The Genomic Landscape of Pancreatic and Periampullary Adenocarcinoma

Author Manuscript Published OnlineFirst on August 3, 2016; DOI: 10.1158/0008-5472.CAN-16-0658 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

The genomic landscape of pancreatic and periampullary adenocarcinoma

Vandana Sandhu1,8, David C Wedge2,11, Inger Marie Bowitz Lothe1,3, Knut Jørgen Labori4, Stefan C Dentro2,11, Trond Buanes4,5, Martina L Skrede1, Astrid M Dalsgaard1, Else Munthe6, Ola Myklebost6, Ole Christian Lingjærde7, Anne-Lise Børresen-Dale1,5, Tone Ikdahl9,10, Peter Van Loo12,13, Silje Nord1, Elin H Kure1,8*

1Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway 2Wellcome Trust Sanger Institute, Hinxton, UK 3Department of Pathology, Oslo University Hospital, Oslo, Norway 4Department of Hepato-Pancreato-Biliary Surgery, Oslo University Hospital, Oslo, Norway 5Institute of Clinical Medicine, University of Oslo, Oslo, Norway 6Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway 7Department of Computer Science, University of Oslo, Oslo, Norway 8Department for Environmental Health and Science, University college of Southeast Norway, Bø in Telemark, Norway 9Department of Oncology, Oslo University Hospital, Oslo, Norway 10Akershus University Hospital, Nordbyhagen, Norway 11Department of Cancer Genomics, Big Data Institute, University of Oxford, Oxford, UK 12The Francis Crick Institute, London, UK 13Department of Human Genetics, University of Leuven, Leuven, Belgium

*Corresponding author: Elin H. Kure, [email protected]

Running title: Copy number aberrations in periampullary adenocarcinomas

Keywords

1. Pancreatic cancer

2. Copy number aberrations

3. Integrative –omics analysis

4. Driver genes

5. Prognostic subtypes

Downloaded from cancerres.aacrjournals.org on October 2, 2021. © 2016 American Association for Cancer Research. Author Manuscript Published OnlineFirst on August 3, 2016; DOI: 10.1158/0008-5472.CAN-16-0658 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Abstract

Despite advances in diagnostics, less than 5% of patients with periampullary tumors

experience an overall survival of five years or more. Periampullary tumors are neoplasms

that arise in the vicinity of the ampulla of Vater, an enlargement of liver and pancreas

ducts where they join and enter the small intestine. In this study, we analyzed copy

number aberrations using Affymetrix SNP 6.0 arrays in 60 periampullary

adenocarcinomas from Oslo University Hospital (OUH) to identify genome-wide copy

number aberrations, putative driver genes, deregulated pathways and potential prognostic markers. Results were validated in a separate cohort derived from the Cancer Genome

Atlas (TCGA) Consortium (n=127). In contrast to many other solid tumors,

periampullary adenocarcinomas exhibited more frequent genomic deletions than gains.

Genes in the frequently co-deleted region 17p13 and 18q21/22 were associated with cell

cycle, apoptosis, p53 and Wnt signaling. By integrating genomics and transcriptomics

data from the same patients, we identified CCNE1 and ERBB2 as candidate driver genes.

Morphological subtypes of periampullary adenocarcinomas (i.e. pancreatobiliary or

intestinal) harbor many common genomic aberrations. However, gain of 13q and 3q, and

deletions of 5q were found specific to the intestinal subtype. Our study also implicated

the use of PAM50 classifier in identifying subgroup of patients with high proliferation

rate, which had impaired survival. Further, gain of 18p11 (18p11.21-23, 18p11.31-32)

and 19q13 (19q13.2, 19q13.31-32) and subsequent overexpression of the genes in these

loci were associated with impaired survival. Our work identifies potential prognostic

markers for periampullary tumors, the genetic characterization of which has lagged.

Introduction

Pancreatic cancer is the fourth most common cause of cancer-related deaths in Western

countries, and it is projected to be the second leading cause of cancer death by 2030 (1).

The incidence and mortality rate for pancreatic cancer are almost equal and the five-year

survival rate is <5%. Across tumor types, tumor evolution is driven either by mutations

or by copy number aberrations (CNA) (2). CNAs play a critical role in activating

oncogenes and inactivating tumor suppressor genes, thereby targeting the hallmarks of

cancer (3,4). Studies have shown that driver alterations in pancreatic cancer include both single nucleotide variants and large-scale rearrangements (5,6). In the field of cancer genomics, the focus has been on identifying altered genomic regions and pathways by

high throughput technologies, and relating these to phenotypic effects. This knowledge

has already led to substantial advances in diagnostics and therapeutics in other cancers

such as targeting the HER2 oncogene in breast cancer patients using the monoclonal

antibody trastuzumab (7).

A number of studies are published on pancreatic ductal adenocarcinomas (PDAC) using

high-throughput data analysis (8-14). Previous studies on relatively small cohorts of

PDAC have documented homozygous deletions of 1p, 3p, 6p, 9p, 12q, 13q, 14q, 17p and

18q, and amplifications of 1q, 2q, 3q, 5q, 7p, 7q, 8q, 11p, 14q, 17q and 20q (11,12,13).

Recently, a study of 75 PDAC and 25 cell lines derived from PDAC patients were

analyzed using Illumina SNP arrays and whole genome SOLID sequencing (5). The

results showed that genomic alterations in PDACs are dominated by structural alterations,

and were classified by the number and distribution of structural variation events. Another

recent publication identified four subtypes of PDAC namely squamous, pancreatic progenitor, immunogenic and aberrantly differentiated endocrine exocrine type (14), which overlapped with Collisson’s subtypes namely quasi-mesenchymal (QM), exocrine and classical subtype, except for the immunogenic subtype (8,14). Despite these studies, our knowledge about pancreatic cancer subtypes is limited, partly due to small samples sizes and lack of validation in different cohorts.

Here we analyzed the copy number profile of 60 tumors from OUH and 127 tumors from

TCGA cohort using SNP arrays. Due to the low tumor purity frequently observed in pancreatic cancer biopsies, copy number alterations may present as subtle changes in copy number signals. The Battenberg analysis pipeline applied herein (15) (doi:

10.5281/zenodo.16107) performs phasing of both parental haplotypes to increase sensitivity. CNAs were identified in tumors originating from the pancreatic ducts, the bile duct, the ampulla and the duodenum, collectively called periampullary adenocarcinomas

(PAs) in the OUH cohort of 60 patients. Several regions of recurrent gain or loss were identified in the OUH cohort and validated in TCGA cohort, providing a set of putative driver genes and deregulated pathways in PA. The frequent gain and overexpression of genes was further associated with poor patient prognosis.

Material and Methods

DNA extraction

DNA was extracted from tumor tissue using the Maxwell Tissue DNA kit on the

Maxwell 16 Instrument (Promega). Briefly 5x20 µm sections were homogenized in 300

µl Lysis Buffer and added to the cartridge. The method is based on purification using

paramagnetic particles as a mobile solid phase for capturing, washing and elution of genomic DNA. Elution volume was 200 µl. DNA was extracted from 6 ml EDTA blood using the QiAamp DNA Blood BioRobot MDx Kit on the BioRobot MDx (Qiagen). This was done at Aros Applied Biotechnology AS, Aarhus, Denmark, and the Department of

Medical Genetics, Oslo University Hospital. The method is based on lysis of the sample using protease, followed by binding of the genomic DNA to a silica-based membrane and washing and elution in 200 µl buffer AE. DNA from normal tissue was extracted at Aros

Applied Biotechnology AS, Aarhus, Denmark according to their Standard Operation

Procedures (SOP’s) for extraction with a column based technology (Qiagen). Tissue specimens were homogenized in Qiagen Tissuelyzer homogenizer. The amount of tumor cells in the sections used for DNA isolation were estimated on HE-stained sections cut before and after cutting of sections used for DNA isolation.

Tumor and matching normal samples

The OUH cohort contained a total of 60 samples of fresh frozen tumor tissue with origin in the four different periampullary locations and corresponding normal DNA samples from EDTA blood; 28 from pancreas, four from bile duct, six from ampulla of pancreatobiliary type, seven from ampulla of intestinal type, nine from duodenum, three

Intraductal Papillary Mucinous Neoplasia (IPMN) samples and three samples from

xenograft cell lines generated from PDAC patients were analyzed using Affymetrix SNP

6.0. The xenograft cell lines were generated at Oslo University Hospital (OUH) between

2010 and 2012. Fresh, surgically excised primary pancreatic adenocarcinoma material

was kept in on ice and transported directly to the animal facility within two hours. STR

fingerprint from the cell lines and the corresponding primary tumors were performed at the genotyping core facility at OUH. The latest mycoplasma test for all three cell lines

was done on 09.02.15. They all tested negative (25). Further, for validation of the results,

127 PDAC samples from the TCGA cohort https://tcga-data.nci.nih.gov/tcga/ were

analyzed.

Affymetrix SNP 6.0 arrays

The Affymetrix SNP 6.0 arrays include 1.8 million genetic markers, including 906,600

SNPs and 946,000 copy number probes. DNA digestion, labeling and hybridization were

performed according to the manufacturer’s instruction (Affymetrix, Santa Clara, CA,

USA).

Data analysis

Copy number aberration profiles from the OUH (n=60) and the TCGA (n=127) cohort

were generated. Segmental copy number information was derived for each sample using

the Battenberg pipeline (https://github.com/cancerit/cgpBattenberg/) as previously

described (15) to estimate tumor cell fraction, tumor ploidy and copy numbers. The

Battenberg pipeline has high sensitivity for samples with low cellularity, frequently

observed in pancreatic tumors. Briefly, the tool phases heterozygous SNPs with use of

the 1000 genomes genotypes as a reference panel using Impute2 (16), and corrects

phasing errors in regions with copy number changes through segmentation (17). After

segmentation of the resulting B-allele frequency (BAF) values, t-tests are performed on

the BAFs of each copy number segment to identify whether they correspond to the value

resulting from a fully clonal copy number change. If not, the copy number segment is represented as a mixture of two different copy number states, with the fraction of cells bearing each copy number state estimated from the average BAF of the heterozygous

SNPs in that segment. The genome instability indices (GII) were calculated for both the cohorts; it is measured as the fraction of aberrant probes throughout the genome above or below the ploidy. Correlation analysis was carried out to identify any association between

GII and tumor ploidy.

Frequency plots

For each tumor, an aberration score was calculated per copy number segment. The aberration score was set to one if total copy number per segment was larger than the ploidy of the tumor, corresponding to a copy number gain, and to -1 if it was smaller than the ploidy of the tumor, corresponding to deletion. Remaining segments were scored to zero. The frequency plots were generated based on aberration score for all samples per segment. The whole genome allelic aberration frequency plots for the OUH cohort based on the four anatomical locations (pancreatic ducts, bile duct, ampulla, and duodenum), the two morphologies (pancreatobiliary and intestinal) and for validation in the TCGA cohort were plotted using ggplot2 library in R version 3.1.2. The radial plots were drawn for regions significantly different at P<0.001 for chi-Squared test in samples under comparison. Hierarchical clustering of the OUH and the TCGA cohort samples were done using Spearman’s distance measure for cytobands and complete linkage method; where gain was given a score of 1, deletion as -1 and 0 otherwise.

mRNA expression analysis

The mRNA expression data for the OUH cohort with GEO accession numbers GSE60979 and GSE58561 has previously been published (9,15). The data was background corrected and quantile normalized. For the TCGA cohort, gene expression levels were assayed by

RNA sequencing, RNaseq by Expectation-Maximization (RSEM) normalized per gene.

The PAM50 gene signature was used for hierarchical clustering of PAs using Spearman’s correlation as distance measure and complete linkage method. The proliferation score was calculated as average gene expression of 11 proliferative genes namely; CCNB1,

UBE2C, BIRC5, CDC20, PTTG1, RRM2, MKI67, TYMS, CEP55, KNTC2 and CDCA1

(18). Two-way ANOVA test was done to estimate the significant difference in proliferation score between the groups.

Driver genes in amplified and deleted regions

The genes located in the amplified and deleted regions were mapped using ENSEMBL

(GRCh37 genome assembly) (19) genome annotation for SNP6 arrays. The genes in

amplified and deleted chromosomal locations were identified based on two criteria. First,

frequency of occurrence with threshold set to >25%. Second, genes that were mapped in

the COSMIC cancer gene census (20) list for most frequently mutated genes in cancer,

census of amplified and overexpressed genes in cancer (n=77) (21) and the tumor

suppressor gene list (n=718) (22).

Correlation analysis of copy number aberrations and gene expression data

The Pearson correlation coefficient was calculated to estimate the correlation between the

copy number state (total copy number subtracted from the absolute ploidy of sample) and

the expression data for 52 PAs and three cell lines from the OUH cohort, and 120 PDACs in TCGA cohort. Expression data for the three IPMNs, and two PAs in OUH cohort and seven PDACs in TCGA cohort were unavailable. The quantile normalized gene

expression values for OUH cohort and RSEM normalized per gene values for TCGA

cohort was used for correlation analysis. The P-values are reported for the significant

association between the allele frequency and the gene expression correlation test at

P<0.05.

Gene Set Enrichment Analysis

We performed KEGG pathway based analysis using the Web based Gene set analysis

tool-kit (WebGestalt) (23-24) to identify biological pathways with enrichment of genes

amplified or deleted in the OUH and the TCGA cohort. WebGestalt uses Hypergeometric

test for enrichment evaluation analysis at P<0.05 after Benjamin and Hochberg’s

correction and the minimum number of genes required for a pathway to be considered

significant is set to 10.

Survival analysis

Survival analysis was performed using the Kaplan–Meier estimator as implemented in

the KMsurv package and the log-rank test in R version 3.1.2. Overall survival (OS) time

was calculated from date of surgery to time of death. OS data were obtained from the

National Population Registry in Norway. Three patients with distant metastases (M1) at

time of resection, and one patient that died from cardiac arrest were excluded from the

analysis. Disease free survival (DFS) time was calculated from date of surgery to date of

recurrence of disease. Recurrence was defined as radiological evidence of intra- abdominal soft tissue around the surgical site or of distant metastasis.

The Kaplan-Meier survival curve is plotted for focally amplified regions of PA samples and for genes located on these regions. The expression for each sample was designated as high if the expression was higher than median expression otherwise low. The P-value from log rank test is reported for significant findings.

Results

1. Genomic aberrations in periampullary adenocarcinomas

The most frequent genomic aberrations in the PAs were identified in both the OUH and the TCGA cohort. In the OUH cohort, deletion of chromosome 18q was the most frequent event, occurring in 77% of the tumors. Focal deletions of 9p21 and 9p23 were found in 70% of the tumors, loss of 17p13 and 17p12 in 68% of the tumors and loss of 6q and 8p in more than 50% of the tumors. Focal gains were observed for the following locations: 8q24.21 (32%), 18q11.2 (33%), 13q33.3 (30%), 3q25.31 (30%), 7p21.3 (28%),

19q13.2 (25%), 1q25.3 (25%) and 1q31 (25%) (Figure 1).

In the TCGA cohort, deletion of 18q was also the most frequent event, deleted in 78% of the tumors. Focal deletion of 9p21 and 9p23 were found in 62% of the tumors, loss of

17p12 and 17p13 in 74% and loss of 6q and 8p were found in 61% and 43% of the tumors, respectively. Focal gains of the chromosomal locations 8q24.21 (43%), 18q11.2

(28%), 13q33.3 (22%), 3q25.31 (20%), 7p21.3 (31%), 19q13.2 (27%), 1q25.3 (42%) and

1q31 (16%) were also observed (Figure 1).

The frequency of chromosomal aberrations in the OUH and TCGA cohort are plotted in

Figure 1. Deletions and gains at the individual tumor level are plotted as a heatmap in

Supplementary Figure S1. The aberration patterns of the PAs were mainly similar in the two cohorts. Approximately 30% of the tumors in both cohorts had acquired copy number gains, and most (>75%) of the tumors carry one or more deletion.

2. Clinicopathological characteristics of periampullary adenocarcinomas

The clinicopathological characteristics of the OUH and TCGA cohort were similar, with the majority of the tumors of stage T3 and grade G2 in both cohorts (Table 1). The average genome instability indices (GII) for the OUH and the TCGA cohort were 0.33

and 0.37, respectively. The clinicopathological characteristics of tumors (excluding three

IPMNs and two xenograft cell lines) are presented in Table 1. The clinical data for the

cell line is available elsewhere (25). The three IPMNs included in the study represent the

benign lesions; hence the clinical features were not presented with malignant

adenocarcinomas in Table 1. CNA profiles of 2/3 xenograft cell lines were compared to

their original tumors. One of the cell lines had a lower ploidy than its corresponding

tumor (Supplementary Figure S2). There was no tumor tissue available for CNA profiling

for the original tumor tissue corresponding to third xenograft cell line. The three IPMN

samples were more normal-like with few chromosomal aberrations, like deletions of

chromosome 9p and 10q and amplification of 1q (Supplementary Figure S3). These

aberrations could be early events in PAs.

Correlation analysis between ploidy and GII showed a positive correlation for both the

OUH cohort (Pearson correlation 0.48; P<0.0001) and the TCGA cohort (Pearson correlation 0.72; P<0.0001) (Supplementary Figure S4).

3. Copy number changes in periampullary adenocarcinomas stratified by morphology

To identify CNAs that had different prevalence between subtypes of PAs, frequencies of copy number gains and deletions were calculated for morphological subgroups (Figure

2A) and sites of origin (Supplementary Figure S5). The aberrations specific to morphological subgroups and sites of origin (P<0.05, chi-squared test) are plotted in

Figures 2B and 2C.

Genomic aberrations specific to the intestinal subtype include loss of 4q, 5q and gains of

3q and 13 chromosomal loci. Also, gains at chromosomes 13q14.3/22.1/32.1/34 and deletions of loci in chromosomes 5q11.2/13.3/21.3, 18p11.22/11.23/11.31 and 18q12.3 are more evident in the intestinal than in the pancreatobiliary subtype (Figure 2A-2B).

Despite small sample sizes (n=41 for the pancreatobiliary and n=16 for the intestinal subtype), these differences are highly significant (P<0.001; chi-squared test). In contrast, whole arm deletion of chromosome arms 6q, 8p, 9p, 17p and 18q21 were more common in the pancreatobiliary than in the intestinal subtype (Figure 2B). Focal deletion of 18q11 was more frequent in the ampulla of the intestinal subtype and the duodenum than in the tumors of pancreatobiliary subtype (P<0.001; chi-squared test) (Figure 2C). The deletions of 4q (57%) and 5q (57%) were observed in the duodenum and ampulla of intestinal subtype, respectively.

Due to strikingly different aberration patterns in the two morphological subtypes of PAs, we hypothesized that the PAM50 gene signature, initially developed for classification of breast cancer subtypes (18) and also used for retrieving prognostic information of non- small cell lung cancers (26) may also help in understanding prognostic information of

PAs. Thus, we performed clustering of the PA using the PAM50 gene signature. The

PAM50 gene signature clustered the samples broadly into intestinal and pancreatobiliary subtypes, where the latter clustered into basal-like and classical groups (Figures 4A).

Clustering our data using the gene signatures defined by Moffitt (n=50) (27) and

Collisson (n=62) (8) showed overlap with Moffitt’s basal and classical subtype, and the

PAM50 basal-like subtype with Collisson’s QM subtype. Collisson’s exocrine subtype was absent in the OUH cohort and the classical subtype was mixed with both the PAM50 basal-like and classical subtypes (Figures 4A). The tumors in the basal-like cluster were poorly differentiated and were highly proliferative as compared to the classical subtype

(P=5.9e-05, two-way ANOVA test) (Figures 4A-4B).

4. Driver genes in periampullary adenocarcinomas

Putative driver genes were identified in the most frequently deleted and amplified chromosomal regions by mapping the genes to known oncogene and tumor suppressor gene lists as described in Materials and Methods. We identified putative driver genes in each chromosomal locus, and the average frequencies of putative driver genes in deleted and amplified genomic loci for both cohorts are presented in Figure 3. The genes located on frequently deleted locations are RUNX3 and EPHB2 on chromosome 1p; PBRM1 and

LTF on chromosome 3p; MYB and PRDM1 on chromosome 6; CDKN2A and CDKN2B on chromosome 9; MAP2K4 and PIK3R5 on chromosome 17. Multiple genes including

MAPK4, SMAD2, SMAD4, DCC and BCL2 were found on chromosome 18, which were the most frequent deletion events in both cohorts. The genes AKT3 on chromosome 1q;

EGFR, PIK3CG on chromosome 7; MYC, PTK2 on chromosome 8; ERCC5 on chromosome 13 and CCNE1 on chromosome 19 were typically amplified. Supplementary

Table S1 shows the frequencies of aberration of amplified or deleted genes in PAs in both the cohorts. Putative driver genes in the regions that were exclusively aberrant in the intestinal subtype are; KLF5, RAP2A and IRS2 on chromosome 13 which were amplified, and PIK3R1, PLK2 and PPAP2A on chromosome 5 and PTPRM gene on chromosome

18p which were deleted. These genes are known tumor suppressors or oncogenes and were frequently deleted or amplified in our intestinal subtype tumors.

5. Integrated analysis of copy number and gene expression data

To determine genomic hotspots of PAs, we carried out correlation analysis of copy number and gene expression data. Gains or losses of several chromosomal loci were associated with gene expression levels in both cohorts. The upregulation of 974 and downregulation of 1060 genes in the OUH cohort and upregulation of 3566 genes and downregulation of 4953 genes in the TCGA cohort were associated with CNA in cis. Of these, 795 of 2034 genes identified in the OUH cohort were validated in the TCGA cohort (Supplementary Tables S2A-2D). Expressions of the genes such as FBXL20 and

MED1 on 17q12 (ERBB2-amplicon) and POP4, CCNE1, C19orf12 and UQCRFS1 on

19q12 were highly correlated with copy number in cis (P<0.001; Pearson’s correlation

test) in the OUH cohort. We validated that the expression of POP4, CCNE1, C19orf12 and UQCRFS1 on 19q12 were associated with the copy number in the TCGA cohort.

Deletions and gains of various chromosomal loci were associated with downregulation of tumor suppressor genes including SMAD2, PBRM1 and TNFRSF10A, and overexpression of oncogenes including JAK2 and FAS (Supplementary Table S2), respectively in both cohorts. Several of our reported driver genes were found significantly correlated with

CNA in cis in both cohorts. Examples include NEK3, GRAMD3, PHLPP1, PINX1,

MLLT3 and CD274.

6. Co-occurrence of chromosomal aberrations in periampullary adenocarcinomas

Co-occurrences of the deletion or amplification events were identified in both cohorts to investigate co-involvement of aberration events in deregulating pathways of PAs. Loss of

17p13 occurred in 63% of the OUH samples and 74% of the TCGA samples, while loss of 18q21/18q22 occurred in 70% of the OUH samples and 79% of the TCGA samples.

Simultaneous loss of 17p13 and 18q21/18q22 occurred in 60% of the OUH samples and in 62% of the TCGA samples, a significant level of co-occurrence in both cohorts

(P<0.05; Fisher exact test). The candidate genes located on chromosome 17p13 and

18q21 are involved in cell cycle regulation (TP53 and YWHAE (17p13), SMAD2 and

SMAD4 (18q21)), p53 signaling (TP53 (17p13) and SERPINB5 and PMAIP1 (18q21)), apoptosis (TP53 and PIK3R5 (17p13) and BCL2 (18q21)) and Wnt signaling (TP53

(17p13) and SMAD4 (18q21)).

7. Gene Set Enrichment Analysis

To identify pathways deregulated in PAs, gene set enrichment analysis was performed using the WebGestalt tool (23, 24). The pathways significantly enriched in both OUH and TCGA (FDR <0.05) and genes deregulated in more than 20% of the samples in each pathway are reported in Supplementary Table S3. The top pathways deregulated in both cohorts and pathways significantly enriched in both gene expression and copy number data are reported in Table 2. The pathways associated with frequent co-deletions of 17p and 18q (cell cycle, apoptosis and p53 signaling) are also reported in Table 2.

8. Prognostic implications of copy number gain

To determine the prognostic implications of copy number gains, survival analysis was performed for the focally amplified regions and also for genes located within these regions. We focused on prognostic values of focal amplicon regions since an oncogene or tumor suppressor is more likely to drive focal rather than whole aberrations. Kaplan-

Meier survival analyses showed that gain of the chromosomal region 18p11 (18p11.21-

23, 18p11.31-32) was associated with decreased DFS and OS at P<0.01 (log rank test).

Amplifications of the genes RAB12 and COLEC12 located on 18p11.22 and 18p11.32 respectively were associated with decreased DFS (Figures 5A-C). Gain of 19q13

(19q13.2, 19q13.31-32) and amplification of the genes SERTAD3 and ERCC1 located on

19q13.2 and 19q13.32 respectively, were associated with decreased OS at P<0.05

(Figures 5D-F).

The prognostic relevance of PAM50 classification into intestinal, basal-like and classical subtypes was determined by plotting a Kaplan-Meier survival curve. Patients with basal like tumor had the worst survival (P=0.006) (Figure 4C).

Discussion

The frequencies of gains and losses were comparable between the OUH and TCGA

cohort, in both of which ≈30% of the samples had copy number gains and ≈75% had

deletions as the most frequent events. The validation using the TCGA cohort supports the

findings in the OUH cohort with respect to genomic aberration patterns, the candidate

driver genes and pathways. The correlation analysis between gene expression and copy

number data identified genes that play an important role in pancreatic cancer tumor

biology. Out of nine genes in the 19q12 amplicon, the expressions of four genes (CCNE1,

POP4, UQCRFS1 and C19orf12) were significantly correlated with gain of 19q12.

Studies have identified 19q12 gain in ER-negative grade III breast cancers, and that

silencing of POP4 and CCNE1 reduce cell viability in cancer cells harboring this

amplification (28). Amplification of CCNE1 is typically found in basal-like breast

cancers, and is associated with increased proliferation (29). We found relatively higher

expression of CCNE1 in the basal-like compared to the classical subtype (P<0.01; t-test).

Further, gain of 17q12 was associated with overexpression of FBXL20 and MED1 genes within the ERBB2 amplicon. These co-amplified genes are important contributor to cancer progression (30).

Recently, Yachida et al. showed similarities between CNA profiles of ampulla of pancreatobiliary and intestinal types (amplification of 1p13.1, 3q26.2, 8q24.21, 12q15 and deletion of 18q21.2 and 9p21.3) (31). We found the same deletion events as reported by Yachida et al. However, the reported amplification events were not frequent in our cohort except for amplifications 3q26.2 and 8q24.21, and were more frequently observed

in the intestinal subtype of ampullary adenocarcinomas. Further the deletion of 5q and amplification of 13q events is novel to the intestinal subtype in our study when comparing ampulla of pancreatobiliary and intestinal subtype. We further extended the comparison of pancreatobiliary and intestinal adenocarcinoma to all periampullary locations. We found that intestinal subtype from either ampulla or duodenum shared more similarities in their CNA profiles compared to the pancreatobiliary type. Gingras et al. compared bile duct, ampulla and duodenal tumor CNA profiles of PAs and reported unique site of origin specific aberrations like 5p amplification of ampulla, 3q amplification of bile duct and 13p amplification of duodenum (32). These sites of origin specific aberrations were not unique in our cohort, where 13p amplification of duodenum was frequently observed in the ampulla of intestinal type. Further, the 3q amplification in the bile duct was very common in the intestinal type, and the 5p amplification of the ampulla was specific to pancreatobiliary type. The difference observed could be related to the fact that Gingras et al. did not stratify samples based on morphology and focused only on site of origin.

The CNA analysis using the Battenberg pipeline is advantageous for low cellularity samples like PAs. As a consequence, we have high power to detect aberrations specific to individual subtypes. One of the important findings is that the PA are more distinct at the level of morphology than at the site of origin, which is consistent with our previously published data of mRNA and miRNA expression profiling of the same tumors (9), suggesting the importance of morphology specific subtyping of PAs. The putative driver genes, PLK2, PIK3R1 and PTPRM, associated with morphological subtypes were also

differentially expressed between the pancreatobiliary and intestinal subtypes in our previous study of the same cohort. Additionally, the prognostic relevance of genes like

PIK3R1, NEK3, SASH1 and RASA3 on chromosome 17p13 was also defined for the intestinal subtype (9). The intestinal subtype specific aberration patterns including deletion of 5q, gain of 3q21-26 and overexpression of CCNL1 and KLF5 and downregulation of PLK2 and PPP3CA were also identified in other tumor types (29, 33-

35). Further, subtyping of PAs using the PAM50 classifier identified a subgroup of pancreatobiliary tumors that are basal-like with worse prognosis as identified by Moffitt et al. as well, however the Collisson’s subtypes were not prognostically different in our cohort. The basal-like subtype is identified in all the classification systems but named differently as squamous type, quasi-mesenchymal type and PAM 50 basal-type (8,14,27).

The classification of PA samples based on PAM50 and “Moffitt’s gene signature” are highly correlated showing the usefulness of PAM50 in subtyping of other tumor types than breast cancer. The PAM50 gene signature classified tumors based on degree of differentiation as it has genes associated with proliferation, cell cycle, keratins and cell adhesion (18). This difference in expression pattern between the two subgroups is unlike to be driven by CNAs, since the aberration patterns between the basal-like and classical subgroups did not significantly differ in either of the two cohorts (data not shown).

Gains of 18p11 in PAs have been linked to poor DFS and OS and gain of 19q13 with poor DFS. The 19q13 chromosomal locus is commonly amplified in various cancers, including ovarian cancer, breast cancer, pancreatic cancer and non-small cell lung cancer

(36-39). The gain of 19q13 is associated with higher grade, stage and outcome in

pancreatic cancer (38). The overexpression of the genes SERTAD3 and ERCC1 located on 19q13.2 and 19q13.32 respectively were found to be associated with impaired overall survival in this study. SERTAD3 is a putative oncogene that induces E2F activity and promotes tumor growth (40). The DNA excision repair protein gene ERCC1 is a known prognostic biomarker of head and neck and non-small cell lung cancer (41-42).

Deletion of 18p11.22 is specific to the intestinal subtype, whereas gain of this locus occurs in ten of the pancreatobiliary samples. 18p11.22 has also been suggested as a novel lung cancer susceptibility locus in never smokers (43). High expression of RAB12 on chromosome 18p11.22 is associated with poor DFS. RAB12 is a Ras oncogene family member and is overexpressed in colorectal cancers (44). Further, the COLEC12 gene is known prognostic marker in anaplastic thyroid cancer and brain tumors (45-46). Due to limited clinical annotation of the TCGA data, we were unable to validate these as prognostic markers in the TCGA cohort.

The present study has a relatively large sample size in both cohorts. The results provide

new knowledge of the genomic changes characteristics of pancreatic cancer and may

prove useful for better understanding the molecular basis for this devastating disease.

Acknowledgements

This research has been supported by grants from the South-Eastern Regional Health

Authority, Hole’s Foundation, The Radium Hospital Foundation, Oslo University

Hospital and University College of Southeast Norway. Silje Nord is financed by a carrier

grant from the Norwegian Regional Health Authorities (Grant number 2014061). We

thank all the patients who participated in the study.

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Table 1: Clinical features of the PAs from the OUH (n=55) and the TCGA cohort (n=127). pT= Tumor size N=Nodal status, M=Metastasis status, R=Resection margin. Note: The original primary tumors of two cell lines are present in 55 PAs and three IPMNs are benign lesions, hence the clinical features of these five samples are not presented in the table below. For comparative reasons, the OS and DFS in patients from the OUH cohort were only calculated for PDAC since the TCGA cohort is primarily composed of PDAC tumors.

OUH (n=55) TCGA (n=127) Clinical Features Frequency Frequency Female 30 (55%) 52 (41%) Gender Male 25 (45%) 75 (59%) PDAC 29 (52%) 111 (87%) PDAC-other - 16 (13%) subtypes Bile duct 4 (7%) - Type Ampulla pancreatobiliary 6 (10%) - subtype Ampulla intestinal 7 (12%) - subtype Duodenum 9 (16%) - T1 4 (7%) 2 (1%) T2 9 (16%) 11 (9%) pT T3 36 (65%) 110 (87%) T4 6 (11%) 3 (2%) TX - 1 (1%) N0 19 (35%) 34 (27%) N1 35 (64%) 91 (72%) N N2 1 (2%) 0 (0%) NX - 2 (1%) M0 52 (95%) 59 (47%) M M1 3 (5%) 4 (3%) MX - 64 (50%) R0 37 (67%) 68 (54%) R1 18 (33%) 42 (33%) R R2 - 4 (3%) RX - 4 (3%) Not available - 9 (7%) Well (G1) 18 (33%) 14 (11%)

Differentiation/ Moderately (G2) 37 (67%) 72 (57%) Grade Poor (G3) - 39 (31%) Undetermined (GX) - 2 (1%) Mean overall survival 578 days 247 days Median disease free survival 259 days - Median age 65 years 66 years

Table 2: Pathway analysis using frequently amplified and deleted genes in the OUH and the TCGA cohort. The table shows the enriched pathways, number of genes enriched in the pathway, P-value and FDR.

OUH cohort TCGA cohort Pathways Number of P-value Adjusted P- Number P-value Adjusted P- genes value of genes value MAPK signaling 44 6.83E-07 5.18E-06 65 2.2E-13 3.1E-12 Jak-STAT 29 3.86E-06 1.95E-05 44 6.0E-12 5.2E-11 signaling Cell cycle 22 0.0001 0.0003 27 1.7E-05 3.8E-05 p53 signaling 13 0.0014 0.0026 20 1.8E-06 5.9E-06 Apoptosis 14 0.005 0.0081 25 1.5E-07 6.9E-07 Insulin signaling 19 0.0071 0.0104 22 0.007 0.008 TGF-beta 12 0.0219 0.0269 16 0.003 0.004 signaling Wnt signaling 18 0.0312 0.0374 34 5.8E-07 2.2E-06

Figure Legends

Figure 1: Copy number aberration in OUH and TCGA cohort. The frequency of

CNAs based on the Battenberg processed SNP-array files in the OUH (top) and the

TCGA (bottom) cohort. The x-axis represents the genomic position while the y-axis is the

frequencies of gains (copies more than individual tumor ploidy)(red) and losses (copies

less than individual tumor ploidy)(green). Note the overrepresentations of deletions in

both cohorts. The three benign IPMN samples in the OUH cohort were not included.

Figure 2: Frequency of CNAs in periampullary adenocarcinomas.

2A) Frequencies of gains and losses in the two morphological subtypes, with the

pancreatobiliary subtype at the top and the intestinal subtype at the bottom. The x-axis

represents the genomic position and is divided into 22 facets for the 22 autosomal

chromosomes. The y-axis represents the frequency of gain (red) and loss (green).

2B-C) Percentage of gains and losses in each of the two morphological subtypes (2B) and

site of origin (2C) for selected genomic regions (cytobands). Regions are marked in red

(gain) and green (loss) to indicate the dominant mode of aberration.

Figure 3: Average frequencies of gains and losses for selected genes in both OUH

and TCGA cohorts. In both panels, the horizontal axis represents the frequencies of

amplifications or deletion of genes, and the color bars on the vertical axis represent the

chromosomes.

Figure 4: Subtyping of PA samples. 4A) The heatmap shows the clustering of PA samples using PAM50 gene signature. The PA samples were clustered using Spearman distance measure and complete linkage method. 4B) The Kaplan Meier survival curve shows the OS for three subclasses found using PAM50 classifier. 4C) The boxplot shows the proliferation score for basal and classical type of PDACs.

Figure 5: Kaplan-Meier OS and DFS for PA patients in the OUH cohort. 5A-C)

Kaplan-Meier DFS curve for OUH cohort patients based on 18p11.22 copy number (5A),

RAB12 (5B) and COLEC12 (5C) gene expression status. 5D-F) Kaplan-Meier OS curve for OUH cohort patients based on 19q13.2 copy number (5D), SERTAD3 (5E) and

ERCC1 (5F) gene expression status. In Figure 5D, tumors carrying deletions and tumors without 19q13 alterations (normal samples) were combined in the survival analysis due to

less number of tumors with deletion events (n=4). The significant p values are marked

in the left corner. The differences between the two curves were determined by log-rank

tests.

Figure 1.

40 OUH (n=57)

80 40 Frequency (%) TCGA (n=127)

Genomic position Downloaded from cancerres.aacrjournals.org on October 2, 2021. © 2016 American Association for Cancer Research. Author Manuscript Published OnlineFirst on August 3, 2016; DOI: 10.1158/0008-5472.CAN-16-0658 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 2.

2A. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Pancreatobiliary type (n=41)

50 Intestinal type (n=16) Frequency (%)

Genomic position

2B. 2C.

3q25.31 3q26.2 3q22.2−23 4q21−24

4q34/35 3q25.31 3q26.2

5q11.2 3q22.2−23 4q21−24 50 4q34/35 5q13.3 40 Ampulla (I) 5q11.2 30 5q21.3 80 5q13.3 20 Ampulla (P) 60 10 6p22.3 40 5q21.3 Intestinal 0 Bile-ducts 20 6q15 0 6p22.3 6q16.1 Duodenum Pancreatobiliary 6q15 6q22.1 PDAC 6q16.1 6q23.3 6q22.1 6q24.3 18q22.3 6q23.3 6q25.1 18q21.2 6q24.3 18q21.33 6q26 18q22.3 6q25.1 6q27 18q21.31 6q26 8p21.3 18q21.33 18q21.2 6q27 9p21.1,9p21.3 18q21.31 8p21.3 9p22.2 18q12.3 9p21.1,9p21.3 9p23 9p23 13q22.1 13q14.3 18q12.3 13q14.3

13q32.1 18p11.31 13q32.1 13q22.1

18p11.23 18p11.31

13q34 18p11.22 18p11.23 13q34 18p11.22

17p13.1-13.3

Downloaded from cancerres.aacrjournals.org on October17p13.1-13.3 2, 2021. © 2016 American Association for Cancer Research. Author Manuscript Published OnlineFirst on August 3, 2016; DOI: 10.1158/0008-5472.CAN-16-0658 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 3. 8p23.1 MCPH1 1p36.11 STMN1 8p23.1 MFHAS1 1p36.11 RUNX3 8p23.1 PINX1 1p36.11 IL22RA1 8q22.1 MTDH 1p36.12 EPHB2 8q22.2 STK3 1 1q25.2 RASAL2 8q22.3 RRM2B 1q25.3 CACNA1E 8q23.1 ANGPT1 1q25.3 RGL1 8q24.11 EXT1 1q31.1 PLA2G4A 8q24.21 MYC 1q41 CAPN2 8q24.21 PCAT1 1q41 TGFB2 8 8q24.21 ASAP1 1q43 AKT3 8q24.21 CCDC26 3p13 FOXP1 8q24.22 NDRG1 3p14.1 ADAMTS9 8q24.22 ADCY8 3p14.2 FHIT 8q24.3 TRIB1 3p14.3 IL17RD 8q24.3 KCNK9 3p21.1 CACNA1D 8q24.3 BAI1 3p21.1 PBRM1 8q24.3 PTK2 3p21.1 PRKCD 9p13.1 IGFBPL1 3p21.31 LTF 9p13.2 PAX5 3p21.31 EXOSC7 9p13.3 RECK 3 3p24.1 TGFBR2 9p21.2 TEK 3p24.2 THRB 9p21.3 MTAP 3p24.2 RARB 9p21.3 CDKN2B−AS1 3q25.1 WWTR1 9p21.3 CDKN2A 3q25.31 CCNL1 9p21.3 MLLT3 4q24 PPP3CA 9p21.3 IFNA1 4 4q24 NFKB1 9p22.3 NFIB 5q11.2 PLK2 9p22.3 PSIP1 5q11.2 PPAP2A 9p23 PTPRD 5 5q13.1 PIK3R1 9 5q23.2 GRAMD3 9p24.1 CD274 6p24.3 C6orf89 9p24.1 UHRF2 6p25.3 EEF1E1 9p24.1 JAK2 6q12 BAI3 9p24.2 SMARCA2 6q13 DUSP22 9p24.3 KANK1 6q16.1 UFL1 13q14.3 NEK3 6q16.2 CCNC 13q22.1 KLF5 6q16.3 HACE1 13q31.1 SPRY2 6q21 PRDM1 13q31.3 GPC5 6q21 FOXO3 13 13q32.1 RAP2A 6q21 WISP3 13q33.1 FGF14 6q22.1 GOPC 13q33.1 ERCC5 6q22.1 ROS1 13q34 IRS2 6q22.1 FRK 13q34 RASA3 6q22.31 GJA1 13q34 TFDP1 6q22.31 STL 17p11.2 FLCN 6q22.33 PTPRK 17p12 MAP2K4 6q23.2 CTGF 17p13.1 GAS7 6 6q23.3 MYB 17p13.1 DNAH2 6q23.3 TNFAIP3 17p13.1 TP53 6q24.1 ECT2L 17 17p13.1 PIK3R5 6q24.1 HECA 17p13.3 SMYD4 6q24.2 PLAGL1 17p13.3 VPS53 6q24.3 SASH1 17p13.3 YWHAE 6q24.3 SHPRH 17p13.3 PAFAH1B1 6q25.1 AKAP12 17p13.3 DPH1 6q25.3 IGF2R 18p11.22 PTPRM 6q25.3 SOD2 18p11.31 EPB41L3 6q25.3 EZR 18p11.31 L3MBTL4 6q26 PARK2 18q11.2 SS18 6q26 PACRG 18q11.2 ZNF521 6q27 RPS6KA2 18q12.3 SETBP1 6q27 FGFR1OP 18q21.1 ZBTB7C 6q27 MLLT4 18q21.1 SMAD2 6q27 RNASET2 18 18q21.1 MAPK4 7p11.2 EGFR 18q21.2 DCC 7p12.3 ADCY1 18q21.2 TCF4 7p14.1 SFRP4 18q21.2 SMAD4 7p15.3 CYCS 18q21.31 MIR122 7p15.3 RAPGEF5 18q21.32 MALT1 7p21.1 HDAC9 18q21.33 BCL2 7 7p21.1 ITGB8 18q21.33 PHLPP1 7p22.2 GNA12 18q21.33 SERPINB5 7p22.3 MAD1L1 18q23 GALR1 7q21.2 CDK6 19p13.3 PLK5 7q21.3 GNGT1 19p13.3 SAFB 7q22.3 PIK3CG 19q12 CCNE1 8p21.1 CLU 19 19q12 POP4 8p21.1 EXTL3 19q12 UQCRFS1 8p21.2 NKX3−1 19q13.11 ANKRD27 8p21.2 BNIP3L 19q13.11 ZNF507 8p21.3 LZTS1 22q11.23 BCR 8p21.3 DOK2 22q12.1 MYO18B 8p21.3 TNFRSF10B 22q12.1 MN1 8 8p21.3 RHOBTB2 22q12.1 CHEK2 8p21.3 PPP3CC 22 22q12.2 SEC14L2 8p22 TUSC3 22q12.2 NF2 8p22 MTUS1 22q12.3 TIMP3 8p22 ZDHHC2 22q13.1 PDGFB 8p22 PDGFRL 22q13.2 BIK 8p22 DLC1 22q13.31 PRR5 8p23.1 CLDN23 80 40 0 40 60 40 20 0 20 Frequency Frequency

Ampli ed Deleted Downloaded from cancerres.aacrjournals.org on October 2, 2021. © 2016 American Association for Cancer Research. Author Manuscript Published OnlineFirst on August 3, 2016; DOI: 10.1158/0008-5472.CAN-16-0658 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 4. 4A. Color - Key

$ −6 0 4 § & Row Z−Score % # BAG1 MLPH $ PA Subtypes NAT1 GPR160 Basal FOXA1 TMEM45B Intestinal NDC80 TYMS Classical UBE2T CENPF ANLN § Mott et al.classication of PDACs UBE2C BIRC5 Basal KIF2C MKI67 CEP55 Classical PTTG1 RRM2 EXO1 & Collisson et al.classication of PDACs ORC6 MELK Quasi-mesenchymal NUF2 CDC20 CCNB1 Classical GRB7 ERBB2 % Site of origin CCNE1 MYBL2 PDAC CDC6 EGFR Bileduct CXXC5 CTR3B Ampulla−P FGFR4 MMP11 Ampulla−I CDH3 KRT5 Duodenum KRT17 KRT14 MIA # Degree of dierentiation FOXC1 PGR Poor MAPT BLVRA BCL2 Moderate SLC39A6 PHGDH MDM2 SFRP1 MYC ESR1

4B. 4C. P = 0.006 P = 5.9e−05 Basal Intestinal Classical Survival Proliferation−score 0.0 0.2 0.4 0.6 0.8 1.0

0 500 1000 1500 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 Time after surgery(days) Basal Classical

Figure 5.

5A. 18p11.(21-32) (DFS) 5B. RAB12 (DFS) 5C. COLEC12 (DFS) 1.0 HR = 0.47 (0.24 - 0.92) P = 0.008 HR = 0.5 (0.25 - 0.98) P = 0.04 P = 0.02 Deleted High Normal High Ampli ed Low Low 0.6 0.8 Survival 0.2 0.4 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0

0 500 1000 1500 0 500 1000 1500 0 500 1000 1500 Time of relapse (days) Time of relapse (days) Time of relapse (days)

5D. 19q13.(2-32) (OS) 5E. SERTAD3 (OS) 5F. ERCC1 (OS)

HR = 2.6 (1.2 - 5.6) HR = 0.45 (0.22 - 0.92) 1.0 HR = 0.46 (0.23 - 0.94) P = 0.01 P = 0.02 P = 0.02 Deleted & Normal High High Ampli ed Low Low Survival 0.4 0.6 0.8 0.2

P = 0.01 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0

0 500 1000 1500 0 500 1000 1500 0 500 1000 1500 Time of death (days) Time of death (days) Time of death (days) Downloaded from cancerres.aacrjournals.org on October 2, 2021. © 2016 American Association for Cancer Research. Author Manuscript Published OnlineFirst on August 3, 2016; DOI: 10.1158/0008-5472.CAN-16-0658 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

The genomic landscape of pancreatic and periampullary adenocarcinoma

Vandana Sandhu, David C. Wedge, Inger Marie Bowitz Lothe, et al.

Cancer Res Published OnlineFirst August 3, 2016.

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