Dataset Information

Rapid characterization of candidate biomarkers for pancreatic cancer using cell microarrays (CMAs).

ABSTRACT: 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.

SUBMITTER: Kim MS

PROVIDER: S-EPMC3565537 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Rapid characterization of candidate biomarkers for pancreatic cancer using cell microarrays (CMAs).

Kim Min-Sik MS Kuppireddy Sarada V SV Sakamuri Sruthi S Singal Mukul M Getnet Derese D Harsha H C HC Goel Renu R Balakrishnan Lavanya L Jacob Harrys K C HK Kashyap Manoj K MK Kashyap Manoj K MK Tankala Shantal G SG Maitra Anirban A Iacobuzio-Donahue Christine A CA Jaffee Elizabeth E Goggins Michael G MG Velculescu Victor E VE Hruban Ralph H RH Pandey Akhilesh A

Journal of proteome research 20121011 11

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 cel ...[more]

PMID: 22985314

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Project description:The identification of new serum biomarkers with high sensitivity and specificity is an important priority in pancreatic cancer research. Through an extensive proteomics analysis of pancreatic cancer cell lines and pancreatic juice, we previously generated a list of candidate pancreatic cancer biomarkers. The present study details further validation of four of our previously identified candidates: regenerating islet-derived 1 beta (REG1B), syncollin (SYCN), anterior gradient homolog 2 protein (AGR2), and lysyl oxidase-like 2 (LOXL2).The candidate biomarkers were validated using enzyme-linked immunosorbent assays in two sample sets of serum/plasma comprising a total of 432 samples (Sample Set A: pancreatic ductal adenocarcinoma (PDAC, n?=?100), healthy (n?=?92); Sample Set B: PDAC (n?=?82), benign (n?=?41), disease-free (n?=?47), other cancers (n?=?70)). Biomarker performance in distinguishing PDAC from each control group was assessed individually in the two sample sets. Subsequently, multiparametric modeling was applied to assess the ability of all possible two and three marker panels in distinguishing PDAC from disease-free controls. The models were generated using sample set B, and then validated in Sample Set A.Individually, all markers were significantly elevated in PDAC compared to healthy controls in at least one sample set (p???0.01). SYCN, REG1B and AGR2 were also significantly elevated in PDAC compared to benign controls (p???0.01), and AGR2 was significantly elevated in PDAC compared to other cancers (p?<?0.01). CA19.9 was also assessed. Individually, CA19.9 showed the greatest area under the curve (AUC) in receiver operating characteristic (ROC) analysis when compared to the tested candidates; however when analyzed in combination, three panels (CA19.9?+?REG1B (AUC of 0.88), CA19.9?+?SYCN?+?REG1B (AUC of 0.87) and CA19.9?+?AGR2?+?REG1B (AUC of 0.87)) showed an AUC that was significantly greater (p?<?0.05) than that of CA19.9 alone (AUC of 0.82). In a comparison of early-stage (Stage I-II) PDAC to disease free controls, the combination of SYCN?+?REG1B?+?CA19.9 showed the greatest AUC in both sample sets, (AUC of 0.87 and 0.92 in Sets A and B, respectively).Additional serum biomarkers, particularly SYCN and REG1B, when combined with CA19.9, show promise as improved diagnostic indicators of pancreatic cancer, which therefore warrants further validation.

Dataset Information

Rapid characterization of candidate biomarkers for pancreatic cancer using cell microarrays (CMAs).

Publications

Rapid characterization of candidate biomarkers for pancreatic cancer using cell microarrays (CMAs).

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