Exome-wide somatic mutation characterization of small bowel adenocarcinoma

RESEARCH ARTICLE Exome-wide somatic mutation characterization of small bowel adenocarcinoma Ulrika A. Ha ¨nninen 1,2 , Riku Katainen 1,2 , Tomas Tanskanen 1,2 , Roosa-Maria Plaketti 1,2 , Riku Laine 1,2 , Jiri Hamberg 1,2 , Ari Ristima ¨ki 1,3 , Eero Pukkala 4,5 , Minna Taipale 6 , JukkaPekka Mecklin 7,8 , Linda M. Forsstro ¨m 1,2 , Esa Pitka ¨nen 1,2 , Kimmo Palin 1,2 , Niko Va ¨lima ¨ki 1,2 , Netta Ma ¨kinen 1,2 , Lauri A. Aaltonen 1,2 * 1Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland, 2Department of Medical and Clinical Genetics, Medicum, University of Helsinki, Helsinki, Finland, 3Department of Pathology, HUSLAB, Helsinki University Hospital and University of Helsinki, Helsinki, Finland, 4Finnish Cancer Registry, Institute for Statistical and Epidemiological Cancer Research, Helsinki, Finland, 5Faculty of Social Sciences, University of Tampere, Tampere, Finland, 6Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden, 7Department of Surgery, Jyva ¨skyla ¨ Central Hospital, Jyva ¨skyla ¨, Finland, 8Faculty of Sport and Health Sciences, University of Jyva ¨skyla ¨, Jyva ¨skyla ¨, Finland *[email protected] Abstract Small bowel adenocarcinoma (SBA) is an aggressive disease with limited treatment options. Despite previous studies, its molecular genetic background has remained somewhat elusive. To comprehensively characterize the mutational landscape of this tumor type, and to identify possible targets of treatment, we conducted the first large exome sequencing study on a population-based set of SBA samples from all three small bowel segments. Archival tissue from 106 primary tumors with appropriate clinical information were available for exome sequencing from a patient series consisting of a majority of confirmed SBA cases diagnosed in Finland between the years 2003–2011. Paired-end exome sequencing was performed using Illumina HiSeq 4000, and OncodriveFML was used to identify driver genes from the exome data. We also defined frequently affected cancer signalling pathways and performed the first extensive allelic imbalance (AI) analysis in SBA. Exome data analysis revealed significantly mutated genes previously linked to SBA (TP53,KRAS,APC,SMAD4, and BRAF), recently reported potential driver genes (SOX9,ATM, and ARID2), as well as novel candidate driver genes, such as ACVR2A,ACVR1B,BRCA2, and SMARCA4. We also identified clear mutation hotspot patterns in ERBB2 and BRAF. No BRAF V600E mutations were observed. Additionally, we present a comprehensive mutation signature analysis of SBA, highlighting established signatures 1A, 6, and 17, as well as U2 which is a previously unvalidated signature. Finally, comparison of the three small bowel segments revealed differences in tumor characteristics. This comprehensive work unveils the mutational landscape and most frequently affected genes and pathways in SBA, providing potential therapeutic targets, and novel and more thorough insights into the genetic background of this tumor type. PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 1 / 23 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Ha¨nninen UA, Katainen R, Tanskanen T, Plaketti R-M, Laine R, Hamberg J, et al. (2018) Exome-wide somatic mutation characterization of small bowel adenocarcinoma. PLoS Genet 14(3): e1007200. https://doi.org/10.1371/journal. pgen.1007200 Editor: Adam Bass, Dana Farber Cancer Institute, UNITED STATES Received: September 20, 2017 Accepted: January 16, 2018 Published: March 9, 2018 Copyright: ©2018 Ha¨nninen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Sequence data has been deposited at the European Genomephenome Archive (EGA), which is hosted by the EBI and the CRG, under study accession number EGAS00001002559. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research" (http:// www.nature.com/ng/journal/v47/n7/full/ng. 3312.html). Data are available on request upon publication from the EGA database by Author summary Small bowel adenocarcinoma is a rare but aggressive disease with limited treatment options. Of gastrointestinal tumors, small bowel tumors account for 3%, of which around one third are adenocarcinomas. Due to the scarcity of evidence-based treatment recommendations there is a dire need for knowledge on the biology of these tumors. Here, we performed the first large exome sequencing effort of 106 small bowel adenocarcinomas from a Finnish population-based cohort to comprehensively characterize the genetic background of this tumor type. The set included tumors from all three small bowel segments allowing us to also compare the genetic differences between these subsets. We defined significantly mutated genes and frequently affected pathways, providing potential therapeutic targets, such as BRAF,ERBB2,ERBB3,ERBB4,PIK3CA,KRAS,ATM, ACVR2A,ACVR1B,BRCA2, and SMARCA4, for this disease. Introduction The gastrointestinal tract, a continuous passageway, includes the main digestive organs: the stomach, the small bowel, and the large bowel. The small bowel makes up 75% of the length of the gastrointestinal tract, yet small bowel tumors constitute only approximately 3% of gastrointestinal tumors [1]. The major histological types of primary small bowel cancers are carcinoids, adenocarcinomas, lymphomas, and sarcomas. Small bowel adenocarcinomas (SBAs) account for around one third of the tumors and are most often found in the duodenum, the first section of the small bowel [2]. SBAs are often sporadic, however, several factors such as inflammatory bowel disease (IBD; Crohn’s disease and ulcerative colitis) and hereditary syndromes such as familial adenomatous polyposis (FAP) and Lynch syndrome (LS) are known to predispose to these tumors [3]. Patients with celiac disease are also at a greater risk of developing SBA compared to general population. Other risk determinants include lifestyle factors, such as alcohol use, obesity, and consumption of red meat [4]. Although diagnostic tools such as imaging and endoscopy have improved, SBAs are often advanced at the time of diagnosis and sometimes found incidentally. The estimated five-year relative survival rate for SBA is 40%, indicating a worse prognosis than for colorectal adenocarcinomas (hereinafter referred as CRC) [2]. The incidence of SBA has also increased over the past decades. This combined with the scarcity of evidence-based treatment recommendations underlines a dire need for knowledge on the biology of these tumors. To date, there have been relatively few large studies on SBA that have either screened a set of known mutation hotspots or cancer genes [5–7], along with two exome sequencing efforts on small sets of duodenal adenocarcinomas [8,9]. The most commonly mutated genes in SBA include TP53,KRAS,SMAD4, and APC [3,7]. The fraction of microsatellite unstable (MSI) tumors in SBA has been reported to vary between 5–35% [10]. These tumors have a defective DNA mismatch repair (MMR) system and thus, compared to microsatellite stable (MSS) tumors, exhibit a remarkably high mutation burden. SBAs share many of the above-mentioned features with CRC. They also share similar carcinogenic pathways; e.g. they are thought to arise through an adenoma-to-carcinoma transition [11]. Regardless, large bowel tumors are much more frequent. Factors that could contribute to the difference include protective factors of the small bowel environment. Due to alkalinity, fewer bacteria, liquid nature of small bowel contents, and shorter transit time, there is less Exome sequencing of small bowel adenocarcinoma PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 2 / 23 contacting the data access committee (DAC accession EGAC00001000649, [email protected]) assigned for this project. Data are restricted due to reasons of patient confidentiality. Funding: This work was supported by grants from the Academy of Finland (Centre of Excellence in Cancer Genetics Research 2012–2017, no. 250345), the Finnish Cancer Society, the Sigrid Juselius Foundation, the Jane and Aatos Erkko Foundation, and SYSCOL (an EU FP7 Collaborative Project, no. 258236). Personal grants were received from the Academy of Finland (no. 295693 to NM and no. 287665 to NV). UAH received the following personal grants for this work: the Finnish Medical Society “Duodecim”, Biomedicum Helsinki Foundation, the Pa¨ivikki and Sakari Sohlberg Foundation, the Ida Montin Foundation, the Gastroenterological Research Foundation, the Maud Kuistila Memorial Foundation, Cancer Foundation Finland sr., and the K. Albin Johansson Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: LAA has received a lecture fee from Roche Oy. exposure to carcinogens [3]. The difference in cancer incidence between the small and large bowel could also be related to a slower rate of stem cell divisions in the small bowel [12]. Since there are limited data available to guide treatment decisions, our aim was to characterize the somatic mutational landscape of SBAs using exome sequencing to gain new insights into the SBA biology and identify potential therapeutic targets. Results Cohort characteristics Clinicopathologic features of the 106 SBA patients are listed in Table 1. Of the 106 tumors, 26 (25%) were duodenal, 52 (49%) jejunal, 18 (17%) ileal, and 10 (9.4%) resided in an unspecified Table 1. Clinicopathologic features of the patient cohort. Characteristic No. (%) of patients All 106 Sex Male 56 (53%) Female 50 (47%) Age Median 62 years Range 24–86 years Celiac disease Celiac 10 (9.4%) Non-celiac 96 (91%) Inflammatory bowel disease Crohn’s disease 4 (3.8%) Ulcerative colitis 1 (0.9%) no inflammatory disease 101 (95.3%) Hereditary syndromes Lynch syndrome 4 (3.8%) FAP 2 (1.9%) no hereditary syndrome 100 (94.3%) Primary tumor location Duodenum 26 (24.5%) Jejunum 52 (49.1%) Ileum 18 (17.0%) not specified 10 (9.4%) Tumor stage (TNM) I 4 (3.8%) II 22 (20.7%) III 25 (23.6%) IV 41 (38.7%) not specified 14 (13.2%) Histological grade G1 18 (17.0%) G2 60 (56.6%) G3 20 (18.9%) not specified 8 (7.5%) MMR status MSI 15 (14.2%) MSS 91 (85.8%) https://doi.org/10.1371/journal.pgen.1007200.t001 Exome sequencing of small bowel adenocarcinoma PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 3 / 23 location. The male-to-female ratio was 1.1, and the median age at diagnosis 62 years (range, 24 to 86 years). Median age at diagnosis was lowest for patients with jejunal tumor (59.5 years for jejunum versus 71.0 for duodenum and 63.0 for ileum; P= 0.00108, Kruskal-Wallis test). Fifteen tumors were designated as MSI based on the exome sequencing data (see below). Ten patients in the cohort had been diagnosed with celiac disease, five with IBD, and six with hereditary syndromes (LS or FAP). The causative germline mutations in LS patients occurred in MLH1 or MSH6, and in FAP patients in APC. All tumors from patients with IBD were MSS, whereas all LS-associated tumors were MSI, and the tumors from the two FAP patients were either MSS or MSI. Evaluation of the clinicopathologic characteristics revealed enrichment of celiac patients amongst the SBA patients: 9.4% compared to 2.4% in the general Finnish population (P= 2.48x10 -4 , exact binomial test) [13]. Five of 10 tumors from patients with celiac disease were microsatellite-unstable, and thus celiac disease was associated with MSI (odds ratio (OR), 8.31; 95% confidence interval (CI), 1.62–43.6; P= 4.83x10 -3 ), which corresponds to previous literature [14]. None of the tumors related to celiac disease resided in ileum. Otherwise the celiac disease-related tumors did not notably differ from other tumors in terms of the characteristics in Table 1. Disease-specific survival was superior for patients with microsatellite-unstable tumors after adjustment for sex, tumor stage, and age at diagnosis (hazard ratio (HR), 0.111; 95% CI, 0.0292–0.419; P= 1.20x10 -3 ) (Table a in S1 Table;S1 Fig). Also, male patients had a worse disease-specific survival, although the difference was not formally significant. Frequently mutated genes in exome data Exome sequencing analysis identified 75,993 somatic mutations across all samples. Of these, 29,120 were non-synonymous and 9,415 synonymous (S2 Table). Fifteen out of 106 (14%) samples were classified as MSI based on high mutation load and overrepresentation of insertions and deletions (indels) at microsatellite loci obtained from Hause et al. [15]. The classification was confirmed by signature analysis (see methods). The average mutation burden in the whole target region was 4.30 mutations per megabase (mut/Mb) per MSS and 63.6 mut/Mb per MSI sample (S2 Fig). The median number of non-synonymous mutations per sample was 88 in MSS (interquartile range (IQR), 64.5–114) and 1,266 in MSI tumors (IQR, 666–1,738). The median number of missense mutations was 79 (IQR, 56.5–105) in MSS and 812 (IQR, 518–1,209) in MSI tumors. For nonsense mutations, the median mutation counts were 10 (IQR, 6.5–15) in MSS and 429 (IQR, 210–498) in MSI tumors and for frameshift mutations 4 (IQR, 2–6) in MSS and 286 (IQR, 180–397) in MSI tumors. In MSS tumors, 6,214 genes harbored a non-synonymous mutation in at least one tumor and 1,921 genes in two or more tumors as compared to 10,716 and 5,055 in MSI tumors, respectively. In MSS tumors, the most frequently mutated known cancer genes were TP53 (44/91, 48%), KRAS (43/91, 47%), APC (20/91, 22%), SMAD4 (14/91, 15%), SOX9 (11/91, 12%), BRAF(10/ 91, 11%), and ERBB2 (10/91, 11%). In MSI tumors, among the most frequently mutated genes were known driver genes ACVR2A (13/15, 87%), BMPR2 (9/15, 60%), KRAS (8/15, 53%), and APC (7/15, 47%). TP53, the most frequently mutated gene in MSS tumors, was also frequently mutated (6/15, 40%) in MSI tumors. Significantly mutated genes in SBAs Next, we sought to identify genes showing statistical evidence of positive selection for mutations in SBA. We applied OncodriveFML to detect candidate driver genes in MSS tumors. In total, 44 genes displayed a nominally significant P-value (<0.05) (Table a in S3 Table). Seven Exome sequencing of small bowel adenocarcinoma PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 4 / 23 genes remained significant after correction for multiple testing (false discovery rate (FDR), qvalue <0.1). However, genes with P<0.05 were also considered as being of potential interest. The most significant genes in MSS tumors consisted of known cancer genes such as TP53, KRAS,APC,SOX9,SMAD4,BRAF, and ACVR2A. (Fig 1, Table a in S3 Table). The twenty-five highest-ranking driver candidates included also recently reported (ATM and ARID2) and novel candidate drivers such as ACVR1B,BRCA2, and SMARCA4 that (to our knowledge) have not been implicated in SBA before. More information on the mutation content of the genes (P<0.05) is displayed in Table b in S3 Table. In addition to KRAS,APC was designated as one of the most significant genes in MSS tumors (20/91, 22%) and was also frequently mutated in MSI (7/15, 46.7%) tumors. Of note, Fig 1. Mutational landscape of the most significant genes in MSS SBAs. The figure includes the 25 highest-ranking genes in MSS tumors (n = 91) according to OncodriveFML, ranked by the P-value (right, red line at P= 0.05). Of these, TP53,KRAS,APC,SOX9,SMAD4,BRAF, and ACVR2A were significant also after correction for multiple testing. Different colors distinguish between the different types of mutations (in the middle). “Double hit” refers to two truncating mutations. The percentage of mutated tumors by gene are shown on the left. The upper bars represent the total number of both synonymous and nonsynonymous mutations per tumor. https://doi.org/10.1371/journal.pgen.1007200.g001 Exome sequencing of small bowel adenocarcinoma PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 5 / 23 37 of 42 (88%) APC mutations were protein-truncating (22 nonsense and 15 frameshift). Of the five patients with IBD, two (40%) harbored an APC nonsense mutation. Atypical mutation hotspots of BRAF BRAF was mutated in 11 tumors (11/106, 10.4%): 10 MSS and one MSI (Fig 2). We did not observe any V600E mutations. Instead, we identified an atypical mutation pattern with two known, less studied hotspots: G469A with two and D594A/G/N with three hits. In addition, we observed other known mutations near these hotspots (G466E, G596R, and K601N). All above-mentioned mutations resided in exons 11 or 15 and have been designated as somatic hotspots in various cancers [16]. In read level inspection, we identified one additional tumor (SIA56) displaying a hotspot mutation in G469A supported by four mutant reads which had not been called. This tumor also harbored one missense mutation in BRAF(T241M). Furthermore, two tumors harbored protein-truncating BRAFvariants: Q257X (SIA214) and A404fs (SIA53). Except for one frameshift mutation, all other mutations occurred in MSS tumors. We compared tumor and patient characteristics according to BRAFmutation status, no significant differences were detected (Table a in S4 Table). BRAF V600E and KRAS mutations are generally mutually exclusive. Regarding atypical hotspot mutations, however, we identified four out of 11 BRAFmutants where BRAFand KRAS mutations co-occurred: KRAS A146T +BRAF D594A (SIA121), KRAS G12R +BRAF G469A (SIA228), KRAS G12D +BRAF Q257X (SIA214), and KRAS G12D +BRAF A404fs (SIA53). Mutation patterns of ERBB2 and other ERBB receptor family member genes We identified 18 ERBB2 mutations in 15 tumors (15/106, 14%): 10 MSS and five MSI (Fig 3). ERBB2did not reach significance in the OncodriveFML analysis; however, it is a known therapeutic target frequently mutated in many tumors of the digestive system, including those of the small bowel [5,7,17,18]. The majority (14/18, 78%) of the mutations clustered into four known hotspots (Fig 3) [16]. One of the hotspots, L755S, was mutated exclusively in MSI tumors, whereas the other hotspots, S310F/Y, R678Q, and V842I, were found both in MSS and MSI tumors. Two samples harbored concurrent hotspot mutations, L755S+V842I and R678Q+V842I. Such co-occurrence has been reported previously at least once in SBA [5]. In addition to the hotspot Fig 2. Mutations in BRAF (ENST00000288602). In total, 12 mutations were identified in 11 tumors (MSS n = 10, MSI n = 1). RBD = Raf-like Rasbinding domain; C1_1 = C1 domain; Pkinase_Tyr = Protein tyrosine kinase. https://doi.org/10.1371/journal.pgen.1007200.g002 Exome sequencing of small bowel adenocarcinoma PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 6 / 23 mutations, three single mutations were identified in MSS (S250F, V777L, and T862A) and one in MSI tumors (P1209T). We compared tumor and patient characteristics of ERBB2 mutant and wild-type cases (Table b in S4 Table). We detected a statistically significant difference in the MMR status (OR, 3.98; 95% CI, 0.886–16.4; P= 0.0368), ERBB2 mutation frequency being higher in MSI tumors. The ERBB family comprises of four receptor tyrosine kinases encoded by EGFR (also known as ERBB1), ERBB2,ERBB3, and ERBB4. Albeit with lower frequencies, also ERBB3 and ERBB4displayed hotspot mutations in our data. We identified 10 ERBB3 mutations in nine tumors, revealing two hotspots: V104M/L in one MSS and in two MSI and S846I in two MSS tumors. These affected either the extracellular domain (V104M/L) or the kinase domain (S846I). We also observed 10 ERBB4mutations in nine tumors. ERBB4 displayed one mutation hotspot, L798R/P in the protein tyrosine kinase domain, supported by two MSS tumors. Moreover, we detected one EGFR mutation (R977C). Thus, there were altogether 29 samples (27%) with a mutation in at least one of the ERBB genes (Fig 3). Of these, four tumors exhibited mutations in more than one of these three genes. All hotspot mutations in different ERBB genes were mutually exclusive. Allelic imbalance in SBA We performed an allelic imbalance (AI) analysis for the whole data set of 106 tumors. The analysis revealed 1,541 loss and 840 gain events across all samples. The number of AI events in MSI tumors (median, 5; IQR, 4–8) was significantly lower compared to that of MSS tumors (median, 22; IQR, 13–35) (P= 1.95x10 -9 ), see Table b in S1 Table. The number of AI events did not differ significantly between tumors from different small bowel segments. The most frequent AI event was partial or whole loss of chromosome 17 short arm (p) harboring TP53, detected in 62/106 (58.5%) samples (Fig 4;S3 Fig). Non-synonymous variants in TP53 cooccurred with loss events in 41/50 (82.0%) of mutated cases (OR, 7.43; 95% CI, 2.86–21.1, P = 4.02x10 -6 ) (S4 Fig). We also observed a high frequency of chromosomal losses in two other significantly mutated known cancer genes: SMAD4 (n = 46) and SOX9 (n = 44). Chromosome Fig 3. Mutation pattern in ERBB receptor family. Mutations in ERBB2(ENST00000269571) grouped into four hotspots (top). Samples (n = 29) with a mutated member of ERBB receptor family are presented in columns (below). In addition to a hotspot mutation, some samples displayed simultaneously a non-hotspot mutation in the same gene, thus all mutations are not shown in the figure. Recep_L = Receptor L domain; Furin-like = Furin-like cysteine rich region; GF_recep = Growth factor receptor domain; Pkinase_Tyr = Protein tyrosine kinase. https://doi.org/10.1371/journal.pgen.1007200.g003 Exome sequencing of small bowel adenocarcinoma PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 7 / 23 or arm level losses were observed at high frequency (n>30) at chromosomes 3p, 8p, 9q, 12q, 15, 17, 18q, 19, and 22 (Fig 4;S3 Fig). Gain events were observed at high frequency at chromosomes 13 and 8q (with MYC as a possible target). In addition, known oncogenes, such as KRAS,BRAF, and PIK3CA that were amongst the highest-ranking genes, were clearly amplified in 20/106 (18.9%), 19/106 (17.9%), and 16/106 (15.1%) samples, respectively. We observed also localized and strong amplification at the ERBB2 locus in 4 samples, two of which had a hotspot mutation in ERBB2 (S3–S5 Figs). Mutational signatures First, we performed mutational signature analysis for all 106 samples. A known MSI signature (signature 6) was identified in 15 tumors (Fig 5; Table a in S5 Table). The signature analysis was then performed separately for the 91 MSS SBAs. This process yielded three mutational signatures (1A, 17 and U2) corresponding to known signatures reported by Alexandrov et al. (Fig 5; Tables b and c in S5 Table) [19]. Signature U2 has not been validated previously due to lack of available biological samples and access to BAM files for the samples. We were able to inspect read sequences in our data set and validate mutations in this signature class. Mutational signatures were studied using multivariable-adjusted negative binomial regression (Table c in S1 Table). Similar to other cancers, the frequency of mutations attributable to Fig 4. Overview of AI events in SBA. Frequency of gains and losses in 106 SBA samples. https://doi.org/10.1371/journal.pgen.1007200.g004 Exome sequencing of small bowel adenocarcinoma PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 8 / 23 Fig 5. Signature contexts. The 15 MSI tumors displayed signature 6. There were three signatures (1A, 17, and U2) that could be extracted from the 91 MSS tumors. https://doi.org/10.1371/journal.pgen.1007200.g005 Exome sequencing of small bowel adenocarcinoma PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 9 / 23 length) from the indel locus. This step was done to exclude low allelic fraction artefacts in regions prone to sequencing errors. Only variants within the targeted region of NimbleGen SeqCap EZ Exome Library v3 Kit were analyzed. BasePlayer [58] was utilized to visualize and analyze the data (allele frequency and quality filtering, allelic imbalance, gene annotation, and calculation of variant statistics). Variant filtering parameters are listed in S8 Table. Ensembl version 87 (GRCh37) was used for gene annotation. Mutation calls have been deposited in the EGA database (EGAS00001002559). OncodriveFML We used OncodriveFML v.2.0.2 [59] to perform significance analysis for somatic mutations within the coding DNA sequence (CDS). OncodriveFML is a permutation-based method that compares a region’s mean functional impact score to its null distribution by randomizing observed mutations. Protein-coding CDS regions were obtained from Gencode release 19 (http://www.gencodegenes.org/). The resulting regions were then merged using bedtools (v.2.25.0). The method’s default scoring framework, CADD [60], was used. OncodriveFML’s default configurations were applied, with the genomic elements file defined as “coding” and the sequencing type defined as “whole exome sequencing”. The focus was, solely, on genes mutated in at least four tumors. Quantile-quantile plots are presented in S7 Fig. Inflation factors for P-value distributions were estimated using the R package GenABEL v.1.8–0. The Benjamini-Hochberg method was applied to adjust for false discovery rate (FDR). Sanger sequencing All non-synonymous mutations in the novel candidate genes (ACVR2A,ACVR1B,BRCA2, and SMARCA4) used in OncodriveFML analysis and genes with a clear mutation hotspot pattern (ERBB2 and BRAF) were selected for validation with Sanger sequencing. Primers were designed using Primer3Plus [61]. Each PCR reaction was performed in triplicates to ensure consistency of the observations. Sequencing reactions were carried out with the Big Dye Terminator v.3.1 kit (Applied Biosystems, Foster City, CA, USA) on an ABI3730 Automatic DNA Sequencer (FIMM Technology Center and DNA sequencing and Genomics laboratory, Institute of Biotechnology, Helsinki, Finland). The sequence graphs were analyzed both with the Mutation Surveyor–software (version v4.0.8, Softgenetics, State College, PA) and manually. Validation was successfully performed for altogether 49/54 mutations. From two tumors (SIA137 and SIA98) no DNA material was left for validation. For 47/49 mutations, we had just enough DNA material from the corresponding normal samples to validate their somatic status. All except two mutations in BRCA2 were validated as somatic. These two rare germline variants (ExAC MAF = 0.00002 & 0.00005) were excluded from the whole study. Even after the removal of these two mutations, BRCA2 remained in the top 25 genes in the OncodriveFML re-run. Allelic imbalance analysis AI regions were called using germline SNVs of the whole sample set of 106 SIA tumors. We selected SNVs for the analysis based on following criteria: • rs-coded • 10 or more coverage at variant call locus • not defined as somatic in this study • within exome target regions Exome sequencing of small bowel adenocarcinoma PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 16 / 23 • does not overlap with regions prone to false allelic imbalance calls (see control analysis below) B allele frequency segmentation (BAFsegmentation) algorithm (described in Staaf et al. [62]) was utilized to call AI regions with parameters: non_informative = 0.97, ai_threshold = 0.6, ai_size = 4, triplet_threshold = 0.8. BAF value was calculated from allelic depth fields of VCF file (ALT calls / total coverage). First, we performed control analysis with 80 normal exomes using the same parameters to detect possible technical artefacts caused by low-complexity genomic loci and usage of exome variant data, which has limited power to detect AI. Control analysis revealed genomic regions more prone to false calls (e.g. centromeres and chromosome ends). Variants overlapping these regions were excluded from the tumor analysis. In addition, we observed median coverage differences between chromosomes (e.g. median coverages across all samples in chromosome 1 and 16 was 38 and 30, respectively). This information was used for chromosome-specific coverage normalization in calculation of log-R ratios for tumor variants. Median coverage for X chromosome was calculated by using only female samples. We ran BAFsegmentation for tumor samples twice. The first run was performed to detect AI regions to get as accurate median coverage for all samples as possible. Median coverages were calculated using all variants, which did not overlap with called AI regions. Variant-specific log-R ratios were calculated using following formula (1): log2ðvarCoverage=ðsampleMedianchromNormalize½chrÞÞ ð1Þ varCoverage was obtained from coverage field (DP) in VCF-file. sampleMedian is sample-specific median coverage value of all chromosomes (AI regions excluded). chromNormalize[chr] corresponds to chromosome specific coverage normalization coefficient, which was processed in control analysis. Second, and last BAFsegmentation run was performed using refined log-R ratios. AI events with median log-R ratios higher than 0.1 were considered as gains and events equal or less than 0.1 were considered as losses (including copy number neutral loss of heterozygosity). Mutation signature analysis First, we performed signature analysis on 106 SBAs, as in Katainen et al., using non-negative matrix factorization of six substitution types in 50-Xp(C/T)pY-30for any nucleotides X and Y [48,63]. All variants within exome target regions were used, including UTRs. We computed the exposure of extracted signatures for each 106 SBAs as a projection of the mutation matrix to the signature weight matrix. The obtained signatures (p) were compared to the published signatures (q) of Alexandrov et al. by mean Kullback-Leibler divergence (D KL (p||q)+D KL (q|| p)]/2. Fifteen samples displayed the MSI signature (Signature 6), consistent with the division of tumors based on the exome data (S2 Table). Mutation signature analysis was subsequently performed in 91 MSS SBAs. Ingenuity Pathway Analysis Ingenuity Pathway Analysis (IPA) version 39480507 was used to determine the frequency of known cancer pathways affected in the MSS tumors. IPA was utilized to define genes linked to each pathway. All genes with at least one non-synonymous mutation were included in the analysis. Statistical analysis of clinical data We used R v.3.4.1 to analyze clinical variables. Fisher’s exact test was used to test for independence of categorical variables. Differences in continuous variables were assessed with the Exome sequencing of small bowel adenocarcinoma PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 17 / 23 Mann-Whitney U test. Disease-specific survival was analyzed by Cox proportional hazards regression with Firth’s penalized likelihood (coxphf package v.1.12). Per-tumor mutation counts attributable to mutational signatures were estimated in MSS tumors, and their associations with clinical features were modeled using negative binomial regression (MASS package v.7.3–47). All P-values are two-sided and unadjusted for multiple comparisons. P-value <0.05 was regarded as statistically significant. Supporting information S1 Table. Cox proportional hazards model for disease-specific survival (a) and negative binomial models for allelic imbalance and mutational signatures (b-c). (PDF) S2 Table. Mutation statistics from exome sequencing data. This table includes sample-wise statistics for all somatic variants within the targeted region. (XLSX) S3 Table. OncodriveFML results of the MSS tumors (a) and mutation content of the genes that received P<0.05 in the OncodriveFML analysis (b). (XLSX) S4 Table. Comparison of clinicopathological characteristics between (a) BRAF and nonBRAF mutants and (b) ERBB2 and non-ERBB2 mutants. (PDF) S5 Table. Signature analysis: (a) division of MSI and MSS tumors, (b) the MSS exposures, and (c) the signature weights (MSS), and (d) the signature weights (MSI). (XLSX) S6 Table. Pathway analysis: (a) Frequencies of mutated pathways in the tumor set, (b) List of genes (with at least one mutation in MSS tumors) per pathway. (PDF) S7 Table. Comparison between the three small bowel segments. (PDF) S8 Table. Filtering criteria for SNVs and indels. Recommended GATK hard filters. (PDF) S1 Fig. Kaplan-Meier estimates of disease-specific survival according to a) MMR status, b) age at operation, c) stage, and d) sex. Eighteen patients were omitted due to missing data, (n = 88). The total duration of follow-up was 379 person-years, and 53 deaths from SBA were observed. In Cox regression model with adjustment for sex, tumor stage, and age at operation, MSI tumors were associated with better disease-specific survival compared to MSS tumors (hazard ratio (HR), 0.111; 95% CI, 0.0292–0.419; P= 1.20x10 -3 ). P-values for unadjusted logrank tests are shown in the figure. (PDF) S2 Fig. Somatic mutation prevalence. The mutation burden in the whole set, n = 106. Median value (red line) = 3.96. (PDF) S3 Fig. AI events in 91 MSS SBAs. Genes highlighted in our study (the 25 highest-ranking genes in OncodriveFML and the ERBB-family genes) (red) and cancer census genes near Exome sequencing of small bowel adenocarcinoma PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 18 / 23 visible AI peaks (purple) are depicted in the graphs. (PDF) S4 Fig. Landscape of mutations and AI events in MSS SBAs. The figure includes the 25 highest-ranking genes in OncodriveFML and the ERBB-family genes. Different colors distinguish between the different types of AI events. Non-synonymous mutations are marked with a black dot. (PDF) S5 Fig. AI events in the ERBB2 and chromosome 17. Four tumors showed a strong localized amplification in ERBB2, of which two tumors harbored also ERBB2 mutation (SIA82, V842I and SIA137, S310Y). (PDF) S6 Fig. Comparison of signature 1A and age at diagnosis between tumors from different segments. Exposure to signature 1A was highest in jejunal tumors even though the median age at diagnosis was lower in patients with jejunal tumor compared to patients with duodenal or ileal tumors. (PDF) S7 Fig. Quantile-quantile plots for MSS (n = 91) OncodriveFML analysis, (a) with all the genes included in the initial run and (b) after filtering the data to contain genes mutated in at least four samples. (PDF) Acknowledgments The authors thank Marjo Rajalaakso, Alison Ollikainen, Iina Vuoristo, Heikki Metsola, Sini Nieminen, Sirpa Soisalo, Inga-Lill Svedberg, Asal Fotouhi, and Lijuan Hu for their excellent technical assistance. We acknowledge the Finnish Cancer Registry and the computational resources provided by the ELIXIR node, hosted at the CSC–IT Center for Science, Finland. Author Contributions Conceptualization: Ulrika A. Ha¨nninen, Jukka-Pekka Mecklin, Lauri A. Aaltonen. Data curation: Riku Katainen, Esa Pitka¨nen. Formal analysis: Riku Katainen, Tomas Tanskanen, Roosa-Maria Plaketti, Riku Laine, Niko Va¨lima¨ki. Funding acquisition: Lauri A. Aaltonen. Investigation: Ulrika A. Ha¨nninen, Tomas Tanskanen, Ari Ristima¨ki, Minna Taipale, Linda M. Forsstro¨m. Methodology: Kimmo Palin. Project administration: Ulrika A. Ha¨nninen, Lauri A. Aaltonen. Resources: Eero Pukkala. Software: Riku Katainen, Jiri Hamberg, Esa Pitka¨nen. Supervision: Netta Ma¨kinen, Lauri A. Aaltonen. Validation: Ulrika A. Ha¨nninen. Exome sequencing of small bowel adenocarcinoma PLOS Genetics | https://doi.org/10.1371/journal.pgen.1007200 March 9, 2018 19 / 23 Visualization: Ulrika A. Ha¨nninen, Riku Katainen, Roosa-Maria Plaketti, Riku Laine. Writing – original draft: Ulrika A. Ha¨nninen, Riku Katainen, Tomas Tanskanen, RoosaMaria Plaketti, Riku Laine, Netta Ma¨kinen. Writing – review & editing: Ulrika A. 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