Glycoprotein biomarker panel for pancreatic cancer discovered by quantitative proteomics analysis

Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers

INTRODUCTION

The identification and characterization of glycopeptides through LC-MS/MS and advanced enrichment techniques are crucial for advancing clinical glycoproteomics, significantly impacting the discovery of disease biomarkers and therapeutic targets. Despite progress in enrichment methods like Lectin Affinity Chromatography (LAC), Hydrophilic Interaction Liquid Chromatography (HILIC), and Electrostatic Repulsion Hydrophilic Interaction Chromatography (ERLIC), issues with specificity, efficiency, and scalability remain, impeding thorough analysis of complex glycosylation patterns crucial for disease understanding.

AREAS COVERED

This review explores the current challenges and innovative solutions in glycopeptide enrichment and mass spectrometry analysis, highlighting the importance of novel materials and computational advances for improving sensitivity and specificity. It outlines the potential future directions of these technologies in clinical glycoproteomics, emphasizing their transformative impact on medical diagnostics and therapeutic strategies.

EXPERT OPINION

The application of innovative materials such as Metal-Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs), functional nanomaterials, and online enrichment shows promise in addressing challenges associated with glycoproteomics analysis by providing more selective and robust enrichment platforms. Moreover, the integration of artificial intelligence and machine learning is revolutionizing glycoproteomics by enhancing the processing and interpretation of extensive data from LC-MS/MS, boosting biomarker discovery, and improving predictive accuracy, thus supporting personalized medicine.

Multibiomarker panels in liquid biopsy for early detection of pancreatic cancer - a comprehensive review

Pancreatic ductal adenocarcinoma (PDAC) is frequently detected in late stages, which leads to limited therapeutic options and a dismal overall survival rate. To date, no robust method for the detection of early-stage PDAC that can be used for targeted screening approaches is available. Liquid biopsy allows the minimally invasive collection of body fluids (typically peripheral blood) and the subsequent analysis of circulating tumor cells or tumor-associated molecules such as nucleic acids, proteins, or metabolites that may be useful for the early diagnosis of PDAC. Single biomarkers may lack sensitivity and/or specificity to reliably detect PDAC, while combinations of these circulating biomarkers in multimarker panels may improve the sensitivity and specificity of blood test-based diagnosis. In this narrative review, we present an overview of different liquid biopsy biomarkers for the early diagnosis of PDAC and discuss the validity of multimarker panels.

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.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Plasma THBS1 as a predictive biomarker for poor prognosis and brain metastasis in patients with HER2-enriched breast cancer

BACKGROUND

Thrombospondin-1 (THBS1) is a secretory adhesive glycoprotein involved in the progression of multiple malignancies, including breast cancer. However, the clinical significance and prognostic role of plasma THBS1 in breast cancer have yet to be clarified.

METHODS

Plasma THBS1 levels in 627 breast cancer patients were analyzed by enzyme-linked immunosorbent assay. Bone marrow blood was drawn from the anterior/posterior superior iliac spine to detect the presence of disseminated tumor cells (DTCs). The effects of plasma THBS1 on the clinicopathological characteristics and survival prediction of breast cancer patients were explored.

RESULTS

Plasma THBS1 did not correlate with overall survival, breast cancer-specific survival (BCSS), and distant disease-free survival (DDFS) in the entire breast cancer cohort. Notably, HER2-enriched patients with high-plasma THBS1 levels had significantly shorter BCSS (P = 0.027) and DDFS (P = 0.011) than those with low levels. Multivariate analyses revealed that plasma THBS1 was an independent prognostic marker of BCSS (P = 0.026) and DDFS (P = 0.007) in HER2-enriched patients. THBS1 levels were 24% higher in positive DTC patients than in negative DTC patients (P = 0.031), and high levels were significantly associated with poor BCSS in positive DTC patients (HR 2.08, 95% CI 1.17-3.71; P = 0.019). Moreover, high-plasma THBS1 levels were specifically associated with an increased occurrence of brain metastasis in HER2-enriched patients (P = 0.041).

CONCLUSION

These findings suggest that plasma THBS1 may be serving as an unfavorable prognosis predictor for HER2-enriched breast cancer and justifies the need for further research.

Aberrant Glycosylation in Pancreatic Ductal Adenocarcinoma 3D Organoids Is Mediated by KRAS Mutations

Aberrant glycosylation in tumor cells is a hallmark during carcinogenesis. KRAS gene mutations are the most well-known oncogenic abnormalities but their association with glycan alterations in pancreatic ductal adenocarcinoma (PDAC) is largely unknown. We employed patient-derived 3D organoids to culture pure live PDAC cells, excluding contamination by fibroblasts and immune cells, to gasp the comprehensive cancer cell surface glycan expression profile using lectin microarray and transcriptomic analyses. Surgical specimens from 24 PDAC patients were digested and embedded into a 3D culture system. Surface-bound glycans of 3D organoids were analyzed by high-density, 96-lectin microarrays. KRAS mutation status and expression of various glycosyltransferases were analyzed by RNA-seq. We successfully established 16 3D organoids: 14 PDAC, 1 intraductal papillary mucinous neoplasm (IPMN), and 1 normal pancreatic duct. KRAS was mutated in 13 (7 G12V, 5 G12D, 1 Q61L) and wild in 3 organoids (1 normal duct, 1 IPMN, 1 PDAC). Lectin reactivity of AAL (Aleuria aurantia) and AOL (Aspergillus oryzae) with binding activity to α1-3 fucose was higher in organoids with KRAS mutants than those with KRAS wild-type. FUT6 (α1-3fucosyltransferase 6) and FUT3 (α1-3/4 fucosyltransferase 3) expression was also higher in KRAS mutants than wild-type. Meanwhile, mannose-binding lectin (rRSL [Ralstonia solanacearum] and rBC2LA [Burkholderia cenocepacia]) signals were higher while those of galactose-binding lectins (rGal3C and rCGL2) were lower in the KRAS mutants. We demonstrated here that PDAC 3D-cultured organoids with KRAS mutations were dominantly covered in increased fucosylated glycans, pointing towards novel treatment targets and/or tumor markers.

Robust Glycoproteomics Platform Reveals a Tetra-Antennary Site-Specific Glycan Capping with Sialyl-Lewis Antigen for Early Detection of Gastric Cancer

The lack of efficient biomarkers for the early detection of gastric cancer (GC) contributes to its high mortality rate, so it is crucial to discover novel diagnostic targets for GC. Recent studies have implicated the potential of site-specific glycans in cancer diagnosis, yet it is challenging to perform highly reproducible and sensitive glycoproteomics analysis on large cohorts of samples. Here, a highly robust N-glycoproteomics (HRN) platform comprising an automated enrichment method, a stable microflow LC-MS/MS system, and a sensitive glycopeptide-spectra-deciphering tool is developed for large-scale quantitative N-glycoproteome analysis. The HRN platform is applied to analyze serum N-glycoproteomes of 278 subjects from three cohorts to investigate glycosylation changes of GC. It identifies over 20 000 unique site-specific glycans from discovery and validation cohorts, and determines four site-specific glycans as biomarker candidates. One candidate has branched tetra-antennary structure capping with sialyl-Lewis antigen, and it significantly outperforms serum CEA with AUC values > 0.89 compared against < 0.67 for diagnosing early-stage GC. The four-marker panel can provide improved diagnostic performances. Besides, discrimination powers of four candidates are also testified with a verification cohort using PRM strategy. This findings highlight the value of this strong tool in analyzing aberrant site-specific glycans for cancer detection.

Mass spectrometry analysis of intact protein N-glycosylation signatures of cells and sera in pancreatic adenocarcinomas

Pancreatic cancer is among the most malignant cancers, and thus early intervention is the key to better survival outcomes. However, no methods have been derived that can reliably identify early precursors of development into malignancy. Therefore, it is urgent to discover early molecular changes during pancreatic tumorigenesis. As aberrant glycosylation is closely associated with cancer progression, numerous efforts have been made to mine glycosylation changes as biomarkers for diagnosis; however, detailed glycoproteomic information, especially site-specific N-glycosylation changes in pancreatic cancer with and without drug treatment, needs to be further explored. Herein, we used comprehensive solid-phase chemoenzymatic glycoproteomics to analyze glycans, glycosites, and intact glycopeptides in pancreatic cancer cells and patient sera. The profiling of N-glycans in cancer cells revealed an increase in the secreted glycoproteins from the primary tumor of MIA PaCa-2 cells, whereas human sera, which contain many secreted glycoproteins, had significant changes of glycans at their specific glycosites. These results indicated the potential role for tumor-specific glycosylation as disease biomarkers. We also found that AMG-510, a small molecule inhibitor against Kirsten rat sarcoma viral oncogene homolog (KRAS) G12C mutation, profoundly reduced the glycosylation level in MIA PaCa-2 cells, suggesting that KRAS plays a role in the cellular glycosylation process, and thus glycosylation inhibition contributes to the anti-tumor effect of AMG-510.

Disease specific urinary biomarkers in the central nervous system

Urinary biomarkers can diagnose and monitor pathophysiologic conditions in the central nervous system (CNS). However, focus is often on single diseases, with limited data on discriminatory capability of this approach in a general setting. Here, we demonstrate that different classes of CNS disease exhibit distinct biomarker patterns, evidence of disease-specific "fingerprinting." Urine from 218 patients with pathology-confirmed tumors or cerebrovascular disease, controls (n = 33) were collected. ELISA and/or bead-based multiplexing quantified levels of 21 putative urinary biomarkers. Analysis identified biomarkers capable of distinguishing each disease from controls and other diseases. Mann-Whitney U tests identified biomarkers with differential expression between disease types and controls (P ≤ 0.001). Subsequent receiver-operating characteristic (ROC) analyses revealed distinguishing biomarkers with high sensitivity and specificity. Areas under the curve (AUCs) ranged 0.8563-1.000 (P values ≤ 0.0003), sensitivities ranged 80.00-100.00%, and specificities ranged 80.95-100.00%. These data demonstrate proof-of-principle evidence that disease-specific urinary biomarker signatures exist. In contrast to non-specific responses to ischemia or injury, these results suggest that urinary biomarkers accurately reflect unique biological processes distinct to different diseases. This work can be used to generate disease-specific panels for enhancing diagnosis, assisting less-invasive follow-up and herald utility by revealing putative disease-specific therapeutic targets.

Blood-Based Biomarkers in the Diagnosis and Risk Stratification of Pancreatic Cysts

The use of blood-based biomarkers for the assessment of pancreatic cystic lesions is a rapidly growing field with incredible potential. CA 19-9 remains the only blood-based marker in common use, while many novel biomarkers are in early stages of development and validation. We highlight current work in the fields of proteomics, metabolomics, cell-free DNA/circulating tumor DNA, extracellular vesicles, and microRNA among others, as well as barriers to development and future directions in the work of blood-based biomarkers for pancreatic cystic lesions.

Chemical conjugation to differentiate monosaccharides by Raman and surface enhanced Raman spectroscopy

Sugars play important roles in numerous biological processes, from providing energy to modifying proteins to alter their function. Glycosylation, the attachment of a sugar residue to a protein, is the most common post translational modification. Identifying the glycans on a protein is a useful tool both for pharmaceutical development as well as probing the proteome and glycome further. Sugars, however, are difficult analytes to probe due to their isomeric nature. In this work, Raman spectroscopy and surface enhanced Raman spectroscopy (SERS) are used to identify different monosaccharide species based on the vibrational modes of these isomeric analytes. The weak scattering of the sugars was overcome through conjugation with phenylboronic acid to provide a larger Raman scattering cross section and induce slight changes in the observed spectra associated with the structure of the monosaccharides. Spontaneous Raman, SERS in flow, and static SERS detection were performed in order to discriminate between arabinose, fructose, galactose, glucose, mannose, and ribose, as well as provide a method for identification and quantification for these sugar conjugates.

The hidden potential of glycomarkers: Glycosylation studies in the service of cancer diagnosis and treatment

Changes in the glycosylation process appear early in carcinogenesis and evolve with the growth and spread of cancer. The correlation of the characteristic glycosylation signature with the tumor stage and the appropriate therapy choice is an important issue in translational medicine. Oncologists also pay attention to extracellular vesicles as reservoirs of new cancer glycomarkers that can be potent for cancer diagnosis/prognosis. In this review, we recall glycomarkers used in oncology and show their new glycoforms of improved clinical relevance. We summarize current knowledge on the biological functions of glycoepitopes in cancer-derived extracellular vesicles and their potential use in clinical practice. Is glycomics a future of cancer diagnosis? It may be, but in combination with other omics analyses than alone.

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.

Systematic characterization of extracellular glycoproteins using mass spectrometry

Surface and secreted glycoproteins are essential to cells and regulate many extracellular events. Because of the diversity of glycans, the low abundance of many glycoproteins, and the complexity of biological samples, a system-wide investigation of extracellular glycoproteins is a daunting task. With the development of modern mass spectrometry (MS)-based proteomics, comprehensive analysis of different protein modifications including glycosylation has advanced dramatically. This review focuses on the investigation of extracellular glycoproteins using MS-based proteomics. We first discuss the methods for selectively enriching surface glycoproteins and investigating protein interactions on the cell surface, followed by the application of MS-based proteomics for surface glycoprotein dynamics analysis and biomarker discovery. We then summarize the methods to comprehensively study secreted glycoproteins by integrating various enrichment approaches with MS-based proteomics and their applications for global analysis of secreted glycoproteins in different biological samples. Collectively, MS significantly expands our knowledge of extracellular glycoproteins and enables us to identify extracellular glycoproteins as potential biomarkers for disease detection and drug targets for disease treatment.

Methods for quantification of glycopeptides by liquid separation and mass spectrometry

Recent advances in analytical techniques provide the opportunity to quantify even low-abundance glycopeptides derived from complex biological mixtures, allowing for the identification of glycosylation differences between healthy samples and those derived from disease states. Herein, we discuss the sample preparation procedures and the mass spectrometry (MS) strategies that have facilitated glycopeptide quantification, as well as the standards used for glycopeptide quantification. For sample preparation, various glycopeptide enrichment methods are summarized including the columns used for glycopeptide separation in liquid chromatography separation. For MS analysis strategies, MS1 level-based quantification and MS2 level-based quantification are described, either with or without labeling, where we have covered isotope labeling, TMT/iTRAQ labeling, data dependent acquisition, data independent acquisition, multiple reaction monitoring, and parallel reaction monitoring. The strengths and weaknesses of these methods are compared, particularly those associated with the figures of merit that are important for clinical biomarker studies and the pathological and functional studies of glycoproteins in various diseases. Possible future developments for glycopeptide quantification are discussed.

Alpha-1-antichymotrypsin as a novel biomarker for diagnosis, prognosis, and therapy prediction in human diseases

The glycoprotein alpha-1-antichymotrypsin (AACT), a serine protease inhibitor, is mainly synthesized in the liver and then secreted into the blood and is involved in the acute phase response, inflammation, and proteolysis. The dysregulation of AACT and its glycosylation levels are associated with tumor progression and recurrence, and could be used as a biomarker for tumor monitoring. In this review, we summarized the expression level, glycosylation modification, and biological characteristics of AACT during inflammation, neurodegenerative or other elderly diseases, and tumorigenesis, as well as, focused on the biological roles of AACT in cancer. The aberrant expression of AACT in cancer might be due to genetic alterations and/or immune by bioinformatics analysis. Moreover, AACT may serve as a diagnostic or prognostic biomarker or therapeutic target in tumors. Furthermore, we found that the expression of AACT was associated with the overall survival of patients with human cancers. Decreased AACT expression was associated with poor survival in patients with liver cancer, increased AACT expression was associated with shorter survival in patients with pancreatic cancer, and decreased AACT expression was associated with shorter survival in patients with early lung cancer. The review confirmed the key roles of AACT in tumorigenesis, suggesting that the glycoprotein AACT may serve as a biomarker for tumor diagnosis and prognosis, and could be a potential therapeutic target for human diseases.

Advances in High Throughput Proteomics Profiling in Establishing Potential Biomarkers for Gastrointestinal Cancer

Gastrointestinal cancers (GICs) remain the most diagnosed cancers and accounted for the highest cancer-related death globally. The prognosis and treatment outcomes of many GICs are poor because most of the cases are diagnosed in advanced metastatic stages. This is primarily attributed to the deficiency of effective and reliable early diagnostic biomarkers. The existing biomarkers for GICs diagnosis exhibited inadequate specificity and sensitivity. To improve the early diagnosis of GICs, biomarkers with higher specificity and sensitivity are warranted. Proteomics study and its functional analysis focus on elucidating physiological and biological functions of unknown or annotated proteins and deciphering cellular mechanisms at molecular levels. In addition, quantitative analysis of translational proteomics is a promising approach in enhancing the early identification and proper management of GICs. In this review, we focus on the advances in mass spectrometry along with the quantitative and functional analysis of proteomics data that contributes to the establishment of biomarkers for GICs including, colorectal, gastric, hepatocellular, pancreatic, and esophageal cancer. We also discuss the future challenges in the validation of proteomics-based biomarkers for their translation into clinics.

Comparative Proteomic Analysis of Fucosylated Glycoproteins Produced by Bacteroides thetaiotaomicron Under Different Polysaccharide Nutrition Conditions

Bacteroides thetaiotaomicron, one of the most eminent representative gut commensal Bacteroides species, is able to use the L-fucose in host-derived and dietary polysaccharides to modify its capsular polysaccharides and glycoproteins through a mammalian-like salvage metabolic pathway. This process is essential for the colonization of the bacteria and for symbiosis with the host. However, despite the importance of fucosylated proteins (FGPs) in B. thetaiotaomicron, their types, distribution, and functions remain unclear. In this study, the effects of different polysaccharide (corn starch, mucin, and fucoidan) nutrition conditions on newly synthesized FGPs expressions and fucosylation are investigated using a chemical biological method based on metabolic labeling and bioorthogonal reaction. According to the results of label-free quantification, 559 FGPs (205 downregulated and 354 upregulated) are affected by the dietary conditions. Of these differentially expressed proteins, 65 proteins show extremely sensitive to polysaccharide nutrition conditions (FGPs fold change/global protein fold change ≥2.0 or ≤0.5). Specifically, the fucosylation of the chondroitin sulfate ABC enzyme, Sus proteins, and cationic efflux system proteins varies significantly upon the addition of mucin, corn starch, or fucoidan. Moreover, these polysaccharides can trigger an appreciable increase in the fucosylation level of the two-component system and ammonium transport proteins. These results highlight the efficiency of the combined metabolic glycan labeling and bio-orthogonal reaction in enriching the intestinal Bacteroides glycoproteins. Moreover, it emphasizes the sensitivity of Bacteroides fucosylation to polysaccharide nutrition conditions, which allows for the regulation of bacterial growth.

Transforming growth factor-β challenge alters the N-, O-, and glycosphingolipid glycomes in PaTu-S pancreatic adenocarcinoma cells

Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor prognosis and high mortality. Transforming growth factor-β (TGF-β) plays a key role in PDAC tumor progression, which is often associated with aberrant glycosylation. However, how PDAC cells respond to TGF-β and the role of glycosylation therein is not well known. Here, we investigated the TGF-β-mediated response and glycosylation changes in the PaTu-8955S (PaTu-S) cell line deficient in SMA-related and MAD-related protein 4 (SMAD4), a signal transducer of the TGF-β signaling. PaTu-S cells responded to TGF-β by upregulating SMAD2 phosphorylation and target gene expression. We found that TGF-β induced expression of the mesenchymal marker N-cadherin but did not significantly affect epithelial marker E-cadherin expression. We also examined differences in N-glycans, O-glycans, and glycosphingolipid-linked glycans in PaTu-S cells upon TGF-β stimulation. TGF-β treatment primarily induced N-glycome aberrations involving elevated levels of branching, core fucosylation, and sialylation in PaTu-S cells, in agreement with TGF-β-induced changes in the expression of glycosylation-associated genes. In addition, we observed differences in O glycosylation and glycosphingolipid glycosylation profiles after TGF-β treatment, including lower levels of sialylated Tn antigen and neoexpression of globosides. Furthermore, the expression of transcription factor sex-determining region Y-related high-mobility group box 4 was upregulated upon TGF-β stimulation, and its depletion blocked TGF-β-induced N-glycomic changes. Thus, TGF-β-induced N-glycosylation changes can occur in a sex-determining region Y-related high-mobility group box 4–dependent and SMAD4-independent manner in the pancreatic PaTu-S cancer cell line. Our results open up avenues to study the relevance of glycosylation in TGF-β signaling in SMAD4-inactivated PDAC.

Recent Advances in Mass Spectrometry-Based Glycomic and Glycoproteomic Studies of Pancreatic Diseases

Modification of proteins by glycans plays a crucial role in mediating biological functions in both healthy and diseased states. Mass spectrometry (MS) has emerged as the most powerful tool for glycomic and glycoproteomic analyses advancing knowledge of many diseases. Such diseases include those of the pancreas which affect millions of people each year. In this review, recent advances in pancreatic disease research facilitated by MS-based glycomic and glycoproteomic studies will be examined with a focus on diabetes and pancreatic cancer. The last decade, and especially the last five years, has witnessed developments in both discovering new glycan or glycoprotein biomarkers and analyzing the links between glycans and disease pathology through MS-based studies. The strength of MS lies in the specificity and sensitivity of liquid chromatography-electrospray ionization MS for measuring a wide range of biomolecules from limited sample amounts from many sample types, greatly enhancing and accelerating the biomarker discovery process. Furthermore, imaging MS of glycans enabled by matrix-assisted laser desorption/ionization has proven useful in complementing histology and immunohistochemistry to monitor pancreatic disease progression. Advances in biological understanding and analytical techniques, as well as challenges and future directions for the field, will be discussed.

Biomarkers in Pancreatic Cancer as Analytic Targets for Nanomediated Imaging and Therapy

As the increase in therapeutic and imaging technologies is swiftly improving survival chances for cancer patients, pancreatic cancer (PC) still has a grim prognosis and a rising incidence. Practically everything distinguishing for this type of malignancy makes it challenging to treat: no approved method for early detection, extended asymptomatic state, limited treatment options, poor chemotherapy response and dense tumor stroma that impedes drug delivery. We provide a narrative review of our main findings in the field of nanoparticle directed treatment for PC, with a focus on biomarker targeted delivery. By reducing drug toxicity, increasing their tumor accumulation, ability to modulate tumor microenvironment and even improve imaging contrast, it seems that nanotechnology may one day give hope for better outcome in pancreatic cancer. Further conjugating nanoparticles with biomarkers that are overexpressed amplifies the benefits mentioned, with potential increase in survival and treatment response.

Identification of Fucosylated SERPINA1 as a Novel Plasma Marker for Pancreatic Cancer Using Lectin Affinity Capture Coupled with iTRAQ-Based Quantitative Glycoproteomics

Pancreatic cancer (PC) is an aggressive cancer with a high mortality rate, necessitating the development of effective diagnostic, prognostic and predictive biomarkers for disease management. Aberrantly fucosylated proteins in PC are considered a valuable resource of clinically useful biomarkers. The main objective of the present study was to identify novel plasma glycobiomarkers of PC using the iTRAQ quantitative proteomics approach coupled with Aleuria aurantia lectin (AAL)-based glycopeptide enrichment and isotope-coded glycosylation site-specific tagging, with a view to analyzing the glycoproteome profiles of plasma samples from patients with non-metastatic and metastatic PC and gallstones (GS). As a result, 22 glycopeptides with significantly elevated levels in plasma samples of PC were identified. Fucosylated SERPINA1 (fuco-SERPINA1) was selected for further validation in 121 plasma samples (50 GS and 71 PC) using an AAL-based reverse lectin ELISA technique developed in-house. Our analyses revealed significantly higher plasma levels of fuco-SERPINA1 in PC than GS subjects (310.7 ng/mL v.s. 153.6 ng/mL, p = 0.0114). Elevated fuco-SERPINA1 levels were associated with higher TNM stage (p = 0.024) and poorer prognosis for overall survival (log-rank test, p = 0.0083). The increased plasma fuco-SERPINA1 levels support the utility of this protein as a novel prognosticator for PC.

Antifungal Mechanism of Volatile Organic Compounds Produced by Bacillus subtilis CF-3 on Colletotrichum gloeosporioides Assessed Using Omics Technology

Bacillus subtilis is commonly used as a biocontrol bacterium owing to its strong antifungal activity, broad-spectrum inhibition, and general safety. In this study, the inhibitory effects of volatile organic compounds (VOCs) produced by B. subtilis CF-3 on Colletotrichum gloeosporioides, a major destructive phytopathogen of litchi anthracnose, were analyzed using proteomics and transcriptomics. Differentially expressed genes (DEGs) and proteins (DEPs) indicated that the inhibition of C. gloeosporioides by B. subtilis CF-3 VOCs downregulated the expression of genes related to cell membrane fluidity, cell wall integrity, energy metabolism, and production of cell wall-degrading enzymes. Particularly, those important DEGs and DEPs related to the ergosterol biosynthetic and biosynthesis of unsaturated fatty acids are most significantly influenced. 2,4-di-tert-butylphenol, a characteristic component of B. subtilis CF-3 VOCs, also showed a similar effect on C. gloeosporioides. Our results provide a theoretical basis for the potential application of B. subtilis CF-3 in the postharvest protection of fruits and vegetables.

Benchmark of site- and structure-specific quantitative tissue N-glycoproteomics for discovery of potential N-glycoprotein markers: a case study of pancreatic cancer

Pancreatic cancer is a highly malignant tumor of the digestive tract that is difficult to diagnose and treat. It is more common in developed countries and has become one of the main causes of death in some countries and regions. Currently, pancreatic cancer generally has a poor prognosis, partly due to the lack of symptoms in the early stages of pancreatic cancer. Therefore, most cases are diagnosed at advanced stage. With the continuous in-depth research of glycoproteomics in precision medical diagnosis, there have been some reports on quantitative analysis of cancer-related cells, plasma or tissues to find specific biomarkers for targeted therapy. This research is based on the developed complete N-linked glycopeptide database search engine GPSeeker, combined with liquid-mass spectrometry and stable diethyl isotope labeling, providing a benchmark of site- and structure-specific quantitative tissue N-glycoproteomics for discovery of potential N-glycoprotein markers. With spectrum-level FDR ≤1%, 20,038 intact N-Glycopeptides corresponding to 4518 peptide backbones, 228 N-glycan monosaccharide compositions 1026 N-glycan putative structures, 4460 N-glycosites and 3437 intact N-glycoproteins were identified. With the criteria of ≥1.5-fold change and p value<0.05, 52 differentially expressed intact N-glycopeptides (DEGPs) were found in pancreatic cancer tussues relative to control, where 38 up-regulated and 14 down-regulated, respectively.

Blood biomarkers for differential diagnosis and early detection of pancreatic cancer

Pancreatic cancer is currently the most lethal tumor entity and case numbers are rising. It will soon be the second most frequent cause of cancer-related death in the Western world. Mortality is close to incidence and patient survival after diagnosis stands at about five months. Blood-based diagnostics could be one crucial factor for improving this dismal situation and is at a stage that could make this possible. Here, we are reviewing the current state of affairs with its problems and promises, looking at various molecule types. Reported results are evaluated in the overall context. Also, we are proposing steps toward clinical utility that should advance the development toward clinical application by improving biomarker quality but also by defining distinct clinical objectives and the respective diagnostic accuracies required to achieve them. Many of the discussed points and conclusions are highly relevant to other solid tumors, too.

Clinical Perspective on Proteomic and Glycomic Biomarkers for Diagnosis, Prognosis, and Prediction of Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is known as a highly aggressive malignant disease. Prognosis for patients is notoriously poor, despite improvements in surgical techniques and new (neo)adjuvant chemotherapy regimens. Early detection of PDAC may increase the overall survival. It is furthermore foreseen that precision medicine will provide improved prognostic stratification and prediction of therapeutic response. In this review, omics-based discovery efforts are presented that aim for novel diagnostic and prognostic biomarkers of PDAC. For this purpose, we systematically evaluated the literature published between 1999 and 2020 with a focus on protein- and protein-glycosylation biomarkers in pancreatic cancer patients. Besides genomic and transcriptomic approaches, mass spectrometry (MS)-based proteomics and glycomics of blood- and tissue-derived samples from PDAC patients have yielded new candidates with biomarker potential. However, for reasons discussed in this review, the validation and clinical translation of these candidate markers has not been successful. Consequently, there has been a change of mindset from initial efforts to identify new unimarkers into the current hypothesis that a combination of biomarkers better suits a diagnostic or prognostic panel. With continuing development of current research methods and available techniques combined with careful study designs, new biomarkers could contribute to improved detection, prognosis, and prediction of pancreatic cancer.

Transcriptome analysis of sinensetin-treated liver cancer cells guided by biological network analysis

Hepatocellular carcinoma is recognized as one of the most frequently occurring malignant types of liver cancer globally, making the identification of biomarkers critically important. The aim of the present study was to identify the genes involved in the anticancer effects of flavonoid compounds so that they may be used as targets for cancer treatment. Sinensetin (SIN), an isolated polymethoxyflavone monomer compound, possesses broad antitumor activities in vitro. Therefore, the identification of a transcriptome profile on the condition of cells treated with SIN may aid to better understand the genes involved and its mechanism of action. Genomic profiling studies of cancer are increasing rapidly in order to provide gene expression data that can reveal prognostic biomarkers to combat liver cancer. In the present study, high-throughput RNA sequencing (RNA-seq) was performed to reveal differential gene expression patterns between SIN-treated and SIN-untreated human liver cancer HepG2 cells. A total of 43 genes were identified to be differentially expressed (39 downregulated and 4 upregulated in the SIN-treated group compared with the SIN-untreated group). An extensive network analysis for these 43 genes resulted in the identification of 10 upregulated highly interconnected hub genes that contributed to the progression of cancer. Functional enrichment analysis of these 10 hub genes revealed their involvement in the regulation of apoptotic processes, immune response and tumor necrosis factor production. Additionally, the mRNA expression levels of these 10 genes were evaluated using reverse transcription-quantitative PCR, and the results were consistent with the RNA-seq data. Overall, the results of the present study revealed differentially expressed genes involved in cancer after SIN treatment in HepG2 cells and may help to develop strategies targeting these genes for treating liver cancer.

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.

The proteomic analysis of breast cell line exosomes reveals disease patterns and potential biomarkers

Cancer cells release small extracellular vesicles, exosomes, that have been shown to contribute to various aspects of cancer development and progression. Differential analysis of exosomal proteomes from cancerous and non-tumorigenic breast cell lines can provide valuable information related to breast cancer progression and metastasis. Moreover, such a comparison can be explored to find potentially new protein biomarkers for early disease detection. In this study, exosomal proteomes of MDA-MB-231, a metastatic breast cancer cell line, and MCF-10A, a non-cancerous epithelial breast cell line, were identified by nano-liquid chromatography coupled to tandem mass spectrometry. We also tested three exosomes isolation methods (ExoQuick, Ultracentrifugation (UC), and Ultrafiltration–Ultracentrifugation) and detergents (n-dodecyl β-d-maltoside, Triton X-100, and Digitonin) for solubilization of exosomal proteins and enhanced detection by mass spectrometry. A total of 1,107 exosomal proteins were identified in both cell lines, 726 of which were unique to the MDA-MB-231 breast cancer cell line. Among them, 87 proteins were predicted to be relevant to breast cancer and 16 proteins to cancer metastasis. Three exosomal membrane/surface proteins, glucose transporter 1 (GLUT-1), glypican 1 (GPC-1), and disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), were identified as potential breast cancer biomarkers and validated with Western blotting and high-resolution flow cytometry. We demonstrated that exosomes are a rich source of breast cancer-related proteins and surface biomarkers that may be used for disease diagnosis and prognosis.

Early detection of pancreatic cancer

Pancreatic ductal adenocarcinoma is most frequently detected at an advanced stage. Such late detection restricts treatment options and contributes to a dismal 5-year survival rate of 3-15%. Pancreatic ductal adenocarcinoma is relatively uncommon and screening of the asymptomatic adult population is not feasible or recommended with current modalities. However, screening of individuals in high-risk groups is recommended. Here, we review groups at high risk for pancreatic ductal adenocarcinoma, including individuals with inherited predisposition and patients with pancreatic cystic lesions. We discuss studies aimed at finding ways of identifying pancreatic ductal adenocarcinoma in high-risk groups, such as among individuals with new-onset diabetes mellitus and people attending primary and secondary care practices with symptoms that suggest this cancer. We review early detection biomarkers, explore the potential of using social media for detection, appraise prediction models developed using electronic health records and research data, and examine the application of artificial intelligence to medical imaging for the purposes of early detection.

Nanoparticle-enabled blood tests for early detection of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is often detected too late to allow adequate treatments with the result that patients are condemned to sufferings and early death. Most efforts have been therefore aimed at identifying sensitive PDAC biomarkers. Although biomarkers have numerous advantages, sample size, intra-individual variability, existence of several biases and confounding variables and cost of investigation make their clinical application challenging. In recent years, nanotechnology is providing new options for early cancer detection. Among recent discoveries, the concept is emerging that the protein corona, i.e. the layer of plasma proteins that surrounds nanomaterials in bodily fluids, is personalized. In particular, the protein corona of cancer patients is significantly different from that of healthy individuals. Herein, we review this concept with a particular focus on clinical relevance. We also discuss the recently developed nanoparticle-enabled blood (NEB) tests that demonstrated to be promising in discriminating PDAC patients from healthy volunteers by global change of the nanoparticle-protein corona. We conclude with a critical discussion of research perspectives aimed at further improving the prediction ability of the test.

Familial Pancreatic Cancer: Current Perspectives

Pancreatic cancer (PC) is a highly lethal disease, mostly incurable when detected. Thus, despite advances in PC treatments, only around 7% of patients survive 5-years after diagnosis. This morbid outcome is secondary to multifactorial reasons, such as late-stage diagnosis, rapid progression and minimal response to chemotherapy. Based on these factors, it is of special relevance to identify PC high-risk individuals in order to establish preventive and early detection measures. Although most PC are sporadic, approximately 10% cases have a familial basis. No main causative gene of PC has been identified but several known germline pathogenic mutations are related with an increased risk of this tumor. These inherited cancer syndromes represent 3% of all PC. On the other hand, in 7% of cases of PC, there is a strong family history without a causative germline mutation, a situation known as familial pancreatic cancer (FPC). In recent years, there is increasing evidence supporting the benefit of genetic germline analysis in PC patients, and periodic pancreatic screening in PC high-risk patients (mainly those with a lifetime risk greater than 5%), although there is no general agreement in the group of patients and individuals to study and screen. In the present review, we expose an update in the field of hereditary and FPC, with the aim of describing the current strategies and implications in genetic counseling, surveillance and therapeutic interventions.

Combined Expression of Plasma Thrombospondin-2 and CA19-9 for Diagnosis of Pancreatic Cancer and Distal Cholangiocarcinoma: A Proteome Approach

Background

Minimally invasive diagnostic biomarkers for patients with pancreatic ductal adenocarcinoma (PDAC) and distal cholangiocarcinoma (dCCA) are warranted to facilitate accurate diagnosis. This study identified diagnostic plasma proteins based on proteomics of tumor secretome.

Materials and Methods

Secretome of tumor and normal tissue was collected after resection of PDAC and dCCA. Differentially expressed proteins were measured by mass spectrometry. Selected candidate biomarkers and carbohydrate antigen 19‐9 (CA19‐9) were validated by enzyme‐linked immunosorbent assay in plasma from patients with PDAC (n = 82), dCCA (n = 29), benign disease (BD; n = 30), and healthy donors (HDs; n = 50). Areas under the curve (AUCs) of receiver operator characteristic curves were calculated to determine the discriminative power.

Results

In tumor secretome, 696 discriminatory proteins were identified, including 21 candidate biomarkers. Thrombospondin‐2 (THBS2) emerged as promising biomarker. Abundance of THBS2 in plasma from patients with cancer was significantly higher compared to HDs (p < .001, AUC = 0.844). Combined expression of THBS2 and CA19‐9 yielded the optimal discriminatory capacity (AUC = 0.952), similarly for early‐ and late‐stage disease (AUC = 0.971 and AUC = 0.911). Remarkably, this combination demonstrated a power similar to CA19‐9 to discriminate cancer from BD (AUC = 0.764), and THBS2 provided an additive value in patients with high expression levels of bilirubin.

Conclusion

Our proteome approach identified a promising set of candidate biomarkers. The combined plasma expression of THBS2/CA19‐9 is able to accurately distinguish patients with PDAC or dCCA from HD and BD.

Implications for Practice

The combined plasma expression of thrombospondin‐2 and carbohydrate antigen 19‐9 is able to accurately diagnose patients with pancreatic cancer and distal cholangiocarcinoma. This will facilitate minimally invasive diagnosis for these patients by distinguishing them from healthy individuals and benign diseases.

This article identifies diagnostic plasma proteins to distinguish patients with pancreatic ductal adenocarcinoma and distal cholangiocarcinoma from benign disease and health donors and evaluates these new markers for additive value with CA19‐9 at different disease stages.

UPLC‑MS/MS‑based metabolomic characterization and comparison of pancreatic adenocarcinoma tissues using formalin‑fixed, paraffin‑embedded and optimal cutting temperature‑embedded materials

The purpose of the present study was to compare metabolites from formalin-fixed and paraffin-embedded (FFPE) pancreatic tissue blocks with those identified in optimal cutting temperature (OCT)-embedded pancreatic tissue blocks. Thus, ultra-performance liquid chromatograph-mass spectrometry/mass spectrometry-based metabolic profiling was performed in paired frozen (n=13) and FFPE (n=13) human pancreatic adenocarcinoma tissue samples, in addition to their benign counterparts. A total of 206 metabolites were identified in both OCT-embedded and FFPE tissue samples. The method feasibility was confirmed through reproducibility and a consistency assessment. Partial least-squares discriminant analysis and heatmap analysis reliably distinguished tumor and normal tissue phenotypes. The expression of 10 compounds, including N-acetylaspartate and creatinine, was significantly different in both OCT-embedded and FFPE tumor samples. These ten compounds may be viable candidate biomarkers of malignant pancreatic tissues. The super-categories to which they belonged exhibited no significant differences between FFPE and OCT-embedded samples. Furthermore, purine, arginine and proline, and pyrimidine metabolism used a shared pathway found in both OCT-embedded and FFPE tissue samples. These results supported the notion that metabolomic data acquired from FFPE pancreatic cancer specimens are reliable for use in retrospective and clinical studies.

Converting the personalized biomolecular corona of graphene oxide nanoflakes into a high-throughput diagnostic test for early cancer detection

Advances in nanotechnology are introducing the exciting possibility of cancer identification at early stages via analysis of the personalized biomolecular corona (BC), i.e. the dynamic "halo" of proteins that adsorbs onto NPs following exposure to patients' plasma. In this study, we develop a blood test for early cancer detection based on the characterization of the BC that forms around Graphene Oxide (GO) nanoflakes. Among its elective properties, GO binds low amounts of albumin, the most abundant protein in the blood and one of the most enriched proteins in the BC of many nanomaterials. This unique property of GO allows strong adsorption of poorly concentrated plasma proteins without abundant protein depletion. In our study, GO nanometric flakes have been used to analyze BCs from 50 subjects, half of them diagnosed with pancreatic cancer and half of them being healthy volunteers. Pancreatic cancer was chosen as the model of a high mortality disease with poor survival rates due to its delayed diagnosis. The receiver operating characteristic (ROC) curve analysis was applied to measure the diagnostic accuracy of the BC-based test. We obtained an area under the curve (AUC) of 0.96 and the test discriminated cancer patients from healthy subjects with a sensitivity of 92%. Finally, a double-blind validation was made using a second test dataset (10 healthy subjects + 10 pancreatic cancer patients) and it confirmed the results obtained on the first training dataset. Being highly accurate, fast, inexpensive and easy to perform, we believe that the BC-enabled blood test has the potential to become a turning point in early detection of cancer and other diseases.

[The role of exosomal tetraspanins and proteases in tumor progression]

Major (CD9, CD63, CD81) and others (CD82, CD151, Tspan8) tetraspanins are widely represented in exosomes, where they interact with various proteins and form functional tetraspanin complexes. Tetraspanin complexes include proteases. Tetraspanin-associated exosomal proteases (ADAM proteases, MMPs, EMMPRIN) play an important role in the processes of cell motility, migration, invasion and formation of metastases. Also, a significant contribution to tumor progression is made by proteases that are not associated with tetraspanins. They destabilize intercellular contacts, promote migration and invasion of tumor cells, participate in the regulation of the expression IGF-I, VEGF and transcription factors activation/deactivation. The role of other proteases of exosomes in the processes of tumor progression is being clarified.

Quantitative proteomics identifies brain acid soluble protein 1 (BASP1) as a prognostic biomarker candidate in pancreatic cancer tissue

BACKGROUND

Pancreatic cancer is a heterogenous disease with a poor prognosis. This study aimed to discover and validate prognostic tissue biomarkers in pancreatic cancer using a mass spectrometry (MS) based proteomics approach.

METHODS

Global protein sequencing of fresh frozen pancreatic cancer and healthy pancreas tissue samples was conducted by MS to discover potential protein biomarkers. Selected candidate proteins were further verified by targeted proteomics using parallel reaction monitoring (PRM). The expression of biomarker candidates was validated by immunohistochemistry in a large tissue microarray (TMA) cohort of 141 patients with resectable pancreatic cancer. Kaplan-Meier and Cox proportional hazard modelling was used to investigate the prognostic utility of candidate protein markers.

FINDINGS

In the initial MS-discovery phase, 165 proteins were identified as potential biomarkers. In the subsequent MS-verification phase, a panel of 45 candidate proteins was verified by the development of a PRM assay. Brain acid soluble protein 1 (BASP1) was identified as a new biomarker candidate for pancreatic cancer possessing largely unknown biological and clinical functions and was selected for further analysis. Importantly, bioinformatic analysis indicated that BASP1 interacts with Wilms tumour protein (WT1) in pancreatic cancer. TMA-based immunohistochemistry analysis showed that BASP1 was an independent predictor of prolonged survival (HR 0.468, 95% CI 0.257-0.852, p = .013) and predicted favourable response to adjuvant chemotherapy, whereas WT1 indicated a worsened survival (HR 1.636, 95% CI 1.083-2.473, p = .019) and resistance to chemotherapy. Interaction analysis showed that patients with negative BASP1 and high WT1 expression had the poorest outcome (HR 3.536, 95% CI 1.336-9.362, p = .011).

INTERPRETATION

We here describe an MS-based proteomics platform for developing biomarkers for pancreatic cancer. Bioinformatic analysis and clinical data from our study suggest that BASP1 and its putative interaction partner WT1 can be used as biomarkers for predicting outcomes in pancreatic cancer patients.

Orientationally Fabricated Zwitterionic Molecularly Imprinted Nanocavities for Highly Sensitive Glycoprotein Recognition

Glycoprotein recognition has recently gained a lot of attention, since glycoproteins play important roles in a diverse range of biological processes. Robustly synthesized glycoprotein receptors, such as molecularly imprinted polymers (MIPs), which can be easily and sustainably handled, are highly attractive as antibody substitutes because of the difficulty in obtaining high-affinity antibodies specific for carbohydrate-containing antigens. Herein, molecularly imprinted nanocavities for glycoproteins have been fabricated via a bottom-up molecular imprinting approach using surface-initiated atom transfer radical polymerization (SI-ATRP). As a model glycoprotein, ovalbumin was immobilized in a specific orientation onto a surface plasmon resonance sensor chip by forming a conventional cyclic diester between boronic acid and cis-diol. Biocompatible polymer matrices were formed around the template molecule, ovalbumin, using SI-ATRP via a hydrophilic comonomer, 2-methacryloyloxyethyl phosphorylcholine, in the presence of pyrrolidyl acrylate (PyA), a functional monomer capable of electrostatically interacting with ovalbumin. The removal of ovalbumin left MIPs with binding cavities containing boronic acid and PyA residues located at suitable positions for specifically binding ovalbumin. Careful analysis revealed that strict control over the polymer significantly improved sensitivity and selectivity for ovalbumin recognition, with a limit of detection of 6.41 ng/mL. Successful detection of ovalbumin in an egg white matrix was demonstrated to confirm the practical utility of this approach. Thus, this strategy of using a polymer-based recognition of a glycoprotein through molecularly imprinted nanocavities precisely prepared using a bottom-up approach provides a potentially powerful approach for detection of other glycoproteins.

Single Amino Acid Variant Discovery in Small Numbers of Cells

We have performed deep proteomic profiling down to as few as 9 Panc-1 cells using sample fractionation, TMT multiplexing, and a carrier/reference strategy. Off line fractionation of the TMT-labeled sample pooled with TMT-labeled carrier Panc-1 whole cell proteome was achieved using alkaline reversed phase spin columns. The fractionation in conjunction with the carrier/reference (C/R) proteome allowed us to detect 47 414 unique peptides derived from 6261 proteins, which provided a sufficient coverage to search for single amino acid variants (SAAVs) related to cancer. This high sample coverage is essential in order to detect a significant number of SAAVs. In order to verify genuine SAAVs versus false SAAVs, we used the SAVControl pipeline and found a total of 79 SAAVs from the 9-cell Panc-1 sample and 174 SAAVs from the 5000-cell Panc-1 C/R proteome. The SAAVs as sorted into high confidence and low confidence SAAVs were checked manually. All the high confidence SAAVs were found to be genuine SAAVs, while half of the low confidence SAAVs were found to be false SAAVs mainly related to PTMs. We identified several cancer-related SAAVs including KRAS, which is an important oncoprotein in pancreatic cancer. In addition, we were able to detect sites involved in loss or gain of glycosylation due to the enhanced coverage available in these experiments where we can detect both sites of loss and gain of glycosylation.

Improving the accuracy of pancreatic cancer clinical staging by exploitation of nanoparticle-blood interactions: A pilot study

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) early diagnosis is  crucial  and new, cheap and user-friendly techniques for biomarker identification  are  needed. "Protein corona" (PC) is emerging a new bio-interface potentially useful in tumor early diagnosis. In a previous investigation, we showed that relevant differences between the  protein patterns of  PCs formed on lipid NPs after exposure to PDAC and non-cancer plasma  samples exist. To extend that research, We performed this pilot study to investigate the effect of PDAC tumor size and distant metastases on PC composition.

METHODS

Twenty PDACs were clinically staged according to the UICC TNM staging system 8 t h Edition. Collected plasma samples were let to interact with lipid NPs; resulting PCs were characterized by SDS-PAGE. To properly evaluate changes in the PC, the protein intensity profiles were reduced to four regions of molecular weight: < 25 kDa, 25-50 kDa, 50-120 kDa, > 120 kDa.  RESULTS: Data analysis allowed to distinguish T1-T2 cases from T3 and above all from metastatic ones (p < 0.05). Discrimination power was particularly due to a subset of plasma proteins with molecular  weight comprised between 25-50 kDa  and 50-120 kDa.

CONCLUSIONS

PC composition is critically influenced by tumor size and presence of distant metastases in PDAC. If our findings will be further confirmed, we envision that future developments of cheap and user-friendly PC-based tools will allow to improve the accuracy of PDAC clinical staging, identifying among resectable  PDACs with potentially better prognosis (i.e. T1 and T2) those at higher risk of occult distant metastases.

Analysis of the plasma proteome using iTRAQ and TMT-based Isobaric labeling

Over the past decade, chemical labeling with isobaric tandem mass tags, such as isobaric tags for relative and absolute quantification reagents (iTRAQ) and tandem mass tag (TMT) reagents, has been employed in a wide range of different clinically orientated serum and plasma proteomics studies. In this review the scope of these works is presented with attention to the areas of research, methods employed and performance limitations. These applications have covered a wide range of diseases, disorders and infections, and have implemented a variety of different preparative and mass spectrometric approaches. In contrast to earlier works, which struggled to quantify more than a few hundred proteins, increasingly these studies have provided deeper insight into the plasma proteome extending the numbers of quantified proteins to over a thousand.

Glycoprotein biomarkers for the detection of pancreatic ductal adenocarcinoma

Pancreatic cancer (PaC) shows a clear tendency to increase in the next years and therefore represents an important health and social challenge. Currently, there is an important need to find biomarkers for PaC early detection because the existing ones are not useful for that purpose. Recent studies have indicated that there is a large window of time for PaC early detection, which opens the possibility to find early biomarkers that could greatly improve the dismal prognosis of this tumor. The present manuscript reviews the state of the art of the existing PaC biomarkers. It focuses on the anomalous glycosylation process and its role in PaC. Glycan structures of glycoconjugates such as glycoproteins are modified in tumors and these modifications can be detected in biological fluids of the cancer patients. Several studies have found serum glycoproteins with altered glycan chains in PaC patients, but they have not shown enough specificity for PaC. To find more specific cancer glycoproteins we propose to analyze the glycan moieties of a battery of glycoproteins that have been reported to increase in PaC tissues and that can also be found in serum. The combination of these new candidate glycoproteins with their aberrant glycosylation together with the existing biomarkers could result in a panel, which would expect to give better results as a new tool for early diagnosis of PaC and to monitor the disease.

Relative and Absolute Quantitation in Mass Spectrometry-Based Proteomics

Mass spectrometry-based quantitative proteomics is a powerful tool for gaining insights into function and dynamics of biological systems. However, peptides with different sequences have different ionization efficiencies, and their intensities in a mass spectrum are not correlated with their abundances. Therefore, various label-free or stable isotope label-based quantitation methods have emerged to assist mass spectrometry to perform comparative proteomic experiments, thus enabling nonbiased identification of thousands of proteins differentially expressed in healthy versus diseased cells. Here, we discuss the most widely used label-free and metabolic-, enzymatic-, and chemical labeling-based proteomic strategies for relative and absolute quantitation. We summarize the specific strengths and weaknesses of each technique in terms of quantification accuracy, proteome coverage, multiplexing capability, and robustness. Applications of each strategy for solving specific biological complexities are also presented.

Signal-Targeted Therapies and Resistance Mechanisms in Pancreatic Cancer: Future Developments Reside in Proteomics

For patients with metastatic pancreatic cancer that are not eligible for surgery, signal-targeted therapies have so far failed to significantly improve survival. These therapeutic options have been tested in phase II/III clinical trials mostly in combination with the reference treatment gemcitabine. Innovative therapies aim to annihilate oncogenic dependency, or to normalize the tumoural stroma to allow immune cells to function and/or re-vascularisation to occur. Large scale transcriptomic and genomic analysis revealed that pancreatic cancers display great heterogeneity but failed to clearly delineate specific oncogene dependency, besides oncogenic Kras. Beyond these approaches, proteomics appears to be an appropriate approach to classify signal dependency and to identify specific alterations at the targetable level. However, due to difficulties in sampling, proteomic data for this pathology are scarce. In this review, we will discuss the current state of clinical trials for targeted therapies against pancreatic cancer. We will then highlight the most recent proteomic data for pancreatic tumours and their metastasis, which could help to identify major oncogenic signalling dependencies, as well as provide future leads to explain why pancreatic tumours are intrinsically resistant to signal-targeted therapies. We will finally discuss how studies on phosphatidylinositol-3-kinase (PI3K) signalling, as the paradigmatic pro-tumoural signal downstream of oncogenic Kras in pancreatic cancer, would benefit from exploratory proteomics to increase the efficiency of targeted therapies.

Human expression patterns: qualitative and quantitative analysis of thrombospondin-1 under physiological and pathological conditions

Thrombospondin-1 (TSP-1), a matricellular protein and one of the first endogenous anti-angiogenic molecules identified, has long been considered a potent modulator of human diseases. While the therapeutic effect of TSP-1 to suppress cancer was investigated in both research and clinical settings, the mechanisms of how TSP-1 is regulated in cancer remain elusive, and the scientific answers to the question of whether TSP-1 expressions can be utilized as diagnostic or prognostic marker for patients with cancer are largely inconsistent. Moreover, TSP-1 plays crucial functions in angiogenesis, inflammation and tissue remodelling, which are essential biological processes in the progression of many cardiovascular diseases, and therefore, its dysregulated expressions in such conditions may have therapeutic significance. Herein, we critically analysed the literature pertaining to TSP-1 expression in circulating blood and pathological tissues in various types of cancer as well as cardiovascular and inflammation-related diseases in humans. We compare the secretion rates of TSP-1 by different cancer and non-cancer cells and discuss the potential connection between the expression changes of TSP-1 and vascular endothelial growth factor (VEGF) observed in patients with cancer. Moreover, the pattern and emerging significance of TSP-1 profiles in cardiovascular disease, such as peripheral arterial disease, diabetes and other related non-cancer disorders, are highlighted. The analysis of published TSP-1 data presented in this review may have implications for the future exploration of novel TSP-1-based treatment strategies for cancer and cardiovascular-related diseases.

Detection of early pancreatic ductal adenocarcinoma with thrombospondin-2 and CA19-9 blood markers

Markers are needed to facilitate early detection of pancreatic ductal adenocarcinoma (PDAC), which is often diagnosed too late for effective therapy. Starting with a PDAC cell reprogramming model that recapitulated the progression of human PDAC, we identified secreted proteins and tested a subset as potential markers of PDAC. We optimized an enzyme-linked immunosorbent assay (ELISA) using plasma samples from patients with various stages of PDAC, from individuals with benign pancreatic disease, and from healthy controls. A phase 1 discovery study (n = 20), a phase 2a validation study (n = 189), and a second phase 2b validation study (n = 537) revealed that concentrations of plasma thrombospondin-2 (THBS2) discriminated among all stages of PDAC consistently. The receiver operating characteristic (ROC) c-statistic was 0.76 in the phase 1 study, 0.84 in the phase 2a study, and 0.87 in the phase 2b study. The plasma concentration of THBS2 was able to discriminate resectable stage I cancer as readily as stage III/IV PDAC tumors. THBS2 plasma concentrations combined with those for CA19-9, a previously identified PDAC marker, yielded a c-statistic of 0.96 in the phase 2a study and 0.97 in the phase 2b study. THBS2 data improved the ability of CA19-9 to distinguish PDAC from pancreatitis. With a specificity of 98%, the combination of THBS2 and CA19-9 yielded a sensitivity of 87% for PDAC in the phase 2b study. A THBS2 and CA19-9 blood marker panel measured with a conventional ELISA may improve the detection of patients at high risk for PDAC.