AGR2, a unique tumor-associated antigen, is a promising candidate for antibody targeting.

Anterior gradient 2 (AGR2), a protein disulfide isomerase, shows two subcellular localizations: intracellular (iAGR2) and extracellular (eAGR2). In healthy cells that express AGR2, the predominant form is iAGR2, which resides in the endoplasmic reticulum. In contrast, cancer cells secrete and express eAGR2 on the cell surface. We wanted to test if AGR2 is a cancer-specific tumor-associated antigen. We utilized two AGR2 antibodies, P3A5 and P1G4, for in vivo tumor localization and tumor growth inhibition. The monoclonal antibodies recognized both human AGR2 and mouse Agr2. Biodistribution experiments using a syngeneic mouse model showed high uptake of P3A5 AGR2 antibody in xenografted eAgr2+ pancreatic tumors, with limited uptake in normal tissues. In implanted human patient-derived eAGR2+ pancreatic cancer xenografts, tumor growth inhibition was evaluated with antibodies and Gemcitabine (Gem). Inhibition was more potent by P1G4 + Gem combination than Gem alone or P3A5 + Gem. We converted these two antibodies to human:mouse chimeric forms: the constructed P3A5 and P1G4 chimeric mVLhCκ and mVHhCγ (γ1, γ2, γ4) genes were inserted in a single mammalian expression plasmid vector, and transfected into human 293F cells. Expressed human:mouse chimeric IgG1, IgG2 and IgG4 antibodies retained AGR2 binding. Increase in IgG yield by transfected cells could be obtained with serial transfection of vectors with different drug resistance. These chimeric antibodies, when incubated with human blood, effectively lysed eAGR2+ PC3 prostate cancer cells. We have, thus, produced humanized anti-AGR2 antibodies that, after further testing, might be suitable for treatment against a variety of eAGR2+ solid tumors.

Perineural invasion in pancreatic cancer: proteomic analysis and in vitro modelling.

Perineural invasion (PNI) is a common and characteristic feature of pancreatic ductal adenocarcinoma (PDAC) that is associated with poor prognosis, tumor recurrence, and generation of pain. However, the molecular alterations in cancer cells and nerves within PNI have not previously been comprehensively analyzed. Here, we describe our proteomic analysis of the molecular changes underlying neuro-epithelial interactions in PNI using liquid chromatography-mass spectrometry (LC-MS/MS) in microdissected PNI and non-PNI cancer, as well as in invaded and noninvaded nerves from formalin-fixed, paraffin-embedded PDAC tissues. In addition, an in vitro model of PNI was developed using a co-culture system comprising PDAC cell lines and PC12 cells as the neuronal element. The overall proteomic profiles of PNI and non-PNI cancer appeared largely similar. In contrast, upon invasion by cancer cells, nerves demonstrated widespread plasticity with a pattern consistent with neuronal injury. The up-regulation of SCG2 (secretogranin II) and neurosecretory protein VGF (nonacronymic) in invaded nerves in PDAC tissues was further validated using immunohistochemistry. The tested PDAC cell lines were found to be able to induce neuronal plasticity in PC12 cells in our in vitro established co-culture model. Changes in expression levels of VGF, as well as of two additional proteins previously reported to be overexpressed in PNI, Nestin and Neuromodulin (GAP43), closely recapitulated our proteomic findings in PDAC tissues. Furthermore, induction of VGF, while not necessary for PC12 survival, mediated neurite extension induced by PDAC cell lines. In summary, here we report the proteomic alterations underlying PNI in PDAC and confirm that PDAC cells are able to induce neuronal plasticity. In addition, we describe a novel, simple, and easily adaptable co-culture model for in vitro study of neuro-epithelial interactions.

Splice variants as novel targets in pancreatic ductal adenocarcinoma.

Despite a wealth of genomic information, a comprehensive alternative splicing (AS) analysis of pancreatic ductal adenocarcinoma (PDAC) has not been performed yet. In the present study, we assessed whole exome-based transcriptome and AS profiles of 43 pancreas tissues using Affymetrix exon array. The AS analysis of PDAC indicated on average two AS probe-sets (ranging from 1-28) in 1,354 significantly identified protein-coding genes, with skipped exon and alternative first exon being the most frequently utilised. In addition to overrepresented extracellular matrix (ECM)-receptor interaction and focal adhesion that were also seen in transcriptome differential expression (DE) analysis, Fc gamma receptor-mediated phagocytosis and axon guidance AS genes were also highly represented. Of note, the highest numbers of AS probe-sets were found in collagen genes, which encode the characteristically abundant stroma seen in PDAC. We also describe a set of 37 'hypersensitive' genes which were frequently targeted by somatic mutations, copy number alterations, DE and AS, indicating their propensity for multidimensional regulation. We provide the most comprehensive overview of the AS landscape in PDAC with underlying changes in the spliceosomal machinery. We also collate a set of AS and DE genes encoding cell surface proteins, which present promising diagnostic and therapeutic targets in PDAC.

Identification of a Three-Biomarker Panel in Urine for Early Detection of Pancreatic Adenocarcinoma.

PURPOSE: Noninvasive biomarkers for early detection of pancreatic ductal adenocarcinoma (PDAC) are currently not available. Here, we aimed to identify a set of urine proteins able to distinguish patients with early-stage PDAC from healthy individuals. EXPERIMENTAL DESIGN: Proteomes of 18 urine samples from healthy controls, chronic pancreatitis, and patients with PDAC (six/group) were assayed using GeLC/MS/MS analysis. The selected biomarkers were subsequently validated with ELISA assays using multiple logistic regression applied to a training dataset in a multicenter cohort comprising 488 urine samples. RESULTS: LYVE-1, REG1A, and TFF1 were selected as candidate biomarkers. When comparing PDAC (n = 192) with healthy (n = 87) urine specimens, the resulting areas under the receiver-operating characteristic curves (AUC) of the panel were 0.89 [95% confidence interval (CI), 0.84-0.94] in the training (70% of the data) and 0.92 (95% CI, 0.86-0.98) in the validation (30% of the data) datasets. When comparing PDAC stage I-II (n = 71) with healthy urine specimens, the panel achieved AUCs of 0.90 (95% CI, 0.84-0.96) and 0.93 (95% CI, 0.84-1.00) in the training and validation datasets, respectively. In PDAC stage I-II and healthy samples with matching plasma CA19.9, the panel achieved a higher AUC of 0.97 (95% CI, 0.94-0.99) than CA19.9 (AUC = 0.88; 95% CI, 0.81-0.95, P = 0.005). Adding plasma CA19.9 to the panel increased the AUC from 0.97 (95% CI, 0.94-0.99) to 0.99 (95% CI, 0.97-1.00, P = 0.04), but did not improve the comparison of stage I-IIA PDAC (n = 17) with healthy urine. CONCLUSIONS: We have established a novel, three-protein biomarker panel that is able to detect patients with early-stage pancreatic cancer in urine specimens.

Noninvasive urinary miRNA biomarkers for early detection of pancreatic adenocarcinoma.

Currently, the majority of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC) present with locally invasive and/or metastatic disease, resulting in five-year survival of less than 5%. The development of an early diagnostic test is, therefore, expected to significantly impact the patient's prognosis. In this study, we successfully evaluated the feasibility of identifying diagnostic cell free microRNAs (miRNAs) for early stage PDAC, through the analysis of urine samples. Using Affymetrix microarrays, we established a global miRNA profile of 13 PDAC, six chronic pancreatitis (CP), and seven healthy (H) urine specimens. Selected differentially expressed miRNAs were subsequently investigated using an independent technique (RT-PCR) on 101 urine samples including 46 PDAC, 29 CP and 26 H. Receiver operating characteristic (ROC) and logistic regression analyses were applied to determine the discriminatory potential of the candidate miRNA biomarkers. Three miRNAs (miR-143, miR-223, and miR-30e) were significantly over-expressed in patients with Stage I cancer when compared with age-matched healthy individuals (P=0.022, 0.035 and 0.04, respectively); miR-143, miR-223 and miR-204 were also shown to be expressed at higher levels in Stage I compared to Stages II-IV PDAC (P=0.025, 0.013 and 0.008, respectively). Furthermore, miR-223 and miR-204 were able to distinguish patients with early stage cancer from patients with CP (P=0.037 and 0.036). Among the three biomarkers, miR-143 was best able to differentiate Stage I (n=6) from healthy (n=26) with area under the curve (AUC) of 0.862 (95% CI 0.695-1.000), with sensitivity (SN) of 83.3% (95% CI 50.0-100.0), and specificity (SP) of 88.5% (95% CI 73.1-100.0). The combination of miR-143 with miR-30e was significantly better at discriminating between these two groups, achieving an AUC of 0.923 (95% CI 0.793-1.000), with SN of 83.3% (95% CI 50.0-100.0) and SP of 96.2% (95% CI 88.5-100.0). In this feasibility study, we demonstrate for the first time the utility of miRNA biomarkers for non-invasive, early detection of PDAC in urine specimens.

Dual-color proteomic profiling of complex samples with a microarray of 810 cancer-related antibodies.

Antibody microarrays have the potential to enable comprehensive proteomic analysis of small amounts of sample material. Here, protocols are presented for the production, quality assessment, and reproducible application of antibody microarrays in a two-color mode with an array of 1,800 features, representing 810 antibodies that were directed at 741 cancer-related proteins. In addition to measures of array quality, we implemented indicators for the accuracy and significance of dual-color detection. Dual-color measurements outperform a single-color approach concerning assay reproducibility and discriminative power. In the analysis of serum samples, depletion of high-abundance proteins did not improve technical assay quality. On the contrary, depletion introduced a strong bias in protein representation. In an initial study, we demonstrated the applicability of the protocols to proteins derived from urine samples. We identified differences between urine samples from pancreatic cancer patients and healthy subjects and between sexes. This study demonstrates that biomedically relevant data can be produced. As demonstrated by the thorough quality analysis, the dual-color antibody array approach proved to be competitive with other proteomic techniques and comparable in performance to transcriptional microarray analyses.

Analysis of the urine proteome in patients with pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) accounts for over 213 000 deaths worldwide each year, largely due to late diagnosis. One of the risk factors for the development of PDAC is chronic pancreatitis (CP); the intense desmoplastic reaction makes differentiation between the two conditions extremely difficult. In order to identify biomarkers for noninvasive diagnosis, we performed 2-D DIGE analysis of urine samples from healthy individuals and patients with PDAC and CP. Despite considerable intersample heterogeneity, a total of 127 statistically valid (p<0.05), differentially expressed protein spots were detected, 101 of which were identified using MALDI-TOF MS. A number of these, including annexin A2, gelsolin and CD59 have already been associated with PDAC, however, their validation using immunoblotting proved challenging. This is probably due to extensive PTMs and processing thus indicating the need for raising specific antibodies for urinary proteins. Despite this, our study clearly demonstrates that urine is a valid source of noninvasive biomarkers in patients with pancreatic diseases.

Pancreatic Expression database: a generic model for the organization, integration and mining of complex cancer datasets.

BACKGROUND: Pancreatic cancer is the 5th leading cause of cancer death in both males and females. In recent years, a wealth of gene and protein expression studies have been published broadening our understanding of pancreatic cancer biology. Due to the explosive growth in publicly available data from multiple different sources it is becoming increasingly difficult for individual researchers to integrate these into their current research programmes. The Pancreatic Expression database, a generic web-based system, is aiming to close this gap by providing the research community with an open access tool, not only to mine currently available pancreatic cancer data sets but also to include their own data in the database. DESCRIPTION: Currently, the database holds 32 datasets comprising 7636 gene expression measurements extracted from 20 different published gene or protein expression studies from various pancreatic cancer types, pancreatic precursor lesions (PanINs) and chronic pancreatitis. The pancreatic data are stored in a data management system based on the BioMart technology alongside the human genome gene and protein annotations, sequence, homologue, SNP and antibody data. Interrogation of the database can be achieved through both a web-based query interface and through web services using combined criteria from pancreatic (disease stages, regulation, differential expression, expression, platform technology, publication) and/or public data (antibodies, genomic region, gene-related accessions, ontology, expression patterns, multi-species comparisons, protein data, SNPs). Thus, our database enables connections between otherwise disparate data sources and allows relatively simple navigation between all data types and annotations. CONCLUSION: The database structure and content provides a powerful and high-speed data-mining tool for cancer research. It can be used for target discovery i.e. of biomarkers from body fluids, identification and analysis of genes associated with the progression of cancer, cross-platform meta-analysis, SNP selection for pancreatic cancer association studies, cancer gene promoter analysis as well as mining cancer ontology information. The data model is generic and can be easily extended and applied to other types of cancer. The database is available online with no restrictions for the scientific community at http://www.pancreasexpression.org/.