Screening for Pancreatic Cancer: US Preventive Services Task Force Reaffirmation Recommendation Statement

14-3-3σ downregulation sensitizes pancreatic cancer to carbon ions by suppressing the homologous recombination repair pathway

This study explored the role of 14-3-3σ in carbon ion-irradiated pancreatic adenocarcinoma (PAAD) cells and xenografts and clarified the underlying mechanism. The clinical significance of 14-3-3σ in patients with PAAD was explored using publicly available databases. 14-3-3σ was silenced or overexpressed and combined with carbon ions to measure cell proliferation, cell cycle, and DNA damage repair. Immunoblotting and immunofluorescence (IF) assays were used to determine the underlying mechanisms of 14-3-3σ toward carbon ion radioresistance. We used the BALB/c mice to evaluate the biological behavior of 14-3-3σ in combination with carbon ions. Bioinformatic analysis revealed that PAAD expressed higher 14-3-3σ than normal pancreatic tissues; its overexpression was related to invasive clinicopathological features and a worse prognosis. Knockdown or overexpression of 14-3-3σ demonstrated that 14-3-3σ promoted the survival of PAAD cells after carbon ion irradiation. And 14-3-3σ was upregulated in PAAD cells during DNA damage (carbon ion irradiation, DNA damaging agent) and promotes cell recovery. We found that 14-3-3σ resulted in carbon ion radioresistance by promoting RPA2 and RAD51 accumulation in the nucleus in PAAD cells, thereby increasing homologous recombination repair (HRR) efficiency. Blocking the HR pathway consistently reduced 14-3-3σ overexpression-induced carbon ion radioresistance in PAAD cells. The enhanced radiosensitivity of 14-3-3σ depletion on carbon ion irradiation was also demonstrated in vivo. Altogether, 14-3-3σ functions in tumor progression and can be a potential target for developing biomarkers and treatment strategies for PAAD along with incorporating carbon ion irradiation.

Identification of pancreatic cancer risk factors from clinical notes using natural language processing

OBJECTIVES

Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of risk factor status is provider knowledge-dependent and requires extensive manual chart review by experts. Natural Language Processing (NLP) has shown promise in automated data extraction from the electronic health record (EHR). We aimed to use NLP for automated extraction of PDAC risk factors from unstructured clinical notes in the EHR.

METHODS

We first developed rule-based NLP algorithms to extract PDAC risk factors at the document-level, using an annotated corpus of 2091 clinical notes. Next, we further improved the NLP algorithms using a cohort of 1138 patients through patient-level training, validation, and testing, with comparison against a pre-specified reference standard. To minimize false-negative results we prioritized algorithm recall.

RESULTS

In the test set (n = 807), the NLP algorithms achieved a recall of 0.933, precision of 0.790, and F1-score of 0.856 for family history of PDAC. For germline genetic mutations, the algorithm had a high recall of 0.851, while precision and F1-score were lower at 0.350 and 0.496 respectively. Most false positives for germline mutations resulted from erroneous recognition of tissue mutations.

CONCLUSIONS

Rule-based NLP algorithms applied to unstructured clinical notes are highly sensitive for automated identification of PDAC risk factors. Further validation in a large primary-care patient population is warranted to assess real-world utility in identifying HRIs for pancreatic cancer screening.

Predicting the risk of pancreatic cancer in women with new-onset diabetes mellitus

BACKGROUND AND AIM

People with new-onset diabetes mellitus (diabetes) could be a possible target population for pancreatic cancer surveillance. However, distinguishing diabetes caused by pancreatic cancer from type 2 diabetes remains challenging. We aimed to develop and validate a model to predict pancreatic cancer among women with new-onset diabetes.

METHODS

We conducted a retrospective cohort study among Australian women newly diagnosed with diabetes, using first prescription of anti-diabetic medications, sourced from administrative data, as a surrogate for the diagnosis of diabetes. The outcome was a diagnosis of pancreatic cancer within 3 years of diabetes diagnosis. We used prescription medications, severity of diabetes (i.e., change/addition of medication within 2 months after first medication), and age at diabetes diagnosis as potential predictors of pancreatic cancer.

RESULTS

Among 99 687 women aged ≥ 50 years with new-onset diabetes, 602 (0.6%) were diagnosed with pancreatic cancer within 3 years. The area under the receiver operating curve for the risk prediction model was 0.73. Age and diabetes severity were the two most influential predictors followed by beta-blockers, acid disorder drugs, and lipid-modifying agents. Using a risk threshold of 50%, sensitivity and specificity were 69% and the positive predictive value (PPV) was 1.3%.

CONCLUSIONS

Our model doubled the PPV of pancreatic cancer in women with new-onset diabetes from 0.6% to 1.3%. Age and rapid progression of diabetes were important risk factors, and pancreatic cancer occurred more commonly in women without typical risk factors for type 2 diabetes. This model could prove valuable as an initial screening tool, especially as new biomarkers emerge.

Guiding post-pancreaticoduodenectomy interventions for pancreatic cancer patients utilizing decision tree models

Background

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in advanced stages, necessitating pancreaticoduodenectomy (PD) as a primary therapeutic approach. However, PD surgery can engender intricate complications. Thus, understanding the factors influencing postoperative complications documented in electronic medical records and their impact on survival rates is crucial for improving overall patient outcomes.

Methods

A total of 749 patients were divided into two groups: 598 (79.84%) chose the RPD (Robotic pancreaticoduodenectomy) procedure and 151 (20.16%) chose the LPD (Laparoscopic pancreaticoduodenectomy) procedure. We used correlation analysis, survival analysis, and decision tree models to find the similarities and differences about postoperative complications and prognostic survival.

Results

Pancreatic cancer, known for its aggressiveness, often requires pancreaticoduodenectomy as an effective treatment. In predictive models, both BMI and surgery duration weigh heavily. Lower BMI correlates with longer survival, while patients with heart disease and diabetes have lower survival rates. Complications like delayed gastric emptying, pancreatic fistula, and infection are closely linked post-surgery, prompting conjectures about their causal mechanisms. Interestingly, we found no significant correlation between nasogastric tube removal timing and delayed gastric emptying, suggesting its prompt removal post-decompression.

Conclusion

This study aimed to explore predictive factors for postoperative complications and survival in PD patients. Effective predictive models enable early identification of high-risk individuals, allowing timely interventions. Higher BMI, heart disease, or diabetes significantly reduce survival rates in pancreatic cancer patients post-PD. Additionally, there's no significant correlation between DGE incidence and postoperative extubation time, necessitating further investigation into its interaction with pancreatic fistula and infection.

Imaging for Early Detection of Pancreatic Ductal Adenocarcinoma: Updates and Challenges in the Implementation of Screening and Surveillance Programs

Pancreatic ductal adenocarcinoma (PDA) is one of the most aggressive cancers. It has a poor 5-year survival rate of 12%, partly because most cases are diagnosed at advanced stages, precluding curative surgical resection. Early-stage PDA has significantly better prognoses due to increased potential for curative interventions, making early detection of PDA critically important to improved patient outcomes. We examine current and evolving early detection concepts, screening strategies, diagnostic yields among high-risk individuals, controversies, and limitations of standard-of-care imaging.

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.

Pancreatic cancer: from early detection to personalized treatment approaches

Pancreatic cancer is notorious for its persistently poor prognosis and health outcomes, so some of the questions that may be begged are "Why is it mostly diagnosed at end stage?", "What could we possibly do with the advancing technology in today's world to detect early pancreatic cancer and intervene?", and "Are there any implementation of the existing novel imaging technologies?". Well, to start with, this is in part because the majority of patients presented would already have reached a locally advanced or metastatic stage at the time of diagnosis due to its highly aggressive characteristics and lack of symptoms. Due to this striking disparity in survival, advancements in early detection and intervention are likely to significantly increase patients' survival. Presently, screening is frequently used in high-risk individuals in order to obtain an early pancreatic cancer diagnosis. Having a thorough understanding of the pathogenesis and risk factors of pancreatic cancer may enable us to identify individuals at high risk, diagnose the disease early, and begin treatment promptly. In this review, the authors outline the clinical hurdles to early pancreatic cancer detection, describe high-risk populations, and discuss current screening initiatives for high-risk individuals. The ultimate goal of this current review is to study the roles of both traditional and novel imaging modalities for early pancreatic cancer detection. A lot of the novel imaging techniques mentioned seem promising, but they need to be put to the test on a large scale and may need to be combined with other non-invasive biomarkers before they can be widely used.

Early features of pancreatic cancer on magnetic resonance imaging (MRI): a case-control study

PURPOSE

Magnetic resonance imaging has been recommended as a primary imaging modality among high-risk individuals undergoing screening for pancreatic cancer. We aimed to delineate potential precursor lesions for pancreatic cancer on MR imaging.

METHODS

We conducted a case-control study at Kaiser Permanente Southern California (2008-2018) among patients that developed pancreatic cancer who had pre-diagnostic MRI examinations obtained 2-36 months prior to cancer diagnosis (cases) matched 1:2 by age, gender, race/ethnicity, contrast status and year of scan (controls). Patients with history of acute/chronic pancreatitis or prior pancreatic surgery were excluded. Images underwent blind review with assessment of a priori defined series of parenchymal and ductal features. We performed logistic regression to assess the associations between individual factors and pancreatic cancer. We further assessed the interaction among features as well as performed a sensitivity analysis stratifying based on specific time-windows (2-3 months, 4-12 months, 13-36 months prior to cancer diagnosis).

RESULTS

We identified 141 cases (37.9% stage I-II, 2.1% III, 31.4% IV, 28.6% unknown) and 292 matched controls. A solid mass was noted in 24 (17%) of the pre-diagnostic MRI scans. Compared to controls, pre-diagnostic images from cancer cases more frequently exhibited the following ductal findings: main duct dilatation (51.4% vs 14.3%, OR [95% CI]: 7.75 [4.19-15.44], focal pancreatic duct stricture with distal (upstream) dilatation (43.6% vs 5.6%, OR 12.71 [6.02-30.89], irregularity (42.1% vs 6.0%, OR 9.73 [4.91-21.43]), focal pancreatic side branch dilation (13.6% vs1.6%, OR 11.57 [3.38-61.32]) as well as parenchymal features: atrophy (57.9% vs 27.4%, OR 46.4 [2.71-8.28], focal area of signal abnormality (39.3% vs 4.8%, OR 15.69 [6.72-44,78]), all p < 0.001).

CONCLUSION

In addition to potential missed lesions, we have identified a series of ductal and parenchymal features on MRI that are associated with increased odds of developing pancreatic cancer.

Five-year recurrence/survival after pancreatoduodenectomy for pancreatic adenocarcinoma: does pre-existing diabetes matter? Results from the Recurrence After Whipple's (RAW) study

BACKGROUND

Diabetes mellitus (DM) has a complex relationship with pancreatic cancer. This study examines the impact of preoperative DM, both recent-onset and pre-existing, on long-term outcomes following pancreatoduodenectomy (PD) for pancreatic ductal adenocarcinoma (PDAC).

METHODS

Data were extracted from the Recurrence After Whipple's (RAW) study, a multi-centre cohort of PD for pancreatic head malignancy (2012-2015). Recurrence and five-year survival rates of patients with DM were compared to those without, and subgroup analysis performed to compare patients with recent-onset DM (less than one year) to patients with established DM.

RESULTS

Out of 758 patients included, 187 (24.7%) had DM, of whom, 47 of the 187 (25.1%) had recent-onset DM. There was no difference in the rate of postoperative pancreatic fistula (DM: 5.9% vs no DM 9.8%; p = 0.11), five-year survival (DM: 24.1% vs no DM: 22.9%; p = 0.77) or five-year recurrence (DM: 71.7% vs no DM: 67.4%; p = 0.32). There was also no difference between patients with recent-onset DM and patients with established DM in postoperative outcomes, recurrence, or survival.

CONCLUSION

We found no difference in five-year recurrence and survival between diabetic patients and those without diabetes. Patients with pre-existing DM should be evaluated for PD on a comparable basis to non-diabetic patients.

The Use of Glucagon-like Peptide-1 Receptor Agonists in Patients with Type 2 Diabetes Mellitus Does Not Increase the Risk of Pancreatic Cancer: A U.S.-Based Cohort Study

BACKGROUND

GLP-1 RAs are widely used for T2DM treatment due to their cardiorenal and metabolic benefits. This study examines the risk of pancreatic cancer with GLP-1 RA use in patients with T2DM.

METHODS

We analyzed TriNetX's deidentified research database using the U.S. Collaborative Network comprising 62 healthcare organizations across the U.S.A. Patients with T2DM were split into two cohorts: one receiving GLP-1 RAs, and one not receiving GLP-1 RAs. We excluded patients with known risk factors for pancreatic cancer, including pancreatic cysts, a personal or family history of BRCA1, BRCA2, CDKN2A, KRAS, MEN1, MLH1, MSH2, NOTCH1, PALB2, PMS2, and PRSS1S genes, family history of pancreatic cancer, and VHL syndrome. Using a 1:1 propensity score-matching model based on baseline characteristics and comorbidities, we created comparable cohorts. We then compared the rate of pancreatic cancer between the two cohorts at a 7-year interval.

RESULTS

Out of 7,146,015 identified patients with T2DM, 10.3% were on a GLP-1 RA and 89.7% were not. Post-PSM, 721,110 patients were in each group. Patients on GLP-1 RAs had a 0.1% risk compared to a 0.2% risk of pancreatic cancer in the 7-year timeframe.

CONCLUSION

The use of GLP-1 RAs in patients with type 2 diabetes mellitus (T2DM) does not appear to substantially elevate the risk of pancreatic cancer; in fact, it may potentially exert a protective effect.

Utilization and Outcomes of Multigene Panel Testing in Patients With Pancreatic Ductal Adenocarcinoma

PURPOSE

Guidelines recommend germline genetic testing (GT) for patients with pancreatic ductal adenocarcinoma (PDAC). This study aims to evaluate the utilization and outcomes of multigene panel GT in patients with PDAC.

METHODS

This retrospective, multisite study included patients with PDAC diagnosed between May 2018 and August 2020 at Mayo Clinic Arizona, Florida, and Minnesota. Discussion, uptake, and outcomes of GT were compared before (May 1, 2018-May 1, 2019) and after (August 1, 2019-August 1, 2020) the guideline update, accounting for a transition period.

RESULTS

The study identified 533 patients with PDAC, with 321 (60.2%) preguideline and 212 (39.8%) postguideline. Patient characteristics did not differ between the preguideline and postguideline periods. GT was discussed in 34.3% (110 of 321) of preguideline and 39.6% (84 of 212) of postguideline patients (odds ratio [OR], 1.26 [95% CI, 0.88 to 1.80]) and subsequently performed in 80.9% (89 of 110) of preguideline and 75.0% (63 of 84) of postguideline patients (OR, 1.10 [95% CI, 0.75 to 1.61]). Of 152 tested patients, 26 (17.1%) had a pathogenic variant (PV), of whom 17 (11.2%; 17 of 152) were PDAC-associated. Over the entire study period, GT was more likely in younger patients (65 v 70 years; P < .001), those seen by a medical oncologist (82.9% v 69.0%; P < .001), and those surviving more than 12 months from diagnosis (70.4% v 43.4%; P < .001). Demographics and personal/family cancer history were comparable between patients with and without a PDAC PV.

CONCLUSION

GT remains underutilized despite National Comprehensive Cancer Network guideline recommendations. Given the poor prognosis of PDAC and potential implications of GT, efforts to increase utilization are needed to provide surveillance and support to both patients with PDAC and at-risk family members.

Liquid Biopsy for Detection of Pancreaticobiliary Cancers by Functional Enrichment and Immunofluorescent Profiling of Circulating Tumor Cells and Their Clusters

Circulating tumor cells (CTCs) have historically been used for prognostication in oncology. We evaluate the performance of liquid biopsy CTC assay as a diagnostic tool in suspected pancreaticobiliary cancers (PBC). The assay utilizes functional enrichment of CTCs followed by immunofluorescent profiling of organ-specific markers. The performance of the assay was first evaluated in a multicentric case-control study of blood samples from 360 participants, including 188 PBC cases (pre-biopsy samples) and 172 healthy individuals. A subsequent prospective observational study included pre-biopsy blood samples from 88 individuals with suspicion of PBC and no prior diagnosis of cancer. CTCs were harvested using a unique functional enrichment method and used for immunofluorescent profiling for CA19.9, Maspin, EpCAM, CK, and CD45, blinded to the tissue histopathological diagnosis. TruBlood® malignant or non-malignant predictions were compared with tissue diagnoses to establish sensitivity and specificity. The test had 95.9% overall sensitivity (95% CI: 86.0-99.5%) and 92.3% specificity (95% CI: 79.13% to 98.38%) to differentiate PBC (n = 49) from benign conditions (n = 39). The high accuracy of the CTC-based TruBlood test demonstrates its potential clinical application as a diagnostic tool to assist the effective detection of PBC when tissue sampling is unviable or inconclusive.

Pancreatic Cysts Greater Than 1 cm Are Associated With an Increased Risk for Developing Pancreatic Cancer in Individuals From Pancreatic-Cancer Prone Kindreds Undergoing Surveillance

BACKGROUND

The International Cancer of the Pancreas Screening Consortium recommended annual imaging for individuals at increased risk for developing a pancreatic ductal adenocarcinoma (PDAC) who did not have concerning pancreatic findings or a cyst <3 cm without worrisome features. We aimed to determine if 3-cm cyst size accurately predicted advanced precursor lesions in high-risk individuals undergoing surveillance.

METHODS

Imaging for high-risk individuals (HRIs) undergoing PDAC surveillance from 2007 to 2021 was reviewed and pancreatic abnormalities were recorded including dominant cyst size and number of cysts. Subjects were excluded if they had the following: (1) no follow-up imaging after baseline, (2) solid lesion at baseline, or (3) development of solid lesion without having cyst on prior imaging.

RESULTS

Five of the 77 HRIs found to have a cystic lesion on surveillance developed a PDAC: 3 with cystic lesion >1 cm as compared with only 2 of 67 HRIs with cystic lesions <1 cm (P < 0.05). None of these cysts developed worrisome findings and 4/5 PDACs did not arise from visualized cystic precursor lesion.

CONCLUSIONS

Patients with a cyst ≥1 cm were at increased risk for developing PDAC compared with patients with cyst <1 cm. Pancreatic ductal adenocarcinoma usually did not arise from a recognized cystic lesion.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Identification of breath volatile organic compounds to distinguish pancreatic adenocarcinoma, pancreatic cystic neoplasm, and patients without pancreatic lesions

BACKGROUND

Volatile organic compounds (VOCs) are a promising potential biomarker that may be able to identify the presence of cancers.

AIM

To identify exhaled breath VOCs that distinguish pancreatic ductal adenocarcinoma (PDAC) from intraductal papillary mucinous neoplasm (IPMN) and healthy volunteers.

METHODS

We collected exhaled breath from histologically proven PDAC patients, radiological diagnosis IPMN, and healthy volunteers using the ReCIVA® device between 10/2021-11/2022. VOCs were identified by thermal desorption-gas chromatography/field-asymmetric ion mobility spectrometry and compared between groups.

RESULTS

A total of 156 participants (44% male, mean age 62.6 ± 10.6) were enrolled (54 PDAC, 42 IPMN, and 60 controls). Among the nine VOCs identified, two VOCs that showed differences between groups were dimethyl sulfide [0.73 vs 0.74 vs 0.94 arbitrary units (AU), respectively; P = 0.008] and acetone dimers (3.95 vs 4.49 vs 5.19 AU, respectively; P < 0.001). After adjusting for the imbalance parameters, PDAC showed higher dimethyl sulfide levels than the control and IPMN groups, with adjusted odds ratio (aOR) of 6.98 (95%CI: 1.15-42.17) and 4.56 (1.03-20.20), respectively (P < 0.05 both). Acetone dimer levels were also higher in PDAC compared to controls and IPMN (aOR: 5.12 (1.80-14.57) and aOR: 3.35 (1.47-7.63), respectively (P < 0.05 both). Acetone dimer, but not dimethyl sulfide, performed better than CA19-9 in PDAC diagnosis (AUROC 0.910 vs 0.796). The AUROC of acetone dimer increased to 0.936 when combined with CA19-9, which was better than CA19-9 alone (P < 0.05).

CONCLUSION

Dimethyl sulfide and acetone dimer are VOCs that potentially distinguish PDAC from IPMN and healthy participants. Additional prospective studies are required to validate these findings.

Enhancing Fractionated Cancer Therapy: A Triple-Anthracene Photosensitizer Unleashes Long-Persistent Photodynamic and Luminous Efficacy

Conventional photodynamic therapy (PDT) is often limited in treating solid tumors due to hypoxic conditions that impede the generation of reactive oxygen species (ROS), which are critical for therapeutic efficacy. To address this issue, a fractionated PDT protocol has been suggested, wherein light irradiation is administered in stages separated by dark intervals to permit oxygen recovery during these breaks. However, the current photosensitizers used in fractionated PDT are incapable of sustaining ROS production during the dark intervals, leading to suboptimal therapeutic outcomes (Table S1). To circumvent this drawback, we have synthesized a novel photosensitizer based on a triple-anthracene derivative that is designed for prolonged ROS generation, even after the cessation of light exposure. Our study reveals a unique photodynamic action of these derivatives, facilitating the direct and effective disruption of biomolecules and significantly improving the efficacy of fractionated PDT (Table S2). Moreover, the existing photosensitizers lack imaging capabilities for monitoring, which constraints the fine-tuning of irradiation parameters (Table S1). Our triple-anthracene derivative also serves as an afterglow imaging agent, emitting sustained luminescence postirradiation. This imaging function allows for the precise optimization of intervals between PDT sessions and aids in determining the timing for subsequent irradiation, thus enabling meticulous control over therapy parameters. Utilizing our novel triple-anthracene photosensitizer, we have formulated a fractionated PDT regimen that effectively eliminates orthotopic pancreatic tumors. This investigation highlights the promise of employing long-persistent photodynamic activity in advanced fractionated PDT approaches to overcome the current limitations of PDT in solid tumor treatment.

Clinical development of a blood biomarker using apolipoprotein-A2 isoforms for early detection of pancreatic cancer

BACKGROUND

We have previously reported apolipoprotein A2-isoforms (apoA2-is) as candidate plasma biomarkers for early-stage pancreatic cancer. The aim of this study was the clinical development of apoA2-is.

METHODS

We established a new enzyme-linked immunosorbent sandwich assay for apoA2-is under the Japanese medical device Quality Management System requirements and performed in vitro diagnostic tests with prespecified end points using 2732 plasma samples. The clinical equivalence and significance of apoA2-is were compared with CA19-9.

RESULTS

The point estimate of the area under the curve to distinguish between pancreatic cancer (n = 106) and healthy controls (n = 106) was higher for apoA2-ATQ/AT [0.879, 95% confidence interval (CI): 0.832-0.925] than for CA19-9 (0.849, 95% CI 0.793-0.905) and achieved the primary end point. The cutoff apoA2-ATQ/AT of 59.5 μg/mL was defined based on a specificity of 95% in 2000 healthy samples, and the reliability of specificities was confirmed in two independent healthy cohorts as 95.3% (n = 106, 95% CI 89.4-98.0%) and 95.8% (n = 400, 95% CI 93.3-97.3%). The sensitivities of apoA2-ATQ/AT for detecting both stage I (47.4%) and I/II (50%) pancreatic cancers were higher than those of CA19-9 (36.8% and 46.7%, respectively). The combination of apoA2-ATQ/AT (cutoff, 59.5 μg/mL) and CA19-9 (37 U/mL) increased the sensitivity for pancreatic cancer to 87.7% compared with 69.8% for CA19-9 alone. The clinical performance of apoA2-is was blindly confirmed by the National Cancer Institute Early Detection Research Network.

CONCLUSIONS

The clinical performance of ApoA2-ATQ/AT as a blood biomarker is equivalent to or better than that of CA19-9.

A Clinical Prediction Model to Assess Risk for Pancreatic Cancer Among Patients With Acute Pancreatitis

OBJECTIVES

We aimed to develop and validate a prediction model as the first step in a sequential screening strategy to identify acute pancreatitis (AP) individuals at risk for pancreatic cancer (PC).

MATERIALS AND METHODS

We performed a population-based retrospective cohort study among individuals 40 years or older with a hospitalization for AP in the US Veterans Health Administration. For variable selection, we used least absolute shrinkage and selection operator regression with 10-fold cross-validation to identify a parsimonious logistic regression model for predicting the outcome, PC diagnosed within 2 years after AP. We evaluated model discrimination and calibration.

RESULTS

Among 51,613 eligible study patients with AP, 801 individuals were diagnosed with PC within 2 years. The final model (area under the receiver operating curve, 0.70; 95% confidence interval, 0.67-0.73) included histories of gallstones, pancreatic cyst, alcohol use, smoking, and levels of bilirubin, triglycerides, alkaline phosphatase, aspartate aminotransferase, alanine aminotransferase, and albumin. If the predicted risk threshold was set at 2% over 2 years, 20.3% of the AP population would undergo definitive screening, identifying nearly 50% of PC associated with AP.

CONCLUSIONS

We developed a prediction model using widely available clinical factors to identify high-risk patients with PC-associated AP, the first step in a sequential screening strategy.

Simulating the healthcare workforce impact and capacity for pancreatic cancer care in Victoria: a model-based analysis

BACKGROUND

The incidence of pancreatic cancer is rising. With improvements in knowledge for screening and early detection, earlier detection of pancreatic cancer will continue to be more common. To support workforce planning, our aim is to perform a model-based analysis that simulates the potential impact on the healthcare workforce, assuming an earlier diagnosis of pancreatic cancer.

METHODS

We developed a simulation model to estimate the demand (i.e. new cases of pancreatic cancer) and supply (i.e. the healthcare workforce including general surgeons, medical oncologists, radiation oncologists, pain medicine physicians, and palliative care physicians) between 2023 and 2027 in Victoria, Australia. The model compares the current scenario to one in which pancreatic cancer is diagnosed at an earlier stage. The incidence of pancreatic cancer in Victoria, five-year survival rates, and Victoria's population size were obtained from Victorian Cancer Registry, Cancer Council NSW, and Australian Bureau of Statistics respectively. The healthcare workforce data were sourced from the Australian Government Department of Health and Aged Care's Health Workforce Data. The model was constructed at the remoteness level. We analysed the new cases and the number of healthcare workforce by profession together to assess the impact on the healthcare workforce.

RESULTS

In the status quo, over the next five years, there will be 198 to 220 stages I-II, 297 to 330 stage III, and 495 to 550 stage IV pancreatic cancer cases diagnosed annually, respectively. Assuming 20-70% of the shift towards pancreatic cancer's earlier diagnosis (shifting from stage IV to stages I-II pancreatic cancer within one year), the stages I-II cases could increase to 351 to 390 or 598 to 665 per year. The shift to early diagnosis led to substantial survival gains, translating into an additional 284 or 795 out of 5246 patients with pancreatic cancer remaining alive up to year 5 post-diagnosis. Workforce supply decreases significantly by the remoteness levels, and remote areas face a shortage of key medical professionals registered in delivering pancreatic cancer care, suggesting travel necessities by patients or clinicians.

CONCLUSION

Improving the early detection and diagnosis of pancreatic cancer is expected to bring significant survival benefits, although there are workforce distribution imbalances in Victoria that may affect the ability to achieve the anticipated survival gain.

Risk factors for early-onset pancreatic ductal adenocarcinoma: A systematic literature review

BACKGROUND

Emerging cancer trends suggest an increase in pancreatic cancer incidence in individuals younger than its typical age of onset, potentially reflecting changes in population exposures and lifestyles.

PATIENTS AND METHODS

We conducted a PRISMA-standard systematic literature review to identify non-heritable risk factors for early-onset pancreatic ductal adenocarcinoma (PDAC) (PROSPERO number: CRD42022299397). Systematic searches of MEDLINE and Embase bibliographic databases were performed (January 2022), and publications were screened against predetermined eligibility criteria; data were extracted using standardised data fields. The STROBE checklist was used to assess the completeness of reporting as a proxy for publication quality. Data were categorised by risk factor and analysed descriptively.

RESULTS

In total, 24 publications were included. All publications reported observational study data; thresholds for age group comparisons ranged between 40 and 65 years. Lifestyle factors investigated included smoking, alcohol consumption, obesity, physical inactivity, meat intake, socioeconomic status and geographical residence. Clinical factors investigated included pancreatitis, diabetes/insulin resistance, prior cancer and cancer stage at diagnosis, hepatitis B infection, metabolic syndrome and long-term proton pump inhibitor exposure. Publication STROBE scores were 6-21 (maximum, 22). Eight studies reported results adjusted for confounders. Potential non-heritable risk factors for early-onset PDAC that warrant further investigation included smoking, alcohol consumption, pancreatitis and hepatitis B infection.

CONCLUSION

Evidence for non-heritable risk factors for early-onset PDAC is heterogeneous, but four factors were identified that might aid the identification of at-risk individuals who may benefit from screening and risk reduction strategies.

Emergency Presentations Predict Worse Outcomes Among Patients with Pancreatic Cancer

BACKGROUND

Emergency presentation (EP) of cancer, a new cancer diagnosis made following an emergency department (ED) visit, is associated with worse patient outcomes and greater organizational stress on healthcare systems. Pancreatic cancer has the highest rate of EPs among European studies but remains understudied in the U.S.

AIMS

To evaluate the association between pancreatic cancer EPs and cancer stage, treatment, and survival.

METHODS

We conducted a retrospective cohort study among patients with pancreatic adenocarcinoma diagnosed from 2007 to 2019 at a tertiary-care Veterans Affairs medical center. Electronic health records were reviewed to identify EP cases, defined as a new pancreatic cancer diagnosis made within 30 days of an ED visit where cancer was suspected. We used multivariate logistic regression models and Cox proportional hazards models to examine the associations between EPs and cancer stage, treatment, and survival.

RESULTS

Of 243 pancreatic cancer patients, 66.7% had EPs. There was no difference in stage by EP status. However, patients diagnosed through EPs were 72% less likely to receive cancer treatment compared to non-emergency presenters (adjusted OR 0.28; 95% CI 0.13-0.57). Patients with EPs also had a 73% higher mortality risk (adjusted HR 1.73; 95% CI 1.29-2.34). This difference in mortality remained statistically significant after adjusting for cancer stage and receipt of cancer treatment (adjusted HR 1.47; 95% CI 1.09-1.99).

CONCLUSIONS

Pancreatic cancer EPs are common and independently associated with lower treatment rates and survival. Enhanced understanding of process breakdowns that lead to EPs can help identify care gaps and inform future quality improvement efforts.

Contributions of the Microbiome-Derived Metabolome for Risk Assessment and Prognostication of Pancreatic Cancer

BACKGROUND

Increasing evidence implicates microbiome involvement in the development and progression of pancreatic ductal adenocarcinoma (PDAC). Studies suggest that reflux of gut or oral microbiota can lead to colonization in the pancreas, resulting in dysbiosis that culminates in release of microbial toxins and metabolites that potentiate an inflammatory response and increase susceptibility to PDAC. Moreover, microbe-derived metabolites can exert direct effector functions on precursors and cancer cells, as well as other cell types, to either promote or attenuate tumor development and modulate treatment response.

CONTENT

The occurrence of microbial metabolites in biofluids thereby enables risk assessment and prognostication of PDAC, as well as having potential for design of interception strategies. In this review, we first highlight the relevance of the microbiome for progression of precancerous lesions in the pancreas and, using liquid chromatography-mass spectrometry, provide supporting evidence that microbe-derived metabolites manifest in pancreatic cystic fluid and are associated with malignant progression of intraductal papillary mucinous neoplasm(s). We secondly summarize the biomarker potential of microbe-derived metabolite signatures for (a) identifying individuals at high risk of developing or harboring PDAC and (b) predicting response to treatment and disease outcomes.

SUMMARY

The microbiome-derived metabolome holds considerable promise for risk assessment and prognostication of PDAC.

Next-generation therapies for pancreatic cancer

INTRODUCTION

Pancreas ductal adenocarcinoma (PDAC) is a frequently lethal malignancy that poses unique therapeutic challenges. The current mainstay of therapy for metastatic PDAC (mPDAC) is cytotoxic chemotherapy. NALIRIFOX (liposomal irinotecan, fluorouracil, leucovorin, oxaliplatin) is an emerging standard of care in the metastatic setting. An evolving understanding of PDAC pathogenesis is driving a shift toward targeted therapy. Olaparib, a poly-ADP-ribose polymerase (PARP) inhibitor, has regulatory approval for maintenance therapy in BRCA-mutated mPDAC along with other targeted agents receiving disease-agnostic approvals including for PDAC with rare fusions and mismatch repair deficiency. Ongoing research continues to identify and evaluate an expanding array of targeted therapies for PDAC.

AREAS COVERED

This review provides a brief overview of standard therapies for PDAC and an emphasis on current and emerging targeted therapies.

EXPERT OPINION

There is notable potential for targeted therapies for KRAS-mutated PDAC with opportunity for meaningful benefit for a sizable portion of patients with this disease. Further, emerging approaches are focused on novel immune, tumor microenvironment, and synthetic lethality strategies.

Association of Glycated Hemoglobin With a Risk of Pancreatic Cancer in High-Risk Individuals Based on Genetic and Family History

INTRODUCTION

Screening for pancreatic cancer (PC) is suggested for high-risk individuals. Additional risk factors may enhance early detection in this population.

METHODS

Retrospective cohort study among patients with germline variants and/or familial pancreatic cancer in an integrated healthcare system between 2003 and 2019. We calculated the incidence rate (IR) by risk category and performed a nested case-control study to evaluate the relationship between HbA1C and PC within 3 years before diagnosis (cases) or match date (controls). Cases were matched 1:4 by age, sex, and timing of HbA1c. Logistic regression was performed to assess an independent association with PC.

RESULTS

We identified 5,931 high-risk individuals: 1,175(19.8%) familial PC, 45(0.8%) high-risk germline variants ( STK11, CDKN2A ), 4,097(69.1%) had other germline variants ( ATM, BRCA 1, BRCA 2, CASR, CDKN2A, CFTR, EPCAM, MLH1, MSH2, MSH6, PALB2, PRSS1, STK11, and TP53 ), and 614(10.4%) had both germline variants and family history. Sixty-eight patients (1.1%) developed PC; 50% were metastatic at diagnosis. High-risk variant was associated with greatest risk of PC, IR = 85.1(95% confidence interval: 36.7-197.6)/10,000 person-years; other germline variants and first-degree relative had IR = 33 (18.4, 59.3), whereas IR among ≥2 first-degree relative alone was 10.7 (6.1, 18.8). HbA1c was significantly higher among cases vs controls (median = 7.0% vs 6.4%, P = 0.02). In multivariable analysis, every 1% increase in HbA1c was associated with 36% increase in odds of PC (odds ratio 1.36, 95% confidence interval: 1.08-1.72). Pancreatitis was independently associated with a risk of PC (odds ratio 3.93, 95% confidence limit 1.19, 12.91).

DISCUSSION

Risk of PC varies among high-risk individuals. HbA1c and history of pancreatitis may be useful additional markers for early detection in this patient population.

Advances in the management of pancreatic cancer

Pancreatic cancer remains among the malignancies with the worst outcomes. Survival has been improving, but at a slower rate than other cancers. Multimodal treatment, including chemotherapy, surgical resection, and radiotherapy, has been under investigation for many years. Because of the anatomical characteristics of the pancreas, more emphasis on treatment selection has been placed on local extension into major vessels. Recently, the development of more effective treatment regimens has opened up new treatment strategies, but urgent research questions have also become apparent. This review outlines the current management of pancreatic cancer, and the recent advances in its treatment. The review discusses future treatment pathways aimed at integrating novel findings of translational and clinical research.

A pancreatic cancer risk prediction model (Prism) developed and validated on large-scale US clinical data

BACKGROUND

Pancreatic Duct Adenocarcinoma (PDAC) screening can enable early-stage disease detection and long-term survival. Current guidelines use inherited predisposition, with about 10% of PDAC cases eligible for screening. Using Electronic Health Record (EHR) data from a multi-institutional federated network, we developed and validated a PDAC RISk Model (Prism) for the general US population to extend early PDAC detection.

METHODS

Neural Network (PrismNN) and Logistic Regression (PrismLR) were developed using EHR data from 55 US Health Care Organisations (HCOs) to predict PDAC risk 6-18 months before diagnosis for patients 40 years or older. Model performance was assessed using Area Under the Curve (AUC) and calibration plots. Models were internal-externally validated by geographic location, race, and time. Simulated model deployment evaluated Standardised Incidence Ratio (SIR) and other metrics.

FINDINGS

With 35,387 PDAC cases, 1,500,081 controls, and 87 features per patient, PrismNN obtained a test AUC of 0.826 (95% CI: 0.824-0.828) (PrismLR: 0.800 (95% CI: 0.798-0.802)). PrismNN's average internal-external validation AUCs were 0.740 for locations, 0.828 for races, and 0.789 (95% CI: 0.762-0.816) for time. At SIR = 5.10 (exceeding the current screening inclusion threshold) in simulated model deployment, PrismNN sensitivity was 35.9% (specificity 95.3%).

INTERPRETATION

Prism models demonstrated good accuracy and generalizability across diverse populations. PrismNN could find 3.5 times more cases at comparable risk than current screening guidelines. The small number of features provided a basis for model interpretation. Integration with the federated network provided data from a large, heterogeneous patient population and a pathway to future clinical deployment.

FUNDING

Prevent Cancer Foundation, TriNetX, Boeing, DARPA, NSF, and Aarno Labs.

Are Tumor Marker Tests Applied Appropriately in Clinical Practice? A Healthcare Claims Data Analysis

Tumor markers (TM) are crucial in the monitoring of cancer treatment. However, inappropriate requests for screening reasons have a high risk of false positive and negative findings, which can lead to patient anxiety and unnecessary follow-up examinations. We aimed to assess the appropriateness of TM testing in outpatient practice in Switzerland. We conducted a retrospective cohort study based on healthcare claims data. Patients who had received at least one out of seven TM tests (CEA, CA19-9, CA125, CA15-3, CA72-4, Calcitonin, or NSE) between 2018 and 2021 were analyzed. Appropriate determinations were defined as a request with a corresponding cancer-related diagnosis or intervention. Appropriateness of TM determination by patient characteristics and prescriber specialty was estimated by using multivariate analyses. A total of 51,395 TM determinations in 36,537 patients were included. An amount of 41.6% of all TM were determined appropriately. General practitioners most often determined TM (44.3%) and had the lowest number of appropriate requests (27.8%). A strong predictor for appropriate determinations were requests by medical oncologists. A remarkable proportion of TM testing was performed inappropriately, particularly in the primary care setting. Our results suggest that a considerable proportion of the population is at risk for various harms associated with misinterpretations of TM test results.

Do Polygenic Risk Scores Add to Clinical Data in Predicting Pancreatic Cancer? A Scoping Review

BACKGROUND

Polygenic risk scores (PRS) summarize an individual's germline genetic risk, but it is unclear whether PRS offer independent information for pancreatic cancer risk prediction beyond routine clinical data.

METHODS

We searched 8 databases from database inception to March 10, 2023 to identify studies evaluating the independent performance of pancreatic cancer-specific PRS for pancreatic cancer beyond clinical risk factors.

RESULTS

Twenty-one studies examined associations between a pancreatic cancer-specific PRS and pancreatic cancer. Seven studies evaluated risk factors beyond age and sex. Three studies evaluated the change in discrimination associated with the addition of PRS to routine risk factors and reported improvements (AUCs: 0.715 to 0.745; AUC 0.791 to 0.830; AUC from 0.694 to 0.711). Limitations to clinical applicability included using source populations younger/healthier than those at risk for pancreatic cancer (n = 10), exclusively of European ancestry (n = 13), or controls without relevant exposures (n = 1).

CONCLUSIONS

While most studies of pancreatic cancer-specific PRS did not evaluate the independent discrimination of PRS for pancreatic cancer beyond routine risk factors, three that did showed improvements in discrimination.

IMPACT

For pancreatic cancer PRS to be clinically useful, they must demonstrate substantial improvements in discrimination beyond established risk factors, apply to diverse ancestral populations representative of those at risk for pancreatic cancer, and use appropriate controls.

Serum immune checkpoint profiling identifies soluble CD40 as a biomarker for pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) responds poorly to systemic treatment, including new immunotherapeutic approaches. Biomarkers are urgently needed for early disease detection, patient stratification for treatment, and response prediction. The role of soluble CD40 (sCD40) is unknown in PDAC. In this study, we performed a quantitative multiplex analysis of 17 immune checkpoint proteins in serum samples from patients with various stages of PDAC in a discovery study (n = 107) and analyzed sCD40 by ELISA in a validation study (n = 317). Youden's J statistic was used for diagnostic cut-off optimization. A Cox proportional hazards regression model was applied in an empiric approach for prognostic threshold optimization. Kaplan-Meier estimator and multivariable Cox regression analyses were used for survival analysis. sCD40 was significantly increased in the serum of patients with PDAC compared to healthy cohorts and patients with IPMN. In the validation cohort, the area under the receiver operating characteristic (ROC) c-statistic was 0.8, and combining sCD40 with CA19-9 yielded a c-statistic of 0.95. sCD40 levels were independent of the tumor stage. However, patients who received neoadjuvant chemotherapy had significantly lower sCD40 levels than those who underwent upfront surgery. Patients with a sCD40 level above the empirical threshold of 0.83 ng/ml had a significantly reduced overall survival with a hazard ratio of 1.4. This observation was pronounced in patients after neoadjuvant chemotherapy. Collectively, soluble CD40 may be considered as both a diagnostic and prognostic non-invasive biomarker in PDAC.

Differential Expression and Diagnostic Value of MUC5AC Glycoforms in Pancreatic Ductal Adenocarcinoma

We explored the differential expression and diagnostic value of two significant Mucin 5AC (MUC5AC) glycoforms, less-glycosylated immature (IM) and heavily-glycosylated mature (MM), in neoplastic diseases (NpD), including pancreatic ductal adenocarcinoma (PDA) and neuroendocrine tumors (NET), and non-neoplastic (non-NpD) diseases. Commercially available tissue microarray (TMA) was constructed from 96 patients, including 38 primary PDA (PT), 5 metastatic lesions (ML), 11 NET, and the rest being non-NpD tissues. Immunohistochemistry for MUC5AC was performed using CHL2 and 45M1 clones for IM and MM isoforms, respectively. MUC5AC (both glycoforms) are not detected in non-NpD. In MUC5AC-positive neoplastic tissues, IM was localized to the cytoplasm (Cy) while MM was identified in apical (Ap) and extracellular (Ec) regions too. One ML positive (omentum) in the TMA expressed both. For PDA vs. non-PDA, the sensitivity (SN) was higher with MM ± IM (71%) than MM (47%) or IM (65%)-alone. The specificity (SP) was 100% with MM-alone, which dropped with the addition of IM (96%) or IM-alone (93%). For NpD vs. non-NpD, the SN (MM + IM-59%, IM-55%, MM-37%) was inferior, and SP was 100% for both glycoforms (MM ± IM). The combination of MUC5AC glycoforms has high SP and reasonable SN to diagnose PDA. They have the potential to be a reliable diagnostic marker and should be investigated further in more extensive studies.

A blood-based metabolomic signature predictive of risk for pancreatic cancer

Emerging evidence implicates microbiome involvement in the development of pancreatic cancer (PaCa). Here, we investigate whether increases in circulating microbial-related metabolites associate with PaCa risk by applying metabolomics profiling to 172 sera collected within 5 years prior to PaCa diagnosis and 863 matched non-subject sera from participants in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cohort. We develop a three-marker microbial-related metabolite panel to assess 5-year risk of PaCa. The addition of five non-microbial metabolites further improves 5-year risk prediction of PaCa. The combined metabolite panel complements CA19-9, and individuals with a combined metabolite panel + CA19-9 score in the top 2.5th percentile have absolute 5-year risk estimates of >13%. The risk prediction model based on circulating microbial and non-microbial metabolites provides a potential tool to identify individuals at high risk of PaCa that would benefit from surveillance and/or from potential cancer interception strategies.

Pancreatic Cancer and Detection Methods

The pancreas is a vital organ with exocrine and endocrine functions. Pancreatitis is an inflammation of the pancreas caused by alcohol consumption and gallstones. This condition can heighten the risk of pancreatic cancer (PC), a challenging disease with a high mortality rate. Genetic and epigenetic factors contribute significantly to PC development, along with other risk factors. Early detection is crucial for improving PC outcomes. Diagnostic methods, including imagining modalities and tissue biopsy, aid in the detection and analysis of PC. In contrast, liquid biopsy (LB) shows promise in early tumor detection by assessing biomarkers in bodily fluids. Understanding the function of the pancreas, associated diseases, risk factors, and available diagnostic methods is essential for effective management and early PC detection. The current clinical examination of PC is challenging due to its asymptomatic early stages and limitations of highly precise diagnostics. Screening is recommended for high-risk populations and individuals with potential benign tumors. Among various PC screening methods, the N-NOSE plus pancreas test stands out with its high AUC of 0.865. Compared to other commercial products, the N-NOSE plus pancreas test offers a cost-effective solution for early detection. However, additional diagnostic tests are required for confirmation. Further research, validation, and the development of non-invasive screening methods and standardized scoring systems are crucial to enhance PC detection and improve patient outcomes. This review outlines the context of pancreatic cancer and the challenges for early detection.

Earlier Diagnosis of Pancreatic Cancer: Is It Possible?

Pancreatic ductal adenocarcinoma has a very high mortality rate which has been only minimally improved in the last 30 years. This high mortality is closely related to late diagnosis, which is usually made when the tumor is large and has extensively infiltrated neighboring tissues or distant metastases are already present. This is a paradoxical situation for a tumor that requires nearly 15 years to develop since the first founding mutation. Response to chemotherapy under such late circumstances is poor, resistance is frequent, and prolongation of survival is almost negligible. Early surgery has been, and still is, the only approach with a slightly better outcome. Unfortunately, the relapse percentage after surgery is still very high. In fact, early surgery clearly requires early diagnosis. Despite all the advances in diagnostic methods, the available tools for improving these results are scarce. Serum tumor markers permit a late diagnosis, but their contribution to an improved therapeutic result is very limited. On the other hand, effective screening methods for high-risk populations have not been fully developed as yet. This paper discusses the difficulties of early diagnosis, evaluates whether the available diagnostic tools are adequate, and proposes some simple and not-so-simple measures to improve it.

Pancreatic Cancer Screening for At-Risk Individuals (Pancreas Scan Study): Yield, Harms, and Outcomes From a Prospective Multicenter Study

INTRODUCTION

Guidelines endorse pancreatic cancer screening in genetically susceptible individuals. We conducted a prospective, multicenter study to determine yield, harms, and outcomes of pancreatic cancer screening.

METHODS

All high-risk individuals undergoing pancreatic cancer screening at 5 centers from 2020 to 2022 were prospectively enrolled. Pancreas findings were designated as low-risk (fatty or chronic pancreatitis-like changes), intermediate-risk (neuroendocrine tumor [NET] <2 cm or branch-duct intraductal papillary mucinous neoplasm [IPMN]), or high-risk lesions (high-grade pancreatic intraepithelial neoplasia/dysplasia, main-duct IPMN, NET >2 cm, or pancreatic cancer). Harms from screening included adverse events during screening or undergoing low-yield pancreatic surgery. Annual screening was performed using endoscopic ultrasound and or magnetic resonance cholangiopancreatography. Annual screening for new-onset diabetes using fasting blood sugar was also performed ( ClinicalTrials.gov : NCT05006131).

RESULTS

During the study period, 252 patients underwent pancreatic cancer screening. Mean age was 59.9 years, 69% were female, and 79.4% were White. Common indications were BRCA 1/2 (36.9%), familial pancreatic cancer syndrome kindred (31.7%), ataxia telangiectasia mutated (3.5%), Lynch syndrome (6.7%), Peutz-Jeghers (4.3%), and familial atypical multiple mole melanoma (3.5%). Low-risk lesions were noted in 23.4% and intermediate-risk lesions in 31.7%, almost all of which were branch-duct IPMN without worrisome features. High-risk lesions were noted in 2 patients (0.8%), who were diagnosed with pancreas cancer at stages T2N1M0 and T2N1M1. Prediabetes was noted in 18.2% and new-onset diabetes in 1.7%. Abnormal fasting blood sugar was not associated with pancreatic lesions. There were no adverse events from screening tests, and no patient underwent low-yield pancreatic surgery.

DISCUSSION

Pancreatic cancer screening detected high-risk lesions with lower frequency than previously reported. No harms from screening were noted.

Outcomes of the IMMray PanCan-d Test in High-Risk Individuals Undergoing Pancreatic Surveillance: Pragmatic Data and Lessons Learned

PURPOSE

An effective blood-based test for pancreatic cancer (PC) screening has remained elusive. The IMMray PanCan-d is the first commercially available blood-based test specifically designed for early detection of PC; however, outcomes from its use in clinical practice have not been reported.

METHODS

We performed a blinded spike-in study of 100 individuals who had an IMMray PanCan-d test, including 94 high-risk individuals (HRIs) undergoing PC surveillance and six individuals with known PC. Specimens were processed blindly following the commercial laboratory's standardized operating procedure. Positive predictive value (PPV) and negative predictive value (NPV) were calculated.

RESULTS

Cohort characteristics included a median age of 63 (IQR, 55-70) years, 57% female, 96% non-Hispanic White, 57% with a pathogenic variant in a PC risk gene (BRCA2 most commonly-18%), and 83% with a family history of PC. Among IMMray PanCan-d results from 94 HRIs undergoing PC surveillance, there was one positive (1%), seven borderlines (7%), 73 negatives (78%), and 13 tests not performed because of low CA19-9 expression (14%). No PC was diagnosed among these HRIs; however, there were two sub-cm pancreatic neuroendocrine tumors, seven clinically diagnosed side branch intraductal papillary mucinous neoplasms ≥1 cm, and a sub-cm solid mass with indeterminate cytology requiring close follow-up; all these individuals had negative IMMray PanCan-d tests. Of the six spiked-in PCs, four (67%) yielded a positive and two (33%) yielded a negative. With an estimated disease prevalence of 2%, the PPV and NPV are 52% and 99%, respectively, if borderline results are considered negative and 12% and 99%, respectively, if borderline tests are considered positive.

CONCLUSION

In clinical practice, IMMray PanCan-d has a robust NPV; however, PPV is dramatically influenced by whether borderline results are characterized as a positive or negative result.

Association between inflammatory bowel disease and pancreatic cancer: results from the two-sample Mendelian randomization study

Background

The nuanced relationship between inflammatory bowel disease (IBD) and pancreatic cancer is noticed in recent years. However, the underlying causal effects of these two diseases are still unclear.

Methods

The two-sample mendelian randomization (MR) was conducted to explore the causal effect of IBD condition on pancreatic cancer. Methods of Wald ratio, inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode were used to investigate the causal relationship between IBD and pancreatic cancer. Besides, Cochrane's Q test, MR-Egger, and leave-one-out method were further conducted to detect heterogeneity, stability, and pleiotropy of MR results.

Results

In the MR analysis, we found Crohn's disease had a significant causal effect on pancreatic cancer. Specifically, Crohn's disease would increase 11.1% the risk of pancreatic cancer by the IVW method (p= 0.022), 33.8% by MR Egger (p= 0.015), by 35.3% by the Weighted model (p= 0.005). Regarding ulcerative colitis, there was no statistically significant causal effect observed on pancreatic cancer (p>0.05). Additionally, the pleiotropic test and Leave-one-out analysis both proved the validity and reliability of the present two-sample MR analyses.

Conclusion

This study indicates that IBD, particularly Crohn's disease, is causality associated with increased risk of pancreatic cancer. Our results may help public health managers to make better follow-up surveillance of IBD patients.

Clinical Value of Circulating microRNAs in Diagnosis and Prognosis of Pancreatic Cancer: A Systematic Review

Pancreatic cancer (PC) is one of the most common cancers and has a high mortality rate due to high invasiveness and rapid progression. Microribonucleic acid (microRNA) plays an essential role in diagnosing PC in the early stages, which improves the five-year survival rate. This systematic review aims to highlight the different subtypes of serum and plasma microRNAs and panel-based assays of microRNAs and how they play a crucial role in the diagnosis and prognosis of PC as a high-sensitive and specific novel biomarker. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines, an in-depth search was performed by using regular keywords and major Medical Subject Heading (MeSH) keywords in PubMed (MEDLINE), PubMed Central, Google Scholar, Science Direct, and Cochrane Library for articles related to this topic and published between 2013 and 2023, up to April 18, 2023. Further eligibility criteria and quality assessment tools were employed to assess the risk of bias, and 13 articles were finalized to be used in this review. The chosen articles included five cross-sectional studies, six systematic reviews and meta-analyses, and two literature reviews. This review provides strong evidence of the usage of microRNA for early diagnosis. It can also be used to exclude differential diagnoses of other diseases, and its prognostic value for determining metastasis and therapeutic efficacy in PC patients. Also, combining microRNA panels with carbohydrate antigen 19.9 (CA19-9) improves the sensitivity and specificity of microRNA as a biomarker.

Machine Learning Predicts Patients With New-onset Diabetes at Risk of Pancreatic Cancer

BACKGROUND

New-onset diabetes represent a high-risk cohort to screen for pancreatic cancer.

GOALS

Develop a machine model to predict pancreatic cancer among patients with new-onset diabetes.

STUDY

A retrospective cohort of patients with new-onset diabetes was assembled from multiple health care networks in the United States. An XGBoost machine learning model was designed from a portion of this cohort (the training set) and tested on the remaining part of the cohort (the test set). Shapley values were used to explain the XGBoost's model features. Model performance was compared with 2 contemporary models designed to predict pancreatic cancer among patients with new-onset diabetes.

RESULTS

In the test set, the XGBoost model had an area under the curve of 0.80 (0.76 to 0.85) compared with 0.63 and 0.68 for other models. Using cutoffs based on the Youden index, the sensitivity of the XGBoost model was 75%, the specificity was 70%, the accuracy was 70%, the positive predictive value was 1.2%, and the negative predictive value was >99%. The XGBoost model obtained a positive predictive value of at least 2.5% with a sensitivity of 38%. The XGBoost model was the only model that detected at least 50% of patients with cancer one year after the onset of diabetes. All 3 models had similar features that predicted pancreatic cancer, including older age, weight loss, and the rapid destabilization of glucose homeostasis.

CONCLUSION

Machine learning models isolate a high-risk cohort from those with new-onset diabetes at risk for pancreatic cancer.

Overview of Pancreatic Cancer Epidemiology in Europe and Recommendations for Screening in High-Risk Populations

Pancreatic cancer is the seventh most common cause of death in the group of oncological diseases. Due to the asymptomatic course, early diagnosis is difficult. Currently, early detection methods are only used in high-risk groups. A literature review based on the available results of observational studies on patients with pancreatic cancer and people from high-risk groups was used to summarize the knowledge on risk factors. The GLOBOCAN 2020 data were used to assess the epidemiological situation in Europe. A summary of screening recommendations was prepared based on the available documents from medical organizations and associations. Pancreatic cancer risk factors are divided into two main groups: non-modifiable factors, e.g., hereditary factors and age, which increase the risk of developing this disease, and modifiable factors-BMI, smoking, and alcohol consumption. Hereditary factors account for 10% of pancreatic cancer cases. The highly specialized methods of early detection, (MRI, CT, or EUS) are used for screening high-risk populations. Of all the imaging methods, EUS is considered the most sensitive for pancreatic cancer and allows an accurate assessment of the size of even small lesions (<30 mm) and the extent of tumour infiltration into blood vessels. The available studies vary on the level of sensitivity and specificity of these methods for the diagnosis of pancreatic cancer. EUS, MRI, and CT are also expensive procedures and in some patients can be invasive, which is one of the arguments against the introduction of population screening programs based on imaging methods. Therefore, it is important to look for viable solutions that would improve early detection. This is important from the point of view of healthcare systems in Europe, where almost 29% of all global pancreatic cancer cases are reported.

Pancreatic cancer among solid organ transplant recipients in the United States

INTRODUCTION

Pancreatic cancer (PC) in solid organ transplant (SOT) recipients is not well studied. Some PC cases may be incidentally detected during hepatobiliary imaging.

METHODS

We evaluated PC among 374,106 SOT recipients during 1995-2017 in the United States using linked data from the national transplant registry and multiple state/regional cancer registries. Standardized incidence ratios (SIRs) were used to compare PC risk in recipients to the general population. We used multivariate Poisson regression to identify independent risk factors for PC. We assessed survival after PC diagnosis using Kaplan-Meier curves and log-rank tests.

RESULTS

SOT recipients had elevated incidence for PC compared with the general population (SIR 1.40, 95% CI 1.29-1.52), and this increase was strongest in liver recipients (1.65, 1.41-1.92). Among all recipients, PC incidence was especially increased for cases arising in the head of the pancreas (SIR 1.50, 95% CI 1.34-1.68) and for cases diagnosed at localized stage (1.85, 1.37-2.44). Among SOT recipients, factors independently associated with increased incidence were consistent with those in general population including male sex, older age, non-O blood type, and history of diabetes. Additionally, compared to other organ recipients, liver transplant recipients had higher PC incidence (adjusted incidence rate ratio 1.28; 95% CI 1.06-1.54). Overall survival after PC diagnosis was poor (median 4 months) and similar between liver and other organ transplant recipients (p = 0.08).

CONCLUSIONS

PC incidence is elevated among SOT recipients, and more commonly diagnosed in liver transplant recipients perhaps related to incidental detection. However, survival is poor even in liver recipients, arguing against routine PC screening.

Advances in the Early Diagnosis of Pancreatic Ductal Adenocarcinoma and Premalignant Pancreatic Lesions

Pancreatic cancer is one of the most lethal human malignancies, in part because it is often diagnosed at late stages when surgery and systemic therapies are either unfeasible or ineffective. Therefore, diagnosing pancreatic cancer in earlier stages is important for effective treatment. However, because the signs and symptoms may be nonspecific and not apparent until the disease is at a late stage, the timely diagnoses of pancreatic cancer can be difficult to achieve. Recent studies have shown that selective screening and increased usage of biomarkers could improve the early diagnosis of pancreatic cancer. In this review, we discuss recent advancements in the early detection of pancreatic ductal carcinoma and precancerous lesions. These include innovations in imaging modalities, the diagnostic utility of various biomarkers, biopsy techniques, and population-based surveillance approaches. Additionally, we discuss how machine learning methods are being applied to develop integrated methods of identifying individuals at high risk of developing pancreatic disease. In the future, the overall survival of pancreatic cancer patients could be improved by the development and adoption of these new methods and techniques.

Machine learning versus regression for prediction of sporadic pancreatic cancer

BACKGROUND/OBJECTIVES

There is currently no widely accepted approach to identify patients at increased risk for sporadic pancreatic cancer (PC). We aimed to compare the performance of two machine-learning models with a regression-based model in predicting pancreatic ductal adenocarcinoma (PDAC), the most common form of PC.

METHODS

This retrospective cohort study consisted of patients 50-84 years of age enrolled in either Kaiser Permanente Southern California (KPSC, model training, internal validation) or the Veterans Affairs (VA, external testing) between 2008 and 2017. The performance of random survival forests (RSF) and eXtreme gradient boosting (XGB) models were compared to that of COX proportional hazards regression (COX). Heterogeneity of the three models were assessed.

RESULTS

The KPSC and the VA cohorts consisted of 1.8 and 2.7 million patients with 1792 and 4582 incident PDAC cases within 18 months, respectively. Predictors selected into all three models included age, abdominal pain, weight change, and glycated hemoglobin (A1c). Additionally, RSF selected change in alanine transaminase (ALT), whereas the XGB and COX selected the rate of change in ALT. The COX model appeared to have lower AUC (KPSC: 0.737, 95% CI 0.710-0.764; VA: 0.706, 0.699-0.714), compared to those of RSF (KPSC: 0.767, 0.744-0.791; VA: 0.731, 0.724-0.739) and XGB (KPSC: 0.779, 0.755-0.802; VA: 0.742, 0.735-0.750). Among patients with top 5% predicted risk from all three models (N = 29,663), 117 developed PDAC, of which RSF, XGB and COX captured 84 (9 unique), 87 (4 unique), 87 (19 unique) cases, respectively.

CONCLUSIONS

The three models complement each other, but each has unique contributions.

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.

Patients with deleterious germline mutations: A heterogeneous population for pancreatic cancer screening?

BACKGROUND AND OBJECTIVES

Modest data exist on the benefits of screening and surveillance for pancreatic cancer (PC) in high-risk individuals. Intraductal papillary mucinous neoplasms (IPMN) are known precursors to PC. We hypothesized that patients with high-risk deleterious germline mutations have a higher prevalence of IPMN.

METHODS

All patients undergoing prospective screening at a single institution from 2013 to 2019 were reviewed.

RESULTS

Of 1166 patients screened, 358 (31%) possessed germline mutations and/or family history of PC (mutations n = 201/358, 56%, family history n = 226/358, 63%) (median follow-up 2.7 years). IPMN was found in 127 patients (35.5%). The prevalence of IPMN in mutation carriers (18%) was higher than in the general population (p < 0.01). Germline mutation was an independent predictor of IPMN (odds ratio [OR] = 3.2; p < 0.01), while family history was not (p = 0.22). IPMN prevalence was distributed unevenly between mutation types (67%-Peutz-Jeghers; 43%-HNPCC, 24%-BRCA2; 17%-ATM; 9%-BRCA1; 0%-CDKN2A and PALB2).

CONCLUSION

In this series, 18% of mutation carriers harbored IPMN, higher than the general population. Germline mutation, but not a family history of PC, was independently associated with IPMN. This prevalence varied across mutation subtypes, suggesting not all mutation carriers develop precancerous lesions. Genetic testing for patients with a positive family history may improve screening modalities for this high-risk population.

Pancreatic cancer: Advances and challenges

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest cancers. Significant efforts have largely defined major genetic factors driving PDAC pathogenesis and progression. Pancreatic tumors are characterized by a complex microenvironment that orchestrates metabolic alterations and supports a milieu of interactions among various cell types within this niche. In this review, we highlight the foundational studies that have driven our understanding of these processes. We further discuss the recent technological advances that continue to expand our understanding of PDAC complexity. We posit that the clinical translation of these research endeavors will enhance the currently dismal survival rate of this recalcitrant disease.

Identification of the molecular subtype and prognostic characteristics of pancreatic cancer based on CD8 + T cell-related genes

CD8 + T lymphocytes are immune cells that play a crucial anti-tumor role in the human body, and prognostic value of CD8 + T cell-related regulatory genes in PAAD remains elusive. Data on 179 expression profiles across 13 immune cell datasets were downloaded from the GEO database, and the expression profiles of CD8 + T cell-related genes were obtained using WGCNA. Molecular subtypes based on CD8 + T cell-related genes were constructed using the ConsensusClusterPlus algorithm. Lasso regression analysis was performed to build a 10-gene signature. GSVA was performed to explore the pathways related to these ten genes. The IMvigor210 cohort was used to explore the predictive efficacy of the signature in terms of immunotherapy response. Four hundred and forty-six CD8 + T cell-related genes were obtained. One hundred and nine genes in TCGA and GEO datasets were closely related to the prognosis of patients and were included in the next study. PAAD samples were divided into two subtypes (IC1 and IC2) according to consensus cluster analysis. These two immune subtypes were significantly different in terms of immune checkpoint genes, immune function, and drug treatment response. Additionally, the 10-gene signature constructed based on CD8 + T cell-related genes showed a stable prognostic performance in TCGA and GEO cohorts. Moreover, it served as an independent prognostic factor for patients with PAAD. Furthermore, the 10-gene signature could effectively predict the response to immunotherapy. The immunophenotyping-derived prognostic model based on CD8 T cell-related genes provides a basis for the clinical treatment of pancreatic cancer.

Early Detection of Cancers in the Era of Precision Oncology

PURPOSE OF REVIEW

The increasing global incidence of cancer demands innovative cancer detection modalities. The current population-based early cancer detection approaches focus on several major types of cancers (breast, prostate, cervical, lung and colon) at their early stages, however, they generally do not target high-risk individuals at precancerous stages.

RECENT FINDINGS

Some cancers, such as pancreatic cancer, are challenging to detect in their early stages. Therefore, there is a pressing need for improved, accessible, noninvasive, and cost-effective early detection methods. Harnessing cell-free-based biomarker-driven strategies paves a new era of precision diagnosis for multicancer early detection. The majority of these tests are in the early stages and expensive, but these approaches are expected to become cost sensitive in the near future.

SUMMARY

This review provides an overview of early cancer detection strategies, highlighting the methods, challenges, and issues to be addressed to revolutionize and improve global early cancer detection.

Risk prediction of hepatobiliary and pancreatic cancers in elderly Chinese: The Dongfeng-Tongji cohort

AIM

Hepatobiliary and pancreatic (HBP) cancers are among the deadliest malignancies. The objective of the study is to build cost-effective models to identify high-risk individuals for early diagnosis and substantially to reduce the burden of HBP cancers.

METHODS

Based on the prospective Dongfeng-Tongji cohort with ∼6 years follow-up, we identified 162 incident cases of hepatocellular carcinoma (HCC), 53 of biliary tract cancer (BTC), and 58 of pancreatic cancer (PC). We matched three controls to each case by age, sex, and hospital. We applied conditional logistic regression to identify predictive clinical variables, from which we constructed clinical risk scores (CRSs). We evaluated the utility of CRSs in stratifying high-risk individuals by 10-fold cross-validation.

RESULTS

Among 50 variables we screened, 6 were independent predictors of HCC, with the top ones being hepatitis (OR = 8.51, 95% CI (3.83, 18.9)), plateletcrit (OR = 0.57, 95% CI (0.42, 0.78)), and alanine aminotransferase (OR = 2.06, 95% CI (1.39, 3.06)). Gallstone (OR = 2.70, 95% CI (1.17, 6.24)) and direct bilirubin (OR = 1.58, 95% CI (1.08, 2.31)) were predictive of BTC, while hyperlipidemia (OR = 2.56, 95% CI (1.12, 5.82)) and fasting blood glucose (OR = 2.00, 95% CI (1.26, 3.15)) were predictive of PC. The CRSs achieved AUCs of 0.784 for HCC, 0.648 for BTC, and 0.666 for PC, respectively. When applying to the full cohort with age and sex included as predictors, the AUCs were increased to 0.818, 0.704, and 0.699, respectively.

CONCLUSIONS

Disease history and routine clinical variables are predictive of incident HBP cancers in elderly Chinese.