Pilot study of blood biomarker candidates for detection of pancreatic cancer

Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development

Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n = 32) and serum (n = 68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC = 0.806. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC = 0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC = 0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of patients with pancreatic cystic lesions.

Logistic regression and other statistical tools in diagnostic biomarker studies

A biomarker is a measured indicator of a variety of processes, and is often used as a clinical tool for the diagnosis of diseases. While the developmental process of biomarkers from lab to clinic is complex, initial exploratory stages often focus on characterizing the potential of biomarkers through utilizing various statistical methods that can be used to assess their discriminatory performance, establish an appropriate cut-off that transforms continuous data to apt binary responses of confirming or excluding a diagnosis, or establish a robust association when tested against confounders. This review aims to provide a gentle introduction to the most common tools found in diagnostic biomarker studies used to assess the performance of biomarkers with an emphasis on logistic regression.

Multianalyte Serum Biomarker Panel for Early Detection of Pancreatic Adenocarcinoma

PURPOSE

We determined whether a large, multianalyte panel of circulating biomarkers can improve detection of early-stage pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

We defined a biologically relevant subspace of blood analytes on the basis of previous identification in premalignant lesions or early-stage PDAC and evaluated each in pilot studies. The 31 analytes that met minimum diagnostic accuracy were measured in serum of 837 subjects (461 healthy, 194 benign pancreatic disease, and 182 early-stage PDAC). We used machine learning to develop classification algorithms using the relationship between subjects on the basis of their changes across the predictors. Model performance was subsequently evaluated in an independent validation data set from 186 additional subjects.

RESULTS

A classification model was trained on 669 subjects (358 healthy, 159 benign, and 152 early-stage PDAC). Model evaluation on a hold-out test set of 168 subjects (103 healthy, 35 benign, and 30 early-stage PDAC) yielded an area under the receiver operating characteristic curve (AUC) of 0.920 for classification of PDAC from non-PDAC (benign and healthy controls) and an AUC of 0.944 for PDAC versus healthy controls. The algorithm was then validated in 146 subsequent cases presenting with pancreatic disease (73 benign pancreatic disease and 73 early- and late-stage PDAC cases) and 40 healthy control subjects. The validation set yielded an AUC of 0.919 for classification of PDAC from non-PDAC and an AUC of 0.925 for PDAC versus healthy controls.

CONCLUSION

Individually weak serum biomarkers can be combined into a strong classification algorithm to develop a blood test to identify patients who may benefit from further testing.

Diagnostic and Prognostic Roles of Thrombospondin-2 in Digestive System Cancers

BACKGROUND

Cancers of digestive system have high case-fatality rate. It is important to find more appropriate methods in diagnosing and predicting gastrointestinal malignances. And thrombospondin-2 (TSP-2) was reported to have the functions, although results were not identical. So we performed this meta-analysis to clarify the significance of TSP-2 in this area.

METHODS

PubMed, Embase, Web of Science, Cochrane Library, and Clinicaltrial.gov were searched for relevant studies. Data were extracted from these involved records. For the meta-analysis of diagnostic test, bivariate mixed effect model was used to estimate diagnostic accuracy. For prognosis part, HRs and their 95% CIs were pooled to compare the overall survival (OS) and disease-free survival (DFS) between patients with high TSP-2 and low TSP-2.

RESULTS

Nine records were eligible for the analysis of diagnostic test. Pooled results were as follows: sensitivity 0.60 (0.52, 0.68), specificity 0.96 (0.91, 0.98), positive likelihood ratio (PLR) 15.4 (7.3, 32.2), negative likelihood ratio (NLR) 0.42 (0.34, 0.50), and diagnostic odds ratio (DOR) 37 (18, 76). While in prognosis part, 10 articles were included. Patients with increased TSP-2 had shorter OS (HR = 1.64, 95% CI = 1.21-2.22); however, no difference was found in DFS between TSP-2 high and low groups (HR = 1.44, 95% CI = 0.28-7.33).

CONCLUSIONS

TSP-2, as a diagnostic marker, has a high specificity but a moderate sensitivity. Meanwhile, it plays a role in predicting OS. Therefore, making TSP-2 a routine assay could be beneficial to high-risk individuals and patients with digestive malignances.

The Role of Adipokines in Pancreatic Cancer

In modern society, inappropriate diets and other lifestyle habits have made obesity an increasingly prominent health problem. Pancreatic cancer (PC), a kind of highly aggressive malignant tumor, is known as a silent assassin and is the seventh leading cause of cancer death worldwide, pushing modern medicine beyond help. Adipokines are coming into notice because of the role of the intermediate regulatory junctions between obesity and malignancy. This review summarizes the current evidence for the relationship between highly concerning adipokines and the pathogenesis of PC. Not only are classical adipokines such as leptin and adiponectin included, but they also cover the recognized chemerin and osteopontin. Through a summary of the biological functions of these adipokines as well as their receptors, it was discovered that in addition to their basic function of stimulating the biological activity of tumors, more studies confirm that adipokines intervene in the progression of PC from the viewpoint of tumor metabolism, immune escape, and reprogramming of the tumor microenvironment (TME). Besides endocrine function, the impact of white adipose tissue (WAT)-induced chronic inflammation on PC is briefly discussed. Furthermore, the potential implication of the acknowledged endocrine behavior of brown adipose tissue (BAT) in relation to carcinogenesis is also explored. No matter the broad spectrum of obesity and the poor prognosis of PC, supplemental research is needed to unravel the detailed network of adipokines associated with PC. Exploiting profound therapeutic strategies that target adipokines and their receptors may go some way to improving the current worrying prognosis of PC patients.

Targeting Macrophage Migration Inhibitory Factor in Acute Pancreatitis and Pancreatic Cancer

Macrophage migration inhibitory factor (MIF) is a pleiotropic cytokine implicated in the pathogenesis of inflammation and cancer. It is produced by various cells and circulating MIF has been identified as a biomarker for a range of diseases. Extracellular MIF mainly binds to the cluster of differentiation 74 (CD74)/CD44 to activate downstream signaling pathways. These in turn activate immune responses, enhance inflammation and can promote cancer cell proliferation and invasion. Extracellular MIF also binds to the C-X-C chemokine receptors cooperating with or without CD74 to activate chemokine response. Intracellular MIF is involved in Toll-like receptor and inflammasome-mediated inflammatory response. Pharmacological inhibition of MIF has been shown to hold great promise in treating inflammatory diseases and cancer, including small molecule MIF inhibitors targeting the tautomerase active site of MIF and antibodies that neutralize MIF. In the current review, we discuss the role of MIF signaling pathways in inflammation and cancer and summarize the recent advances of the role of MIF in experimental and clinical exocrine pancreatic diseases. We expect to provide insights into clinical translation of MIF antagonism as a strategy for treating acute pancreatitis and pancreatic cancer.

Systemic Proteome Alterations Linked to Early Stage Pancreatic Cancer in Diabetic Patients

BACKGROUND

Diabetes is a risk factor associated with pancreatic ductal adenocarcinoma (PDAC), and new adult-onset diabetes can be an early sign of pancreatic malignancy. Development of blood-based biomarkers to identify diabetic patients who warrant imaging tests for cancer detection may represent a realistic approach to facilitate earlier diagnosis of PDAC in a risk population.

METHODS

A spectral library-based proteomic platform was applied to interrogate biomarker candidates in plasma samples from clinically well-defined diabetic cohorts with and without PDAC. Random forest algorithm was used for prediction model building and receiver operating characteristic (ROC) curve analysis was applied to evaluate the prediction probability of potential biomarker panels.

RESULTS

Several biomarker panels were cross-validated in the context of detection of PDAC within a diabetic background. In combination with carbohydrate antigen 19-9 (CA19-9), the panel, which consisted of apolipoprotein A-IV (APOA4), monocyte differentiation antigen CD14 (CD14), tetranectin (CLEC3B), gelsolin (GSN), histidine-rich glycoprotein (HRG), inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3), plasma kallikrein (KLKB1), leucine-rich alpha-2-glycoprotein (LRG1), pigment epithelium-derived factor (SERPINF1), plasma protease C1 inhibitor (SERPING1), and metalloproteinase inhibitor 1 (TIMP1), demonstrated an area under curve (AUC) of 0.85 and a two-fold increase in detection accuracy compared to CA19-9 alone. The study further evaluated the correlations of protein candidates and their influences on the performance of biomarker panels.

CONCLUSIONS

Proteomics-based multiplex biomarker panels improved the detection accuracy for diagnosis of early stage PDAC in diabetic patients.

Proteome alterations in pancreatic ductal adenocarcinoma

Proteins are the essential functional biomolecules profoundly implicated in all aspects of pancreatic tumorigenesis and its progression. While common genomic factors, such as KRAS, TP53, SMAD4, and CDKN2A have been well recognized in association of pancreatic ductal adenocarcinoma (PDAC), our understanding of functional changes at the proteome level merits further investigation. Malignance associated proteome alterations can be attributed to the convoluted outcomes from genetic, epigenetic and environmental factors in initiating and progressing PDAC, and may reflect on changes in protein expressional level, structure, localization, as well as post-translational modifications (PTMs) status. The study of localized or systemic proteome alterations in PDAC, as well as its precursor lesions, such as pancreatic intraepithelial neoplasia (PanIN) and mucinous pancreatic cystic neoplasm, would provide unique perspectives in elucidating functional molecular events underlying PDAC. While efforts have been made, challenges still exist to comprehensively integrate much of the proteomic discovery to the perspectives gained from genomic studies in the context of biomarker discovery. Novel approaches and data from well-defined longitudinal clinical studies and experimental models are needed to facilitate the study of PDAC and precursor lesions for early detection and intervention.

Proteome characterization of human pancreatic cyst fluid from intraductal papillary mucinous neoplasm by liquid chromatography/tandem mass spectrometry

RATIONALE

In recent years, the molecular components of pancreatic cyst fluid have been used for diagnosis and prognosis. Because the protein markers that are currently used in clinical tests are unreliable, proteomic studies to find new protein markers are being conducted. However, such researches have been limited due to the complexity of pancreatic cyst fluid and the immaturity of proteomic techniques.

METHODS

To overcome these limitations and provide a pancreatic cyst proteome dataset, we examined cyst fluid proteome with tandem mass spectrometry. The proteomic analysis was performed using a Orbitrap-based mass spectrometer (Q-Exactive) coupled with a 50-cm-long nano-liquid chromatography column. Protein mutations were identified using mutation sequence database search.

RESULTS

A total of 5850 protein groups were identified from microliters of cyst fluid. Among those, 3934 protein groups were reported for the first time in pancreatic cyst fluid. Although high-abundance proteins were not depleted in the experiment, our dataset detected almost all pancreatic tumor markers such as mucin family members, S100 proteins, and CEA-related proteins. In addition, 590 protein mutation marker candidates were discovered.

CONCLUSIONS

We provide a comprehensive cyst proteome dataset that includes cystic cellular proteins and mutated proteins. Our findings would serve as a rich resource for further IPMN studies and clinical applications. The MS data have been deposited in the ProteomeXchange with identifier PXD005671 (http://proteomecentral.proteomexchange.org/dataset/PXD005671).

Human osteopontin: Potential clinical applications in cancer (Review)

Human osteopontin (OPN) is a glycosylated phosphoprotein which is expressed in a variety of tissues in the body. In recent years, accumulating evidence has indicated that the aberrant expression of OPN is closely associated with tumourigensis, progression and most prominently with metastasis in several tumour types. In this review, we present the current knowledge on the expression profiles of OPN and its main splice variants in human cancers, as well as the potential implications in patient outcome. We also discuss its putative clinical application as a cancer biomarker and as a therapeutic target.

Osteopontin as a discriminating marker for pancreatic cancer and chronic pancreatitis

INTRODUCTION

We analyzed concentrations of osteopontin (OPN) in patients with pancreatic ductal adenocarcinoma (PDAC) in order to determine firstly whether it is useful to distinguish between PDAC patients and those with chronic non-hereditary pancreatitis (CP) and type 2 diabetes mellitus (T2DM), and secondly whether OPN concentrations depend on the PDAC stage.

METHODS

Groups consisting of 64 patients with PDAC, 71 with CP, 67 with T2DM and 48 healthy controls (CON) were enrolled in the study. Controls were compared with regard to levels of OPN, oxidative stress markers, conventional tumor markers and other biochemical parameters.

RESULTS

Levels of OPN were higher in patients with PDAC compared with CP patients (P< 0.001), T2DM (P< 0.001) and CON (P< 0.001). There were increased OPN levels in CP patients in comparison with T2DM (P< 0.001) and CON (P< 0.001). Patients with PDAC in stage IV had higher OPN levels than PDAC patients in stage III (P< 0.01). There was no difference in OPN levels of PDAC patients in stage III compared to patients in stage II.

CONCLUSION

Our pilot study demonstrates the usefulness of estimating OPN levels to differentiate between pancreatic cancer and chronic pancreatitis. Higher OPN levels over 102 ng/ml could be a potential diagnostic biomarker for pancreatic cancer.

Cytokines as Biomarkers of Pancreatic Ductal Adenocarcinoma: A Systematic Review

OBJECTIVES

A systematic review of the role of cytokines in clinical medicine as diagnostic, prognostic, or predictive biomarkers in pancreatic ductal adenocarcinoma was undertaken.

MATERIALS AND METHODS

A systematic review was conducted according to the 2009 PRISMA guidelines. PubMed database was searched for all original articles on the topic of interest published until June 2015, and this was supplemented with references cited in relevant articles. Studies were evaluated for risk of bias using the Quality in Prognosis Studies tools.

RESULTS

Forty one cytokines were investigated with relation to pancreatic ductal adenocarcinoma (PDAC) in 65 studies, ten of which were analyzed by more than three studies. Six cytokines (interleukin[IL]-1β, -6, -8, -10, vascular endothelial growth factor, and transforming growth factor) were consistently reported to be increased in PDAC by more than four studies; irrespective of sample type; method of measurement; or statistical analysis model used. When evaluated as part of distinct panels that included CA19-9, IL-1β, -6 and -8 improved the performance of CA19-9 alone in differentiating PDAC from healthy controls. For example, a panel comprising IL-1β, IL-8, and CA 19-9 had a sensitivity of 94.1% vs 85.9%, specificity of 100% vs 96.3%, and area under the curve of 0.984 vs 0.925. The above-mentioned cytokines were associated with the severity of PDAC. IL-2, -6, -10, VEGF, and TGF levels were reported to be altered after patients received therapy or surgery. However, studies did not show any evidence of their ability to predict treatment response.

CONCLUSION

Our review demonstrates that there is insufficient evidence to support the role of individual cytokines as diagnostic, predictive or prognostic biomarkers for PDAC. However, emerging evidence indicates that a panel of cytokines may be a better tool for discriminating PDAC from other non-malignant pancreatic diseases or healthy individuals.

Proteomics analysis of bodily fluids in pancreatic cancer

Proteomics study of pancreatic cancer using bodily fluids emphasizes biomarker discovery and clinical application, presenting unique prospect and challenges. Depending on the physiological nature of the bodily fluid and its proximity to pancreatic cancer, the proteomes of bodily fluids, such as pancreatic juice, pancreatic cyst fluid, blood, bile, and urine, can be substantially different in terms of protein constitution and the dynamic range of protein concentration. Thus, a comprehensive discovery and specific detection of cancer-associated proteins within these varied fluids is a complex task, requiring rigorous experiment design and a concerted approach. While major challenges still remain, fluid proteomics studies in pancreatic cancer to date have provided a wealth of information in revealing proteome alterations associated with pancreatic cancer in various bodily fluids.

SIBLINGs and SPARC families: Their emerging roles in pancreatic cancer

Pancreatic cancer has a considerably poor prognosis with a 5-year survival probability of less than 5% when all stages are combined. Pancreatic cancer is characterized by its dense stroma, which is involved in the critical interplay with the tumor cells throughout tumor progression and furthermore, creates a barrier restricting efficient penetration of therapeutics. Alterations in a large number of genes are reflected by a limited number of signaling pathways, which are potential targets. Understanding more about the molecular basis of this devastating cancer type regarding tumor microenvironment, distinct subpopulations of cells, epithelial-to-mesenchymal transition and inflammation will lead to the development of various targeted therapies for controlling tumor growth and metastasis. In this complex scenario of pancreatic cancer, especially members of the “small integrin binding ligand N-linked glycoproteins” (SIBLINGs) and “secreted protein acidic and rich in cysteine” (SPARC) families have emerged due to their prominent roles in properties including proliferation, differentiation, apoptosis, adhesion, migration, angiogenesis, wound repair and regulation of extracellular matrix remodeling. SIBLINGs consist of five members, which include osteopontin (OPN), bone sialoprotein, dentin matrix protein 1, dentin sialophosphoprotein and matrix extracellular phosphoglycoprotein. The SPARC family of modular extracellular proteins is comprised of SPARC/osteonectin (ON) and SPARC-like 1 (hevin); secreted modular calcium binding proteins; testicans and follistatin-like protein. In this review, we especially focus on OPN and ON, elaborating on their special and growing importance in pancreatic cancer diagnosis and prognosis.

Spatiotemporal proteomic analyses during pancreas cancer progression identifies serine/threonine stress kinase 4 (STK4) as a novel candidate biomarker for early stage disease

Pancreas cancer, or pancreatic ductal adenocarcinoma, is the deadliest of solid tumors, with a five-year survival rate of <5%. Detection of resectable disease improves survival rates, but access to tissue and other biospecimens that could be used to develop early detection markers is confounded by the insidious nature of pancreas cancer. Mouse models that accurately recapitulate the human condition allow disease tracking from inception to invasion and can therefore be useful for studying early disease stages in which surgical resection is possible. Using a highly faithful mouse model of pancreas cancer in conjunction with a high-density antibody microarray containing ∼2500 antibodies, we interrogated the pancreatic tissue proteome at preinvasive and invasive stages of disease. The goal was to discover early stage tissue markers of pancreas cancer and follow them through histologically defined stages of disease using cohorts of mice lacking overt clinical signs and symptoms and those with end-stage metastatic disease, respectively. A panel of seven up-regulated proteins distinguishing pancreas cancer from normal pancreas was validated, and their levels were assessed in tissues collected at preinvasive, early invasive, and moribund stages of disease. Six of the seven markers also differentiated pancreas cancer from an experimental model of chronic pancreatitis. The levels of serine/threonine stress kinase 4 (STK4) increased between preinvasive and invasive stages, suggesting its potential as a tissue biomarker, and perhaps its involvement in progression from precursor pancreatic intraepithelial neoplasia to pancreatic ductal adenocarcinoma. Immunohistochemistry of STK4 at different stages of disease revealed a dynamic expression pattern further implicating it in early tumorigenic events. Immunohistochemistry of a panel of human pancreas cancers confirmed that STK4 levels were increased in tumor epithelia relative to normal tissue. Overall, this integrated approach yielded several tissue markers that could serve as signatures of disease stage, including early (resectable), and therefore clinically meaningful, stages.

Diagnostic significance of serum osteopontin level for pancreatic cancer: a meta-analysis

OBJECTIVE

This meta-analysis aimed to identify the significance of serum osteopontin (OPN) level for the diagnosis of pancreatic cancer (PC).

METHODS

Through searching the following electronic databases-the Cochrane Library Database (Issue 12, 2013), Web of Science (1945-2013), PubMed (1966-2013), CINAHL (1982-2013), EMBASE (1980-2013), and the Chinese Biomedical Database (CBM) (1982-2013)-related articles were determined without any language restrictions. The STATA statistical software (version 12.0; Stata Corporation, College Station, TX) was chosen to deal with statistical data. Standard mean difference (SMD) and its corresponding 95% confidence interval (95% CI) was calculated. Eleven clinical case-control studies, which recruited 491 PC patients and 481 healthy controls, were selected for statistical analysis.

RESULTS

Combined SMD of OPN suggested that the serum OPN level in PC patients was significantly higher than that in healthy controls (SMD=3.58, 95% CI=2.42-4.74, p<0.001). Ethnicity stratified analysis indicated a higher serum OPN level in PC patients compared with control subjects among both Caucasians and Asians (Caucasians: SMD=2.62, 95% CI=1.33-3.91, p<0.001; Asians: SMD=4.54, 95% CI=2.80-6.27, p<0.001; respectively).

CONCLUSION

The main finding of our meta-analysis revealed that an elevated serum OPN level may be used as a promising diagnostic tool for early identification of PC.

Prediagnostic serum biomarkers as early detection tools for pancreatic cancer in a large prospective cohort study

BACKGROUND

The clinical management of pancreatic cancer is severely hampered by the absence of effective screening tools.

METHODS

Sixty-seven biomarkers were evaluated in prediagnostic sera obtained from cases of pancreatic cancer enrolled in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO).

RESULTS

The panel of CA 19-9, OPN, and OPG, identified in a prior retrospective study, was not effective. CA 19-9, CEA, NSE, bHCG, CEACAM1 and PRL were significantly altered in sera obtained from cases greater than 1 year prior to diagnosis. Levels of CA 19-9, CA 125, CEA, PRL, and IL-8 were negatively associated with time to diagnosis. A training/validation study using alternate halves of the PLCO set failed to identify a biomarker panel with significantly improved performance over CA 19-9 alone. When the entire PLCO set was used for training at a specificity (SP) of 95%, a panel of CA 19-9, CEA, and Cyfra 21-1 provided significantly elevated sensitivity (SN) levels of 32.4% and 29.7% in samples collected <1 and >1 year prior to diagnosis, respectively, compared to SN levels of 25.7% and 17.2% for CA 19-9 alone.

CONCLUSIONS

Most biomarkers identified in previously conducted case/control studies are ineffective in prediagnostic samples, however several biomarkers were identified as significantly altered up to 35 months prior to diagnosis. Two newly derived biomarker combinations offered advantage over CA 19-9 alone in terms of SN, particularly in samples collected >1 year prior to diagnosis. However, the efficacy of biomarker-based tools remains limited at present. Several biomarkers demonstrated significant velocity related to time to diagnosis, an observation which may offer considerable potential for enhancements in early detection.

Serum osteopontin and tissue inhibitor of metalloproteinase 1 as diagnostic and prognostic biomarkers for pancreatic adenocarcinoma

OBJECTIVES

Pancreatic ductal adenocarcinoma (PDAC) has a dismal 5-year survival rate of 5%. There is an urgent need for early detection while the tumors are small and surgically resectable. We assessed serum osteopontin (OPN) and tissue inhibitor of metalloproteinase 1 (TIMP-1) as possible diagnostic and prognostic biomarkers in a novel cohort of patients with pancreatic cancer.

METHODS

Osteopontin and TIMP-1 levels were determined in sera from 86 patients with PDAC, 86 healthy control subjects, and 48 patients with chronic pancreatitis. Regression models were used to relate OPN and TIMP-1 to sex, age, stage, class, and treatment. Survival analyses were performed using univariate and multivariate Cox models.

RESULTS

The serum levels of both OPN and TIMP-1 distinguished PDAC from chronic pancreatitis (P ≤ 0.0001) and healthy control subjects (P < 0.0001). The serum levels of both OPN and TIMP-1 also distinguished early-stage resectable PDAC cases from chronic pancreatitis (P < 0.04) and healthy control subjects (P < 0.01). High serum levels of OPN were significantly correlated with reduced patient survival.

CONCLUSIONS

Serum OPN and TIMP-1 have use as diagnostic biomarkers in PDAC. Our data suggest a potential benefit of using OPN, TIMP-1, and CA 19-9 in a panel to improve diagnostic accuracy in PDAC.

Multiplex targeted proteomic assay for biomarker detection in plasma: a pancreatic cancer biomarker case study

Biomarkers are most frequently proteins that are measured in the blood. Their development largely relies on antibody creation to test the protein candidate performance in blood samples of diseased versus nondiseased patients. The creation of such antibody assays has been a bottleneck in biomarker progress due to the cost, extensive time, and effort required to complete the task. Targeted proteomics is an emerging technology that is playing an increasingly important role to facilitate disease biomarker development. In this study, we applied a SRM-based targeted proteomics platform to directly detect candidate biomarker proteins in plasma to evaluate their clinical utility for pancreatic cancer detection. The characterization of these protein candidates used a clinically well-characterized cohort that included plasma samples from patients with pancreatic cancer, chronic pancreatitis, and healthy age-matched controls. Three of the five candidate proteins, including gelsolin, lumican, and tissue inhibitor of metalloproteinase 1, demonstrated an AUC value greater than 0.75 in distinguishing pancreatic cancer from the controls. In addition, we provide an analysis of the reproducibility, accuracy, and robustness of the SRM-based proteomics platform. This information addresses important technical issues that could aid in the adoption of the targeted proteomics platform for practical clinical utility.

Stool DNA testing for the detection of pancreatic cancer: assessment of methylation marker candidates

BACKGROUND

Pancreatic cancer (PanC) presents at late stage with high mortality. Effective early detection methods are needed. Aberrantly methylated genes are unexplored as markers for noninvasive detection by stool testing. The authors aimed to select discriminant methylated genes and to assess accuracy of these and mutant KRAS in stool to detect PanC.

METHODS

Nine target genes were assayed by real-time methylation-specific polymerase chain reaction (MSP) in bisulfite-treated DNA from microdissected frozen specimens of 24 PanC cases and 30 normal colon controls. Archived stools from 58 PanC cases and 65 controls matched on sex, age, and smoking were analyzed. Target genes from fecal supernatants were enriched by hybrid capture, bisulfite-treated, and assayed by MSP. KRAS mutations were assayed using the QuARTS technique.

RESULTS

Areas under the receiver operating characteristics curves (AUCs) for tissue BMP3, NDRG4, EYA4, UCHL1, MDFI, Vimentin, CNTNAP2, SFRP2, and TFPI2 were 0.90, 0.79, 0.78, 0.78, 0.77, 0.77, 0.69, 0.67, and 0.66, respectively. The top 4 markers and mutant KRAS were evaluated in stool. BMP3 was the most discriminant methylation marker in stool. At 90% specificity, methylated BMP3 alone detected 51% of PanCs, mutant KRAS detected 50%, and combination detected 67%. AUCs for methylated BMP3, mutant KRAS, and combination in stool were 0.73, 0.75, and 0.85, respectively.

CONCLUSIONS

This study demonstrates that stool assay of a methylated gene marker can detect PanC. Among candidate methylated markers discriminant in tissue, BMP3 alone performed well in stool. Combining methylated BMP3 and mutant KRAS increased stool detection over either marker alone.

Stool DNA testing for the detection of pancreatic cancer: assessment of methylation marker candidates

BACKGROUND

Pancreatic cancer (PanC) presents at late stage with high mortality. Effective early detection methods are needed. Aberrantly methylated genes are unexplored as markers for noninvasive detection by stool testing. The authors aimed to select discriminant methylated genes and to assess accuracy of these and mutant KRAS in stool to detect PanC.

METHODS

Nine target genes were assayed by real-time methylation-specific polymerase chain reaction (MSP) in bisulfite-treated DNA from microdissected frozen specimens of 24 PanC cases and 30 normal colon controls. Archived stools from 58 PanC cases and 65 controls matched on sex, age, and smoking were analyzed. Target genes from fecal supernatants were enriched by hybrid capture, bisulfite-treated, and assayed by MSP. KRAS mutations were assayed using the QuARTS technique.

RESULTS

Areas under the receiver operating characteristics curves (AUCs) for tissue BMP3, NDRG4, EYA4, UCHL1, MDFI, Vimentin, CNTNAP2, SFRP2, and TFPI2 were 0.90, 0.79, 0.78, 0.78, 0.77, 0.77, 0.69, 0.67, and 0.66, respectively. The top 4 markers and mutant KRAS were evaluated in stool. BMP3 was the most discriminant methylation marker in stool. At 90% specificity, methylated BMP3 alone detected 51% of PanCs, mutant KRAS detected 50%, and combination detected 67%. AUCs for methylated BMP3, mutant KRAS, and combination in stool were 0.73, 0.75, and 0.85, respectively.

CONCLUSIONS

This study demonstrates that stool assay of a methylated gene marker can detect PanC. Among candidate methylated markers discriminant in tissue, BMP3 alone performed well in stool. Combining methylated BMP3 and mutant KRAS increased stool detection over either marker alone.

Osteopontin as a key mediator for vasculogenic mimicry in hepatocellular carcinoma

Osteopontin (OPN) is overexpressed in a variety of cancers including hepatocellular carcinoma (HCC), and is likely involved in the process of vasculogenic mimicry (VM) in some tumor cells. In this study, we explored whether OPN plays a role for VM in HCC. Metastatic MHCC97-H human HCC cells and non-metastatic Hep3B human HCC cells were compared for their abilities to establish VM. Three dimensional-culture assays showed that MHCC97-H cells but not Hep3B cells were able to form the chord-like structure that represents VM. Real-time RT-PCR arrays were used to detect gene expression profiles of the two HCC cell lines in three-dimensional culture. PCR array analyses revealed the increased expression of OPN in MHCC97-H cells forming VM compared with Hep3B cells. Small interfering RNA was employed to investigate whether OPN knockdown could influence VM, and the expression of matrix metalloproteinase (MMP)-2, MMP-9 and urokinase-type plasminogen activator (uPA) in MHCC97-H cells. OPN knockdown resulted in a significant decrease in the ability of MHCC97-H cells to form VM, which was accompanied by the down-regulation of MMP-2 and uPA expression. Furthermore, human HCC tissue samples were studied by immunohistochemistry to analyze the correlations between VM and the expression of OPN, MMP-2 and uPA. There existed significant positive correlations between VM and the expression of OPN, MMP-2 and uPA in HCC tissue samples. In conclusion, OPN is required for VM in HCC cells, and its action may be associated with activation of MMP-2 and uPA. OPN-targeted therapeutics may be useful for patients with advanced HCC.

Protein alterations associated with pancreatic cancer and chronic pancreatitis found in human plasma using global quantitative proteomics profiling

Pancreatic cancer is a lethal disease that is difficult to diagnose at early stages when curable treatments are effective. Biomarkers that can improve current pancreatic cancer detection would have great value in improving patient management and survival rate. A large scale quantitative proteomics study was performed to search for the plasma protein alterations associated with pancreatic cancer. The enormous complexity of the plasma proteome and the vast dynamic range of protein concentration therein present major challenges for quantitative global profiling of plasma. To address these challenges, multidimensional fractionation at both protein and peptide levels was applied to enhance the depth of proteomics analysis. Employing stringent criteria, more than 1300 proteins total were identified in plasma across 8-orders of magnitude in protein concentration. Differential proteins associated with pancreatic cancer were identified, and their relationship with the proteome of pancreatic tissue and pancreatic juice from our previous studies was discussed. A subgroup of differentially expressed proteins was selected for biomarker testing using an independent cohort of plasma and serum samples from well-diagnosed patients with pancreatic cancer, chronic pancreatitis, and nonpancreatic disease controls. Using ELISA methodology, the performance of each of these protein candidates was benchmarked against CA19-9, the current gold standard for a pancreatic cancer blood test. A composite marker of TIMP1 and ICAM1 demonstrate significantly better performance than CA19-9 in distinguishing pancreatic cancer from the nonpancreatic disease controls and chronic pancreatitis controls. In addition, protein AZGP1 was identified as a biomarker candidate for chronic pancreatitis. The discovery and technical challenges associated with plasma-based quantitative proteomics are discussed and may benefit the development of plasma proteomics technology in general. The protein candidates identified in this study provide a biomarker candidate pool for future investigations.