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

Prognostic and diagnostic value of circulating IGFBP2 in pancreatic cancer

Insulin-like growth factor binding protein 2 (IGFBP2) is overexpressed in tumor tissues of several malignancies, including pancreatic cancer. Because of its role in tumor progression, IGFBP2 has been investigated as a tumor biomarker. However, little is known about its utility in pancreatic cancer. Plasma IGFBP2 levels were determined using enzyme-linked immunosorbent assay in 75 patients with pancreatic ductal adenocarcinoma (PDAC), 73 matched healthy controls, and 17 chronic pancreatitis patients. Our results showed that the plasma IGFPB2 level was significantly higher in PDAC patients than in patients with chronic pancreatitis and healthy controls. At a cut-off value of 333.9 ng/mL, the specificity and sensitivity were 78.08 and 65.33%, respectively. IGFBP2 level alone did not outperform carbohydrate antigen 19-9 (CA19-9) in diagnostic accuracy, but it successfully identified 9 out of 24 PDAC patients who were misidentified by CA19-9. The combination of IGFBP2 and CA19-9 was more accurate in the detection of PDAC than CA19-9 alone. IGFBP2 was more accurate than the other in discriminating between chronic pancreatitis and PDAC. Plasma IGFBP2, rather than CA19-9, was higher in the new-onset diabetes, lymph node involvement, and distant metastasis subgroups. IGFBP2 level was notably higher in stage IV cases than in stage I/II or stage III disease. However, CA19-9 did not show a difference between stages. After adjusting for lymph node involvement and distant metastasis, plasma IGFBP2 was identified as an independent prognostic marker for PDAC. The median survival time for patients with an IGFBP2 level ≥333.9 ng/mL was significantly shorter than that for patients with an IGFBP2 level <333.9 ng/mL. Marked elevation of plasma IGFBP2 in PDAC is associated with poorer survival. IGFBP2 may be considered as a supplementary biomarker for the diagnosis and prognostic prediction in Chinese pancreatic cancer patients.

The function of the inter-alpha-trypsin inhibitors in the development of disease

Through the formation of covalent connections with hyaluronic acid (HA), the inter-α-trypsin inhibitor (IαI) family collaborates to preserve the stability of the extracellular matrix (ECM). The five distinct homologous heavy chains (ITIH) and one type of light chain make up the IαI family. ITIH alone or in combination with bikunin (BK) has been proven to have important impacts in a number of earlier investigations. This implies that BK and ITIH might be crucial to both physiological and pathological processes. The functions of BK and ITIH in various pathophysiological processes are discussed independently in this paper. In the meanwhile, this study offers suggestions for further research on the roles of BK and ITIH in the course of disease and summarizes the plausible mechanisms of the previous studies.

Novel serum protein biomarker panel for early diagnosis of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Late presentation of disease at the time of diagnosis is one of the major reasons for dismal prognostic outcomes for PDAC patients. Currently, there is a lack of clinical biomarkers, which can be used to diagnose PDAC patients at an early resectable stage. This study performed proteomic mass spectrometry to identify novel blood-based biomarkers for early diagnosis of PDAC. Serum specimens from 88 PDAC patients and 88 healthy controls (60 discovery cohort and 28 validation cohort) were analyzed using data independent acquisition high resolution mass spectrometry to identify candidate biomarker proteins. A total of 249 proteins were identified and quantified by the mass spectrometric analysis. Six proteins were markedly (>1.5 fold) and significantly (p < .05; q < 0.1) increased in PDAC patients compared to healthy controls in discovery cohort. Notably, four of these six proteins were significantly upregulated in an independent validation cohort. The top three upregulated proteins (i.e., Polymeric Immunoglobulin Receptor [PIGR], von Willebrand Factor [vWF], and Fibrinogen) were validated using enzyme linked immunosorbent assay, which led to selection of PIGR and vWF as a diagnostic biomarker panel for PDAC. The panel showed high ability to diagnose early stage (stage I and II) PDAC patients (area under the curve [AUC]: 0.8926), which was further improved after the addition of clinically used prognostic biomarker (Ca 19-9) to the panel (AUC: 0.9798). In conclusion, a novel serum protein biomarker panel for early diagnosis of PDAC was identified.

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.

Diagnostic Differentiation between Pancreatitis and Pancreatic Cancer: A Scoping Review

Pancreatitis, encompassing acute and chronic forms, and pancreatic cancer pose significant challenges to the exocrine tissue of the pancreas. Recurrence rates and complications following acute pancreatitis episodes can lead to long-term risks, including diabetes mellitus. Chronic pancreatitis can develop in approximately 15% of cases, regardless of the initial episode's severity. Alcohol-induced pancreatitis, idiopathic causes, cigarette smoking, and hereditary pancreatitis contribute to the progression to chronic pancreatitis. Chronic pancreatitis is associated with an increased risk of pancreatic cancer, with older age at onset and smoking identified as risk factors. This scoping review aims to synthesise recent publications (2017-2022) on the diagnostic differentiation between pancreatitis and pancreatic cancer while identifying knowledge gaps in the field. The review focuses on biomarkers and imaging techniques in individuals with pancreatitis and pancreatic cancer. Promising biomarkers such as faecal elastase-1 and specific chemokines offer non-invasive ways to assess pancreatic insufficiency and detect early biomarkers for chronic pancreatitis. Imaging techniques, including computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasound (EUS), and positron emission tomography (PET), aid in differentiating between chronic pancreatitis and pancreatic cancer. However, accurately distinguishing between the two conditions remains a challenge, particularly when a mass is present in the head of the pancreas. Several knowledge gaps persist despite advancements in understanding the association between pancreatitis and pancreatic cancer, including the correlation between histopathological grading systems, non-invasive imaging techniques, and biomarkers in chronic pancreatitis to determine the risk of progression to pancreatic cancer, as well as differentiating between the two conditions. Further research is necessary to enhance our understanding of these aspects, which can ultimately improve the diagnosis and management of pancreatitis and pancreatic cancer.

Altered microheterogeneity at several N-glycosylation sites in OPSCC in constant protein expression conditions

Protein glycosylation responds sensitively to disease states. It is implicated in every hallmark of cancer and has recently started to be considered as a hallmark itself. Changes in N-glycosylation microheterogeneity are more dramatic than those of protein expression due to the non-template nature of protein glycosylation. This enables their potential use in serum-based diagnostics. Here, we perform glycopeptidomics on serum from patients with oropharyngeal squamous cell carcinoma (OPSCC), compared to controls and comparing between cancers based on etiology (human papilloma virus- positive or negative). Using MS2, we then targeted glycoforms, significantly different between the groups, to identify their glycopeptide compositions. Simultaneously we investigate the same serum proteins, comparing whether N-glycosylation changes reflect protein-level changes. Significant glycoforms were identified from proteins such as alpha-1-antitrypsin (SERPINA1), haptoglobin, and different immunoglobulins. SERPINA1 had glycovariance at 2 N-glycosylation sites, that were up to 35 times more abundant in even early-stage OPSCCs, despite minimal differences between SERPINA1 protein levels between groups. Some identified glycoforms' fold changes (FCs) were in line with serum protein level FCs, others were less abundant in early-stage cancers but with great variance in higher-stage cancers, such as on immunoglobulin heavy constant gamma 2, despite no change in protein levels. Such findings indicate that glycovariant analysis might be more beneficial than proteomic analysis, which is yet to be fruitful in the search for biomarkers. Highly sensitive glycopeptide changes could potentially be used in the future for cancer screening. Additionally, characterizing the glycopeptide changes in OPSCC is valuable in the search for potential therapeutic targets.

Bile Acids and Microbiota Interplay in Pancreatic Cancer

Evidence suggests the involvement of the microbiota, including oral, intra-tumoral and gut, in pancreatic cancer progression and response to therapy. The gut microbiota modulates the bile acid pool and is associated with maintaining host physiology. Studies have shown that the bile acid/gut microbiota axis is dysregulated in pancreatic cancer. Bile acid receptor expression and bile acid levels are dysregulated in pancreatic cancer as well. Studies have also shown that bile acids can cause pancreatic cell injury and facilitate cancer cell proliferation. The microbiota and its metabolites, including bile acids, are also altered in other conditions considered risk factors for pancreatic cancer development and can alter responses to chemotherapeutic treatments, thus affecting patient outcomes. Altogether, these findings suggest that the gut microbial and/or bile acid profiles could also serve as biomarkers for pancreatic cancer detection. This review will discuss the current knowledge on the interaction between gut microbiota interaction and bile acid metabolism in pancreatic cancer.

Research of Prostate Cancer Urinary Diagnostic Biomarkers by Proteomics: The Noteworthy Influence of Inflammation

Nowadays, in the case of suspected prostate cancer (PCa), tissue needle biopsy remains the benchmark for diagnosis despite its invasiveness and poor tolerability, as serum prostate-specific antigen (PSA) is limited by low specificity. The aim of this proteomic study was to identify new diagnostic biomarkers in urine, an easily and non-invasively available sample, able to selectively discriminate cancer from benign prostatic hyperplasia (BPH), evaluating whether the presence of inflammation may be a confounding parameter. The analysis was performed by two-dimensional gel electrophoresis (2-DE), mass spectrometry (LC-MS/MS) and Enzyme-Linked Immunosorbent Assay (ELISA) on urine samples from PCa and BPH patients, divided into subgroups based on the presence or absence of inflammation. Significant quantitative and qualitative differences were found in the urinary proteomic profile of PCa and BPH groups. Of the nine differentially expressed proteins, only five can properly be considered potential biomarkers of PCa able to discriminate the two diseases, as they were not affected by the inflammatory process. Therefore, the proteomic research of novel and reliable urinary biomarkers of PCa should be conducted considering the presence of inflammation as a realistic interfering element, as it could hinder the detection of important protein targets.

Multitasking dynamic contrast enhanced magnetic resonance imaging can accurately differentiate chronic pancreatitis from pancreatic ductal adenocarcinoma

Background and aims

Accurate differentiation of chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) is an area of unmet clinical need. In this study, a novel Multitasking dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) technique was used to quantitatively evaluate the microcirculation properties of pancreas in CP and PDAC and differentiate between them.

Methods

The Multitasking DCE technique was able to acquire one 3D image per second during the passage of MRI contrast agent, allowing the quantitative estimation of microcirculation properties of tissue, including blood flow F_p, plasma volume fraction v_p, transfer constant K^trans, and extravascular extracellular volume fraction v_e. Receiver operating characteristic (ROC) analysis was performed to differentiate the CP pancreas, PDAC pancreas, normal control pancreas, PDAC tumor, PDAC upstream, and PDAC downstream. ROCs from quantitative analysis and conventional analysis were compared.

Results

Fourteen PDAC patients, 8 CP patients and 20 healthy subjects were prospectively recruited. The combination of F_p, v_p, K^trans, and v_e can differentiate CP versus PDAC pancreas with good AUC (AUC [95% CI] = 0.821 [0.654 - 0.988]), CP versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), PDAC pancreas versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC tumor with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC downstream with excellent AUC (0.917 [0.795 - 1.000]), and CP versus PDAC upstream with fair AUC (0.722 [0.465 - 0.980]). This quantitative analysis outperformed conventional analysis in differentiation of each pair.

Conclusion

Multitasking DCE MRI is a promising clinical tool that is capable of unbiased quantitative differentiation between CP from PDAC.

Quantitative bile and serum proteomics for the screening and differential diagnosis of primary sclerosing cholangitis

Background

Primary sclerosing cholangitis (PSC) is a chronic liver disease characterized by biliary strictures, cholestasis, and a markedly increased risk of cholangiocarcinoma. New markers for the screening and differential diagnosis of PSC are needed. In this pilot study, we have analyzed both the bile and serum proteomic profiles of 80 PSC patients and non-PSC controls (n = 6 for bile and n = 18 for serum).

Aim

The aim of this study was to discover candidates for new biomarkers for the differential diagnosis of PSC.

Methods

Bile and serum samples were processed and subsequently analyzed using ultra performance liquid chromatography-ultra definition mass spectrometry (UPLC-UDMSE). Further analysis included statistical analyses such as receiver operating characteristic curve analysis as well as pathway analysis using Ingenuity Pathway Analysis.

Results and conclusions

In bile, we discovered 64 proteins with significantly different levels between the groups, with fold changes of up to 129. In serum, we discovered 112 proteins with significantly different levels. Receiver operating characteristic curve analysis found multiple proteins with high area under the curve values, up to 0.942, indicating that these serum proteins are of value as new non-invasive classifiers of PSC. Pathway analysis revealed multiple canonical pathways that were enriched in the dataset, which have roles in bile homeostasis and metabolism. We present several serum proteins that could serve as new blood-based markers for the diagnosis of PSC after further validation. The measurement of serum levels of these proteins could be of use in the screening of patients with suspected PSC.

ConSIG: consistent discovery of molecular signature from OMIC data

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.

POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability

Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.

Application of Proteomics in Pancreatic Ductal Adenocarcinoma Biomarker Investigations: A Review

Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with a poor prognosis is usually detected at the advanced stage of the disease. The only US Food and Drug Administration-approved biomarker that is available for PDAC, CA 19-9, is most useful in monitoring treatment response among PDAC patients rather than for early detection. Moreover, when CA 19-9 is solely used for diagnostic purposes, it has only a recorded sensitivity of 79% and specificity of 82% in symptomatic individuals. Therefore, there is an urgent need to identify reliable biomarkers for diagnosis (specifically for the early diagnosis), ascertain prognosis as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies are growing exponentially at an accelerated rate for a wide range of applications in cancer research. In this review, we discussed the current status of biomarker research for PDAC using various proteomic technologies. This review will explore the potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage, ascertain prognosis of patients with the disease in addition to monitoring treatment response and tumour recurrence of patients.

Online-2D NanoLC-MS for Crude Serum Proteome Profiling: Assessing Sample Preparation Impact on Proteome Composition

Although current LC-MS technology permits scientists to efficiently screen clinical samples in translational research, e.g., steroids, biogenic amines, and even plasma or serum proteomes, in a daily routine, maintaining the balance between throughput and analytical depth is still a limiting factor. A typical approach to enhance the proteome depth is employing offline two-dimensional (2D) fractionation techniques before reversed-phase nanoLC-MS/MS analysis (1D-nanoLC-MS). These additional sample preparation steps usually require extensive sample manipulation, which could result in sample alteration and sample loss. Here, we present and compare 1D-nanoLC-MS with an automated online-2D high-pH RP × low pH RP separation method for deep proteome profiling using a nanoLC system coupled to a high-resolution accurate-mass mass spectrometer. The proof-of-principle study permitted the identification of ca. 500 proteins with ∼10,000 peptides in 15 enzymatically digested crude serum samples collected from healthy donors in 3 laboratories across Europe. The developed method identified 60% more peptides in comparison with conventional 1D nanoLC-MS/MS analysis with ca. 4 times lower throughput while retaining the quantitative information. Serum sample preparation related changes were revealed by applying unsupervised classification techniques and, therefore, must be taken into account while planning multicentric biomarker discovery and validation studies. Overall, this novel method reduces sample complexity and boosts the number of peptide and protein identifications without the need for extra sample handling procedures for samples equivalent to less than 1 μL of blood, which expands the space for potential biomarker discovery by looking deeper into the composition of biofluids.

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.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

Proteomic analysis of pancreatic ductal adenocarcinoma

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC), which represents approximately 80% of all pancreatic cancers, is a highly aggressive malignant disease and one of the most lethal among all cancers. Overall, the 5-year survival rate among all pancreatic cancer patients is less than 9%; these rates have shown little change over the past 30 years. A more comprehensive understanding of the molecular mechanisms underlying this complex disease is crucial to the development of new diagnostic tools for early detection and disease monitoring, as well as to identify new and more effective therapeutics to improve patient outcomes.

AREA COVERED

We summarize recent advances in proteomic strategies and mass spectrometry to identify new biomarkers for early detection and monitoring of disease progression, predict response to therapy, and to identify novel proteins that have the potential to be 'druggable' therapeutic targets. An overview of proteomic studies that have been conducted to further our mechanistic understanding of metastasis and chemotherapy resistance in PDAC disease progression will also be discussed.

EXPERT COMMENTARY

The results from these PDAC proteomic studies on a variety of PDAC sample types (e.g., blood, tissue, cell lines, exosomes, etc.) provide great promise of having a significant clinical impact and improving patient outcomes.

PSMD11, PTPRM and PTPRB as novel biomarkers of pancreatic cancer progression

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate of all major cancers. Surgery is the only curative intent therapy, but the majority of patients experience disease relapse. Thus, patients who do not benefit from highly morbid surgical resection needs to be identified and offered palliative chemotherapy instead. In this pilot study, we aimed to identify differentially regulated proteins in plasma and plasma derived microparticles from PDAC patients with poor and good prognosis.

METHODS

Plasma and plasma derived microparticle samples were obtained before surgical resection from PDAC patients. Sequential Windowed Acquisition of all Theoretical fragment ion spectra - Mass Spectrometry (SWATH-MS) proteomic analysis was performed to identify and quantify proteins in these samples. Statistical analysis was performed to identify biomarkers for poor prognosis.

RESULTS

A total of 482 and 1024 proteins were identified from plasma and microparticle samples, respectively, by SWATH-MS analysis. Statistical analysis of the data further identified nine and six differentially (log₂ratio > 1, p < .05) expressed proteins in plasma and microparticles, respectively. Protein tyrosine phosphatases, PTPRM and PTPRB, were decreased in plasma of patients with poor PDAC prognosis, while proteasomal subunit PSMD11 was increased in microparticles of patients with poor prognosis.

CONCLUSION AND GENERAL SIGNIFICANCE

A novel blood-based biomarker signature for PDAC prognosis was identified.

A rare case of acute pancreatitis: a diagnostic conundrum and unusual complications

A 63-year-old man with a history of recurrent idiopathic acute pancreatitis (AP) was admitted to our surgical ward due to severe abdominal pain. He denied chronic excessive alcohol use. Other typical causes of AP, such as gallstones, hypertriglyceridemia, and trauma, were ruled out. After considering all possible etiologies, the most likely factor producing AP was medication that had been administered to him two weeks before the very first episode of the disease. Medication should always be considered as a possible trigger of AP, especially if the first episode occurs shortly after drug administration and the etiology is unclear. During patient’s hospitalization, laboratory reports revealed significant fluctuations in the serum levels of pancreatic enzymes, which can be attributed to recurrent bacteremia. After the 30-day period of hospitalization and long-lasting antibiotic therapy, he was discharged in a good condition with normal levels of serum pancreatic enzymes.

Label-free proteomics reveals serum proteins whose levels differ between pancreatic ductal adenocarcinoma patients with short or long survival

Pancreatic ductal adenocarcinoma is the most common and aggressive type of pancreatic cancer, with a 5-year survival rate that is less than 10%. New biomarkers to aid in predicting the prognosis of pancreatic ductal adenocarcinoma patients are needed. Previous proteomic studies have to a great extent focused on finding proteins of value for the diagnosis of pancreatic ductal adenocarcinoma. There is a lack of studies that have profiled the serum or plasma proteome in order to discover candidates for new prognostic biomarkers. In this study, we have used ultra-performance liquid chromatography-ultra-definition mass spectrometry to analyze the serum samples of 21 pancreatic ductal adenocarcinoma patients with short or long survival. Statistical analysis discovered 31 proteins whose expression differed significantly between pancreatic ductal adenocarcinoma patients with short or long survival. Pathway analysis discovered multiple canonical pathways enriched in this data set, with several pathways having roles in inflammation and lipid metabolism. The serum proteins identified here, which include complement components and several enzymes, could be of value as candidates for new noninvasive prognostic markers.

Label-free plasma proteomics identifies haptoglobin-related protein as candidate marker of idiopathic pulmonary fibrosis and dysregulation of complement and oxidative pathways

Idiopathic pulmonary fibrosis (IPF) is a lung parenchymal disease of unknown cause usually occurring in older adults. It is a chronic and progressive condition with poor prognosis and diagnosis is largely clinical. Currently, there exist few biomarkers that can predict patient outcome or response to therapies. Together with lack of markers, the need for novel markers for the detection and monitoring of IPF, is paramount. We have performed label-free plasma proteomics of thirty six individuals, 17 of which had confirmed IPF. Proteomics data was analyzed by volcano plot, hierarchical clustering, Partial-least square discriminant analysis (PLS-DA) and Ingenuity pathway analysis. Univariate and multivariate statistical analysis overlap identified haptoglobin-related protein as a possible marker of IPF when compared to control samples (Area under the curve 0.851, ROC-analysis). LXR/RXR activation and complement activation pathways were enriched in t-test significant proteins and oxidative regulators, complement proteins and protease inhibitors were enriched in PLS-DA significant proteins. Our pilot study points towards aberrations in complement activation and oxidative damage in IPF patients and provides haptoglobin-related protein as a new candidate biomarker of IPF.

Oncobiosis and Microbial Metabolite Signaling in Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma is one of the most lethal cancers in both men and women, with a median five-year survival of around 5%. Therefore, pancreatic adenocarcinoma represents an unmet medical need. Neoplastic diseases, such as pancreatic adenocarcinoma, often are associated with microbiome dysbiosis, termed oncobiosis. In pancreatic adenocarcinoma, the oral, duodenal, ductal, and fecal microbiome become dysbiotic. Furthermore, the pancreas frequently becomes colonized (by Helicobacter pylori and Malassezia, among others). The oncobiomes from long- and short-term survivors of pancreatic adenocarcinoma are different and transplantation of the microbiome from long-term survivors into animal models of pancreatic adenocarcinoma prolongs survival. The oncobiome in pancreatic adenocarcinoma modulates the inflammatory processes that drive carcinogenesis. In this review, we point out that bacterial metabolites (short chain fatty acids, secondary bile acids, polyamines, indole-derivatives, etc.) also have a role in the microbiome-driven pathogenesis of pancreatic adenocarcinoma. Finally, we show that bacterial metabolism and the bacterial metabolome is largely dysregulated in pancreatic adenocarcinoma. The pathogenic role of additional metabolites and metabolic pathways will be identified in the near future, widening the scope of this therapeutically and diagnostically exploitable pathogenic pathway in pancreatic adenocarcinoma.

Identification of several plasma proteins whose levels in colorectal cancer patients differ depending on outcome

Colorectal cancer (CRC) stands for 10% of the worldwide cancer burden and has recently become the second most common cause of cancer death. The 5-year survival rate depends mainly on stage at diagnosis. Mass spectrometric proteomic analysis is widely used to study the plasma proteome, which is complex and contains multitudes of proteins. In this study, we have used Ultra Performance Liquid Chromatography-Ultra Definition Mass Spectrometry (UPLC-UDMSE)-based proteomics to analyze plasma samples from 76 CRC patients. We identified several plasma proteins, such as CP, TVP23C, FETUB, and IGFBP3, of which altered levels led to significant differences in survival, as seen by Cox regression and Kaplan-Meier analysis. Additionally, during Cox regression analysis, samples were adjusted for age and/or tumor stage, enabling stringent analysis. These proteins, although in need of further validation, could be of use during patient follow-up, as their levels can non-invasively be measured from blood samples, and could be of use in predicting patient outcome. Several of these proteins additionally have roles in metabolism and inflammation, two processes central to the development and progression of cancer, further indicating their importance in cancer.

Thromboinflammatory changes in plasma proteome of pregnant women with PCOS detected by quantitative label-free proteomics

Polycystic ovary syndrome (PCOS) is the most common endocrinological disorder of fertile-aged women. Several adverse pregnancy outcomes and abnormalities of the placenta have been associated with PCOS. By using quantitative label-free proteomics we investigated whether changes in the plasma proteome of pregnant women with PCOS could elucidate the mechanisms behind the pathologies observed in PCOS pregnancies. A total of 169 proteins with ≥2 unique peptides were detected to be differentially expressed between women with PCOS (n = 7) and matched controls (n = 20) at term of pregnancy, out of which 35 were significant (p-value < 0.05). A pathway analysis revealed that networks related to humoral immune responses, inflammatory responses, cardiovascular disease and cellular growth and proliferation were affected by PCOS. Classification of cases and controls was carried out using principal component analysis, orthogonal projections on latent structure-discriminant analysis (OPLS-DA), hierarchical clustering, self-organising maps and ROC-curve analysis. The most significantly enriched proteins in PCOS were properdin and insulin-like growth factor II. In the dataset, properdin had the best predictive accuracy for PCOS (AUC = 1). Additionally, properdin abundances correlated with AMH levels in pregnant women.

Proteomic analyses of ECM during pancreatic ductal adenocarcinoma progression reveal different contributions by tumor and stromal cells

Pancreatic ductal adenocarcinoma (PDAC) has prominent extracellular matrix (ECM) that compromises treatments yet cannot be nonselectively disrupted without adverse consequences. ECM of PDAC, despite the recognition of its importance, has not been comprehensively studied in patients. In this study, we used quantitative mass spectrometry (MS)-based proteomics to characterize ECM proteins in normal pancreas and pancreatic intraepithelial neoplasia (PanIN)- and PDAC-bearing pancreas from both human patients and mouse genetic models, as well as chronic pancreatitis patient samples. We describe detailed changes in both abundance and complexity of matrisome proteins in the course of PDAC progression. We reveal an early up-regulated group of matrisome proteins in PanIN, which are further up-regulated in PDAC, and we uncover notable similarities in matrix changes between pancreatitis and PDAC. We further assigned cellular origins to matrisome proteins by performing MS on multiple lines of human-to-mouse xenograft tumors. We found that, although stromal cells produce over 90% of the ECM mass, elevated levels of ECM proteins derived from the tumor cells, but not those produced exclusively by stromal cells, tend to correlate with poor patient survival. Furthermore, distinct pathways were implicated in regulating expression of matrisome proteins in cancer cells and stromal cells. We suggest that, rather than global suppression of ECM production, more precise ECM manipulations, such as targeting tumor-promoting ECM proteins and their regulators in cancer cells, could be more effective therapeutically.

Urinary Proteomics Profiles Are Useful for Detection of Cancer Biomarkers and Changes Induced by Therapeutic Procedures

Boron neutron capture therapy (BNCT) is a binary cancer treatment modality where two different agents (¹⁰B and thermal neutrons) have to be present to produce an effect. A dedicated trial design is necessary for early clinical trials. The concentration of ¹⁰B in tissues is an accepted surrogate to predict BNCT effects on tissues. Tissue, blood, and urines were sampled after infusion of two different boron carriers, namely BSH and BPA in the frame of the European Organisation for Research and Treatment of Cancer (EORTC) trial 11001. In this study, urine samples were used to identify protein profiles prior and after drug infusion during surgery. Here, an approach that is based on the mass spectrometry (MS)-based proteomic analysis of urine samples from head and neck squamous cell carcinoma (HNSCC) and thyroid cancer patients is presented. This method allowed the identification of several inflammation- and cancer-related proteins, which could serve as tumor biomarkers. In addition, changes in the urinary proteome during and after therapeutic interventions were detected. In particular, a reduction of three proteins that were involved in inflammation has been observed: Galectin-3 Binding Protein, CD44, and osteopontin. The present work represents a proof of principle to follow proteasome changes during complex treatments based on urine samples.

Plasma Proteomics Analysis Reveals Dysregulation of Complement Proteins and Inflammation in Acquired Obesity-A Study on Rare BMI-Discordant Monozygotic Twin Pairs

PURPOSE

The purpose of this study is to elucidate the effect of excess body weight and liver fat on the plasma proteome without interference from genetic variation.

EXPERIMENTAL DESIGN

The effect of excess body weight is assessed in young, healthy monozygotic twins from pairs discordant for body mass index (intrapair difference (Δ) in BMI > 3 kg m^-2 , n = 26) with untargeted LC-MS proteomics quantification. The effect of liver fat is interrogated via subgroup analysis of the BMI-discordant twin cohort: liver fat discordant pairs (Δliver fat > 2%, n = 12) and liver fat concordant pairs (Δliver fat < 2%, n = 14), measured by magnetic resonance spectroscopy.

RESULTS

Seventy-five proteins are differentially expressed, with significant enrichment for complement and inflammatory response pathways in the heavier co-twins. The complement dysregulation is found in obesity in both the liver fat subgroups. The complement and inflammatory proteins are significantly associated with adiposity measures, insulin resistance and impaired lipids.

CONCLUSIONS AND CLINICAL RELEVANCE

The early pathophysiological mechanisms in obesity are incompletely understood. It is shown that aberrant complement regulation in plasma is present in very early stages of clinically healthy obese persons, independently of liver fat and in the absence of genetic variation that typically confounds human studies.

Plasma Gelsolin: Indicator of Inflammation and Its Potential as a Diagnostic Tool and Therapeutic Target

Gelsolin, an actin-depolymerizing protein expressed both in extracellular fluids and in the cytoplasm of a majority of human cells, has been recently implicated in a variety of both physiological and pathological processes. Its extracellular isoform, called plasma gelsolin (pGSN), is present in blood, cerebrospinal fluid, milk, urine, and other extracellular fluids. This isoform has been recognized as a potential biomarker of inflammatory-associated medical conditions, allowing for the prediction of illness severity, recovery, efficacy of treatment, and clinical outcome. A compelling number of animal studies also demonstrate a broad spectrum of beneficial effects mediated by gelsolin, suggesting therapeutic utility for extracellular recombinant gelsolin. In the review, we summarize the current data related to the potential of pGSN as an inflammatory predictor and therapeutic target, discuss gelsolin-mediated mechanisms of action, and highlight recent progress in the clinical use of pGSN.

Patients with early-stage oropharyngeal cancer can be identified with label-free serum proteomics

Background

The increasing incidence of oropharyngeal squamous cell carcinoma (OPSCC) is mainly related to human papillomavirus (HPV) infection. As OPSCCs are often diagnosed at an advanced stage, mortality and morbidity remain high. There are no diagnostic biomarkers for early detection of OPSCC.

Methods

Serum from 25 patients with stage I–II OPSCC, and 12 healthy controls, was studied with quantitative label-free proteomics using ultra-definition MS^E. Statistical analyses were performed to identify the proteins most reliably distinguishing early-stage OPSCCs from controls. P16 was used as a surrogate marker for HPV. P16-positive and P16-negative tumours were analysed separately.

Results

With two or more unique proteins per identification, 176 proteins were quantified. A clear separation between patients with early-stage tumours and controls was seen in principal component analysis. Latent structures discriminant analysis identified 96 proteins, most reliably differentiating OPSCC patients from controls, with 13 upregulated and 83 downregulated proteins in study cases. The set of proteins was studied further with network, pathway and protein–protein interaction analyses, and found to participate in lipid metabolism, for example.

Conclusions

We found a set of serum proteins distinguishing early-stage OPSCC from healthy individuals, and suggest a protein set for further evaluation as a diagnostic biomarker panel for OPSCC.

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.

Colorectal cancer patients with different C-reactive protein levels and 5-year survival times can be differentiated with quantitative serum proteomics

Over 1.4 million people are diagnosed with colorectal cancer (CRC) each year, making it the third most common cancer in the world. Increased screening and therapeutic modalities including improved combination treatments have reduced CRC mortality, although incidence and mortality rates are still increasing in some areas. Serum-based biomarkers are mainly used for follow-up of cancer, and are ideal due to the ease and minimally invasive nature of sample collection. Unfortunately, CEA and other serum markers have too low sensitivity for screening and preoperative diagnostic purposes. Increasing interest is focused on the possible use of biomarkers for predicting treatment response and prognosis in cancer. In this study, we have performed mass spectrometry analysis (UPLC-UDMS^E) of serum samples from 19 CRC patients. Increased levels of C-reactive protein (CRP), which occur during local inflammation and the presence of a systemic inflammatory response, have been linked to poor prognosis in CRC patients. We chose to analyze samples according to CRP values by dividing them into the categories CRP <30 and >30, and, separately, according to short and long 5-year survival. The aim was to discover differentially expressed proteins associated with poor prognosis and shorter survival. We quantified 256 proteins and performed detailed statistical analyses and pathway analysis. We discovered multiple proteins that are up- or downregulated in patients with CRP >30 as compared to CRP <30 and in patients with short as compared to long 5-year survival. Pathways that were enriched include LXR/RXR activation, FXR/RXR activation, complement and coagulation cascades and acute phase signaling response, with some of the proteins we identified having roles in these pathways. In this study, we have identified multiple proteins, of which a few have been previously identified as potential biomarkers, and others that have been identified as potential biomarkers for CRC for the first time, to the best of our knowledge. While these proteins still need to be validated in larger patient series, this pilot study will pave the way for future studies aiming to provide better biomarkers for patients with CRC.

Identification of MST1 as a potential early detection biomarker for colorectal cancer through a proteomic approach

Colorectal cancer (CRC) is a common malignant neoplasm worldwide. It is important to identify new biomarkers for the early detection of CRC. In this study, magnetic beads and the Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) platform were used to analyse CRC and healthy control (HC) serum samples. The CRC diagnosis pattern was established to have a specificity of 94.7% and sensitivity of 92.3% in a blind test. The candidate biomarker serine/threonine kinase 4 (STK4, also known as MST1) was identified by Tandem mass spectrometry (MS/MS) and verified with western blotting and enzyme-linked immunosorbent assay (ELISA). The results indicated that there was a higher concentration of MST1 in HC subjects than stage I CRC patients for the early detection of CRC and a lower concentration in stage IV patients than in other CRC patients. The sensitivity and specificity of MST1 combined with carcinoembryonic antigen (CEA) and faecal occult blood test (FOBT) in diagnosis of colorectal cancer were 92.3% and 100%, respectively. Additionally, low MST1 expression was associated with the poor prognosis. These results illustrate that MST1 is a potential biomarker for early detection, prognosis and prediction of distant metastasis of CRC.

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.