Dysregulation of ILC3s unleashes progression and immunotherapy resistance in colon cancer

Jeremy Goc; Mengze Lv; Nicholas J Bessman; Anne-Laure Flamar; Sheena Sahota; Hiroaki Suzuki; Fei Teng; Gregory G Putzel; JRI Live Cell Bank; Gerard Eberl; David R Withers; Janelle C Arthur; Manish A Shah; Gregory F Sonnenberg

doi:10.1016/j.cell.2021.07.029

. Author manuscript; available in PMC: 2022 Sep 16.

Published in final edited form as: Cell. 2021 Aug 17;184(19):5015–5030.e16. doi: 10.1016/j.cell.2021.07.029

Dysregulation of ILC3s unleashes progression and immunotherapy resistance in colon cancer

Jeremy Goc ^1,^2,³, Mengze Lv ^1,^2,³, Nicholas J Bessman ^1,^2,^3,^†, Anne-Laure Flamar ^1,^2,^3,^††, Sheena Sahota ^1,^2,³, Hiroaki Suzuki ^1,^2,^3,^†††, Fei Teng ^1,^2,³, Gregory G Putzel ¹; JRI Live Cell Bank¹, Gerard Eberl ⁴, David R Withers ⁵, Janelle C Arthur ^6,^7,⁸, Manish A Shah ^9,¹⁰, Gregory F Sonnenberg ^1,^2,^3,¹¹

¹Jill Roberts Institute for Research in Inflammatory Bowel Disease, Weill Cornell Medicine, Cornell University, New York, NY USA

²Joan and Sanford I. Weill Department of Medicine, Division of Gastroenterology and Hepatology, Weill Cornell Medicine, Cornell University, New York, NY USA

³Department of Microbiology and Immunology, Weill Cornell Medicine, Cornell University, New York, NY USA

⁴Microenvironment & Immunity Unit, Institut Pasteur, Paris, France

⁵College of Medical and Dental Sciences, Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United Kingdom

⁶Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

⁷Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

⁸Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

⁹Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA

¹⁰Gastrointestinal Oncology Program, Center for Advanced Digestive Care, Sandra and Edward Meyer Cancer Center, New York-Presbyterian Hospital, Weill Cornell Medicine, Cornell University, New York, NY 10021, USA

¹¹Lead Contact

^†

Present address: New Jersey Medical School, Center for Immunity and Inflammation, Department of Medicine, Newark, NJ, USA

^††

Present address: Swim Across America and Ludwig Collaborative Laboratory, Immunology Program, Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA

^†††

Present address: EA Pharma Co., Ltd. Kanagawa, Japan.

Author contributions

Conceptualization, J.G, and G.F.S.; Methodology, J.G. and G.F.S.; Software and formal Analysis, J.G. and G.G.P.; Investigation, J.G., M.L, N.J.B., H.S., A.L.F., S.S., F.T. D.R.W and J.A.C.; Resources, JRI LCB, G.E., and M.A.S.; Visualization and Writing – Original Draft, Review & Editing, J.G. and G.F.S.; Project Administration and Funding Acquisition, J.G. and G.F.S.

^✉

Correspondence: gfsonnenberg@med.cornell.edu

PMCID: PMC8454863 NIHMSID: NIHMS1728516 PMID: 34407392

Summary:

Group 3 innate lymphoid cells (ILC3s) regulate immunity and inflammation, yet their role in cancer remains elusive. Here, we identify that colorectal cancer (CRC) manifests with altered ILC3s that are characterized by reduced frequencies, increased plasticity, and an imbalance with T cells. We evaluated the consequences of these changes in mice and determined that a dialogue between ILC3s and T cells via major histocompatibility complex class II (MHCII) is necessary to support colonization with microbiota that subsequently induce type-1 immunity in the intestine and tumor microenvironment. As a result, mice lacking ILC3-specific MHCII develop invasive CRC and resistance to anti-PD-1 immunotherapy. Finally, humans with dysregulated intestinal ILC3s harbor microbiota that fail to induce type-1 immunity and immunotherapy responsiveness when transferred to mice. Collectively, these data define a protective role for ILC3s in cancer and indicate that their inherent disruption in CRC drives dysfunctional adaptive immunity, tumor progression and immunotherapy resistance.

Graphical Abstract

graphic file with name nihms-1728516-f0001.jpg

In brief:

ILC3s are altered in the tumor microenvironment of humans with colorectal cancer, resembling those found in the inflamed intestine. In mice, ILC3s regulate adaptive immunity, shape the microbiota composition and protect from tumor progression as well as colorectal cancer immunotherapy resistance.

Introduction:

Interactions between mammals and microbes profoundly impact cancer (Dzutsev et al., 2017; Routy et al., 2018a; Helmink et al., 2019). For example, several chronic infections are directly linked to cancer development (Garrett, 2015; Grivennikov et al., 2010; de Martel and Franceschi, 2009; de Martel et al., 2020). This occurs through a number of well-described mechanisms and vaccination is an important approach to prevent these infection-associated cancers (Grivennikov et al., 2010; de Martel and Franceschi, 2009; de Martel et al., 2020). In contrast, mammals are continuously colonized with trillions of normally beneficial microbes, termed the microbiota, that also substantially impact cancer, but the mechanisms by which this occur remain poorly understood (Dzutsev et al., 2017; Garrett, 2015; Grivennikov et al., 2010).

One important connection with cancer is the ability of the microbiota to modulate the immune system and promote chronic inflammation (Belkaid and Hand, 2014; Grivennikov et al., 2010; Honda and Littman, 2012; Hanahan and Weinberg, 2011; Trinchieri, 2012). This can occur in diseases such as inflammatory bowel disease (IBD), which is associated with a loss of immunologic tolerance, the development of inflammatory immune responses directed against intestinal microbiota (Maloy and Powrie, 2011; Blander et al., 2017; Uhlig and Powrie, 2018; Plichta et al., 2019), and an increased risk for colorectal cancer (CRC) (Beaugerie and Itzkowitz, 2015). However, most CRC cases develop independently of IBD and yet are still directly linked with substantial microbiota-dependent engagement of inflammatory pathways, such as the cytokine IL-23 and associated T helper (T_H)17 cell responses (Langowski et al., 2006; Grivennikov et al., 2012; Wang et al., 2014; Grivennikov et al., 2010). It remains poorly defined how malignant transformation in the intestine promotes these inflammatory responses to the microbiota, and the development of novel strategies to prevent this outcome could be beneficial in CRC.

Emerging evidence indicates that microbiota also substantially impacts the responsiveness of tumors to therapies, including revolutionary immune checkpoint blockade (Iida et al., 2013; Viaud et al., 2013; Vétizou et al., 2015; Sivan et al., 2015; Daillère et al., 2016; Gopalakrishnan et al., 2018; Matson et al., 2018; Routy et al., 2018b; Tanoue et al., 2019; Mager et al., 2020; Davar et al., 2021; Baruch et al., 2021). Provocative data from these studies demonstrated that the microbiota is required for the success of checkpoint blockade in mouse models, and further, that the composition or presence of specific microbes in patient populations are sufficient to augment therapeutic outcome when transferred to germ-free or antibiotics (ABX)-treated recipient mice (Vétizou et al., 2015; Sivan et al., 2015; Gopalakrishnan et al., 2018; Matson et al., 2018; Routy et al., 2018b; Tanoue et al., 2019). Moreover, recent studies demonstrated that fecal microbiota transplantation (FMT) may be harnessed as a method to boost responsiveness to checkpoint blockade in resistant cancer patients (Baruch et al., 2021; Davar et al., 2021). These studies provide one explanation for the variable response of individual cancer patients or tumor types to checkpoint blockade therapy (Sharma et al., 2017). In particular, CRC and other gastrointestinal cancers demonstrate a remarkably poor response to checkpoint blockade, with a notable exception of tumors harboring high microsatellite instability (Le et al., 2015). These studies provoke a number of fundamental gaps in knowledge, including a need to define the mechanisms by which the microbiota regulate checkpoint blockade, the pathways that control colonization with beneficial microbes, as well as the relevance of these findings to CRC patients who are known to exhibit microbiota dysbiosis and resistance to checkpoint blockade (Brennan and Garrett, 2016; Sharma et al., 2017).

Innate lymphoid cells (ILCs) are a recently appreciated family of tissue-resident innate lymphocytes that play critical roles in regulating host-microbe interactions at mucosal barrier surfaces of the mammalian body (Artis and Spits, 2015; Colonna, 2018; Vivier et al., 2018). In particular, group 3 ILCs (ILC3s) are innate counterparts of T_H17 cells, express retinoic acid-related orphan receptor gamma t (RORγt), are enriched in the intestine, and regulate interactions with the microbiota through production of cytokines such as IL-17 and IL-22 (Sonnenberg and Artis, 2015; Vivier et al., 2018). ILC3s also play a broader role in modulating adaptive immunity through direct cellular interactions with T cells (Sonnenberg and Hepworth, 2019). The CCR6⁺ lymphoid tissue inducer (LTi)-like subset of ILC3s express major histocompatibility complex class II (MHCII) and have the potential to process and present antigens to CD4 T cells (von Burg et al., 2014; Hepworth et al., 2013, 2015). MHCII⁺ ILC3s inhibit microbiota-specific effector CD4 T cell responses and prevent intestinal inflammation (Hepworth et al., 2013, 2015; Melo-Gonzalez et al., 2019). Further, translational analyses revealed that ILC3s or effector functions are dramatically reduced in the inflamed intestine of individuals with IBD (Bernink et al., 2013, 2015; Hepworth et al., 2015; Li et al., 2017; Zhou et al., 2019; Martin et al., 2019), a process that is driven in part by pro-inflammatory stimuli and conversion to a group 1 ILC (ILC1) phenotype (Vonarbourg et al., 2010; Bernink et al., 2013, 2015).

Early studies thus far have reported that ILC3s infiltrate tumors and influence cancer mainly via production of cytokines (Bergmann et al., 2017; Chan et al., 2014; Goc et al., 2016; Irshad et al., 2017; Kirchberger et al., 2013; Koh et al., 2019; Liu et al., 2019; Xuan et al., 2019). Conversely, other reports suggest that ILC3s induce lymphoid neogenesis (Carrega et al., 2015), promote vasculature changes (Eisenring et al., 2010; Nussbaum et al., 2017) or protect from genotoxic stress (Gronke et al., 2019). Many of these studies were necessarily performed using mice that lack adaptive immunity, raising questions about whether cross-talk between ILC3 and adaptive immunity is also important in cancer (Hanahan and Weinberg, 2011; Fridman et al., 2017; Bruni et al., 2020). In this study, we hypothesize that ILC3s are involved in the regulation of adaptive immunity, the control of host-microbiota interactions, and the progression or therapeutic responsiveness of tumors in the context of CRC.

Results:

CRC in humans and mice manifests with substantially altered ILC3s

We performed a comprehensive analysis of ILCs from resected colorectal tumors, adenomas and matched-adjacent non-malignant tissues from a cohort of 72 individuals. The clinical and histopathologic characteristics of this cohort are described in Table S1. ILCs were identified by gating on viable CD4⁺5cells that lack lineage markers and express CD127. We subsequently gated for CRTH2 and CD117 to distinguish all ILC subsets (Fig. 1A) as previously described (Bernink et al., 2013, 2015; Björklund et al., 2016). ILC3s were dominant among total ILCs within both non-inflamed adjacent and tumor tissues (Fig. 1A), however tumor tissues exhibited a significant decrease in ILC3s and a concomitant increase in ILC1s (Fig. 1B). The frequency of ILC3s among CD45⁺ cells was also significantly decreased within tumors relative to matched non-malignant adjacent tissues (Fig. S1A). We further observed a similar pattern in pre-cancerous adenoma tissues (Fig. 1C and S1B). These alterations in ILC3s from colorectal tumors and adenomas were comparable to what is observed within inflamed tissues of patients with IBD (Fig. 1D and S1C), and is consistent with chronic inflammation documented in the tumor microenvironment of CRC (Mantovani et al., 2008; Terzić et al., 2010).

Figure 1: — Tumor-infiltrating and lamina propria cells were respectively isolated from resected tumors, adenoma, IBD lesions and adjacent tissues from CRC and IBD patients. (A) ILCs were gated as CD45⁺ and lineage (CD3, CD4, CD8, CD11c, CD14, CD19, CD34, CD94, CD123, FcεR1α) negative, CD127⁺ and further divided by expression of CRTH-2 (ILC2s; red) or CD117 (ILC3s; blue) or as lacking expression of both markers (ILC1s; black). After data analysis and filtering, 47 CRC and 7 adenoma paired-samples reached minimal threshold for ILC3 numbers and were validated for further analyses. (B-D) Frequencies of ILC subsets among total ILCs were compared between (B) tumors, (C) adenomas, (D) IBD lesions and non-malignant/non-inflamed adjacent tissues. (E,F) Age- and sex-matched CDX2^Cre-APCmin^+/F mice developing spontaneous colonic adenoma-carcinoma were examined for the frequency of ILC3s within adenoma and adjacent non-malignant colon tissues. (E) ILCs were gated as CD45⁺ and lineage (B220, CD3ε, CD5, CD8, CD11b, CD11c, Ly6G) negative, CD90.2⁺CD127⁺ and further divided by expression of GATA-3 (ILC2s; red) or RORγt (ILC3s; blue) or as lacking expression of both markers and expressing T-bet and NKp46 (ILC1s). (F) Frequencies of ILC subsets among total ILCs and CD45⁺ cells were compared between adenoma and non-malignant colon tissues. (G) Representative pictures of immunofluorescence staining of frozen-tissue sections from mouse colon adenomas isolated from APC^min/+ mice. Sections were stained for DAPI (grey), CD3 (red), RORγt(green), and IL-7Rα(blue). Scale bar = 200 μm. White stars indicate CD3^-IL-7Ro⁺RORy⁺ILC3s. Data include (A,B) n=47 CRC, (C) n=7 adenomas (D) n=16 IBD patients or (E,F) three independent experiments pooled. (B-D,F) Results are shown as box plots with 10/25/50/75/90 percentiles. Statistical analyses between groups are performed using a (B) paired Student’s t, (C) Wilcoxon or (D,F) Mann-Whitney U test. * p < 0.05, ** p < 0.01, *** p <0.001, **** p <0.0001. See also Figure S1 and Table S1.

We next examined a spontaneous mouse model of colonic adenoma-carcinoma by crossing CDX2^Cre with APCmin^{floxed/floxed} mice (CDX2^Cre-APCmin^+/F) (Hinoi et al., 2007). ILCs lacking lineage markers and expressing CD90.2 and CD127 were identified within the colon adenoma of CDX2^Cre-APCmin^+/F mice and further gated ILC1s, ILC2s and ILC3s (Fig. 1E). Consistent with human samples, a significant decrease of ILC3 frequencies among total ILCs and a concomitant but non-significant ILC1 increase was observed in adenomas relative to the non-malignant colon (Fig. 1F). In contrast with human, ILC subsets are dominantly represented by ILC2s in the mouse colon, but their frequency was not altered in tumors (Fig. 1F). Further, the frequency of all ILC subsets was significantly decreased among CD45⁺ cells within the adenoma relative to non-malignant colon (Fig. 1F). A similar reduction in ILC3s occurred in a chemically-induced CRC model (Fig S1D-G). Immunofluorescence analyses revealed that infiltrating ILC3s within spontaneously developing or chemically-induced mouse adenomas selectively localized within lymphoid aggregates (resembling induced-tertiary lymphoid structures) arising at the invasive margin of the tumor microenvironment (Fig. 1G and S1H). We further detected ILC3s that localized closely with T cells within these structures suggesting the possibility of cell-cell interactions between these cells (Fig. 1G and S1H). Altogether, these results indicate that both human and experimental mouse CRC manifests with substantially altered ILC3s that are present at reduced frequencies relative to adjacent tissues and localize primarily within tertiary lymphoid structures.

ILC3s exhibit increased cellular plasticity in CRC

To define how ILC3s change in the context of human CRC, we sort-purified these cells and performed transcriptional profiling by RNA sequencing. For all the samples, we validated an abundant expression of ILC-related (IL2RA, IL2RG, IL7R, RORA) and ILC3-related (IL1R1, RORC, IL23R, ID2, KIT) transcripts as previously described (Björklund et al., 2016), as well as low expression of specific transcripts related to other cellular lineages (Fig. 2A and S2A). Unbiased principal component analysis (PCA) revealed significantly distinct clustering between ILC3s isolated from CRC tumors versus matched adjacent tissues (Fig. 2B). One distinction is the appearance of an ILC1-related gene signature selectively in tumor-infiltrating ILC3s, including a significant increase of CXCR3, SH2D1A, IL6R, CD28, and TNFRSF1B transcripts (Fig. 2C), suggesting that ILC3s are actively transitioning to an ILC1 phenotype as reported in other contexts (Cella et al., 2019; Mazzurana et al., 2019). Consistent with this, tumor-infiltrating ILC3s exhibited a significant increase of ILC1-related and cytotoxic genes that are gradually expressed during an ILC3-to-ILC1 transition (Cella et al., 2019), including TIGIT, CD247, CD244, SIRPG, PRF1, GNLY, GZMA, GZMB and GZMK (Fig. 2D). Conversely, we observed a significant decrease of IL1R1, IL2, IL22, CCL20, CSF2, FOXO1, TNFSF13B and ZBTB24 (Fig. 2D), a group of ILC3-associated genes that are down-regulated during an ILC1 transition (Cella et al., 2019). Transcripts for Aiolos (IKZF3), a transcriptional regulator that favors ILC1 conversion, and downstream target MYC, were also over-expressed in tumor-infiltrating ILC3s (Fig. 2D).

Figure 2: — (A) Histogram demonstrating mean normalized counts from RNA-Seq of genes expressed in sort-purified ILC3s. Markers of highly expressed core ILC and ILC3-related transcripts are indicated. (B) PCA analysis of cell-sorted ILC3 RNA-Seq performed on 4 tumor and 4 non-malignant adjacent tissues from CRC patients. (C,D) Heatmap of normalized counts comparing gene expression for transcripts related to (C) ILC1 genes and (D) to ILC3-ILC1 transitional signature. Scale based on Z-score of Log₂(normalized counts). (E,F) Analysis of transitional ILC3a to ILC1a subsets on 20 CRC patients. (E) ILC3s, ILC1s and transitional populations were defined as Lineage (CD3, CD4, CD8, CD14, CD19, CD34, CD123, FcεRIa) negative and NKp44⁺CD56⁺. ILC3a (CD103^-CD300LF⁺CCR6⁺), ILC3b (CD103⁺CD300LF⁺CCR6⁺), ILC1b (CD103⁺CD300LF^-CCR6⁺) and ILC1a (CD103⁺CD300LF^-CCR6^-) frequencies were then compared between tumors and healthy adjacent tissues. (F) Mean Fluorescence Intensity (MFI) expression of markers related to ILC3-ILC1 transition among ILC3a and ILC1a subsets. (G) ILC3 subsets were gated according to the expression of CD45RA and NKp44 to identify NKp44⁺, CD45RA⁺ and NKp44^-CD45RA^- ILC3s and (H) frequencies of ILC3 subsets among total ILC3s were compared between tumor and non-malignant adjacent tissues from CRC patients. (I,J) Age- and sex-matched RORγt-eGFP mice were treated with the AOM/DSS CRC protocol and examined for the frequency of ILC3 subsets within adenoma and non-malignant colon tissues. (I) ILC3 subsets were divided by expression of NKp46 (red) and CCR6 (blue) and (J) compared between adenoma and non-malignant colon tissues. Data include (E,F) n=20, (H) n=47 CRC or (J) three independent experiments pooled. (E,F,H,J) Results are shown as (E) the mean ± SEM or (F,H,J) box plots with 10/25/50/75/90 percentiles. Statistical analyses between groups are performed using a (B) PERMANOVA, (C-D) the DESeq2 R package with FDR threshold of 0.1, (E,F) Wilcoxon, (H) paired Student’s t or (J) Mann-Whitney U test. * p < 0.05, ** p < 0.01, *** p <0.001, **** p <0.0001. See also Figure S2.

We utilized a previously described protocol to quantify several intermediate cellular states (ILC3a>ILC3b>ILC1b>ILC1a) in the spectrum of ILC3-to-ILC1 transition (Cella et al., 2019). In a cohort of 20 patients, we identified that CRC tumors contain a significant decrease of an ILC3a population and a concomitant significant increase of an ILC1a population, in both frequency and total cell numbers relative to adjacent non-malignant tissues (Fig. 2E and S2B). We further observed a progressive decrease of the ILC3-related markers IL-1R, CD117 and CD127 in ILC3a and total ILC3s, as well as an increase of the ILC1-transitioning markers CD39 and SIRPG in the ILC3a population (Fig 2F and S2C). We further confirmed an increase of ILC1-related markers CD39, CD244 and SIRPγ within the ILC1a population (Fig 2F).

The transcriptional identity of ILC3s in CRC was also altered, including a significant increase of transcripts related to antigen presentation, co-stimulation, cell migration, inhibitory receptors, proliferation and cytokine signaling (Fig. S2D). Interestingly, we detected a group of genes (CD244, CD274, CTLA4, PDCD1, PDCD1LG2) related to an “exhaustion-like” phenotype previously described in T cells (Fig. S2D). A previous single cell RNA sequencing approach suggested that human ILC3s exhibit three distinct populations, consisting of cytokine-producing NKp44⁺ ILC3s, naïve-like CD45RA⁺ ILC3s, and NKp44^- CD45RA^- ILC3s (Björklund et al., 2016). Utilizing flow cytometry (Fig. 2G), we identified that NKp44⁺ ILC3s, which are the dominant subset in non-malignant colon, are significantly decreased in adenomas and tumors (Fig. 2H and S2E), consistent with this population converting to an ILC1 phenotype (Bernink et al., 2013; Cella et al., 2019). Conversely, we detected a significant increase in CD45RA⁺ ILC3s in tumors, while the proportion of NKp44^-CD45RA^- ILC3s was significantly higher in both tumors and adenomas relative to adjacent tissues (Fig. 2H and S2E).

We examined experimental mouse CRC and observed comparable changes in ILC3 subsets, including a significant decrease in NKp46⁺ ILC3s and a significant increase in CCR6⁺ ILC3s (Fig. 2I, 2J and S2F). We also confirmed an increased staining of PD-1 on tumor-infiltrating ILCs in CRC (Fig. S2G) and fate-mapping experiments revealed that a substantial portion of colonic ILC3s lost RORγt protein (converting to a so called “ex-ILC3”) in a chemically-induced CRC model (Fig. S2H). Collectively, these results demonstrate that ILC3s within CRC are fundamentally altered, exhibiting reduced frequencies, altered subset heterogeneity, and increased plasticity towards ILC1s or ex-ILC3s.

ILC3 and T cell interactions support microbiota that drive type-1 immunity and become altered in CRC

We next assessed the interactions between ILC3s and adaptive immunity in CRC and observed significant increases in the ratio of T cells to ILC3s in tumors relative to non-malignant adjacent tissues, and this is associated with an increase in the frequency of T_H17 cells and decrease in the frequency of T_H1 cells (Fig. 3A and S3A). A similar pattern was found within pre-cancerous adenoma tissues (Fig 3B) and inflamed tissues from IBD patient (Fig. 3C). We previously reported that ILC3s limit T_H17 cells and intestinal inflammation through direct interactions via MHCII (Hepworth et al., 2013, 2015). Examination of MHCII on ILC3s in our human samples revealed an overall increase in HLA-DR (Fig. S3B), however the ratio of CD4 T cells to MHCII⁺ ILC3s remained significantly increased in CRC (Fig. 3D and S3C). In our mouse models, ILC3s were the dominant MHCII⁺ innate lymphocyte (Fig. S3D) and it was possible to detect MHCII⁺ ILC3s at tertiary lymphoid structures within CRC (Fig. S3E). Further, the overall ratio of CD4 T cells to MHCII⁺ ILC3s was also increased relative to adjacent tissues (Fig 3E, 3F and S3F, S3G). This suggests that there is a disruption in the dialogue between MHCII⁺ ILC3s and T cells in CRC.

Figure 3: — (A-D) Tumor-infiltrating and lamina propria cells were respectively isolated from resected (A,D) CRC tumors, (B) adenomas, (C) IBD lesions and adjacent tissues from CRC and IBD patients. (A-F) Ratios and frequencies of ILC3s and T cells were determined in human (A,D) CRC tumors, (B) adenomas, (C) IBD lesions and adenomas from (E) CDX2^Cre-APCmin^+/F and (F) RORγt-eGFP mice treated with the AOM-DSS chemically-induced CRC protocol. (G) Proportion of colonic T_H1 CD4 and T-bet⁺ CD8 T cells were compared in age- and sex-matched MHCII^∆ILC3 and MHCII^F/F littermate control mice. (H) Principal Coordinates Analysis (PCoA) analysis of 16S microbiota composition, determined by 16S sequencing, from *Rag1*^−/− mice before (day 0) and after (day 46) adoptive transfer with CD4 T cell from MHCII^∆ILC3 and MHCII^F/F mice. (I) Proportion of colonic T_H1 CD4 and T-bet⁺ CD8 T cells were compared in C57BL/6J mice after FMT with luminal microbiota from MHCII^∆ILC3 and MHCII^F/F mice. Data include (A,D) n=47 CRC, (B) n=7 adenomas, (C) n=16 IBD patients or (E,F) three or (G-I) two independent experiments. Results are shown as box plots with 10/25/50/75/90 percentiles. Statistical analyses between patient groups are performed using a (A,D) paired Student’s t, (B) Wilcoxon, (C,E-G,I) Mann-Whitney U or (H) a PERMANOVA test. * p < 0.05, ** p < 0.01, *** p <0.001, **** p <0.0001. See also Figure S3.

While MHCII⁺ ILC3s can limit T_H17 cells (Hepworth et al., 2013, 2015), it remains unclear if there is a link between these populations and the altered T_H1 cell response in CRC. To directly explore this, we examined the colon of mice with an ILC3-specific deletion of MHCII (MHCII^ΔILC3). In this context we observed a significant decrease of T_H1 cells, as well as T-bet⁺ CD8 T cells within the colon relative to controls (Fig. 3G). We hypothesized that this broad impact on type-1 immunity is indirect and results from T cell-dependent alterations in the microbiota. Indeed, recent reports demonstrated that specific microbiota support T_H1 cell and CD8 T cell immunity in the gut (Atarashi et al., 2017; Gopalakrishnan et al., 2018; Tanoue et al., 2019). To test this hypothesis, we performed 16S sequencing and determined that MHCII^ΔILC3 mice exhibit significantly distinct microbiota from littermate controls (Fig. S3H). Altered T cell responses promoted this phenotype, as transfer of colonic CD4 T cells from MHCII^ΔILC3 mice was sufficient to induce greater compositional changes in the microbiota of recipient Rag1^−/− mice relative to recipients receiving CD4 T cells from littermate controls (Fig. 3H and S3H). We further observed that FMT from MHCII^ΔILC3 mice into ABX-treated wild-type recipient mice induced significantly lower frequencies of colonic T_H1 and T-bet⁺ CD8 T cells relative to recipients of FMT from littermate controls (Fig. 3I). Overall, our results demonstrate that a dialogue between MHCII⁺ ILC3s and CD4 T cells is disrupted in CRC, and that these cellular interactions are necessary for immunologic homeostasis in the gut, subsequently supporting colonization of microbiota that promote type-1 immunity.

MHCII⁺ ILC3s limit the progression and invasion of CRC

We next directly examined how MHCII⁺ ILC3s impact experimental CRC. Strikingly, deletion of ILC3-specific MHCII resulted in significantly higher weight loss over the course of the experiment, increased spleen weight, and reduced colon length (Fig. S4A-C). We further confirmed that this mouse model remained selective to ILC3 and does not target MHCII on other innate or adaptive immune cells (Fig. S4D). MHCII^ΔILC3 mice also exhibited a significant decrease of colonic T_H1 and T-bet⁺ CD8 T cells in this CRC model (Fig. 4A). While littermate controls exhibit typical luminal polyps selectively localized at the distal colon, MHCII^ΔILC3 mice display significantly reduced number, size and load of macroscopically visible polyps (Fig. 4B, C). However, histological analyses revealed that MHCII^ΔILC3 mice were not protected from tumorigenesis and rather displayed highly differentiated and invasive β-catenin⁺ adenocarcinoma that predominantly arises within the submucosa and the muscularis of the colon (Fig. 4D), corresponding to more aggressive progression of CRC. These highly invasive and flat colorectal tumors have been reported in CRC patients, are associated with IBD, and are difficult to clinically manage (Bressenot et al., 2014; Beaugerie and Itzkowitz, 2015). Blinded histopathologic analyses revealed that MHCII^ΔILC3 mice exhibited a significantly increased dysplasia score (Fig. 4E), characterized by the development of adenocarcinoma, an overall increased dysplasia involvement in the entire colon, and invasive tumorigenesis (Fig. 4F) in the submucosa layers of the colon as compared to littermate controls.

Figure 4: — (A-F) Age- and sex-matched MHCII^∆ILC3 mice and MHCII^F/F littermate controls were treated with the AOM-DSS chemically-induced CRC protocol. (A) Proportion of colonic T_H1 CD4 and T-bet⁺ CD8 T cells and (B) macroscopic visualization of colon tumor at the end-point of the protocol with (C) polyp counts, size and load. (D) Swiss-rolled colons were fixed in formalin, paraffin-embedded, cut for 5 μm sections and H&E (top) or β-catenin (bottom) stained for histology. Scale bar: 200 μm, black arrows indicate malignant areas. (E) Score for tumor dysplasia and (F) dysplasia and invasion involvement. (G-I) APC^min/+-MHCII^ΔILC3 crossed-mice and APC^min/+-MHCII^F/F littermate controls were scored for (G) tumor dysplasia, (H) tumor number and (I) were followed for survival. (A-I) Data include (A) two, (C-F) five or (G-I) three independent experiments pooled. (A,C,F,H) Results are shown as the mean ± SEM. Statistical analyses were evaluated using a (A,C,E,F,H) Mann-Whitney U or (I) Logrank Mantel-Cox test. * p < 0.05, ** p < 0.01, *** p <0.001, **** p <0.0001. See also Figure S4.

These findings were not specific to the chemically-induced model, as MHCII^ΔILC3 mice crossed with the APC^min/+ CRC model (MHCII^ΔILC3-APC^min/+) also resulted in a similar increased dysplasia score (Fig. 4G) and a higher number of advanced tumors (Fig. 4H). Further, MHCII^ΔILC3-APC^min/+ mice exhibited a significant reduction in overall survival relative to littermates (Fig. 4I). These results collectively demonstrate that an appropriate dialogue between MHCII⁺ ILC3s and T cells is necessary to preserve type-1 immunity in the context of cancer, thus limiting susceptibility to highly invasive colon tumors.

MHCII⁺ ILC3s limit resistance to checkpoint immunotherapy

We next investigated whether this ILC3 dialogue with T cells and the microbiota impacts checkpoint blockade immunotherapy. To address this question, we subcutaneously implanted the MC38 colon tumor cell line into littermate control and MHCII^ΔILC3 mice. In this context, we observed that ILC3-specific MHCII did not impact tumor growth (Fig. S5A). In contrast, when we treated these mice with anti-PD-1 monoclonal antibody (mAb), we observed that tumors are robustly controlled in littermate controls, while MHCII^ΔILC3 mice exhibited a significant resistance (Fig. 5A, 5B). Comparable results were observed in the melanoma B16-F10 model (Fig S5B, S5C). To explore the mechanisms by which this occurs, we profiled the implanted tumor microenvironment. Although ILC3s did not infiltrate the tumor in this subcutaneous model (Fig. S5D), we observed that MHCII^ΔILC3 mice exhibited a striking decrease in both T_H1 cells and T-bet⁺ CD8 T cells within the MC38 tumor (Fig. 5C). Following anti-PD-1 mAb treatment, there was also a significant increase in responding PD-1⁺ CD8 T cells in littermate controls, as previously reported (Wei et al., 2017), while there was a lack of response in MHCII^ΔILC3 mice (Fig. 5D). Thus, MHCII⁺ ILC3s also support type-1 immunity in a systemic context and prevent T cell resistance to checkpoint blockade immunotherapies.

Figure 5: — (A-F) MHCII^ΔILC3 mice and MHCII^F/F littermate controls, (G,H) germ-free mice transplanted with luminal fecal microbiota from MHCII^ΔILC3 mice or littermate controls, or (I,J) C57BL/6J mice treated with DSS were injected subcutaneously with MC38 and treated with either an anti-PD-1 or a control mAb at the indicated days. (A,G,I) Tumor growth curve and (B,H,J) tumor weight with mean ± SEM pooled from (A,B,I,J) three or (G,H) two independent experiments are shown. (C) Proportion of tumor-infiltrating T_H1 CD4 and T-bet⁺ CD8 T cells and (D) PD-1⁺ CD8 T cells were compared between MHCII^∆ILC3 and littermate control mice. (E,F) Weighted UniFrac PCoA analysis of 16S sequencing luminal colonic microbiota from MC38 tumor-bearing MHCII^∆ILC3 and littermate control mice treated with (E) control or (F) anti-PD-1 mAbs. Results are shown as the mean ± SEM. Statistical analyses were evaluated using a (A,G,I) two-way ANOVA, (B-D,H,J) Mann-Whitney U or (E,F) PERMANOVA test. * p < 0.05, ** p < 0.01, *** p <0.001, **** p <0.0001. See also Figure S5 and Table S2.

MC38-implanted MHCII^ΔILC3 mice exhibited a comparable α-diversity but significant and consistent changes in the composition of the intestinal microbiota that were independent of anti-PD-1 mAb treatment (Fig. 5E, 5F and S5E, S5F). Compositional changes included bacteria from the Bacteroidales and Clostridiales orders, with increased abundance of operational taxonomic units (OTUs) in MHCII^ΔILC3 mice belonging to the Bacteroides genus, and Ruminococcaceae and Lachnospiraceae families. We also observed a decrease of OTUs from the Clostridiales and Bacteroidales orders (Table S2). Further, FMT of the microbiota from non-responsive MHCII^ΔILC3 mice was sufficient to transfer significant resistance to anti-PD-1 mAb treatment in wild-type germ-free recipient mice (Fig. 5G, 5H). We also tested whether we could recapitulate this by inducing intestinal inflammation alone. We accomplished this with repeated exposure to dextran sulfate sodium (DSS), a context where we observe a significant decrease in colonic ILC3s (Fig. S5G). Following recovery, we observed that mice with prior DSS-induced intestinal inflammation exhibited a comparable tumor growth, but a significant resistance to anti-PD-1 mAb treatment relative to naïve mice (Fig. 5I, 5J). Collectively, our results demonstrate a critical role for MHCII⁺ ILC3s in preventing resistance to checkpoint blockade immunotherapy and indicate that this mechanistically occurs by controlling immune homeostasis in the intestine and supporting colonization with microbiota that are required for optimal type-1 immunity.

Humans with IBD and altered ILC3s harbor microbiota that promote resistance to immunotherapy

We next hypothesized that IBD patients harbor a microbiota that fails to support type-1 immunity and hinders immunotherapy success. We found that colorectal biopsies of patients with active IBD contained reduced frequencies of ILC3s and increased T cell/ILC3 ratios relative to biopsies from healthy controls (Fig. 6A). Microbial sequencing from these donors revealed significant compositional modifications in the luminal microbiota (Fig. 6B). To test whether these microbial changes impact type-1 immune responses and efficacy of checkpoint blockade, we performed a FMT from healthy subjects and IBD patients (Table S3) to cohorts of wild-type mice pre-treated with ABX as previously described (Routy et al., 2018b). Permutation-based testing of microbiome similarity between mice sharing the same human donor confirmed a successful transfer of numerous microbiota strains (Fig. 6C). Likewise, a linear mixed effect model showed that the relative abundance of several OTUs in recipient mice was significantly correlated with donor identity, even controlling for cage effects. These analyses allowed us to identify the most significantly transferred OTUs, including members of the Bacteroides, Erysipelotrichia and Alistipes genera (Table S4).

Figure 6: — (A) Frequencies of ILC3s among CD45⁺ cells and T cell/ILC3 ratio comparison between biopsies from healthy and IBD donors. (B) Weighted UniFrac PCoA analysis of 16S sequencing of microbiota from 3 IBD and 4 healthy donors. (C) Weighted UniFrac distances between pairs of mouse fecal samples, showing donor effect. (D-H) ABX-treated mice were transplanted with microbiota from IBD and healthy donors, injected subcutaneously with MC38, and treated with either anti-PD-1 or an isotype control mAb at the indicated days. (D) Proportion of T-bet⁺ colonic CD4 T cell, (E) tumor growth curve and (F) tumor weight. (G-H) Correlation analyses between the response score to anti-PD-1 mAb and the relative bacterial abundance in animals treated with anti-PD-1 mAb. Results are shown as (A) box plots with 25/50/75 quartiles or (D-F) the mean ± SEM. (D-H) Data include (D) three or (E-H) four independent experiments pooled. Statistical analyses were evaluated using a (B) PERMANOVA, (C) permutation testing (D,F) Mann-Whitney U, (E) two-way ANOVA and (G,H) Spearman correlation test. * p < 0.05, ** p < 0.01, *** p <0.001, **** p <0.0001. See also Figure S6 and Table S3 and S4.

We next examined the impact on type-1 immunity and observed that mice receiving FMT from IBD patients had a significant decrease in colonic T_H1 cells relative to recipients of FMT from healthy donors (Fig. 6D). These mice were then inoculated subcutaneously with MC38 and treated with isotype or anti-PD-1 mAb (Fig. S6A). There were no significant differences in α-diversity or tumor growth among recipient mice, but mice receiving microbiota from healthy donors responded to anti-PD-1 immunotherapy, while mice receiving microbiota from IBD donors exhibited a significant resistance (Fig. 6E and S6B). These results were reproduced in cohorts of mice receiving FMTs from four healthy individuals and three IBD patients (Fig. 6F and S6C). Compositional shifts in microbiota between experiments included significantly decreased representation of Bacteroidetes phylum in mice receiving microbiota from IBD patients (Fig. S6D).

Our analyses also revealed an inverse relationship between Bacteroidales and Clostridiales orders, with Bacteroidales abundance positively associated, and Clostridiales negatively associated with anti-PD1 mAb responsiveness (Fig. 6G). We further identified 6 bacterial OTUs (Fig. 6H) that are significantly correlated with anti-PD-1 response, all of them belonging to the Bacteroides genus and successfully transferred in our FMT (Table S4 and Fig. S6E). Specifically, the abundance of 4 OTUs (OTU2, OTU92, OTU38 and OTU78) significantly correlated with responsiveness to therapy, whereas 2 OTUs (OTU6 and OTU163) were associated with resistance (Fig. 6H). Altogether, our results demonstrate that in the healthy intestine, a dialogue between ILC3s and T cells is necessary to control immunologic homeostasis and shape the microbiota in a manner that supports type-1 immunity (Fig. S6F). This dialogue is disrupted in the context of intestinal inflammation or CRC, resulting in specific compositional shifts of the microbiota that fail to induce optimal type-1 immunity, facilitating CRC progression or resistance to checkpoint blockade (Fig. S6F).

Discussion

ILC3s play a pivotal role in host-microbiota homeostasis (Sonnenberg and Artis, 2015; Vivier et al., 2018), but their role in cancer remains elusive. Our study demonstrates that ILC3s are fundamentally altered within the tumor microenvironment of CRC patients and in mouse models of CRC. This impairment of ILC3 is associated with an increased transitional state to ILC1s or ex-ILC3s, which have recently been reported to be driven by IL-23 and TGF-β (Cella et al., 2019), two cytokines that are abundant in CRC (Friedman et al., 1995; Langowski et al., 2006; Grivennikov et al., 2012; Tsushima et al., 1996). Therefore, while ILC3s infiltrate tumors and localize in tertiary lymphoid structures, the inherent development of CRC re-directs these populations towards an ILC1 phenotype. Similar findings have been reported in IBD, where an impairment of ILC3s may contribute to the development of an inflammatory environment (Bernink et al., 2013, 2015; Hepworth et al., 2015; Li et al., 2017; Zhou et al., 2019; Martin et al., 2019).

We found that human colon tumors are characterized by a significant decrease in ILC3s, a strong imbalance with T cells, a concomitant increase of T_H17 cells, and a substantial decrease in T_H1 cells relative to adjacent mucosa, suggesting that CRC progression is associated with impaired dialogue between innate and adaptive immunity in the gut. We previously reported a direct link by which antigen-presenting ILC3s limit microbiota-specific T_H17 cells in the intestine (Hepworth et al., 2013, 2015), and now we provide new data demonstrating that impairment of antigen-presenting ILC3s indirectly limits T_H1 cells and type-1 immunity in the intestine. The latter pathway involves a T cell-dependent shift in the microbiota composition, which subsequently reduces the ability of the microbiota to support T_H1 cell and CD8 T cell mediated type-1 immunity relative to microbiota from a healthy intestine. These compositional shifts in the microbiota occur in patients with IBD and are sufficient to transfer altered type-1 immunity to wild-type recipient mice. This is in agreement with prior reports that specific consortia of microbes are sufficient to promote type-1 immune responses (Sivan et al., 2015; Atarashi et al., 2017; Gopalakrishnan et al., 2018; Matson et al., 2018; Routy et al., 2018b; Tanoue et al., 2019), and our data provide evidence that a dialogue between MHCII⁺ ILC3s and CD4 T cells is important for maintaining this function of the microbiota.

Deletion of ILC3-specific MHCII in mice also results in increased susceptibility to invasive and flat colon tumor development, suggesting that MHCII⁺ ILC3s act as critical regulators that prevent intestinal inflammation and the transition to cancer. Elevated T_H17 cells and defects in type-1 immunity have been associated with CRC progression (Wu et al., 2009; Grivennikov et al., 2012) and linked to poor clinical outcomes (Tosolini et al., 2011; Bindea et al., 2013). Therefore, it is likely that this aggressive tumor phenotype is collaboratively driven by an exacerbated T_H17 cell response and impaired type-1 immunity. Further, the phenotype of tumors in mice lacking ILC3-specific MHCII also mirrors those that develop in humans with colitis-associated cancers (CAC). CAC are molecularly distinct, often develop in flat dysplasia with indistinct margins, and in a field of concomitant inflammation, thus making endoscopic detection and resection challenging (Mattar et al., 2011; Beaugerie and Itzkowitz, 2015). Our results demonstrate that disruption of a dialogue between MHCII⁺ ILC3s, T cells and the microbiota is a previously unappreciated mechanism driving invasive CAC, and further provides a new model to study this aggressive disease.

Revolutionary checkpoint blockade immunotherapies are an important approach to combat cancer, but responsiveness remains highly heterogeneous across patients (Sharma et al., 2017). CRC and other gastrointestinal cancers exhibit poor responsiveness, with a recent study reporting only 11% of mismatch repair-proficient tumors responding to checkpoint blockade (Le et al., 2015). Variations in gut microbiota could explain these diverse therapeutic responses as distinct compositions are associated with success or failure in some cancer patients, and FMT is sufficient to transfer these phenotypes to mice (Gopalakrishnan et al., 2018; Mager et al., 2020; Matson et al., 2018; Routy et al., 2018b). FMT also has the potential to break immunotherapy resistance in melanoma cancer patients (Baruch et al., 2021; Davar et al., 2021), and often a type-1 immune response is one of the best predictors of treatment response (Litchfield et al., 2021; Rozeman et al., 2021). Our data indicate that impaired ILC3s and intestinal inflammation are sufficient to shape the microbiota in a manner that fails to promote anti-tumor type-1 immunity and rather causes resistance to checkpoint blockade. These results suggest that MHCII⁺ ILC3s may serve as gatekeepers to limit microbial shifts in these contexts, and subsequently support type-1 immune responses, uncovering a new pathway to protect against checkpoint blockade resistance. This also suggests that the inherent reduction of ILC3s elicits inflammation and microbial shifts that drive resistance to checkpoint blockade in CRC.

Defining how microbiota impact therapy responsiveness is currently incomplete, therefore limiting the potential to predict or prevent resistance. We identify that patients with impaired ILC3s and intestinal inflammation exhibit distinct microbial signatures in Bacteroides species that significantly track with resistance to anti-PD-1 immunotherapy. These findings have implications both for the management of IBD patients and the use of checkpoint blockade in gastrointestinal cancers. For example, our results suggest that supporting ILC3s, limiting intestinal inflammation or manipulating the microbiota could hold the key to boosting type-1 immunity in CRC patients, thus limiting tumor progression and increasing responsiveness to checkpoint blockade. These results also have broad implications to our understanding of ILC3s and host-microbiota interactions in multiple cancer types and will pave the way towards targeted approaches to manipulate these pathways to improve cancer therapies.

Limitations of the Study

Although our study provides a comprehensive understanding of the role of ILC3s in cancer, several limitations should be mentioned. First, while we define how a dialogue between MHCII⁺ ILC3s and T cells impacts T_H17 cells and type-1 immunity, it remains possible that other T cell populations are impacted and deeper analyses with single-cell RNA-sequencing would comprehensively define the evolution of every T cell compartments during homeostasis, cancer and immunotherapy. Second, the precise localization of ILC3s within human CRC was not achievable due to technical limitations, and it will be important for future studies to quantify ILC3s in this manner across multiple mouse models and clinical samples. Finally, our data demonstrating the impact of altered microbiota from MHCII^ΔILC3 mice and IBD patients on immunotherapy resistance was performed in the gold-standard subcutaneous tumor implantation model, but additional studies should be conducted to extend our findings to other types of tumors and in spontaneous or orthotropic models, as well as to determine whether FMT could improve immunotherapy responsiveness in CRC patients.

STAR★Methods

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact:

Gregory F. Sonnenberg (gfsonnenberg@med.cornell.edu)

Materials Availability

This study did not generate new unique reagents.

Data and Code Availability

All data including raw sequencing reads are uploaded to the SRA and GEO and are publicly available as of the date of publication. The accession numbers are listed in the key resources table.

The paper does not report original code.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Experimental Model and Subject Details

Human samples collection

A total of 92 CRC and adenoma and 16 IBD colon tissue samples were obtained and cryopreserved from adult patients. Informed consent was obtained from all subjects and tissues were obtained following Institutional Review Board-approved protocols from the Center for Advanced Digestive Diseases (CADC) and the JRI IBD Live Cell Bank Consortium at Weill Cornell Medicine. Subject baseline characteristics for these samples are listed in Table S1 and S3. Twenty samples were provided by the Cooperative Human Tissue Network (CHTN), which is funded by the National Cancer Institute. Other investigators may have received specimens from the same subjects.

Animal models

C57BL/6J mice and APC^min/+ mice were purchased from the Jackson Laboratory, and were bred and/or maintained at Weill Cornell Medicine. C57BL/6J RORγ-t eGFP mice were provided by G.E. MHCII^∆ILC3 mice and MHCII^F/F littermate controls were generated as previously described (Hepworth et al., 2013) by crossing Rorc^cre mice and H2-Ab1^{floxed/floxed} mice. CDX2^Cre-APCmin^+/F mice were generated by crossing CDX2^Cre with APCmin^{floxed/floxed} mice (Hinoi et al., 2007) and were obtained from the Jackson Laboratory. The APCmin^{floxed/floxed} mice used were targeted on exon 15 (Shibata et al., 1997). ROSA26-RORc^cre-YFP mice were generated by crossing the above Rorc^cre mice with ROSA26-floxed-STOP-YFP mice from the Jackson Laboratory (Srinivas et al., 2001). Wild-type C57BL/6J mice were purchased from Jackson Laboratories, and all other mice were bred and maintained in specific-pathogen-free facilities at Weill Cornell Medicine. Littermates were used as controls in all experiments. Germ-free C57BL/6J were maintained at the Weill Cornell Medicine Gnotobiotic Mouse Facility. Germ-free C57BL/6J and ABX-treated animals were maintained in sterile isocages during all experimental procedures. If not mentioned specifically, adult mice were between 6 and 10-week of age. Male and female mice were used for experiments and were gender-matched; no differences were observed within the parameters analyzed. Littermates of the same sex were randomly assigned to experimental groups. For tumor experiments, mice developing skin ulcerations were euthanized according to approved protocols and excluded from analyses. All protocols were approved by the Weill Cornell Medicine Institutional Animal Care and Use Committee (IACUC), and all experiments were performed according to the guidelines of the IACUC.

Cell lines

The C57BL/6J -derived colon cancer cell line MC38 (Corbett et al., 1975) and the melanoma cell line B16-F10 were maintained at 37°C with 5% CO₂ in RPMI medium supplemented with 10% heat-inactivated fetal bovine serum, penicillin, and streptomycin (Cellgro). The MC38 cell line was kindly provided by Dr. Dmitriy Zamarin and the B16-F10 cell line was purchased from the ATCC. MC38 cells are derived from female mice and B16-F10 cells are derived from male mice. These cell lines have not been authenticated.

Method details

Murine chronic colitis and colorectal cancer induction protocol

The DSS and AOM/DSS mouse models of chronic colitis and CRC, respectively, were established using the protocol previously described (Thaker et al., 2012; Wirtz et al., 2017) with slight modifications. In brief, for the development of chronic colitis, mice were given 3% DSS (MP Biochemicals, colitis grade) in drinking water for 7 days, followed by drinking water for 14 days. This cycle was repeated once. For CRC, mice were given an initial intraperitoneal injection of 12.5 mg/kg AOM and DSS treatment was then started at day 5 after AOM injection. DSS cycle was repeated once for MHCII^∆ILC3 and littermate controls or twice for C57BL/6J mice. Mice were sacrificed, stool and tissue were collected, and colons were examined macroscopically for tumor analyses, then fixed in formalin for paraffin embedding and histology (Bialkowska et al., 2016). Macroscopically visible colon adenomas were quantified with an Olympus SZX12 dissecting scope and sized with a digital caliper.

Murine intestinal tissue isolation

For intestinal lamina propria lymphocyte preparations, intestines were isolated, attached fat removed and tissues cut open longitudinally. Luminal contents were removed by shaking in ice-cold PBS (Corning). For AOM/DSS protocol, distal luminal polyps were excised from the colon with an Olympus SZX12 dissecting scope. Afterwards, mucus was gently removed by forceps. Epithelial cells and intraepithelial lymphocytes were then removed by shaking tissue in HANKS free media (Sigma-Aldrich) containing 2% FBS (Omega Scientific) and 5 mM EDTA (Thermo Fisher Scientific) stripping buffer for 30 min at 37 °C. Samples were then vortexed and the epithelial fraction was discarded. Afterwards, samples were washed by PBS and enzymatic digestion was performed in RPMI containing 10% FBS and 0.4 U/ml dispase (Thermo Fisher Scientific), 1 mg/ml collagenase III (Worthington) and 20 μg/ml DNase I (Sigma Aldrich) on a shaker for 45 min at 37 °C. Leukocytes were filtered through a 70 μm cell strainer and further enriched by a 40% Percoll gradient centrifugation (GE Healthcare).

Human intestinal tissue isolation

Surgical-resection samples from the colon or ileum of patients with colon cancer or IBD were obtained and tumor, IBD-inflamed and non-malignant or non-inflamed regions were isolated by a trained pathologist. Single cell suspensions from intestinal tissues were obtained by incubating adjacent non-malignant tissue for 30 min at 37°C with shaking in stripping buffer containing 5% FCS (Omega Scientific), 1 mM EDTA (Thermo Fisher Scientific) and 1 mM DTT (Sigma-Aldrich) to remove the epithelial layer. Supernatants were then discarded. Tumor and the remaining adjacent tissues were then mechanically dissociated with a sterile scalpel. The lamina propria fraction was obtained by incubating the dissociated tissues for 1 hour at 37°C with shaking in 2 mg/ml collagenase D (Roche), 0.1 mg/ml DNase I (Sigma) and 1 mg/ml of Trypsin Inhibitor (Gibco) digestion solution. Remaining tissues were then filtered through a 70 μm cell strainer. All cells were then viably cryopreserved in 90% FBS and 10% DMSO for side-by-side analysis at a later time point.

For human biopsy samples, de-identified intestinal biopsies from the terminal ileum or the colon of IBD patients or healthy controls donors who did not have IBD were obtained following Institutional Review Board-approved protocols from the JRI IBD Live Cell Bank Consortium at Weill Cornell Medicine. Tissues were processed by first incubating in 1 mM EDTA, 1 mM DTT and 5% FBS for 30 min at 37°C with shaking to remove intestinal epithelial cells. Supernatants were then discarded and the remaining tissues were incubated in 0.5 mg/ml collagenase D and 20 μg/ml DNase I for 1 hour at 37°C with shaking to obtain the lamina propria fraction. Any remaining tissues were also included following mechanical dissociation and filtering through a 70 μm cell strainer. All cells were then viably cryopreserved in 90% FBS and 10% DMSO for future side-by-side analyses. Following thawing and filtering through a 70 μm cell strainer, cryopreserved cells were stained with antibodies for flow cytometry acquisition.

RNA sequencing

Human ILC3s (CD45⁺CD3^-CD4^-CD8^-CD19^-CD94^-CD14^-CD123^-FcεRIa^-CD34^-CD11c^-CRTH-2^-CD127⁺CD117⁺) were sort-purified from surgical-resection samples from the colon of patients with colon cancer. Sorted cells were used to prepare RNA sequencing libraries by the Epigenomics Core at Weill Cornell Medicine, using the Clontech SMARTer Ultra Low Input RNA Kit V4 (Clontech Laboratories). Sequencing was performed on an Illumina HiSeq 4000, yielding 50-bp single-end reads. Raw sequencing reads were demultiplexed with Illumina CASAVA (v.1.8.2). Adapters were trimmed from reads using FLEXBAR (v.2.4) (Dodt et al., 2012) and reads were aligned to the NCBI GRCh37/hg19 human genome using the STAR aligner (v.2.3.0) (Dobin et al., 2013) with default settings. Reads per gene were counted using Rsubread (Liao et al., 2019). Prior to differential expression analysis, genes were prefiltered, keeping only those genes with 50 or more counts in at least two samples. Differential expression analysis was performed using DESeq2 version 1.20.0 (Love et al., 2014) using both site (tumor/adjacent) and patient ID as factors in the design. A false discovery rate of 0.1 was taken to indicate significance. Principal components analysis (PCA) was performed using the top 500 highest variance genes after applying DESeq2’s variance stabilizing transformation. The degree to which samples clustered by site (tumor versus adjacent) in the PCA was assessed using PERMANOVA (Anderson, 2001) as implemented by the adonis function of the vegan R package (Oksanen et al., 2019) using the Euclidean metric and 20,000 permutations.

Immunofluorescence and image analysis

Tissue sections from experimental mice were cut and stained as described previously (Mackley et al., 2015). Briefly, 6-μm-thick sections of tissue were cut, fixed in formalin at 4 °C for 20 min and then stored at −20 °C before staining. Antibodies raised against the following mouse antigens were used: CD3 (clone 145–2C11, dilution 1:100, eBioscience) IL-7Rα (clone A7R34, dilution 1:25, Thermo Scientific), purified RORγt (clone AFKJS-9, dilution 1:30, Thermo Scientific) and HLA-DR (clone M5/114.15.2, dilution 1:200, Biolegend). Detection of RORγt expression required amplification of the signal as described previously (Mackley et al., 2015). Purified rat primary antibodies against the transcription factors were detected with donkey anti-rat-IgG-FITC (dilution 1:100, molecular probes), and then rabbit anti-FITC-AF488 (dilution 1:100, molecular probes) or with donkey anti-rabbit-IgG-AF488 (dilution 1:100, molecular probes). Biotinylated anti-CD3 antibodies were detected with SA-AF555 (dilution 1:100, molecular probes). Sections were counterstained with DAPI (Invitrogen) and mounted using ProLong Gold (Invitrogen). Slides were analyzed on a Zeiss 780 Zen microscope (Zeiss).

Histological staining and histopathology

Tissue samples from the colon tumors and intestines of mice were fixed with formalin, embedded in paraffin and 5-μm sections were stained with hematoxylin and eosin. Histology was scored for dysplasia/tumors by a blinded experienced investigator (J.C.A.). For mice treated with the AOM/DSS chemically-induced CRC protocol (Thaker et al., 2012), dysplasia scoring was based upon a published scoring system (Arthur et al., 2012) as follows: I = no dysplasia; II = mild dysplasia characterized by aberrant crypt foci (ACF); III = gastrointestinal neoplasia (GIN); IV = adenoma, non-invasive severe or high grade dysplasia restricted to the mucosa; V = adenocarcinoma, invasive through the muscularis mucosa; VI = adenocarcinoma, fully invasive through the submucosa and into or through the muscularis propria. Due to the extensive dysplasia throughout the colon of MHCII^∆ILC3 mice, we expanded our scoring system to further distinguish percent involvement of dysplasia and invasive lesions. “Overall dysplasia involvement” score multiplies dysplasia score by % involvement: multiplied by 1 for no dysplasia, 2 for 0–25% colon affected, 3 for 25–50%, 4 for 50% or greater. We used a similar approach to score invasion involvement. Invasive lesions were first categorized as non-invasive (0), invasive through the muscularis mucosa (1), or fully transmural into the muscularis propria (2). This invasion score was then multiplied by the % involvement as above for dysplasia, resulting in an “invasion involvement” score. For the APC^min/+ spontaneous CRC model, dysplasia scoring was based upon a published scoring system (Nalbantoglu et al., 2016) and adapted as follow: I = no dysplasia; II = mild dysplasia characterized by aberrant crypt foci (ACF); III = gastrointestinal neoplasia (GIN); IV = adenoma or adenocarcinoma including at least 9 crypts.

T-cell adoptive transfer

2 × 10⁶ CD4⁺CD3⁺ T cells were sorted from the mesenteric lymph nodes of control or experimental mice to a purity >98% and transferred intravenously to naive C57BL/6J Rag1^−/− recipient mice.

Subcutaneous tumor implantation

5 × 10⁵ tumor cells were injected subcutaneously in 100 μL PBS on the right flank. Tumor size was determined by the formula L * W * D where L = length, W = width, and D = depth, on the indicated days.

In vivo administration of antibodies

Anti-PD-1 (clone RMP1–14) monoclonal antibody was purchased from BioXCell and administered intraperitoneally every 2 days at a dose of 200 μg per mouse starting on day 7 and ending on day 11 post-tumor injections.

Preparation of tumor cell suspensions

Subcutaneous tumors were excised and single cell suspension were obtained after mechanical dilacerations and tumor digestion with 2 mg/ml of collagenase D (Roche) at 37°C for 1 hour. Hematopoietic cells were then enriched by density gradient centrifugation with 40 % Percoll (GE Healthcare).

16S rRNA bacterial sequencing

16S rRNA gene sequencing and OTUs table generation for experiment 1 of the MHCII^ΔILC3 mouse microbiome characterization were performed at the Alkek Center for Metagenomics and Microbiome Research at the Baylor College of Medicine using methods adapted from the NIH Human Microbiome Project (NIH HMP Working Group et al., 2009). In brief, the 16S rDNA V4 region was amplified by PCR and sequenced on the MiSeq platform (Illumina) using a 2×250 bp paired-end protocol. The read pairs were demultiplexed based on unique molecular barcodes, and subsequently merged using USEARCH v7.0.1090 (Edgar, 2010), allowing zero mismatches and a minimum overlap of 50 bases. Merged reads were trimmed at the first base with Q5 after which a quality filter was applied discarding reads with > 0.05 expected errors. Merged and filtered 16S sequences were clustered into Operational Taxonomic Units (OTUs) using the UPARSE algorithm (Edgar, 2013) with a similarity cutoff value of 97%. OTUs were mapped to a modified version of the SILVA Database containing only the 16S V4 region to determine taxonomies. Abundances were recovered by mapping the demultiplexed reads to the UPARSE OTUs.

16S rRNA gene sequencing and OTU table generation for experiment 2 of the MHCII^ΔILC3 mouse microbiome characterization, as well as for the human-to-mouse FMT experiments, were performed at the Microbiome Core Lab of Weill Cornell Medicine. DNA extraction, library preparation, and sequencing were performed following the protocols of the Earth Microbiome Project (Thompson et al., 2017) using primers 515F and 926R (Parada et al., 2016). Libraries were sequenced on an Illumina MiSeq instrument, producing paired-end 250 base pair reads. Raw reads were processed using USEARCH version 11 (Edgar, 2010). Specifically, forward and reverse reads were merged, discarding read pairs with more than 5 mismatches or less than 90% sequence identity in the overlap region. Merged sequences shorter than 300 bp or with an overlap region less than 16bp were discarded. Sequences matching PhiX phage were removed, followed by quality filtering with a maximum expected error number of 1.0. OTU clustering was performed using the USEARCH cluster_otus command with default settings. Merged (pre-filter) reads were mapped to the OTUs, generating a OTUs table. OTUs representative sequences were classified using the SINTAX algorithm (Edgar, 2016) using the Ribosomal Database Project’s training set version 16 (Cole et al., 2014) and with a confidence threshold of 0.8. The phylogenetic tree used to calculate weighted UniFrac distances was generated from the OTUs representative sequences using the USEARCH cluster_aggd command with default settings.

Alpha diversity estimation and principal coordinates analysis (PCoA) were performed using the phyloseq R package (McMurdie and Holmes, 2013) after rarefying OTU tables to at least 10,000 reads. The extent to which samples clustered according to genotype in the PCoA was assessed by applying PERMANOVA (Anderson, 2001) to the weighted UniFrac distances using 20,000 permutations.

Differential abundance testing was carried out on all OTUs with average relative abundances of at least 0.01 using the DESeq2 R package (Love et al., 2014) with default parameters after exporting the data using the function phyloseq_to_deseq2 of the phyloseq R package. The p values from differential abundance testing were corrected for multiple comparisons, with a false discovery rate of 0.1 being taken to indicate significance.

To generate the phylogenetic tree shown in Fig. S6E, bacterial reference genome sequences were downloaded from NCBI and their respective 16S regions were extracted using the USEARCH command search_pcr using the 515F and 926R primer sequences (Parada et al., 2016). A single sequence was allowed to represent each species so long as all paralogs within a genome were at least 99% similar to each other. The standard_fasttree method of ETE-tools (Huerta-Cepas et al., 2016) was used to generate the phylogenetic tree, making use of Clustal Omega (Sievers et al., 2011) for multiple sequence alignment and FastTree 2 (Price et al., 2010) for phylogenetic tree construction.

Fecal microbiota transplantation

For FMT studies, donor fecal samples from five healthy subjects and four IBD patients (with one technical replicate for one patient) were collected and resuspended in anaerobic PBS supplemented with 10% glycerol in an anaerobic chamber. In some cases, a cocktail of antibiotics, ampicillin (1 mg/ml), streptomycin (5 mg/ml), and colistin (1 mg/ml) (Sigma-Aldrich) was added to the sterile water of mice. Fresh fecal suspension (200 μL per mouse) was then administered to recipient germ-free or antibiotics pre-treated C57BL/6J mice by oral gavage. In addition, another 100 μL was applied on the fur of each animal. Transplanted animals were subsequently maintained in a sterile isocage with autoclaved food and water. Two weeks after FMT, tumor cells were injected subcutaneously and mice were treated with anti-PD-1 or control mAbs as described above. Animals were evaluated for successful microbiota-transplantation via 16S sequencing comparison between human donor and recipient mice before further analyzes. Two individuals (one healthy subject and one IBD patient) demonstrating FMT failure were excluded from the analyses.

Quantification and statistical analysis

Standard statistical analysis

Statistical tests were performed with Prism (GraphPad) and R version 3.6.3 (http://www.r-project.org/). Results represent either the mean and error bars depict the SEM or box plots with 10/25/50/75/90 percentiles. PD-1 response score was calculated following the formula: 1 – ((tumor size after anti-PD-1 treatment at day 21)/(Mean tumor size of isotype treated animals at day 21)). P values of mouse datasets were determined by paired or unpaired two-tailed Student’s t-test with a 95% confidence interval. Variance was analyzed using F-test. Welch’s correction was performed in case of unequal variance. Where appropriate, Mann–Whitney U-test, Wilcoxon matched-pairs test, one way or two-way ANOVA followed by Bonferroni post-tests were performed. Unless otherwise stated, p values < 0.05 were considered statistically significant. For the comparison of Kaplan-Meier survival curves, Log-rank (mantel-Cox) test was used. Details of samples number, data representation, and statistical comparison method of each plot can be found in the figures and figure legends.

Permutation test

Permutation testing was used to assess whether the microbiota of mice receiving FMT from the same donor were more similar to each other than those of mice receiving FMT from different donors, taking into account the strong within-cage correlations in microbiome composition. Specifically, the Weighted UniFrac distance was calculated between all pairs of recipient microbiota. The “donor effect” was defined to be the difference between the mean “different cage, different donor” distance and the mean “different cage, same donor” distance. The statistical significance of this donor effect was determined by recalculating the donor effect in 10,000 null datasets generated from the true data by permuting the cages with respect to donors. Thus, in the null datasets any true effect of donor identity is destroyed, while within-cage correlations are preserved. The permutation p value is the proportion of null datasets with donor effects larger than that seen in the true dataset.

Linear mixed-effects model

The correlation of donor identity with recipient microbiome was assessed at the OTU level by means of a linear mixed-effects model, which was fitted using the lme4 R package (Bates et al., 2015). Specifically, the relative abundance of an OTU in recipients was modeled using donor identity (a categorical factor) as a fixed effect and cage identity as a random effect. This model was compared to one without the donor effect, yielding a p value. The test was applied to all OTUs with average relative abundances of at least 0.01. The resulting p values were corrected for multiple comparisons, with false discovery rates less than 0.05 being considered significant.

Additional resources

This study did not generate any additional resources.

Supplementary Material

NIHMS1728516-supplement-1.pdf^{(476.3KB, pdf)}

Figure S1: ILC3s from cancerous or inflamed intestines exhibit a unique phenotype, Related to Figure 1. Tumor-infiltrating and lamina propria cells were respectively isolated from resected (A) tumor, (B) adenoma, (C) IBD lesions or non-malignant/non-inflamed adjacent tissues from CRC and IBD patients. (A-C) Frequencies of ILC subsets among CD45⁺ cells were compared between lesions and adjacent non-inflammatory tissues. (D-H) Age- and sex-matched RORγt-eGFP mice were treated with the AOM/DSS chemically-induced CRC protocol and examined for the frequency of ILC3s within adenoma and non-malignant adjacent colon tissues. (D) ILCs were gated as CD45⁺ and lineage (B220, CD3ε, CD5, CD8, CD11b, CD11c, Ly6G) negative, CD90.2⁺CD127⁺ and further divided (E) by expression of KLRG1 (ILC2s; red) and RORγt (ILC3s; blue) or as lacking expression of both markers and expressing NKp46 and NK1.1 (ILC1s). (F,G) Frequencies of ILC subsets among (F) total ILCs or (G) CD45⁺ cells were compared between adenoma and non-malignant adjacent colon tissues. (H) Representative pictures of immunofluorescence staining of frozen-tissue sections from mouse colon adenomas isolated at the endpoint of the AOM-DSS chemically-induced CRC protocol. Sections were stained for DAPI (grey), CD3 (red), RORγt (green), and IL-7Rα (blue). Scale bar = 50 μm. White stars indicate CD3^-IL-7Rα⁺RORγt⁺ ILC3s. Data include (A) n=47 CRC, (B) n=7 adenomas (C) n=16 IBD patients or (D-G) at least 3 independent experiments pooled. (A-C,F,G). Results are shown as box plots with 10/25/50/75/90 percentiles. Statistical analyses between patient groups were performed using a (A) paired Student’s t, (B) Wilcoxon and (C,F,G) Mann-Whitney U test. * p < 0.05, ** p < 0.01, **** p <0.0001.

NIHMS1728516-supplement-2.pdf^{(1.7MB, pdf)}

Figure S2: ILC3s from CRC exhibit altered heterogeneity and plasticity, Related to Figure 2. (A) Heatmap comparison of counts transcripts from RNA-Seq analysis of ILC3s isolated from CRC and non-malignant adjacent colon tissues. (B) Analysis of transitional ILC3a to ILC1a subsets on 20 CRC patients. ILC3a (CD103^-CD300LF⁺CCR6⁺) and ILC1a (CD103⁺CD300LF^-CCR6^-) cell numbers per grams of tissues were compared between tumors and healthy adjacent tissues. (C) MFI for human ILC3 bona fide markers and (D) Heatmap of normalized counts comparing gene expression for transcripts related to ILC3 functions. Scale based on Z-score of Log₂(normalized counts). (E) Frequencies of ILC3 subsets among ILC3s were determined and compared between colon adenoma and non-malignant adjacent tissues from CRC patients. (F,G) Age- and sex-matched CDX2^Cre-APCmin^+/F mice were examined for the frequency of ILC3 subsets within adenoma and non-malignant colon tissues. (F) ILC3 subsets were divided by expression of NKp46 and compared between adenoma and non-malignant colon tissues. (G) Proportion of PD-1⁺ cells among ILCs was determined between adenoma and non-malignant colon. (H) RORγt expression among “fate-mapped” ILC3s was determined in colon and mesenteric lymph nodes from mice treated with the AOM-DSS chemically-induced CRC protocol. Data include (B) n=20 or (C) n=47 CRC, (E) n=7 adenomas or (F) at least three or (G) two independent experiments pooled. Results are shown as (B) the mean ± SEM or as (C,E-G) box plots with 10/25/50/75/90 percentiles. Statistical analyses between groups were performed using a (B,E) Wilcoxon, (C) paired Student’s t, (D) the DESeq2 R package with FDR threshold of 0.1, or (F,G) Mann-Whitney U test. * p < 0.05, *** p <0.001, **** p <0.0001.

NIHMS1728516-supplement-3.pdf^{(193KB, pdf)}

Figure S3: MHCII⁺ILC3s are imbalanced with CD4 T cells in CRC and required to maintain microbiota-dependent type-1 immunity. Related to Figure 3. (A-C) Tumor-infiltrating and lamina propria cells were isolated from resected tumor or non-malignant adjacent tissues of human CRC patients. (A) T cell/ILC3 ratio normalized per grams of tissue, (B) MHCII MFI among ILC3s cells were determined and compared between tumors and non-malignant adjacent tissues from CRC patients. (C) CD4 T cells/MHCII⁺ ILC3s ratios were determined in adenomas and compared with adjacent non-malignant tissues. (D) MHCII expression on colonic ILCs from CDX2^Cre-APCmin^+/F mice. (E) Representative pictures of immunofluorescence staining of frozen-tissue sections from mouse colon adenomas isolated from APC^min/+ mice. Sections were stained for CD3 or MHCII (red), RORγt (green), and IL-7Rα (blue). Scale bar = 50 μm. (F-G) T cell/ILC3s ratios were determined and compared between adenomas from (F) CDX2^Cre-APCmin^+/F or (G) mice treated with the AOM-DSS chemically-induced CRC protocol and non-malignant colon tissues. (H) PCoA analysis of 16S sequencing microbiota from Rag1^−/− mice before (day 0) and after (day 46) adoptive transfer with CD4 T cell from MHCII^∆ILC3 or MHCII^F/F mice (donors). Data include (A,B) n=47 CRC, (C) n=7 adenomas, (D,F,G) three or (H) are representative of two independent experiments. Results are shown as (A) the mean ± SEM or (B-D,F,G) box plots with 10/25/50/75/90 percentiles. Statistical analyses were performed using a (A,B) paired Student’s t, (C) Wilcoxon, (D) one-way ANOVA, (F,G) Mann-Whitney U or (H) a PERMANOVA test. * p < 0.05, *** p <0.001, **** p <0.0001.

NIHMS1728516-supplement-4.pdf^{(5MB, pdf)}

Figure S4: Selective deletion of ILC3-specific MHCII alters experimental CRC, Related to Figure 4. Age- and sex-matched MHCII^∆ILC3 mice and MHCII^F/F littermate controls were treated with the AOM-DSS chemically-induced CRC protocol. (A) Weight-curve follow-up, (B) spleen weight and (C) colon length at the endpoint of the protocol. (D) Frequencies of MHCII⁺ cells on innate and adaptive immune cell subsets were evaluated in the colon of MHCII^∆ILC3 mice and MHCII^F/F littermate controls at the endpoint of the protocol. ILCs were gated as CD45⁺, lineage (CD3ε, CD5, CD8, B220, CD11c, CD11b, Ly6G) negative, CD90.2⁺CD127⁺ with ILC1s as GATA-3^-RORγt^-T-bet⁺, ILC2s as GATA-3⁺ and ILC3s as RORγt⁺; T cells as CD45⁺CD3ε⁺CD5⁺CD90.2⁺; B cells as CD45⁺CD11b^-CD11c^-B220⁺; cDC as CD45⁺, lineage (CD3ε, CD49b, Siglec-F) negative, CD90.2^-CD64^-LY6C^-CD11C⁺ with cDC1s as CD11b^-XCR1⁺, cDC2s as CD11b⁺XCR1^-; Macrophages as CD45⁺, lineage (CD3ε, CD49b, Siglec-F) negative, CD90.2^-CD64⁺Ly6C^- and Neutrophils as CD45⁺, lineage (CD3ε, CD49b, Siglec-F) negative, CD90.2^-Ly6C⁺Ly6G⁺. Data include (A-C) five or (D) two independent experiments. Results are shown as the mean ± SEM. Statistical analyses were evaluated using a (A) two-way ANOVA or (B-D) Mann-Whitney U test. * p < 0.05, ** p < 0.01, *** p <0.001, **** p <0.0001.

NIHMS1728516-supplement-5.pdf^{(69.6KB, pdf)}

Figure S5: MHCII⁺ILC3s control the efficacy of anti-PD-1 immunotherapy, Related to Figure 5. (A,D) Age- and sex-matched RORγt-eGFP mice or (B,C,E,F) MHCII^∆ILC3 mice and MHCII^F/F littermate controls were injected subcutaneously with (A,D-F) MC38 or (B,C) B16-F10 cell line on the flank and (B,C) treated with either anti-PD-1 or a control mAb at the indicated days. (A,B) Tumor growth curve and (C) weight with mean ± SEM (B,C) pooled from three independent experiments are shown. (D) ILC3s were gated in inguinal draining lymph node (diLN) or tumor cells from MC38 tumor-bearing RORγt-eGFP mice as CD45⁺ lineage (CD3ε, CD5, CD8, NK1.1, B220, CD11c, CD11b, Ly6G) negative, CD90.2⁺CD127⁺RORγt⁺. (E) α-diversity comparison and (F) representative PCoA analysis of 16S sequencing from luminal fecal microbiota composition between MHCII^ΔILC3 mice and MHCII^F/F littermate controls. (G) C57BL/6J mice were treated with water or DSS and proportion of colonic ILC3s among total ILCs was compared. Data include (A-C) three or (E-G) two independent experiments. Shown are (A-C) mean ± SEM or (E,G) box plots with 10/25/50/75/90 percentiles. Statistical analyses were evaluated using a (A,B) two- or (E) one-way ANOVA, (C,G) Mann-Whitney U and (F) PERMANOVA test. p < 0.05, *** p <0.001, **** p <0.0001.

NIHMS1728516-supplement-6.pdf^{(125.4KB, pdf)}

Figure S6: Fecal microbiota transplantation from human donors to recipient mice, Related to Figure 6. (A) Experimental scheme illustrating the experimental FMT procedure from human donors. (B) α-diversity (C) mean tumor weight per groups of recipient mice and (D) microbiota phylum-level composition (averaged per experiment) relative abundance comparison from four experiments between mice transplanted with human IBD or healthy donors and treated with anti-PD-1 mAb. (E) Phylogenetic tree comparing ribosomal rRNA gene sequences from NCBI reference genomes to OTUs significantly associated with donor identity according to a linear mixed-effects model. (F) Schematic model summarizing how dysregulation of an ILC3 and T cell dialogue during IBD or CRC impacts the microbiota, type-1 adaptive immunity, CRC progression and immunotherapy responsiveness. (B-E) Data include four independent experiments. (B,C) Shown are box plots with 10/25/50/75/90 percentiles. Statistical analyses were evaluated using a (B) one-way ANOVA, (C) paired Student’s t or (D) unpaired Student’s t test of cage-averaged relative abundances with False Discovery Rate (FDR) threshold of 0.1. * p < 0.05.

NIHMS1728516-supplement-7.pdf^{(367.4KB, pdf)}

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies

CD94-FITC (DX22) eBioscience Cat # 11–0949-42; RRID:AB_11149673	Thermo Fisher Scientific	Cat # 11–0949-42; RRID:AB_11149673
CD19-FITC (HiB19)	Thermo Fisher Scientific	Cat # 11–0199-42; RRID:AB_10669461
CD123-FITC (6H6)	Thermo Fisher Scientific	Cat # 11–1239-42; RRID:AB_10854578
FcεR1-FITC (AER-37)	Thermo Fisher Scientific	Cat # 11–5899-42; RRID:AB_10732835
CD11c-FITC (BU15)	Thermo Fisher Scientific	Cat # MA1–10085; RRID:AB_11156194
CD14-FITC (TuK4)	Thermo Fisher Scientific	Cat # MHCD1401; RRID:AB_10373108
CD34-FITC (581)	Thermo Fisher Scientific	Cat # CD3458101; RRID:AB_2536499
HLA-DR-PE (L243)	Thermo Fisher Scientific	Cat # 12–9952-42; RRID:AB_1272093
CRTH2-PeCF594 (BM16)	BD Biosciences	Cat # 563501; RRID:AB_2738244
CD3-Alexa Fluor 700 (UCHT1)	Biolegend	Cat # 300424; RRID:AB_493741
CD45RA-Alexa Fluor 700 (HI100)	Biolegend	Cat # 304120; RRID:AB_493763
CD336-APC (P44–8)	Biolegend	Cat # 325110; RRID:AB_2149432
CD183-APC/Cyanine7 (G025H7)	Biolegend	Cat # 353722; RRID:AB_2561423
CD117-PerCP-eFluor 710 (104D2)	Thermo Fisher Scientific	Cat # 46–1178-42; RRID:AB_10598525
CD127-PeCy7 (A019D5)	Biolegend	Cat # 351320; RRID:AB_10897098
CD196-Brilliant Violet 421 (G034E3)	Biolegend	Cat # 353408; RRID:AB_2561356
CD3-Brilliant Violet 650 (UCHT1)	Biolegend	Cat # 300468; RRID:AB_2629574
CD45-Brilliant Violet 605 (HI30)	Biolegend	Cat # 304042; RRID:AB_2562106
CD4-BUV395 (SK3)	BD Biosciences	Cat # 563550; RRID:AB_2738273
CD8-BUV737 (SK1)	BD Biosciences	Cat # 564629; RRID:AB_2744464
CD336-eFluor 450 (44.189)	Thermo Fisher Scientific	Cat # 48–3369-42; RRID:AB_2574057
CCR6-PE (29–2L17)	Biolegend	Cat# 129804, RRID:AB_1279137
CD4-Pe-TexasRed (RM4–5)	Biolegend	Cat# 100566, RRID:AB_2563685
CD90.2-Alexa Fluor 700 (30-H12)	Biolegend	Cat# 105320, RRID:AB_493725
KLRG1-APC (2FA/JLRG1)	Biolegend	Cat# 138412, RRID:AB_10641560
B220-APC/Fire 750 (RA3–6B2)	Biolegend	Cat# 103260, RRID:AB_2572109
CD11b-APC/Fire 750 (M1/70)	Biolegend	Cat# 101262, RRID:AB_2572122
CD11c-APC/Fire 750 (N418)	Biolegend	Cat# 117352, RRID:AB_2572124
CD3-PerCP-Cy5.5 (145–2C11)	Biolegend	Cat# 45–0031-82, RRID:AB_1107000
CD5-PerCP-Cy5.5 (53–7.3)	Biolegend	Cat# 100624, RRID:AB_2563433
CD8-PerCP-Cy5.5 (53–6.7)	Biolegend	Cat# 100734, RRID:AB_2075238
Ly6G-PerCP-eFluor 710 (1A8-Ly6g)	Thermo Fisher Scientific	Cat# 46–9668-82, RRID:AB_2573893
CD127-Pe-Cy7 (A7R34)	Biolegend	Cat# 135014, RRID:AB_1937265
MHCII-eFluor 450 (M5/114.15.2)	Thermo Fisher Scientific	Cat# 48–5321-82, RRID:AB_1272204
CD45-eVolve 655 (30-F11)	Thermo Fisher Scientific	Cat# 86–0451-42, RRID:AB_2574792
NKp46-Brilliant Violet 605 (29A1.4)	Biolegend	Cat# 137619, RRID:AB_2562452
NK1.1-BUV395 (PK136)	BD Biosciences	Cat# 564144, RRID:AB_2738618
CD8-PE-eFluor610 (53–6.7)	Thermo Fisher Scientific	Cat# 61–0081-82, RRID:AB_2574524
RORγt-PE (B2D)	Thermo Fisher Scientific	Cat# 12–6981-82, RRID:AB_10807092
CD335-PE-Dazzle 594 (29A1.4)	Biolegend	Cat# 137630, RRID:AB_2616666
T-bet-eFluor 660 (eBio4B10)	Thermo Fisher Scientific	Cat# 50–5825-82, RRID:AB_10596655
Gata-3-PerCP-eFluor 710 (TWAJ)	Thermo Fisher Scientific	Cat# 46–9966-42, RRID:AB_10804487
CD3-PE/Cy7 (145–2C11)	Biolegend	Cat# 100320, RRID:AB_312685
CD5-PE/Cy7 (53–7.3)	Biolegend	Cat# 100622, RRID:AB_2562773
CD8α-PE/Cy7 (53–6.7)	Biolegend	Cat# 100722, RRID:AB_312761
Ly6G-PE/Cy7 (1A8)	Biolegend	Cat# 127618, RRID:AB_1877261
CD3-FITC (UCHT1)	Biolegend	Cat# 300440, RRID:AB_2562046
CD4-FITC (SK3)	Biolegend	Cat# 344604, RRID:AB_1937227
CD8-FITC (SK1)	Biolegend	Cat# 344704, RRID:AB_1877178
CD103-Brilliant Violet 650 (Ber-ACT8)	BD Biosciences	Cat# 743653, RRID:AB_2741655
CD56-PE-eFluor 610 (CMSSB)	Thermo Fisher Scientific	Cat# 61–0567-42, RRID:AB_2574568
CD336-BUV395 (p44–8)	BD Biosciences	Cat# 744305, RRID:AB_2742135
CD300f-APC (UP-D1)	Thermo Fisher Scientific	Cat# 50–3008-42, RRID:AB_2574195
CD172 g-PE(LSB2.20)	Biolegend	Cat# 336606, RRID:AB_1227766
IL-1RI-Alexa Fluor 700	R and D Systems	Cat# FAB269N, RRID:AB_10971619
CD39-Brilliant Violet 711 (A1)	Biolegend	Cat# 329536, RRID:AB_2814198
CD244 -Brilliant Violet 605 (C1.7)	Biolegend	Cat# 329536, RRID:AB_2814198
CD45-Brilliant Violet 785 (HI30)	Biolegend	Cat# 304048, RRID:AB_2563129
CD279-Brilliant Violet 785 (29F.1A12)	Biolegend	Cat# 135225, RRID:AB_2563680
FOXP3-FITC (FJK-16 s)	Thermo Fisher Scientific	Cat# 11–5773-82, RRID:AB_465243
RORγt-PE-eFluor 610 (B2D)	Thermo Fisher Scientific	Cat# 61–6981-82, RRID:AB_2574650
CD3e-PE-eFluor 610 (145–2C11)	Thermo Fisher Scientific	Cat# 61–0031-82, RRID:AB_2574514
GATA-3-eFluor 660 (TWAJ)	Thermo Fisher Scientific	Cat# 50–9966-42, RRID:AB_10596663
T-bet-Brilliant Violet 421 (4B10)	Biolegend	Cat# 644816, RRID:AB_10959653
I-A/I-E-Brilliant Violet 650 (M5/114.15.2)	Biolegend	Cat# 107641, RRID:AB_2565975
CD335-Brilliant Violet 711 (29A1.4)	Biolegend	Cat# 137621, RRID:AB_2563289
CD8-Brillant Violet 650 (53–6.7)	Biolegend	Cat# 100742, RRID:AB_2563056
I-A/I-E-BUV 395 (2G9)	BD Biosciences	Cat# 743876, RRID:AB_2741827
CD3-APC (145–2C11)	Thermo Fisher Scientific	Cat# 17–0031-82, RRID:AB_469315
CD5-APC (53–7.3)	Biolegend	Cat# 100626, RRID:AB_2563929
CD4-Brilliant Violet 605 (GK1.5)	Biolegend	Cat# 100451, RRID:AB_2564591
CD45-FITC (30-F11)	Thermo Fisher Scientific	Cat# 11–0451-85, RRID:AB_465051
B220-PE (RA3–6B2)	Thermo Fisher Scientific	Cat# 12–0452-82, RRID:AB_465671
I-A/I-E-APC (M5/114.15.2)	Biolegend	Cat# 107614, RRID:AB_313329
Ly6C-APC-eFluor 780 (HK1.4)	Thermo Fisher Scientific	Cat# 47–5932-82, RRID:AB_2573992
CD11b-PerCP-Cyanine5.5 (M1/70)	Thermo Fisher Scientific	Cat# 45–0112-82, RRID:AB_953558
CD11c-PE/Cyanine7 (N418)	Biolegend	Cat# 117318, RRID:AB_493568
CD3-Brilliant Violet 421 (145–2C11)	Biolegend	Cat# 100341, RRID:AB_2562556
CD49b-Brilliant Violet 421 (DX5)	BD Biosciences	Cat# 563063, RRID:AB_2737983
Siglec-F-BV421 (E50–2440)	BD Biosciences	Cat# 562681, RRID:AB_2722581
CD64-Brilliant Violet 605 (Clone X54–5/7.1)	Biolegend	Cat# 139323, RRID:AB_2629778
XCR1-Brilliant Violet 650 (ZET)	Biolegend	Cat# 148220, RRID:AB_2566410
CD127-Brilliant Violet 711 (A7R34)	Biolegend	Cat# 135035, RRID:AB_2564577
Ly6G-BUV395 (1A8)	BD Biosciences	Cat# 563978, RRID:AB_2716852

Biological samples

Human CRC and IBD samples	Center for Advanced Digestive Diseases (CADC) and the JRI IBD Live Cell Bank Consortium at Weill Cornell Medicine	N/A
Human CRC samples	Cooperative Human Tissue Network (CHTN)	N/A

Chemicals, peptides, and recombinant proteins

Colistin sulfate salt	Sigma-Aldrich	Cat# C4461
Streptomycin sulfate salt	Sigma-Aldrich	Cat# S6501
Ampicillin Trihydrate	Chemcruz	Cat# sc-254945A
Azoxymethane	Sigma Aldrich	Cat # A5486
Dextran Sulfate Sodium Salt, colitis grade	MP Biochemical	Cat # 160110
DNase I	Sigma	Cat # D5025
Collagenase D	Millapore-Sigma	Cat # 11088882001
Collagenase Type III	Worthington Chemicals	Cat # LS004183
16% paraformaldehyde	Electron Microscopy Sciences	Cat#15710
Formalin solution, neutral buffered, 10%	Sigma-Aldrich	HT501128–4L
InVivoMab anti-mouse PD-1 (CD279) antibody	Bio X Cell	Cat# BE0146, RRID:AB_10949053
Rat IgG isotype control-Purified	Sigma-Aldrich	Cat#I4131
L-Glutamine (200 mM)	GIBCO	Cat#25030081
Penicillin- Streptomycin (10,000 U/mL)	GIBCO	Cat#15140122
RPMI 1640 with L-glutamine	Corning	Cat#10–040-CV
Percoll	GE Healthcare	Cat#GE17–0891-01
EDTA	Thermo Fisher Scientific	Cat#15575020

Critical commercial assays

Live/Dead Fixable Aqua Dead Cell Stain Kit	Thermo Fisher Scientific	Cat#L34957
Foxp3 transcription factor staining buffer set	Thermo Fisher Scientific	Cat#00–5523-00
CD16/CD32 FcBlock (24G2)	Thermo Fisher Scientific	Cat# 16–0161-85, RRID:AB_468899

Deposited data

ILC3s RNaseq data	This paper	GEO: GSE165814
16S microbiota sequencing	This paper	SRA: PRJNA699540

Experimental models: Cell lines

MC38	Dr. Dmitriy Zamarin, MSKCC	N/A
B16-F10	ATCC	Cat# CRL-6475, RRID:CVCL_0159

Experimental Models: Organisms/Strains

C57BL/6J mice	Jackson Laboratory	RRID:IMSR_JAX:000664
Rorc^cre mice	Dr. Gerard Eberl	N/A
RORγt-eGFP mice	Dr. Gerard Eberl	N/A
B6.Cg-Tg(CDX2-cre)101Erf/J mice	Jackson Laboratory	RRID:IMSR_JAX:009350
C57BL/6-Apc^tm1Tyj/J mice	Jackson Laboratory	RRID:IMSR_JAX:009045
C57BL/6J-Apc^Min/J mice	Jackson Laboratory	RRID:IMSR_JAX:002020
B6.129X1-Gt(ROSA)26Sor^tm1(EYFP)Cos/J mice	Jackson Laboratory	RRID:IMSR_JAX:006148

Software and algorithms

FACS Diva software	BD Biosciences	https://www.flowjo.com/solutions/flowjo/downloads
FlowJo V9.9.3 software	TreeStar	https://www.flowjo.com/solutions/flowjo/downloads
GraphPad Prism 6.0 software	GraphPad	https://www.graphpad.com
R version 3.6.3	R Core Team	https://www.r-project.org/
CASAVA (v.1.8.2).	Illumina	https://support.illumina.com/sequencing/sequencing_software/casava2/questions.html
FLEXBAR (v.2.4)	Dodt et al., 2012	https://github.com/seqan/flexbar
STAR (v.2.3.0)	Dobin et al., 2013	https://github.com/alexdobin/STAR
Rsubread	Liao et al., 2019	https://bioconductor.org/packages/release/bioc/html/Rsubread.html
DESeq2 version 1.20.0	Love et al., 2014	https://bioconductor.org/packages/release/bioc/html/DESeq2.html
vegan version 2.5	Oksanen et al., 2019	https://cran.r-project.org/web/packages/vegan/index.html
USEARCH versions v7.0.1090 and 11	Edgar 2010	https://www.drive5.com/usearch/
phyloseq version 1.30.0	McMurdie and Holmes, 2013	https://www.bioconductor.org/packages/release/bioc/html/phyloseq.html
ETE-tools version 3	Huerta-Cepas et al., 2016	http://etetoolkit.org/
Clustal Omega version 1.2.0	Sievers et al., 2011	https://www.ebi.ac.uk/Tools/msa/clustalo/
FastTree version 2	Price et al., 2010	http://www.microbesonline.org/fasttree/
lme4 version 1.1.27	Bates et al., 2015	https://cran.r-project.org/web/packages/lme4/index.html

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Highlights:

Profiling of human and mouse CRC reveal a substantial dysregulation of ILC3s.
ILC3 and T cell interactions support microbiota that drive type-1 immunity.
ILC3 impairment drives CRC progression and immunotherapy resistance in mice.
Humans with altered ILC3s harbor microbiota that cause immunotherapy resistance.

Acknowledgements

We thank members of the Sonnenberg Laboratory for discussions and critical reading of the manuscript. Research in the Sonnenberg Laboratory is supported by the National Institutes of Health (R01AI143842, R01AI123368, R01AI145989, U01AI095608, R21CA249274 and R01AI162936), an Investigators in the Pathogenesis of Infectious Disease Award from the Burroughs Wellcome Fund, a Wade F.B. Thompson/Cancer Research Institute (CRI) CLIP Investigator grant, the Meyer Cancer Center Collaborative Research Initiative, The Dalton Family Foundation, and Linda and Glenn Greenberg. J.G. is supported by fellowships from the Crohn’s and Colitis Foundation (519428) and the Philippe Foundation. N.J.B. was supported by a fellowship from the NIH (F32AI124517). G.F.S. is a CRI Lloyd J. Old STAR. J.C.A is supported by K01DK103952 and R01DK124617. We would like to thank the Epigenomics and the Microbiome Cores of Weill Cornell Medicine and all contributing members of the JRI IBD Live Cell Bank, which is supported by the JRI, Jill Roberts Center for IBD, Cure for IBD, the Rosanne H. Silbermann Foundation, the Sanders Family and Weill Cornell Medicine Division of Pediatric Gastroenterology and Nutrition. JRI Live Cell Bank consortium members include David Artis, Randy Longman, Gregory Sonnenberg, Ellen Scherl, Robbyn Sockolow, Dana Lukin, Robert Battat, Thomas Ciecierega, Aliza Solomon, Elaine Barfield, Kimberley Chien, Johanna Ferriera, Jasmin Williams, Shaira Khan, Peik Sean Chong, Samah Mozumder, Lance Chou, Wenqing Zhou, Mohd Ahmed, Connie Zhong, and Ann Joseph.

Footnotes

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Declaration of interests

The authors declare no competing interests.

Inclusion and diversity

We worked to ensure gender balance in the recruitment of human subjects. We worked to ensure ethnic or other types of diversity in the recruitment of human subjects. We worked to ensure that the study questionnaires were prepared in an inclusive way. We worked to ensure sex balance in the selection of non-human subjects. While citing references scientifically relevant for this work, we also actively worked to promote gender balance in our reference list.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHMS1728516-supplement-1.pdf^{(476.3KB, pdf)}

NIHMS1728516-supplement-2.pdf^{(1.7MB, pdf)}

NIHMS1728516-supplement-3.pdf^{(193KB, pdf)}

NIHMS1728516-supplement-4.pdf^{(5MB, pdf)}

NIHMS1728516-supplement-5.pdf^{(69.6KB, pdf)}

NIHMS1728516-supplement-6.pdf^{(125.4KB, pdf)}

NIHMS1728516-supplement-7.pdf^{(367.4KB, pdf)}

Data Availability Statement

All data including raw sequencing reads are uploaded to the SRA and GEO and are publicly available as of the date of publication. The accession numbers are listed in the key resources table.

The paper does not report original code.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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PERMALINK

Dysregulation of ILC3s unleashes progression and immunotherapy resistance in colon cancer

Jeremy Goc

Mengze Lv

Nicholas J Bessman

Anne-Laure Flamar

Sheena Sahota

Hiroaki Suzuki

Fei Teng

Gregory G Putzel

Gerard Eberl

David R Withers

Janelle C Arthur

Manish A Shah

Gregory F Sonnenberg

Summary:

Graphical Abstract

In brief:

Introduction:

Results:

CRC in humans and mice manifests with substantially altered ILC3s

Figure 1: ILC3s are dysregulated in human and mouse CRC.

ILC3s exhibit increased cellular plasticity in CRC

Figure 2: ILC3s in CRC exhibit increased plasticity towards ILC1s.

ILC3 and T cell interactions support microbiota that drive type-1 immunity and become altered in CRC

Figure 3: ILC3s are imbalanced with T cells in CRC and disruption of these interactions impairs microbiota-dependent type-1 immunity.

MHCII+ ILC3s limit the progression and invasion of CRC

Figure 4: MHCII+ ILC3s protect from experimental CRC progression and invasion.

MHCII+ ILC3s limit resistance to checkpoint immunotherapy

Figure 5: MHCII+ ILC3s preserve type-1 immunity in tumors and prevent resistance to immunotherapy.

Humans with IBD and altered ILC3s harbor microbiota that promote resistance to immunotherapy

Figure 6: IBD patients harbor microbiota that cause resistance to immunotherapy.

Discussion

Limitations of the Study

STAR★Methods

Resource availability

Lead contact

Materials Availability

Data and Code Availability

Experimental Model and Subject Details

Human samples collection

Animal models

Cell lines

Method details

Murine chronic colitis and colorectal cancer induction protocol

Murine intestinal tissue isolation

Human intestinal tissue isolation

RNA sequencing

Immunofluorescence and image analysis

Histological staining and histopathology

T-cell adoptive transfer

Subcutaneous tumor implantation

In vivo administration of antibodies

Preparation of tumor cell suspensions

16S rRNA bacterial sequencing

Fecal microbiota transplantation

Quantification and statistical analysis

Standard statistical analysis

Permutation test

Linear mixed-effects model

Additional resources

Supplementary Material

Highlights:

Acknowledgements

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

MHCII⁺ ILC3s limit the progression and invasion of CRC

Figure 4: MHCII⁺ ILC3s protect from experimental CRC progression and invasion.

MHCII⁺ ILC3s limit resistance to checkpoint immunotherapy

Figure 5: MHCII⁺ ILC3s preserve type-1 immunity in tumors and prevent resistance to immunotherapy.