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

Multi-omics profiling to identify early plasma biomarkers in pre-diagnostic pancreatic ductal adenocarcinoma: a nested case-control study

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations. A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant. Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31-7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429-0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls. CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.

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

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

Circulating microRNAs in association with pancreatic cancer risk within 5 years

Early detection is critical for improving pancreatic cancer prognosis. Our study aims to identify circulating microRNAs (miRNAs) associated with pancreatic cancer risk. The two-stage study used plasma samples collected ≤5 years prior to cancer diagnosis, from case-control studies nested in five prospective cohort studies. The discovery stage included 185 case-control pairs from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Replication stage samples comprised 277 pairs from Shanghai Women's Health Study/Shanghai Men's Health Study, Southern Community Cohort Study, and Multiethnic Cohort Study. Seven hundred and ninety-eight miRNAs were measured using the NanoString nCounter Analysis System. Odds ratios (OR) and 95% confidence intervals (CI) for per 10% change in miRNAs in association with pancreatic cancer risk were derived from conditional logistic regression analysis in discovery and replication studies, separately, and then meta-analyzed. Stratified analysis was conducted by age at diagnosis (<65/≥65 years) and time interval between sample collection and diagnosis (≤2/>2 years). In the discovery stage, 120 risk associated miRNAs were identified at p < .05. Three were validated in the replication stage: hsa-miR-199a-3p/hsa-miR-199b-3p, hsa-miR-767-5p, and hsa-miR-191-5p, with respective ORs (95% CI) being 0.89 (0.84-0.95), 1.08 (1.02-1.13), and 0.90 (0.85-0.95). Five additional miRNAs, hsa-miR-640, hsa-miR-874-5p, hsa-miR-1299, hsa-miR-22-3p, and hsa-miR-449b-5p, were validated among patients diagnosed at ≥65 years, with OR (95% CI) of 1.23 (1.09-1.39), 1.33 (1.16-1.52), 1.25 (1.09-1.43), 1.28 (1.12-1.46), 0.76 (0.65-0.89), and 1.22 (1.07-1.39), respectively. The miRNA targets were enriched in pancreatic carcinogenesis/progression-related pathways. Our study suggests that circulating miRNAs may identify individuals at high risk for pancreatic cancer ≤5 years prior to diagnosis, indicating its potential utility in cancer screening and surveillance.

Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank

Background

Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers.

Methods

We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score.

Results

Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714-0.854] compared to 0.681 (95% CI: 0.597-0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1].

Conclusions

Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.

Cyfra 21.1: A Useful Tumour Marker in Pancreatic Ductal Adenocarcinoma: Cross-Sectional Study

BACKGROUND

Pancreatic cancer is a challenging disease, often requiring invasive procedures for diagnosis. Reliable tumour markers are essential for ensuring early detection and better patient outcomes. Although Carbohydrate Antigen 19-9 is the most commonly used marker, it is marred by low predictive accuracy and high false positivity. Carcino Embryonic Antigen also has limited practical use. A novel antigen, Cytokeratin fragment 21-1, is gaining significance for its diagnostic value in various tumours.

MATERIALS AND METHODS

This prospective study aimed to evaluate the potential of Cytokeratin fragment 21-1 in comparison with Carbohydrate Antigen 19-9 and Carcino Embryonic Antigen in diagnosing pancreatic cancer. From January 2016 to December 2019, 45 patients with confirmed pancreatic ductal adenocarcinoma were included in this cross-sectional study.

RESULTS

Carbohydrate Antigen 19-9 was raised in 22 patients, Carcino Embryonic Antigen was elevated in 17, and Cytokeratin fragment 21-1 was elevated in 30 cases. Carbohydrate Antigen 19-9 was found to be elevated in the presence of jaundice. Both Carbohydrate Antigen 19-9 and Cytokeratin fragment 21-1 had good correlation with stage of cancer, while Carcino Embryonic Antigen had very minimal correlation.

CONCLUSION

In this study, Cytokeratin fragment 21-1 was elevated in a higher number of cases than Carbohydrate Antigen 19-9 and Carcino Embryonic Antigen. Both Cytokeratin fragment 21-1 and Carbohydrate Antigen 19-9 correlated well with cancer stage. Also Cytokeratin fragment 21-1 was not affected by jaundice, unlike Carbohydrate Antigen 19-9. Therefore, Cytokeratin fragment 21-1 has the potential to be an effective individual tumour marker in pancreatic cancer.

The role of carcinoembryonic antigen-related cell adhesion molecule 1 in cancer

The Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1), also known as CD66a, is a member of the immunoglobulin superfamily. CEACAM1 was shown to be a prognostic marker in patients suffering from cancer. In this review, we summarize pre-clinical and clinical evidence linking CEACAM1 to tumorigenicity and cancer progression. Furthermore, we discuss potential CEACAM1-based mechanisms that may affect cancer biology.

Diagnostic Performance of a Tumor Marker Gene Test to Personalize Serum CA19-9 Reference Ranges

PURPOSE

CA19-9 synthesis is influenced by common variants in the fucosyltransferase (FUT) enzymes FUT3 and FUT2. We developed a clinical test to detect FUT variants, and evaluated its diagnostic performance for pancreatic ductal adenocarcinoma (PDAC).

EXPERIMENTAL DESIGN

A representative set of controls from the Cancer of the Pancreas Screening study was identified for each FUT functional group. Diagnostic sensitivity was determined first in a testing set of 234 PDAC cases, followed by a 134-case validation set, all of whom had undergone resection with curative intent without neoadjuvant therapy. Tumor marker gene testing was performed in the Johns Hopkins Molecular Diagnostics Laboratory. CA19-9 levels were measured in the Hopkins Clinical Chemistry lab. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative ability of CA19-9 alone versus with the gene test.

RESULTS

Applying the CA19-9 standard cutoff (<36 U/mL) to all 716 subjects yielded a 68.8% sensitivity in the test set of cases, 67.2% in the validation set, at 91.4% specificity. Applying 99th percentile cutoffs according to each individual's FUT group (3, 34.9, 41.8, and 89.2, for the FUT3-null, FUT-low, FUT-intermediate, and FUT-high groups, respectively) yielded a diagnostic sensitivity for CA19-9 in the first set of cases of 66.7%, 65.7% in the validation set, at 98.9% specificity. ROC analysis for CA19-9 alone yielded an AUC of 0.84; with the tumor marker gene test, AUC improved to 0.92 (P < 0.001).

CONCLUSIONS

Using a tumor marker gene test to personalize an individual's CA19-9 reference range significantly improves diagnostic accuracy.

Immunohistochemical Evaluation of the Expression of Specific Membrane Antigens in Patients with Pancreatic Ductal Adenocarcinoma

(1) Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. The lack of validated disease biomarkers makes timely diagnosis challenging in most cases. Cell membrane and surface proteins play a crucial role in several routes of oncogenesis. The aim of this study was to evaluate the expression of six membrane antigens on PDAC (CA 19-9, mucin 1 and 4 (MUC1, MUC4), mesothelin (MSLN), Annexin A10 (ANXA10), Glypican-1 (GPC-1)) and their correlation with oncologic outcomes. (2) Methods: Immunohistochemical staining for CA 19.9, MUC1, MUC4, MSLN, ANXA10, and GPC-1 of surgical samples of 50 consecutive patients with PDAC was performed. Antigen expression for tumor, ductal, and acinar tissues was classified according to the histo-score (H-score) by two pathologists. (3) Results: Recurrence rate was 47% and 18 patients (36%) deceased (median follow-up 21.5 months). Immunostaining for CA 19-9 and MUC1 showed a significantly higher expression in the neoplastic tissue compared to non-tumor ductal and acinar tissues (p < 0.001). MUC4, MSLN, ANXA10, and GPC-1 were selectively expressed in the neoplastic tissue (p < 0.001). A CA 19-9 H-score value >270 was independently associated with a worse overall survival (p = 0.05) and disease-free survival (p = 0.05). (4) Conclusions: CA 19-9 and MUC1 are highly expressed in PDAC cells. The histological expression of CA 19-9 may predict prognosis. MUC4, MSLN, ANXA10, and GPC-1 are selectively expressed by neoplastic tissue and may represent a potential histological biomarker of disease.

Diagnostic and Prognostic Performance of MicroRNA-25, Carbohydrate Antigen 19-9, Carcinoembryonic Antigen, and Carbohydrate Antigen 125 in Pancreatic Ductal Adenocarcinoma

Background

Pancreatic cancer is a malignancy with high mortality due to the difficulties in early detection. We investigated and compared the diagnostic and prognostic performance of several blood biomarkers, including microRNA-25 (miR-25), carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), and carbohydrate antigen 125 (CA125).

Methods

A retrospective study was conducted at the Chinese People's Liberation Army General Hospital from May 2014 to September 2018. Serum specimens were collected, and miR-25 expression levels were measured using real-time quantitative polymerase chain reaction. Serum CA19-9, CEA, and CA125 levels were measured using enzyme-linked immunosorbent assay (ELISA). Statistical analyses including nonparametric test, receiver operator characteristic (ROC) curves, Kaplan-Meier analysis, and subsequent log-rank test were performed with PRISM 5.0 software. Univariate and multivariate analyses were performed with the R software. P<0.05 was considered statistically significant.

Results

A total of 250 individuals were recruited, including 75 with pancreatic ductal adenocarcinoma (PDAC), 75 with benign lesions, and 100 healthy controls. miR-25, CA19-9, CEA, and CA125 exhibited an area under the curve (AUC) of 0.88, 0.91, 0.81, and 0.76 with a sensitivity of 78.7%, 74.7%, 37.3%, and 35.7% and specificity of 91.5%, 97.0%, 98.2%, and 98.3%, respectively. The combination of miR-25 and CA19-9 further increased the sensitivity to 93.3% with a specificity of 88.5%. Stage-dependent sensitivity was observed with CA19-9, CEA, and CA125. miR-25 levels significantly stratified the prognosis by median level (4,989.97 copies/mL). CA19-9, CEA, and CA125 levels significantly stratified the prognosis by median levels. Univariate and subsequent multivariate analyses identified tumor (T) stage, CA19-9, and CA125 as independent risk factors for PDAC prognosis.

Conclusion

The combination of miR-25 and CA19-9 significantly enhanced the detection sensitivity of PDAC. T stage, CA19-9, and CA125 levels were independent risk factors for PDAC prognosis.

Urine biomarkers enable pancreatic cancer detection up to 2 years before diagnosis

The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is mainly attributed to late diagnosis. We assessed the predictive performance of our previously reported urine biomarker panel for earlier detection of PDAC (LYVE1, REG1B and TFF1) in prediagnostic samples, alone and in combination with plasma CA19-9. This nested case-control study included 99 PDAC cases with urine samples prospectively collected up to 5 years prior to PDAC diagnosis and 198 matched controls. The samples were obtained from the Shanghai Women's Health Study (SWHS), the Shanghai Men's Health Studies (SMHS) and the Southern Community Cohort Study (SCCS). The urine biomarkers were measured by ELISA. Plasma CA19-9 was quantified by Luminex. Multiple logistic regression and Wilcoxon rank-sum and Mann-Whitney test were used for analysis. The internal validation approach was applied and the validated AUC estimators are reported on. The algorithm of urinary protein panel, urine creatinine and age named PancRISK, displayed similar AUC as CA19-9 up to 1 year before PDAC diagnosis (AUC = 0.79); however, the combination enhanced the AUCs to 0.89, and showed good discriminative ability (AUC = 0.77) up to 2 years. The combination showed sensitivity (SN) of 72% at 90% specificity (SP), and SP of 59% at 90% SN up to 1 year and 60% SN with 80% SP and 53% SP with 80% SN up to 2 years before PDAC diagnosis. Adding the clinical information on BMI value resulted in the overall improvement in performance of the PancRISK score. When combined with CA19-9, the urinary panel reached a workable model for detecting PDAC cases up to 2 years prior to diagnosis.

Artificial intelligence in pancreatic cancer: diagnosis, limitations, and the future prospects-a narrative review

PURPOSE

This review aims to explore the role of AI in the application of pancreatic cancer management and make recommendations to minimize the impact of the limitations to provide further benefits from AI use in the future.

METHODS

A comprehensive review of the literature was conducted using a combination of MeSH keywords, including "Artificial intelligence", "Pancreatic cancer", "Diagnosis", and "Limitations".

RESULTS

The beneficial implications of AI in the detection of biomarkers, diagnosis, and prognosis of pancreatic cancer have been explored. In addition, current drawbacks of AI use have been divided into subcategories encompassing statistical, training, and knowledge limitations; data handling, ethical and medicolegal aspects; and clinical integration and implementation.

CONCLUSION

Artificial intelligence (AI) refers to computational machine systems that accomplish a set of given tasks by imitating human intelligence in an exponential learning pattern. AI in gastrointestinal oncology has continued to provide significant advancements in the clinical, molecular, and radiological diagnosis and intervention techniques required to improve the prognosis of many gastrointestinal cancer types, particularly pancreatic cancer.

Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images

Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is complicated as PDAC remains asymptomatic until cancer advances to late stages when treatment is mostly ineffective. Stratifying the risk of developing PDAC can improve early detection as subsequent screening of high-risk individuals through specialized surveillance systems reduces the chance of misdiagnosis at the initial stage of cancer. Risk stratification is however challenging as PDAC lacks specific predictive biomarkers. Studies reported that the pancreas undergoes local morphological changes in response to underlying biological evolution associated with PDAC development. Accurate identification of these changes can help stratify the risk of PDAC. In this retrospective study, an extensive radiomic analysis of the precancerous pancreatic subregions was performed using abdominal Computed Tomography (CT) scans. The analysis was performed using 324 pancreatic subregions identified in 108 contrast-enhanced abdominal CT scans with equal proportion from healthy control, pre-diagnostic, and diagnostic groups. In a pairwise feature analysis, several textural features were found potentially predictive of PDAC. A machine learning classifier was then trained to perform risk prediction of PDAC by automatically classifying the CT scans into healthy control (low-risk) and pre-diagnostic (high-risk) classes and specifying the subregion(s) likely to develop a tumor. The proposed model was trained on CT scans from multiple phases. Whereas using 42 CT scans from the venous phase, model validation was performed which resulted in ~89.3% classification accuracy on average, with sensitivity and specificity reaching 86% and 93%, respectively, for predicting the development of PDAC (i.e., high-risk). To our knowledge, this is the first model that unveiled microlevel precancerous changes across pancreatic subregions and quantified the risk of developing PDAC. The model demonstrated improved prediction by 3.3% in comparison to the state-of-the-art method that considers the global (whole pancreas) features for PDAC prediction.

Clinical characteristics and outcomes in carbohydrate antigen 19-9 negative pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of death from cancer worldwide. Tumor markers like carbohydrate antigen 19-9 (CA 19-9) have been proven valuable as a diagnostic tool and a predictor for tumor staging and response to therapy.

AIM

To delineate the phenotype of normal CA 19-9 PDAC according to clinical features, disease staging and prognosis as compared with high CA 19-9 PDAC cases.

METHODS

We performed a retrospective single-center analysis of all PDAC cases admitted in our Gastroenterology department over a period of 30 mo that were diagnosed by endoscopic ultrasound-guided tissue acquisition. Patients were divided into two groups according to CA 19-9 levels over a threshold of 37 U/mL. We performed a comparison between the two groups with regard to demographic and clinical data, biomarkers, tumor staging and 6-mo survival.

RESULTS

Altogether 111 patients were recruited with 29 having documented normal CA 19-9 (< 37 U/mL). In the CA 19-9 negative group of patients, 20.68% had elevated levels of both CEA and CA 125, 13.79% for CA 125 only whilst 17.24% for CEA only. The two groups had similar demographic characteristics. Abdominal pain was more frequently reported in positive vs negative CA 19-9 PDAC cases (76.83% vs 55.17%), while smoking was slightly more prevalent in the latter group (28.04% vs 31.03%). Tumors over 2 cm were more frequently seen in the positive CA 19-9 group, reflecting a higher proportion of locally advanced and metastatic neoplasia (87.7% vs 79.3%). Six-month survival was higher for the negative CA 19-9 group (58.62% vs 47.56%).

CONCLUSION

Elevated CA 19-9 at diagnosis seems to be associated with a more pronounced symptomatology, high tumor burden and poor prognosis compared to negative CA 19-9 PDAC cases. CEA and CA 125 can be adjunctive useful markers for PDAC, especially in CA 19-9 negative cases.

Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images

BACKGROUND

Early stage diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is challenging due to the lack of specific diagnostic biomarkers. However, stratifying individuals at high risk of PDAC, followed by monitoring their health conditions on regular basis, has the potential to allow diagnosis at early stages.

OBJECTIVE

To stratify high risk individuals for PDAC by identifying predictive features in pre-diagnostic abdominal Computed Tomography (CT) scans.

METHODS

A set of CT features, potentially predictive of PDAC, was identified in the analysis of 4000 raw radiomic parameters extracted from pancreases in pre-diagnostic scans. The naïve Bayes classifier was then developed for automatic classification of CT scans of the pancreas with high risk for PDAC. A set of 108 retrospective CT scans (36 scans from each healthy control, pre-diagnostic, and diagnostic group) from 72 subjects was used for the study. Model development was performed on 66 multiphase CT scans, whereas external validation was performed on 42 venous-phase CT scans.

RESULTS

The system achieved an average classification accuracy of 86% on the external dataset.

CONCLUSIONS

Radiomic analysis of abdominal CT scans can unveil, quantify, and interpret micro-level changes in the pre-diagnostic pancreas and can efficiently assist in the stratification of high risk individuals for PDAC.

The Impact of Biomarkers in Pancreatic Ductal Adenocarcinoma on Diagnosis, Surveillance and Therapy

Simple Summary

Pancreatic ductal adenocarcinoma is a leading cause of cancer death worldwide. Due to the frequently late diagnosis, early metastasis and high therapy resistance curation is rare and prognosis remains poor overall. To provide early diagnostic and therapeutic predictors, various molecules from blood, tissue and other origin e.g., saliva, urine and stool, have been identified as biomarkers. This review summarizes current trends in biomarkers for diagnosis and therapy of pancreatic ductal adenocarcinoma.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is still difficult to treat due to insufficient methods for early diagnosis and prediction of therapy response. Furthermore, surveillance after curatively intended surgery lacks adequate methods for timely detection of recurrence. Therefore, several molecules have been analyzed as predictors of recurrence or early detection of PDAC. Enhanced understanding of molecular tumorigenesis and treatment response triggered the identification of novel biomarkers as predictors for response to conventional chemotherapy or targeted therapy. In conclusion, progress has been made especially in the prediction of therapy response with biomarkers. The use of molecules for early detection and recurrence of PDAC is still at an early stage, but there are promising approaches in noninvasive biomarkers, composite panels and scores that can already ameliorate the current clinical practice. The present review summarizes the current state of research on biomarkers for diagnosis and therapy of pancreatic cancer.

Renalase is a novel tissue and serological biomarker in pancreatic ductal adenocarcinoma

Dysregulated expression of the secretory protein renalase can promote pancreatic ductal adenocarcinoma (PDAC) growth in animal models. We characterized renalase expression in premalignant and malignant PDAC tissue and investigated whether plasma renalase levels corresponded to clinical PDAC characteristics. Renalase immunohistochemistry was used to determine the presence and distribution of renalase in normal pancreas, chronic pancreatitis, PDAC precursor lesions, and PDAC tissues. Associations between pretreatment plasma renalase and PDAC clinical status were assessed in patients with varied clinical stages of PDAC and included tumor characteristics, surgical resection in locally advanced/borderline resectable PDAC, and overall survival. Data were retrospectively obtained and correlated using non-parametric analysis. Little to no renalase was detected by histochemistry in the normal pancreatic head in the absence of abdominal trauma. In chronic pancreatitis, renalase immunoreactivity localized to peri-acinar spindle-shaped cells in some samples. It was also widely present in PDAC precursor lesions and PDAC tissue. Among 240 patients with PDAC, elevated plasma renalase levels were associated with worse tumor characteristics, including greater angiolymphatic invasion (80.0% vs. 58.1%, p = 0.012) and greater node positive disease (76.5% vs. 56.5%, p = 0.024). Overall survival was worse in patients with high plasma renalase levels with median follow-up of 27.70 months vs. 65.03 months (p < 0.001). Renalase levels also predicted whether patients with locally advanced/borderline resectable PDAC underwent resection (AUC 0.674; 95%CI 0.42-0.82, p = 0.04). Overall tissue renalase was increased in both premalignant and malignant PDAC tissues compared to normal pancreas. Elevated plasma renalase levels were associated with advanced tumor characteristics, decreased overall survival, and reduced resectability in patients with locally advanced/borderline resectable PDAC. These studies show that renalase levels are increased in premalignant pancreatic tissues and that its levels in plasma correspond to the clinical behavior of PDAC.

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).

Non-Invasive Biomarkers for Earlier Detection of Pancreatic Cancer-A Comprehensive Review

Simple Summary

Pancreatic ductal adenocarcinoma (PDAC), which represents approximately 90% of all pancreatic cancers, is an extremely aggressive and lethal disease. It is considered a silent killer due to a largely asymptomatic course and late clinical presentation. Earlier detection of the disease would likely have a great impact on changing the currently poor survival figures for this malignancy. In this comprehensive review, we assessed over 4000 reports on non-invasive PDAC biomarkers in the last decade. Applying the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, we selected and reviewed in more detail 49 relevant studies reporting on the most promising candidate biomarkers. In addition, we also highlight the present challenges and complexities of translating novel biomarkers into clinical use.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) carries a deadly diagnosis, due in large part to delayed presentation when the disease is already at an advanced stage. CA19-9 is currently the most commonly utilized biomarker for PDAC; however, it lacks the necessary accuracy to detect precursor lesions or stage I PDAC. Novel biomarkers that could detect this malignancy with improved sensitivity (SN) and specificity (SP) would likely result in more curative resections and more effective therapeutic interventions, changing thus the present dismal survival figures. The aim of this study was to systematically and comprehensively review the scientific literature on non-invasive biomarkers in biofluids such as blood, urine and saliva that were attempting earlier PDAC detection. The search performed covered a period of 10 years (January 2010—August 2020). Data were extracted using keywords search in the three databases: MEDLINE, Web of Science and Embase. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was applied for study selection based on establishing the risk of bias and applicability concerns in Patient Selection, Index test (biomarker assay) and Reference Standard (standard-of-care diagnostic test). Out of initially over 4000 published reports, 49 relevant studies were selected and reviewed in more detail. In addition, we discuss the present challenges and complexities in the path of translating the discovered biomarkers into the clinical setting. Our systematic review highlighted several promising biomarkers that could, either alone or in combination with CA19-9, potentially improve earlier detection of PDAC. Overall, reviewed biomarker studies should aim to improve methodological and reporting quality, and novel candidate biomarkers should be investigated further in order to demonstrate their clinical usefulness. However, challenges and complexities in the path of translating the discovered biomarkers from the research laboratory to the clinical setting remain and would have to be addressed before a more realistic breakthrough in earlier detection of PDAC is achieved.

Early prediction of pancreatic cancer from new-onset diabetes: an Associazione Italiana Oncologia Medica (AIOM)/Associazione Medici Diabetologi (AMD)/Società Italiana Endocrinologia (SIE)/Società Italiana Farmacologia (SIF) multidisciplinary consensus position paper

Pancreatic cancer (PC) is a common cause of cancer-related death, due to difficulties in detecting early-stage disease, to its aggressive behaviour, and to poor response to systemic therapy. Therefore, developing strategies for early diagnosis of resectable PC is critical for improving survival. Diabetes mellitus is another major public health problem worldwide. Furthermore, diabetes can represent both a risk factor and a consequence of PC: nowadays, the relationship between these two diseases is considered a high priority for research. New-onset diabetes can be an early manifestation of PC, especially in a thin adult without a family history of diabetes. However, even if targeted screening for patients at higher risk of PC could be a promising approach, this is not recommended in asymptomatic adults with new-onset diabetes, due to the much higher incidence of hyperglycaemia than PC and to the lack of a safe and affordable PC screening test. Prompted by a well-established and productive multidisciplinary cooperation, the Italian Association of Medical Oncology (AIOM), the Italian Medical Diabetologists Association (AMD), the Italian Society of Endocrinology (SIE), and the Italian Society of Pharmacology (SIF) here review available evidence on the mechanisms linking diabetes and PC, addressing the feasibility of screening for early PC in patients with diabetes, and sharing a set of update statements with the aim of providing a state-of-the-art overview and a decision aid tool for daily clinical practice.

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The incidence of PC is increasing and its prognosis is very poor; therefore, early detection is fundamental.

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New-onset diabetes may be an early manifestation of PC, often disappearing after its resection.

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Screening for PC is not currently recommended among people with new-onset diabetes, due to its high incidence.

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Thin subjects >50 years old at the time of diabetes onset, with sudden weight loss and severe hyperglycaemia are at higher risk.

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Currently some clinical models are promising for stratifying cancer risk in people with new-onset diabetes.

Lead-Time Trajectory of CA19-9 as an Anchor Marker for Pancreatic Cancer Early Detection

BACKGROUND & AIMS

There is substantial interest in liquid biopsy approaches for cancer early detection among subjects at risk, using multi-marker panels. CA19-9 is an established circulating biomarker for pancreatic cancer; however, its relevance for pancreatic cancer early detection or for monitoring subjects at risk has not been established.

METHODS

CA19-9 levels were assessed in blinded sera from 175 subjects collected up to 5 years before diagnosis of pancreatic cancer and from 875 matched controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. For comparison of performance, CA19-9 was assayed in blinded independent sets of samples collected at diagnosis from 129 subjects with resectable pancreatic cancer and 275 controls (100 healthy subjects; 50 with chronic pancreatitis; and 125 with noncancerous pancreatic cysts). The complementary value of 2 additional protein markers, TIMP1 and LRG1, was determined.

RESULTS

In the PLCO cohort, levels of CA19-9 increased exponentially starting at 2 years before diagnosis with sensitivities reaching 60% at 99% specificity within 0 to 6 months before diagnosis for all cases and 50% at 99% specificity for cases diagnosed with early-stage disease. Performance was comparable for distinguishing newly diagnosed cases with resectable pancreatic cancer from healthy controls (64% sensitivity at 99% specificity). Comparison of resectable pancreatic cancer cases to subjects with chronic pancreatitis yielded 46% sensitivity at 99% specificity and for subjects with noncancerous cysts, 30% sensitivity at 99% specificity. For prediagnostic cases below cutoff value for CA19-9, the combination with LRG1 and TIMP1 yielded an increment of 13.2% in sensitivity at 99% specificity (P = .031) in identifying cases diagnosed within 1 year of blood collection.

CONCLUSION

CA19-9 can serve as an anchor marker for pancreatic cancer early detection applications.

Diagnostic model for pancreatic cancer using a multi-biomarker panel

Purpose

Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage.

Methods

Individual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups).

Results

The Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value.

Conclusion

This diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.

Circulating Cancer Biomarkers

Cancer is among the major public health problems worldwide, representing the leading cause of morbidity and mortality in industrialized countries [...].

Reproductive Factors, Use of Exogenous Hormones, and Pancreatic Cancer Incidence: The Norwegian Women and Cancer Study

Introduction

The incidence of pancreatic cancer is increasing worldwide and characterized by a particularly low survival rate. Studies have reported weak and inconsistent evidence for associations among reproductive factors, use of exogenous hormones, and pancreatic cancer incidence in women.

Purpose

To investigate relationships between reproductive factors, exogenous hormones, and the rate of pancreatic cancer incidence in a large population-based prospective cohort of women in Norway.

Methods

We used data from the Norwegian Women and Cancer study on 588 incident cases of pancreatic cancer diagnosed among 165,419 women, with mean follow-up of 18.7 years. Cox proportional-hazard models were used to estimate HRs and 95% CIs for associations of interest.

Results

Cumulative breastfeeding duration >24 months was associated with 63% decreased incidence of pancreatic cancer compared to no breastfeeding. We observed an inverse linear dose–response trend between cumulative breastfeeding duration and pancreatic cancer incidence, which was confirmed in parous women and ever-smokers. Higher age at first birth and menopause were inversely associated with pancreatic cancer incidence, though with less precise effect estimates. Current use of oral contraceptives was associated with a doubling of pancreatic cancer incidence, but the analysis was hampered by a small number of cases. There was no evidence of any associations between age at menarche, parity or use of menopausal hormone therapy, and incidence of pancreatic cancer.

Conclusion

Our results suggest a potential protective effect of breastfeeding duration against pancreatic cancer incidence. Inconsistent results for the other reproductive factors suggested no important role of estrogens in pancreatic cancer etiology.

Biomarkers and Strategy to Detect Preinvasive and Early Pancreatic Cancer: State of the Field and the Impact of the EDRN

Patients afflicted with pancreatic ductal adenocarcinoma (PDAC) face a dismal prognosis, but headway could be made if physicians could identify the disease earlier. A compelling strategy to broaden the use of surveillance for PDAC is to incorporate molecular biomarkers in combination with clinical analysis and imaging tools. This article summarizes the components involved in accomplishing biomarker validation and an analysis of the requirements of molecular biomarkers for disease surveillance. We highlight the significance of consortia for this research and highlight resources and infrastructure of the Early Detection Research Network (EDRN). The EDRN brings together the multifaceted expertise and resources needed for biomarker validation, such as study design, clinical care, biospecimen collection and handling, molecular technologies, and biostatistical analysis, and studies coming out of the EDRN have yielded biomarkers that are moving forward in validation. We close the article with an overview of the current investigational biomarkers, an analysis of their performance relative to the established benchmarks, and an outlook on the current needs in the field. The outlook for improving the early detection of PDAC looks promising, and the pace of further research should be quickened through the resources and expertise of the EDRN and other consortia.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

THBS2/CA19-9 Detecting Pancreatic Ductal Adenocarcinoma at Diagnosis Underperforms in Prediagnostic Detection: Implications for Biomarker Advancement

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed too late for effective therapy. The classic strategy for early detection biomarker advancement consists of initial retrospective phases of discovery and validation with tissue samples taken from individuals diagnosed with disease, compared with controls. Using this approach, we previously reported the discovery of a blood biomarker panel consisting of thrombospondin-2 (THBS2) and CA19-9 that together could discriminate resectable stage I and IIa PDAC as well as stages III and IV PDAC, with c-statistic values in the range of 0.96 to 0.97 in two phase II studies. We now report that in two studies of blood samples prospectively collected from 1 to 15 years prior to a PDAC diagnosis (Mayo Clinic and PLCO cohorts), THBS2 and/or CA19-9 failed to discriminate cases from healthy controls at the AUC = 0.8 needed. We conclude that PDAC progression may be heterogeneous and for some individuals can be more rapid than generally appreciated. It is important that PDAC early-detection studies incorporate high-risk, prospective prediagnostic cohorts into discovery and validation studies.Prevention Relevance: A blood biomarker panel of THBS2 and CA19-9 detects early stages of pancreatic ductal adenocarcinoma at diagnosis, but not when tested across a population up to 1 year earlier. Our findings suggest serial sampling over time, using prospectively collected samples for biomarker discovery, and more frequent screening of high-risk individuals.

Investigating a novel multiplex proteomics technology for detection of changes in serum protein concentrations that may correlate to tumor burden

Background: To account for cancer heterogeneity, we previously introduced the concept of "personalized" tumor markers, which are biomarkers that are informative in subsets of patients or even a single patient. Recent developments in various multiplex protein technologies create excitement for the discovery of markers of tumor burden in individual patients, but the reliability of the technologies remains to be tested for this purpose. Here, we sought to explore the potential of a novel proteomics platform, which utilizes a multiplexed antibody microarray, to detect changes in serum protein concentration that may correlate to tumor burden in pancreatic cancer. Methods: We applied the Quantibody® Human Kiloplex Array to simultaneously measure 1,000 proteins in sera obtained pre- and post-surgically from five pancreatic cancer patients. We expected that proteins which decreased post-surgery may correlate to tumor burden. Sera from two healthy individuals, split into two aliquots each, were used as controls. To validate the multiplexed results, we used single-target ELISA assays to measure the proteins with the largest serum concentration changes after surgery in sera collected pre- and post-surgically from the previous five patients and 10 additional patients. Results: The multiplexed array revealed nine proteins with more than two-fold post-surgical decrease in at least two of five patients. However, validation using single ELISAs showed that only two proteins tested displayed more than two-fold post-surgical decrease in one of the five original patients. In the independent cohort, six of the proteins tested showed at least a two-fold decrease post-surgery in at least one patient. Conclusions: Our study found that the Quantibody® Human Kiloplex Array results could not be reliably replicated with individual ELISA assays and most hits would likely represent false positives if applied to biomarker discovery. These findings suggest that data from novel, high-throughput proteomic platforms need stringent validation to avoid false discoveries.

Investigating a novel multiplex proteomics technology for detection of changes in serum protein concentrations that may correlate to tumor burden

Background: To account for cancer heterogeneity, we previously introduced the concept of "personalized" tumor markers, which are biomarkers that are informative in subsets of patients or even a single patient. Recent developments in various multiplex protein technologies create excitement for the discovery of markers of tumor burden in individual patients, but the reliability of the technologies remains to be tested for this purpose. Here, we sought to explore the potential of a novel proteomics platform, which utilizes a multiplexed antibody microarray, to detect changes in serum protein concentration that may correlate to tumor burden in pancreatic cancer. Methods: We applied the Quantibody® Human Kiloplex Array to simultaneously measure 1,000 proteins in sera obtained pre- and post-surgically from five pancreatic cancer patients. We expected that proteins which decreased post-surgery may correlate to tumor burden. Sera from two healthy individuals, split into two aliquots each, were used as controls. To validate the multiplexed results, we used single-target ELISA assays to measure the proteins with the largest serum concentration changes after surgery in sera collected pre- and post-surgically from the previous five patients and 10 additional patients. Results: The multiplexed array revealed nine proteins with more than two-fold post-surgical decrease in at least two of five patients. However, validation using single ELISAs showed that only two proteins tested displayed more than two-fold post-surgical decrease in one of the five original patients. In the independent cohort, six of the proteins tested showed at least a two-fold decrease post-surgery in at least one patient. Conclusions: Our study found that the Quantibody® Human Kiloplex Array results could not be reliably replicated with individual ELISA assays and most hits would likely represent false positives if applied to biomarker discovery. These findings suggest that data from novel, high-throughput proteomic platforms need stringent validation to avoid false discoveries.

SST gene hypermethylation acts as a pan-cancer marker for pancreatic ductal adenocarcinoma and multiple other tumors: toward its use for blood-based diagnosis

Aberrant DNA methylation is often involved in carcinogenesis. Our initial goal was to identify DNA methylation biomarkers associated with pancreatic cancer. A genomewide methylation study was performed on DNA from pancreatic ductal adenocarcinoma (PDAC) and endocrine pancreas tumors. Validation of DNA methylation patterns and concomitant alterations in expression of gene candidates was performed on patient samples and pancreatic cancer cell lines. Furthermore, validation was done on independent data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Finally, droplet digital PCR was employed to detect DNA methylation marks in cell-free (cf) DNA isolated from plasma samples of PDAC patients and cancer-free blood donors. Hypermethylation of the SST gene (encoding somatostatin) and concomitant downregulation of its expression were discovered in PDAC and endocrine tumor tissues while not being present in chronic pancreatitis (inflamed) tissues and normal pancreas. Fittingly, treatment with a somatostatin agonist (octreotide) reduced cell proliferation and migration of pancreatic cancer cells. Diagnostic performance of SST methylation in a receiver operating characteristic curve analysis was 100% and 89% for tissue and plasma samples, respectively. A large body of TCGA and GEO data confirmed SST hypermethylation and downregulation in PDAC and showed a similar effect in a broad spectrum of other tumor entities. SST promoter methylation represents a sensitive and promising molecular, pan-cancer biomarker detectable in tumor tissue, and liquid biopsy samples.

Management of patients with increased risk for familial pancreatic cancer: updated recommendations from the International Cancer of the Pancreas Screening (CAPS) Consortium

BACKGROUND AND AIM

The International Cancer of the Pancreas Screening Consortium met in 2018 to update its consensus recommendations for the management of individuals with increased risk of pancreatic cancer based on family history or germline mutation status (high-risk individuals).

METHODS

A modified Delphi approach was employed to reach consensus among a multidisciplinary group of experts who voted on consensus statements. Consensus was considered reached if ≥75% agreed or disagreed.

RESULTS

Consensus was reached on 55 statements. The main goals of surveillance (to identify high-grade dysplastic precursor lesions and T1N0M0 pancreatic cancer) remained unchanged. Experts agreed that for those with familial risk, surveillance should start no earlier than age 50 or 10 years earlier than the youngest relative with pancreatic cancer, but were split on whether to start at age 50 or 55. Germline ATM mutation carriers with one affected first-degree relative are now considered eligible for surveillance. Experts agreed that preferred surveillance tests are endoscopic ultrasound and MRI/magnetic retrograde cholangiopancreatography, but no consensus was reached on how to alternate these modalities. Annual surveillance is recommended in the absence of concerning lesions. Main areas of disagreement included if and how surveillance should be performed for hereditary pancreatitis, and the management of indeterminate lesions.

CONCLUSIONS

Pancreatic surveillance is recommended for selected high-risk individuals to detect early pancreatic cancer and its high-grade precursors, but should be performed in a research setting by multidisciplinary teams in centres with appropriate expertise. Until more evidence supporting these recommendations is available, the benefits, risks and costs of surveillance of pancreatic surveillance need additional evaluation.

Prolactin Promotes Fibrosis and Pancreatic Cancer Progression

Pancreatic ductal adenocarcinoma (PDAC) is associated with significant fibrosis. Recent findings have highlighted the profibrotic activity of tissue-resident macrophages in the pancreatic cancer microenvironment. Here, we show that neoplastic pancreatic epithelium, as well as a subset of tissue-resident macrophages, expresses the prolactin-receptor (PRLR). High mobility group box 1-induced prolactin expression in the pancreas maintained FAK1 and STAT3 phosphorylation within the epithelium and stroma. Gain-of-function and loss-of-function experiments demonstrated the essential role of prolactin in promoting collagen deposition and fibrosis. Finally, the signaling cascade downstream of prolactin/PRLR activated STAT3 rather than STAT5 in PDAC. These findings suggest that targeting prolactin together with IL6, a known major activator of STAT3, could represent a novel therapeutic strategy for treating pancreatic cancer. SIGNIFICANCE: Prolactin is a key factor in the cross-talk between the stroma and neoplastic epithelium, functioning to promote fibrosis and PDAC progression.

CA19-9 and apolipoprotein-A2 isoforms as detection markers for pancreatic cancer: a prospective evaluation

Recently, we identified unique processing patterns of apolipoprotein A2 (ApoA2) in patients with pancreatic cancer. Our study provides a first prospective evaluation of an ApoA2 isoform ("ApoA2-ATQ/AT"), alone and in combination with carbohydrate antigen 19-9 (CA19-9), as an early detection biomarker for pancreatic cancer. We performed ELISA measurements of CA19-9 and ApoA2-ATQ/AT in 156 patients with pancreatic cancer and 217 matched controls within the European EPIC cohort, using plasma samples collected up to 60 months prior to diagnosis. The detection discrimination statistics were calculated for risk scores by strata of lag-time. For CA19-9, in univariate marker analyses, C-statistics to distinguish future pancreatic cancer patients from cancer-free individuals were 0.80 for plasma taken ≤6 months before diagnosis, and 0.71 for >6-18 months; for ApoA2-ATQ/AT, C-statistics were 0.62, and 0.65, respectively. Joint models based on ApoA2-ATQ/AT plus CA19-9 significantly improved discrimination within >6-18 months (C = 0.74 vs. 0.71 for CA19-9 alone, p = 0.022) and ≤ 18 months (C = 0.75 vs. 0.74, p = 0.022). At 98% specificity, and for lag times of ≤6, >6-18 or ≤ 18 months, sensitivities were 57%, 36% and 43% for CA19-9 combined with ApoA2-ATQ/AT, respectively, vs. 50%, 29% and 36% for CA19-9 alone. Compared to CA19-9 alone, the combination of CA19-9 and ApoA2-ATQ/AT may improve detection of pancreatic cancer up to 18 months prior to diagnosis under usual care, and may provide a useful first measure for pancreatic cancer detection prior to imaging.

Trefoil factor(s) and CA19.9: A promising panel for early detection of pancreatic cancer

Background

Trefoil factors (TFF1, TFF2, and TFF3) are small secretory molecules that recently have gained significant attention in multiple studies as an integral component of pancreatic cancer (PC) subtype-specific gene signature. Here, we comprehensively investigated the diagnostic potential of all the member of trefoil family, i.e., TFF1, TFF2, and TFF3 in combination with CA19.9 for detection of PC.

Methods

Trefoil factors (TFFs) gene expression was analyzed in publicly available cancer genome datasets, followed by assessment of their expression in genetically engineered spontaneous mouse model (GEM) of PC (KrasG12D; Pdx1-Cre (KC)) and in human tissue microarray consisting of normal pancreas adjacent to tumor (NAT), precursor lesions (PanIN), and various pathological grades of PC by immunohistochemistry (IHC). Serum TFFs and CA19.9 levels were evaluated via ELISA in comprehensive sample set (n = 362) comprised of independent training and validation sets each containing benign controls (BC), chronic pancreatitis (CP), and various stages of PC. Univariate and multivariate logistic regression and receiver operating characteristic curves (ROC) were used to examine their diagnostic potential both alone and in combination with CA19.9.

Findings

The publicly available datasets and expression analysis revealed significant increased expression of TFF1, TFF2, and TFF3 in human PanINs and PC tissues. Assessment of KC mouse model also suggested upregulated expression of TFFs in PanIN lesions and early stage of PC. In serum analyses studies, TFF1 and TFF2 were significantly elevated in early stages of PC in comparison to benign and CP control group while significant elevation in TFF3 levels were observed in CP group with no further elevation in its level in early stage PC group. In receiver operating curve (ROC) analyses, combination of TFFs with CA19.9 emerged as promising panel for discriminating early stage of PC (EPC) from BC (AUC_{TFF1+TFF2+TFF3+CA19.9} = 0.93) as well as CP (AUC_{TFF1+TFF2+TFF3+CA19.9} = 0.93). Notably, at 90% specificity (desired for blood-based biomarker panel), TFFs combination improved CA19.9 sensitivity by 10% and 25% to differentiate EPC from BC and CP respectively. In an independent blinded validation set, the combination of TFFs and CA19.9 (AUC_{TFF1+TFF2+TFF3+CA19.9} = 0.82) also improved the overall efficacy of CA19.9 (AUC_CA19.9 = 0.66) to differentiate EPC from CP proving unique biomarker capabilities of TFFs to distinguish early stage of this deadly lethal disease.

Interpretation

In silico, tissue and serum analyses validated significantly increased level of all TFFs in precursor lesions and early stages of PC. The combination of TFFs enhanced sensitivity and specificity of CA19.9 to discriminate early stage of PC from benign control and chronic pancreatitis groups.

Plasma Micro-RNA Alterations Appear Late in Pancreatic Cancer

Objectives:

The aim of this research was to study whether plasma microRNAs (miRNA) can be used for early detection of pancreatic cancer (PC) by analyzing prediagnostic plasma samples collected before a PC diagnosis.

Background:

PC has a poor prognosis due to late presenting symptoms and early metastasis. Circulating miRNAs are altered in PC at diagnosis but have not been evaluated in a prediagnostic setting.

Methods:

We first performed an initial screen using a panel of 372 miRNAs in a retrospective case-control cohort that included early-stage PC patients and healthy controls. Significantly altered miRNAs at diagnosis were then measured in an early detection case-control cohort wherein plasma samples in the cases are collected before a PC diagnosis. Carbohydrate antigen 19–9 (Ca 19–9) levels were measured in all samples for comparison.

Results:

Our initial screen, including 23 stage I-II PC cases and 22 controls, revealed 15 candidate miRNAs that were differentially expressed in plasma samples at PC diagnosis. We combined all 15 miRNAs into a multivariate statistical model, which outperformed Ca 19–9 in receiver-operating characteristics analysis. However, none of the candidate miRNAs, individually or in combination, were significantly altered in prediagnostic plasma samples from 67 future PC patients compared with 132 matched controls. In comparison, Ca 19–9 levels were significantly higher in the cases at <5 years before diagnosis.

Conclusion:

Plasma miRNAs are altered in PC patients at diagnosis, but the candidate miRNAs found in this study appear late in the course of the disease and cannot be used for early detection of the disease.

A Plasma-Derived Protein-Metabolite Multiplexed Panel for Early-Stage Pancreatic Cancer

BACKGROUND

We applied a training and testing approach to develop and validate a plasma metabolite panel for the detection of early-stage pancreatic ductal adenocarcinoma (PDAC) alone and in combination with a previously validated protein panel for early-stage PDAC.

METHODS

A comprehensive metabolomics platform was initially applied to plasmas collected from 20 PDAC cases and 80 controls. Candidate markers were filtered based on a second independent cohort that included nine invasive intraductal papillary mucinous neoplasm cases and 51 benign pancreatic cysts. Blinded validation of the resulting metabolite panel was performed in an independent test cohort consisting of 39 resectable PDAC cases and 82 matched healthy controls. The additive value of combining the metabolite panel with a previously validated protein panel was evaluated.

RESULTS

Five metabolites (acetylspermidine, diacetylspermine, an indole-derivative, and two lysophosphatidylcholines) were selected as a panel based on filtering criteria. A combination rule was developed for distinguishing between PDAC and healthy controls using the Training Set. In the blinded validation study with early-stage PDAC samples and controls, the five metabolites yielded areas under the curve (AUCs) ranging from 0.726 to 0.842, and the combined metabolite model yielded an AUC of 0.892 (95% confidence interval [CI] = 0.828 to 0.956). Performance was further statistically significantly improved by combining the metabolite panel with a previously validated protein marker panel consisting of CA 19-9, LRG1, and TIMP1 (AUC = 0.924, 95% CI = 0.864 to 0.983, comparison DeLong test one-sided P= .02).

CONCLUSIONS

A metabolite panel in combination with CA19-9, TIMP1, and LRG1 exhibited substantially improved performance in the detection of early-stage PDAC compared with a protein panel alone.

A Prospective Study to Establish a New-Onset Diabetes Cohort: From the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer

The National Cancer Institute and the National Institute for Diabetes and Digestive and Kidney Diseases initiated the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer (CPDPC) in 2015 (the CPDPC's origin, structure, governance, and research objectives are described in another article in this journal). One of the key objectives of CPDPC is to assemble a cohort of 10,000 subjects 50 years or older with new-onset diabetes, called the NOD cohort. Using a define, enrich, and find early detection approach, the aims of the NOD study are to (a) estimate the 3-year probability of pancreatic ductal adenocarcinoma (PDAC) in NOD (define), (b) establish a biobank of clinically annotated biospecimens from presymptomatic PDAC and control new-onset type 2 diabetes mellitus subjects, (c) conduct phase 3 validation studies of promising biomarkers for identification of incident PDAC in NOD patients (enrich), and (d) provide a platform for development of a future interventional screening protocol for early detection of PDAC in patients with NOD that incorporates imaging studies and/or clinical algorithms (find). It is expected that 85 to 100 incidences of PDAC will be diagnosed during the study period in this cohort of 10,000 patients.

Identification of diagnostic upper gastrointestinal cancer tissue type-specific urinary biomarkers

Several potential urinary biomarkers exhibiting an association with upper gastrointestinal tumour growth have been previously identified, of which S100A6, S100A9, rabenosyn-5 and programmed cell death 6-interacting protein (PDCD6IP) were further validated and found to be upregulated in malignant tumours. The cancer cohort from our previous study was subclassified to assess whether distinct molecular markers can be identified for each individual cancer type using a similar approach. Urine samples from patients with cancers of the stomach, oesophagus, oesophagogastric junction or pancreas were analysed by surface-enhanced laser desorption/ionization-time-of-flight mass spectrometry using both CM10 and IMAC30 (Cu²⁺-complexed) chip types and LC-MS/MS-based mass spectrometry after chromatographic enrichment. This was followed by protein identification, pattern matching and validation by western blotting. We found 8 m/z peaks with statistical significance for the four cancer types investigated, of which m/z 2447 and 2577 were identified by pattern matching as fragments of cathepsin-B (CTSB) and cystatin-B (CSTB); both molecules are indicative of pancreatic cancer. Additionally, we observed a potential association of upregulated α-1-antichymotrypsin with pancreatic and gastric cancers, of PDCD6IP, vitelline membrane outer layer protein 1 homolog (VMO1) and triosephosphate isomerase (TPI1) with oesophagogastric junctional cancers, and of complement C4-A, prostatic acid phosphatase, azurocidin and histone-H1 with oesophageal cancer. Furthermore, the potential pancreatic cancer biomarkers CSTB and CTSB were validated independently by western blotting. Therefore, the present study identified two new potential urinary biomarkers that appear to be associated with pancreatic cancer. This may provide a simple, non-invasive screening test for use in the clinical setting.

Vitamin K intake and prostate cancer risk in the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) Screening Trial

Background

Vitamin K inhibits prostate cancer cells, and an altered expression of vitamin K-dependent proteins in prostate tumors has been linked to their aggressiveness and progression. However, little is known about the effect of vitamin K intake on prostate cancer in human populations.

Objectives

We evaluated the associations of dietary intake of phylloquinone (vitamin K-1), menaquinones (vitamin K-2), and total vitamin K with the development of prostate cancer among participants in the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) Screening Trial.

Design

Dietary intake of vitamin K was assessed with the Dietary Questionnaire (DQX) at baseline and the Dietary History Questionnaire (DHQ) at the third anniversary of randomization by using high-performance liquid chromatography-based food-composition data obtained from the USDA and published studies. During a median follow-up of 11.8 y, 2978 cases of prostate cancer (including 490 advanced cases) were identified from the 28,356 men who completed DQX. Similarly, 2973 cases of prostate cancer (including 647 advanced cases) were documented from the 48,090 men who completed DHQ. Cox proportional hazards regression was used to estimate prostate cancer risk in relation to the dietary intake of vitamin K.

Results

After adjustment for confounders, dietary intakes of phylloquinone, menaquinones, and total vitamin K, assessed with either the DQX or DHQ, were not significantly associated with the risk of advanced, nonadvanced, and total prostate cancer. These results remained virtually the same when vitamin K intake was modeled as a categorical (divided into quintiles) or continuous (per IQR increase) variable or after outliers of total vitamin K intake (defined as a value that falls above the sum of third quartile and twice the IQR) were excluded.

Conclusions

The present study does not suggest that vitamin K intake influences the occurrence of total and advanced prostate cancer in the general US population.

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.

Untargeted lipidomic features associated with colorectal cancer in a prospective cohort

BACKGROUND

Epidemiologists are beginning to employ metabolomics and lipidomics with archived blood from incident cases and controls to discover causes of cancer. Although several such studies have focused on colorectal cancer (CRC), they all followed targeted or semi-targeted designs that limited their ability to find discriminating molecules and pathways related to the causes of CRC.

METHODS

Using an untargeted design, we measured lipophilic metabolites in prediagnostic serum from 66 CRC patients and 66 matched controls from the European Prospective Investigation into Cancer and Nutrition (Turin, Italy). Samples were analyzed by liquid chromatography-high-resolution mass spectrometry (LC-MS), resulting in 8690 features for statistical analysis.

RESULTS

Rather than the usual multiple-hypothesis-testing approach, we based variable selection on an ensemble of regression methods, which found nine features to be associated with case-control status. We then regressed each selected feature on time-to-diagnosis to determine whether the feature was likely to be either a potentially causal biomarker or a reactive product of disease progression (reverse causality).

CONCLUSIONS

Of the nine selected LC-MS features, four appear to be involved in CRC etiology and merit further investigation in prospective studies of CRC. Four other features appear to be related to progression of the disease (reverse causality), and may represent biomarkers of value for early detection of CRC.

On the Dual Role of Carcinoembryonic Antigen-Related Cell Adhesion Molecule 1 (CEACAM1) in Human Malignancies

Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) is a glycoprotein belonging to the carcinoembryonic antigen (CEA) family that is expressed on a wide variety of cells and holds a complex role in inflammation through its alternate splicing and generation of various isoforms, mediating intricate mechanisms of modulation and dysregulation. Initially regarded as a tumor suppressor as its expression shows considerable downregulation within the epithelia in the early phases of many solid cancers, CEACAM1 has been linked lately to the progression of malignancy and metastatic spread as various papers point to its role in tumor progression, angiogenesis, and invasion. We reviewed the literature and discussed the various expression patterns of CEACAM1 in different types of tumors, describing its structure and general biologic functions and emphasizing the most significant findings that link this molecule to poor prognosis. The importance of understanding the role of CEACAM1 in cell transformation stands not only in this adhesion molecule's value as a prognostic factor but also in its promising premise as a potential new molecular target that could be exploited as a specific cancer therapy.

Prediagnostic detection of mesothelioma by circulating calretinin and mesothelin - a case-control comparison nested into a prospective cohort of asbestos-exposed workers

Malignant mesothelioma (MM) is strongly associated with a previous asbestos exposure. To improve timely detection of MM in asbestos workers, better screening tools – like minimally-invasive biomarkers – are desirable. Between 2008 and 2018 2,769 patients with benign asbestos-related diseases were recruited to participate in annual screens. Using a nested case-control design the protein markers calretinin and mesothelin were determined by enzyme-linked immunosorbent assays in prediagnostic plasma samples of 34 MM cases as well as 136 matched controls from the cohort. Conditional on a pre-defined specificity of 98% for calretinin and 99% for mesothelin the markers reached individual sensitivities of 31% and 23%, respectively, when including the incident cases with samples taken between one and 15 months before diagnosis. The combination of both markers increased the sensitivity to 46% at 98% specificity. Marker complementation increased with earlier sampling. The marker combination improves the sensitivity of the individual markers, indicating a useful complementation and suggesting that additional markers may further improve the performance. This is the first prospective cohort study to evaluate a detection of MM by calretinin and its combination with mesothelin up to about a year before clinical diagnosis. Whether an earlier diagnosis will result in reduced mortality has yet to be demonstrated.

Identification of LEA, a podocalyxin-like glycoprotein, as a predictor for the progression of colorectal cancer

Large external antigen (LEA) is considered as a colorectal cancer (CRC)‐associated antigen, which was found via mAb ND‐1 generated using hybridoma technology, but its molecular features remain unknown. To facilitate the clinical application of LEA, we identified LEA as a podocalyxin‐like protein 1 (PODXL) with molecular weight of approximately 230 kDa, a hyperglycosylated protein, using immunoprecipitation and mass spectrometry in combination, and verified that ND‐1‐recognized epitope is on the terminal sialic acid of LEA. Correlation analysis between LEA and PODXL in molecular weight, immunological cross‐reactivity, and gene expression dependence supported the PODXL identity of the LEA. Moreover, we assessed the clinical significance of the LEA in 89 pairs of primary CRC tissues and adjacent nontumor colorectal tissues using ND‐1 by quantum dot‐based immunohistochemistry (QD‐IHC). High LEA expression was correlated significantly with T stage (P = 0.010). Patients with high LEA expression showed significantly poorer prognosis than those with LEA low expression (P = 0.007). Multivariate analysis indicated LEA expression as an independent predictor. Furthermore, the comparative analysis showed that mAb ND‐1‐based IHC analysis toward sugar residue of PODXL has higher sensitivity and specificity to evaluate the LEA/PODXL expression than mAb 3D3‐based method toward core protein of PODXL in CRC cell lines and clinical samples. In addition, we first found that LEA/PODXL can be secreted in exosomes from cancer cells and CRC patient peripheral blood. Our results demonstrate that LEA is an independent predictor for CRC progression and has the potential to be applied for clinical setting with high sensitivity, high specificity, and noninvasive access.

Metabolomics based predictive classifier for early detection of pancreatic ductal adenocarcinoma

The availability of robust classification algorithms for the identification of high risk individuals with resectable disease is critical to improving early detection strategies and ultimately increasing survival rates in PC. We leveraged high quality biospecimens with extensive clinical annotations from patients that received treatment at the Medstar-Georgetown University hospital. We used a high resolution mass spectrometry based global tissue profiling approach in conjunction with multivariate analysis for developing a classification algorithm that would predict early stage PC with high accuracy. The candidate biomarkers were annotated using tandem mass spectrometry. We delineated a six metabolite panel that could discriminate early stage PDAC from benign pancreatic disease with >95% accuracy of classification (Specificity = 0.85, Sensitivity = 0.9). Subsequently, we used multiple reaction monitoring mass spectrometry for evaluation of this panel in plasma samples obtained from the same patients. The pattern of expression of these metabolites in plasma was found to be discordant as compared to that in tissue. Taken together, our results show the value of using a metabolomics approach for developing highly predictive panels for classification of early stage PDAC. Future investigations will likely lead to the development of validated biomarker panels with potential for clinical translation in conjunction with CA-19-9 and/or other biomarkers.

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

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

Validation of Biomarkers for Early Detection of Pancreatic Cancer: Summary of The Alliance of Pancreatic Cancer Consortia for Biomarkers for Early Detection Workshop

Pancreatic cancer is the third leading cause of cancer death in the United States, and the 5-year relative survival for patients diagnosed with pancreatic cancer is less than 10%. Early intervention is the key to a better survival outcome. Currently, there are no biomarkers that can reliably detect pancreatic cancer at an early stage or identify precursors that are destined to progress to malignancy. The National Cancer Institute in partnership with the Kenner Family Research Fund and the Pancreatic Cancer Action Network convened a Data Jamboree on Biomarkers workshop on December 5, 2016, to discuss and evaluate existing or newly developed biomarkers and imaging methods for early detection of pancreatic cancer. The primary goal of this workshop was to determine if there are any promising biomarkers for early detection of pancreatic cancer that are ready for clinical validation. The Alliance of Pancreatic Cancer Consortia for Biomarkers for Early Detection, formed under the auspices of this workshop, will provide the common platform and the resources necessary for validation. Although none of the biomarkers evaluated seemed ready for a large-scale biomarker validation trial, a number of them had sufficiently high sensitivity and specificity to warrant additional research, especially if combined with other biomarkers to form a panel.

Metabolomic biomarkers of pancreatic cancer: a meta-analysis study

Pancreatic cancer (PC) is an aggressive disease with high mortality rates, however, there is no blood test for early detection and diagnosis of this disease. Several research groups have reported on metabolomics based clinical investigations to identify biomarkers of PC, however there is a lack of a centralized metabolite biomarker repository that can be used for meta-analysis and biomarker validation. Furthermore, since the incidence of PC is associated with metabolic syndrome and Type 2 diabetes mellitus (T2DM), there is a need to uncouple these common metabolic dysregulations that may otherwise diminish the clinical utility of metabolomic biosignatures. Here, we attempted to externally replicate proposed metabolite biomarkers of PC reported by several other groups in an independent group of PC subjects. Our study design included a T2DM cohort that was used as a non-cancer control and a separate cohort diagnosed with colorectal cancer (CRC), as a cancer disease control to eliminate possible generic biomarkers of cancer. We used targeted mass spectrometry for quantitation of literature-curated metabolite markers and identified a biomarker panel that discriminates between normal controls (NC) and PC patients with high accuracy. Further evaluation of our model with CRC, however, showed a drop in specificity for the PC biomarker panel. Taken together, our study underscores the need for a more robust study design for cancer biomarker studies so as to maximize the translational value and clinical implementation.