Interferon-Gamma–Producing CD8+ Tissue Resident Memory T Cells Are a Targetable Hallmark of Immune Checkpoint Inhibitor–Colitis

1See Covering the Cover synopsis on page 1079; See editorial on page 1106.

2See Covering the Cover synopsis on page 1079; See editorial on page 1106.

3ICI-colitis is a common adverse effect of checkpoint inhibitors, can mimic IBD, and currently has empirically derived treatment guidelines.We identify both unique and overlapping immunopathology in ICI-colitis and UC. CD8+ TRM cells are the key effector cells in ICI-colitis. TRM cells strongly express checkpoint proteins and IFNG. We present the first PD1-inhibitor–associated colitis single-cell analysis that demonstrates consistent TRM cell activation. IFNG-JAK-STAT activation identified tofacitinib as a potential therapy, although IFNG blockade could negatively affect oncological response.This is a small human cohort study. Further investigation will be required to understand the role of the microbiome in TRM cell activation and the safety of JAK inhibition.This analysis of CD8+ TRM cells and associated immune pathways in ICI-colitis provides a basis for targeted therapy development. We provide an immunologic rationale for the use of JAK inhibitor therapy in refractory ICI-colitis.

4ICI-colitis is a common adverse effect of checkpoint inhibitors, can mimic IBD, and currently has empirically derived treatment guidelines.

5We identify both unique and overlapping immunopathology in ICI-colitis and UC. CD8+ TRM cells are the key effector cells in ICI-colitis. TRM cells strongly express checkpoint proteins and IFNG. We present the first PD1-inhibitor–associated colitis single-cell analysis that demonstrates consistent TRM cell activation. IFNG-JAK-STAT activation identified tofacitinib as a potential therapy, although IFNG blockade could negatively affect oncological response.

6This is a small human cohort study. Further investigation will be required to understand the role of the microbiome in TRM cell activation and the safety of JAK inhibition.

7This analysis of CD8+ TRM cells and associated immune pathways in ICI-colitis provides a basis for targeted therapy development. We provide an immunologic rationale for the use of JAK inhibitor therapy in refractory ICI-colitis.

8Immune checkpoint inhibitors (ICIs) are revolutionizing the treatment of melanoma and other cancers but come at the cost of immune-related adverse events (irAEs). These irAEs commonly affect the gastrointestinal tract, with those receiving combination anti–CTLA-4 and PD-1 therapy displaying increased rates of ICI-colitis (32%–37%) compared to those treated with anti–PD-1 monotherapy (4%–6%)., There is a higher incidence of ICI-diarrhea (44%), probably due to unconfirmed colitis. ICI-colitis results in the greatest overall mortality of irAE, although other rarer toxicities (eg, myocarditis) have lower individual survival rates.

9Current management for ICI-colitis includes systemic corticosteroids and subsequent anti-TNFα therapy (infliximab) for inadequately controlled disease. Alternative therapies include anti-α4β7 integrin (vedolizumab) and fecal microbiota transplantation (FMT). These therapeutic approaches are empirically derived from the treatment of idiopathic inflammatory bowel disease (IBD), without an understanding of how analogous this newer entity is to more classical forms of colitis. Refractory cases of ICI-colitis occur, resulting in steroid toxicity and, on occasion, colectomy. It is anticipated that greater insight into the mechanisms underlying ICI-colitis will lead to more targeted treatments. ICI-colitis is heterogeneous, but can mimic UC and, less commonly, Crohn’s disease. We opted to use UC as the external disease control, as both conditions typically affect the rectum and/or sigmoid colon and are amenable to flexible sigmoidoscopy.

10At the outset of this study, little was known about the cellular and molecular pathogenesis of ICI-colitis. The available data suggested that anti–CTLA-4–associated colitis is associated with CD8+ T cells and an up-regulation of Th1 and Th17 effector pathways, including interferon-gamma (IFNG). We previously demonstrated that anti–CTLA-4/PD-1 colitis is associated with high levels of activated (HLA-DR+CD38+) memory CD8+ T cells, and lower proportions of regulatory T cells compared with UC. We hypothesized that CD8+ TRM cells are implicated in the pathogenesis of ICI-colitis, postulating that they would become activated in an off-target consequence, and sought to understand the signaling pathways involved (Figure 1).

11TRM cells are specialist lymphocytes enriched at mucosal sites, including the gut, and display minimal circulation. CD8+ TRM cells classically express CD69 and CD103, and play an important role in mucosal immunity (reviewed in Sasson et al). They are implicated in the pathogenesis of autoimmune skin conditions. TRM-like tumor infiltrating lymphocytes also mediate anti-tumor responses,, and a higher proportion of TRM-like tumor infiltrating lymphocytes correlates with disease-free survival., TRM-like tumor infiltrating lymphocytes express high levels of checkpoint proteins,,, which appear to act primarily as negative regulators rather than markers of exhaustion.

12Recent data utilized single-cell technology to identify cytotoxic CD8+ T cells as the main pathogenic gastrointestinal population in anti–CTLA-4/PD-1 colitis. It was inferred through T cell receptor sequence analysis that these may derive, in part, from TRM cells. Up-regulation of both IFNG and TNFA signaling pathways was identified in the CD4+ and CD8+ T cells of patients with ICI-colitis, acting on the myeloid cellular compartment. Our study supports and extends the prior analysis via a broader range of experimental techniques. We also provide a direct comparison with idiopathic UC and include analysis of anti–PD-1 monotherapy colitis and anti–CTLA-4/PD-1 gastritis. Our data taken together directly implicate IFNG-expressing CD8+ TRM cells as a major pathogenic T cell population in the colon.

13Finally, on the basis of our laboratory data, we predicted that tofacitinib would be an effective therapy in a case of refractory anti–PD-1 colitis and used this successfully. These data, consistent with the subsequently published case reports of successful tofacitinib therapy,, provide compelling insights into the underlying pathogenic mechanisms in this clinical setting and novel pathways to target therapeutically.

14We studied patients with anti–CTLA-4/PD-1 colitis (dual checkpoint inhibitor colitis [DCC]; n = 15), anti–CTLA-4/PD-1 treated with no colitis (DCNC; n = 10), anti–PD-1 colitis (PDC; n = 6), anti–PD-1–treated with no colitis (n = 5), active UC (disease flare assessment; n = 10), and healthy volunteers (HVs; n = 22). The patients with UC and ICI-colitis are reasonably matched in terms of inflammation severity and previous and current therapy but do have a longer median acute flare duration in the UC cohort (see Supplementary Table 1), and the usual caveats must apply when interpreting real-world human data from subjects that are not perfectly aligned. All patients provided written informed consent for participation (Oxford GI Illness Biobank 16/YH/0247 or PRISE [Predicting Immunotherapy Side Effects] study, London-Surrey Research Ethics Committee: REC18/LO/0412). This consent enabled invitation for sigmoidoscopy pre-ICI, at week 5–7 post ICI treatment and at the development of colitis symptoms (see Figure 1). Colitis disease activity is comparable across UC and ICI-colitis. The duration of active colitis and the treatment at the time of sampling are detailed in Supplementary Table 1. Rectal and sigmoid biopsies were protocolized. Individual patient samples studied in each experiment are shown in Supplementary Table 2.

15The clinical characteristics of patients with anti–CTLA-4/PD-1–associated gastritis (n = 4) and HV controls (n = 7) are shown in Supplementary Table 3.

16Colonic biopsies were placed in RPMI media containing penicillin and streptomycin and 10% fetal calf serum. Biopsies underwent enzymatic and mechanical digestion with 1 mg/mL Collagenase D (Sigma-Aldrich) and 100 μg/mL DNAse I (ThermoFisher) and were shaken at 37°C for 1 hour. Biopsies were dissociated using gentleMACS Dissociator (Miltenyi Biotech) and passed through a 70-μm strainer.

17Flow cytometry was performed on freshly isolated mononuclear cells using a near-infrared live/dead stain (Invitrogen) and an initial monoclonal antibody panel (see Supplementary Methods) was performed on a 3-laser LSR Fortessa X-20 (BD Biosciences). An extended TRM cell phenotyping monoclonal antibody panel (see Supplementary Methods) was performed on an Aurora spectral analyzer (Cytek).

18Data were analyzed using FACSDIVA software, version 8.0.1 (BD Biosciences). Gating strategies are shown in Supplementary Figure 2A. Lymphocyte populations are reported as a proportion of parent populations.

19Differences between groups were determined using the unpaired nonparametric Mann-Whitney test. Correlation analysis was performed using the nonparametric Spearman test. All analyses were performed using SPSS software (IBM, Armonk, NY). Medians and interquartile ranges are reported throughout. A P value <.05 was considered statistically significant. When multiple comparisons were performed, a Bonferroni correction was made (see figure legends).

20Multiplex immunofluorescence staining was carried out on 4-μm formalin-fixed paraffin embedded sections using the OPAL protocol (AKOYA Biosciences) on the Leica BOND RXm autostainer (Leica Microsystems). Six consecutive staining cycles were performed using primary antibody-Opal fluorophore pairings. Whole slide scans and multispectral images were obtained on the AKOYA Biosciences Vectra Polaris. Batched analyzed multispectral images were fused in HALO (Indica Labs) to produce a spectrally unmixed reconstructed whole tissue image.

21Targeted gene expression was measured using 150 μg of RNA extracted from pinch biopsies and a 770-gene human autoimmune profiling panel with a custom 10-gene spike in set (Supplementary Methods). Samples were analyzed on an nCounter Sprint profiler with downstream analysis using nSolver freeware (Nanostring), Gene Set Enrichment Analysis (Broad Institute), and R studio (Boston).

22Bulk RNA sequencing (RNASeq) analysis was performed using 900 ng per sample of RNA extracted from pinch biopsies and the GRCH37.EBVB95-8wt reference genome. Total RNA was converted to complementary DNA (cDNA) with second-strand cDNA incorporating a 2'-deoxyuridine 5'-triphosphate. cDNA was end-repaired with PolyA tails and was adapter-ligated. Sequencing was performed on a NovaSeq6000 (Illumina). Bulk RNASeq was analyzed using Partek Flow software. Library generation and sequencing were performed at the Oxford Genomics Centre. Data analysis was performed according to published standards.,,,

23Single-cell (sc)RNASeq libraries were generated using 10X Genomics Chromium scRNA Reagents Kits (v1 Chemistry). Live CD45+ cells were sorted using a FACSAriaIII cell sorter (BD Biosciences) and resuspended in phosphate-buffered saline with 0.04% bovine serum albumin at approximately 1000 cells/μL and loaded onto 2 lanes of the Chromium Controller. Captured cell number was 5876. Library quality and concentration was determined using a TapeStation (Agilent) and Qubit 2.0 Fluorometer (Thermo Fisher). Libraries were sequenced on an Illumina HiSeq 4000 to a mean depth of 64,000 mean reads/cell. Library generation and sequencing were performed at the Oxford Genomics Centre.

24FastQ generation, read alignment, barcode counting, and UMI counting was performed using the Cell Ranger Pipeline, version 2.2.0. Downstream processing steps were performed using Seurat, version 2.3.4. Genes expressed in fewer than 10 cells were removed. Cells with a local minimum of the UMI distribution to the left of the mode UMI count, <500 genes, and >10,000 UMIs, > 2500 genes, and/or > 10% mitochondrial reads were removed. Data were log-normalized and scaled, with cell–cell variation due to UMI counts, percent mitochondrial reads, and S and G2M cell cycle scores regressed out.

25Demultiplexing scRNASeq was run with the inferred genotypes from the bulk RNASeq data that we have sequenced as part of this same project. We used the GATK variant calling pipeline on the samples included in each pool (GX06/GX18) and fed that to demuxlet as described in Kang et al.

26Cellranger (version 3.0.2) mkfastq was applied to the Illumina BCL output to produce FASTQ files. Cellranger count was then applied to each FASTQ file to produce a feature barcoding and gene expression library. Cellranger aggr was used to combine samples for merged analysis.

27We applied scater package to filter out single-cell profiles that were outliers for any metric, as low-quality libraries. Data analysis was performed according to published standards.,,, All datasets used and additional scripts are available online (https://bitbucket.org/Fairfaxlab/prise-sarah-sassion/src/master/).

28We used the area under the curve to calculate whether a T cell reference gene set was enriched within the expressed genes for each cell. We used the Human Protein Atlas database reference gene list for T cells, downloaded from https://www.proteinatlas.org/humanproteome/blood/blood+cells+summary, cell type group enriched genes. the repository is provided as a supplementary file (repository_reference_gene_sets.txt).

29Live CD45+CD3+ T cells were sorted using a FACSAriaIII cell sorter (BD Biosciences). A total of 46,000 T cells were sorted and stained with a cocktail of 70 oligo-conjugated AbSeq antibodies (BD Biosciences; see Supplementary Table 4) for 45 minutes at 4ºC. Cells were then washed to remove residual unbound AbSeq antibodies and loaded onto 3 BD Rhapsody cartridges (BD Biosciences) for single-cell capture.

30Single-cell capture and cDNA library preparation were performed using the BD Rhapsody Express single-cell analysis system (BD Biosciences) and a customized T cell expression panel (Supplementary Table 5), according to the manufacturer’s instructions (for further details including data analysis and quality control, see Supplementary Methods).

31We previously demonstrated that the majority of T cells in the colon of ICI-colitis were CD8+. We sought to determine whether these CD8+ T cells had a TRM cell phenotype. We found that both UC and DCC groups are associated with increased proportions of CD3+ T cells in the affected tissue compared with healthy gut and DCNC groups, respectively (Figure 2Ai). Compared with UC, DCC is associated with proportionately fewer CD4+ T cells and more CD8+ T cells (Figure 2Aii–iii). The proportion of CD8+CD103+ T cells is lower in active UC compared with HVs (Figure 2Aiv). Patients with DCC have a very high proportion of activated CD8+CD103+ T cells, as defined by the co-expression of HLA-DR and CD38 (median 65% of CD8+CD103+ T cells), and this is higher than in DCNC (3%; P <.0001) and UC (13%; P <.01) groups (Figure 2Avi). There is some activation of CD8+103– “non–tissue-resident” T cells in DCC compared with DCNC; however, the proportion is much lower than in the CD103+ subset (Figure 2Av). We used a more stringent definition of CD8+ TRM cells, that is, co-expression of CD69 and CD103 to confirm high levels of cellular activation of CD8+ TRM cells in DCC compared with DCNC (Figure 2Avii–viii).

32Higher cellular activation of CD103+CD69+ CD8+ TRM cells compared to CD103– CD8+ T cells was detected across all patient groups and is represented in Figure 1Aix.

33We investigated whether the proportion of activated CD8+ TRM cells in the colon was an accurate biomarker of the presence of DCC and response to therapy. Overall, CD8+ TRM cell activation positively correlates with endoscopic severity of DCC using the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) score (Figure 2B). UCEIS score details are provided in Supplementary Figure 1. We previously reported that UCEIS can be used as an objective endoscopic marker of ICI-colitis clinical outcomes. We confirmed by multiplexed spectral fluorescence microscopy that in DCC, CD8+ TRM cells reside both in gastrointestinal crypts and in the lamina propria (Figure 2C).

34We investigated whether activation of CD8+ TRM cells was a phenomenon specific to ICI-colitis or was evident in other forms of irAEs, such as ICI-gastritis (Figure 2Di). CD8+ TRM comprise the majority of T cells in the gastric mucosa in both health and anti–CTLA-4/PD-1 gastritis (Figure 2Dii–iii). The proportion of activated CD8+ TRM cells was low in health (<1%; Figure 2Div) and increased in anti–CTLA-4/PD-1 gastritis (30%–51%; Figure 2Div).

35We performed an extended flow cytometric panel to further characterize the CD8+ TRM cells in anti–CTLA-4/PD-1 colitis (Supplementary Figure 2B).

36We performed a 780-gene autoimmune profiling panel using colonic RNA to determine unique and common features among DCC, DCNC, and UC groups. A heatmap of the top 50 defining features demonstrates that DCC is associated with up-regulated STAT1, GBP2, IFI30, GZMB, PSMB9, IFITM1, HLAB, S100A9, and CXCL1 (Figure 3A).

37We identified 259 genes that are up-regulated more than 2-fold across the DCC, DCNC, and UC groups compared to HVs (Figure 3B). Of the 173 genes up-regulated in DCC, only 12 of 173 are common to the DCNC group, indicating these changes are not simply an on-treatment effect. Of the 173 genes up-regulated in DCC, 144 of 173 are in common with UC, and 29 are unique (Figure 3B).

38Exploration of the 173 up-regulated genes in DCC using g:Profiler pathway analysis highlights a number of biological pathways (Figure 3C). The top 30 pathways include defense response and response to external biotic stimulus and cytokine-mediated pathways. The most pathway-specific results include a response/cellular response to IFNG (Figure 3C; Supplementary Table 6). We confirm that the canonical JAK/STAT components of IFNG signaling are up-regulated in anti–CTLA-4/PD-1 colitis compared to controls (Figure 3D).

39Volcano plots showing differentially expressed genes in UC vs HV, DCC vs DCNC, and DCC vs UC are shown in Supplementary Figure 3. Up-regulated genes common to DCC and UC include S100A8, S100A9, and IDO1. DCC is associated with lower expression of canonical B cell markers CD19, MS4A1(CD20), and CD22 compared to UC.

40RNASeq analysis from bulk RNA extracted from colonic biopsies confirms the transcriptomic signature associated with DCC is distinct from UC (Figure 4A and B). As opposed to the Nanostring panel, which selects for genes expressed by lymphocytes, the bulk RNASeq analysis is predominated by epithelial signals. Analysis of modular hallmark gene sets demonstrates patients with DCC have highly expressed IFNG response, in excess of TNFA signaling (Figure 4C).

41Single-cell analysis formed 8 main clusters (Figure 5A) that include T cells (clusters 2, 4, and 5), B cells (clusters 1, 6, and 8), plasmablasts (cluster 7), and monocytes (cluster 3). UC has a higher proportion of B cells and plasmablasts compared with ICI-colitis groups and HV (Figure 5B). The defining features of each cluster are shown in Figure 5C and Supplementary Table 7. Co-expression of ITGAE (CD103) and CD69 is strongest in cluster 4 and identifies cells with a TRM cell phenotype. The DCC group has the highest proportion of cells with a TRM cell phenotype (cluster 4; Figure 5B). These cells have a very high expression of immune checkpoint transcripts, including CTLA4, PDCD1 (PD-1), TIGIT, HAVCR2(TIM-3), and LAG3, which are minimally detected or absent in the other clusters (Figure 5D).

42We further investigated the T cell component of the scRNAseq dataset (Figure 5E and G). CD8+ T cells with a TRM cell phenotype can be identified as cells co-expressing transcripts for CD8A, CD8B, ITGAE, and CD69 (Figure 5F), and there is evidence for these cells expressing high amounts of HLADR, GZMB, and PRF1 (Figure 5H). We have demonstrated by Nanostring and bulk RNASeq that IFNG signaling pathways are enriched in anti–CTLA-4/PD-1 colitis, but the source of IFNG message cannot be determined at a bulk RNASeq level. Using scRNASeq, we are able to confirm IFNG production in T cells that overlap with the TRM cell zones (Figure 5F and I) and that IFNG production is detected in all UC, DCC, and anti–PD-1 colitis (PDC) groups (Figure 5I).

43Heatmap analysis demonstrates that each group has a distinct set of up-regulated genes (Figure 5J). T cells from patients with DCC have significantly higher expression of MT2A, S100A11, GNLY, MT1E, GZMB, and CCL20. T cells from patients with PDC have significantly higher expression of DUSP4, NR3C1, HLADPB1, HLADR85, and HSPA1A. T cells from patients with UC have significantly higher expression of SELL, CCR7, IFITM3, FAM118A, and FCMR.

44The defining characteristics of ITGAE+(CD103+) colonic T cells (compared to ITGAE– T cells) are shown in Figure 5K. These cells have significantly higher expression of CCL5, NKG7, CD8A, GZMA, TMIGD2, TNFRSF9, GZMB, GNLY, IFNG, CXCR6, LAG3, and TIGIT, and lower expression of CCR7, CD28, and CD4.

45To confirm our finding that CD8+ TRM cells display high expression of IFNG, including in patients with PDC, we performed a second single-cell protein and RNASeq experiment, this time sorting on live CD45+CD3+ T cells (Figure 6). The cells form 7 clusters (Figure 6A and Supplementary Table 8), including 3 clusters of CD8+ TRM cells (clusters 1, 2, and 6), a tissue-resident CD4+ T cell population (cluster 4), and 3 populations of nonresident classical T cells (clusters 0, 3, and 5). There is a clear separation of CD4+ (Figure 6B) and CD8+ T cell populations (Figure 6C), with CD103(ITGAE) predominantly overlapping with CD8+ T cells (Figure 6D). Co-expression of CD8, CD103(ITGAE), and CD69 defines the CD8+ TRM cell clusters (Figure 6E). Expression of IFNG overlaps with CD8+ TRM cell populations and, to a lesser degree, the CD4+CD103+ T cells (Figure 6F). T cells from patients with PD-1 colitis are predominantly in the CD8+ TRM cell populations (clusters 1 and 6) and are distinct from T cells from active UC, which are predominantly in the conventional and CD4+ T cell zones (Figure 6G).

46We extracted data pertaining to the 3 CD8+ TRM cell populations (Figure 6H) and found that the CD8+ TRM cell population 2, comprised mostly cells from patients with PD-1 colitis, has markedly high expression of activation markers (HLADR, CD38) and checkpoint molecules (CTLA4, TIM3). This transcriptome is distinct from CD8+ TRM cell population 1, which has a greater representation in health and expresses high levels of IL7R and CCL4. The smaller CD8+ TRM cell population 3 again contains unique features, including KIR2DL4, KLRC3, and includes a γδ T cell component.

47A 61-year-old man with metastatic non-small-cell lung cancer was treated with combination chemotherapy (carboplatin and pemetrexed) and pembrolizumab (see Figure 7A). After 2 cycles, he developed symptoms of ICI-colitis, which was confirmed endoscopically and histologically (UCEIS and Nancy score details are in Supplementary Figure 1). He did not respond to intravenous steroids or 2 doses of infliximab. We had previously seen a rapid resolution of refractory anti–CTLA-4/PD-1 colitis with FMT treatment in a different patient, with corresponding decreases in CD8+ TRM cell activation (Figure 7B). We prioritized FMT over vedolizumab as he required rapid induction therapy. There was a modest initial clinical response to FMT; however, on follow-up sigmoidoscopies over the subsequent 12 weeks, he continued to have refractory colitis of an equivalent endoscopic and histologic severity. This was confirmed by flow cytometry (Figure 7C). Our data collectively provided us with evidence that tofacitinib, a JAK inhibitor, was a potential therapeutic option. After discussion with the patient, tofacitinib was prescribed at 10 mg twice per day, with concomitant venous thromboembolism prophylaxis. He made an immediate clinical response and achieved endoscopic and histologic remission by 5 weeks (Figure 7A), commensurate with resolution of activated CD8+ TRM cells (61%–7%; Figure 7D). Tofacitinib was ceased after 6 weeks, and he restarted chemotherapy. He has made a good oncological response and 10 months later colitis has not recurred.

48An RNAplex assay of colonic mucosal RNA pre- and post-tofacitinib therapy shows down-regulation of key JAK-STAT signaling components known to be down-stream of IFNG signaling (Figure 7F). The overall transcriptional response to tofacitinib therapy demonstrates down-regulation of transcripts including S100A8, IDO1, and S100A9 (Figure 7G).

49The profound success of ICIs has resulted in broader applications, and an increasing incidence of ICI-colitis that is frequent and has the greatest overall irAE mortality.

50Here we present a comprehensive analysis of ICI-colitis using multiparameter and spectral flow cytometry, RNAplex, and bulk and single-cell analysis. We find that in all patient groups, the majority of colon-derived CD8+ T cells are TRM cells, and that in anti–CTLA-4/PD-1 colitis, the highest activation levels are seen in TRM cells. CD8+ TRM cell activation anti–CTLA-4/PD-1 colitis correlates with clinical, endoscopic, and histopathologic findings, and the response to treatment over time. In addition, CD8+ TRM cell activation is also present in ICI gastritis, which involves a distinct epithelium and microenvironment from the colon, and this may have implications for the pathogenesis of extragastrointestinal irAEs.

51We have made a direct comparison with active UC and find that the level of CD8+CD103+ T cell activation is significantly greater in the DCC group. We identify both the immunologic commonalities with UC and disparate features. In addition to differences in T cell populations, we demonstrate that B lineage populations are over-represented in the UC samples and comparatively absent in ICI groups, indicating that pathogenic B cells likely play a smaller role in ICI-colitis. The IFNG-response pathway is up-regulated in both UC and DCC, but with an enhanced activation in both DCC and PDC indicating that targeting this pathway may be even more effective in ICI-colitis.

52Our scRNASeq experiments confirm that CD8+ TRM cells are enriched in ICI-colitis, and display the highest proportion of immune checkpoint transcripts, including CTLA4 and PCDC1 (PD-1). This supports recently published data and provides a likely mechanism by which these cells become disproportionally and rapidly activated after ICI administration. We confirm that the production of IFNG clusters in the same region as CD8+ TRM cells, extending the data from Luoma et al, which identified IFNG and TNFA up-regulation in the CD4+ and CD8+ T cells, and acting on the myeloid cellular compartment. Our analysis of ITGAE(CD103)+ T cells reveals an expression profile similar to CD8+ TRM cells in vitiligo, with 5 up-regulated genes that largely relate to cytotoxicity featuring in both datasets: GZMB, GNLY, NKG7, CCL5, and IFNG. Our data suggest that in health, CD8+ TRM cells express a homeostatic signature, including IL7R, but that in ICI-associated colitis, there is significant up-regulation of activation molecules and checkpoint molecules.

53We find compelling evidence for up-regulated IFNG signaling in ICI-colitis, more so than TNFA, which is the current target of ICI-colitis rescue therapy. We present a patient with refractory ICI-colitis who we treated successfully with tofacitinib, and with robust resolution of CD8+ TRM cell activation. This aligns with the recent case reports of successful tofacitinib therapy for treatment-refractory ICI-colitis., We acknowledge that a series of cases cannot conclusively determine a treatment’s large-scale efficacy or safety. IFNG signaling is a well-established pathway in tumor control. Early work in murine models found that neutralizing IFNG interferes with tumor rejection in immunocompetent hosts. Mice lacking IFNG signaling components IFNGR1 or STAT1 develop a higher percentage of tumors at a faster rate. We recognize concern that use of JAK inhibition in ICI-colitis may deactivate not only colonic but also intra-tumoral CD8+ TRM cells, which are a key therapeutic target., In human melanoma, IL-7 signaling, T cell infiltration, and IFNG signaling signatures have a high association with tumor response to immune checkpoint inhibitors, and conversely defects in IFNG signaling, including loss-of function mutations in IFNGR1, JAK1, JAK2, and STAT1, are associated with resistance to checkpoint blockade.,,,, Although larger clinical trials are needed to establish the safety and efficacy of tofacitinib for ICI-colitis, our analysis provides a complete bench-to-bedside cycle: from a novel disease entity, hypothesis, and mechanistic study, to intervention with a rationally repurposed therapeutic. Our data suggest that tofacitinib may be a useful therapy in patients with refractory ICI-colitis as a salvage therapy.

54Our study has limitations. We acknowledge our relatively small cohort of patients and, given the real-world nature of our study, these patients are not perfectly matched. In addition, the relatively low cell number in some of the experiments, notably the initial scRNASeq, mean that the UC and ICI-colitis comparison could bias toward commonalities. Comparison of a chronic disease (UC) with longstanding inflammatory response against an acute iatrogenic colitis may also confound the data. Although the patients with UC do have a longer median flare duration, the cohorts are otherwise well-matched in relation to inflammation severity, and previous and current therapy (see Supplementary Table 1). Future studies could aim to reduce the heterogenicity of enrolled ICI-colitis and comparator where possible, and this may require larger multicenter studies. In addition, although we attempted to study ICI-colitis CD8+ TRM cells ex vivo, the high rates of apoptosis, in keeping with down-regulated Bcl-2 expression (see Supplementary Figure 2), made this challenging. We suggest cluster 4 in our scRNASeq experiment is likely numerically under-represented, given our flow cytometry results were conducted on fresh tissue.

55There remain many unanswered questions, including what factors drive a subset of patients to develop ICI-colitis, leaving others unaffected. We postulate that activated CD8+ TRM cells in ICI-colitis are responding to commensal or pathogenic microbes, and that this results in high levels of cellular activation and IFNG signaling that propagates downstream and widespread tissue activation. Furthermore, the use of FMT for treatment of ICI-colitis (for which we describe one treatment success) implies that replacement of the microbiome may remove the instigating antigen.

56Our work has identified that IFNG-producing CD8+ TRM cells are a cellular hallmark of ICI-colitis. This has important implications for targeted therapy for ICI-colitis, as evidenced by the successful application of a JAK inhibitor. These findings also suggest that medications that specifically target CD103 may prove effective therapy, and note monoclonal antibody targeting the β7 integrin chain. Finally, our data on CD8+ TRM cell activation in both colon and gastric epithelium may have broader relevance for other extra-gastrointestinal irAEs.