Dysregulation of ILC3s unleashes progression and immunotherapy resistance in colon cancer

Group 3 innate lymphoid cells (ILC3s) are critical regulators of intestinal homeostasis, yet their role in cancer remains elusive. Here, we identify that the tumor microenvironment of humans and mice with colorectal cancer (CRC) contains altered ILC3 responses that resemble those found during intestinal inflammation and are characterized by reduced frequencies, altered subset distribution, and an imbalance with T cells. We evaluated the consequences of these changes in mice and determined that ILC3-specific major histocompatibility complex class II (MHCII) was required to limit the progression of CRC. Interestingly, ILC3-specific MHCII also prevented resistance to anti-PD-1 immunotherapy by regulating intestinal inflammation and microbiota composition. Finally, humans with intestinal inflammation and dysregulated ILC3s also harbor specific microbiota that are sufficient to promote immunotherapy resistance in mice. Collectively, these data define a protective role for antigen-presenting ILC3s in cancer, and indicate that inherent disruption of this pathway drives CRC progression and immunotherapy resistance. 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 or ileum of patients with colon cancer or IBD. 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) and reads were aligned to the NCBI GRCh37/hg19 human genome using the STAR aligner (v.2.3.0) 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.