Identification of serum biomarker signatures associated with pancreatic cancer

Optimizing hybrid ensemble feature selection strategies for transcriptomic biomarker discovery in complex diseases

Biomedical research takes advantage of omic data, such as transcriptomics, to unravel the complexity of diseases. A conventional strategy identifies transcriptomic biomarkers characterized by expression patterns associated with a phenotype by relying on feature selection approaches. Hybrid ensemble feature selection (HEFS) has become increasingly popular as it ensures robustness of the selected features by performing data and functional perturbations. However, it remains difficult to make the best suited choices at each step when designing such approaches. We conducted an extensive analysis of four possible HEFS scenarios for the identification of Stage IV colorectal, Stage I kidney and lung and Stage III endometrial cancer biomarkers from transcriptomic data. These scenarios investigate the use of two types of feature reduction by filters (differentially expressed genes and variance) conjointly with two types of resampling strategies (repeated holdout by distribution-balanced stratified and random stratified) for downstream feature selection through an aggregation of thousands of wrapped machine learning models. Based on our results, we emphasize the advantages of using HEFS approaches to identify complex disease biomarkers, given their ability to produce generalizable and stable results to both data and functional perturbations. Finally, we highlight critical issues that need to be considered in the design of such strategies.

N-glycan biosignatures as a potential diagnostic biomarker for early-stage pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with a 5-year survival rate of less than 10%, owing to its late-stage diagnosis. Early detection of pancreatic cancer (PC) can significantly increase survival rates.

AIM

To identify the serum biomarker signatures associated with early-stage PDAC by serum N-glycan analysis.

METHODS

An extensive patient cohort was used to determine a biomarker signature, including patients with PDAC that was well-defined at an early stage (stages I and II). The biomarker signature was derived from a case-control study using a case-cohort design consisting of 29 patients with stage I, 22 with stage II, 4 with stage III, 16 with stage IV PDAC, and 88 controls. We used multiparametric analysis to identify early-stage PDAC N-glycan signatures and developed an N-glycan signature-based diagnosis model called the "Glyco-model".

RESULTS

The biomarker signature was created to discriminate samples derived from patients with PC from those of controls, with a receiver operating characteristic area under the curve of 0.86. In addition, the biomarker signature combined with cancer antigen 19-9 could discriminate patients with PDAC from controls, with a receiver operating characteristic area under the curve of 0.919. Glyco-model demonstrated favorable diagnostic performance in all stages of PC. The diagnostic sensitivity for stage I PDAC was 89.66%.

CONCLUSION

In a prospective validation study, this serum biomarker signature may offer a viable method for detecting early-stage PDAC.

Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data

BACKGROUND

Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development.

RESULTS

Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed.

CONCLUSIONS

Validation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites and also suggest cancer treatment strategies.

Innate and adaptive immune-directed tumour microenvironment in pancreatic ductal adenocarcinoma

One of the most deadly and aggressive cancers in the world, pancreatic ductal adenocarcinoma (PDAC), typically manifests at an advanced stage. PDAC is becoming more common, and by the year 2030, it is expected to overtake lung cancer as the second greatest cause of cancer-related death. The poor prognosis can be attributed to a number of factors, including difficulties in early identification, a poor probability of curative radical resection, limited response to chemotherapy and radiotherapy, and its immunotherapy resistance. Furthermore, an extensive desmoplastic stroma that surrounds PDAC forms a mechanical barrier that prevents vascularization and promotes poor immune cell penetration. Phenotypic heterogeneity, drug resistance, and immunosuppressive tumor microenvironment are the main causes of PDAC aggressiveness. There is a complex and dynamic interaction between tumor cells in PDAC with stromal cells within the tumour immune microenvironment. The immune suppressive microenvironment that promotes PDAC aggressiveness is contributed by a range of cellular and humoral factors, which itself are modulated by the cancer. In this review, we describe the role of innate and adaptive immune cells, complex tumor microenvironment in PDAC, humoral factors, innate immune-mediated therapeutic advances, and recent clinical trials in PDAC.

A Comprehensive Review of the Potential Role of Liquid Biopsy as a Diagnostic, Prognostic, and Predictive Biomarker in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma is one of the most lethal malignant diseases, with a mortality rate being close to incidence. Due to its heterogeneity and plasticity, as well as the lack of distinct symptoms in the early phases, it is very often diagnosed at an advanced stage, resulting in poor prognosis. Traditional tissue biopsies remain the gold standard for making a diagnosis, but have an obvious disadvantage in their inapplicability for frequent sampling. Blood-based biopsies represent a non-invasive method which potentially offers easy and repeated sampling, leading to the early detection and real-time monitoring of the disease and hopefully an accurate prognosis. Given the urgent need for a reliable biomarker that can estimate a patient's condition and response to an assigned treatment, blood-based biopsies are emerging as a potential new tool for improving patients' survival and surveillance. In this article, we discuss the current advances and challenges in using liquid biopsies for pancreatic cancer, focusing on circulating tumour DNA (ctDNA), extracellular vesicles (EVs), and circulating tumour cells (CTCs), and compare the performance and reliability of different biomarkers and combinations of biomarkers.

Serum protein profiling of lung, pancreatic, and colorectal cancers reveals alcohol consumption-mediated disruptions in early-stage cancer detection

While the link between serum proteins and cancer has been studied in an effort to enable early-stage cancer detection, factors that might perturb this link has been poorly understood. To ask this question, we performed serum protein profiling on a prospective cohort of 601 individuals with or without lung, pancreatic, or colorectal cancers and identified ten distinct serum protein signatures with distinct link to the patient metadata. Importantly, we discovered that a positive history of alcohol consumption is a major factor that diminishes the sensitivity of serum protein-mediated liquid biopsy in early-stage malignancies, resulting in a 44% decline in the sensitivity of detecting American Joint Committee on Cancer (AJCC) stage I malignancies. Our data provide evidence that patient lifestyle can affect the sensitivity of liquid biopsy and suggest the potential need for abstinence from alcohol before measurement during serum protein-based cancer screening.

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

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

Rapid, Sensitive, and Selective Quantification of Bacillus cereus Spores Using xMAP Technology

Bacillus cereus is a spore-forming ubiquitous bacterium notable as a food poisoning agent. Detection of B. cereus spores using selective media is laborious and non-specific. Herein, the quantitative detection of B. cereus spores was investigated with commercial antibodies and published aptamer sequences. Several detection reagents were screened for affinity to Bacillus collagen-like protein A (BclA), an abundant exosporium glycoprotein. Sensitivity and selectivity toward B. cereus spores were tested using immunoassays and multi-analyte profiling (xMAP). A recombinant antibody developed in llama against BclA protein showed B. cereus spore selectivity and sensitivity between 10² and 10⁵ spores/mL using xMAP. DNA aptamer sequences demonstrated sensitivity from 10³ to 10⁷ spores/mL and no cross-reaction to B. megaterium and B. subtilis. Selectivity for B. cereus spores was also demonstrated in a mixture of several diverse microorganisms and within a food sample with no compromise of sensitivity. As proof of concept for multiplexed measurement of human pathogens, B. cereus and three other microorganisms, E. coli, P. aeruginosa, and S. cerevisiae, were simultaneously detected using xMAP. These data support the development of a rapid, sensitive, and selective system for quantitation of B. cereus spores and multiplexed monitoring of human pathogens in complex matrices.

Advanced iron oxide nanotheranostics for multimodal and precision treatment of pancreatic ductal adenocarcinoma

Despite current advances in new approaches for cancer detection and treatment, pancreatic cancer remains one of the most lethal cancer types. Difficult to detect early, aggressive tumor biology, and resistance to chemotherapy, radiotherapy, and immunotherapy result in a poor prognosis of pancreatic cancer patients with a 5-year survival of 10%. With advances in cancer nanotechnology, new imaging and drug delivery approaches that allow the development of multifunctional nanotheranostic agents offer opportunities for improving pancreatic cancer treatment using precision oncology. In this review, we will introduce potential applications of innovative theranostic strategies to address major challenges in the treatment of pancreatic cancer at different disease stages. Several important issues concerning targeted delivery of theranostic nanoparticles and tumor stromal barriers are discussed. We then focus on the development of a magnetic iron oxide nanoparticle platform for multimodal therapy of pancreatic cancer, including MRI monitoring targeted nanoparticle/drug delivery, therapeutic response, and tumor re-staging, activation of tumor immune response by immunoactivating nanoparticle and magnetic hyperthermia therapy, and intraoperative interventions for improving the outcome of targeted therapy. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.

Detection of Early-Stage Pancreatic Ductal Adenocarcinoma From Blood Samples: Results of a Multiplex Biomarker Signature Validation Study

INTRODUCTION

The IMMray PanCan-d test combines an 8-plex biomarker signature with CA19-9 in a proprietary algorithm to detect pancreatic ductal adenocarcinoma (PDAC) in serum samples. This study aimed to validate the clinical performance of the IMMray PanCan-d test and to better understand test performance in Lewis-null (le/le) individuals who cannot express CA19-9.

METHODS

Serum samples from 586 individuals were analyzed with the IMMray PanCan-d biomarker signature and CA19-9 assay, including 167 PDAC samples, 203 individuals at high risk of familial/hereditary PDAC, and 216 healthy controls. Samples were collected at 11 sites in the United States and Europe. The study was performed by Immunovia, Inc (Marlborough, MA), and sample identity was blinded throughout the study. Test results were automatically generated using validated custom software with a locked algorithm and predefined decision value cutoffs for sample classification.

RESULTS

The IMMray PanCan-d test distinguished PDAC stages I and II (n = 56) vs high-risk individuals with 98% specificity and 85% sensitivity and distinguished PDAC stages I-IV vs high-risk individuals with 98% specificity and 87% sensitivity. We identified samples with a CA19-9 value of 2.5 U/mL or less as probable Lewis-null (le/le) individuals. Excluding these 55 samples from the analysis increased the IMMray PanCan-d test sensitivity to 92% for PDAC stages I-IV (n = 157) vs controls (n = 379) while maintaining specificity at 99%; test sensitivity for PDAC stages I and II increased from 85% to 89%.

DISCUSSION

These results demonstrate the IMMray PanCan-d blood test can detect PDAC with high specificity (99%) and sensitivity (92%).

Early Diagnostic Ability of Human Complement Factor B in Pancreatic Cancer Is Partly Linked to Its Potential Tumor-Promoting Role

Although plasma complement factor B (CFB, NX_P00751), both alone and in combination with CA19-9 (i.e., the ComB-CAN), previously exhibited a reliable diagnostic ability for pancreatic cancer (PC), its detectability of the early stages and the cancer detection mechanism remained elusive. We first evaluated the diagnostic accuracy of ComB-CAN using plasma samples from healthy donors (HDs), patients with chronic pancreatitis (CP), and patients with different PC stages (I/II vs III/IV). An analysis of the area under the curve (AUC) by PanelComposer using logistic regression revealed that ComB-CAN has a superior diagnostic ability for early-stage PC (97.1.% [95% confidence interval (CI): (97.1-97.2)]) compared with CFB (94.3% [95% CI: 94.2-94.4]) or CA19-9 alone (34.3% [95% CI: 34.1-34.4]). In the comparisons of all stages of patients with PC vs CP and HDs, the AUC values of ComB-CAN, CFB, and CA19-9 were 0.983 (95% CI: 0.983-0.983), 0.950 (95% CI: 0.950-0.951), and 0.873 (95% CI: 0.873-0.874), respectively. We then investigated the molecular mechanism underlying the detection of early-stage PC by using stable cell lines of CFB knockdown and CFB overexpression. A global transcriptomic analysis coupled to cell invasion assays of both CFB-modulated cell lines suggested that CFB plays a tumor-promoting role in PC, which likely initiates the PI3K-AKT cancer signaling pathway. Thus our study establishes ComB-CAN as a reliable early diagnostic marker for PC that can be clinically applied for early PC screening in the general public.

UEG position paper on pancreatic cancer. Bringing pancreatic cancer to the 21st century: Prevent, detect, and treat the disease earlier and better

BACKGROUND

Pancreatic ductal adenocarcinoma is the deadliest cancer worldwide with a 98% loss-of-life expectancy and a 30% increase in the disability-adjusted life years during the last decade in Europe. The disease cannot be effectively prevented nor being early detected. When diagnosed, 80% of patients have tumors that are in incurable stages, while for those who undergo surgery, 80% of patients will present with local or distant metastasis. Importantly, chemotherapies are far from being effective.

OBJECTIVE

Pancreatic cancer represents a great challenge and, at the same time, a huge opportunity for advancing our understanding on the basis of the disease, the molecular profiles, that would lead to develop tools for early detection and effective treatments, thus, boosting patient survival.

RESULTS

Research on pancreatic cancer has being receiving little or minimal funds from European funding bodies. UEG is calling for public-private partnerships that would effectively fund research on pancreatic cancer.

CONCLUSION

This would increase our understanding of this disease and better treatment, through pan-European efforts that take advantage of the strong academic European research landscape on pancreatic cancer, and the contribution by the industry of all sizes.

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

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

The current role of blood-based biomarkers in surgical decision-making in patients with localised pancreatic cancer: A systematic review

INTRODUCTION

The role of blood-based biomarkers in surgical decision-making in patients with localised pancreatic cancer remains unclear. This review aimed to report the utility of blood-based biomarkers focusing on prediction of response to neoadjuvant therapy, prediction of surgical resectability and early relapse after surgery.

MATERIALS AND METHODS

MEDLINE/PubMed, Embase and Web of Science were searched till October 2019. Studies published between January 2000 and September 2019 with a minimum of 20 patients with pancreatic adenocarcinoma, reporting the utility of at least one blood-based biomarker in predicting response to neoadjuvant therapy and predicting surgical resectability or early relapse after surgery were included.

RESULTS

A total of 2604 studies were identified, of which 24 comprising of 3367 patients and 12 blood-based biomarkers were included. All included studies were observational. Levels of carbohydrate antigen (CA)19-9 were reported in the majority of the studies. Levels of CA19-9 predicted the response to neoadjuvant therapy and early relapse in 10 studies. CA125 levels above 35 U/ml were predictive of surgical irresectability in two studies. However, marked variation in both timing of sampling and cut-off values was noted between studies.

CONCLUSION

Despite some evidence of potential benefit, the utility of currently available blood-based biomarkers in aiding surgical decision-making in patients undergoing potentially curative treatment for pancreatic cancer is limited by methodological heterogeneity. Standardisation of future studies may allow a more comprehensive analysis of the biomarkers described in this review.

Estimation and construction of confidence intervals for biomarker cutoff-points under the shortest Euclidean distance from the ROC surface to the perfection corner

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive type of cancer with a 5-year survival rate of less than 5%. As in many other diseases, its diagnosis might involve progressive stages. It is common that in biomarker studies referring to PDAC, recruitment involves three groups: healthy individuals, patients that suffer from chronic pancreatitis, and PDAC patients. Early detection and accurate classification of the state of the disease are crucial for patients' successful treatment. ROC analysis is the most popular way to evaluate the performance of a biomarker and the Youden index is commonly employed for cutoff derivation. The so-called generalized Youden index has a drawback in the three-class case of not accommodating the full data set when estimating the optimal cutoffs. In this article, we explore the use of the Euclidean distance of the ROC to the perfection corner for the derivation of cutoffs in trichotomous settings. We construct an inferential framework that involves both parametric and nonparametric techniques. Our methods can accommodate the full information of a given data set and thus provide more accurate estimates in terms of the decision-making cutoffs compared with a Youden-based strategy. We evaluate our approaches through extensive simulations and illustrate them on a PDAC biomarker study.

Discovery and Validation of Circulating EVL mRNA as a Prognostic Biomarker in Pancreatic Cancer

Background

Circulating plasma mRNAs can be analyzed to identify putative cancer biomarkers. This study was conducted in an effort to detect plasma mRNA biomarkers capable of predicting pancreatic cancer (PACA) patient prognosis. Material and Methods. First, prognostic mRNAs that were differentially expressed in PACA in The Cancer Genome Atlas (TCGA) were established, after which microarray expression profiles from PACA patient plasma samples were utilized to specifically identify potential prognostic plasma mRNA biomarkers associated with this cancer type. In total, plasma samples were then collected from 79 PACA patients and 19 healthy controls to confirm differential mRNA expression via qPCR, while Kaplan–Meier analyses were used to examine the link between mRNA expression and patient overall survival.

Results

In total, three prognostic differentially expressed genes were identified in PACA patient plasma samples, including SMAP2, PTPN6, and EVL (Ena/VASP-like). Plasma EVL levels were confirmed via qPCR to be correlated with tumor pathology (p < 0.01), while the overall survival of patients with low plasma EVL levels was poor (p < 0.01). Multivariate Cox regression analyses further confirmed that plasma EVL levels were independent predictors of PACA patient prognosis.

Conclusion

We found that PACA is associated with the downregulation of plasma EVL mRNA levels, indicating that this mRNA may be a viable biomarker associated with patient prognosis.

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.

Plasma suPAR may help to distinguish between chronic pancreatitis and pancreatic cancer

OBJECTIVES

SuPAR (soluble urokinase-type plasminogen activator receptor) is a biomarker reflecting the inflammatory state of the human body. Earlier studies suggest that urinary suPAR/creatinine ratio levels are elevated in chronic pancreatitis (CP), and that plasma suPAR (P-suPAR) level is elevated in pancreatic cancer (PC). Our aim was to study the levels of P-suPAR in CP in a long-term prospective follow-up setting to explore the possibility of distinguishing between PC and CP.

MATERIALS AND METHODS

Two patient groups were compared. The first group included 83 patients who were prospectively followed up after their first acute alcohol-induced pancreatitis (AAP) for median 7.0 (range 0.3-9.8) years. Twelve patients in this group developed CP during follow-up, and two patients were further excluded from the CP cohort. The second group consisted of 25 patients operated on for suspicion of pancreatic malignancy and final pathological diagnosis of PC. P-suPAR levels were measured and compared within and between these groups.

RESULTS

P-suPAR levels remained low during follow-up despite the development of CP. P-suPAR was significantly higher in PC patients [median 3.7 (IQR 3.1-4.4) ng/mL] than in CP patients [2.6 (1.8-3.6) ng/mL]; p = .014. A cutoff value of 2.8 ng/mL resulted from a ROC curve with area under curve (AUC) of 0.79 (95% CI 0.61-0.97), p = .009 in differentiation between PC and CP with a sensitivity and a specificity of 88% and 70% respectively.

CONCLUSION

P-suPAR is higher in patients with PC than in patients with CP, and it could thus be used in differentiating between PC and CP.

Immune-related protein signature in serum stratify relapsed mantle cell lymphoma patients based on risk

Background

Response to modern treatment strategies, which combine cytotoxic compounds with immune stimulatory agents and targeted treatment is highly variable among MCL patients. Thus, providing prognostic and predictive markers for risk adapted therapy is warranted and molecular information that can help in patient stratification is a necessity. In relapsed MCL, biopsies are rarely available and molecular information from tumor tissue is often lacking. Today, the main tool to access risk is the MCL international prognostic index (MIPI), which does not include detailed biological information of relevance for different treatment options. To enable continuous monitoring of patients, non-invasive companion diagnostic tools are needed which can further reduce cost and patient distress and enable efficient measurements of biological markers.

Methods

We have assessed if serum-based protein profiling can identify immune related proteins that stratify relapsed MCL patients based on risk. Overall, 371 scFv targeting 158 proteins were assessed using an antibody microarray platform. We profiled patients (n = 44) who had been treated within the MCL6-Philemon trial combining targeted and immune-modulatory treatment.

Results

The downstream processing led to the identification of the relapsed immune signature (RIS) consisting of 11 proteins with potential to stratify patients with long and short overall survival (OS). Moreover, in this population, MIPI alone failed to separate high, intermediate and low risk patients, but a combined index based on MIPI together with RIS, MIPI_ris, showed improved performance and significantly stratified all three risk groups based on OS.

Conclusions

Our results show that addition of biological parameters to previous prognostic indices improves patient stratification among patients treated with BTK inhibitor triplet combination, particularly, in the identification of an extreme high risk group.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07678-4.

Using protein microarray to identify and evaluate autoantibodies to tumor-associated antigens in ovarian cancer

The aim of this study was to develop a noninvasive serological diagnostic approach in identifying and evaluating a panel of candidate autoantibodies to tumor‐associated antigens (TAAs) based on protein microarray technology for early detection of ovarian cancer (OC). Protein microarray based on 154 proteins encoded by 138 cancer driver genes was used to screen candidate anti‐TAA autoantibodies in a discovery cohort containing 17 OC and 27 normal controls (NC). Indirect enzyme‐linked immunosorbent assay (ELISA) was used to detect the content of candidate anti‐TAA autoantibodies in sera from 140 subjects in the training cohort. Differential anti‐TAA autoantibodies were further validated in the validation cohort with 328 subjects. Subsequently, 112 sera from the patients with ovarian benign diseases with 104 OC sera and 104 NC sera together were recruited to identify the specificity of representative autoantibodies to OC among ovarian diseases. Five TAAs (GNAS, NPM1, FUBP1, p53, and KRAS) were screened out in the discovery phase, in which four of them presented higher levels in OC than controls (P < .05) in the training cohort, which was consistent with the result in the subsequent validation cohort. An optimized panel of three anti‐TAA (GNAS, p53, and NPM1) autoantibodies was identified to have relatively high sensitivity (51.2%), specificity (86.0%), and accuracy (68.6%), respectively. This panel can identify 51% of OC patients with CA125 negative. This study supports our assumption that anti‐TAA autoantibodies can be considered as potential diagnostic biomarkers for detection of OC; especially a panel of three anti‐TAA autoantibodies could be a good tool in immunodiagnosis of OC.

Preoperative Fasting and General Anaesthesia Alter the Plasma Proteome

Background: Blood plasma collected at time of surgery is an excellent source of patient material for investigations into disease aetiology and for the discovery of novel biomarkers. Previous studies on limited sets of proteins and patients have indicated that pre-operative fasting and anaesthesia can affect protein levels, but this has not been investigated on a larger scale. These effects could produce erroneous results in case-control studies if samples are not carefully matched. Methods: The proximity extension assay (PEA) was used to characterize 983 unique proteins in a total of 327 patients diagnosed with ovarian cancer and 50 age-matched healthy women. The samples were collected either at time of initial diagnosis or before surgery under general anaesthesia. Results: 421 of the investigated proteins (42.8%) showed statistically significant differences in plasma abundance levels comparing samples collected at time of diagnosis or just before surgery under anaesthesia. Conclusions: The abundance levels of the plasma proteome in samples collected before incision, i.e., after short-time fasting and under general anaesthesia differs greatly from levels in samples from awake patients. This emphasizes the need for careful matching of the pre-analytical conditions of samples collected from controls to cases at time of surgery in the discovery as well as clinical use of protein biomarkers.

Multiplex profiling of serum proteins in solution using barcoded antibody fragments and next generation sequencing

The composition of serum proteins is reflecting the current health status and can, with the right tools, be used to detect early signs of disease, such as an emerging cancer. An earlier diagnosis of cancer would greatly increase the chance of an improved outcome for the patients. However, there is still an unmet need for proficient tools to decipher the information in the blood proteome, which calls for further technological development. Here, we present a proof-of-concept study that demonstrates an alternative approach for multiplexed protein profiling of serum samples in solution, using DNA barcoded scFv antibody fragments and next generation sequencing. The outcome shows high accuracy when discriminating samples derived from pancreatic cancer patients and healthy controls and represents a scalable alternative for serum analysis.

Brofelth, Ekstrand et al use DNA barcoded scFv antibody fragments and next generation sequencing for multiplex profiling of proteins in serum from pancreatic cancer patients with high accuracy. This approach can potentially be used in high throughput precision diagnosis.

Proteomic insights on the metabolism in inflammatory bowel disease

Inflammatory bowel diseases (IBD) are chronic and relapsing inflammatory conditions of the gut that include Crohn's disease and ulcerative colitis. The pathogenesis of IBD is not completely unraveled, IBD are multi-factorial diseases with reported alterations in the gut microbiota, activation of different immune cell types, changes in the vascular endothelium, and alterations in the tight junctions’ structure of the colonic epithelial cells. Proteomics represents a useful tool to enhance our biological understanding and to discover biomarkers in blood and intestinal specimens. It is expected to provide reproducible and quantitative data that can support clinical assessments and help clinicians in the diagnosis and treatment of IBD. Sometimes a differential diagnosis of Crohn's disease and ulcerative colitis and the prediction of treatment response can be deducted by finding meaningful biomarkers. Although some non-invasive biomarkers have been described, none can be considered as the “gold standard” for IBD diagnosis, disease activity and therapy outcome. For these reason new studies have proposed an “IBD signature”, which consists in a panel of biomarkers used to assess IBD. The above described approach characterizes “omics” and in this review we will focus on proteomics.

Serum IGFBP-1 as a potential biomarker for diagnosis of early-stage upper gastrointestinal tumour

BACKGROUND

Early detection would improve upper gastrointestinal cancer prognosis. We aimed to identify serum protein biomarker for the detection of early-stage upper gastrointestinal cancer.

METHODS

We performed a three-tiered study including 2028 participants from three medical centres. First, we applied two different antibody arrays to screen candidate serum proteins that increased in 20 patients with oesophageal squamous cell carcinoma (ESCC) compared with 20 normal controls. We then evaluated the selected protein by enzyme-linked immunosorbent assay in 1064 participants including 731 upper gastrointestinal cancer patients (287 ESCCs, 237 oesophagogastric junction adenocarcinomas (EJAs), and 207 stomach cancers) and 333 normal controls. The diagnostic value of the selected protein was finally validated in two independent cohorts of ESCC patients and controls (n=472 and 452, respectively). The receiver operating characteristic was used to calculate diagnostic accuracy.

FINDINGS

Serum insulin-like growth factor binding protein-1 (IGFBP-1) identified in both antibody arrays showed significantly elevated levels in upper gastrointestinal cancers, compared with normal controls. Serum IGFBP-1 provided high diagnostic accuracy of early-stage ESCC, EJA, stomach and cancer (areas under the curve: 0·898, 0·936 and 0·864, respectively). This protein maintained diagnostic performance for early-stage ESCC in independent cohorts 1 and 2 (0·849 and 0·911, respectively). Additionally, serum levels of IGFBP-1 dropped significantly after surgical resection of primary tumours, compared with the corresponding pre-operative ESCC samples (p < 0·05).

INTERPRETATION

Serum IGFBP-1 represents a promising diagnostic biomarker to detect early-stage upper gastrointestinal cancer.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

Validation and evaluation of a common biomarker in human cancers sera protein detected by a monoclonal antibody UNIVmAb

OBJECTIVE

Management and diagnosis of multiple human cancers remains a challenge and search for a common biomarker is still debatable. In this manuscript we have evaluated the use of monoclonal antibody UNIVmAb, to detect the protein (H11) as a common biomarker for all cancers irrespective of the grade and origin. We have shown by both ELISA and Western Blot that the H11 protein, is a unique hyaluronan binding protein that has not been detected earlier. H11 protein was fractionated in an anion exchange column followed by cibacron blue gel exclusion chromatography. Hyaluronan binding H11 protein reacted with Monoclonal antibody UNIVmAb and b-HA inspite of b-Hyaluronan (biotinylated Hyaluronan) interaction and HA-Oligo (Hyaluronan oligosaccharides) competition from various grades of Human cancers sera.

RESULTS

ELISA, Western blot and b-Hyaluronan interactions clearly showed an over-expression of UNIVmAb reacted H11 protein in all fifty cancer's sera when compared with seventy normal sera. UNIVmAb reactive H11 protein can be used as a common biomarker. We believe, UNIVmAb detected H11 protein, is a unique hyaluronan binding protein, that can be used as a common biomarker for all cancers.

Proteomic and genomic profiling of pancreatic cancer

Pancreatic cancer remains the most fatal human tumor type. The aggressive tumor biology coupled with the lack of early detection strategies and effective treatment are major reasons for the poor survival rate. Collaborative research efforts have been devoted to understand pancreatic cancer at the molecular level. Large-scale genomic studies have generated important insights into the genetic drivers of pancreatic cancer. In the post-genomic era, protein sequencing of tumor tissue, cell lines, pancreatic juice, and blood from patients with pancreatic cancer has provided a fundament for the development of new diagnostic and prognostic biomarkers. The integration of mass spectrometry and genomic sequencing strategies may help characterize protein identities and post-translational modifications that relate to a specific mutation. Consequently, proteomic and genomic techniques have become a compulsory requirement in modern medicine and health care. These types of proteogenomic studies may usher in a new era of precision diagnostics and treatment in patients with pancreatic cancer.

A review of lifestyle, metabolic risk factors, and blood-based biomarkers for early diagnosis of pancreatic ductal adenocarcinoma

We aimed to review the epidemiologic literature examining lifestyle and metabolic risk factors, and blood-based biomarkers including multi-omics (genomics, proteomics, and metabolomics) and to discuss how these predictive markers can inform early diagnosis of pancreatic ductal adenocarcinoma (PDAC). A search of the PubMed database was conducted in June 2018 to review epidemiologic studies of (i) lifestyle and metabolic risk factors for PDAC, genome-wide association studies, and risk prediction models incorporating these factors and (ii) blood-based biomarkers for PDAC (conventional diagnostic markers, metabolomics, and proteomics). Prospective cohort studies have reported at least 20 possible risk factors for PDAC, including smoking, heavy alcohol drinking, adiposity, diabetes, and pancreatitis, but the relative risks and population attributable fractions of individual risk factors are small (mostly < 10%). High-throughput technologies have continued to yield promising genetic, metabolic, and protein biomarkers in addition to conventional biomarkers such as carbohydrate antigen 19-9. Nonetheless, most studies have utilized a hospital-based case-control design, and the diagnostic accuracy is low in studies that collected pre-diagnostic samples. Risk prediction models incorporating lifestyle and metabolic factors as well as other clinical parameters have shown good discrimination and calibration. Combination of traditional risk factors, genomics, and blood-based biomarkers can help identify high-risk populations and inform clinical decisions. Multi-omics investigations can provide valuable insights into disease etiology, but prospective cohort studies that collect pre-diagnostic samples and validation in independent studies are warranted.

The Interface of Pancreatic Cancer With Diabetes, Obesity, and Inflammation: Research Gaps and Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop

A workshop on "The Interface of Pancreatic Cancer with Diabetes, Obesity, and Inflammation: Research Gaps and Opportunities" was held by the National Institute of Diabetes and Digestive and Kidney Diseases on October 12, 2017. The purpose of the workshop was to explore the relationship and possible mechanisms of the increased risk of pancreatic ductal adenocarcinoma (PDAC) related to diabetes, the role of altered intracellular energy metabolism in PDAC, the mechanisms and biomarkers of diabetes caused by PDAC, the mechanisms of the increased risk of PDAC associated with obesity, and the role of inflammatory events and mediators as contributing causes of the development of PDAC. Workshop faculty reviewed the state of the current knowledge in these areas and made recommendations for future research efforts. Further knowledge is needed to elucidate the basic mechanisms contributing to the role of hyperinsulinemia, hyperglycemia, adipokines, and acute and chronic inflammatory events on the development of PDAC.

Serum Biomarker Signature-Based Liquid Biopsy for Diagnosis of Early-Stage Pancreatic Cancer

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with a 5-year survival of < 10% because of diffuse symptoms leading to late-stage diagnosis. That survival could increase significantly if localized tumors could be detected early. Therefore, we used multiparametric analysis of blood samples to obtain a novel biomarker signature of early-stage PDAC. The signature was derived from a large patient cohort, including patients with well-defined early-stage (I and II) PDAC. This biomarker signature was validated subsequently in an independent patient cohort.

PATIENTS AND METHODS

The biomarker signature was derived from a case-control study, using a Scandinavian cohort, consisting of 16 patients with stage I, 132 patients with stage II, 65 patients with stage III, and 230 patients with stage IV PDAC, and 888 controls. This signature was validated subsequently in an independent case-control cohort in the United States with 15 patients with stage I, 75 patients with stage II, 15 patients with stage III, and 38 patients with stage IV PDAC, and 219 controls. An antibody microarray platform was used to identify the serum biomarker signature associated with early-stage PDAC.

RESULTS

Using the Scandinavian case-control study, a biomarker signature was created, discriminating samples derived from patients with stage I and II from those from controls with a receiver operating characteristic area under the curve value of 0.96. This signature, consisting of 29 biomarkers, was then validated in an independent case-control study in the United States. The biomarker signature could discriminate patients with stage I and II PDAC from controls in this independent patient cohort with a receiver operating characteristic area under the curve value of 0.96.

CONCLUSION

This serum biomarker signature might represent a tenable approach to detecting early-stage, localized PDAC if these findings are supported by a prospective validation study.

Current applications of antibody microarrays

The concept of antibody microarrays is one of the most versatile approaches within multiplexed immunoassay technologies. These types of arrays have increasingly become an attractive tool for the exploratory detection and study of protein abundance, function, pathways, and potential drug targets. Due to the properties of the antibody microarrays and their potential use in basic research and clinical analytics, various types of antibody microarrays have already been developed. In spite of the growing number of studies utilizing this technique, few reviews about antibody microarray technology have been presented to reflect the quality and future uses of the generated data. In this review, we provide a summary of the recent applications of antibody microarray techniques in basic biology and clinical studies, providing insights into the current trends and future of protein analysis.

Comparison of the tumor cell secretome and patient sera for an accurate serum-based diagnosis of pancreatic ductal adenocarcinoma

Pancreatic cancer is the currently most lethal malignancy. Toward an accurate diagnosis of the disease in body liquids, we studied the protein composition of the secretomes of 16 primary and established cell lines of pancreatic ductal adenocarcinoma (PDAC). Compared to the secretome of non-tumorous cells, 112 proteins exhibited significantly different abundances. Functionally, the proteins were associated with PDAC features, such as decreased apoptosis, better cell survival and immune cell regulation. The result was compared to profiles obtained from 164 serum samples from two independent cohorts - a training and a test set - of patients with PDAC or chronic pancreatitis and healthy donors. Eight of the 112 secretome proteins exhibited similar variations in their abundance in the serum profile specific for PDAC patients, which was composed of altogether 189 proteins. The 8 markers shared by secretome and serum yielded a 95.1% accuracy of distinguishing PDAC from healthy in a Receiver Operating Characteristic curve analysis, while any number of serum-only markers produced substantially less accurate results. Utility of the identified markers was confirmed by classical enzyme linked immunosorbent assays (ELISAs). The study highlights the value of cell secretome analysis as a means of defining reliable serum biomarkers.

Serum miRNA signature diagnoses and discriminates murine colitis subtypes and predicts ulcerative colitis in humans

Inflammatory bowel disease (IBD) is difficult to diagnose due to nonspecific and variable symptoms, and lack of reliable diagnostic tests. Current methods are invasive, non-sensitive, non-predictive, and do not easily discriminate between its two main forms. Consequently, there remains a great need for reliable serum markers for IBD. Here, using a longitudinal study of various mouse models of colitis, we identified a serum miRNA signature that indicated the development of colitis and discriminated between inflammations of various origins (colitis from arthritis). Unlike the existing biomarkers, the newly identified signature also serves to distinguish individuals at risk, predict the type of inflammation, and evaluate the response to therapeutics. Moreover, the miRNA signature identified in mice predicted ulcerative colitis with 83.3% accuracy. In future, the signature identified herein could play a central role in monitoring inflammatory disorders and therapeutic responses in patients, thereby paving the way for personalized medicine.

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

Background

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

Methods

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

Results

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

Conclusion

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

Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer

Interest in precision diagnostics has been fuelled by the concept that early detection of cancer would benefit patients; that is, if detected early, more tumours should be resectable and treatment more efficacious. Serum contains massive amounts of potentially diagnostic information, and affinity proteomics has risen as an accurate approach to decipher this, to generate actionable information that should result in more precise and evidence-based options to manage cancer. To achieve this, we need to move from single to multiplex biomarkers, a so-called signature, that can provide significantly increased diagnostic accuracy. This Opinion article focuses on the progress being made in identifying protein biomarker signatures of clinical utility, using blood-based proteomics.

Diagnostic value of selected markers and apoptotic pathways for pancreatic cancer

Pancreatic cancer occupies the fourth place as a cause of death from cancer, and the mortality rate is similar to the number of newly detected cases. Due to the late diagnosis, only 5-6% of patients with pancreatic cancer survive for five years. Given that early diagnosis is critical for improving patients' survival rates, there is an urgent need for the discovery and validation of new biomarkers with sufficient sensitivity and specificity to help diagnose pancreatic cancer early. Detection of serum tumor markers (CA19-9, CEA, CA125 and CA242) is conducive to the early diagnosis of pancreatic cancer. The combination of miR-16, miR-196a and CA19-9 plasma level was more effective, especially in early tumor screening. Furthermore, recent studies reported that mainly miR-21, miR-155 and miR-196 were dysregulated in IPMN (intraductal papillary mucinous neoplasms) and PanIN (pancreatic intraepithelial neoplasia) lesions, suggesting their usefulness as early biomarkers of these diseases. The reduced rate of apoptosis plays a crucial role in carcinogenesis, and it is one of the most important characteristics acquired by pancreatic cancer cells, which protects them from attack by the immune system and reduces the effectiveness of pharmacological treatment. This review summarizes the data concerning the clinical utility of selected biomarkers in pancreatic cancer patients. The review mainly focuses on the genetic aspects of signaling pathway disorders associated with apoptosis in the pathogenesis and diagnosis of pancreatic cancer.

Potent EMT and CSC Phenotypes Are Induced By Oncostatin-M in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is referred to as a silent killer due to the lack of clear symptoms, a lack of early detection methods, and a high frequency of metastasis at diagnosis. In addition, pancreatic cancer is remarkably resistant to chemotherapy, and clinical treatment options remain limited. The tumor microenvironment (TME) and associated factors are important determinants of metastatic capacity and drug resistance. Here, oncostatin M (OSM), an IL6 cytokine family member, was identified as an important driver of mesenchymal and cancer stem cell (CSC) phenotypes. Furthermore, the generation of cells that harbor mesenchymal/CSC properties following OSM exposure resulted in enhanced tumorigenicity, increased metastasis, and resistance to gemcitabine. OSM induced the expression of ZEB1, Snail (SNAI1), and OSM receptor (OSMR), engaging a positive feedback loop to potentiate the mesenchymal/CSC program. Suppression of JAK1/2 by ruxolitinib prevented STAT3-mediated transcription of ZEB1, SNAI1 and OSMR, as well as the emergence of a mesenchymal/CSC phenotype. Likewise, ZEB1 silencing, by shRNA-mediated knockdown, in OSM-driven mesenchymal/CSC reverted the phenotype back to an epithelial/non-CSC state. Importantly, the generation of cells with mesenchymal/CSC properties was unique to OSM, and not observed following IL6 exposure, implicating OSMR and downstream effector signaling as a distinct target in PDAC. Overall, these data demonstrate the capacity of OSM to regulate an epithelial-mesenchymal transition (EMT)/CSC plasticity program that promotes tumorigenic properties.Implications: Therapeutic targeting the OSM/OSMR axis within the TME may prevent or reverse the aggressive mesenchymal and CSC phenotypes associated with poor outcomes in patients with PDAC. Mol Cancer Res; 15(4); 478-88. ©2017 AACR.

Potent EMT and CSC Phenotypes Are Induced By Oncostatin-M in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is referred to as a silent killer due to the lack of clear symptoms, a lack of early detection methods, and a high frequency of metastasis at diagnosis. In addition, pancreatic cancer is remarkably resistant to chemotherapy, and clinical treatment options remain limited. The tumor microenvironment (TME) and associated factors are important determinants of metastatic capacity and drug resistance. Here, oncostatin M (OSM), an IL6 cytokine family member, was identified as an important driver of mesenchymal and cancer stem cell (CSC) phenotypes. Furthermore, the generation of cells that harbor mesenchymal/CSC properties following OSM exposure resulted in enhanced tumorigenicity, increased metastasis, and resistance to gemcitabine. OSM induced the expression of ZEB1, Snail (SNAI1), and OSM receptor (OSMR), engaging a positive feedback loop to potentiate the mesenchymal/CSC program. Suppression of JAK1/2 by ruxolitinib prevented STAT3-mediated transcription of ZEB1, SNAI1 and OSMR, as well as the emergence of a mesenchymal/CSC phenotype. Likewise, ZEB1 silencing, by shRNA-mediated knockdown, in OSM-driven mesenchymal/CSC reverted the phenotype back to an epithelial/non-CSC state. Importantly, the generation of cells with mesenchymal/CSC properties was unique to OSM, and not observed following IL6 exposure, implicating OSMR and downstream effector signaling as a distinct target in PDAC. Overall, these data demonstrate the capacity of OSM to regulate an epithelial-mesenchymal transition (EMT)/CSC plasticity program that promotes tumorigenic properties.Implications: Therapeutic targeting the OSM/OSMR axis within the TME may prevent or reverse the aggressive mesenchymal and CSC phenotypes associated with poor outcomes in patients with PDAC. Mol Cancer Res; 15(4); 478-88. ©2017 AACR.

Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) is the fifth most common cause of cancer-related death in Europe with a 5-year survival rate of <5%. Chronic pancreatitis (CP) is a risk factor for PDAC development, but in the majority of cases malignancy is discovered too late for curative treatment. There is at present no reliable diagnostic marker for PDAC available.

OBJECTIVES

The aim of the study was to identify single blood-based metabolites or a panel of metabolites discriminating PDAC and CP using liquid chromatography-mass spectrometry (LC-MS).

METHODS

A discovery cohort comprising PDAC (n = 44) and CP (n = 23) samples was analyzed by LC-MS followed by univariate (Student's t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Discriminative metabolite features were subject to raw data examination and identification to ensure high feature quality. Their discriminatory power was then confirmed in an independent validation cohort including PDAC (n = 20) and CP (n = 31) samples.

RESULTS

Glycocholic acid, N-palmitoyl glutamic acid and hexanoylcarnitine were identified as single markers discriminating PDAC and CP by univariate analysis. OPLS-DA resulted in a panel of five metabolites including the aforementioned three metabolites as well as phenylacetylglutamine (PAGN) and chenodeoxyglycocholate.

CONCLUSION

Using LC-MS-based metabolomics we identified three single metabolites and a five-metabolite panel discriminating PDAC and CP in two independent cohorts. Although further study is needed in larger cohorts, the metabolites identified are potentially of use in PDAC diagnostics.

Synergistic Communication between CD4+ T Cells and Monocytes Impacts the Cytokine Environment

Physiological cytokine environments arise from factors produced by diverse cell types in coordinated concert. Understanding the contributions of each cell type in the context of cell-cell communication is important for effectively designing disease modifying interventions. Here, we present multi-plexed measurement of 48 cytokines from a coculture system of primary human CD4+ T cells and monocytes across a spectrum of stimuli and for a range of relative T cell/monocyte compositions, coupled with corresponding measurements from PBMCs and plasma from the same donors. Computational analysis of the resulting data-sets elucidated communication-independent and communication-dependent contributions, including both positive and negative synergies. We find that cytokines in cell supernatants were uncorrelated to those found in plasma. Additionally, as an example of positive synergy, production levels of CXCR3 cytokines IP-10 and MIG, depend non-linearly on both IFNγ and TNFα levels in cross-talk between T cells and monocytes. Overall, this work demonstrates that communication between cell types can significantly impact the consequent cytokine environment, emphasizing the value of mixed cell population studies.

Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics

In the quest for deciphering disease-associated biomarkers, high-performing tools for multiplexed protein expression profiling of crude clinical samples will be crucial. Affinity proteomics, mainly represented by antibody-based microarrays, have during recent years been established as a proteomic tool providing unique opportunities for parallelized protein expression profiling. But despite the progress, several main technical features and assay procedures remains to be (fully) resolved. Among these issues, the handling of protein microarray data, i.e. the biostatistics parts, is one of the key features to solve. In this study, we have therefore further optimized, validated, and standardized our in-house designed recombinant antibody microarray technology platform. To this end, we addressed the main remaining technical issues (e.g. antibody quality, array production, sample labelling, and selected assay conditions) and most importantly key biostatistics subjects (e.g. array data pre-processing and biomarker panel condensation). This represents one of the first antibody array studies in which these key biostatistics subjects have been studied in detail. Here, we thus present the next generation of the recombinant antibody microarray technology platform designed for clinical immunoproteomics.

Pancreatic cancer stem cell markers and exosomes - the incentive push

Pancreatic cancer (PaCa) has the highest death rate and incidence is increasing. Poor prognosis is due to late diagnosis and early metastatic spread, which is ascribed to a minor population of so called cancer stem cells (CSC) within the mass of the primary tumor. CSC are defined by biological features, which they share with adult stem cells like longevity, rare cell division, the capacity for self renewal, differentiation, drug resistance and the requirement for a niche. CSC can also be identified by sets of markers, which for pancreatic CSC (Pa-CSC) include CD44v6, c-Met, Tspan8, alpha6beta4, CXCR4, CD133, EpCAM and claudin7. The functional relevance of CSC markers is still disputed. We hypothesize that Pa-CSC markers play a decisive role in tumor progression. This is fostered by the location in glycolipid-enriched membrane domains, which function as signaling platform and support connectivity of the individual Pa-CSC markers. Outside-in signaling supports apoptosis resistance, stem cell gene expression and tumor suppressor gene repression as well as miRNA transcription and silencing. Pa-CSC markers also contribute to motility and invasiveness. By ligand binding host cells are triggered towards creating a milieu supporting Pa-CSC maintenance. Furthermore, CSC markers contribute to the generation, loading and delivery of exosomes, whereby CSC gain the capacity for a cell-cell contact independent crosstalk with the host and neighboring non-CSC. This allows Pa-CSC exosomes (TEX) to reprogram neighboring non-CSC towards epithelial mesenchymal transition and to stimulate host cells towards preparing a niche for metastasizing tumor cells. Finally, TEX communicate with the matrix to support tumor cell motility, invasion and homing. We will discuss the possibility that CSC markers are the initial trigger for these processes and what is the special contribution of CSC-TEX.

Plasma protein profiling in a stage defined pancreatic cancer cohort - Implications for early diagnosis

Pancreatic ductal adenocarcinoma (PDAC) is a disease where detection preceding clinical symptoms significantly increases the life expectancy of patients. In this study, a recombinant antibody microarray platform was used to analyze 213 Chinese plasma samples from PDAC patients and normal control (NC) individuals. The cohort was stratified according to disease stage, i.e. resectable disease (stage I/II), locally advanced (stage III) and metastatic disease (stage IV). Support vector machine analysis showed that all PDAC stages could be discriminated from controls and that the accuracy increased with disease progression, from stage I to IV. Patients with stage I/II PDAC could be discriminated from NC with high accuracy based on a plasma protein signature, indicating a possibility for early diagnosis and increased detection rate of surgically resectable tumors.

Protein Profiling Gastric Cancer and Neighboring Control Tissues Using High-Content Antibody Microarrays

In this study, protein profiling was performed on gastric cancer tissue samples in order to identify proteins that could be utilized for an effective diagnosis of this highly heterogeneous disease and as targets for therapeutic approaches. To this end, 16 pairs of postoperative gastric adenocarcinomas and adjacent non-cancerous control tissues were analyzed on microarrays that contain 813 antibodies targeting 724 proteins. Only 17 proteins were found to be differentially regulated, with much fewer molecules than the numbers usually identified in studies comparing tumor to healthy control tissues. Insulin-like growth factor-binding protein 7 (IGFBP7), S100 calcium binding protein A9 (S100A9), interleukin-10 (IL‐10) and mucin 6 (MUC6) exhibited the most profound variations. For an evaluation of the proteins’ capacity for discriminating gastric cancer, a Receiver Operating Characteristic curve analysis was performed, yielding an accuracy (area under the curve) value of 89.2% for distinguishing tumor from non-tumorous tissue. For confirmation, immunohistological analyses were done on tissue slices prepared from another cohort of patients with gastric cancer. The utility of the 17 marker proteins, and particularly the four molecules with the highest specificity for gastric adenocarcinoma, is discussed for them to act as candidates for diagnosis, even in serum, and targets for therapeutic approaches.

Dysbindin as a novel biomarker for pancreatic ductal adenocarcinoma identified by proteomic profiling

Pancreatic adenocarcinoma (PDAC) is known to have a poor prognosis partly because of lack of effective biomarkers. In the test set, we investigated dysbindin (DTNBP1) as a potential biomarker for PDAC by comparing preoperative and postoperative serum mass spectrometry (MS) proteomic profilings. Of the included 50 PDAC patients, 42 (positivity of 84.0%) detected a lower MS peak in postoperative serums than preoperative ones which was then identified as dysbindin. In the verification set, receiver operating characteristics (ROC) were used to assess diagnostic efficiency. 550 participants were included in the verification set [250 with PDAC, 80 with benign biliary obstruction (BBO), 70 with chronic pancreatitis (CP) and 150 healthy donors (HD)]. Dysbindin was increased in PDAC patient sera than in all controls. ROC curves revealed the optimum diagnostic cutoff for dysbindin was 699.16 pg/ml [area under curve (AUC) 0.849 (95% CI 0.812-0.885), sensitivity 81.9% and specificity 84.7%]. Raised concentration of dysbindin in sera could differentiate PDAC from BBO, CP and HD. Moreover, dysbindin maintained its diagnostic accuracy for PDAC patients who were CA19-9 negative [AUC 0.875 (95% CI 0.804-0.945), sensitivity 83.0%, specificity 89.0%] and for patients with benign biliary obstruction [AUC 0.849 (95% CI 0.803-0.894), sensitivity 82.3%, specificity 84.0%].Our discovery of dysbindin may complement measurement of CA19-9 in the diagnosis of PDAC and help to discriminate PDAC from other pancreatic diseases or begin biliary obstruction.

Accurate Quantification of Disease Markers in Human Serum Using Iron Oxide Nanoparticle-linked Immunosorbent Assay

Accurate and reliable quantification of biomarkers in the blood is essential in disease screening and diagnosis. Here we describe an iron oxide nanoparticle (IONP)-linked immunosorbent assay (ILISA) for detecting biomolecules in human serum. Sandwich ILISA was optimized for the detection of four important serological markers, IgA, IgG, IgM, and C-reactive protein (CRP), and assessed with normal sera, simulated disease-state sera and the serum samples from patients infected with West Nile virus (WNV) or human herpes virus (HHV). Our study shows that using the detection assay formulated with 18.8 nm wüstite nanocrystals, ILISA can achieve sub-picomolar detection sensitivity, and all four markers can be accurately quantified over a large dynamic range. In addition, ILISA is not susceptible to variations in operating procedures and shows better linearity and higher stability compared with ELISA, which facilitates its integration into detection methods suitable for point of care. Our results demonstrate that ILISA is a simple and versatile nanoplatform for highly sensitive and reliable detection of serological biomarkers in biomedical research and clinical applications.

Variation in serum biomarkers with sex and female hormonal status: implications for clinical tests

Few serum biomarker tests are implemented in clinical practice and recent reports raise concerns about poor reproducibility of biomarker studies. Here, we investigated the potential role of sex and female hormonal status in this widespread irreproducibility. We examined 171 serum proteins and small molecules measured in 1,676 participants from the Netherlands Study of Depression and Anxiety. Concentrations of 96 molecules varied with sex and 66 molecules varied between oral contraceptive pill users, postmenopausal females, and females in the follicular and luteal phases of the menstrual cycle (FDR-adjusted p-value <0.05). Simulations of biomarker studies yielded up to 40% false discoveries when patient and control groups were not matched for sex and up to 41% false discoveries when premenopausal females were not matched for oral contraceptive pill use. High accuracy (over 90%) classification tools were developed to label samples with sex and female hormonal status where this information was not collected.

Cellular and molecular aspects of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy that affects nearly 50,000 patients each year. The overall 5-year survival rate for this malignancy remains the lowest of any cancer at around 7% due to limited diagnostic methods, disease aggressiveness and a lack of targeted therapeutic interventions. This review highlights the successes achieved over the past several decades as well as the significant cellular and molecular hurdles that remain in combatting this deadly disease at a translational level.