Early Detection Initiative: A randomized controlled trial of algorithm-based screening in patients with new onset hyperglycemia and diabetes for early detection of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is the only leading cause of cancer death without an early detection strategy. In retrospective studies, 0.5-1% of subjects >50 years of age who newly develop biochemically-defined diabetes have been diagnosed with PDAC within 3 years of meeting new onset hyperglycemia and diabetes (NOD) criteria. The Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) algorithm further risk stratifies NOD subjects based on age and changes in weight and diabetes parameters. We present the methodology for the Early Detection Initiative (EDI), a randomized controlled trial of algorithm-based screening in patients with NOD for early detection of PDAC. We hypothesize that study interventions (risk stratification with ENDPAC and imaging with Computerized Tomography (CT) scan) in NOD will identify earlier stage PDAC. EDI uses a modified Zelen's design with post-randomization consent. Eligible subjects will be identified through passive surveillance of electronic medical records and eligible study participants randomized 1:1 to the Intervention or Observation arm. The sample size is 12,500 subjects. The ENDPAC score will be calculated only in those randomized to the Intervention arm, with 50% (n = 3125) expected to have a high ENDPAC score. Consenting subjects in the high ENDPAC group will undergo CT imaging for PDAC detection and an estimate of potential harm. The effectiveness and efficacy evaluation will compare proportions of late stage PDAC between Intervention and Observation arm per randomization assignment or per protocol, respectively, with a planned interim analysis. The study is designed to improve the detection of sporadic PDAC when surgical intervention is possible.

Biomarkers of Chronic Pancreatitis: A systematic literature review.

BACKGROUND: Chronic pancreatitis (CP) does not have diagnostic or prognostic biomarkers. CP is the end stage of a progressive inflammatory syndrome that is diagnosed at late stages by morphologic features. To diagnose earlier stages of the disease, a new mechanistic definition was established based on identifying underlying pathogenic processes and biomarker evidence of disease activity and stage. Although multiple risk factors are known, the corresponding biomarkers needed to make a highly accurate diagnosis of earlier disease stages have not been established. The goal of this study is to systematically analyze the literature to identify the most likely candidates for development into biomarkers of CP. METHODS: We conducted a systematic review of candidate analytes from easily accessible biological fluids and identified 67 studies that compared CP to nonpancreatic-disease controls. We then ranked candidate biomarkers for sensitivity and specificity by area under the receiver operator curves (AUROCs). RESULTS: Five biomarkers had a large effect size (an AUROC > 0.96), whereas 30 biomarkers had a moderate effect size (an AUROC between 0.96 and 0.83) for distinguishing CP cases from controls or other diseases. However, the studies reviewed had marked variability in design, enrollment criteria, and biospecimen sample handling and collection. CONCLUSIONS: Several biomarkers have the potential for evaluation in prospective cohort studies and should be correlated with risk factors, clinical features, imaging studies and outcomes. The Consortium for the Study of Chronic Pancreatitis, Diabetes and Pancreas Cancer provides recommendations for avoiding design biases and heterogeneity in sample collection and handling in future studies.

Prediagnostic Image Data, Artificial Intelligence, and Pancreatic Cancer: A Tell-Tale Sign to Early Detection.

Pancreatic cancer continues to be one of the deadliest malignancies and is the third leading cause of cancer-related mortality in the United States. Based on several models, it is projected to become the second leading cause of cancer-related deaths by 2030. Although the overall survival rate for patients diagnosed with pancreatic cancer is less than 10%, survival rates are increasing in those whose cancers are detected at an early stage, when intervention is possible. There are, however, no reliable biomarkers or imaging technology that can detect early-stage pancreatic cancer or accurately identify precursors that are likely to progress to malignancy. The Alliance of Pancreatic Cancer Consortia, a virtual consortium of researchers, clinicians, and advocacies focused on early diagnosis of pancreatic cancer, was formed in 2016 to provide a platform and resources to discover and validate biomarkers and imaging methods for early detection. The focus of discussion at the most recent alliance meeting was on imaging methods and the use of artificial intelligence for early detection of pancreatic cancer.

Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer: From Concept to Reality.

Research progress in diseases of the exocrine pancreas [chronic pancreatitis (CP), pancreatogenic diabetes mellitus, and pancreatic cancer] has been hampered by the disorders' heterogeneity, the limitations of previous small cross-sectional studies, the inability to safely obtain pancreatic tissue for study, and the lack of structured epidemiology tools, genetic testing, and biomarker development. Mechanism-based research of these diseases has suffered from the lack of systematically collected clinical measures in longitudinal cohort studies linked with biospecimens. Given the increasing incidence and prevalence of CP and its association to the development of pancreatic cancer, its complications, high mortality rate, and associated health care cost, the National Institute for Diabetes and Digestive and Kidney Diseases and the National Cancer Institute funded the Consortium for the Study of Chronic Pancreatitis, Diabetes and Pancreatic Cancer to identify research gaps and foster multidisciplinary collaborations to better diagnose, characterize, and manage CP and its sequelae. The CPDPC structure, governance, and research objectives are described in this article. Studies undertaken by the CPDPC are described in other articles in this journal's issue.

Standard Operating Procedures for Biospecimen Collection, Processing, and Storage: From the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer.

High-quality and well-annotated biorepositories are needed to better understand the pathophysiology and biologic mechanisms of chronic pancreatitis (CP) and its consequences. We report a methodology for the development of a robust standard operating procedure (SOP) for a biorepository based on the experience of the clinical centers within the consortium to study Chronic Pancreatitis, Diabetes and Pancreas Cancer Clinical Centers (CPDPC), supported by the National Cancer Institute and the National Institute for Diabetes and Digestive and Kidney Diseases as a unique multidisciplinary model to study CP, diabetes, and pancreatic cancer in both children and adults. Standard operating procedures from the CPDPC centers were evaluated and consolidated. The literature was reviewed for standard biorepository operating procedures that facilitated downstream molecular analysis. The existing literature on biobanking practices was harmonized with the SOPs from the clinical centers to produce a biorepository for pancreatic research. This article reports the methods and basic principles behind the creation of SOPs to develop a biorepository for the CPDPC. These will serve as a guide for investigators developing biorepositories in pancreas research. Rigorous and meticulous adherence to standardized biospecimen collection will facilitate investigations to better understand the pathophysiology and biologic mechanisms of CP, diabetes, and pancreatic cancer.

PROspective Evaluation of Chronic Pancreatitis for EpidEmiologic and Translational StuDies: Rationale and Study Design for PROCEED From the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer.

Prospective Evaluation of Chronic Pancreatitis for Epidemiologic and Translational Studies (PROCEED) is the first prospective, observational cohort study of chronic pancreatitis (CP) in the United States. The primary goals of PROCEED are to define disease progression, test the predictive capability of candidate biomarkers, and develop a platform to conduct translational and mechanistic studies in CP. Using objective and consensus-driven criteria, PROCEED will enroll adults at different stages of CP-controls, suspected CP, and definite CP. In addition to collecting detailed information using structured case report forms and protocol-mandated evaluations at baseline and during follow-up, PROCEED will establish a linked biorepository of blood, urine, saliva, stool, pancreatic fluid, and pancreatic tissue. Enrollment for PROCEED began in June 2017. As of July 1, 2018, nine clinical centers of the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer are enrolling, and 350 subjects have completed baseline evaluation. In conclusion, PROCEED will provide the most accurate and reliable estimates to date on progression of CP. The established cohort and biorepository will facilitate numerous analyses, leading to new strategies for diagnosis, methods to monitor disease progression, and treatment of CP.

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.

Evaluation of a Mixed Meal Test for Diagnosis and Characterization of PancrEaTogEniC DiabeTes Secondary to Pancreatic Cancer and Chronic Pancreatitis: Rationale and Methodology for the DETECT Study From the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer.

Pancreatogenic diabetes mellitus is most commonly the result of chronic pancreatitis but can also occur secondary to pancreatic cancer. The early identification of pancreatogenic diabetes and distinction from the more prevalent type 2 diabetes are clinically significant; however, currently, there is no validated method to differentiate these diabetes subtypes. We describe a study, "Evaluation of a Mixed Meal Test for Diagnosis and Characterization of PancrEaTogEniC DiabeTes Secondary to Pancreatic Cancer and Chronic Pancreatitis: the DETECT study," that seeks to address this knowledge gap. The DETECT study is a multicenter study that will examine differences in hormone and glucose excursions after a mixed meal test. The study will also create a biorepository that will be used to evaluate novel diagnostic biomarkers for differentiating these diabetes subtypes.

Pancreatic Cancer Chemoprevention Translational Workshop: Meeting Report.

Pancreatic cancer is the fourth leading cause of cancer related deaths in the United States with a 5-year survival rate of less than 10%. The Division of Cancer Prevention of the National Cancer Institute sponsored the Pancreatic Cancer Chemoprevention Translational Workshop on September 10 to 11, 2015. The goal of the workshop was to obtain information regarding the current state of the science and future scientific areas that should be prioritized for pancreatic cancer prevention research, including early detection and intervention for high-risk precancerous lesions. The workshop addressed the molecular/genetic landscape of pancreatic cancer and precursor lesions, high-risk populations and criteria to identify a high-risk population for potential chemoprevention trials, identification of chemopreventative/immunopreventative agents, and use of potential biomarkers and imaging for assessing short-term efficacy of a preventative agent. The field of chemoprevention for pancreatic cancer is emerging, and this workshop was organized to begin to address these important issues and promote multi-institutional efforts in this area. The meeting participants recommended the development of an National Cancer Institute working group to coordinate efforts, provide a framework, and identify opportunities for chemoprevention of pancreatic cancer.

The Doylestown Algorithm: A Test to Improve the Performance of AFP in the Detection of Hepatocellular Carcinoma.

Biomarkers for the early diagnosis of hepatocellular carcinoma (HCC) are needed to decrease mortality from this cancer. However, as new biomarkers have been slow to be brought to clinical practice, we have developed a diagnostic algorithm that utilizes commonly used clinical measurements in those at risk of developing HCC. Briefly, as α-fetoprotein (AFP) is routinely used, an algorithm that incorporated AFP values along with four other clinical factors was developed. Discovery analysis was performed on electronic data from patients who had liver disease (cirrhosis) alone or HCC in the background of cirrhosis. The discovery set consisted of 360 patients from two independent locations. A logistic regression algorithm was developed that incorporated log-transformed AFP values with age, gender, alkaline phosphatase, and alanine aminotransferase levels. We define this as the Doylestown algorithm. In the discovery set, the Doylestown algorithm improved the overall performance of AFP by 10%. In subsequent external validation in over 2,700 patients from three independent sites, the Doylestown algorithm improved detection of HCC as compared with AFP alone by 4% to 20%. In addition, at a fixed specificity of 95%, the Doylestown algorithm improved the detection of HCC as compared with AFP alone by 2% to 20%. In conclusion, the Doylestown algorithm consolidates clinical laboratory values, with age and gender, which are each individually associated with HCC risk, into a single value that can be used for HCC risk assessment. As such, it should be applicable and useful to the medical community that manages those at risk for developing HCC.