Genetic and Epigenetic Characteristics of Inflammatory Bowel Disease–Associated Colorectal Cancer

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY-NC-ND 4.0 https://creativecommons.org/licenses/by-nc-nd/4.0/ Genetic and Epigenetic Characteristics of Inflammatory Bowel Disease–Associated Colorectal Cancer © 2021 the AGA Institute Published version Rajamäki, Kristiina; Taira, Aurora; Katainen, Riku; Välimäki, Niko; Kuosmanen, Anna; Plaketti, Roosa-Maria; Seppälä, Toni T.; Ahtiainen, Maarit; Wirta, ErkkiVille; Vartiainen, Emilia; Sulo, Päivi; Ravantti, Janne; Lehtipuro, Suvi; Granberg, Kirsi J.; Nykter, Matti; Tanskanen, Tomas; Ristimäki, Ari; Koskensalo, Selja; Renkonen-Sinisalo, Laura; Lepistö, Anna; Böhm, Jan; Taipale, Jussi; Mecklin, Jukka-Pekka; Aavikko, Mervi; Palin, Kimmo; Aaltonen, Lauri A. Rajamäki, K., Taira, A., Katainen, R., Välimäki, N., Kuosmanen, A., Plaketti, R.-M., Seppälä, T. T., Ahtiainen, M., Wirta, E.-V., Vartiainen, E., Sulo, P., Ravantti, J., Lehtipuro, S., Granberg, K. J., Nykter, M., Tanskanen, T., Ristimäki, A., Koskensalo, S., Renkonen-Sinisalo, L., . . . Aaltonen, L. A. (2021). Genetic and Epigenetic Characteristics of Inflammatory Bowel Disease–Associated Colorectal Cancer. Gastroenterology, 161(2), 592-607. https://doi.org/10.1053/j.gastro.2021.04.042 2021 Genetic and Epigenetic Characteristics of Inflammatory Bowel Disease–Associated Colorectal Cancer Kristiina Rajamäki, 1,2, *Aurora Taira, 1,2, *Riku Katainen, 1,2 Niko Välimäki, 1,2 Anna Kuosmanen, 1,2 Roosa-Maria Plaketti, 1,2 Toni T. Seppälä, 2,3,4 Maarit Ahtiainen, 5 Erkki-Ville Wirta, 6 Emilia Vartiainen, 1,2 Päivi Sulo, 1,2 Janne Ravantti, 1,2 Suvi Lehtipuro, 7,8 Kirsi J. Granberg, 7,8 Matti Nykter, 7,8 Tomas Tanskanen, 9 Ari Ristimäki, 2,10 Selja Koskensalo, 11 Laura Renkonen-Sinisalo, 11 Anna Lepistö, 11 Jan Böhm, 5 Jussi Taipale, 2,12,13 Jukka-Pekka Mecklin, 14,15 Mervi Aavikko, 1,2,16 Kimmo Palin, 1,2 and Lauri A. Aaltonen 1,2 1 Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland; 2 Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland; 3 Department of Surgery, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland; 4 Department of Surgical Oncology, Johns Hopkins University, Baltimore, Maryland; 5 Department of Pathology, Central Finland Health Care District, Jyväskylä, Finland; 6 Department of Gastroenterology and Alimentary Tract Surgery, Tampere University Hospital, Tampere, Finland; 7 Prostate Cancer Research Center, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; 8 Tays Cancer Center, Tampere University Hospital, Tampere, Finland; 9 Finnish Cancer Registry, Institute for Statistical and Epidemiological Cancer Research, Helsinki, Finland; 10 Department of Pathology, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; 11 Department of Gastrointestinal Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland; 12 Division of Functional Genomics and Systems Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden; 13 Department of Biochemistry, University of Cambridge, Cambridge, UK; 14 Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland; 15 Department of Education and Research, Central Finland Central Hospital, Jyväskylä, Finland; and 16 Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland See Covering the Cover synopsis on page 380. BACKGROUND & AIMS: Inflammatory bowel disease (IBD) is a chronic, relapsing inflammatory disorder associated with an elevated risk of colorectal cancer (CRC). IBD-associated CRC (IBD-CRC) may represent a distinct pathway of tumorigenesis compared to sporadic CRC (sCRC). Our aim was to comprehensively characterize IBD-associated tumorigenesis integrating multiple high-throughput approaches, and to compare the results with in-house data sets from sCRCs. METHODS: Whole-genome sequencing, single nucleotide polymorphism arrays, RNA sequencing, genome-wide methylation analysis, and immunohistochemistry were performed using fresh-frozen and formalin-fixed tissue samples of tumor and corresponding normal tissues from 31 patients with IBD-CRC. RESULTS: Transcriptome-based tumor subtyping revealed the complete absence of canonical epithelial tumor subtype associated with WNT signaling in IBD-CRCs, dominated instead by mesenchymal stroma-rich subtype. Negative WNT regulators AXIN2 and RNF43 were strongly down-regulated in IBD-CRCs and chromosomal gains at HNF4A, a negative regulator of WNTinduced epithelial–mesenchymal transition (EMT), were less frequent compared to sCRCs. Enrichment of hypomethylation at HNF4abinding sites was detected solely in sCRC genomes. Gastroenterology 2021;161:592–607 BASIC AND TRANSLATIONAL AT PIGR and OSMR involved in mucosal immunity were dysregulated via epigenetic modifications in IBD-CRCs. Genome-wide analysis showed significant enrichment of noncoding mutations to 50untranslated region of TP53 in IBD-CRCs. As reported previously, somatic mutations in APC and KRAS were less frequent in IBD-CRCs compared to sCRCs. CONCLUSIONS: Distinct mechanisms of WNT pathway dysregulation skew IBDCRCs toward mesenchymal tumor subtype, which may affect prognosis and treatment options. Increased OSMR signaling may favor the establishment of mesenchymal tumors in patients with IBD. Keywords: Colorectal Cancer; Inflammatory Bowel Disease; Epithelial–Mesenchymal Transition; DNA Methylation; Consensus Molecular Subtype. Inflammatory bowel disease (IBD), comprising ulcerative colitis (UC) and Crohn’s disease (CD), involves a complex interplay of genetic predisposition and environmental factors that alter host–microbiota interactions causing dysregulation of gut immune responses. 1 The growing prevalence and diminishing age at onset of IBD amplify the risk of comorbidities and the associated economic burden. 2 Patients with IBD have an elevated risk of colorectal cancer (CRC) 3 attributed to chronic inflammation, yet the detailed mechanisms remain elusive. 4 Accumulating evidence suggests that IBD-associated CRC (IBD-CRC) may emerge through a distinct pathway of tumorigenesis compared to sporadic CRC (sCRC). Patients with IBD are younger at CRC diagnosis and the tumors develop at inflamed areas of the colon with characteristic clinicopathologic features. 4,5 IBD-CRCs show lower frequency of somatic APC and KRAS mutations, whereas TP53 mutations occur earlier in tumorigenesis compared to sCRCs. 4,6–10 Despite reduced APC mutations, nuclear accumulation of b-catenin is prevalent in IBD-CRCs, 11 suggesting an alternative mechanism of WNT pathway activation. Additional suggested driver genes have varied across studies, 6–10 while the frequency of hypermutated 9 and microsatellite unstable tumors, 12 level of somatic copy number alterations, 9,10 and distribution of somatic mutational signatures 6,9,10 appear similar to sCRCs. Studies on DNA methylation patterns characterizing IBD-CRC have focused on a limited number of genes 13–15 or microarray data, 16 warranting further genome-wide analyses. Transcriptome-based classification of CRCs has emerged as a powerful tool to describe tumor transcriptional, genetic, epigenetic, and microenvironment characteristics. 17,18 A large-scale international effort resulted in amalgamation of 4 consensus molecular subtypes (CMSs). 17 CMS distribution remains unknown in IBD-CRC; in sCRC, epithelial WNTassociated CMS2 is the most common and mesenchymal CMS4 associates with poor prognosis. 17 Here, we integrate multiple high-throughput sequencing approaches to comprehensively characterize IBD-CRC and to identify differences compared to sCRC. Our most striking finding was the complete absence of CMS2 tumors among IBD-CRCs that were instead skewed toward CMS4. Materials and Methods Samples The study adhered to the Declaration of Helsinki and was approved by the local ethics committee (details are in Supplementary Material). CRC patient samples were collected in 1994–2017 at 9 regional hospitals in Finland. 19–21 Thirtyone IBD-CRC cases were identified from our collection comprising approximately 2,500 CRC patients (UC: n ¼27, CD: n¼2, unclassified IBD: n ¼2 sharing features of UC and CD) (Table 1). The availability of sample material for downstream analyses is summarized in Supplementary Figure 1. Whole-Genome Sequencing DNA was isolated from fresh-frozen tumor, normal colon, or blood of 29 patients with IBD-CRC. Libraries were prepared using TruSeq Nano DNA HT or TruSeq PCR-Free Kit (Illumina), followed by paired-end sequencing using Illumina platform (HiSeqXTen/HiSeq2000). sCRCs were sequenced as described previously. 22 WHAT YOU NEED TO KNOW BACKGROUND AND CONTEXT Inflammatory bowel disease increases the risk of colorectal cancer and the tumors developing in patients may be genetically and epigenetically distinct compared to sporadic tumors. NEW FINDINGS Transcriptomic analyses of colorectal cancer specimens from patients with inflammatory bowel disease revealed the absence of canonical epithelial tumor subtype and predominance of mesenchymal tumors associated with oncostatin M receptor overexpression. LIMITATIONS The intensive surveillance of colorectal cancer in patients with inflammatory bowel disease makes these tumors rare, limiting the sample size of the study. IMPACT The results suggest that colon inflammation may favor the development of mesenchymal tumor subtype, which has previously been associated with poor survival in large colorectal cancer cohorts. *Authors share co-first authorship. Abbreviations used in this paper: AI, allelic imbalance; CD, Crohn’s disease; CMS, consensus molecular subtype; CRC, colorectal cancer; DE, differentially expressed; DML, differentially methylated loci; EMT, epithelial-mesenchymal transition; FDR, false discovery rate; IBD, inflammatory bowel disease; IBD-CRC, inflammatory bowel disease–associated colorectal cancer; MSI, microsatellite instability; MSS, microsatellite stable; SBS, single base substitution; sCRC, sporadic CRC; SV, structural variant; TGF-b, transforming growth factor b; TSS, transcription start site; UC, ulcerative colitis; UTR, untranslated region; WGS, whole-genome sequencing. Most current article © 2021 by the AGA Institute. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/). 0016-5085 https://doi.org/10.1053/j.gastro.2021.04.042 August 2021 Inflammatory Bowel Disease and Colon Cancer 593 BASIC AND TRANSLATIONAL AT Somatic Variant Calling and Analysis Sequence alignment to GRCh38 reference genome, other data preprocessing steps, and somatic variant calling were performed with GenomeAnalysisToolkit GATK4 best practices workflow (version 4.0.4.0.) for all tumor/normal pairs. Gene annotation (Ensembl genes release 89) for somatic single Table 1.Clinical Characteristics of the Patients With Inflammatory Bowel Disease and Colorectal Cancer Sample Sex IBD diagnosis Age at IBD diagnosis, y a Age at CRC diagnosis, yTumor location Tumor histology TNM stage Grade MSI status c174.1T F UC 38 54 Cecum AC I 2 MSS c175.1T M UC 33 48 Rectum AC II 3 MSS c269.1T F UC 41 41 Cecum ACPM II 2 MSS c3.1T M IBD-U 27 48 Transverse colon AC II 1 MSS c424.1T1 M UC 61 72 Rectosigmoid junction AC III 3 MSS c461.1T M UC 82 82 Ascending colon ACPM III 3 MSS c492.1T M UC 57 57 Rectum AC III 2-3 MSS c589.1T F UC 43 50 Transverse colon AC III 1 MSS c596.1T M UC 25 51 Rectum AC II 2 MSS c696.1T F UC 19 42 Cecum AC III 3 MSS c745.1T M CD 25 55 Rectum AC II 2 MSS c989.1T F UC 26 62 Cecum AC III 3 MSS s1111.1T M UC 36 64 Rectum AC II 1 MSS s1138.1T M UC 41 61 Rectum AC II 2 MSS s1170.1T M UC 65 65 Sigmoid colon AC III NA MSS s1179.1T M UC 15 50 Transverse colon ACPM III 2 MSS s205.1T M UC 22 60 Descending colon AC II 2 MSS s576.1T M UC 22 41 Ascending colon AC III 3 NA s617.1T M UC 20 33 Rectum ACM IV NA MSS s649.1T M UC 20 33 Cecum ACM III NA MSS s660.1T F IBD-U 56 66 Ascending colon AC II 2 MSI s683.1T M UC 8 46 Cecum ACM II NA MSS s700.1T M UC 41 64 Transverse colon AC I 1 MSS s703.1T F UC 11 22 Descending colon ACPM III 2 MSS s750.1T F UC 13 30 Cecum ACPM III 3 MSS s751.1T M UC 61 61 Sigmoid colon AC III 3 MSS s763.1T M CD 18 44 Rectum AC I 3 MSS s814.1T F UC 42 72 Rectum ACPM I 3 MSS s842.1T F UC 16 53 Rectum ACPM III 3 MSS s85.1T M UC 64 64 Cecum AC I NA MSS s982.1T1 b,c M UC 34 34 Cecum ACPM III 2 MSI s982.1T2 b,c M UC 34 34 Splenic flexure ACPM III 2 MSI AC, adenocarcinoma; ACM, mucinous adenocarcinoma; ACPM, partially mucinous adenocarcinoma; IBD-U, unclassified IBD; NA, not available. a In cases where IBD was diagnosed together with CRC based on pathologic findings from the surgical resection, there was often a history of several years of undiagnosed gastrointestinal symptoms mentioned in the clinical data. b Two tumors sampled from the same individual. c The individual was also diagnosed with hereditary nonpolyposis CRC. 594 Rajamäki et al Gastroenterology Vol. 161, No. 2 BASIC AND TRANSLATIONAL AT nucleotide variants and small insertions/deletions was performed with BasePlayer. 23 Mutational Signatures Somatic mutational signatures were extracted from wholegenome sequencing (WGS) data of 237 colorectal tumor/ normal pairs, including the 27 paired IBD-CRCs. Briefly, single base substitutions (SBSs) were classified based on their flanking sequence context (±1 bp) into 96 possible mutation types. These mutational spectra were analyzed with standard nonnegative matrix factorization, as described previously. 22,24 OncodriveFML OncodriveFML (version 2.2.0) 25 was used to analyze the coding sequence, 30untranslated region (UTR), and 50UTR for signals of positive selection using the somatic mutations from 27 microsatellite stable (MSS) IBD-CRCs. Allelic Imbalance Single nucleotide polymorphism array data were analyzed previously from 1699 colorectal tumor/normal pairs, 26 including 23 IBD-CRCs; allelic imbalance (AI) regions of somatic allelic loss and gain were processed with the same pipeline. 26 Variant calls (HaplotypeCaller) from WGS data were used to calculate AI for 6 additional IBD-CRCs lacking single nucleotide polymorphism array data. The presence of AI in each tumor was evaluated at loci found heterozygous in the corresponding normal sample, as described previously. 26 Analysis was limited to MSS IBD-CRCs (n ¼27) and MSS sCRCs (n ¼ 1360; microsatellite instability [MSI] statuses from Palin et al 26 ). RNA Sequencing Trizol-extracted RNA from 64 CRCs, including 17 IBD-CRCs, underwent HiSeq LncRNA-Seq library preparation and pairedend sequencing using Illumina HiSeqXTen. Raw sequences were mapped onto the human transcriptome (ensembl release 79) using Salmon (version 0.12.0). 27 Gene-level quantification was done with DESeq2 (version 1.18.1), 28 followed by limma (version 3.34.9) 29 correction of sequencing batch effects. Differential Expression Analysis Differential gene expression was analyzed using Partek Genomics Suite 6.6 (Partek Inc) Gene Expression workflow, followed by pathway analysis using PANTHER (version 15.0). 30 Consensus Molecular Subtypes Random forest classifier of CMSClassifier R package 17 was used to call CMS for each RNA-sequenced tumor, expressed here as the nearest CMS (RF.1) predicted by the classifier. Deconvolution The proportions of tumor-infiltrating immune cells were estimated from RNA sequencing data using CIBERSORT. 31,32 Reference was created as described previously 33 by combining bulk RNA sequencing data from isolated blood immune cells (accession GSE60424) and representative median expression profiles from CRCs and normal colon samples from an independent data set. 34 See Supplementary Table 16. Cell type–specific gene expression profiles were inferred from RNA sequencing data using PRISM, 35 combining singlecell RNA sequencing data of healthy colon 36 and CRCs 37 as reference. Immune Cell Score Whole-section slides from 265 formalin-fixed, paraffinembedded CRCs, including 26 IBD-CRCs, were stained with anti-CD3 (LN10, 1:200; Novocastra) and anti-CD8 (SP16, 1:400; Thermo Scientific) antibodies. Positively stained cells were analyzed using QuPath, 38 as described previously. 39 The immune cell score was formulated as described previously, 40 following the original method by Galon et al. 41 Nanopore Long-Read Sequencing Libraries were prepared for 20 IBD-CRCs, 36 sCRCs, and 12 normal colon samples from IBD-CRC patients following Genomic DNA by Ligation (SQK-LSK109) protocol (Oxford Nanopore Technologies). Sequencing and base-calling on PromethION platform employed Live base-calling with MinKNOW. Reads were aligned against the reference genome GRCh38 using minimap2 (version 2.16; preset: map-ont). 42 Structural variants (SVs) were identified using Sniffles (version 1.0.11) 43 and merged together from all tumors and normals with SURVIVOR (version 1.0.6) 44 to filter out SVs found in any normal sample. Genome-wide methylation patterns were obtained using Nanopolish. 45 Differentially methylated loci (DMLs) at autosomal regions were identified and analyzed with R, version 3.5.1 using R packages DSS (version 2.28.0,) 46 bsseq (version 1.16.1), annotatr (version 1.12.1), 47 and Locus Overlap Analysis (version 1.16.0). 48 Chromatin immunoprecipitation sequencing and DNase-sequencing data and 15 chromatin states provided by Roadmap Epigenomics 49 project (Supplementary Table 12) and transcription factor binding sites from CRC cell lines 50 served as region set databases in Locus Overlap Analysis. Data Availability Genome-wide somatic single nucleotide variant and insertion/deletion calls (GRCh38) are deposited in the EGA database under accession code EGAS00001004710. Results Somatic Point Mutations in Known Colorectal Cancer Driver Genes and Immunity-Related Genes Characterize Inflammatory Bowel Disease–Associated Colorectal Cancers The study comprised 31 cases of IBD-CRC (Table 1); 29 underwent WGS. A total of 1,104,175 somatic single nucleotide variants and insertion/deletions were identified (Supplementary Figure 2). Two outliers with high somatic variant counts were explained by MSI, a distinct pathway of August 2021 Inflammatory Bowel Disease and Colon Cancer 595 BASIC AND TRANSLATIONAL AT CRC tumorigenesis driven by mismatch repair deficiency. We focused on the more typical MSS IBD-CRCs. The 27 MSS IBD-CRCs harbored a median of 18,194 somatic variants per tumor (3387–73,003), which was highly similar to 259 MSS sCRCs previously whole-genome sequenced in-house (median, 17,319; range, 253–84,646) (Supplementary Figure 2); genome-wide mutation densities showed similar distributions. Coding sequences displayed 4702 variants, with 2816 genes affected by nonsynonymous variants. We ranked the 100 genes mutated in 3 or more tumors by mutation density (Figure 1A). Top 20 genes featured several known CRC driver genes, including TP53 and KRAS having the highest mutation densities. Analysis of mutual exclusivity and co-occurrence of mutations in these genes revealed no significant results (Supplementary Table 1). As expected, KRAS and APC mutations were few in IBD-CRCs compared to sCRCs (22% vs 49% and 22% vs 73%, respectively, Fisher exact test, P¼.0087 and P¼2.5 10 7 ); TP53 was frequently mutated in both groups (63% vs 62%) (Figure 1A,Supplementary Table 2). A comparison of all variants in COSMIC Cancer Gene Census 51 genes revealed paucity of genes mutated uniquely in IBD-CRC (Supplementary Table 2). Immunity-related CARD8 and PIGR ranked high by mutation density (Figure 1A). All 3 CARD8 variants were missense, and 2 of the 4 PIGR variants present in 3 of 27 IBD-CRCs (11% vs 1% in MSS sCRCs) were truncating (Supplementary Table 3). Noncoding TP53 Mutations Are Enriched in Inflammatory Bowel Disease–Associated Colorectal Cancers To discover candidate driver genes, we applied OncodriveFML 25 to all somatic point mutations from the 27 MSS IBD-CRCs, totaling 622,366 variants. Coding regions of 2 genes showed significant evidence of positive selection after false discovery rate (FDR) correction (Q<0.1), TP53 (P<1.1 10 –6 ,Q¼.00061) mutated in 16 tumors and GBA2 (P¼.00014, Q¼.043) mutated in 2 tumors (Supplementary Figure 2,Supplementary Table 4). We further analyzed 30UTR and 50UTR of all protein-coding genes. Overlapping 50UTR of TP53 and WRAP53 genes was the only region showing significant evidence of positive selection (P¼.00036, Q¼.022) in IBD-CRCs, while remaining nonsignificant in sCRCs (P¼.015, Q¼1.00) (Supplementary Figure 2,Supplementary Table 4). This region harbored 4 mutations in 3/27 IBD-CRCs and 6 mutations in 5 of 239 sCRCs, resulting in low TP53 expression (Figure 1Band C). Uncoupling of Age-Related Mutational Signature From Age in Inflammatory Bowel Disease– Associated Colorectal Cancers Detection of genome-wide mutational signatures 22,24 in IBD-CRCs revealed 5 somatic SBS signatures that resembled the signatures SBS1 (age-related spontaneous deamination of 5-methylcytosine), SBS8 (unknown), SBS17 (CTCF/ cohesin binding sites), and SBS15 and SBS20 (defective DNA mismatch repair) in human cancers described previously 22,52 (Figure 2A). Compared to MSS sCRCs, MSS IBDCRCs showed a decreased exposure to SBS1 (P¼.0037) (Figure 2B) and a significantly lower rate of SBS1 mutations during life before CRC diagnosis (ordinary least squares, P¼.023). Neither the mean difference nor difference in SBS1 mutation rate could be explained in Bayesian analysis by the younger age at onset in IBD-CRC patients (mean ± SD, 54 ±11 years vs 69 ±11 years in IBD-CRC vs sCRC) (Figure 2C). SBS17 exposure was elevated (P¼.0082) in IBD-CRCs (Figure 2B); tumors dominated by SBS17 showed no obvious correlations to clinical characteristics or known driver mutations. Analysis of Chromosomal Rearrangements Reveals Tumor Type–Specific Allelic Imbalance Chromosomal stability of the MSS IBD-CRCs was inspected using both nanopore WGS data (n ¼19) and single nucleotide polymorphism array data (n ¼27). Numbers of somatic nanopore-detected SVs did not differ between IBD-CRCs and sCRCs (Mann-Whitney U test P>.76) (Figure 3A–C). Coding region SVs in IBD-CRC revealed 5 genes with a recurring somatic breakpoint: CCSER1 (n ¼2 tumors), FHIT (n ¼2), IMMP2L (n ¼3), MACROD2 (n ¼2), and PIBF1 (n ¼2) (Supplementary Table 5), all known fragile site genes. 53,54 We previously characterized AI in 1699 CRCs 26 and now compared MSS IBD-CRCs (n ¼27) to MSS sCRCs (n ¼1360). No overall difference between IBD-CRCs (mean, 9.2 10 8 bp of AI per tumor) and sCRCs (mean, 1.1 10 9 bp) was observed (Mann-Whitney U test P¼.29) (Figure 3D). The only outstanding difference genome-wide (at FDR <10%) was found at 5p13.1-p12, where IBD-CRC was enriched for gains (10 of 27 tumors (37%); Figure 3E;Supplementary Table 6). Of the genes in this region, OSMR and LIFR cytokine receptors sharing the ligand oncostatin M 55 were significantly up-regulated in IBD-CRCs compared to sCRCs (Supplementary Table 7). Closer inspection of 38 previously characterized AI target genes in CRC 26 revealed 4 genes with differential AI (FDR <10%): CDKN2B and SMARCA2 enriched for allelic losses, FOXA1 for allelic gains, and HNF4A having fewer gains in IBD-CRC compared to sCRC (Supplementary Table 8). Differential Gene Expression Highlights Changes Related to Stromal and Immune Cells in Inflammatory Bowel Disease–Associated Colorectal Cancers Bulk RNA sequencing revealed 870 significantly differentially expressed (DE) genes between MSS IBD-CRCs and MSS sCRCs (FDR <10%; Supplementary Table 7, Supplementary Figure 3). In IBD-CRCs, pathway analysis showed a strong overenrichment (overexpression) of gene sets related to complement activation and extracellular matrix organization (Figure 4A,Supplementary Table 9). Very few gene sets were significantly underenriched, 596 Rajamäki et al Gastroenterology Vol. 161, No. 2 BASIC AND TRANSLATIONAL AT including only 1 of 44 significant results for Reactome database, TCF-dependent signaling in response to WNT (RHSA-201681). Genes in this set included RNF43 and AXIN2, the most significantly down-regulated genes in IBD-CRCs. The most significantly up-regulated gene, OSMR, appeared in a large overenriched Immune System gene set (R-HSA168256) comprising 146 DE genes related to myeloid/ lymphoid immune cells, complement, and, interestingly, EMT (TWIST1,ZEB1,VIM)(Supplementary Table 9). We estimated cell type–specific gene expression profiles for epithelial, stromal, and immune cells using PRISM deconvolution tool35 (Supplementary Figure 4). PRISMestimated cell-type proportions for each tumor consensus molecular subtype (Supplementary Figure 5) matched those Figure 1. Somatic point mutations. (A) OncoPrint (https://www.cbioportal.org/oncoprinter) showing somatic point mutations (filled squares) and copy number alterations (filled bars) in genes mutated in 3 or more MSS IBD-CRCs and ranked by mutation density (mutations/Mb; top 20 genes and APC ranking no. 41 presented with percentages of mutated tumors). F, female; IBDU, unclassified inflammatory bowel disease; M, male; TNM, tumor-nodes-metastases. (B) Variants observed in the overlapping 50UTR of TP53 and WRAP53. IRES, internal ribosome entry site. (C) Gene expression in 64 RNA-sequenced CRCs as transcripts per million (TPM). Red dot signifies an IBD-CRC carrying TP53/WRAP53 50UTR variant (no coding TP53 variants). August 2021 Inflammatory Bowel Disease and Colon Cancer 597 BASIC AND TRANSLATIONAL AT reported previously using a different algorithm. 17 Differential expression analysis between MSS IBD-CRCs and MSS sCRCs yielded 376, 626, and 66 DE genes for epithelial, stromal, and immune cells, respectively (Supplementary Table 7). Pathway analysis of these separate gene lists allowed connecting each pathway change to a particular cell type (Supplementary Table 9). Epithelial DE genes, predominantly down-regulated in IBD-CRCs, were enriched for pathways related to epithelial cell differentiation and development. Stromal DE genes, predominantly upregulated, were enriched for extracellular matrix organization, integrin interactions, vasculature development, and insulin-like growth factor metabolism. Immune DE genes, predominantly up-regulated, were enriched for classical complement activation and other antibody-mediated immune responses. Common Epithelial Colorectal Cancer Subtype Associated With WNT Signaling Is Absent in Inflammatory Bowel Disease–Associated Colorectal Cancers We defined the nearest CMS for each of the 64 RNAsequenced CRCs, as described previously. 17 As reported, MSI sCRCs were highly enriched for CMS1 (Supplementary Table 10). Based on PRISM deconvolution, immune cell proportion was highest in CMS1, stromal in CMS4, and epithelial in CMS2/CMS3 tumors (Supplementary Figure 5), as reported previously. 17 Comparison of MSS tumors in IBDCRCs revealed a striking paucity of the canonical epithelial CMS2 subtype associated with WNT and MYC signaling (0% of IBD-CRCs vs 39% of sCRCs; P¼.0048, P adj ¼.019) (Figure 4B,Supplementary Table 10). IBD-CRCs were Figure 2. Somatic mutational signatures. (A) Contributions of SBS signatures to somatic mutations in IBD-CRCs. CBS, CTCF/ cohesin binding site. (B) SBS signatures in IBD-CRCs (MSS n ¼25) and sCRCs (MSS n ¼194, MSI n ¼12). Robust linear model regression was used to compare the MSS groups (**P<.01). (C) Analysis of age-dependence of SBS1. After Bayesian analysis with and without intercept, depending on the IBD status, the slope for IBD-CRCs for SBS1 exposure is significantly lower than for sCRCs. On average, the MSS IBD-CRCs had 7.4–61 fewer SBS1-associated mutations per year (95% credible interval). 598 Rajamäki et al Gastroenterology Vol. 161, No. 2 BASIC AND TRANSLATIONAL AT dominated by mesenchymal CMS4 (57% vs 21%; P¼.019, P adj ¼.057), with concomitant up-regulation of transcription factors mediating EMT (TWIST1, TWIST2, SNAI2, ZEB1, ZEB2)(Supplementary Table 7). Mesenchymal Tumors Show a Distinct Pattern of Immune Cell Infiltration We interrogated the immune cell contexture of RNAsequenced CRCs using CIBERSORT deconvolution. 31,32 MSI and CMS1 tumors were found in clusters with high estimated proportions of CD8 þ cytotoxic T cells (Supplementary Figure 5), as reported previously. 17 MSS tumors showed 3 clusters dominated by CD4 þ T, CD8 þ T, and B cells, respectively (Figure 4C). IBD-CRCs were divided between the B cell and the CD4 þ T cell clusters, forming in the latter a distinct CMS4-enriched subcluster distinguished by high proportions of monocytes. We further analyzed immune cell score, a prognostic measure of tumor T cell infiltration reflecting numbers of total (CD3 þ ) and cytotoxic (CD8 þ ) T cells. 41 Rates of high immune cell score (3–4) were similar between 24 MSS IBDCRCs and 196 MSS sCRCs (54% vs 43%; Fisher exact test, P¼.39) (Supplementary Table 11). There were no significant differences in the 4 individual stainings (Figure 4D)or in the ratios of CD8 þ to CD3 þ T cells (Supplementary Figure 5) between these groups (Mann-Whitney U test, Holm-Bonferroni correction). Similar Genome-Wide Methylation Patterns in Inflammatory Bowel Disease–Associated Colorectal Cancers and Sporadic Colorectal Cancer Methylation analyses were carried out using wholegenome nanopore sequencing data. Based on methylation values at CpG islands, IBD-CRCs and a pool of nondysplastic normal colon samples from patients with IBD (“IBD-normals”) mainly clustered separately from sCRCs (Supplementary Figure 6). All subsequent methylation analyses focused on samples from patients with MSS tumors. IBD-CRCs showed, on average, higher genome-wide methylation compared to sCRCs (Figure 5A). Neither age nor cancer type was significantly associated with the average methylation level (joint model P¼.078). Differentially methylated loci (DMLs) were studied in autosomes comparing IBD-CRCs to IBD-normals and sCRCs to IBDnormals, resulting in 553,390 DMLs (4.4% hypermethylated) and 2,413,663 DMLs (3.7% hypermethylated), respectively. Five of the 10 IBD-normals were matched with the studied IBD-CRCs, likely decreasing the IBD-CRC DML count, potentially affected also by shared IBD-derived methylation changes. In both tumor groups, genomic annotation of the DMLs (Figure 5B–D,Supplementary Figure 6) revealed the majority of hypomethylated loci at non-CpG island (“CpG inter”) areas and modest enrichment only on quiescent/low chromatin areas, out of 15 chromatin states studied. Figure 3. Overview of somatic structural aberrations. Numbers of somatic (A) intraand (B) inter-chromosomal, and (C) total SVs in 19 nanopore-sequenced MSS IBD-CRCs and 32 MSS sCRCs. The percentages in (B) refer to the proportion of tumors lacking inter-chromosomal SVs. (D,E) Somatic AI in 27 MSS IBD-CRCs and 1360 MSS sCRCs across the autosomes. (D) Total amount of base pairs affected by somatic AI. Dashed lines denote the mean per group (orange/blue) and overall mean (gray). Y-axis is logarithmic and truncated to a minimum 100 kbp. (E) Observed proportion of tumors with allelic loss (or gain) at each genomic position. August 2021 Inflammatory Bowel Disease and Colon Cancer 599 BASIC AND TRANSLATIONAL AT 32. Chen B, Khodadoust MS, Liu CL, et al. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol Biol 2018;1711:243–259. 33. Luoto S, Hermelo I, Vuorinen EM, et al. Computational characterization of suppressive immune microenvironments in glioblastoma. Cancer Res 2018;78:5574– 5585. 34. Ongen H, Andersen CL, Bramsen JB, et al. Putative cisregulatory drivers in colorectal cancer. Nature 2014; 512:87–90. 35. Häkkinen A, Zhang K, Alkodsi A, et al. 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Oncostatin-M promotes phenotypic changes associated with mesenchymal and stem cell-like differentiation in breast cancer. Oncogene 2014;33:1485–1494. Author names in bold designate shared co-first authorship. Received January 18, 2021. Accepted April 16, 2021. Correspondence Address correspondence to: Kristiina Rajamäki, PhD, Department of Medical and Clinical Genetics and Applied Tumor Genomics Research Program, Research Programs Unit, PO Box 63 (Haartmaninkatu 8) FI-00014 University of Helsinki, Helsinki, Finland. e-mail: ekristiina.rajamaki@helsinki.fi; or Lauri A. Aaltonen, MD, PhD, Department of Medical and Clinical Genetics and Applied Tumor Genomics Research Program, Research Programs Unit, PO Box 63 (Haartmaninkatu 8) FI-00014 University of Helsinki, Helsinki, Finland. e-mail: lauri.aaltonen@helsinki.fi. Acknowledgments The authors thank Marjo Rajalaakso, Sini Marttinen, Sirpa Soisalo, Inga-Lill Åberg, Iina Vuoristo, Alison London, Justyna Kolakowska, and Heikki Metsola for excellent technical support, and Iikka Järvinen for helping in the immune cell scoring. The authors acknowledge the computational resources provided by the ELIXIR node, hosted at the CSC–IT Center for Science, Finland. Kristiina Rajamäki and Aurora Taira contributed equally to this work. CRediT Authorship Contributions Aurora Taira, MSc (Conceptualization: Lead; Data curation: Equal; Formal analysis: Lead; Investigation: Lead; Methodology: Equal; Software: Equal; Visualization: Lead; Writing –original draft: Lead; Writing –review & editing: Lead). Kristiina Rajamäki, PhD (Conceptualization: Lead; Data curation: Equal; Formal analysis: Lead; Investigation: Lead; Project administration: Lead; Visualization: Equal; Writing –original draft: Lead; Writing –review & editing: Lead). Riku Katainen, PhD (Conceptualization: Supporting; Data curation: Equal; Formal analysis: Equal; Investigation: Supporting; Methodology: Equal; Software: Equal; Visualization: Supporting; Writing –original draft: Equal; Writing –review & editing: Supporting). Niko Välimäki, PhD (Conceptualization: Supporting; Data curation: Equal; Formal analysis: Equal; Investigation: Equal; Methodology: Equal; Software: Equal; Visualization: Supporting; Writing –original draft: Equal; Writing – review & editing: Supporting). Anna Kuosmanen, PhD (Conceptualization: Supporting; Data curation: Equal; Formal analysis: Equal; Investigation: Equal; Software: Equal; Visualization: Equal; Writing –original draft: Equal; Writing –review & editing: Supporting). Roosa-Maria Plaketti, BSc (Data curation: Supporting; Formal analysis: Equal; Investigation: Supporting; Methodology: Supporting; Software: Equal; Visualization: Supporting; Writing –original draft: Equal; Writing –review & editing: Supporting). Toni T. Seppälä, MD, PhD (Funding acquisition: Supporting; Investigation: Supporting; Methodology: Supporting; Writing –review & editing: Equal). Maarit Ahtiainen, PhD (Investigation: Supporting; Methodology: Supporting; Writing –original draft: Supporting; Writing –review & editing: Supporting). Erkki-Ville Wirta, MD, PhD (Investigation: Supporting; Methodology: Supporting; Writing –review & editing: Supporting). Emilia Vartiainen, BSc (Data curation: Supporting; Formal analysis: Supporting; Investigation: Supporting; Software: Supporting; Visualization: Supporting; Writing –review & editing: Supporting). Päivi Sulo, MSc (Data curation: Supporting; Formal analysis: Supporting; Investigation: Supporting; Methodology: Supporting; Software: Supporting; Visualization: Supporting; Writing –review & editing: Supporting). Janne Ravantti, PhD (Methodology: Supporting; Resources: Equal; Software: Supporting; Writing –review & editing: Supporting). Suvi Lehtipuro, PhD (Methodology: Supporting; Software: Supporting; Writing –review & editing: Supporting). Kirsi J. Granberg, PhD (Methodology: Supporting; Writing –review & editing: Equal). Matti Nykter, PhD (Methodology: Supporting; Supervision: Supporting; Writing –review & editing: Supporting). Tomas Tanskanen, MD, PhD (Data curation: Supporting; Investigation: Supporting; Writing –review & editing: Supporting). Ari Ristimäki, MD, PhD (Data curation: Equal; Resources: Equal; Writing – review & editing: Supporting). Selja Koskensalo, MD, PhD (Resources: Equal; Writing –review & editing: Supporting). Laura Renkonen-Sinisalo, MD, PhD (Resources: Equal; Writing –review & editing: Supporting). Anna Lepistö, MD, PhD (Resources: Equal; Writing –review & editing: Supporting). Jan Böhm, MD, PhD (Investigation: Supporting; Methodology: Supporting; Writing –review & editing: Supporting). Jussi Taipale, PhD (Conceptualization: Supporting; Resources: Supporting; Writing –review & editing: Supporting). Jukka-Pekka Mecklin, MD, PhD (Funding acquisition: Equal; Supervision: Supporting; Writing –review & editing: Supporting). Mervi Aavikko, PhD (Conceptualization: Equal; Data curation: Equal; Formal analysis: Supporting; Investigation: Equal; Project administration: Equal; Supervision: Lead; Writing –review & editing: Equal). Kimmo Palin, PhD (Conceptualization: Supporting; Data curation: Equal; Formal analysis: Equal; Investigation: Equal; Methodology: Equal; Software: Equal; Supervision: Lead; Visualization: Supporting; Writing –review & editing: Supporting). Lauri A. Aaltonen, MD, PhD (Conceptualization: Equal; Funding acquisition: Lead; Project administration: Equal; Supervision: Lead; Writing –review & editing: Equal). Conflicts of interest The authors disclose no conflicts. Funding This study was supported by The Finnish Center of Excellence in Tumor Genetics and other Academy of Finland grants 312041, 335823, 250345, 319083, 320149, and 320185. Cancer Foundation Finland (Lauri A. Aaltonen, Jukka-Pekka Mecklin, Toni T. Seppälä), iCAN Digital Precision Cancer Medicine Flagship (Lauri A. Aaltonen, Kimmo Palin), Sigrid Jusélius Foundation (Lauri A. Aaltonen, Ari Ristimäki, Toni T. Seppälä), Doctoral Programme in Biomedicine, University of Helsinki (Aurora Taira), Jane and Aatos Erkko Foundation (Jukka-Pekka Mecklin), UEF state research funding (Jukka-Pekka Mecklin), Emil Aaltonen Foundation (Toni T. Seppälä), Finnish Medical Foundation (Toni T. Seppälä), Finnish Cancer Organizations (Ari Ristimäki), Finska Läkaresällskapet (Ari Ristimäki), Helsinki University Central Hospital Research Funds (Ari Ristimäki), and Instrumentarium Science Foundation (Toni T. Seppälä). August 2021 Inflammatory Bowel Disease and Colon Cancer 607 BASIC AND TRANSLATIONAL AT