panels B, C, and E use color to indicate gene expression (Log2CPM). Number and percentage of cells with detected expression of each candidate gene are reported at the bottom of each feature plot. Related to Fig. 5.
Extended Data Fig. 8 Receptor-ligand interactions predict altered cellular communication in irColitis.
(a) Schematic of cell-cell communication inference. Predicted communication between two cell types is defined as proportional to the transcript levels of ligand and receptor genes in the two cell types. (b) Left: PDCD1 (log2CPM) (encoding PD-1) in tissue CD8 T cells (x-axis) and CD274 (encoding PD-L1) in epithelial and mesenchymal nuclei (y-axis). Right: Gene expression of PDCD1 and its ligands CD274 and PDCD1LG2 (encoding PD-L2) across blood immune cell types (top panels) and tissue immune, epithelial, and mesenchymal cells/nuclei (epi./mes.) (lower panels). Error-bars show 95% CI of fold-changes (black indicates FDR < 5%), cases (orange) and controls (black). Box plots show median and interquartile range. (c) Gene expression for ligand-receptor gene pairs, dots represent patients. X-axis indicates expression of HAVCR2, ITGB2, ITGAL, IL17A, IL26, CXCL13 in CD8 T cluster 3 cells (top row) or cluster 5 cells (bottom row). Y-axis indicates expression in epithelial cluster 2 cells (CEACAM1, ICAM1, IL17RA, IL17RC, IL20RA, IL10RB) or CD4 T cluster 6 cells (CXCR5). Unadjusted p-value and FDR (q-value) from differential expression (t-test) of the mean of each gene pair between cases and controls. (d) Differential expression analysis of means of gene pairs, focusing on putative communication between CD8 T cell clusters from Fig. 2a, b and other immune, epithelial, and mesenchymal cell lineages (Methods). Top: Tissue ITGB2HI circulatory (CD8-11, 3). Bottom: Activated effector CD8 TRM (ITGAEHI GZMBHI) (CD8-7, 2, 5). Heatmap color indicates FC between irColitis cases and controls. White dot indicates FDR < 5%. (e) DE analysis with five major tissue cell lineages (columns). Gene pairs are in five biological themes. White dots indicate FDR < 5%. (f) Left: correlations of cell cluster abundances within cases (x-axis) and within controls (y-axis), signed Spearman p-values. Right: abundance of tissue CD8-5 cells (x-axis) vs E-18 cells (y-axis). (g) Left: correlations of cell cluster abundances (cases and controls), x-axis Spearman correlation, y-axis -log10 p-value. Right: abundance of tissue CD8-11 (x-axis) vs E-13 (y-axis). E/M: epithelial and mesenchymal nuclei, B: B cells, MP: mononuclear phagocytes, CD8: CD8 T/gamma delta T/NK cells, CD4: CD4 T cells. Related to Figs. 2–5.
Extended Data Fig. 9 Spearman analysis of ligand-receptor gene pairs across multiple colon mucosal cell types.
(a) Volcano plot of Spearman correlations of percent of cells expressing pairs of genes, for all 10,101 gene-and-cell-type pairs (1,441 total unique gene pairs, tested for each pair of cell lineages). X-axis indicates correlation coefficient and y-axis p-value. (b) Number of gene pairs with FDR < 5% is depicted as an edge connecting each pair of cell lineages. Edge thickness and color is proportional to the number of gene pairs. (c) Spearman correlation of percent of cells with gene expression for selected pairs of genes for each pair of major cell lineages. (d) Heatmap color depicts the signed Spearman p-value for each pair of genes, for each pair of cell lineages. White dots indicate FDR < 5%. Columns indicate pairs of different cell lineages. E: epithelial and mesenchymal nuclei, B: B cells, MP: mononuclear phagocytes, CD8: CD8 T/GDT/NK cells, CD4: CD4 T cells. Pairs of genes in boldface are shown in panel (C). Related to Figs. 2–5.
Extended Data Fig. 10 Illustration of epithelial-immune interactions associated with irColitis.
Cartoon illustrating the major findings of our study showing that irColitis is defined by the colon mucosal expansion of ITGAEHI CD8 TRM T cells expressing CXCL13 and Th17 gene programs, ITGB2Hi CD8 T cells that may recirculate, Tregs, CD4 T cells expressing CXCL13 and IL17A, and ISGHi MP cells. Putative ligand/receptor pathways that recruit circulating cells to the endothelium (CX3CR1-CX3CL1, ITGAL/ITGB2-ICAM-1/2, CXCR3-CXCL9/10/11) and retain expanded CD8 T cells in tissue (ITGAL/ITGB2-ICAM-1, CXCR3-CXCL9/10/11) are shown. Epithelial defects in irColitis include decreased stem cells, increased transit amplifying cells, and top crypt epithelial cells with marked upregulation of interferon-stimulated genes (ISGs), CASP1, ZBP1, ICAM1, CD274/PD-L1, and CXCL10/11 and downregulation of aquaporin (AQP) water channel genes. Mesenchymal alterations in irColitis are notable for increased endothelial cells. The white oval at the bottom right shows the part of the crypt depicted in greater detail in the upper part of the cartoon. Related to Figs. 1–6.
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Thomas, M.F., Slowikowski, K., Manakongtreecheep, K. et al. Single-cell transcriptomic analyses reveal distinct immune cell contributions to epithelial barrier dysfunction in checkpoint inhibitor colitis. Nat Med (2024). https://doi.org/10.1038/s41591-024-02895-x
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DOI: https://doi.org/10.1038/s41591-024-02895-x
