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Correlation of the three urinary biomarkers and plasma CA19.9 (CA19.9p). A, Correlation plots (Navy blue: Healthy; Turquoise: chronic pancreatitis (CP); Purple: pancreatic adenocarcinoma (PDAC). B, Pearson correlation coefficients and corresponding significance (NS: non-significant, *: p

Diagnostic performance of urine biomarkers in discriminating pancreatic adenocarcinoma all stages (A-C) and stage I-II (D-F) from chronic pancreatitis patients. A, ROC curves of PDAC (n=143) versus CP (n=62) patients for individual urine biomarkers in the training set (70% of the data). B, ROC curves of PDAC versus CP patients for the panel in the training set and in the independent validation set (30% of the data, PDAC n=49, CP n=30). C, Summary table. D, ROC curves of individual urine biomarkers in training dataset (70%, PDAC stage I-II n= 56, CP=66). E, ROC curves of the panel in training and validation (PDAC stage I-II n=15, CP n=26) dataset. F, Summary table. Cnorm, creatinine-normalised, creat, creatinine, AUC: area under the curve SN: sensitivity, SP: specificity with 95% Confidence Interval (CI). SN and SP in the validation set were derived for optimal cutpoint determined in the training dataset.

Expression of the protein biomarkers in pancreatic cancer tissues. A, Immunohistochemical analysis of REG1A: i) REG1A in poorly differentiated PDAC, ii) luminal REG1A in malignant glands. B, TFF1: i) heterogenous expression in cancer,, ii) luminal TFF1 in malignant gland. C, LYVE1 expression in the scattered lymphatic vessels i) in the muscle layer and ii) in the stroma surrounding malignant gland. D, The biomarker levels during monitoring of pancreatic adenocarcinoma patients: LYVE1, REG1A and TFF1 were measured using ELISA in urine samples collected before surgery and during the patient's follow up. Each point represents log-transformed ELISA values at a particular time point (x-axis).

Exploratory comparison of plasma CA19.9 and the urine biomarker panel in discriminating early pancreatic adenocarcinoma from chronic pancreatitis patients. A, ROC curves of the biomarker panel with corresponding plasma CA19.9 alone and in combination comparing CP urine (n=50), and urines from PDAC stages I-II (n=71) and, I-IIA (n=16) (B). C, Summary table. AUC: area under the curve, SN: sensitivity, SP: specificity with 95% Confidence Interval (CI). SN and SP in the validation set were derived for optimal cutpoint determined in the training dataset.

Supplementary Figure Legends from Identification of a Three-Biomarker Panel in Urine for Early Detection of Pancreatic Adenocarcinoma

Urine biomarker concentrations in different tumours. Scatter dot plots of urine LYVE1, REG1A and plasma CA19.9 in different hepatobiliary pathologies and early stages of pancreatic adenocarcinoma (I-IIA, n=16) and I-II (n=71). The level of TFF1 protein was not measured in these samples due to substantial modifications made to the original ELISA assay by the source company at the moment of this analysis. IPMN (n=33): intraductal papillary mucinous neoplasm, AMP (n=26): ampullary cancer, NET (n=18): neuroendocrine tumour, CHL (n=24): cholangiocarcinoma, DuCA (n=16): duodenal cancer. Bars indicate median and IQR values. Upper bars: Kruskal-Wallis/Dunn's post test, *: p0.05 (no adjustment for multiplicity was made).

Expression of non-redundant proteins identified by GeLC-MS/MS from males and females in healthy controls, chronic pancreatitis and pancreatic adenocarcinoma patients.

Urine proteome analysis. A, schematic outline of the study. B, classification of total identified proteins according to sub-cellular localisation and C, functional activity determined by Ingenuity Pathway Analysis. H: healthy, CP: chronic pancreatitis, PDAC: pancreatic ductal adenocarcinoma, GeLC/MS/MS: SDS-PAGE-Liquid Chromatography-Tandem Mass Spectrometry.

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.

S100P, a calcium-binding protein, is known to advance tumor progression and metastasis in pancreatic and several other cancers. Herein is described the in silico identification of a putative binding pocket of S100P to identify, synthesize and evaluate novel small molecules with the potential to selectively bind S100P and inhibit its activation of cell survival and metastatic pathways. The virtual screening of a drug-like database against the S100P model led to the identification of over 100 clusters of diverse scaffolds. A representative test set identified a number of structurally unrelated hits that inhibit S100P-RAGE interaction, measured by ELISA, and reduce in vitro cell invasion selectively in S100P-expressing pancreatic cancer cells at 10 μM. This study establishes a proof of concept in the potential for rational design of small molecule S100P inhibitors for drug candidate development., Graphical abstract Image 1, Highlights • Virtual screening identified hits against S100P, a protein target involved in tumor progression and metastasis. • In vitro ELISA screening identified 13 hits that inhibit S100P interaction with its extracellular binding target RAGE. • Synthesis of a short analog series of hit 4 demonstrated proof of concept for inhibitor design and optimization. • Active hits reduce in vitro cell invasiveness selectively in S100P-expressing pancreatic cancer cells.

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.

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.

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. Clin Cancer Res; 21(15); 3512–21. ©2015 AACR.

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

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/.