Haplotype structure of the <i>TYMS</i> genetic region.

<p><b>A.</b> Linear map of the 80 kb <i>TYMS</i> genetic region covering the <i>TYMS</i> gene (coordinates 657,604–673,499), upstream region (600,000–657,603) and downstream region (673,500–680,000). All coordinate positions are according to UCSC genomic build GRCh37/hg19. SNPs along this region were selected from the HapMap database (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034426#s4" target="_blank">Materials and Methods</a>) and from our sequence analysis. <b>B.</b> Nine haplotype blocks (in triangular shape), numbered 1 to 9, were obtained by haplotype analysis using Haploview (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034426#s4" target="_blank">Materials and Methods</a>). The reference SNP numbers (rs) are indicated on top. The linkage disequilibrium (D′) is indicated in the small boxes colored red or blue (a color legend is provided). Some newly discovered SNPs that were not in the public database at the time of analysis were named as TYMS_SG 1, 2, 3, 16,19, 22, and 24. At the time of submission of the new SNPs, we noticed they were deposited by others and had assigned SNP numbers of rs12964837, rs11872762, rs11877806, rs36124867, rs75363899, rs2853533, and rs72634355 respectively. <b>C.</b> The largest haplotype block spanning the <i>TYMS</i> gene and some parts in the 5′ UTR, including the VNTR and the mononucleotide repeats, and the 3′UTR, is expanded. Blocks 1 and 2 in this figure corresponds to blocks 8 and 9 respectively, of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034426#pone-0034426-g001" target="_blank">Figure 1B</a>. The unmatched marker 87 corresponding to SNP number rs3826626 (in panel B) was removed in this figure. The locations of the VNTR, MR (mononucleotide repeats), and the 6-bp deletion/insertion polymorphism are shown. The <i>TYMS</i> translational start codon is 13 bp downstream of the VNTR. Enlarged versions of figures B and C are provided in supporting information as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034426#pone.0034426.s002" target="_blank">figure S2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034426#pone.0034426.s003" target="_blank">figure S3</a>, respectively.</p

VNTR genotypes among 40 genomes.

<p>VNTR genotypes among 40 genomes.</p

Length variability (polymorphism) within the MR2 repeat.

1<p>Machine-generated measurement of DNA length at the top of the observed peak, in nucleotide scale.</p>2<p>Peak width at half-maximal heights of both peak slopes, in nucleotide scale.</p>3<p>The peak width was between 4 and 5 nucleotides (half-maximal peak height for this sample was more than 4 nucleotides but less than 5). Analysis of the graphs from mixtures made of differing ratios produced a migration of the peak as expected, confirming a variation in the polymorphic forms of samples PN9 and PN104.</p

The nine most common SNP haplotypes.

<p>Common haplotypes and estimated haplotype frequencies as determined using Haploview across the region under survey. Numbers on top of the figure indicate the ‘SNP number’ from the 80 kb analyzed region (refer <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034426#pone-0034426-g001" target="_blank">Figure 1A</a>) as listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034426#pone.0034426.s005" target="_blank">Table S2</a>. Numbers in the middle reflect frequencies of the individual haplotype. These frequencies sum up to the numbers at the bottom because they reflect only fairly common haplotypes (i.e., the number at the end ‘0.94’, explains frequencies of 94% of individuals, the rest of the individuals have rare haplotypes).</p

Activated Epidermal Growth Factor Receptor as a Novel Target in Pancreatic Cancer Therapy

Pancreatic cancer is one of the most fatal among all solid malignancies. Targeted therapeutic approaches have the potential to transform cancer therapy as exemplified by the success of several tyrosine kinase inhibitors. Prompted by this, comprehensive profiling of tyrosine kinases and their substrates was carried out using a panel of low passage pancreatic cancer cell lines. One of the pancreatic cancer cell lines, P196, which showed dramatic upregulation of tyrosine kinase activity as compared to non-neoplastic cells, was systematically studied using a quantitative proteomic approach called stable isotope labeling with amino acids in cell culture (SILAC). A careful analysis of activated tyrosine kinase pathways revealed aberrant activation of epidermal growth factor receptor pathway in this cell line. Mouse xenograft based studies using EGFR inhibitor erlotinib confirmed EGFR pathway to be responsible for proliferation in these tumors. By a systematic study across low passage pancreatic cancer cell lines and mice carrying pancreatic cancer xenografts, we have demonstrated activated epidermal growth factor receptor as an attractive candidate for targeted therapy in a subset of pancreatic cancers. Further, we propose immunohistochemical labeling of activated EGFR (pEGFR¹⁰⁶⁸) as an efficient screening tool to select patients who are more likely to respond to EGFR inhibitors

Rapid Characterization of Candidate Biomarkers for Pancreatic Cancer Using Cell Microarrays (CMAs)

Tissue microarrays have become a valuable tool for high-throughput analysis using immunohistochemical labeling. However, the large majority of biochemical studies are carried out in cell lines to further characterize candidate biomarkers or therapeutic targets with subsequent studies in animals or using primary tissues. Thus, cell line-based microarrays could be a useful screening tool in some situations. Here, we constructed a cell microarray (CMA) containing a panel of 40 pancreatic cancer cell lines available from American Type Culture Collection in addition to those locally available at Johns Hopkins. As proof of principle, we performed immunocytochemical labeling of an epithelial cell adhesion molecule (Ep-CAM), a molecule generally expressed in the epithelium, on this pancreatic cancer CMA. In addition, selected molecules that have been previously shown to be differentially expressed in pancreatic cancer in the literature were validated. For example, we observed strong labeling of CA19-9 antigen, a prognostic and predictive marker for pancreatic cancer. We also carried out a bioinformatics analysis of a literature curated catalog of pancreatic cancer biomarkers developed previously by our group and identified two candidate biomarkers, HLA class I and transmembrane protease, serine 4 (TMPRSS4), and examined their expression in the cell lines represented on the pancreatic cancer CMAs. Our results demonstrate the utility of CMAs as a useful resource for rapid screening of molecules of interest and suggest that CMAs can become a universal standard platform in cancer research

Rapid Characterization of Candidate Biomarkers for Pancreatic Cancer Using Cell Microarrays (CMAs)

Tissue microarrays have become a valuable tool for high-throughput analysis using immunohistochemical labeling. However, the large majority of biochemical studies are carried out in cell lines to further characterize candidate biomarkers or therapeutic targets with subsequent studies in animals or using primary tissues. Thus, cell line-based microarrays could be a useful screening tool in some situations. Here, we constructed a cell microarray (CMA) containing a panel of 40 pancreatic cancer cell lines available from American Type Culture Collection in addition to those locally available at Johns Hopkins. As proof of principle, we performed immunocytochemical labeling of an epithelial cell adhesion molecule (Ep-CAM), a molecule generally expressed in the epithelium, on this pancreatic cancer CMA. In addition, selected molecules that have been previously shown to be differentially expressed in pancreatic cancer in the literature were validated. For example, we observed strong labeling of CA19-9 antigen, a prognostic and predictive marker for pancreatic cancer. We also carried out a bioinformatics analysis of a literature curated catalog of pancreatic cancer biomarkers developed previously by our group and identified two candidate biomarkers, HLA class I and transmembrane protease, serine 4 (TMPRSS4), and examined their expression in the cell lines represented on the pancreatic cancer CMAs. Our results demonstrate the utility of CMAs as a useful resource for rapid screening of molecules of interest and suggest that CMAs can become a universal standard platform in cancer research