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

Serum proteomics reveals survival-associated biomarkers in pancreatic cancer patients treated with chemoimmunotherapy

Immunotherapy has transformed the landscape of cancer treatment but remains largely ineffective for patients with pancreatic ductal adenocarcinoma (PDAC). Some patients, however, show improved outcomes when treated with a combination of immunotherapy and chemotherapy. Here, we conducted deep serum proteome analysis to investigate the protein profiles of PDAC patients and changes during this combinatorial treatment. Utilizing an advanced serum workflow, we quantified 1,011 proteins across 211 samples from 62 patients. Glycolytic enzymes were associated with survival in anti-PD-1-treated patients, with their abundances significantly correlating with expression levels in tumor biopsies. Notably, a set of protein biomarkers was found to be highly predictive of survival in anti-PD-1-treated patients (area under the curve [AUC] = 0.91). Overall, our data demonstrate the potential of deep serum proteomics for precision medicine, offering a powerful tool to guide patient selection for treatment through minimally invasive serum protein biomarker measurements.

Deciphering molecular crosstalk mechanisms between skeletal muscle atrophy and KRAS-mutant pancreatic cancer: a literature review

Background and Objective

Cachexia-induced skeletal muscle atrophy is a critical manifestation in Kirsten rat sarcoma viral oncogene homologue (KRAS)-mutant pancreatic cancer (PC) patients, predominantly characterized by a shift in metabolic equilibrium towards catabolism that accelerates protein degradation in myofibers and leads to muscle atrophy. This metabolic reprogramming not only supports tumor growth but also precipitates energy depletion in skeletal muscle tissues. Exploring these mechanisms reveals potential therapeutic targets in the metabolic and proteolytic pathways associated with KRAS-mutant PC.

Methods

A comprehensive search for literature was conducted in PubMed, Web of Science, Google Scholar and other search engines up to May 21st, 2024. Studies on PC models and patients were included.

Key Content and Findings

The crosstalk between KRAS-mutant PC and skeletal muscle atrophy can be categorized into four principal domains: (I) KRAS-driven metabolic reprogramming in cancer cells leads to the depletion of muscle energy reserves, thereby influencing the reallocation of myofiber energy towards fueling cancer cell; (II) KRAS-mutant cancer cells rely on nutrient-scavenging pathways, resulting in altered cytokine profiles, increased ubiquitin mRNA expression and autophagy-lysosome pathway, which facilitate myotube degradation and inhibit muscle regeneration, thereby disrupting muscular homeostasis and causing a one-way nutrient flux; (III) tumor-induced oxidative stress inflicts damage on myotubes, highlighting the detrimental effects of reactive oxygen species on muscle structure; (IV) KRAS-mutant cancer cells remodulate immune cell dynamics within the tumor environment, thereby reshaping host immunity. Together, these findings illuminate the intricate interplay between KRAS-mutant PC and skeletal muscle atrophy, mapping the pathophysiological framework that is crucial for understanding sarcopenia and related disorders.

Conclusions

This comprehensive analysis advances our understanding of the complex etiology of cancer cachexia and stimulates the development of targeted therapeutic strategies.

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.

The Past, Present, and Future of Biomarkers for the Early Diagnosis of Pancreatic Cancer

Pancreatic cancer is one of the most aggressive cancers with a very poor 5-year survival rate and reduced therapeutic options when diagnosed in an advanced stage. The dismal prognosis of pancreatic cancer has guided significant efforts to discover novel biomarkers in order to anticipate diagnosis, increasing the population of patients who can benefit from curative surgical treatment. CA 19-9 is the reference biomarker that supports the diagnosis and guides the response to treatments. However, it has significant limitations, a low specificity, and is inefficient as a screening tool. Several potential biomarkers have been discovered in the serum, urine, feces, and pancreatic juice of patients. However, most of this evidence needs further validation in larger cohorts. The advent of advanced omics sciences and liquid biopsy techniques has further enhanced this field of research. The aim of this review is to analyze the historical evolution of the research on novel biomarkers for the early diagnosis of pancreatic cancer, focusing on the current evidence for the most promising biomarkers from different body fluids and the novel trends in research, such as omics sciences and liquid biopsy, in order to favor the application of modern personalized medicine.

AZGP1 Aggravates Macrophage M1 Polarization and Pyroptosis in Periodontitis

Periodontal tissue destruction in periodontitis is a consequence of the host inflammatory response to periodontal pathogens, which could be aggravated in the presence of type 2 diabetes mellitus (T2DM). Accumulating evidence highlights the intricate involvement of macrophage-mediated inflammation in the pathogenesis of periodontitis under both normal and T2DM conditions. However, the underlying mechanism remains elusive. Alpha-2-glycoprotein 1 (AZGP1), a glycoprotein featuring an MHC-I domain, has been implicated in both inflammation and metabolic disorders. In this study, we found that AZGP1 was primarily colocalized with macrophages in periodontitis tissues. AZGP1 was increased in periodontitis compared with controls, which was further elevated when accompanied by T2DM. Adeno-associated virus-mediated overexpression of Azgp1 in the periodontium significantly enhanced periodontal inflammation and alveolar bone loss, accompanied by elevated M1 macrophages and pyroptosis in murine models of periodontitis and T2DM-associated periodontitis, while Azgp1-/- mice exhibited opposite effects. In primary bone marrow-derived macrophages stimulated by lipopolysaccharide (LPS) or LPS and palmitic acid (PA), overexpression or knockout of Azgp1 markedly upregulated or suppressed, respectively, the expression of macrophage M1 markers and key components of the NLR Family Pyrin Domain Containing 3 (NLRP3)/caspase-1 signaling. Moreover, conditioned medium from Azgp1-overexpressed macrophages under LPS or LPS+PA stimulation induced higher inflammatory activation and lower osteogenic differentiation in human periodontal ligament stem cells (hPDLSCs). Furthermore, elevated M1 polarization and pyroptosis in macrophages and associated detrimental effects on hPDLSCs induced by Azgp1 overexpression could be rescued by NLRP3 or caspase-1 inhibition. Collectively, our study elucidated that AZGP1 could aggravate periodontitis by promoting macrophage M1 polarization and pyroptosis through the NLRP3/casapse-1 pathway, which was accentuated in T2DM-associated periodontitis. This finding deepens the understanding of AZGP1 in the pathogenesis of periodontitis and suggests AZGP1 as a crucial link mediating the adverse effects of diabetes on periodontal inflammation.

Mass spectrometry–based proteomics technology in pancreatic cancer research

Pancreatic ductal adenocarcinoma (PDAC) has become a significant health concern with increasing incidence and mortality rates over the past few decades. Researchers have turned their attention to cutting-edge mass spectrometry (MS) technology due to its high-throughput and accurate detection capacity, which plays a vital role in understanding the mechanisms and discovering biomarkers for pancreatic diseases. In this review, we comprehensively investigate various methodologies of quantitative and qualitative proteomics MS technologies, alongside bioinformatical platforms employed in pancreatic cancer research. The integration of these optimized approaches provides novel insights into the molecular mechanisms underlying tumorigenesis and disease progression, ultimately facilitating the discovery of potential diagnostic, prognostic biomarkers, and therapeutic targets. The robust MS-based strategy shows promise in paving the way for early diagnosis and personalized medicine for pancreatic cancer patients.

Characterization of the plasma proteome from healthy adult dogs

Introduction

Bloodwork is a widely used diagnostic tool in veterinary medicine, as diagnosis and therapeutic interventions often rely on blood biomarkers. However, biomarkers available in veterinary medicine often lack sensitivity or specificity. Mass spectrometry-based proteomics technology has been extensively used in the analysis of biological fluids. It offers excellent potential for a more comprehensive characterization of the plasma proteome in veterinary medicine.

Methods

In this study, we aimed to identify and quantify plasma proteins in a cohort of healthy dogs and compare two techniques for depleting high-abundance plasma proteins to enable the detection of lower-abundance proteins via label-free quantification liquid chromatography-mass spectrometry. We utilized surplus lithium-heparin plasma from 30 healthy dogs, subdivided into five groups of pooled plasma from 6 randomly selected individuals each. Firstly, we used a commercial kit to deplete high-abundance plasma proteins. Secondly, we employed an in-house method to remove albumin using Blue-Sepharose.

Results and discussion

Among all the samples, some of the most abundant proteins identified were apolipoprotein A and B, albumin, alpha-2-macroglobulin, fibrinogen beta chain, fibronectin, complement C3, serotransferrin, and coagulation factor V. However, neither of the depletion techniques achieved significant depletion of highly abundant proteins. Despite this limitation, we could detect and quantify many clinically relevant proteins. Determining the healthy canine proteome is a crucial first step in establishing a reference proteome for canine plasma. After enrichment, this reference proteome can later be utilized to identify protein markers associated with different diseases, thereby contributing to the diagnosis and prognosis of various pathologies.

Circulating Biomarkers Involved in the Development of and Progression to Chronic Pancreatitis-A Literature Review

Chronic pancreatitis (CP) is the end-stage of continuous inflammation and fibrosis in the pancreas evolving from acute- to recurrent acute-, early, and, finally, end-stage CP. Currently, prevention is the only way to reduce disease burden. In this setting, early detection is of great importance. Due to the anatomy and risks associated with direct sampling from pancreatic tissue, most of our information on the human pancreas arises from circulating biomarkers thought to be involved in pancreatic pathophysiology or injury. The present review provides the status of circulating biomarkers involved in the development of and progression to CP.

Targeted protein quantitation in human body fluids by mass spectrometry

Human body fluids (biofluids) contain various proteins, some of which reflect individuals' physiological conditions or predict diseases. Therefore, the analysis of biofluids can provide substantial information on novel biomarkers for clinical diagnosis and prognosis. In the past decades, mass spectrometry (MS)-based technologies have been developed as proteomic strategies not only for the identification of protein biomarkers but also for biomarker verification/validation in body fluids for clinical applications. The main advantage of targeted MS-based methodologies is the accurate and specific simultaneous quantitation of multiple biomarkers with high sensitivity. Here, we review MS-based methodologies that are currently used for the targeted quantitation of protein components in human body fluids, especially in plasma, urine, cerebrospinal fluid, and saliva. In addition, the currently used MS-based methodologies are summarized with a specific focus on applicable clinical sample types, MS configurations, and acquisition modes.

A niche-mimicking polymer hydrogel-based approach to identify molecular targets for tackling human pancreatic cancer stem cells

BACKGROUND

Pancreatic adenocarcinoma (PAAD) is one of the most fatal human cancers, but effective therapies remain to be established. Cancer stem cells (CSCs) are highly resistant to anti-cancer drugs and a deeper understanding of their microenvironmental niche has been considered important to provide understanding and solutions to cancer eradication. However, as the CSC niche is composed of a wide variety of biological and physicochemical factors, the development of multidisciplinary tools that recapitulate their complex features is indispensable. Synthetic polymers have been studied as attractive biomaterials due to their tunable biofunctionalities, while hydrogelation technique further renders upon them a diversity of physical properties, making them an attractive tool for analysis of the CSC niche.

METHODS

To develop innovative materials that recapitulate the CSC niche in pancreatic cancers, we performed polymer microarray analysis to identify niche-mimicking scaffolds that preferentially supported the growth of CSCs. The niche-mimicking activity of the identified polymers was further optimized by polyethylene glycol (PEG)-based hydrogelation. To reveal the biological mechanisms behind the activity of the optimized hydrogels towards CSCs, proteins binding onto the hydrogel were analyzed by liquid chromatography with tandem mass spectrometry (LC-MS/MS), and the potential therapeutic targets were validated by looking at gene expression and patients' outcome in the TCGA database.

RESULTS

PA531, a heteropolymer composed of 2-methoxyethyl methacrylate (MEMA) and 2-(diethylamino)ethyl methacrylate (DEAEMA) (5.5:4.5) that specifically supports the growth and maintenance of CSCs was identified by polymer microarray screening using the human PAAD cell line KLM1. The polymer PA531 was converted into five hydrogels (PA531-HG1 to HG5) and developed to give an optimized scaffold with the highest CSC niche-mimicking activities. From this polymer that recapitulated CSC binding and control, the proteins fetuin-B and angiotensinogen were identified as candidate target molecules with clinical significance due to the correlation between gene expression levels and prognosis in PAAD patients and the proteins associated with the niche-mimicking polymer.

CONCLUSION

This study screened for biofunctional polymers suitable for recapitulation of the pancreatic CSC niche and one hydrogel with high niche-mimicking abilities was successfully fabricated. Two soluble factors with clinical significance were identified as potential candidates for biomarkers and therapeutic targets in pancreatic cancers. Such a biomaterial-based approach could be a new platform in drug discovery and therapy development against CSCs, via targeting of their niche.

Metabolic Functions of Biliverdin IXβ Reductase in Redox-Regulated Hematopoietic Cell Fate

Cytoprotective heme oxygenases derivatize heme to generate carbon monoxide, ferrous iron, and isomeric biliverdins, followed by rapid NAD(P)H-dependent biliverdin reduction to the antioxidant bilirubin. Recent studies have implicated biliverdin IXβ reductase (BLVRB) in a redox-regulated mechanism of hematopoietic lineage fate restricted to megakaryocyte and erythroid development, a function distinct and non-overlapping from the BLVRA (biliverdin IXα reductase) homologue. In this review, we focus on recent progress in BLVRB biochemistry and genetics, highlighting human, murine, and cell-based studies that position BLVRB-regulated redox function (or ROS accumulation) as a developmentally tuned trigger that governs megakaryocyte/erythroid lineage fate arising from hematopoietic stem cells. BLVRB crystallographic and thermodynamic studies have elucidated critical determinants of substrate utilization, redox coupling and cytoprotection, and have established that inhibitors and substrates bind within the single-Rossmann fold. These advances provide unique opportunities for the development of BLVRB-selective redox inhibitors as novel cellular targets that retain potential for therapeutic applicability in hematopoietic (and other) disorders.

The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection

Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.

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.

Differential Plasma Proteins Identified via iTRAQ-Based Analysis Serve as Diagnostic Markers of Pancreatic Ductal Adenocarcinoma

Objective

We aimed to identify differentially expressed proteins in the plasma of patients with pancreatic cancer and control subjects, which could serve as potential tumor biomarkers.

Methods

Differentially expressed proteins were determined via isostatic labeling and absolute quantification (iTRAQ). Potential protein biomarkers were identified via enzyme-linked immunosorbent assay (ELISA) in 40 patients and 40 control subjects, and those eventually selected were further validated in 40 pancreatic cancer and normal pancreatic tissues.

Results

In total, 30 proteins displayed significant differences in expression among which 21 were downregulated and 9 were upregulated compared with the control group. ELISA revealed downregulation of peroxiredoxin-2 (PRDX2) and upregulation of alpha-1-antitrypsin (AAT), Ras-related protein Rab-2B (RAB2B), insulin-like growth factor-binding protein 2 (IGFBP2), Rho-related GTP-binding protein RhoC (RHOC), and prelamin-A/C (LMNA) proteins in 40 other samples of pancreatic cancer. Notably, only AAT, RAB2B, and IGFBP2 levels were consistent with expression patterns obtained with iTRAQ. Moreover, all three proteins displayed a marked increase in pancreatic cancer tissues. Data from ROC curve analysis indicated that the diagnostic ability of AAT, RAB2B, and IGFBP2 combined with carbohydrate antigen 19-9 (CA19-9) for pancreatic cancer was significantly greater than that of the single indexes (area under the curve (AUC): 90% vs. 75% (CA19-9), 76% (AAT), 71% (RAB2B), and 71% (IGFBP2), all P < 0.01).

Conclusion

AAT, RAB2B, and IGFBP2 could serve as effective biomarkers to facilitate the early diagnosis of pancreatic cancer.

Diagnostic accuracy and added value of blood-based protein biomarkers for pancreatic cancer: A meta-analysis of aggregate and individual participant data

BACKGROUND

Novel blood-based protein biomarkers may be of value for efficient, accurate, and non-invasive diagnosis of pancreatic cancer. This study assesses the diagnostic accuracy of newly recognized blood-based protein biomarkers for detecting pancreatic cancer, and investigates their added value to CA19-9, the common blood-based biomarker in clinical use for pancreatic cancer.

METHODS

PubMed, Embase, Web of Science, and the Wiley/Cochrane Library were systematically searched from inception until June 2022. A meta-analysis of aggregate and individual participant data was conducted using frequentist and Bayesian hierarchical random-effects models. The added clinical utility of protein biomarkers was investigated using bootstrap bias-corrected decision curve analyses.

FINDINGS

Aggregate data from 28 primary studies (6127 participants) were included, of which 8 studies (1790 participants) provided individual participant data. CA19-9 was significantly more accurate than MIC-1 for distinguishing pancreatic cancer from benign disease (AUC, 0.83 vs 0.74; relative diagnostic odds ratio [rDOR], 2.10 [95% CI, 0.98-4.48]; p = 0.002), THBS2 (AUC, 0.87 vs 0.69; rDOR, 4.53 [2.16-9.39]; p < 0.0001), TIMP-1 (AUC, 0.91 vs 0.70; rDOR, 8.00 [3.81-16.9]; p < 0.0001), OPN (AUC, 0.89 vs 0.74; rDOR, 4.22 [1.13-15.6]; p < 0.0001), ICAM-1 (AUC, 0.91 vs 0.68; rDOR 9.30 [0.87-99.5]; p < 0.0001), and IGFBP2 (AUC, 0.91 vs 0.68; rDOR, 4.48 [0.78-24.3]; p < 0.0001). The addition of these novel protein biomarkers to CA19-9 did not significantly improve the AUC, and resulted in minor increases or limited decreases in clinical utility.

INTERPRETATION

Novel protein biomarkers have moderate diagnostic accuracy, do not outperform CA19-9 in differentiating pancreatic cancer from benign disease, and show limited added clinical value to CA19-9. We propose recommendations to aid the development of minimally invasive diagnostic tests with sufficient clinical utility to improve the management of patients with suspected pancreatic cancer.

FUNDING

Bennink Foundation, Dutch Cancer Foundation (KWF Kankerbestrijding), and AIRC.

Role of circulating exosomal biomarkers and their diagnostic accuracy in pancreatic cancer

Background and Aim

New biomarkers have the potential to facilitate early diagnosis of pancreatic cancer (PC). Circulating exosomes are cell-derived protein complexes containing RNA that can be used as indicators of cancer development. The aim of this review is to evaluate the current literature involving PC patient groups for highly accurate exosomal biomarkers.

Methods

The literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eight-hundred and seventy-five studies were identified across various databases (Ovid MEDLINE, Embase, and Cochrane) published between 2009 and 2020. Nine studies fulfilled the inclusion criteria: human PC patients, diagnosis as outcome of interest, serum biomarker of exosomal content, reporting of diagnostic values, and disease progress. Area under the curve (AUC) of the exosomal biomarker was compared against that of CA19-9.

Results

Nine papers were reviewed for relevant outcomes based on the inclusion criteria. These studies involved 565 participants (331 PC, 234 controls; male/female ratio 1.21; mean age 64.1). Tumor staging was reported in all studies, with 45.6% of PC patients diagnosed with early-stage PC (T1-2). The mRNA panel (ARG1, CD63, CK18, Erbb3, GAPDH, H3F3A, KRAS, ODC1) and GPC 1 reported the highest performing sensitivity and specificity at 100% each. The microRNA panel (miR-10b, miR-21, miR-30c, miR-181a, and miR-let7a), mRNA panel (ARG1, CD63, CK18, Erbb3, GAPDH, H3F3A, KRAS, ODC1), and GPC 1 showed a perfect AUC of 1.0. Five studies compared the AUC of the exosomal biomarker against CA19-9, each being superior to that of CA19-9.

Conclusion

The potential of exosomal biomarkers remains promising in PC diagnosis. Standardization of future studies will allow for larger comparative analyses and overcoming contrasting findings.

RNA-sequencing for transcriptional profiling of whole blood in early stage and metastatic pancreatic cancer patients

We investigated the transcriptional profile of whole blood in early and metastatic stages of pancreatic cancer (PaC) patients to identify potential diagnostic factors for early diagnosis. Blood samples from 18 participants (6 healthy individuals, 6 patients in early stage (I/II) PaC, and 6 patients in metastatic PaC) were analyzed by RNA-sequencing. The expression levels of identified genes were subsequently compared with their expression in pancreatic tumor tissues based on TCGA data reported in UALCAN and GEPIA2 databases. Overall, 331 and 724 genes were identified as differentially expressed genes in early and metastatic stages, respectively. Of these, 146 genes were shared by early and metastatic stages. Upregulation of PTCD3 and UBA52 genes and downregulation of A2M and ARID1B genes in PaC patients were observed from early stage to metastasis. TCGA database showed increasing trend in expression levels of these genes from stage I to IV in pancreatic tumor tissue. Finally, we found that low expression of PTCD3, A2M, and ARID1B genes and high expression of UBA52 gene were positively correlated with PaC patients survival. We identified a four-gene set (PTCD3, UBA52, A2M, and ARID1B) expressed in peripheral blood of early stage and metastatic PaC patients that may be useful for PaC early diagnosis.

Proteomic analysis of serum proteins in children with brain death

BACKGROUND

Brain death (BD) is a catastrophic physiological outcome that can occur in individuals with terminal illness and can adversely affect the graft quality after donation of their organs. As BD has no specific symptoms, it can be difficult to diagnose in a timely manner. The present study was designed to investigate the serum protein expression profiles of children affected by BD in an effort to define diagnostic biomarkers for this condition.

METHODS

Blood samples were collected from 8 patients with BD and 8 healthy controls during the same time period. Tandem mass tags and mass spectrometry were used to conduct a proteomic analysis of serum extracted from the samples. The potential regulatory roles of the top 5 upregulated and downregulated proteins identified through the analysis were then explored using bioinformatics analyses and a review of the related literature.

RESULTS

The top 5 upregulated proteins in the serum samples from patients with BD were lipopolysaccharide-binding protein (LBP), α1-acid glycoprotein (α1-AGP), α1-antichymotrypsin (α1-ACT), leucine-rich α1-glycoprotein (LRG1), and lactate dehydrogenase B heavy chain (LDHB), and the 5 most downregulated proteins in these samples were actin-binding protein 2 (transgelin-2), platelet basic protein (PBP), tropomyosin α4 chain (TPM4), tropomyosin α3 chain (TPM3), and peptidase inhibitor 16 (PI16). Literature searches indicated that several of the identified proteins influence the pathogeneses of various diseases, with LBP, α1-AGP, α1-ACT, LRG1, transgelin-2, and PBP all being related to inflammatory activity.

CONCLUSIONS

Through a proteomics-based analysis, several differentially expressed proteins were identified in patients with BD relative to healthy controls. Most of these proteins are associated with inflammatory responses that have the potential to persist after the occurrence of BD. Further clinical work is needed to clarify the functional roles of the identified proteins.

HBFP: a new repository for human body fluid proteome

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL:https://bmbl.bmi.osumc.edu/HBFP/

Biomarkers in the diagnosis of pancreatic cancer: Are we closer to finding the golden ticket?

Pancreatic cancer (PC) is a leading cause of cancer related mortality on a global scale. The disease itself is associated with a dismal prognosis, partly due to its silent nature resulting in patients presenting with advanced disease at the time of diagnosis. To combat this, there has been an explosion in the last decade of potential candidate biomarkers in the research setting in the hope that a diagnostic biomarker may provide a glimmer of hope in what is otherwise quite a substantial clinical dilemma. Currently, serum carbohydrate antigen 19-9 is utilized in the diagnostic work-up of patients diagnosed with PC however this biomarker lacks the sensitivity and specificity associated with a gold-standard marker. In the search for a biomarker that is both sensitive and specific for the diagnosis of PC, there has been a paradigm shift towards a focus on liquid biopsy and the use of diagnostic panels which has subsequently proved to have efficacy in the diagnosis of PC. Currently, promising developments in the field of early detection on PC using diagnostic biomarkers include the detection of microRNA (miRNA) in serum and circulating tumour cells. Both these modalities, although in their infancy and yet to be widely accepted into routine clinical practice, possess merit in the early detection of PC. We reviewed over 300 biomarkers with the aim to provide an in-depth summary of the current state-of-play regarding diagnostic biomarkers in PC (serum, urinary, salivary, faecal, pancreatic juice and biliary fluid).

Extracellular Vesicle Analysis Allows for Identification of Invasive IPMN

BACKGROUND AND AIMS

Advances in cross-sectional imaging have resulted in increased detection of intraductal papillary mucinous neoplasms (IPMNs), and their management remains controversial. At present, there is no reliable noninvasive method to distinguish between indolent and high risk IPMNs. We performed extracellular vesicle (EV) analysis to identify markers of malignancy in an attempt to better stratify these lesions.

METHODS

Using a novel ultrasensitive digital extracellular vesicle screening technique (DEST), we measured putative biomarkers of malignancy (MUC1, MUC2, MUC4, MUC5AC, MUC6, Das-1, STMN1, TSP1, TSP2, EGFR, EpCAM, GPC1, WNT-2, EphA2, S100A4, PSCA, MUC13, ZEB1, PLEC1, HOOK1, PTPN6, and FBN1) in EV from patient-derived cell lines and then on circulating EV obtained from peripheral blood drawn from patients with IPMNs. We enrolled a total of 133 patients in two separate cohorts: a clinical discovery cohort (n = 86) and a validation cohort (n = 47).

RESULTS

From 16 validated EV proteins in plasma samples collected from the discovery cohort, only MUC5AC showed significantly higher levels in high-grade lesions. Of the 11 patients with invasive IPMN (inv/HG), 9 had high MUC5AC expression in plasma EV of the 11 patients with high-grade dysplasia alone, only 1 had high MUC5AC expression (sensitivity of 82%, specificity of 100%). These findings were corroborated in a separate validation cohort. The addition of MUC5AC as a biomarker to imaging and high-riskstigmata allowed detection of all cases requiring surgery, whereas imaging and high-risk stigmata alone would have missed 5 of 14 cases (36%).

CONCLUSIONS

MUC5AC in circulating EV can predict the presence of invasive carcinoma within IPMN. This approach has the potential to improve the management and follow-up of patients with IPMN including avoiding unnecessary surgery.

Biomarkers of Chronic Pancreatitis: A systematic literature review

BACKGROUND

Chronic pancreatitis (CP) does not have diagnostic or prognostic biomarkers. CP is the end stage of a progressive inflammatory syndrome that is diagnosed at late stages by morphologic features. To diagnose earlier stages of the disease, a new mechanistic definition was established based on identifying underlying pathogenic processes and biomarker evidence of disease activity and stage. Although multiple risk factors are known, the corresponding biomarkers needed to make a highly accurate diagnosis of earlier disease stages have not been established. The goal of this study is to systematically analyze the literature to identify the most likely candidates for development into biomarkers of CP.

METHODS

We conducted a systematic review of candidate analytes from easily accessible biological fluids and identified 67 studies that compared CP to nonpancreatic-disease controls. We then ranked candidate biomarkers for sensitivity and specificity by area under the receiver operator curves (AUROCs).

RESULTS

Five biomarkers had a large effect size (an AUROC > 0.96), whereas 30 biomarkers had a moderate effect size (an AUROC between 0.96 and 0.83) for distinguishing CP cases from controls or other diseases. However, the studies reviewed had marked variability in design, enrollment criteria, and biospecimen sample handling and collection.

CONCLUSIONS

Several biomarkers have the potential for evaluation in prospective cohort studies and should be correlated with risk factors, clinical features, imaging studies and outcomes. The Consortium for the Study of Chronic Pancreatitis, Diabetes and Pancreas Cancer provides recommendations for avoiding design biases and heterogeneity in sample collection and handling in future studies.

Human body-fluid proteome: quantitative profiling and computational prediction

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

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.

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

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.

Bacterial DNA induces the formation of heat-resistant disease-associated proteins in human plasma

Our study demonstrated for the first time that bacterial extracellular DNA (eDNA) can change the thermal behavior of specific human plasma proteins, leading to an elevation of the heat-resistant protein fraction, as well as to de novo acquisition of heat-resistance. In fact, the majority of these proteins were not known to be heat-resistant nor do they possess any prion-like domain. Proteins found to become heat-resistant following DNA exposure were named "Tetz-proteins". Interestingly, plasma proteins that become heat-resistant following treatment with bacterial eDNA are known to be associated with cancer. In pancreatic cancer, the proportion of proteins exhibiting eDNA-induced changes in thermal behavior was found to be particularly elevated. Therefore, we analyzed the heat-resistant proteome in the plasma of healthy subjects and in patients with pancreatic cancer and found that exposure to bacterial eDNA made the proteome of healthy subjects more similar to that of cancer patients. These findings open a discussion on the possible novel role of eDNA in disease development following its interaction with specific proteins, including those involved in multifactorial diseases such as cancer.

Omics Approaches in Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma, which represents 80% of pancreatic cancers, is mainly diagnosed when treatment with curative intent is not possible. Consequently, the overall five-year survival rate is extremely dismal—around 5% to 7%. In addition, pancreatic cancer is expected to become the second leading cause of cancer-related death by 2030. Therefore, advances in screening, prevention and treatment are urgently needed. Fortunately, a wide range of approaches could help shed light in this area. Beyond the use of cytological or histological samples focusing in diagnosis, a plethora of new approaches are currently being used for a deeper characterization of pancreatic ductal adenocarcinoma, including genetic, epigenetic, and/or proteo-transcriptomic techniques. Accordingly, the development of new analytical technologies using body fluids (blood, bile, urine, etc.) to analyze tumor derived molecules has become a priority in pancreatic ductal adenocarcinoma due to the hard accessibility to tumor samples. These types of technologies will lead us to improve the outcome of pancreatic ductal adenocarcinoma patients.

Predictive proteomic signatures for response of pancreatic cancer patients receiving chemotherapy

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer that is characterized by its poor prognosis, rapid progression and development of drug resistance. Chemotherapy is a vital treatment option for most of PDAC patients. Stratification of PDAC patients, who would have a higher likelihood of responding to chemotherapy, could facilitate treatment selection and patient management.

METHODS

A quantitative proteomic study was performed to characterize the protein profiles in the plasma of PDAC patients undergoing chemotherapy to determine if specific biomarkers could be used to predict likelihood of therapeutic response.

RESULTS

By comparing the plasma proteome of the PDAC patients with positive therapeutic response and longer overall survival (Good-responders) to those who did not respond as well with shorter survival time (Limited-responders), we identified differential proteins and protein variants that could effectively segregate Good-responders from Limited-responders. Functional clustering and pathway analysis further suggested that many of these differential proteins were involved in pancreatic tumorigenesis. Four proteins, including vitamin-K dependent protein Z (PZ), sex hormone-binding globulin (SHBG), von Willebrand factor (VWF) and zinc-alpha-2-glycoprotein (AZGP1), were further evaluated as single or composite predictive biomarker with/without inclusion of CA 19-9. A composite biomarker panel that consists of PZ, SHBG, VWF and CA 19-9 demonstrated the best performance in distinguishing Good-responders from Limited-responders.

CONCLUSION

Based on the cohort investigated, our data suggested that systemic proteome alterations involved in pathways associated with inflammation, immunoresponse, coagulation and complement cascades may be reporters of chemo-treatment outcome in PDAC patients. For the majority of the patients involved, the panel consisting of PZ, SHBG, VWF and CA 19-9 was able to segregate Good-responders from Limited-responders effectively. Our data also showed that dramatic fluctuations of biomarker concentration in the circulating system of a PDAC patient, which might result from biological heterogeneity or confounding complications, could diminish the performance of a biomarker. Categorization of PDAC patients in terms of their tumor stages and histological types could potentially facilitate patient stratification for treatment.

Unraveling altered RNA metabolism in pancreatic cancer cells by liquid-chromatography coupling to ion mobility mass spectrometry

Ion mobility coupling to mass spectrometry facilitates enhanced identification certitude. Further coupling to liquid chromatography results in multi-dimensional analytical methods, especially suitable for complex matrices with structurally similar compounds. Modified nucleosides represent a large group of very similar members linked to aberrant proliferation. Besides basal production under physiological conditions, they are increasingly excreted by transformed cells and subsequently discussed as putative biomarkers for various cancer types. Here, we report a method for modified nucleosides covering 37 species. We determined collisional cross-sections with high reproducibility from pure analytical standards. For sample purification, we applied an optimized phenylboronic acid solid-phase extraction on media obtained from four different pancreatic cancer cell lines. Our analysis could discriminate different subtypes of pancreatic cancer cell lines. Importantly, they could clearly be separated from a pancreatic control cell line as well as blank medium. m1A, m27G, and Asm were the most important features discriminating cancer cell lines derived from well-differentiated and poorly differentiated cancers. Eventually, we suggest the analytical method reported here for future tumor-marker identification studies. Graphical abstract.

Soluble stroma-related biomarkers of pancreatic cancer

The clinical management of pancreatic ductal adenocarcinoma (PDAC) is hampered by the lack of reliable biomarkers. This study investigated the value of soluble stroma‐related molecules as PDAC biomarkers. In the first exploratory phase, 12 out of 38 molecules were associated with PDAC in a cohort of 25 PDAC patients and 16 healthy subjects. A second confirmatory phase on an independent cohort of 131 PDAC patients, 30 chronic pancreatitis patients, and 131 healthy subjects confirmed the PDAC association for MMP7, CCN2, IGFBP2, TSP2, sICAM1, TIMP1, and PLG. Multivariable logistic regression model identified biomarker panels discriminating respectively PDAC versus healthy subjects (MMP7 + CA19.9, AUC = 0.99, 99% CI = 0.98–1.00) (CCN2 + CA19.9, AUC = 0.96, 99% CI = 0.92–0.99) and PDAC versus chronic pancreatitis (CCN2 + PLG+FN+Col4 + CA19.9, AUC = 0.94, 99% CI = 0.88–0.99). Five molecules were associated with PanIN development in two GEM models of PDAC (PdxCre/LSL‐Kras^G12D and PdxCre/LSL‐Kras^G12D/+/LSL‐Trp53^R172H/+), suggesting their potential for detecting early disease. These markers were also elevated in patient‐derived orthotopic PDAC xenografts and associated with response to chemotherapy. The identified stroma‐related soluble biomarkers represent potential tools for PDAC diagnosis and for monitoring treatment response of PDAC patients.

Disease-specific protein corona sensor arrays may have disease detection capacity

Protein corona sensor array technology identifies diseases through specific proteomics pattern recognition.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

Quiescin sulfhydryl oxidase 1 (QSOX1) glycosite mutation perturbs secretion but not Golgi localization

Quiescin sulfhydryl oxidase 1 (QSOX1) catalyzes the formation of disulfide bonds in protein substrates. Unlike other enzymes with related activities, which are commonly found in the endoplasmic reticulum, QSOX1 is localized to the Golgi apparatus or secreted. QSOX1 is upregulated in quiescent fibroblast cells and secreted into the extracellular environment, where it contributes to extracellular matrix assembly. QSOX1 is also upregulated in adenocarcinomas, though the extent to which it is secreted in this context is currently unknown. To achieve a better understanding of factors that dictate QSOX1 localization and function, we aimed to determine how post-translational modifications affect QSOX1 trafficking and activity. We found a highly conserved N-linked glycosylation site to be required for QSOX1 secretion from fibroblasts and other cell types. Notably, QSOX1 lacking a glycan at this site arrives at the Golgi, suggesting that it passes endoplasmic reticulum quality control but is not further transported to the cell surface for secretion. The QSOX1 transmembrane segment is dispensable for Golgi localization and secretion, as fully luminal and transmembrane variants displayed the same trafficking behavior. This study provides a key example of the effect of glycosylation on Golgi exit and contributes to an understanding of late secretory sorting and quality control.

Exact association test for small size sequencing data

Background

Recent statistical methods for next generation sequencing (NGS) data have been successfully applied to identifying rare genetic variants associated with certain diseases. However, most commonly used methods (e.g., burden tests and variance-component tests) rely on large sample sizes. Notwithstanding, due to its-still high cost, NGS data is generally restricted to small sample sizes, that cannot be analyzed by most existing methods.

Methods

In this work, we propose a new exact association test for sequencing data that does not require a large sample approximation, which is applicable to both common and rare variants. Our method, based on the Generalized Cochran-Mantel-Haenszel (GCMH) statistic, was applied to NGS datasets from intraductal papillary mucinous neoplasm (IPMN) patients. IPMN is a unique pancreatic cancer subtype that can turn into an invasive and hard-to-treat metastatic disease.

Results

Application of our method to IPMN data successfully identified susceptible genes associated with progression of IPMN to pancreatic cancer.

Conclusions

Our method is expected to identify disease-associated genetic variants more successfully, and corresponding signal pathways, improving our understanding of specific disease’s etiology and prognosis.

Circulating biomarkers for early diagnosis of pancreatic cancer: facts and hopes

Pancreatic cancer (PC) is characterized by extremely high mortality and poor prognosis, which are largely ascribed to difficulties in early diagnosis and limited therapeutics. Although there is a sufficient window for intervention before preneoplastic lesions progress to invasive disease, effective early detection of PC remains difficult using current biomarkers and imaging techniques. Biomarkers with satisfactory diagnostic efficacy and convenient analysis methods are urgently required. In this review, we summarized recent advances in the identification of biomarkers in circulation for early detection of PC. A number of novel circulating biomarkers, such as metabolites, cell-free DNA (cfDNA), noncoding RNA, and exosomes, that show promising diagnostic value have been discovered using advances in sequencing techniques and "omics" analyses. Panels comprising several biomarkers may also exhibit better diagnostic performance. In the future, we need more efficient circulating biomarkers for the identification of noninvasive precursor lesions and early disease. Collaborative large-scale studies are also required to show the clinical validity and applicability of potential biomarkers.

Elevated systemic levels of the matrix metalloproteinase inhibitor TIMP-1 correlate with clinical markers of cachexia in patients with chronic pancreatitis and pancreatic cancer

BACKGROUND

Tissue inhibitor of metalloproteinases-1 (TIMP-1) is a candidate diagnostic and prognostic biomarker for pancreatic ductal adenocarcinoma (PDAC). Here, we determined the possible association of systemic TIMP-1 levels with cachexia and jaundice, two common PDAC-associated conditions.

METHODS

Plasma TIMP-1 was measured by ELISA in patients diagnosed with PDAC (n = 36) and chronic pancreatitis (CP) (n = 25). Patients without pancreatic pathologies and known malignancies of other origin served as controls (n = 13). TIMP-1 levels in these patients were tested for asscociation with jaundice and chachexia, and furthermore correlated with cachexia-related clinical parameters such as weight loss and ferritin, parameters of lung function, hemoglobin and liver synthesis parameters.

RESULTS

TIMP-1 plasma levels were mostly higher in CP and PDAC patients with concomitant jaundice or cachexia. Elevated plasma TIMP-1 levels were also associated with clinical cachexia markers, including absolute and relative values of weight loss and lung function, as well as ferritin, hemoglobin, and cholinesterase levels. TIMP-1 levels significantly correlated with cachexia only in patients without jaundice. Jaundice also impaired the use of TIMP-1 as a prognostic marker in cancer patients. Relating to cachexia status alone, a slightly improved association of TIMP-1 levels with survival of PDAC patients was observed.

CONCLUSION

This retrospective study reports for the first time that plasma levels of TIMP-1 are associated with pancreatic lesion-induced cachexia in patients without jaundice. TIMP-1 is counterindicated as a survival marker in patients with jaundice.

Pancreatic Cancer Diagnosis and Management: Has the Time Come to Prick the Bubble?

Pancreatic cancer (PC) is associated with poor prognosis and very dismal survival rates. The most effective possibility of cure is tumor resection, which is only possible in about 15% of patients diagnosed at early stages of disease progression. Recent whole-genome sequencing studies pointed genetic alterations in 12 core signaling pathways in PC. These observations hint at the possibility that the initial mutation in PC might appear nearly 20 years before any symptoms occur, suggesting that a large window of opportunity may exist for early detection. Biomarkers with the potential to identify pre-neoplastic disease or very early stages of cancer are of great promise to improve patient survival. The concept of liquid biopsy refers to a minimally invasive sampling and analysis of liquid biomarkers that can be isolated from body fluids, primarily blood, urine and saliva. A myriad of circulating molecules may be useful as tumor markers, including cell-free DNA (cfDNA), cell-free RNA (cfRNA), circulating tumor cells (CTC), circulating tumor proteins, and extracellular vesicles, more specifically exosomes. In this review, we discuss with more detail the potential role of exosomes in several aspects related to PC, from initiation to tumor progression and its applicability in early detection and treatment. Exosomes are small circulating extracellular vesicles of 50-150 nm in diameter released from the plasma membrane by almost all cells and exhibit some advantages over other biomarkers. Exosomes are central players of intercellular communication and they have been implicated in a series of biological process, including tumorigenesis, migration and metastasis. Several exosomal microRNAs and proteins have been observed to distinguish PC from benign pancreatic diseases and healthy controls. Besides their possible role in diagnosis, understanding exosomes functions in cancer has clarified the importance of microenvironment in PC progression as well as its influence in proliferation, metastasis and resistance to chemotherapy. Increasing knowledge on cancer exosomes provides valuable insights on new therapeutic targets and can potentially open new strategies to treat this disease. Continuous research is needed to ascertain the reliability of using exosomes and their content as potential biomarkers, so that, hopefully, in the near future, they will provide the opportunity for early diagnosis, treatment intervention and increase survival of PC patients.

Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel

Due to its high mortality rate and asymptomatic nature, early detection rates of pancreatic ductal adenocarcinoma (PDAC) remain poor. We measured 1000 biomarker candidates in 134 clinical plasma samples by multiple reaction monitoring-mass spectrometry (MRM-MS). Differentially abundant proteins were assembled into a multimarker panel from a training set (n=684) and validated in independent set (n=318) from five centers. The level of panel proteins was also confirmed by immunoassays. The panel including leucine-rich alpha-2 glycoprotein (LRG1), transthyretin (TTR), and CA19-9 had a sensitivity of 82.5% and a specificity of 92.1%. The triple-marker panel exceeded the diagnostic performance of CA19-9 by more than 10% (AUC_CA19-9 = 0.826, AUC_panel= 0.931, P < 0.01) in all PDAC samples and by more than 30% (AUC_CA19-9 = 0.520, AUC_panel = 0.830, P < 0.001) in patients with normal range of CA19-9 (<37U/mL). Further, it differentiated PDAC from benign pancreatic disease (AUC_CA19-9 = 0.812, AUC_panel = 0.892, P < 0.01) and other cancers (AUC_CA19-9 = 0.796, AUC_panel = 0.899, P < 0.001). Overall, the multimarker panel that we have developed and validated in large-scale samples by MRM-MS and immunoassay has clinical applicability in the early detection of PDAC.

Revisiting biomarker discovery by plasma proteomics

Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)-based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous "triangular strategies" aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a "rectangular" plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision-making, and we discuss some first steps in this direction.

iTRAQ based investigation of plasma proteins in HIV infected and HIV/HBV coinfected patients - C9 and KLK are related to HIV/HBV coinfection

OBJECTIVES

Human immunodeficiency virus (HIV) and hepatitis B virus (HBV) share similar routes of transmission, and rapid progression of hepatic and immunodeficiency diseases has been observed in coinfected individuals. Our main objective was to investigate the molecular mechanism of HIV/HBV coinfections.

METHODS

We selected HIV infected and HIV/HBV coinfected patients with and without Highly Active Antiretroviral Therapy (HAART). Low abundance proteins enriched using a multiple affinity removal system (MARS) were labeled with isobaric tags for relative and absolute quantitation (iTRAQ) kits and analyzed using liquid chromatography-mass spectrometry (LC-MS). The differential proteins were analyzed by Gene Ontology (GO) database.

RESULTS

A total of 41 differential proteins were found in HIV/HBV coinfected patients as compared to HIV mono-infected patients with or without HAART treatment, including 7 common HBV-regulated proteins. The proteins involved in complement and coagulation pathways were significantly enriched, including plasma kallikrein (KLK) and complement component C9 (C9). C9 and KLK were verified to be down-regulated in HIV/HBV coinfected patients through ELISA analysis.

CONCLUSION

The present iTRAQ based proteomic analyses identified 7 proteins that are related to HIV/HBV coinfection. HBV might influence hepatic and immune functions by deregulating complement and coagulation pathways. C9 and KLK could potentially be used as targets for the treatment of HIV/HBV coinfections.

Silencing IGFBP-2 decreases pancreatic cancer metastasis and enhances chemotherapeutic sensitivity

Pancreatic cancer has remained one of the most devastating and lethal malignancies characterized by local invasion, distant metastasis and a high degree of chemoresistance. Insulin-like growth factor binding protein 2 (IGFBP-2) is a member of the IGFBP family of proteins, and it is highly expressed in pancreatic cancer patients’ serum and tumor tissues. IGFBP-2 also mediates tumor cell growth, invasion and resistance, while the mechanisms remain unclear. In this study, we sought to determine the impact of IGFBP-2 expression on pancreatic cancer tumorigenesis and metastasis in vitro and in vivo. Wound healing, migration and invasion assays revealed that knockdown of IGFBP-2 inhibits cancer cell migration and invasion. Downregulation of IGFBP-2 attenuates EMT via increasing the E-cadherin and reducing the vimentin and N-cadherin. PTCH-1 is found contribute to the function of IGFBP-2 in suppressing metastasis and EMT of pancreatic cancer. Silencing IGFBP-2 inhibited invasion and metastatic properties, partially through inhibiting PTCH1 in pancreatic cancer. Additionally, inhibition of IGFBP-2 enhanced the sensitivity of pancreatic cancer cells to gemcitabine, suppressed tumor growth and potentiated the anti-tumor effect of gemcitabine in the orthotopic tumor model. Our results provide novel insight of IGFBP-2 as a promising target to inhibit the metastasis and overcome the chemoresistance in pancreatic cancer.

Key biological processes driving metastatic spread of pancreatic cancer as identified by multi-omics studies

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive malignancy, characterized by a high metastatic burden, already at the time of diagnosis. The metastatic potential of PDAC is one of the main reasons for the poor outcome next to lack of significant improvement in effective treatments in the last decade. Key mutated driver genes, such as activating KRAS mutations, are concordantly expressed in primary and metastatic tumors. However, the biology behind the metastatic potential of PDAC is not fully understood. Recently, large-scale omic approaches have revealed new mechanisms by which PDAC cells gain their metastatic potency. In particular, genomic studies have shown that multiple heterogeneous subclones reside in the primary tumor with different metastatic potential. The development of metastases may be correlated to a more mesenchymal transcriptomic subtype. However, for cancer cells to survive in a distant organ, metastatic sites need to be modulated into pre-metastatic niches. Proteomic studies identified the influence of exosomes on the Kuppfer cells in the liver, which could function to prepare this tissue for metastatic colonization. Phosphoproteomics adds an extra layer to the established omic techniques by unravelling key functional signaling. Future studies integrating results from these large-scale omic approaches will hopefully improve PDAC prognosis through identification of new therapeutic targets and patient selection tools. In this article, we will review the current knowledge on the biology of PDAC metastasis unravelled by large scale multi-omic approaches.

Comparative proteomic profiling of the serum differentiates pancreatic cancer from chronic pancreatitis

Finland ranks sixth among the countries having highest incidence rate of pancreatic cancer with mortality roughly equaling incidence. The average age of diagnosis for pancreatic cancer is 69 years in Nordic males, whereas the average age of diagnosis of chronic pancreatitis is 40–50 years, however, many cases overlap in age. By radiology, the evaluation of a pancreatic mass, that is, the differential diagnosis between chronic pancreatitis and pancreatic cancer is often difficult. Preoperative needle biopsies are difficult to obtain and are demanding to interpret. New blood based biomarkers are needed. The accuracy of the only established biomarker for pancreatic cancer, CA 19‐9 is rather poor in differentiating between benign and malignant mass of the pancreas. In this study, we have performed mass spectrometry analysis (High Definition MS^E) of serum samples from patients with chronic pancreatitis (13) and pancreatic cancer (22). We have quantified 291 proteins and performed detailed statistical analysis such as principal component analysis, orthogonal partial least square discriminant analysis and receiver operating curve analysis. The proteomic signature of chronic pancreatitis versus pancreatic cancer samples was able to separate the two groups by multiple statistical techniques. Some of the enriched pathways in the proteomic dataset were LXR/RXR activation, complement and coagulation systems and inflammatory response. We propose that multiple high‐confidence biomarker candidates in our pilot study including Inter‐alpha‐trypsin inhibitor heavy chain H2 (Area under the curve, AUC: 0.947), protein AMBP (AUC: 0.951) and prothrombin (AUC: 0.917), which should be further evaluated in larger patient series as potential new biomarkers for differential diagnosis.

Laser Capture Microdissection of Pancreatic Acinar Cells to Identify Proteomic Alterations in a Murine Model of Caerulein-Induced Pancreatitis

Objectives:

Chronic pancreatitis (CP) is characterized by inflammation and fibrosis of the pancreas, leading to pain, parenchymal damage, and loss of exocrine and endocrine function. There are currently no curative therapies; diagnosis remains difficult and aspects of pathogenesis remain unclear. Thus, there is a need to identify novel biomarkers to improve diagnosis and understand pathophysiology. We hypothesize that pancreatic acinar regions contain proteomic signatures relevant to disease processes, including secreted proteins that could be detected in biofluids.

Methods:

Acini from pancreata of mice injected with or without caerulein were collected using laser capture microdissection followed by mass spectrometry analysis. This protocol enabled high-throughput analysis that captured altered protein expression throughout the stages of CP.

Results:

Over 2,900 proteins were identified, whereas 331 were significantly changed ≥2-fold by mass spectrometry spectral count analysis. Consistent with pathogenesis, we observed increases in proteins related to fibrosis (e.g., collagen, P<0.001), several proteases (e.g., trypsin 1, P<0.001), and altered expression of proteins associated with diminished pancreas function (e.g., lipase, amylase, P<0.05). In comparison with proteomic data from a public data set of CP patients, a significant correlation was observed between proteomic changes in tissue from both the caerulein model and CP patients (r=0.725, P<0.001).

CONCLUSIONS:

This study illustrates the ability to characterize proteome changes of acinar cells isolated from pancreata of caerulein-treated mice and demonstrates a relationship between signatures from murine and human CP.

Sequential Validation of Blood-Based Protein Biomarker Candidates for Early-Stage Pancreatic Cancer

Background

CA19-9, which is currently in clinical use as a pancreatic ductal adenocarcinoma (PDAC) biomarker, has limited performance in detecting early-stage disease. We and others have identified protein biomarker candidates that have the potential to complement CA19-9. We have carried out sequential validations starting with 17 protein biomarker candidates to determine which markers and marker combination would improve detection of early-stage disease compared with CA19-9 alone.

Methods

Candidate biomarkers were subjected to enzyme-linked immunosorbent assay based sequential validation using independent multiple sample cohorts consisting of PDAC cases (n = 187), benign pancreatic disease (n = 93), and healthy controls (n = 169). A biomarker panel for early-stage PDAC was developed based on a logistic regression model. All statistical tests for the results presented below were one-sided.

Results

Six out of the 17 biomarker candidates and CA19-9 were validated in a sample set consisting of 75 PDAC patients, 27 healthy subjects, and 19 chronic pancreatitis patients. A second independent set of 73 early-stage PDAC patients, 60 healthy subjects, and 74 benign pancreatic disease patients (combined validation set) yielded a model that consisted of TIMP1, LRG1, and CA19-9. Additional blinded testing of the model was done using an independent set of plasma samples from 39 resectable PDAC patients and 82 matched healthy subjects (test set). The model yielded areas under the curve (AUCs) of 0.949 (95% confidence interval [CI] = 0.917 to 0.981) and 0.887 (95% CI = 0.817 to 0.957) with sensitivities of 0.849 and 0.667 at 95% specificity in discriminating early-stage PDAC vs healthy subjects in the combined validation and test sets, respectively. The performance of the biomarker panel was statistically significantly improved compared with CA19-9 alone (P < .001, combined validation set; P = .008, test set).

Conclusion

The addition of TIMP1 and LRG1 immunoassays to CA19-9 statistically significantly improves the detection of early-stage PDAC.

Bioactive Nutrients and Nutrigenomics in Age-Related Diseases

The increased life expectancy and the expansion of the elderly population are stimulating research into aging. Aging may be viewed as a multifactorial process that results from the interaction of genetic and environmental factors, which include lifestyle. Human molecular processes are influenced by physiological pathways as well as exogenous factors, which include the diet. Dietary components have substantive effects on metabolic health; for instance, bioactive molecules capable of selectively modulating specific metabolic pathways affect the development/progression of cardiovascular and neoplastic disease. As bioactive nutrients are increasingly identified, their clinical and molecular chemopreventive effects are being characterized and systematic analyses encompassing the "omics" technologies (transcriptomics, proteomics and metabolomics) are being conducted to explore their action. The evolving field of molecular pathological epidemiology has unique strength to investigate the effects of dietary and lifestyle exposure on clinical outcomes. The mounting body of knowledge regarding diet-related health status and disease risk is expected to lead in the near future to the development of improved diagnostic procedures and therapeutic strategies targeting processes relevant to nutrition. The state of the art of aging and nutrigenomics research and the molecular mechanisms underlying the beneficial effects of bioactive nutrients on the main aging-related disorders are reviewed herein.

Proteomic strategies in the search for novel pancreatic cancer biomarkers and drug targets: recent advances and clinical impact

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers; despite a low incidence rate it is the fourth leading cause of cancer-related death in the world. Improvement of the diagnosis, prognosis and treatment remains the main focus of pancreatic cancer research. Rapid developments in proteomic technologies has improved our understanding of the pancreatic cancer proteome. Here, the authors summarise the recent proteomic strategies undertaken in the search for: novel biomarkers for early diagnosis, pancreatic cancer-specific proteins which may be used for novel targeted therapies and proteins which may be useful for monitoring disease progression post-therapy. Recent advances and findings discussed here provide great promise of having a significant clinical impact and improving the outcome of patients with this malignancy.