Genetic Mechanisms of Immune Evasion in Colorectal Cancer

Abstract

To understand the genetic drivers of immune recognition and evasion in colorectal cancer, we analyzed 1,211 colorectal cancer primary tumor samples, including 179 classified as microsatellite instability-high (MSI-high). This set includes The Cancer Genome Atlas colorectal cancer cohort of 592 samples, completed and analyzed here. MSI-high, a hypermutated, immunogenic subtype of colorectal cancer, had a high rate of significantly mutated genes in important immune-modulating pathways and in the antigen presentation machinery, including biallelic losses of B2M and HLA genes due to copy-number alterations and copy-neutral loss of heterozygosity. WNT/β-catenin signaling genes were significantly mutated in all colorectal cancer subtypes, and activated WNT/β-catenin signaling was correlated with the absence of T-cell infiltration. This large-scale genomic analysis of colorectal cancer demonstrates that MSI-high cases frequently undergo an immunoediting process that provides them with genetic events allowing immune escape despite high mutational load and frequent lymphocytic infiltration and, furthermore, that colorectal cancer tumors have genetic and methylation events associated with activated WNT signaling and T-cell exclusion.Significance: This multi-omic analysis of 1,211 colorectal cancer primary tumors reveals that it should be possible to better monitor resistance in the 15% of cases that respond to immune blockade therapy and also to use WNT signaling inhibitors to reverse immune exclusion in the 85% of cases that currently do not. Cancer Discov; 8(6); 730-49. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 663.

Conflict of interest statement

M. Leiserson is a consultant with Microsoft. C. Wu is a co-founder of Neon Therapeutics, and a member of their scientific advisory board. C. Fuchs is a consultant for Eli Lilly, Entrinsic Health, Genentech, Merck, Sanofi, Five Prime Therapeutics, Merrimack, Bayer, Agios, Taiho, Kew, Bain Capital, and a board member of CytomX. L. Garraway is a Senior Vice President of Global Development and Affairs at Eli Lilly. The other authors declare no potential conflicts of interest.

©2018 American Association for Cancer Research.

Figures

Figure 1. Frequently mutated genes in CRC

Mutation landscape showing the subtypes of the 592 TCGA tumor samples, sorted by increasing mutation load, including MSI status, POLE status and Lynch status. Samples reported on previously by TCGA (2012) are indicated. Molecular subtypes (see Methods) enriched in MSI-high or MSS are indicated: * P<0.01, ** P<1.0×10−4, *** P<1.0×10−6. Consensus Molecular Subtypes (CMSs) are shown; MSI-high is enriched for CMS1, the microsatellite instability immune subset (P<1×10−15). Mutations defining genomic subtypes are in Methods.

Figure 2. Data sets and summary of significantly mutated genes

a, Sources of data, breakdown of subtypes, and types of analyses performed on each cohort. b, Significantly mutated genes (by MutsigCV) in MSS relative to MSI-high; all genes with Q<0.01 in the combined TCGA+NHS/HPFS set and at least one of the individual cohorts are shown. Genes are labeled by functional class(es), prevalence of microsatellite mutations (*: significant with Q<0.01 in indel-specific calculation), and incidence of biallelic disruptive mutations (ˆ: present in at least one sample).

Figure 3. Biallelic loss events in significantly mutated genes in CRC broken down by MSI status

a, Stacked barplot showing genes with recurrent biallelic disruptions in MSS and MSI-high tumors, by number of biallelic disruptions of each type. b, CN-LOH and single copy loss events in MSI-high and MSS. Each panel shows all disruptive somatic mutations (i.e., splice-site mutations, nonsense mutations, start site mutations, and frame-shift indels) that overlap single-copy losses and CN-LOH events. Each horizontal segment represents a single event, with the length of the segment proportional to the length of the alteration; colored dots represent mutations.

Figure 4. Mutation landscape of immune-related genes and consequences in MSI-high tumors

a, Mutation landscape of 9 genes frequently mutated in MSI-high and involved in antigen presentation, as well as 11 significantly mutated immune-related genes that regulate other hematopoietic cell types beyond antigen presentation. Gene expression for HLA Class I (red bar) and Class II (blue bar) genes is shown for comparison. Total number of coding mutations in MSS samples for each gene is shown to the left. Samples were clustered by gene expression; the four main clusters are indicated above the dendrogram: Cluster I (green), Cluster II (purple), Cluster III (yellow), and Cluster IV (orange) bars). b, Decreases in HLA-A and HLA-B expression in samples with disruptive mutations in those genes. c, Decreases in HLA Class I gene expression in samples with mutations in either NLRC5 or RFX5. d, Decreases in TAP2 and B2M expression in samples with disruptive mutations in those genes. (* P<0.01, ** P<0.001, *** P<1.0×10−4 for b-d.) e, Mutation counts in pathways affecting antigen presentation broken down by type.

Figure 5. WNT signaling anti-correlated with T-cell infiltration

a, Mutation landscape of significantly mutated genes involved in WNT signaling in MSS versus MSI-high tumors. b, Scatterplot showing T-cell average versus the number of coding mutations in MSS and MSI-high samples. Box and whisker plot of the number of coding mutations versus T-cell-inflamed status (“T-cell high” samples are those with T-cell average greater than the median for MSS and MSI-high.) c, Scatterplot showing the T-cell average versus AXIN2 gene expression in MSS and MSI-high samples. d, Immunohistochemical analysis of T lymphocyte infiltrates according to nuclear CTNNB1 (beta-catenin) status. Box plot showing that nuclear CTNNB1 is anti-correlated with tumor-infiltrating lymphocytes (P=0.027). Boxes are 95% confidence intervals; lines are estimated odds ratios. Crohn’s-like reaction was defined as transmural lymphoid reaction. Peritumoral lymphocytic reaction was defined as discrete lymphoid reactions surrounding tumor. Intratumoral periglandular reaction was defined as lymphocytic reaction in tumor stroma within tumor mass. TIL was defined as lymphocytes on top of cancer cells. e, Box plot showing that nuclear CTNNB1 is anti-correlated with CD3+ density (P=0.054), CD8+ density (P=0.0019), CD45RO+ density (P=0.0080), but not FOXP3+ density (P>0.1). (* P<0.1, ** P<0.01 for d-e.) f, The top row depicts a tumor with positive nuclear staining for CTNNB1 (left panel, highlighted by inset) that harbors a low level of CD3, CD8, and CD45RO-positive tumor infiltrating lymphocytes (arrows) compared to the tumor in the bottom row that shows a membranous-only expression pattern for CTNNB1 and harbors higher levels of CD3, CD8, and CD45RO-positive tumor infiltrating lymphocytes. In contrast, levels of FOXP3-positive T lymphocytes are lower than the other T cell subsets and do not show a significantly different density based on nuclear CTNNB1 status. Note that the degree of periglandular lymphocytic response (marked by asterisks) within the stroma is not significantly impacted by nuclear CTNNB1 status.

Figure 6. APC biallelic loss a genomic driver of immunosuppression of TILs by WNT-signaling

a, Scatterplot showing T-cell average versus AXIN2 gene expression for MSS biallelic APC loss cases (APC++), MSI-high biallelic APC loss cases, and MSS and MSI-high cases without disruptive mutations in APC (APC-). b, Differences in AXIN2 expression and T-cell average for all CRC samples without APC disruptive mutations (APC-) and with biallelic APC disruptive mutations (APC++). c, Differences in AXIN2 expression and T-cell average for samples without APC disruptive mutations and with biallelic APC disruptive mutations separated by MSS and MSI-high status (* P<0.01, ** P<0.001, *** P<1.0×10−4; NS: not significant).

Figure 7. AXIN2 super-enhancer hypomethylation a cis-driver of immunosuppression of TILs by WNT-signaling

a, Gene structure for AXIN2 showing the location of the super-enhancer (black horizontal bar) and the level of anti-correlation of the CpG sites in this region with the T-cell average. Red CpG sites are averaged to yield a beta-value for the AXIN2 hypomethylated region (HMR); black are not included in the average. b, Histogram showing the AXIN2 HMR beta-value for matched normal (green, N = 35), MSS (blue, N = 322) and MSI-high (red, N = 55) samples. In MSS, 112 samples are hypomethylated and 210 are hypermethylated based on an average beta of 0.4 after adjusting for tumor purity. c, Differences in AXIN2 expression and T-cell average for hypomethylated (HypoMR+) AXIN2 MSS cases, hypermethylated (HypoMR-) AXIN2 MSS cases, and MSI-high cases. Mutation load for hypomethylated and hypermethylated AXIN2 MSS cases. d, Histogram showing the AXIN2 HMR beta-value for matched normal (green, N = 35), MSI-high (red, N = 55), and MSS restricted to APC biallelic loss cases (light blue, N = 206) and cases without APC disruptions (dark blue, N = 46). e, Differences in AXIN2 expression and T-cell average for MSS broken down by AXIN2 hypomethylation status and APC biallelic mutation status.