Detectable Symptomatology Preceding the Diagnosis of Pancreatic Cancer and Absolute Risk of Pancreatic Cancer Diagnosis

Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment

INTRODUCTION

Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction.

METHODS

Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool.

RESULTS

Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals.

DISCUSSION

AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.

Identifying Symptoms Prior to Pancreatic Ductal Adenocarcinoma Diagnosis in Real-World Care Settings: Natural Language Processing Approach

BACKGROUND

Pancreatic cancer is the third leading cause of cancer deaths in the United States. Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, accounting for up to 90% of all cases. Patient-reported symptoms are often the triggers of cancer diagnosis and therefore, understanding the PDAC-associated symptoms and the timing of symptom onset could facilitate early detection of PDAC.

OBJECTIVE

This paper aims to develop a natural language processing (NLP) algorithm to capture symptoms associated with PDAC from clinical notes within a large integrated health care system.

METHODS

We used unstructured data within 2 years prior to PDAC diagnosis between 2010 and 2019 and among matched patients without PDAC to identify 17 PDAC-related symptoms. Related terms and phrases were first compiled from publicly available resources and then recursively reviewed and enriched with input from clinicians and chart review. A computerized NLP algorithm was iteratively developed and fine-trained via multiple rounds of chart review followed by adjudication. Finally, the developed algorithm was applied to the validation data set to assess performance and to the study implementation notes.

RESULTS

A total of 408,147 and 709,789 notes were retrieved from 2611 patients with PDAC and 10,085 matched patients without PDAC, respectively. In descending order, the symptom distribution of the study implementation notes ranged from 4.98% for abdominal or epigastric pain to 0.05% for upper extremity deep vein thrombosis in the PDAC group, and from 1.75% for back pain to 0.01% for pale stool in the non-PDAC group. Validation of the NLP algorithm against adjudicated chart review results of 1000 notes showed that precision ranged from 98.9% (jaundice) to 84% (upper extremity deep vein thrombosis), recall ranged from 98.1% (weight loss) to 82.8% (epigastric bloating), and F1-scores ranged from 0.97 (jaundice) to 0.86 (depression).

CONCLUSIONS

The developed and validated NLP algorithm could be used for the early detection of PDAC.

An Integrative Pancreatic Cancer Risk Prediction Model in the UK Biobank

Pancreatic cancer (PaCa) is a lethal cancer with an increasing incidence, highlighting the need for early prevention strategies. There is a lack of a comprehensive PaCa predictive model derived from large prospective cohorts. Therefore, we have developed an integrated PaCa risk prediction model for PaCa using data from the UK Biobank, incorporating lifestyle-related, genetic-related, and medical history-related variables for application in healthcare settings. We used a machine learning-based random forest approach and a traditional multivariable logistic regression method to develop a PaCa predictive model for different purposes. Additionally, we employed dynamic nomograms to visualize the probability of PaCa risk in the prediction model. The top five influential features in the random forest model were age, PRS, pancreatitis, DM, and smoking. The significant risk variables in the logistic regression model included male gender (OR = 1.17), age (OR = 1.10), non-O blood type (OR = 1.29), higher polygenic score (PRS) (Q5 vs. Q1, OR = 2.03), smoking (OR = 1.82), alcohol consumption (OR = 1.27), pancreatitis (OR = 3.99), diabetes (DM) (OR = 2.57), and gallbladder-related disease (OR = 2.07). The area under the receiver operating curve (AUC) of the logistic regression model is 0.78. Internal validation and calibration performed well in both models. Our integrative PaCa risk prediction model with the PRS effectively stratifies individuals at future risk of PaCa, aiding targeted prevention efforts and supporting community-based cancer prevention initiatives.

Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records

Pancreatic cancer is one of the deadliest cancer types with poor treatment options. Better detection of early symptoms and relevant disease correlations could improve pancreatic cancer prognosis. In this retrospective study, we used symptom and disease codes (ICD-10) from the Danish National Patient Registry (NPR) encompassing 6.9 million patients from 1994 to 2018,, of whom 23,592 were diagnosed with pancreatic cancer. The Danish cancer registry included 18,523 of these patients. To complement and compare the registry diagnosis codes with deeper clinical data, we used a text mining approach to extract symptoms from free text clinical notes in electronic health records (3078 pancreatic cancer patients and 30,780 controls). We used both data sources to generate and compare symptom disease trajectories to uncover temporal patterns of symptoms prior to pancreatic cancer diagnosis for the same patients. We show that the text mining of the clinical notes was able to complement the registry-based symptoms by capturing more symptoms prior to pancreatic cancer diagnosis. For example, 'Blood pressure reading without diagnosis', 'Abnormalities of heartbeat', and 'Intestinal obstruction' were not found for the registry-based analysis. Chaining symptoms together in trajectories identified two groups of patients with lower median survival (<90 days) following the trajectories 'Cough→Jaundice→Intestinal obstruction' and 'Pain→Jaundice→Abnormal results of function studies'. These results provide a comprehensive comparison of the two types of pancreatic cancer symptom trajectories, which in combination can leverage the full potential of the health data and ultimately provide a fuller picture for detection of early risk factors for pancreatic cancer.

Predicting 10-Year Risk of Pancreatic Cancer Using a Combined Genetic and Clinical Model

Background and Aims

Pancreatic cancer has the poorest 5-year survival rate of any major solid tumor, but when diagnosed at an early stage, survival rates improve. Population screening is impractical because pancreatic cancer is rare with a lifetime risk of 1.7%, but accurate risk stratification in the general population could enable health care providers to focus early detection strategies to at-risk individuals. Here, we validate a combined risk prediction model that integrates a polygenic risk score and a clinical risk model.

Methods

Using the UK Biobank, we conducted a prospective cohort study assessing 10-year pancreatic cancer risks based on a polygenic risk score, a clinical risk score, and a combined risk score. We assessed the association, discrimination, calibration, cumulative hazards, and standardized incidence ratios compared to population incidence rates for the risk scores. We also conducted net reclassification analyses.

Results

While all of the risk scores discriminated well between affected and unaffected participants, the combined risk score - with a Harrell's C-index of 0.714 (95% confidence interval [CI] = 0.698, 0.730) - discriminated better than both the polygenic risk score (P = .001) and the clinical risk score (P = .02). In terms of calibration, there was no problem with dispersion for the combined risk score (β = 0.952, 95% CI = 0.865-1.039, P = .3) and overall there was a small overestimation of risk (α = -0.089, 95% CI = -0.156 to -0.021, P = .009). Participants in the top decile of 10-year risk were at 1.413 (95% CI = 1.242-1.607) times population risk.

Conclusion

The combined risk score was able to identify individuals at substantially increased risk of pancreatic cancer and to whom targeted screening could be useful.

Pancreatic cancer is associated with medication changes prior to clinical diagnosis

Patients with pancreatic ductal adenocarcinoma (PDAC) commonly develop symptoms and signs in the 1-2 years before diagnosis that can result in changes to medications. We investigate recent medication changes and PDAC diagnosis in Nurses' Health Study (NHS; females) and Health Professionals Follow-up Study (HPFS; males), including up to 148,973 U.S. participants followed for 2,994,057 person-years and 991 incident PDAC cases. Here we show recent initiation of antidiabetic (NHS) or anticoagulant (NHS, HFS) medications and cessation of antihypertensive medications (NHS, HPFS) are associated with pancreatic cancer diagnosis in the next 2 years. Two-year PDAC risk increases as number of relevant medication changes increases (P-trend <1 × 10^-5), with participants who recently start antidiabetic and stop antihypertensive medications having multivariable-adjusted hazard ratio of 4.86 (95%CI, 1.74-13.6). These changes are not associated with diagnosis of other digestive system cancers. Recent medication changes should be considered as candidate features in multi-factor risk models for PDAC, though they are not causally implicated in development of PDAC.

Early Diagnosis of Pancreatic Cancer: Clinical Premonitions, Timely Precursor Detection and Increased Curative-Intent Surgery

BACKGROUND

The overall poor prognosis in pancreatic cancer is related to late clinical detection. Early diagnosis remains a considerable challenge in pancreatic cancer. Unfortunately, the onset of clinical symptoms in patients usually indicate advanced disease or presence of metastasis.

ANALYSIS AND RESULTS

Currently, there are no designated diagnostic or screening tests for pancreatic cancer in clinical use. Thus, identifying risk groups, preclinical risk factors or surveillance strategies to facilitate early detection is a target for ongoing research. Hereditary genetic syndromes are a obvious, but small group at risk, and warrants close surveillance as suggested by society guidelines. Screening for pancreatic cancer in asymptomatic individuals is currently associated with the risk of false positive tests and, thus, risk of harms that outweigh benefits. The promise of cancer biomarkers and use of 'omics' technology (genomic, transcriptomics, metabolomics etc.) has yet to see a clinical breakthrough. Several proposed biomarker studies for early cancer detection lack external validation or, when externally validated, have shown considerably lower accuracy than in the original data. Biopsies or tissues are often taken at the time of diagnosis in research studies, hence invalidating the value of a time-dependent lag of the biomarker to detect a pre-clinical, asymptomatic yet operable cancer. New technologies will be essential for early diagnosis, with emerging data from image-based radiomics approaches, artificial intelligence and machine learning suggesting avenues for improved detection.

CONCLUSIONS

Early detection may come from analytics of various body fluids (eg 'liquid biopsies' from blood or urine). In this review we present some the technological platforms that are explored for their ability to detect pancreatic cancer, some of which may eventually change the prospects and outcomes of patients with pancreatic cancer.

Clinical Prediction Models for Pancreatic Cancer in General and At-Risk Populations: A Systematic Review

INTRODUCTION

Identifying high-risk individuals using a risk prediction model could be a crucial first stage of screening pathways to improve the early detection of pancreatic cancer. A systematic review was conducted to critically evaluate the published primary literature on the development or validation of clinical risk prediction models for pancreatic cancer risk.

METHODS

MEDLINE, Embase, and Web of Science were searched for relevant articles from the inception of each database up to November 2021. Study selection and data extraction were conducted by 2 independent reviewers. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was applied to assess risk of bias.

RESULTS

In total, 33 studies were included, describing 38 risk prediction models. Excluding studies with an overlapping population, this study consist of 15,848,100 participants, of which 58,313 were diagnosed with pancreatic cancer. Eight studies externally validated their model, and 13 performed internal validation. The studies described risk prediction models for pancreatic cancer in the general population (n = 14), patients with diabetes (n = 8), and individuals with gastrointestinal (and other) symptoms (symptoms included abdominal pain, unexplained weight loss, jaundice, and change in bowel habits and indigestion; n = 11). The commonly used clinical risk factors in the model were cigarette smoking (n = 27), age (n = 25), diabetes history (n = 22), chronic pancreatitis (n = 18), and body mass index (n = 14). In the 25 studies that assessed model performance, C-statistics ranged from 0.61 to 0.98. Of the 33 studies included, 6 were rated as being at a low risk of bias based on PROBAST.

DISCUSSION

Many clinical risk prediction models for pancreatic cancer had been developed for different target populations. Although low risk-of-bias studies were identified, these require external validation and implementation studies to ensure that these will benefit clinical decision making.

Difference of risk of pancreatic cancer in new-onset diabetes and long-standing diabetes: population-based cohort study

CONTEXT

Considering the absence of methods to find pancreatic cancer early, surveillance of high-risk groups is needed for early diagnosis.

OBJECTIVE

The study aimed to investigate the effect in the incidence of pancreatic cancer and the differences between new-onset DM (NODM) and long-standing DM (LSDM) since NODM group is a representative high-risk group.

METHODS

The Korean National Health Insurance Service-National Sample Cohort between 2002 and 2013 data was used. Regarding 88,396 people with DM (case group), we conducted a 1:1 propensity score matching to select a matched non-DM population (control group). To investigate the interaction between DM and the time variable distinguishing NODM and LSDM, we performed a multi-variable time-dependent Cox regression analysis.

RESULTS

The incidence of pancreatic cancer was higher in the DM group compared to the non-DM group (0.52% vs. 0.16%, P < 0.001). The DM group had shown different risk of pancreatic cancer development according to the duration since the DM diagnosis (NODM hazard ratio (HR): 3.81, 95% confidence interval (CI): 2.97-4.88, P < 0.001; LSDM HR: 1.53, 95% CI: 1.11-2.11, P < 0.001). When the NODM and the LSDM groups were compared, the risk of pancreatic cancer was higher in the NODM group than LSDM group (HR: 1.55, P = 0.020). In subgroup analysis, NODM group showed that men (HR = 4.42 95% CI: 3.15-6.19, P < 0.001) and patients who were in their 50 s (HR = 7.54, 95% CI: 3.24-17.56, P < 0.001) were at a higher risk of developing pancreatic cancer than matched same sex or age control group (non-DM population), respectively.

CONCLUSION

The risk of pancreatic cancer was greater in people with DM than non-DM population. Among people with DM, NODM showed a higher risk of pancreatic cancer than long standing DM.

Does Chronic Use of High Dose Proton Pump Inhibitors Increase Risk for Pancreatic Cancer?

OBJECTIVES

To analyze whether use of proton pump inhibitors increase the risk for pancreatic cancer in a mouse model and human clinical cohorts.

METHODS

p48-Cre/LSL-KrasG12D mice that develop precancerous pancreatic intraepithelial neoplasia (PanINs) were treated with low- or high-dose proton pump inhibitors (PPIs) orally for 1 and 4 months. The mechanism for the cholecystokinin receptor 2 (CCK-2R) activation was investigated in vitro. Two resources were employed to analyze the risk of pancreatic cancer in human subjects with PPI use.

RESULTS

Serum gastrin levels were increased 8-fold (P < 0.0001) in mice treated with chronic high-dose PPIs, and this change correlated with an increase (P = 0.02) in PanIN grade and the development of microinvasive cancer. The CCK-2R expression was regulated by microRNA-148a in the p48-Cre/LSL-KrasG12D mice pancreas and in human pancreatic cancer cells in vitro. Proton pump inhibitor consumption in human subjects was correlated with pancreatic cancer risk (odds ratio, 1.54). A validation analysis conducted using the large-scale United Kingdom Biobank database confirmed the correlation (odds ratio, 1.9; P = 0.00761) of pancreatic cancer risk with PPI exposure.

CONCLUSIONS

This investigation revealed in both murine models and human subjects, PPI use is correlated with a risk for development of pancreatic cancer.

Using proton pump inhibitors increases the risk of hepato-biliary-pancreatic cancer. A systematic review and meta-analysis

Background: More and more studies are focusing on the adverse effects and damage caused by PPI abuse, we carried out a systematic review and meta-analysis for assessing whether the proton pump inhibitor (PPI) leads to hepato-biliary-pancreatic cancer.

Methods: PubMed, EMBASE and Web of Science were searched until 1 July 2022, 25 studies (17 case-control and 8 cohort studies; 2741853 individuals) included in this study. Pooled Odd Ratios (ORs) were used for random effect models. Sensitivity analysis and dose-response analysis, subgroup analysis were all conducted.

Results: The aggregate OR of the meta-analysis was 1.69 (95% confidence interval (CI): 1.42–2.01, p = 0.01) and heterogeneity (I² = 98.9%, p < 0.001) was substantial. According to stratified subgroup analyses, the incidence of hepato-biliary-pancreatic cancer was associated, expect for study design, study quality and region. Risk of hepato-biliary-pancreatic cancer is highest when people is treated with normal doses of PPI. The risks decrease and become insignificant when the cumulative defined daily dose (cDDD) increases.

Conclusion: The use of PPI may be associated with an increased risk of hepato-biliary-pancreatic cancer. Hence, caution is needed when using PPIs among patients with a high risk of hepato-biliary-pancreatic cancer.

Serum Syndecan-1: A Novel Biomarker for Pancreatic Ductal Adenocarcinoma

INTRODUCTION

Syndecan-1 (SDC1) has multiple functions in tumorigenesis in general and specifically in pancreatic cancer. We aimed to evaluate SDC1 as a diagnostic and prognostic biomarker in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

In this case-control study, patients newly diagnosed with a biopsy-proven PDAC were enrolled alongside healthy individuals in a derivation-validation cohort design. Serum SDC1 was measured by enzyme-linked immunoassay. The diagnostic accuracy of SDC1 levels for diagnosing PDAC was computed. A unified cohort enriched with additional early-stage patients with PDAC was used to evaluate the association of SDC1 with survival outcomes and patient characteristics.

RESULTS

In the derivation cohort, serum SDC1 levels were significantly higher in patients with PDAC (n = 39) compared with healthy controls (n = 20) (40.1 ng/mL, interquartile range 29.8-95.3 vs 25.6 ng/mL, interquartile range 17.1-29.8, respectively; P < 0.001). The receiver operating characteristic analysis area under the curve was 0.847 (95% confidence interval 0.747-0.947, P < 0.001). These results were replicated in a separate age-matched validation cohort (n = 38 PDAC, n = 38 controls; area under the curve 0.844, 95% confidence interval 0.757-0.932, P < 0.001). In the combined-enriched PDAC cohort (n = 110), using a cutoff of 35 ng/mL, the median overall 5-year survival between patients below and above this cutoff was not significantly different, although a trend for better survival after 1 year was found in the lower level group (P = 0.06). There were 12 of the 110 patients with PDAC (11%) who had normal CA 19-9 in the presence of elevated SDC1.

DISCUSSION

These findings suggest serum SDC1 as a promising novel biomarker for early blood-based diagnosis of pancreatic cancer.

Prediction Model for Pancreatic Cancer-A Population-Based Study from NHIRD

Simple Summary

Pancreatic cancer has been ranked seventh in the top ten cancer mortality rates for the past three year in Taiwan. It is one of the more difficult cancers to detect early due to the lack of early diagnostic tools. This is a population-based study from NHIRD. A higher performance pancreatic cancer prediction model has been established. This predictive model can improve the awareness of the risk of pancreatic cancer and give patients with pancreatic cancer a simpler tool for early screening in the golden period when the disease can still be eradicated.

Abstract

(1) Background: Cancer has been the leading cause of death in Taiwan for 39 years, and among them, pancreatic cancer has been ranked seventh in the top ten cancer mortality rates for the past three years. While the incidence rate of pancreatic cancer is ranked at the bottom of the top 10 cancers, the survival rate is very low. Pancreatic cancer is one of the more difficult cancers to detect early due to the lack of early diagnostic tools. Early screening is important for the treatment of pancreatic cancer. Only a few studies have designed predictive models for pancreatic cancer. (2) Methods: The Taiwan Health Insurance Database was used in this study, covering over 99% of the population in Taiwan. The subset sample was not significantly different from the original NHIRD sample. A machine learning approach was used to develop a predictive model for pancreatic cancer disease. Four models, including logistic regression, deep neural networks, ensemble learning, and voting ensemble were used in this study. The ROC curve and a confusion matrix were used to evaluate the accuracy of the pancreatic cancer prediction models. (3) Results: The AUC of the LR model was higher than the other three models in the external testing set for all three of the factor combinations. Sensitivity was best measured by the stacking model for the first factor combinations, and specificity was best measured by the DNN model for the second factor combination. The result of the model that used only nine factors (third factor combinations) was equal to the other two factor combinations. The AUC of the previous models for the early assessment of pancreatic cancer ranged from approximately 0.57 to 0.71. The AUC of this study was higher than that of previous studies and ranged from 0.71 to 0.76, which provides higher accuracy. (4) Conclusions: This study compared the performances of LR, DNN, stacking, and voting models for pancreatic cancer prediction and constructed a pancreatic cancer prediction model with accuracy higher than that of previous studies. This predictive model will improve awareness of the risk of pancreatic cancer and give patients with pancreatic cancer a simpler tool for early screening in the golden period when the disease can still be eradicated.

Prediction of Pancreatic Cancer in Diabetes Patients with Worsening Glycemic Control

BACKGROUND

Worsening glycemic control indicates elevated risk of pancreatic ductal adenocarcinoma (PDAC). We developed prediction models for PDAC among those with worsening glycemic control after diabetes diagnosis.

METHODS

In 2000-2016 records within the Veterans Affairs Health System (VA), we identified three cohorts with progression of diabetes: (i) insulin initiation (n = 449,685), (ii) initiation of combination oral hypoglycemic medication (n = 414,460), and (iii) hemoglobin A1c (HbA1c) ≥8% with ≥Δ1% within 15 months (n = 593,401). We computed 12-, 36-, and 60-month incidence of PDAC and developed prediction models separately for males and females, with consideration of >30 demographic, behavioral, clinical, and laboratory variables. Models were selected to optimize Akaike's Information Criterion, and performance for predicting 12-, 36-, and 60-month incident PDAC was evaluated by bootstrap.

RESULTS

Incidence of PDAC was highest for insulin initiators and greater in males than in females. Optimism-corrected c-indices of the models for predicting 36-month incidence of PDAC in the male population were: (i) 0.72, (ii) 0.70, and (iii) 0.71, respectively. Models performed better for predicting 12-month incident PDAC [c-index (i) 0.78, (ii) 0.73, (iii) 0.76 for males], and worse for predicting 60-month incident PDAC [c-index (i) 0.69, (ii) 0.67, (iii) 0.68 for males]. Model performance was lower among females. For subjects whose model-predicted 36-month PDAC risks were ≥1%, the observed incidences were (i) 1.9%, (ii) 2.2%, and (iii) 1.8%.

CONCLUSIONS

Sex-specific models for PDAC can estimate risk of PDAC at the time of progression of diabetes.

IMPACT

Our models can identify diabetes patients who would benefit from PDAC screening.

A Circulating Exosome RNA Signature Is a Potential Diagnostic Marker for Pancreatic Cancer, a Systematic Study

Simple Summary

Most patients with pancreatic cancer are diagnosed at an advanced stage due to the lack of tools with high sensitivity and specificity for early detection. Aberrant gene expression occurs in pancreatic cancer, which can be packaged into nanoparticles (also known as exosomes or nano-sized extracellular vesicles) and then released into blood. In this study, we aimed to evaluate the diagnostic value of a circulating exosome RNA signature in pancreatic cancer. Our findings indicate that the circulating exosome RNA signature is a potential marker for the early detection or diagnosis of pancreatic cancer.

Abstract

Several exosome proteins, miRNAs and KRAS mutations have been investigated in the hope of carrying out the early detection of pancreatic cancer with high sensitivity and specificity, but they have proven to be insufficient. Exosome RNAs, however, have not been extensively evaluated in the diagnosis of pancreatic cancer. The purpose of this study was to investigate the potential of circulating exosome RNAs in pancreatic cancer detection. By retrieving RNA-seq data from publicly accessed databases, differential expression and random-effects meta-analyses were performed. The results showed that pancreatic cancer had a distinct circulating exosome RNA signature in healthy individuals, and that the top 10 candidate exosome RNAs could distinguish patients from healthy individuals with an area under the curve (AUC) of 1.0. Three (HIST2H2AA3, LUZP6 and HLA-DRA) of the 10 genes in exosomes had similar differential patterns to those in tumor tissues based on RNA-seq data. In the validation dataset, the levels of these three genes in exosomes displayed good performance in distinguishing cancer from both chronic pancreatitis (AUC = 0.815) and healthy controls (AUC = 0.8558), whereas a slight difference existed between chronic pancreatitis and healthy controls (AUC = 0.586). Of the three genes, the level of HIST2H2AA3 was positively associated with KRAS status. However, there was no significant difference in the levels of the three genes across the disease stages (stages I–IV). These findings indicate that circulating exosome RNAs have a potential early detection value in pancreatic cancer, and that a distinct exosome RNA signature exists in distinguishing pancreatic cancer from healthy individuals.

Syndecan-1 and KRAS Gene Expression Signature Associates With Patient Survival in Pancreatic Cancer

OBJECTIVES

The purpose of this study was to investigate the association of syndecan-1 (SDC1) and KRAS molecular characteristics with patient survival in pancreatic cancer.

METHODS

Both SDC1 mRNA and methylation and KRAS mRNA and somatic mutations, as well as clinical data were retrieved from The Cancer Genome Alta pancreatic cancer data set for survival analyses. Kyoto Encyclopedia of Gene and Genomes pathway analysis for coexpressed genes for either SDC1 or KRAS was performed, respectively.

RESULTS

A significantly negative correlation existed between SDC1 mRNA and DNA methylation. Patients with KRAS somatic mutations had a significantly higher SDC1 mRNA but lower methylation than those without the mutations. Compared with patients with KRASSDC1 signature, those with a high level of KRAS and SDC1 alone or both had a significantly elevated mortality. The adjusted hazard ratios (95% confidence interval) were 2.30 (1.16-4.54, P = 0.017) for KRASSDC1, 2.85 (1.48-5.49, P = 0.002) for KRASSDC1, and 2.48 (1.31-4.70, P = 0.005) for KRASSDC1, respectively. Several Kyoto Encyclopedia of Gene and Genomes pathways were shared, whereas there were distinct pathways between KRAS and SDC1 coexpressed genes.

CONCLUSIONS

SDC1 interplays with KRAS, and targeting both KRAS and SDC1 in combination may be more beneficial to pancreatic cancer patients.

A pooled genome-wide association study identifies pancreatic cancer susceptibility loci on chromosome 19p12 and 19p13.3 in the full-Jewish population

Jews are estimated to be at increased risk of pancreatic cancer compared to non-Jews, but their observed 50-80% excess risk is not explained by known non-genetic or genetic risk factors. We conducted a GWAS in a case-control sample of American Jews, largely Ashkenazi, including 406 pancreatic cancer patients and 2332 controls, identified in the dbGaP, PanScan I/II, PanC4 and GERA data sets. We then examined resulting SNPs with P < 10^-7 in an expanded sample set, of 539 full- or part-Jewish pancreatic cancer patients and 4117 full- or part-Jewish controls from the same data sets. Jewish ancestries were genetically determined using seeded FastPCA. Among the full Jews, a novel genome-wide significant association was detected on chromosome 19p12 (rs66562280, per-allele OR = 1.55, 95% CI = 1.33-1.81, P = 10^-7.6). A suggestive relatively independent association was detected on chromosome 19p13.3 (rs2656937, OR = 1.53, 95% CI = 1.31-1.78, P = 10^-7.0). Similar associations were seen for these SNPs among the full and part Jews combined. This is the first GWAS conducted for pancreatic cancer in the increased-risk Jewish population. The SNPs rs66562280 and rs2656937 are located in introns of ZNF100-like and ARRDC5, respectively, and are known to alter regulatory motifs of genes that play integral roles in pancreatic carcinogenesis.

Early detection of pancreatic cancer

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

Association of Glycated Hemoglobin Levels With Risk of Pancreatic Cancer

IMPORTANCE

New-onset diabetes after the age of 50 years is a potential indicator of pancreatic cancer. Understanding the associations between hyperglycemia, diabetes, and pancreatic cancer, including pancreatic ductal adenocarcinoma, is key to developing an approach to early detection.

OBJECTIVE

To assess the association of elevation in glycated hemoglobin (HbA1c) with the risk of pancreatic cancer.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study was conducted using data collected from an integrated health care system in California. A total of 851 402 patients aged 50 to 84 years who had HbA1c measurements taken between 2010 and 2014 were identified as the base cohort, with 12 contemporaneous cohorts created based on varying HbA1c thresholds (ie, 6.1%, 6.3%, 6.5%, and 6.7%) and prior diabetes status. Data analysis was conducted from August 2018 to September 2019.

MAIN OUTCOMES AND MEASURES

New cases of pancreatic cancer identified through cancer registry and California death files during a 3-year period. Three-year risk, incidence rate, sensitivity, number of patients needed to screen to detect 1 case, timing, and stage at diagnosis were determined.

RESULTS

Among 851 402 patients in the base cohort, 447 502 (52.5%) were women, 255 441 (30.0%) were Hispanic participants, 383 685 (45.1%) were non-Hispanic white participants, 100 477 (11.8%) were Asian participants, and 88 969 (10.4%) were non-Hispanic black participants, with a median (interquartile range) age of 62 (56-69) years and a median (interquartile range) HbA1c level of 6.0% (5.7%-6.6%). The incidence rate of pancreatic cancer was 0.45 (95% CI, 0.43-0.49) per 1000 person-years. After excluding prior diabetes as well as confirmation of new-onset hyperglycemia based on an HbA1c level of 6.5%, a total of 20 012 patients remained, with 74 of 1041 pancreatic ductal adenocarcinoma cases (7.1%) from the base cohort included. The rate of pancreatic cancer was 0.72 (95% CI, 0.32-1.42) per 1000 person-years among Asian patients, 0.83 (95% CI, 0.35-1.71) per 1000 person-years among non-Hispanic black patients, 0.84 (95% CI, 0.48-1.37) per 1000 person-years among Hispanic patients, and 2.37 (95% CI, 1.75-3.14) per 1000 person-years among non-Hispanic white patients. Overall, 42 of 74 cancers (56.8%) were diagnosed within 1 year of the index laboratory test. Among 1041 total cases, 708 (68.0%) had staging information available, of whom 465 (65.7%) had stage III or IV disease at diagnosis. In the base cohort, the number needed to undergo evaluation to identify a single case of pancreatic ductal adenocarcinoma was 818 (95% CI, 770-869), with estimates ranging from 206 (95% CI, 160-264) to 600 (95% CI, 540-666) in the subcohorts.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that screening patients for pancreatic cancer based solely on elevation in HbA1c level is unlikely to represent an effective strategy. Future efforts to identify a high-risk population based on changes in glycemic parameters should account for racial/ethnic differences.

Genetic and Circulating Biomarker Data Improve Risk Prediction for Pancreatic Cancer in the General Population

BACKGROUND

Pancreatic cancer is the third leading cause of cancer death in the United States, and 80% of patients present with advanced, incurable disease. Risk markers for pancreatic cancer have been characterized, but combined models are not used clinically to identify individuals at high risk for the disease.

METHODS

Within a nested case-control study of 500 pancreatic cancer cases diagnosed after blood collection and 1,091 matched controls enrolled in four U.S. prospective cohorts, we characterized absolute risk models that included clinical factors (e.g., body mass index, history of diabetes), germline genetic polymorphisms, and circulating biomarkers.

RESULTS

Model discrimination showed an area under ROC curve of 0.62 via cross-validation. Our final integrated model identified 3.7% of men and 2.6% of women who had at least 3 times greater than average risk in the ensuing 10 years. Individuals within the top risk percentile had a 4% risk of developing pancreatic cancer by age 80 years and 2% 10-year risk at age 70 years.

CONCLUSIONS

Risk models that include established clinical, genetic, and circulating factors improved disease discrimination over models using clinical factors alone.

IMPACT

Absolute risk models for pancreatic cancer may help identify individuals in the general population appropriate for disease interception.

Exposure to proton pump inhibitors and risk of pancreatic cancer: a meta-analysis

Objectives: To estimate the pancreatic cancer risk among subjects exposed versus not exposed to proton pump inhibitors.Methods: The authors searched PubMed, EMBASE, Scopus, Cochrane Library, and clinicaltrials.gov to identify relevant studies. The authors quantified pancreatic cancer risk among subjects exposed versus not exposed to PPIs, expressed as the pooled (adjusted) odds ratio (OR/aOR) and 95% confidence interval (95%CI) in overall and sensitivity analyses.Results: One randomized trial, two cohort, four case-control, and five nested case-control studies with 700,178 subjects (73,985 cases; 626,193 controls) were retained. PPI exposure was associated with pancreatic cancer risk (OR = 1.75, 95%CI = 1.12-2.72, I2 = 99%); confirmed in sensitivity analyses for high-quality studies, observational studies, case-control studies, studies with pancreatic cancer as the primary outcome, and in sensitivity analyses for diabetes and obesity but not for pancreatitis and smoking. This association was independent of the duration and Defined Daily Dose of PPI exposure. Rabeprazole had a singular significant association with pancreatic cancer (OR = 5.40, 95%CI = 1.98-14.703, I2 = 87.9%).Conclusion: The class of PPIs is associated with a 1.75-fold increase in pancreatic cancer risk, confirmed in sensitivity analyses.

Prediction of Pancreatic Cancer Based on Imaging Features in Patients With Duct Abnormalities

OBJECTIVES

Abnormalities of the main pancreatic duct may be an early indicator of pancreatic ductal adenocarcinoma (PDAC). We develop and validate algorithms that predict the risk of PDAC using features identified on cross-sectional imaging and other clinical characteristics collected through electronic medical records.

METHODS

Adult patients with abdominal computed tomography or magnetic resonance imaging in January 2006 to June 2016 demonstrating dilatation of main pancreatic duct were identified. Pancreas-related morphologic features were extracted from radiology reports using natural language processing. The cumulative incidence of PDAC with death as a competing risk was estimated using multistate models. Model discrimination was assessed using c-index. The models were internally validated using bootstrapping.

RESULTS

The cohort consisted of 7819 patients (mean age, 71 years; 65% female). A total of 781 patients (10%) developed PDAC within 3 years after the first eligible imaging study. The final models achieved reasonable discrimination (c-index, 0.825-0.833). The 3-year average risk of PDAC in the top 5% of the total eligible patients was 56.0%, more than 20 times of the average risk among the bottom 50% of patients.

CONCLUSIONS

Prediction models combining imaging features and clinical measures can be used to further stratify the risk of pancreatic cancer among patients with pancreas ductal dilatation.

Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients

Background

An accurate and simple risk prediction model that would facilitate earlier detection of pancreatic adenocarcinoma (PDAC) is not available at present. In this study, we compare different algorithms of risk prediction in order to select the best one for constructing a biomarker-based risk score, PancRISK.

Methods

Three hundred and seventy-nine patients with available measurements of three urine biomarkers, (LYVE1, REG1B and TFF1) using retrospectively collected samples, as well as creatinine and age, were randomly split into training and validation sets, following stratification into cases (PDAC) and controls (healthy patients). Several machine learning algorithms were used, and their performance characteristics were compared. The latter included AUC (area under ROC curve) and sensitivity at clinically relevant specificity.

Results

None of the algorithms significantly outperformed all others. A logistic regression model, the easiest to interpret, was incorporated into a PancRISK score and subsequently evaluated on the whole data set. The PancRISK performance could be even further improved when CA19-9, commonly used PDAC biomarker, is added to the model.

Conclusion

PancRISK score enables easy interpretation of the biomarker panel data and is currently being tested to confirm that it can be used for stratification of patients at risk of developing pancreatic cancer completely non-invasively, using urine samples.

Development of a stratification tool to identify pancreatic intraductal papillary mucinous neoplasms at lowest risk of progression

BACKGROUND

Because most pancreatic intraductal papillary mucinous neoplasms (IPMNs) will never become malignant, currently advocated long-term surveillance is low-yield for most individuals.

AIM

To develop a score chart identifying IPMNs at lowest risk of developing worrisome features or high-risk stigmata.

METHODS

We combined prospectively maintained pancreatic cyst surveillance databases of three academic institutions. Patients were included if they had a presumed side-branch IPMN, without worrisome features or high-risk stigmata at baseline (as defined by the 2012 international Fukuoka guidelines), and were followed ≥ 12 months. The endpoint was development of one or more worrisome features or high-risk stigmata during follow-up. We created a multivariable prediction model using Cox-proportional logistic regression analysis and performed an internal-external validation.

RESULTS

875 patients were included. After a mean follow-up of 50 months (range 12-157), 116 (13%) patients developed worrisome features or high-risk stigmata. The final model included cyst size (HR 1.12, 95% CI 1.09-1.15), cyst multifocality (HR 1.49, 95% CI 1.01-2.18), ever having smoked (HR 1.40, 95% CI 0.95-2.04), history of acute pancreatitis (HR 2.07, 95% CI 1.21-3.55), and history of extrapancreatic malignancy (HR 1.34, 95% CI 0.91-1.97). After validation, the model had good discriminative ability (C-statistic 0.72 in the Mayo cohort, 0.71 in the Columbia cohort, 0.64 in the Erasmus cohort).

CONCLUSION

In presumed side branch IPMNs without worrisome features or high-risk stigmata at baseline, the Dutch-American Risk stratification Tool (DART-1) successfully identifies pancreatic lesions at low risk of developing worrisome features or high-risk stigmata.

Environmental Risk Factors of Pancreatic Cancer

Despite the advancement in medical knowledge that has improved the survival rate of many cancers, the survival rate of pancreatic cancer has remained dismal with a five-year survival rate of only 9%. The poor survival of pancreatic cancer emphasizes the urgent need to identify the causes or the risk factors of pancreatic cancer in order to establish effective preventive strategies. This review summarizes the current evidence regarding the environmental (non-genetic, including lifestyle, and clinical factors) risk factors of pancreatic cancer. Based on the current evidence, the established risk factors of pancreatic cancer are cigarette smoking, chronic diabetes, and obesity. Other strong risk factors include low consumption of fruits and vegetables, excess consumption of alcohol, poor oral hygiene, and the lack of allergy history. In the future, more studies are needed to identify additional risk factors of pancreatic cancer, especially the modifiable risk factors that could be included in a public health campaign to educate the public in order to reduce the incidence of pancreatic cancer.

Do changes in health reveal the possibility of undiagnosed pancreatic cancer? Development of a risk-prediction model based on healthcare claims data

Background and objective

Early detection methods for pancreatic cancer are lacking. We aimed to develop a prediction model for pancreatic cancer based on changes in health captured by healthcare claims data.

Methods

We conducted a case-control study on 29,646 Medicare-enrolled patients aged 68 years and above with pancreatic ductal adenocarcinoma (PDAC) reported to the Surveillance Epidemiology an End Results (SEER) tumor registries program in 2004–2011 and 88,938 age and sex-matched controls. We developed a prediction model using multivariable logistic regression on Medicare claims for 16 risk factors and pre-diagnostic symptoms of PDAC present within 15 months prior to PDAC diagnosis. Claims within 3 months of PDAC diagnosis were excluded in sensitivity analyses. We evaluated the discriminatory power of the model with the area under the receiver operating curve (AUC) and performed cross-validation by bootstrapping.

Results

The prediction model on all cases and controls reached AUC of 0.68. Excluding the final 3 months of claims lowered the AUC to 0.58. Among new-onset diabetes patients, the prediction model reached AUC of 0.73, which decreased to 0.63 when claims from the final 3 months were excluded. Performance measures of the prediction models was confirmed by internal validation using the bootstrap method.

Conclusion

Models based on healthcare claims for clinical risk factors, symptoms and signs of pancreatic cancer are limited in classifying those who go on to diagnosis of pancreatic cancer and those who do not, especially when excluding claims that immediately precede the diagnosis of PDAC.

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.

Pancreatic Cancer Following Incident Diabetes in African Americans and Latinos: The Multiethnic Cohort

Background

Diabetes has been proposed to be a risk factor for and a consequence of pancreatic cancer (PC). The relationship between recent-onset diabetes and PC is not well understood, and data in minorities are sparse. We examined the relationships between recent-onset diabetes and PC incidence in African Americans and Latinos in the Multiethnic Cohort.

Methods

A total of 48 995 African Americans and Latinos without prior diabetes and cancer at baseline (1993-1996) were included in the study. Questionnaires, Medicare data, and California hospital discharge files were used to identify new diabetes diagnoses. Cox regressions were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for cancer associated with diabetes and with diabetes duration.

Results

A total of 15 833 (32.3%) participants developed diabetes between baseline and 2013. A total of 408 incident PC cases were identified during follow-up. Diabetes was associated with PC (HRage75 = 2.39, 95% CI = 1.91 to 2.98). Individuals with recent-onset diabetes (within three or fewer years of PC diagnosis) had a greater risk compared with those with long-term diabetes across all ages. The HRage75 for recent-onset diabetes was 4.08 (95% CI = 2.76 to 6.03) in Latinos and 3.38 (95% CI = 2.30 to 4.98) in African Americans.

Conclusions

Diabetes was associated with a more than twofold higher risk of PC in African Americans and Latinos, but recent-onset diabetes was associated with a 2.3-fold greater increase in risk of PC than long-standing diabetes. Our findings support the hypothesis that recent-onset diabetes is a manifestation of PC and that long-standing diabetes is a risk factor for this malignancy.

Association between proton pump inhibitor use and the risk of pancreatic cancer: A Korean nationwide cohort study

PURPOSE

Proton pump inhibitor (PPI) therapy causes hypergastrinemia, which could promote the development and progression of pancreatic cancer. Accordingly, this study aimed to investigate the association between PPI exposure and the risk of pancreatic cancer.

METHODS

We conducted a twelve-year longitudinal population-based study (2002-2013) using the Korean National Health Insurance Corporation claims database merged with national health examination data. The study cohort included 453,655 cancer-free individuals in January 2007 (index date). Incident pancreatic cancer was assessed throughout follow up until December 2013. The exposure to PPIs before the index date was assessed using a standardized Defined Daily Dose (DDD) system. We calculated the hazard ratios (HRs) and their 95% confidence intervals (CIs) for pancreatic cancer risk associated with cumulative PPI use using Cox proportional hazard regression models.

RESULTS

There were 3,086 cases of pancreatic cancer during the period of 2,920,000 person-years. PPI users exceeding 60 DDDs were at a higher risk of pancreatic cancer compared with non-users (HR, 1.34; 95% CI, 1.04-1.72). Subgroup analyses revealed that a significant association existed between PPI use and pancreatic cancer in low risk groups including individuals who were female, engaged in healthy lifestyle habits, and had no history of diabetes or chronic pancreatitis.

CONCLUSION

Exposure to PPI appears to increase the risk of pancreatic cancer, independent of conventional risk factors.

Prediction model for pancreatic cancer risk in the general Japanese population

Genome-wide association studies (GWASs) have identified many single nucleotide polymorphisms (SNPs) that are significantly associated with pancreatic cancer susceptibility. We sought to replicate the associations of 61 GWAS-identified SNPs at 42 loci with pancreatic cancer in Japanese and to develop a risk model for the identification of individuals at high risk for pancreatic cancer development in the general Japanese population. The model was based on data including directly determined or imputed SNP genotypes for 664 pancreatic cancer case and 664 age- and sex-matched control subjects. Stepwise logistic regression uncovered five GWAS-identified SNPs at five loci that also showed significant associations in our case-control cohort. These five SNPs were included in the risk model and also applied to calculation of the polygenic risk score (PRS). The area under the curve determined with the leave-one-out cross-validation method was 0.63 (95% confidence interval, 0.60–0.66) or 0.61 (0.58–0.64) for versions of the model that did or did not include cigarette smoking and family history of pancreatic cancer in addition to the five SNPs, respectively. Individuals in the lowest and highest quintiles for the PRS had odds ratios of 0.62 (0.42–0.91) and 1.98 (1.42–2.76), respectively, for pancreatic cancer development compared with those in the middle quintile. We have thus developed a risk model for pancreatic cancer that showed moderately good discriminatory ability with regard to differentiation of pancreatic cancer patients from control individuals. Our findings suggest the potential utility of a risk model that incorporates replicated GWAS-identified SNPs and established demographic or environmental factors for the identification of individuals at increased risk for pancreatic cancer development.

Retrospective cohort of pancreatic and Vater ampullary adenocarcinoma from a reference center in Mexico

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) and ampulla of Vater adenocarcinomas (AVAC) are periampullary tumors. These tumors have overlapping symptoms and a common treatment, but present differences in their survival and biology. No recent studies in Mexico have been published that describe the clinicopathological characteristics of these tumors. Therefore, the aim of this study was to describe the clinicopathological characteristics of PDAC and AVAC in patients at a reference center in Mexico.

METHODS

A retrospective cohort of patients with PDAC or AVAC was analyzed at our institution (July 2007 to June 2016). Inferential analysis of the clinical data was performed with Student's t-test or a χ2 test with odds ratios (OR) and confidence intervals (CI), depending on the variables. Overall survival was compared using Kaplan-Meier curves with log-rank p values.

RESULTS

Forty patients with PDAC and 76 with AVAC were analyzed, including 77 females and 39 males with a mean age of 60.6 years and a mean evolution time of 5.7 months. PDAC patients had more abdominal pain, a larger tumor size and more advanced stages than AVAC patients. In contrast, AVAC patients had more jaundice, a higher percentage of complete resections and higher overall survival. Up to 70% of patients were overweight. PDAC cohort included a higher proportion of smokers.

CONCLUSIONS

Our cohort was slightly younger, had a larger percentage of females, and a greater percentage of obese patients than those in many international reports. A high proportion of PDAC patients are diagnosed in advanced stages and have a low likelihood of resectability.

Racial/ethnic differences in the epidemiology of ovarian cancer: a pooled analysis of 12 case-control studies

Background

Ovarian cancer incidence differs substantially by race/ethnicity, but the reasons for this are not well understood. Data were pooled from the African American Cancer Epidemiology Study (AACES) and 11 case-control studies in the Ovarian Cancer Association Consortium (OCAC) to examine racial/ethnic differences in epidemiological characteristics with suspected involvement in epithelial ovarian cancer (EOC) aetiology.

Methods

We used multivariable logistic regression to estimate associations for 17 reproductive, hormonal and lifestyle characteristics and EOC risk by race/ethnicity among 10 924 women with invasive EOC (8918 Non-Hispanic Whites, 433 Hispanics, 911 Blacks, 662 Asian/Pacific Islanders) and 16 150 controls (13 619 Non-Hispanic Whites, 533 Hispanics, 1233 Blacks, 765 Asian/Pacific Islanders). Likelihood ratio tests were used to evaluate heterogeneity in the risk factor associations by race/ethnicity.

Results

We observed statistically significant racial/ethnic heterogeneity for hysterectomy and EOC risk (P = 0.008), where the largest odds ratio (OR) was observed in Black women [OR = 1.64, 95% confidence interval (CI) = 1.34-2.02] compared with other racial/ethnic groups. Although not statistically significant, the associations for parity, first-degree family history of ovarian or breast cancer, and endometriosis varied by race/ethnicity. Asian/Pacific Islanders had the greatest magnitude of association for parity (≥3 births: OR = 0.38, 95% CI = 0.28-0.54), and Black women had the largest ORs for family history (OR = 1.77, 95% CI = 1.42-2.21) and endometriosis (OR = 2.42, 95% CI = 1.65-3.55).

Conclusions

Although racial/ethnic heterogeneity was observed for hysterectomy, our findings support the validity of EOC risk factors across all racial/ethnic groups, and further suggest that any racial/ethnic population with a higher prevalence of a modifiable risk factor should be targeted to disseminate information about prevention.

Safety signal detection and evaluation in clinical development programs: A case study of tofacitinib

Adverse events are anticipated during a clinical development program. Tofacitinib is an oral Janus kinase inhibitor for the treatment of rheumatoid arthritis (RA). We describe here the process undertaken by Pfizer to investigate a safety signal for pancreatic cancer with tofacitinib. Potential cases of pancreatic cancer across indications from Pfizer's clinical trials and safety databases were identified and underwent in‐depth case review and external expert consultation. The magnitude of the signal was quantified. The feasibility of formal signal evaluation via a hypothesis‐testing study was explored. As of July 2016, 14 cases of potential pancreatic cancer were identified: eight cases in clinical development trials (psoriasis n = 6; RA n = 1; psoriatic arthritis n = 1), four cases in a postmarketing study in RA patients in Japan, and two spontaneous reports. Incidence rates (95% confidence intervals) per 100 patient‐years ranged from 0 (0, 0.02) to 0.14 in RA, 0.05 (0.01, 0.15) to 0.07 (0.02, 0.16) in psoriasis, and 0.25 (0.01, 1.37) in psoriatic arthritis. The majority of patients had established risk factors for pancreatic cancer. The pharmaceutical industry's rapid and transparent response to safety signals is essential for ensuring patient safety and enabling physicians and patients to adequately assess a drug's risk:benefit. Safety signals emerging through pharmacovigilance may be true or false indicators of a causative association with drug exposure. In this example, it was determined that tofacitinib exposure was unlikely to be related to induction and promotion of pancreatic cancer; however, a relationship with pancreatic cancer promotion could not be excluded.

Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.

Informative Patterns of Health-Care Utilization Prior to the Diagnosis of Pancreatic Ductal Adenocarcinoma

Early-detection tests for pancreatic ductal adenocarcinoma (PDAC) are needed. Since a hypothetical screening test would be applied during antecedent clinical encounters, we sought to define the variability in health-care utilization leading up to PDAC diagnosis. This was a retrospective cohort study that included patients diagnosed with PDAC in the Indianapolis, Indiana, area between 1999 and 2013 with at least 1 health-care encounter during the antecedent 36-month period (n = 1,023). Patients were classified by unique patterns of health-care utilization using a group-based trajectory model. The prevalences of PDAC signals, such as diabetes mellitus (DM) and chronic pancreatitis, were compared. Four distinct trajectories were identified, the most common (42.0%) being having few clinical encounters more than 6 months prior to PDAC diagnosis (late acceleration). In all cases, a minority of persons had DM (30.6%, with 9.5% <1.5 years before PDAC) or any pancreatic disorder (39.9%); these were least common in the late-acceleration group (DM, 14.7%; any pancreatic disorder, 32.1% (P < 0.001)). The most common pattern of antecedent care was having few clinical encounters until shortly before PDAC diagnosis. Since the majority of patients diagnosed with PDAC do not have an antecedent PDAC signal, early-detection strategies limited to these groups may not apply to the majority of cases.

Impact of Sixteen Established Pancreatic Cancer Susceptibility Loci in American Jews

Background: The higher risk of pancreatic cancer in Ashkenazi Jews compared with non-Jews is only partially explained by the increased frequency of BRCA1 and BRCA2 mutations in Ashkenazi Jews.Methods: We evaluated the impact of 16 established pancreatic cancer susceptibility loci in a case-control sample of American Jews, largely Ashkenazi, including 406 full-Jewish pancreatic cancer patients and 2,332 full-Jewish controls, genotyped as part of the Pancreatic Cancer Cohort and Case-Control Consortium I/II (PanScan I/II), Pancreatic Cancer Case-Control Consortium (PanC4), and Resource for Genetic Epidemiology Research on Adult Health and Aging (GERA) datasets. We compared risk in full-Jewish subjects with risk in part-Jewish; non-Jewish Southern European; and in the combined non-Jewish Eastern, Northern, Southern, and Western European (non-Jewish white European) subjects from the same datasets. Jewish ancestries were genetically identified using seeded Fast principal component analysis. Data were analyzed by unconditional logistic regression, and adjusted for age, sex, and principal components.Results: One SNP on chromosome 13q22.1 (rs9543325; OR, 1.36; 95% confidence interval, 1.16-1.58; P = 10-4.1) was significant in full-Jews. Individual ORs and minor allele frequencies were similar between Jewish and non-Jewish white European subjects. The average ORs across the 16 pancreatic cancer susceptibility loci for full-Jewish, full- plus part-Jewish, non-Jewish Southern European, and non-Jewish white European subjects were 1.25, 1.30, 1.31, and 1.26, respectively.Conclusions: The 16 pancreatic cancer susceptibility loci similarly impact Jewish and non-Jewish white European subjects, both individually and as summary odds.Impact: These 16 pancreatic cancer susceptibility loci likely do not explain the higher risk seen in Ashkenazi Jews. Cancer Epidemiol Biomarkers Prev; 26(10); 1540-8. ©2017 AACR.

Plasma microRNAs as biomarkers of pancreatic cancer risk in a prospective cohort study

Noninvasive biomarkers for early pancreatic ductal adenocarcinoma (PDAC) diagnosis and disease risk stratification are greatly needed. We conducted a nested case-control study within the Prospective Investigation into Cancer and Nutrition (EPIC) cohort to evaluate prediagnostic microRNAs (miRs) as biomarkers of subsequent PDAC risk. A panel of eight miRs (miR-10a, -10b, -21-3p, -21-5p, -30c, -106b, -155 and -212) based on previous evidence from our group was evaluated in 225 microscopically confirmed PDAC cases and 225 controls matched on center, sex, fasting status and age/date/time of blood collection. MiR levels in prediagnostic plasma samples were determined by quantitative RT-PCR. Logistic regression was used to model levels and PDAC risk, adjusting for covariates and to estimate area under the receiver operating characteristic curves (AUC). Plasma miR-10b, -21-5p, -30c and -106b levels were significantly higher in cases diagnosed within 2 years of blood collection compared to matched controls (all p-values <0.04). Based on adjusted logistic regression models, levels for six miRs (miR-10a, -10b, -21-5p, -30c, -155 and -212) overall, and for four miRs (-10a, -10b, -21-5p and -30c) at shorter follow-up time between blood collection and diagnosis (≤5 yr, ≤2 yr), were statistically significantly associated with risk. A score based on the panel showed a linear dose-response trend with risk (p-value = 0.0006). For shorter follow-up (≤5 yr), AUC for the score was 0.73, and for individual miRs ranged from 0.73 (miR-212) to 0.79 (miR-21-5p).

Factors influencing survival of patients with pancreatic adenocarcinoma and synchronous liver metastases receiving palliative care

BACKGROUND AND AIMS

Patients with pancreatic ductal adenocarcinoma and synchronous liver metastases (PACLM) have an extremely limited life expectancy. We performed a single-center analysis to explore the clinical results and prognostic factors of patients with PACLM receiving palliative care.

METHODS

We retrospectively reviewed 189 patients undergoing palliative care at Tianjin Medical University Cancer Hospital over a 15-year period. Clinical characteristics, survival condition, and factors associated with survival were analyzed. Treatment methods included palliative bypass surgery, percutaneous transhepatic cholangiodrainage, drug analgesia, symptomatic treatment, and other nutritional or supportive measures.

RESULTS

The overall survival (OS) was 3.6 months for all patients. Multivariate analysis for clinical features showed that Karnofsky performance score (KPS), ascites, cigarette smoking, primary tumor size, and lactate dehydrogenase (LDH) were prognostic variables with statistical significance (P < 0.05). The patients were classified into three groups of patients according to how many of these 5 risk factors were present: 0-1, 2, or 3-5 risk factors. The median OS of the 3 groups of patients were 5.0, 3.3, and 2.5 months, respectively, with a notable statistical significance (P < 0.0001).

CONCLUSIONS

KPS<80, ascites, cigarette smoking, primary tumor size≥5 cm, and LDH≥250U/L are effective predictive factors of poor prognosis for patients with PACLM. The stratification of treatment outcome groups based on these factors facilitates evaluation of individual prognosis and can guide clinical decisions.

Early recognition of multiple sclerosis using natural language processing of the electronic health record

BACKGROUND

Diagnostic accuracy might be improved by algorithms that searched patients' clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS). The focus this study was to determine if patients with MS could be identified from their clinical notes prior to the initial recognition by their healthcare providers.

METHODS

An MS-enriched cohort of patients with well-established MS (n = 165) and controls (n = 545), was generated from the adult outpatient clinic. A random sample cohort was generated from randomly selected patients (n = 2289) from the same adult outpatient clinic, some of whom had MS (n = 16). Patients' notes were extracted from the data warehouse and signs and symptoms mapped to UMLS terms using MedLEE. Approximately 1000 MS-related terms occurred significantly more frequently in MS patients' notes than controls'. Synonymous terms were manually clustered into 50 buckets and used as classification features. Patients were classified as MS or not using Naïve Bayes classification.

RESULTS

Classification of patients known to have MS using notes of the MS-enriched cohort entered after the initial ICD9[MS] code yielded an ROC AUC, sensitivity, and specificity of 0.90 [0.87-0.93], 0.75[0.66-0.82], and 0.91 [0.87-0.93], respectively. Similar classification accuracy was achieved using the notes from the random sample cohort. Classification of patients not yet known to have MS using notes of the MS-enriched cohort entered before the initial ICD9[MS] documentation identified 40% [23-59%] as having MS. Manual review of the EHR of 45 patients of the random sample cohort classified as having MS but lacking an ICD9[MS] code identified four who might have unrecognized MS.

CONCLUSIONS

Diagnostic accuracy might be improved by mining patients' clinical notes for signs and symptoms of specific diseases using NLP. Using this approach, we identified patients with MS early in the course of their disease which could potentially shorten the time to diagnosis. This approach could also be applied to other diseases often missed by primary care providers such as cancer. Whether implementing computerized diagnostic support ultimately shortens the time from earliest symptoms to formal recognition of the disease remains to be seen.

Surveillance for neoplasia in the pancreas

Despite its low incidence in the general population, pancreatic cancer is one of the leading causes of cancer-related mortality. Survival greatly depends on operability, but most patients present with unresectable disease. Therefore, there is great interest in the early detection of pancreatic cancer and its precursor lesions by surveillance. Worldwide, several programs have been initiated for individuals at high risk for pancreatic cancer. Their first results suggest that surveillance in high-risk individuals is feasible, but their effectiveness in decreasing mortality remains to be proven. This review will discuss which individuals are eligible for surveillance, which lesions are aimed to be detected, and which surveillance modalities are being used in current clinical practice. Furthermore, it addresses the management of abnormalities found during surveillance and topics for future research.

Serum microRNA-196 and microRNA-200 in pancreatic ductal adenocarcinoma of patients with diabetes mellitus

BACKGROUND/OBJECTIVES

Our aim was to compare expressions of 6 microRNAs (miRNAs) in patients with pancreatic ductal adenocarcinoma (PAC) and non-cancer patients, moreover according to the presence or absence of diabetes mellitus.

METHODS

Expressions of miRNA-192, -196, -200, -21, -30 and -423 were measured in 77 patients with PAC and 64 non-cancer patients (34 patients with type 2 DM and 30 control persons). 60 patients with PAC (78%) had DM or prediabetes and it was of new-onset (less than 2 years before the cancer diagnosis) in 44 out of them.

RESULTS

The expressions of all microRNAs were 1.4-3.7 times higher (significantly) in the PAC group compared to non-cancer patients. No difference was found between PAC diabetic and PAC non-diabetic patients. MicroRNA-200 was significantly higher in PAC patients with significant body weight loss against those without weight loss. Adding miRNA-196 and -200 to the current marker CA 19-9 improved the discriminative ability of the test (compared to CA 19-9 alone).

CONCLUSION

MicroRNA-196 and -200 could be used as additional markers in PAC diagnosis.

Pancreatic cancer

Pancreatic cancer is a major cause of cancer-associated mortality, with a dismal overall prognosis that has remained virtually unchanged for many decades. Currently, prevention or early diagnosis at a curable stage is exceedingly difficult; patients rarely exhibit symptoms and tumours do not display sensitive and specific markers to aid detection. Pancreatic cancers also have few prevalent genetic mutations; the most commonly mutated genes are KRAS, CDKN2A (encoding p16), TP53 and SMAD4 - none of which are currently druggable. Indeed, therapeutic options are limited and progress in drug development is impeded because most pancreatic cancers are complex at the genomic, epigenetic and metabolic levels, with multiple activated pathways and crosstalk evident. Furthermore, the multilayered interplay between neoplastic and stromal cells in the tumour microenvironment challenges medical treatment. Fewer than 20% of patients have surgically resectable disease; however, neoadjuvant therapies might shift tumours towards resectability. Although newer drug combinations and multimodal regimens in this setting, as well as the adjuvant setting, appreciably extend survival, ∼80% of patients will relapse after surgery and ultimately die of their disease. Thus, consideration of quality of life and overall survival is important. In this Primer, we summarize the current understanding of the salient pathophysiological, molecular, translational and clinical aspects of this disease. In addition, we present an outline of potential future directions for pancreatic cancer research and patient management.

Exosomes: potential for early detection in pancreatic cancer

Progress in the treatment of patients with pancreatic cancer at earlier stages has motivated research in identifying novel noninvasive or minimally invasive biomarkers for early detection. Exosomes, which contain bioactive molecules (such as proteins, RNAs and lipids), are membrane-structured nanovesicles that are secreted from living cells and are found in human body fluids. As functional mediators, exosomes play key roles in cell-cell communications, regulating diverse biological processes. Here we aim to examine recent findings in the potential diagnostic value of serum exosomes in pancreatic cancer.

Invited Commentary: Clinical Utility of Prediction Models for Rare Outcomes--The Example of Pancreatic Cancer

Translating relative risk estimates into absolute risks is important in evaluating the potential clinical and public health relevance of etiologic discoveries. Predicting high absolute risk is challenging, particularly for rare endpoints such as pancreatic cancer. Recent efforts to develop risk prediction models for pancreatic cancer have found moderate risk levels for very small parts of the population. A new approach in which clinical symptoms and medication use are evaluated in addition to information on risk factors is presented by Risch et al. in this issue of the Journal (Am J Epidemiol. 2015;182(1):26-34). The authors estimated absolute risks based on the relative risks obtained from their case-control study. Their absolute risk estimates were higher than those from previous approaches but remained restricted to a very small proportion of the general population. In the present commentary, we address issues of absolute risk stratification (particularly for rare diseases), specific analytic methods, and how actionable information will differ based on the disease and possible intervention. We suggest that moving from cancer-specific models to broader models used to predict risk for multiple outcomes can make risk prediction for rare diseases more effective. When considering translational goals, it is important to estimate absolute risk at the early stages of etiologic research. The results can be sobering but allow focusing on the most promising goals.