Genome-Wide DNA Copy Number Analysis in Pancreatic Cancer Using High-Density Single Nucleotide Polymorphism Arrays

Home , NFE2L3, SKAP2

Oncogene (2008) 27, 1951–1960 & 2008 Nature Publishing Group All rights reserved 0950-9232/08 $30.00 www.nature.com/onc ONCOGENOMICS Genome-wide DNA copy number analysis in pancreatic cancer using high-density single nucleotide polymorphism arrays

T Harada1, C Chelala1, V Bhakta1, T Chaplin2, K Caulee1, P Baril1, BD Young2 and NR Lemoine1

1Centre for Molecular Oncology, Cancer Research UK, Institute of Cancer, Barts and The London School of Medicine and Dentistry, Queen Mary, University of London, London, UK and 2Centre for Medical Oncology, Cancer Research UK, Institute of Cancer, Barts and The London School of Medicine and Dentistry, Queen Mary, University of London, London, UK

To identify genomic abnormalities characteristic of pan- significant impact on the course of the disease, so that creatic ductal adenocarcinoma (PDAC) in vivo, a panel of the prognosis for patients still remains dismal with a 27 microdissected PDAC specimens were analysed using median survival of approximately 6 months from diag- high-density microarrays representing B116 000 single nosis and an overall 5-year survival of less than 5% (Jemal nucleotide polymorphism (SNP) loci. We detected fre- et al., 2005). There is an urgent need for innovative quent gains of1q, 2, 3, 5, 7p, 8q, 11, 14q and 17q ( X78% approaches to early diagnosis and specifically targeted ofcases), and losses of1p, 3p, 6, 9p, 13q, 14q, 17p and 18q therapies, and this will only be made possible by a (X44%). Although the results were comparable with comprehensive understanding of the molecular events that those from array CGH, regions of those genetic changes make this such an aggressively malignant tumour type. were defined more accurately by SNP arrays. Integrating Genomic alterations can contribute to the dysregula- the Ensembl public data, we have generated ‘gene’ copy tion of expression levels of oncogenes and tumour number indices that facilitate the search for novel suppressor genes in cancer cells, the accumulation of candidates involved in pancreatic carcinogenesis. Copy which is correlated with tumour progression (Ried et al., numbers in a subset ofthe genes were validated using 1999; Bardeesy and DePinho, 2002; Li et al., 2004; Lips quantitative real-time PCR. The SKAP2/SCAP2 gene et al., 2007). The introduction of genotyping by single (7p15.2), which belongs to the src family kinases, was nucleotide polymorphism (SNP) arrays has allowed most frequently (63%) amplified in our sample set and its genome-wide, high-resolution analysis of both DNA recurrent overexpression (67%) was confirmed by reverse copy number (DCN) alterations and loss of hetero- transcription–PCR. Furthermore, fluorescence in situ zygosity (LOH) events in cancer cells (Lindblad-Toh hybridization and in situ RNA hybridization analyses for et al., 2000; Bignell et al., 2004; Huang et al., 2004; Janne this gene have demonstrated a significant correlation et al., 2004; Zhao et al., 2004, 2005; Midorikawa et al., between DNA copy number and mRNA expression level in 2006). High-density SNP microarrays permit highly an independent sample set (Po0.001). These findings accurate mapping of those genetic changes across the indicate that the dysregulation of SKAP2/SCAP2, which entire genome, providing a promising starting point for is mostly caused by its increased gene copy number, is the identification of novel candidate genes affected by likely to be associated with the development ofPDAC. such genomic abnormalities. In the present study, to Oncogene (2008) 27, 1951–1960; doi:10.1038/sj.onc.1210832; identify genomic changes characteristic of PDAC cells published online 22 October 2007 in vivo, we analysed a panel of 27 microdissected PDAC tissue samples using the Affymetrix 100 K SNP arrays. Keywords: pancreatic cancer; tissue microdissection; Based on the obtained DCN data, we found a novel SNP array; genetic alterations; SKAP2/SCAP2 candidate gene, SCAP2 (SKAP2 is the latest official gene symbol), which is located at the minimal overlapping region of 7p15.2 gain. The general applicability of this

observation was prospectively validated in an indepen- ONCOGENOMICS Introduction dent sample set using ﬂuorescence in situ hybridization (FISH) and in situ RNA hybridization (ISH). Pancreatic ductal adenocarcinoma (PDAC) is one of the most challenging malignancies facing oncologists today. Essentially, no conventional treatment has made a Results

Correspondence: Professor NR Lemoine, Centre for Molecular Genome-wide analysis of DCN alterations in PDAC cells Oncology, Cancer Research UK, Institute of Cancer, Barts and The in vivo London School of Medicine and Dentistry, Queen Mary, University of Genome-wide copy number analysis was performed in a London, Charterhouse Square, London, EC1M 6BQ, UK. E-mail: [email protected] total of 27 microdissected PDAC samples (Supplemen- Received 13 June 2007; revised 29 August 2007; accepted 6 September tary Table 1). The average genotype call rate was 2007; published online 22 October 2007 96.3±3.4 and 97.0±2.3% in HindIII and XbaI50K Genome-wide analysis in pancreatic cancer T Harada et al 1952

Figure 1 The overview of genomic changes of all chromosomes in a total of 27 microdissected PDAC tissues, determined by the Affymetrix 100 K SNP arrays. (see Supplementary Figure 1 for all the details) ‘Genetic gains’ are shown as green bars and ‘losses’ as red bars according to the genomic position (Build 35). Thick bars are used to depict ‘high-level ampliﬁcations’ and ‘homozygous deletions’. Blue bars indicate ‘LOH regions’ and grey, thick bars are used for ‘UPD regions’.

SNP arrays, respectively. All the raw data are available in SNP arrays delineated more precise physical boundaries the Gene Expression Omnibus (GEO) (http://www.ncbi. of chromosomal breakpoints in PDAC (Figure 2). As a nlm.nih.gov/geo/) with the accession number GSE7130. result, we have identified homozygous deletions at The Copy Number Analyzer for Affymetrix Gene- 9p21.3 (45 kb) and high-level amplifications in three Chip (CNAG) analysis for all 27 PDAC samples regions of 8q: 8q24.13–q24.21 (2.2 Mb), 8q24.22 identified chromosomal regions of both DCN altera- (177 kb) and 8q24.23–q24.3 (2.7 Mb) (X19% of cases). tions and LOH throughout the whole genome of These were detected as minimal regions of frequent PDACs. We summarize genetic abnormalities of all genetic alterations and therefore, considered to be chromosomes in Figure 1. (All the details including epicentres in those changes (Supplementary Figure 1). published copy number variation (CNV) data are shown in Supplementary Figure 1) The most frequent genetic gain was detected on 8q in our sample set (26 out of 27 DCN alterations in individual genes across the entire cases; 96%). Gains of 1q, 2, 3, 5, 7p, 11, 14q and 17q genome were also identified at a high frequency (X78% of By integrating the Ensembl public data with our DCN cases). On the other hand, the most recurrent genetic data, we sought to identify ‘gene’ copy numbers loss was observed on 9p in 21 out of 27 cases (78%), throughout the entire genome of PDAC. We have followed by 18q, 6, 1p, 13q, 17p, 3p and 14q (X44% of documented the genes included in regions of frequent cases). Overall, the spectrum patterns of genetic changes genetic changes in Table 1 and the complete list of copy identified by SNP arrays were similar to our previous numbers in all the genes is provided in Supplementary results from both metaphase and array-based compara- Table 2. These gene copy number indices enabled tive genomic hybridization (array CGH) (Harada et al., comparison of our results with the previously published 2002a, b, 2007). However, with the increased resolution, data. SCAP2 (SKAP2, 7p15.2) was identified as the

Oncogene Genome-wide analysis in pancreatic cancer T Harada et al 1953 genes (FN1, SCAP2, RAB2 and CDKN2A) using genomic DNA from 19 microdissected PDACs (Supple- mentary Table 3). Due to very limited amounts of microdissected tumour DNA, eight out of 27 samples were not available in q-PCR analysis. We chose the HAND1 gene (5q33.2) as a reference gene in this assay because there were no DCN changes detected at the locus of this gene in SNP array analysis of these 19 cases. In general, inferred DCNs were concordant in SNP arrays and q-PCR (Figure 3). Although the absolute values of DCN were different between two analyses in some samples, a strong correlation was observed between two data sets, with a Spearman’s correlation coefﬁcient r ¼ 0.72. These results have demonstrated the overall validity of the DCN status determined by genotyping-based microarrays.

Genome-wide detection of LOH and uniparental disomy (UPD) regions A total of 579 LOH regions (2–70regions per case, 0.78– 174 Mb in size) were detected in 27 PDAC tissues (Figure 1). The frequent LOH regions were observed in various chromosome arms; 4q (63% of cases), 18q (63%), 9p (56%), 6p (56%), 6q (56%), 8p (56%), 2q (52%), 1p (48%), 5q (48%), 7q (48%) and 3p (44%) (Supplementary Figure 1). These results were consistent with the previous reports (Iacobuzio-Donahue et al., 2004; Calhoun et al., 2006). Combining these with the DCN data, we identified a total of 223 UPD regions (1–23 region(s) per case, 1–64 Mb in size) in 27 PDAC cases and therefore, 39% of LOH regions were considered to be Figure 2 (a) Comparison of the results between CGH and SNP UPD. Remarkably, common UPD regions (4 out of 27 arrays. Upper: Chromosome 18 in PC16, determined by array cases; 15%) were preferentially identified in only three CGH (see ref. Harada et al., 2007). The blue line is used to depict chromosome loci: 4q22.3–q23 (2.8 Mb), 4q31.21–q31.23 the smoothed DCN values. Lower: The same sample was analysed by SNP arrays, showing more distinct physical boundaries that (2.0Mb) and 18q21.1 (1.3 Mb) (Supplementary Table 4). enabled to identify small size of DCN alterations (asterisks). (b) ‘High-level amplification’ detected by SNP arrays (chromosome 11 in PC33). This amplicon size was approximately 1.35 Mb. Screening for genes within the minimal amplicon at 7p15.2 region by reverse transcription–PCR SNP arrays enabled us to narrow down the minimal most frequently (63% of cases) amplified gene in 27 overlapping regions of DCN alterations, which are likely PDACs, which has not been described in any type of to contain critical oncogenes or tumour suppressor genes. cancer. We also detected increased copy numbers in In this study, we identified frequent gains on 7p in 27 MYC (8q24.21, 48%), NCOA3/AIB1 (20q13.12, 44%), PDAC cases (78%). Although 7p copy number gain has KRAS (12p12.1, 44%), ERBB2 (17q12, 41%) and EGFR been detected as a large region, our array results revealed X (7p11.2, 33%) genes. On the other hand, two tumour the minimal region at 7p15.2, with 3 copies found in suppressor genes, CDKN2A and CDKN2B, were in- 59–63% of cases (Figure 4A). According to the Ensembl cluded in the locus of 9p21.3 that was deleted at the database, this region is approximately 1 Mb in size and highest frequency (63% of cases). Genetic losses were includes five known genes (NFE2L3, HNRPA2B1, also detected in genes such as DCC (18q21.1, 48%), CBX3, SNX10 and SCAP2). This region was not SMAD4 (18q21.1, 33%), MAP2K4 (17p12, 30%), TP53 involved in CNV regions and therefore, thought to be (17p13.1, 26%) and RUNX3 (1p36.11, 22%). Thus, ‘acquired’ alteration (Supplementary Figure 1) (Redon recurrent genetic changes that were previously charac- et al., 2006). We postulated that some of these genes may terized in PDAC were commonly observed in our be incidentally coamplified due to the ongoing genomic analysis (Bardeesy and DePinho, 2002; Li et al., 2004). instability, along with the target gene(s). To screen for the gene(s) whose DCN alteration(s) could lead to significant change(s) in transcript level, reverse transcription–PCR Verification of gene copy numbers by quantitative (RT–PCR) was performed for five candidate genes within real-time PCR (q-PCR) this amplicon (Figure 4B). The transcripts of four genes To validate gene copy numbers identified by SNP (NFE2L3, HNRPA2B1, CBX3 and SNX10)wereex- arrays, we performed q-PCR for a subset of candidate pressed in normal pancreas tissues, whereas various levels

Oncogene Genome-wide analysis in pancreatic cancer T Harada et al 1954 Table 1 Frequent gene copy numbers identiﬁed by SNP arrays (A) Increased gene copy numbers

Cytoband Start (bp) End (bp) Gene symbol Ensembl ID Totala DCN ¼ 3or4b DCNX5c

7p15.2 2647993026677581 SCAP2 ENSG00000005020 18 17 1 8q24.23 139332265 139449408 NULL ENSG00000169438 18 12 6 1q21.1 142189379 142565232 PDE4DIP ENSG00000178104 17 17 0 1q21.1 142433716 142433772 NULL ENSG00000190646 17 17 0 1q21.1 142433716 142433860 U2 ENSG00000201685 17 17 0 1q21.1 142478353 142480409 Q9H762_HUMAN ENSG00000168681 17 17 0 1q21.1 142585472 142606033 SEC22L1 ENSG00000155878 17 17 0 1q21.1 142628069 142628613 NULL ENSG00000177144 17 17 0 1q21.1 142638784 142638882 U6 ENSG00000201789 17 17 0 1q23.3 159333913 159481893 DDR2 ENSG00000162733 17 17 0 1q23.3 160210932 160211020 NULL ENSG00000193661 17 17 0 1q25.1 172023592 172107588 TNR ENSG00000116147 17 12 5 1q25.2 173163964 173543625 PAPPA2 ENSG00000116183 17 17 0 1q25.2 173317061 173317576 NULL ENSG00000172760 17 17 0 1q25.2 173561861 173865681 ASTN ENSG00000152092 17 17 0 1q25.2 173730156 173730238 hsa-mir-488 ENSG00000202609 17 17 0 1q25.2 173872188 173983209 FAM5B ENSG00000198797 17 17 0 1q25.3 179724210179846384 LAMC1 ENSG00000135862 17 17 0 1q25.3 179887056 179945696 LAMC2 ENSG00000058085 17 16 1 1q25.3 179949035 180119394 NMNAT2 ENSG00000157064 17 16 1 1q25.3 180173291 180254982 SMG7_HUMAN ENSG00000116698 17 17 0 3q22.1 132736261 133241863 CPNE4 ENSG00000196353 17 16 1 7p15.2 25762765 25762863 mmu-mir-148a ENSG00000199085 17 17 0 7p15.2 25762779 25762846 NULL ENSG00000192920 17 17 0 8q21.11 76482826 76641614 HNF4G ENSG00000164749 17 14 3 8q24.12 120469769 120469890 NULL ENSG00000192356 17 14 3 8q24.12 120469769 120469890 snoACA32 ENSG00000199918 17 14 3

2q35 216050687 216126302 FN1 ENSG00000115414 13 12 1 8q24.21 128817686 128822853 MYC ENSG00000136997 13 9 4 20q13.12 45645347 45715724 NCOA3 ENSG00000124151 12 12 0 12p12.1 25249447 25295121 KRAS ENSG00000133703 12 11 1 17q12 35109920 35138436 ERBB2 ENSG00000141736 11 11 0 7p11.2 54860934 55049239 EGFR ENSG00000146648 9 9 0 8q12.1 61592113 61696183 RAB2 ENSG00000104388 9 6 3

(B) Decreased gene copy numbers

Cytoband Start (bp) End (bp) Gene symbol Ensembl ID Totald DCN ¼ 1e DCN ¼ 0f

9p21.3 21957751 21984490 CDKN2A ENSG00000147889 17 12 5 9p21.3 21992909 21999312 CDKN2B ENSG00000147883 17 12 5 9p21.3 22002115 22002528 NULL ENSG00000187088 17 12 5 9p21.3 21957137 21957738 NSGX_HUMAN ENSG00000173515 16 11 5 9p21.3 21792635 21921198 MTAP ENSG00000099810 14 10 4 9p21.3 2243684022442472 DMRTA1 ENSG00000176399 14 11 3 18q21.33 58005521 58125333 KIAA1468 ENSG00000134444 14 14 0 18q21.33 58143566 58204482 TNFRSF11A ENSG00000141655 14 14 0 18q21.33 58149025 58149121 NULL ENSG00000191838 14 14 0 18q21.33 58149025 58149125 Y ENSG00000199867 14 14 0 18q21.33 5826242058262484 NULL ENSG00000193871 14 14 0 18q21.33 58468521 58468807 NULL ENSG00000192667 14 14 0 18q21.33 58534939 58798645 PHLPP ENSG00000081913 14 14 0 18q21.33 58549439 58549545 NULL ENSG00000193542 14 14 0 18q21.33 58549439 58549545 U6 ENSG00000199195 14 14 0 18q21.33 5865662058656928 Q9H380_HUMAN ENSG00000171825 14 14 0 18q21.33 58941559 59137489 BCL2 ENSG00000171791 14 14 0 18q21.33 59148813 59185465 FVT1 ENSG00000119537 14 14 0 18q21.33 59207407 59240673 VPS4B ENSG00000119541 14 14 0 18q21.33 59315202 59315613 NULL ENSG00000176042 14 14 0 18q22.3 6956315069563445 NULL ENSG00000191507 14 14 0 18q22.3 69891581 69966020 FBXO15 ENSG00000141665 14 14 0 18q22.3 69966766 69977000 TI21L_HUMAN ENSG00000075336 14 14 0 18q22.3 70045721 70046017 NULL ENSG00000192188 14 14 0 18q22.3 70071512 70110155 CYB5 ENSG00000166347 14 14 0

18q21.1 48121156 49311021 DCC ENSG00000187323 13 12 1 18q21.1 46810611 46860139 SMAD4 ENSG00000141646 9 7 2

Oncogene Genome-wide analysis in pancreatic cancer T Harada et al 1955 Table 1 (continued ) (B) Decreased gene copy numbers

Cytoband Start (bp) End (bp) Gene symbol Ensembl ID Totald DCN ¼ 1e DCN ¼ 0f

17p12 11864929 11987872 MAP2K4 ENSG00000065559 8 7 1 17p13.1 7512464 7531642 TP53 ENSG00000141510 7 7 0 1p36.11 24971364 25036918 RUNX3 ENSG00000020633 6 6 0

Abbreviations: DCN, DNA copy number. The complete list of copy numbers in all the genes across the entire genome is available in Supplementary Table 2. aTotal number of regions in which a gene copy number was increased. bThe number of cases in which low-level gain (DCN ¼ 3 or 4) was observed. cThe number of cases in which high-level ampliﬁcation (DCNX5) was observed. dTotal number of regions in which a gene copy number was decreased. eThe number of cases in which hemizygous deletion (DCN ¼ 1) was observed. fThe number of cases in which homozygous deletion (DCN ¼ 0) was observed.

Figure 3 Comparison of DCN alterations identified by SNP arrays (blue columns) and q-PCR (violet columns) analyses. A significant correlation (r ¼ 0.72) was observed between two data sets. All the data are shown in Supplementary Table 3. of expression were observed in PDAC cases. In contrast, tissues except for one damaged tissue (Figure 4D). In SCAP2 was the only gene that was not expressed in normal pancreas and liver tissues, there was no SCAP2 normal tissues, but it was upregulated in eight out of 12 expression detected in ductal, islet and hepatic cells, PDAC cases (67%). These results raised the possibility whereas very subtle, patchy signals were observed in that genetic gain of this region could be caused solely by acinar cells. In contrast, the SCAP2 transcript was the SCAP2 gene. upregulated (score 1–2) in 62 out of 91 PDAC cases (68%), which was in good accordance with the RT–PCR results (Supplementary Table 5). Moreover, SCAP2 A strong correlation between DCN and mRNA expression overexpression was observed consistently from early- level of SCAP2 (I–II) to late-stage (III–IVb) tumours, indicating that this To validate the high frequency of SCAP2 gain, inter- gene may be involved in the development of PDAC. phase FISH analysis was applied to an independent Finally, in order to assess directly whether an increased sample set that consists of 92 PDAC cases (see Materials gene copy number of SCAP2 is associated with over- and methods). However, we used 36 cases for the expression of its transcript, we compared the FISH and analysis because the remaining 56 specimens were not ISH results from 36 PDAC tissues that were commonly interpretable probably due to formalin overfixation of used for both analyses (Table 2). The SCAP2 transcript tissues (Table 2). DCN of the SCAP2 gene was increased was upregulated in all 19 cases with genetic gain of this in 19 out of 36 PDAC tissues (53%), which was a similar gene. Despite an increase in transcript level, there were frequency to the DCN data by SNP arrays (Figure 4C). no DCN changes of SCAP2 observed in seven samples Next, to evaluate further the frequency of SCAP2 (No. 39, 47, 50, 57, 61, 64 and 84 in Table 2). Notably, overexpression, ISH was carried out using all PDAC we found a significant correlation between gene copy

Oncogene Genome-wide analysis in pancreatic cancer T Harada et al 1956

Figure 4 (A) The minimal common region (7p15.2) of 7p gain, defined by DCN analysis of 27 PDAC cases. The green bars below the chromosome define the region of copy number gain in each case analysed in this study. The approximately 1 Mb region, which was most frequently gained in our sample set, was surrounded by black dots. The genes contained in the minimal region at 7p15.2 are listed below. SNP markers spotted on Affymetrix 100 K arrays are shown at the bottom. (B) Transcript levels of five candidate genes within the minimal region in normal pancreas and PDAC tissues, determined by RT–PCR. Samples were run in the following order: lane 1–2, normal pancreas; lane 3–14, PDACs; lane 15, negative control. Among five genes, the only SCAP2 transcript was not detectable in normal pancreas tissues, whereas it was upregulated in eight out of 12 PDAC tissues (67%). (C) The FISH results in two representative cases. (a) No copy number change of SCAP2 in PC58 and (b) a genetic gain in PC63. Original magnification: Â 1000. (D) SCAP2 mRNA expression in normal epithelial cells and PDAC cells, determined by ISH. (a) No signals (score 0) were detected in ductal (arrow) and islet cells (arrow heads) of normal pancreas, whereas very subtle, patchy signals were observed in acinar cells. (b) ISH conducted with a sense SCAP2 riboprobe, used as a negative control. Arrow indicates ductal cells of normal pancreas. (c) Strong signal (score 2) in moderately differentiated PDAC cells (PC53). (d) Higher level of expression (score 1) in metastatic PDAC cells (PC66) compared to normal hepatic cells (asterisk). Arrows indicate two micrometastatic lesions. Original magnification: (a and b), Â 400; (c), Â 100; (d), Â 40.

number and expression level of this gene (Po0.001, have shown that the quality of the data from SNP arrays Fisher’s exact test). is highly dependent on tumour purity: Up to 20% of contamination with non-neoplastic cells should be acceptable for the detection of genomic abnormalities, whereas more than 30% of contamination resulted in a Discussion signiﬁcant reduction of the sensitivity of the analysis (Lindblad-Toh et al., 2000; Huang et al., 2004; Zhao It is well known that PDAC tissues are especially et al., 2004). Tissue microdissection enables us to collect heterogeneous, with neoplastic cells constituting only a puriﬁed populations of cancer cells by trimming out small proportion of the tumour mass. Previous studies dense stromal components (Harada et al., 2002a, 2007;

Oncogene Genome-wide analysis in pancreatic cancer T Harada et al 1957 Table 2 FISH and ISH results in an independent sample set of 36 2005; Andersen et al., 2007). However, the use of micro- PDAC tissues dissected tumour samples is thought to be advantageous No. Sample Age Sex Histologya Stageb FISHc ISHd for more accurate determination of LOH regions in case constitutive DNA is not available (Yamamoto et al., 3 PA801/C3 30 F Mod I 1.17 1 2007). We found that approximately 40% of LOH 7 PA801/D5 49 M Mod I 0.91 0 14 PA801/E6 65 M Mod I 0.98 0 regions were considered to be UPD in PDAC tissues, 15 PA801/G7 30 M Mod I 1.43 1 whereas a previous study has shown that UPD was 18 PA801/F6 40 F Poor I 1.41 2 detected in almost 50% of LOH regions using 26 19 PC56 57 F Mod II 1.07 0 PDAC-derived cell lines (Calhoun et al., 2006). Accord- 28 PC41 69 F Well III 0.93 0 ingly, this copy-neutral event is likely to occur fre- 29 PC57 62 F Well III 1.34 1 30PC40 58 M Mod III 1.30 2 quently in PDAC cells in vivo as well as in vitro. 31 PC44 59 M Mod III 1.52 2 Interestingly, we found that common UPD regions were 32 PC58 59 M Mod III 0.96 0 preferentially observed in only three chromosome loci, 36 PC42 51 M Poor III 1.18 1 indicating that these UPD events may be nonrandom. 39 PC46 53 M Mod IVa 1.13 1 40PC48 57 F Mod IVb 0.94 0 Our previous study has shown that UPD in acute 41 PC49 65 F Mod IVb 1.24 1 myeloid leukaemia can result in homozygosity for gene 42 PC51 61 M Mod IVb 1.26 1 mutations (Fitzgibbon et al., 2005). Therefore, we 43 PC63 51 M Mod IVb 1.37 2 speculate that common UPD regions in PDAC may 44 PC65 69 M Mod IVb 1.00 0 harbour genes that are targets for somatic mutations 47 PC53 57 F Mod IVb 1.14 2 48 PC54 61 M Mod IVb 1.50 1 (Supplementary Table 4). 49 PC66 69 M Mod IVb 1.22 1 Chromosome arm 7p has long been suspected to 50PC52 74 M Poor IVb 0.97 1 include critical oncogenes in PDAC (Griffin et al., 1995; 51 PC59 67 M Poor IVb 1.04 0 Harada et al., 2002a; Karhu et al., 2006). Our SNP array 52 PC61 76 M Poor IVb 1.20 2 53 PC67 55 F Poor IVb 1.28 2 analysis has revealed the minimal common region 55 PC55 74 M Poor IVb 1.50 1 (7p15.2) of 7p gain, which was most frequently 56 PC6067 M Poor IVb 1.11 0 (approximately 60%) identified in 27 PDAC cases 57 PC62 76 M Poor IVb 1.00 1 (Figure 4A). Among five known genes included in this 58 PC68 55 F Poor IVb 1.41 2 region, SCAP2 was the only gene to show the possibility 61 PA801/B8 65 M Well NA 1.09 1 63 PA801/A2 73 F Mod NA 0.97 0 of a causal relationship between gene dosage and 64 PA801/A4 53 F Mod NA 0.96 1 mRNA expression level. Therefore, we hypothesized 74 PA801/C2 57 M Mod NA 1.21 1 that SCAP2 may potentially be the ‘driver’ gene in a 81 PA801/C8 68 F Poor NA 1.20 2 frequently observed gain of this 1 Mb region. 84 PA801/F8 50 M Poor NA 0.90 1 87 PA801/G1 62 M Poor NA 1.33 2 In order to evaluate this hypothesis, we performed FISH and ISH analyses on the identical tissue speci- Abbreviations: FISH, fluorescence in situ hybridization; ISH, in situ mens, which allowed for the direct comparison between RNA hybridization; PDAC, pancreatic ductal adenocarcinoma. aMod, DNA and mRNA status of SCAP2 in vivo. Our FISH moderately; poor, poorly differentiated tubular adenocarcinoma. bNA, results have shown a genetic gain of SCAP2 in 53% of c the information is not available. Significant differences are outlined in another population of 36 PDAC cases, which was a bold. A part of the FISH results (PC40–42, 44, 46, 48–49, 51–55) in this table has been reported in our previous manuscript (Table 4 in ref. similar frequency to those from SNP arrays (63%). Harada et al., 2007). dSignificant differences are outlined in bold. All These findings demonstrate that an increased DCN of ISH results are shown in Supplementary Table 5. SCAP2 could be a prevalent genetic change in the majority of PDAC cases. More importantly, there was a significant correlation (Po0.001) between genetic gain Andersen et al., 2007). In the present study, we applied and overexpression of the SCAP2 gene, suggesting that only microdissected samples to Affymetrix 100 K SNP the transcript level of this gene may be regulated by its arrays, which was crucial to identify genetic changes DNA copy number. High-level amplification of SCAP2 that reflect the intrinsic characteristics of PDAC cells was not frequent in our sample set, but it is known that in vivo at a high sensitivity. In addition, integrating the even a low-level copy number change can have a Ensembl public data with our results, we identified copy significant effect on gene expression in cancer (Chin number changes in individual genes across the entire et al., 2006; Saramaki et al., 2006). genome (Supplementary Table 2). The ‘gene’ copy SCAP2 was first identified as a cancer-related gene by number indices we provide here facilitate the search Buchholz et al., showing that this gene is overexpressed in for novel candidate genes that may be useful as potential pancreatic intraepithelial neoplasia (PanIN) lesions (Buch- diagnostic and prognostic markers. Copy numbers in a holz et al., 2005). The present study has shown that subset of the genes were then validated by q-PCR to SCAP2 is upregulated in PDAC tissues at a high prove the relevance of our DCN data from SNP arrays. frequency (67 and 68% in RT–PCR and ISH, respec- Analysing matched normal DNA in parallel with tively). Furthermore, we found that a genetic gain of tumour DNA, SNP arrays can be a powerful tool for the SCAP2 was frequently observed in PDAC tissues using genome-wide detection of LOH/UPD regions because both CGH and SNP arrays as well as FISH (Harada et al., we could completely exclude consanguinity as a cause of 2007). Taken together, the dysregulation of SCAP2,which homozygosity (Fitzgibbon et al., 2005; Raghavan et al., is mostly caused by its increased gene copy number, is

Oncogene Genome-wide analysis in pancreatic cancer T Harada et al 1958 likely to be associated with the development of PDAC. et al., 2005). We chose ‘automatic analysis’ mode in which SCAP2 was originally described as a substrate for the src the software performs pair-wise tests using all the references. family kinases able to regulate the phosphorylation of the ‘Genetic gains’ (DCNX3) and ‘losses’ (DCNp1) were defined presynaptic protein a-Synuclein; a process considered to according to working criteria of CNAG. In addition, ‘high- be crucial in the pathogenesis of several neurodegenerative level amplifications’ and ‘homozygous deletions’ were determined to be DCNX5 and DCN ¼ 0, respectively. In order to diseases including Parkinson’s disease (Takahashi et al., avoid false-positive changes due to random noise in signal 2003). Although SCAP2 structurally seems to be an intensity at each SNP, we set a minimum physical length of at adaptor protein with a SH3 domain providing a binding least five consecutive SNPs for putative genetic alterations. site for the focal adhesion kinase RAFTK, the biological The LOH data from two 50K chips were analysed separately role of this gene is still unknown. However, based on the and both results were then merged to determine LOH regions. ability of SCAP2 to interact with RAFTK,itislikelyto Chromosome X was not analysed to avoid gender-related modulate the spreading and motility of PDAC cells complications (Andersen et al., 2007). UPD regions were (McLean et al., 2005). Interestingly, a recent study has defined as LOH regions without DCN alterations. demonstrated that overexpression of a-Synuclein in Gene annotation and data integration osteosarcoma cells results in a prolonged G1 phase of the cell cycle and initiates tumour differentiation (Fujita The physical position of all SNPs (n ¼ 116 204) on the arrays et al., 2006). Therefore, we speculate that SCAP2 could was mapped according to the Human Genome Sequence also control the growth and differentiation of PDAC cells. (Build 35). We developed our own visualization software to merge all genetic aberrations with the gene annotation from This hypothesis is supported by the data that SCAP2 is the Ensembl Ver.37 (the most updated version based on Build overexpressed in PanIN lesions as well as in PDACs. 35) (http://www.ensembl.org) public database. Taking struc- Based on these observations, we propose that SCAP2 tural variation in the human genome into account, our could be a potential marker gene for early diagnosis and a software integrated the public data of CNVs (http://projects. possible target for therapeutic intervention in PDAC. tcag.ca/variation/) into our analysis (Iafrate et al., 2004; Redon et al., 2006).

Materials and methods DCN alterations determined by q-PCR Relative gene copy numbers were determined by q-PCR using Tumour and reference samples the ABI PRISM 7500 Sequence Detection System (Applied A total of 27 fresh-frozen PDAC tissue specimens were Biosystems, Cheshire, UK) and a TaqMan Universal PCR obtained surgically or at autopsy from Yamaguchi University Master Mix kit (Applied Biosystems), as described previously, School of Medicine, Japan (Supplementary Table 1). Tissue with minor modifications (Zhao et al., 2004, 2005). PCR microdissection was manually performed to collect tumour condition as well as primers and TaqMan MGB probes cells at more than 90% purity in all cases, and DNA was sequence for five genes (FN1, SCAP2, RAB2, CDKN2A and extracted as described previously (Harada et al., 2002a). HAND1) are available in Supplementary Table 6. PCR Reference DNA was obtained from lymphocytes of a total reactions were performed in triplicate for each primer set of 13 healthy volunteers (6 males and 7 females) whose and the data were analysed using the Sequence Detection ethnicity is anonymous. These tumour and reference genomic Software Ver1.3 (Applied Biosystems). Each tumour DNA DNAs were used for both SNP array and q-PCR analyses. was quantified by comparing the target locus to the reference Another series of 12 fresh-frozen PDAC and two normal locus of the HAND1 gene (5q33.2), for which DCN of 2 was pancreas tissues were obtained from the Human Biomaterials confirmed by SNP arrays in all 19 samples analysed. The Resource Centre, Department of Histopathology, Charing standard curve method was used to calculate target gene copy Cross Hospital, London, UK, and were subjected to RT–PCR. number in the tumour DNA sample normalized to the The clinicopathological information was not available for reference gene (HAND1) and calibrated to normal DNA. these anonymous samples. In addition, an independent sample set of 92 formalin-fixed, paraffin-embedded PDAC tissue Reverse transcription–PCR sections (4–5 mm thickness) were prepared for FISH and ISH RT–PCR was performed using 12 PDAC and two normal analyses: 25 samples from Yamaguchi University and 67 pancreas tissues, as described previously (Harada et al., 2007). samples from a tissue microarray (US Biomax Inc., Rockville, cDNAs were synthesized from 1 mg of total RNA using an MD, USA; http://www.biomax.us/tissue-arrays/Pancreas/ oligo (dT) primer. Reverse transcription was followed by 30 PA801). All the clinical samples were obtained with ethical PCR cycles to amplify a cDNA fragment of each of five genes approval of the host institutions. This study was approved by a (NFE2L3, HNRPA2B1, CBX3, SNX10 and SCAP2). 18S Research Ethical Committee (REC) with the REC Project rRNA was used as an endogenous control. All the primer sets Number 05/80408/6S. used are shown in Supplementary Table 6. Amplified products were separated on 1% agarose gels and visualized with Affymetrix 100 K SNP arrays and data analysis ethidium bromide. We used the GeneChip Human Mapping 100 K Set (Affyme- trix, Santa Clara, CA, USA) that is composed of two 50K Two-colour FISH for SCAP2 arrays (Hind240and Xba240). DNA digestion, labelling and Two-colour FISH was performed as described previously hybridization were performed according to the manufacturer’s (Harada et al., 2007). The target probe was prepared from the instructions as described elsewhere (Zhao et al., 2005). The raw BAC clone, RP11-232C20(BACPAC Resources, Oakland, images were analysed using the GCOS (Ver1.4) and GTYPE CA, USA) including the SCAP2 gene and its DNA was (Ver4.1) software (Affymetrix). labelled with Cy3-dCTP. Centromeric probes for chromo- To assess DCN alterations and LOH, we used the CNAG somes 7 (CEP7) labelled with SpectrumGreen were purchased (Ver2.0) software (http://www.genome.umin.jp/) (Nannya from Vysis (Downers Grove, IL, USA). The threshold of gain

Oncogene Genome-wide analysis in pancreatic cancer T Harada et al 1959 and loss was deﬁned as ratios (BAC/centromeric probes) of USA). SCAP2 mRNA expression was judged using a 0–2 score X1.16 and p0.87, respectively, corresponding to ±2 standard (0 ¼ no signal, 1 ¼ weak intensity, 2 ¼ strong intensity). deviation (see Harada et al., 2007 for more details).

ISH for SCAP2 Abbreviations The SCAP2 probe was amplified by PCR from OriGene clone TC117697 (OriGene Technologies Inc., Rockville, MD, USA) array CGH, array-based comparative genomic hybridization; that encodes full-length cDNA of SCAP2. The primers used to DCN, DNA copy number; FISH, fluorescence in situ amplify SCAP2 product are the same as those used in RT– hybridization; ISH, in situ RNA hybridization; LOH, loss of PCR (Supplementary Table 6). The PCR product was cloned heterozygosity; PDAC, pancreatic ductal adenocarcinoma; into the pCR4-TOPO vector using the TOPO cloning kit q-PCR, quantitative real-time PCR; RT–PCR, reverse trans- (Invitrogen Ltd, Paisley, UK) to create pCR4-SCAP2-ISH. cription–PCR; SNP, single nucleotide polymorphism; UPD, Positive clones were verified by sequence analysis. Riboprobes uniparental disomy. were synthesized from 1 mg of linearized pCR4-SCAP2-ISH plasmid DNA and digoxigenin were labelled using a DIG Acknowledgements RNA labelling kit (Roche Diagnostics, Mannheim, Germany). T3 and T7 polymerases were used to synthesize anti-sense and This work was supported by Cancer Research UK (C355/ sense probes, respectively. Riboprobes for SCAP2 (30ng per A6254). We thank Professor Kiwamu Okita (Department of case) were hybridized to PDAC tissues using the Ventana Gastroenterology and Hepatology, Yamaguchi University Discovery System (Ventana Medical Systems, Tucson, AZ, School of Medicine) for providing clinical samples of PDAC.

References

Andersen CL, Wiuf C, Kruhoffer M, Korsgaard M, Laurberg S, allelotype of pancreaticobiliary carcinoma provides quantitative Orntoft TF. (2007). Frequent occurrence of uniparental disomy in estimates of genome-wide allelic loss. Cancer Res 64: 871–875. colorectal cancer. Carcinogenesis 28: 38–48. Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y Bardeesy N, DePinho RA. (2002). Pancreatic cancer biology and et al. (2004). Detection of large-scale variation in the human genetics. Nat Rev Cancer 2: 897–909. genome. Nat Genet 36: 949–951. Bignell GR, Huang J, Greshock J, Watt S, Butler A, West S et al. Janne PA, Li C, Zhao X, Girard L, Chen TH, Minna J et al. (2004). (2004). High-resolution analysis of DNA copy number using High-resolution single-nucleotide polymorphism array and cluster- oligonucleotide microarrays. Genome Res 14: 287–295. ing analysis of loss of heterozygosity in human lung cancer cell lines. Buchholz M, Braun M, Heidenblut A, Kestler HA, Kloppel G, Oncogene 23: 2716–2726. Schmiegel W et al. (2005). Transcriptome analysis of microdissected Jemal A, Murray T, Ward E, Samuels A, Tiwari RC, Ghafoor A et al. pancreatic intraepithelial neoplastic lesions. Oncogene 24: 6626–6636. (2005). Cancer statistics, 2005. CA Cancer J Clin 55: 10–30. Calhoun ES, Hucl T, Gallmeier E, West KM, Arking DE, Maitra A Karhu R, Mahlamaki E, Kallioniemi A. (2006). Pancreatic adeno- et al. (2006). Identifying allelic loss and homozygous deletions in carcinoma—genetic portrait from chromosomes to microarrays. pancreatic cancer without matched normals using high-density Genes Chromosomes Cancer 45: 721–730. single-nucleotide polymorphism arrays. Cancer Res 66: 7920–7928. Li D, Xie K, Wolff R, Abbruzzese JL. (2004). Pancreatic cancer. Chin K, DeVries S, Fridlyand J, Spellman PT, Roydasgupta R, Kuo WL Lancet 363: 1049–1057. et al. (2006). Genomic and transcriptional aberrations linked to breast Lindblad-Toh K, Tanenbaum DM, Daly MJ, Winchester E, Lui WO, cancer pathophysiologies. Cancer Cell 10: 529–541. Villapakkam A et al. (2000). Loss-of-heterozygosity analysis of Fitzgibbon J, Smith LL, Raghavan M, Smith ML, Debernardi S, small-cell lung carcinomas using single-nucleotide polymorphism Skoulakis S et al. (2005). Association between acquired uniparental arrays. Nat Biotechnol 18: 1001–1005. disomy and homozygous gene mutation in acute myeloid leukemias. Lips EH, de Graaf EJ, Tollenaar RAEM, van Eijk R, Oosting J, Cancer Res 65: 9152–9154. Szuhai K et al. (2007). Single nucleotide polymorphism array Fujita M, Sugama S, Nakai M, Takenouchi T, Wei J, Urano T et al. analysis of chromosomal instability patterns discriminates rectal (2006). Alpha-synuclein stimulates differentiation of osteosarcoma adenomas from carcinomas. J Pathol 212: 269–277. cells: Relevance to downregulation of proteasome activity. J Biol McLean GW, Carragher NO, Avizienyte E, Evans J, Brunton VG, Chem 282: 5736–5748. Frame MC. (2005). The role of focal-adhesion kinase in cancer—a Griffin CA, Hruban RH, Morsberger LA, Ellingham T, Long PP, new therapeutic opportunity. Nat Rev Cancer 5: 505–515. Jaffee EM et al. (1995). Consistent chromosome abnormalities in Midorikawa Y, Yamamoto S, Ishikawa S, Kamimura N, Igarashi H, adenocarcinoma of the pancreas. Cancer Res 55: 2394–2399. Sugimura H et al. (2006). Molecular karyotyping of human Harada T, Baril P, Gangeswaran R, Kelly G, Chelala C, Bhakta V hepatocellular carcinoma using single-nucleotide polymorphism et al. (2007). Identification of genetic alterations in pancreatic cancer arrays. Oncogene 25: 5581–5590. by the combined use of tissue microdissection and array-based Nannya Y, Sanada M, Nakazaki K, Hosoya N, Wang L, Hangaishi A comparative genomic hybridisation. Br J Cancer 96: 373–382. et al. (2005). A robust algorithm for copy number detection using Harada T, Okita K, Shiraishi K, Kusano N, Furuya T, Oga A et al. high-density oligonucleotide single nucleotide polymorphism geno- (2002a). Detection of genetic alterations in pancreatic cancers by typing arrays. Cancer Res 65: 6071–6079. comparative genomic hybridization coupled with tissue microdissec- Raghavan M, Lillington DM, Skoulakis S, Debernardi S, Chaplin T, tion and degenerate oligonucleotide primed polymerase chain Foot NJ et al. (2005). Genome-wide single nucleotide polymorphism reaction. Oncology 62: 251–258. analysis reveals frequent partial uniparental disomy due to somatic Harada T, Okita K, Shiraishi K, Kusano N, Kondoh S, Sasaki K. (2002b). recombination in acute myeloid leukemias. Cancer Res 65: 375–378. Interglandular cytogenetic heterogeneity detected by comparative Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD genomic hybridization in pancreatic cancer. Cancer Res 62: 835–839. et al. (2006). Global variation in copy number in the human genome. Huang J, Wei W, Zhang J, Liu G, Bignell GR, Stratton MR et al. Nature 444: 444–454. (2004). Whole genome DNA copy number changes identified by Ried T, Heselmeyer-Haddad K, Blegen H, Schrock E, Auer G. (1999). high density oligonucleotide arrays. Hum Genomics 1: 287–299. Genomic changes defining the genesis, progression, and malignancy Iacobuzio-Donahue CA, van der Heijden MS, Baumgartner MR, potential in solid human tumors: a phenotype/genotype correlation. Troup WJ, Romm JM, Doheny K et al. (2004). Large-scale Genes Chromosomes Cancer 25: 195–204.

Oncogene Genome-wide analysis in pancreatic cancer T Harada et al 1960 Saramaki OR, Porkka KP, Vessella RL, Visakorpi T. (2006). Genetic of Affymetrix single-nucleotide–polymorphism genotyping micro- aberrations in prostate cancer by microarray analysis. Int J Cancer arrays. Am J Hum Genet 81: 114–126. 119: 1322–1329. Zhao X, Li C, Paez JG, Chin K, Janne PA, Chen TH et al. (2004). An Takahashi T, Yamashita H, Nagano Y, Nakamura T, Ohmori H, integrated view of copy number and allelic alterations in the cancer Avraham H et al. (2003). Identiﬁcation and characterization of a novel genome using single nucleotide polymorphism arrays. Cancer Res Pyk2/related adhesion focal tyrosine kinase-associated protein that 64: 3060–3071. inhibits alpha-synuclein phosphorylation. JBiolChem278: 42225–42233. Zhao X, Weir BA, LaFramboise T, Lin M, Beroukhim R, Garraway L Yamamoto G, Nannya Y, Kato M, Sanada M, Levine RL, Kawamata et al. (2005). Homozygous deletions and chromosome ampliﬁcations N et al. (2007). Highly sensitive method for genome-wide detection in human lung carcinomas revealed by single nucleotide polymorph- of allelic composition in nonpaired, primary tumor specimens by use ism array analysis. Cancer Res 65: 5561–5570.

Supplementary Information accompanies the paper on the Oncogene website (http://www.nature.com/onc).

Oncogene