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

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