Pancreatic Ductal Adenocarcinoma Screening Research Articles

The microbial composition of pancreatic ductal adenocarcinoma: A systematic review of 16S rRNA gene sequencing.

Pancreatic cancer, specifically pancreatic ductal adenocarcinoma (PDAC), continues to pose a significant clinical and scientific challenge. The most significant finding of recent years is that PDAC tumours harbour their specific microbiome, which differs amongst tumour entities and is distinct from healthy tissue. This review aims to evaluate and summarise all PDAC studies that have used the next-generation technique, 16S rRNA gene amplicon sequencing within each bodily compartment. As well as establishing a causal relationship between PDAC and the microbiome. This systematic review was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. A comprehensive search strategy was designed, and 1727 studies were analysed. In total, 38 studies were selected for qualitative analysis and summarised significant PDAC bacterial signatures. Despite the growing amount of data provided, we are not able to state a universal 16S rRNA gene microbial signature that can be used for PDAC screening. This is most certainly due to the heterogeneity of the presentation of results, lack of available datasets and the intrinsic selection bias between studies. Several key studies have begun to shed light on causality and the influence the microbiome constituents and their produced metabolites could play in tumorigenesis and influencing outcomes. The challenge in this field is to shape the available microbial data into targetable signatures. Making sequenced data readily available is critical, coupled with the coordinated standardisation of data and the need for consensus guidelines in studies investigating the microbiome in PDAC.

Estimating risk for pancreatic cancer among 9.4 million veterans in care.

10544 Background: Screening for pancreatic ductal adenocarcinoma (PDAC) has focused on individuals with a hereditary component, accounting for only 10% of PDAC events. Using the electronic health record, we developed a PDAC risk prediction model in a general population in the Veterans Health Administration (VA), the largest integrated health system in the US. Methods: We included all individuals from 2002- 2021 with ≥1 inpatient or outpatient VA encounter and recorded race, starting follow-up 1.5 years after first VA encounter. We excluded those who developed PDAC before start of follow-up. We used the multivariable Cox proportional hazards model in the derivation dataset (random 70% of cohort) to estimate the coefficients associated with each predictor for our outcome, time to PDAC (3 and 10 years), censoring at loss to follow-up or 12/31/2021. Candidate predictors included age, gender, race/ethnicity, sex, BMI, smoking status, alcohol use (alcohol use/related disorders and AUDIT-C), diabetes, pancreatitis, Hepatitis C, HIV, liver disease, cholecystectomy, pancreatic cysts, Charlson comorbidity index and common laboratory values. Selection of predictors was based on clinical reasoning, extent of missing values and model fit (Akaike Information Criterion and Harrell’s c–statistic). We evaluated model discrimination using c – statistic and model calibration comparing observed Kaplan – Meier estimated incidence vs. predicted risk score. Results: Among 9.4 million individuals, we observed 19,839 cases of PDAC within 10 years (6,022 cases within 3 years). Median baseline age was 58.5 years, 75.0% were White, and 16.3% were Black. Final predictors included age, gender, smoking, alcohol use (alcohol use/related disorders and AUDIT-C), BMI, diabetes, pancreatitis, pancreatic cyst, and history of cancer (Table, c-statistic in the derivation cohort 3 years: 0.79; 10 years: 0.77). Ten-year risk ranged maximally from 0-5.5%. Preliminary studies suggest labs do not clinically improve model performance. Conclusions: We developed a novel prediction model using available clinical data in a general population that identifies PDAC with high accuracy. Future work will try to identify additional predictors to improve the range of risk predicted. We will validate our findings in the testing set and in external cohorts. [Table: see text]

The Potential of Salivary Biomarkers in Early Detection of Pancreatic Ductal Adenocarcinoma: A Systematic Review.

Pancreatic ductal adenocarcinoma (PDAC) is a formidable global health concern with a dire prognosis, highlighting the critical need for early detection strategies. This systematic review delves into the potential of salivary biomarkers as a non-invasive means for identifying PDAC at its incipient stages. Saliva's proximity to the circulatory system enables the detection of tumor-derived biomolecules, making it an ideal candidate for mass screening. The analysis of three selected studies reveals promising candidates such as Neisseria mucosa, Fusobacterium periodonticum, polyamines, and specific long non-coding RNAs (lncRNAs). Notably, polyamines like spermine show potential in distinguishing PDAC, while lncRNAs HOX transcript antisense RNA (HOTAIR) and plasmacytoma variant translocation 1 (PVT1) exhibit superior sensitivity and specificity compared to traditional serum markers. However, challenges, including small sample sizes and a lack of validation, underscore the need for standardized diagnostic panels and large-scale collaborative studies. Advancements in nanotechnology, machine learning, and ethical considerations are crucial for harnessing the diagnostic potential of saliva. The review emphasizes the imperative for extensive clinical trials to validate salivary biomarkers, ensuring not only diagnostic accuracy but also cost-effectiveness, patient compliance, and long-term benefits in the realm of PDAC screening. Longitudinal studies are recommended to unravel temporal changes in salivary biomarkers, shedding light on disease progression and treatment response.

Cost-Effectiveness Analysis of Screening for Pancreatic Cancer Among High-Risk Populations.

We evaluated the potential cost-effectiveness of combined magnetic resonance imaging (MRI) and endoscopic ultrasound (EUS) screening for pancreatic ductal adenocarcinoma (PDAC) among populations at high risk for the disease. We used a microsimulation model of the natural history of PDAC to estimate the lifetime health benefits, costs, and cost-effectiveness of PDAC screening among populations with specific genetic risk factors for PDAC, including BRCA1 and BRCA2, PALB2, ATM, Lynch syndrome, TP53, CDKN2A, and STK11. For each high-risk population, we simulated 29 screening strategies, defined by starting age and frequency. Screening included MRI with follow-up EUS in a subset of patients. Costs of tests were based on Medicare reimbursement for MRI, EUS, fine-needle aspiration biopsy, and pancreatectomy. Cancer-related cost by stage of disease and phase of treatment was based on the literature. For each high-risk population, we performed an incremental cost-effectiveness analysis, assuming a willingness-to-pay (WTP) threshold of $100,000 US dollars (USD) per quality-adjusted life year (QALY) gained. For men with relative risk (RR) 12.33 (CDKN2A) and RR 28 (STK11), annual screening was cost-effective, starting at age 55 and 40 years, respectively. For women, screening was only cost-effective for those with RR 28 (STK11), with annual screening starting at age 45 years. Combined MRI/EUS screening may be a cost-effective approach for the highest-risk populations (among mutations considered, those with RR >12). However, for those with moderate risk (RR, 5-12), screening would only be cost-effective at higher WTP thresholds (eg, $200K USD/QALY) or with once-only screening.

Pancreatic cancer-screening or surveillance?

Despite continuous improvement of diagnostic and therapeutic procedures, the number of new pancreatic ductal adenocarcinoma (PDAC) cases diagnosed annually almost equals the number of PDAC-related deaths. Prerequisite for curative treatment is aresectable tumor at the time of diagnosis. Individuals with genetic and/or familial risk profiles should therefore be screened and included in structured surveillance programs. Description of the status quo and usefulness of current PDAC screening and surveillance concepts. A selective literature search of current national and international guidelines including underlying literature was performed. Nearly half of pancreatic cancer cases are missed by currently available surveillance programs, even in high-risk cohorts. Magnetic resonance imaging and endoscopic ultrasound supplemented by CA19‑9 (±HbA1c) are not accurate enough to ensure robust earlier pancreatic cancer detection. Complementary biomarker panels will take on acrucial diagnostic role in the future.

S129 Pancreatic Findings by Germline Mutation Status and Imaging Modality in Patients Undergoing Screening for Pancreatic Ductal Adenocarcinoma

S129 Pancreatic Findings by Germline Mutation Status and Imaging Modality in Patients Undergoing Screening for Pancreatic Ductal Adenocarcinoma

Pancreatic Cancer Surveillance in Carriers of a Germline Pathogenic Variant in CDKN2A

Three percent of patients with pancreatic ductal adenocarcinoma (PDAC) present a germline pathogenic variant (GPV) associated with an increased risk of this tumor, CDKN2A being one of the genes associated with the highest risk. There is no clear consensus on the recommendations for surveillance in CDKN2A GPV carriers, although the latest guidelines from the International Cancer of the Pancreas Screening Consortium recommend annual endoscopic ultrasound (EUS) or magnetic resonance imaging (MRI) regardless of family history. Our aim is to describe the findings of the PDAC surveillance program in a cohort of healthy CDKN2A GPV heterozygotes. This is an observational analysis of prospectively collected data from all CDKN2A carriers who underwent screening for PDAC at the high-risk digestive cancer clinic of the "Hospital Clínic de Barcelona" between 2013 and 2021. A total of 78 subjects were included. EUS or MRI was performed annually with a median follow-up of 66 months. Up to 17 pancreatic findings were described in 16 (20.5%) individuals under surveillance, although most of them were benign. No significant precursor lesions were identified, but an early PDAC was detected and treated. While better preventive strategies are developed, we believe that annual surveillance with EUS and/or MRI in CDKN2A GPV heterozygotes may be beneficial.

Exosomal miRNA Biomarker Panel for Pancreatic Ductal Adenocarcinoma Detection in Patient Plasma: A Pilot Study

Pancreatic ductal adenocarcinoma (PDAC) is rapidly becoming one of the leading causes of cancer-related deaths in the United States, and with its high mortality rate, there is a pressing need to develop sensitive and robust methods for detection. Exosomal biomarker panels provide a promising avenue for PDAC screening since exosomes are highly stable and easily harvested from body fluids. PDAC-associated miRNAs packaged within these exosomes could be used as diagnostic markers. We analyzed a series of 18 candidate miRNAs via RT-qPCR to identify the differentially expressed miRNAs (p < 0.05, t-test) between plasma exosomes harvested from PDAC patients and control patients. From this analysis, we propose a four-marker panel consisting of miR-93-5p, miR-339-3p, miR-425-5p, and miR-425-3p with an area under the curve (AUC) of the receiver operator characteristic curve (ROC) of 0.885 with a sensitivity of 80% and a specificity of 94.7%, which is comparable to the CA19-9 standard PDAC marker diagnostic.

Untargeted metabolomics characterization of the resectable pancreatic ductal adenocarcinoma.

Diagnosis of pancreatic ductal adenocarcinoma (PDAC) is difficult due to the lack of specific symptoms and screening methods. Only less than 10% of PDAC patients are candidates for surgery at the time of diagnosis. Thus, there is a great global unmet need for valuable biomarkers that could improve the opportunity to detect PDAC at the resectable stage. This study aimed to develop a potential biomarker model for the detection of resectable PDAC by tissue and serum metabolomics. Ultra-high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS/MS) was performed for metabolome quantification in 98 serum samples (49 PDAC patients and 49 healthy controls (HCs)) and 20 pairs of matched pancreatic cancer tissues (PCTs) and adjacent noncancerous tissues (ANTs) from PDAC patients. Univariate and multivariate analyses were used to profile the differential metabolites between PDAC and HC. A total of 12 differential metabolites were present in both serum and tissue samples of PDAC. Among them, a total of eight differential metabolites showed the same expressional levels, including four upregulated and four downregulated metabolites. Finally, a panel of three metabolites including 16-hydroxypalmitic acid, phenylalanine, and norleucine was constructed by logistic regression analysis. Notably, the panel was capable of distinguishing resectable PDAC from HC with an AUC value of 0.942. Additionally, a multimarker model based on the 3-metabolites-based panel and CA19-9 showed a better performance than the metabolites panel or CA19-9 alone (AUC: 0.968 vs. 0.942, 0.850). Taken together, the resectable early-stage PDAC has unique metabolic features in serum and tissue samples. The defined panel of three metabolites has the potential value for early screening of PDAC at the resectable stage.

Multidrug resistance genes screening of pancreatic ductal adenocarcinoma based on sensitivity profile to chemotherapeutic drugs

BackgroundsPancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer types and chemotherapeutic drug resistance is a stumbling block in improving the overall survival of PDAC patients. The nature of specific drug resistant subpopulation within pancreatic ductal adenocarcinoma is believed to be partly attributed to epithelial-mesenchymal transition (EMT) and cell stemness. Various PDAC cell lines show various degrees of resistance to chemotherapeutic agents including gemcitabine (GEM) and 5-fluorouracil (5-FU). In-depth understanding of drug resistance mechanisms and profile heterogeneities could lead to the development of novel and precise therapeutic strategies for addressing the chemo-resistant dilemma in PDAC patients.MethodsCytotoxicity assays were performed by CCK8 in ten common PDAC cell lines including AsPC-1, BxPC-3, CAPAN-1, CFPAC, HPAFII, MIA PaCa-2, PANC-1, Patu-8988, SW1990 and T3M4. RNA-seq data of the ten cell lines were downloaded from Cancer Cell Line Encyclopedia (CCLE) database and subsequently analyzed for differentially expressed genes (DEGs). Based on first-line chemotherapy regimens of PDAC, DEGs between resistant and sensitive cell lines were validated by qRT-PCR. Enriched pathways of differentially expressed genes between the resistant and sensitive cell lines were acquired by Metascape database.ResultsWe found that the top two toxic drugs for PDAC cell lines were paclitaxel (PTX) and GEM. Among the ten PDAC cell lines, SW1990 was the most resistant PDAC cell line with the highest IC50 levels for three drugs, while MIA PaCa-2 and BxPC-3 were the most sensitive PDAC cell lines. Differential expression analysis revealed the highest number of DEGs associated with cisplatin (CIS) sensitivity up to 642 genes, of which 181 genes were upregulated and 461 genes were downregulated in CIS-resistant cell lines. The least number of DEGs are associated with GEM sensitivity, of which 37 genes were highly expressed in GEM-resistant PDAC cell lines and 25 genes were lowly expressed. Enrichment analysis of the DEGs revealed that pathways associated with drug resistance were mainly extracellular matrix and cell–cell junction related pathways.ConclusionsPDAC cell lines showed diverse sensitivities to commonly used chemotherapeutic agents, which was caused by differential gene expression between the resistant and sensitive cell lines. The heterogeneity and its associated genes were enriched in extracellular matrix and cell–cell junction related pathways. Our study first portrayed the sensitivity profile to chemotherapeutic drugs of PDAC, which would benefit the chemoresistance mechanism study by reemphasizing the vital role of extracellular matrix and cell–cell junction related pathways and helping the selection of suitable PDAC cell lines.

Pankreaskarzinom – Screening oder Surveillance: Was ergibt Sinn?

BackgroundDespite continuous improvement of diagnostic and therapeutic procedures, the number of new pancreatic ductal adenocarcinoma (PDAC) cases diagnosed annually almost equals the number of PDAC-related deaths. Prerequisite for curative treatment is a resectable tumor at the time of diagnosis. Individuals with genetic and/or familial risk profiles should therefore be screened and included in structured surveillance programs.ObjectivesDescription of the status quo and usefulness of current PDAC screening and surveillance concepts.MethodsA selective literature search of current national and international guidelines including underlying literature was performed.ResultsNearly half of pancreatic cancer cases are missed by currently available surveillance programs, even in high-risk cohorts. Magnetic resonance imaging and endoscopic ultrasound supplemented by CA19‑9 (± HbA1c) are not accurate enough to ensure robust earlier pancreatic cancer detection. Complementary biomarker panels will take on a crucial diagnostic role in the future.

Abstract A028: The Early Detection Initiative trial version 2: A platform trial to test novel approaches to pancreatic cancer screening in patients with new onset hyperglycemia and diabetes

Abstract The Early Detection Initiative (EDI) is an innovative study designed to build a platform that will evaluate novel approaches to early detection of resectable pancreatic ductal adenocarcinoma (PDAC) in patients over age 50 with new onset hyperglycemia and diabetes (NOD). This randomized controlled trial (RCT) of algorithm-based screening for PDAC uses a modified Zelen’s design with post-randomization consent. Eligible subjects are identified through electronic medical record (EMR) surveillance and randomized 1:1 to the Observation or Intervention Arm. The Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) score, an algorithm that further risk stratifies NOD based on age and changes in weight and glycemic parameters, is calculated in the Intervention Arm. Consenting subjects with ENDPAC &amp;gt;0 have Computerized Tomography (CT) scan imaging for PDAC detection; potential harm, including over-diagnosis, is also estimated. At the time of abstract submission &amp;gt;2000 individuals have been randomized and &amp;gt;50 subjects consented for CT imaging. EDI version 2 adds several innovations in response to challenges encountered and experience gained so far. Most significantly, EDI v2 has a platform design with a common control arm and allows for multiple intervention arms. This opens the door for evaluating future new biomarkers for PDAC early detection in a high–risk NOD population, including new blood-based biomarkers for the early detection of cancer that are now commercially available. EDI v2 focuses primarily on co-efficacy endpoints (PDAC stage shift in ENDPAC &amp;gt;0 group, and in all patients, excluding non-consented patients); effectiveness evaluation becomes a secondary objective. This addresses the challenges of low consent rate (~25%) due to lack of awareness of connection between NOD and PDAC and low acceptance for a novel research screening modality. The analyses of primary endpoints were adjusted to accommodate potential over-diagnosis due to screening and imbalance between ENDPAC &amp;gt;0 and ENDPAC ≤0 groups due to low consent rate and likely imperfect enrichment by ENDPAC, when evaluating all patients. Electronic eligibility checks and consent shorten the recruitment time, a critical factor for stage shift due to rapid progression of PDAC. A pre-randomization screening pilot study is planned to enhance the post-randomization consent rate. Intervention to Control randomization ratio is changed to 2:1, allowing more rapid recruitment and, when factoring the consent rate, doubling the sample size of the observation arm. Through these innovations, EDI v2 maintains the full advantage of Zelen’s design that made the trial feasible in sample size and cost. Simulation studies demonstrate that our approach correctly controls the type-1 error and the trial has adequate statistical power. The EDI trial represents an innovative and contemporary approach to developing and refining an early detection strategy for pancreatic cancer. Citation Format: Ying-Qi Zhao, Suresh T. Chari, Anirban Maitra, Lynn M. Matrisian, Eva E. Shrader, Bechien U. Wu, Avinash Kambadakone, Barbara Kenner, Jo Ann S. Rinaudo, Sudhir Srivastava, Ying Huang, Ziding Feng. The Early Detection Initiative trial version 2: A platform trial to test novel approaches to pancreatic cancer screening in patients with new onset hyperglycemia and diabetes [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A028.

The association of new-onset diabetes with subsequent diagnosis of pancreatic cancer-novel use of a large administrative database.

Screening options for pancreatic ductal adenocarcinoma (PDAC) are limited. New-onset type 2 diabetes (NoD) is associated with subsequent diagnosis of PDAC in observational studies and may afford an opportunity for PDAC screening. We evaluated this association using a large administrative database. Patients were identified using claims data from the OptumLabs® Data Warehouse. Adult patients with NoD diagnosis were matched 1:3 with patients without NoD using age, sex and chronic obstructive pulmonary disease (COPD) status. The event of PDAC diagnosis was compared between cohorts using the Kaplan-Meier method. Factors associated with PDAC diagnosis were evaluated with Cox's proportional hazards modeling. We identified 640421 patients with NoD and included 1921263 controls. At 3years, significantly more PDAC events were identified in the NoD group vs control group (579 vs 505; P<0.001). When controlling for patient factors, NoD was significantly associated with elevated risk of PDAC (HR 3.474, 95% CI 3.082-3.920, P<0.001). Other factors significantly associated with PDAC diagnosis were increasing age, increasing age among Black patients, and COPD diagnosis (P≤0.05). NoD was independently associated with subsequent diagnosis of PDAC within 3years. Future studies should evaluate the feasibility and benefit of PDAC screening in patients with NoD.

Exosomal glypican-1 is elevated in pancreatic cancer precursors and can signal genetic predisposition in the absence of endoscopic ultrasound abnormalities.

BACKGROUNDIndividuals within specific risk groups for pancreatic ductal adenocarcinoma (PDAC) [mucinous cystic lesions (MCLs), hereditary risk (HR), and new-late onset diabetes mellitus (NLOD)] represent an opportunity for early cancer detection. Endoscopic ultrasound (EUS) is a premium image modality for PDAC screening and precursor lesion characterization. While no specific biomarker is currently clinically available for this purpose, glypican-1 (GPC1) is overexpressed in the circulating exosomes (crExos) of patients with PDAC compared with healthy subjects or those harboring benign pancreatic diseases.AIMTo evaluate the capacity of GPC1+ crExos to identify individuals at higher risk within these specific groups, all characterized by EUS. METHODSThis cross-sectional study with a prospective unicentric cohort included 88 subjects: 40 patients with MCL, 20 individuals with HR, and 20 patients with NLOD. A control group (CG) was submitted to EUS for other reasons than pancreatic pathology, with normal pancreas and absence of hereditary risk factors (n = 8). The inclusion period was between October 2016 and January 2019, and the study was approved by the Ethics Committee of Centro Hospitalar Universitário de São João, Porto, Portugal. All patients provided written informed consent. EUS and blood tests for quantification of GPC1+ crExos by flow cytometry and carbohydrate antigen 19-9 (CA 19-9) levels by ELISA were performed in all subjects. EUS-guided tissue acquisition was done whenever necessary. For statistical analysis, SPSS® 27.0 (IBM Corp., Armonk, NY, United States) version was used. All graphs were created using GraphPad Prism 7.00 (GraphPad Software, San Diego, CA, United States).RESULTSHalf of MCLs harbored worrisome features (WF) or high-risk stigmata (HRS). Pancreatic abnormalities were detected by EUS in 10.0% and 35.0% in HR and NLOD individuals, respectively, all considered non-malignant and “harmless.” Median levels of GPC1+ crExos were statistically different: MCL [99.4%, interquartile range (IQR): 94.9%-99.8%], HR (82.0%, IQR: 28.9%-98.2%), NLOD (12.6%, IQR: 5.2%-63.4%), and CG (16.2%, IQR: 6.6%-20.1%) (P < 0.0001). Median levels of CA 19-9 were within the normal range in all groups (standard clinical cut-off of 37 U/mL). Within HR, individuals with a positive history of cancer had higher median levels of GPC1+ crExos (97.9%; IQR: 61.7%-99.5%), compared to those without (59.7%; IQR: 26.3%-96.4%), despite no statistical significance (P = 0.21). Pancreatic cysts with WF/HRS were statistically associated with higher median levels of GPC1+ crExos (99.6%; IQR: 97.6%-99.8%) compared to those without (96.5%; IQR: 81.3%-99.5%) (P = 0.011), presenting an area under the receiver operating characteristic curve value of 0.723 (sensitivity 75.0% and specificity 67.7%, using a cut-off of 98.5%; P = 0.012).CONCLUSIONGPC1+ crExos may act as biomarker to support the diagnosis and stratification of PDAC precursor lesions, and in signaling individuals with genetic predisposition in the absence of EUS abnormalities.

Cascade testing of first-degree relatives of patients with pancreatic cancer with confirmed germline genetic mutations: A simulation modeling study.

10513 Background: Universal germline genetic testing is recommended for all patients with pancreatic ductal adenocarcinoma (PDAC). In addition to treatment implications for the patient, it is valuable for family members of the PDAC patient to undertake germline testing to understand their familial cancer risk. We evaluated the potential effectiveness of cascade testing of first-degree relatives (FDRs) of PDAC patients to guide MRI-based screening for PDAC. Methods: We used a microsimulation model of PDAC to estimate the potential life expectancy (LE) benefit of cascade genetic testing of FDRs of PDAC patients. Analysis was performed for eight mutations of interest (ATM, BRCA1, BRCA2, PALB2, Lynch, TP53, CDKN2A, STK11) with relative risk of PDAC ranging from 1.6 – 24. FDRs were defined by their sex, age, and information state (not informed, informed but not tested, test negative, or test positive). Family size and composition, communication of genetic testing results, and uptake of cascade testing were based on published studies. For each FDR group, we simulated multiple MRI-based screening strategies, defined by starting age (FDR age at patient diagnosis to age 75) and frequency (once or annual until age 75), and identified the strategy that resulted in the highest LE. Results: Each PDAC patient would have an average of 4.35 living FDRs. 1.32 of these living FDRs would be informed of the patient’s test results but not undergo testing themselves, and 1.03 would test positive for the mutation of interest. For lower risk mutations (ATM, BRCA2, PALB2), FDRs would only undergo screening if they test positive. For Lynch syndrome, not tested FDRs would undergo screening starting at age 65 or 70, whereas FDRs who test positive would start at 50 or 55. For STK11, the highest risk mutation, FDRs would start screening annually as soon as they are informed of the patient’s test results. The total LE gain for cascade testing of FDRs ranged from 0 (BRCA1) to 1.05 years (STK11). Conclusions: For each PDAC patient identified to have a germline genetic mutation, cascade testing of FDRs could add up to a year life expectancy for the family, in addition to the value of the germline information to the PDAC patient’s care. [Table: see text]

Genetic testing to guide screening for pancreatic ductal adenocarcinoma: Results of a microsimulation model

BackgroundFirst-degree relatives (FDRs) of patients with pancreatic ductal adenocarcinoma (PDAC) have elevated PDAC risk, partially due to germline genetic variants. We evaluated the potential effectiveness of genetic testing to target MRI-based screening among FDRs. MethodsWe used a microsimulation model of PDAC, calibrated to Surveillance, Epidemiology, and End Results (SEER) data, to estimate the potential life expectancy (LE) gain of screening for each of the following groups of FDRs: individuals who test positive for each of eight variants associated with elevated PDAC risk (e.g., BRCA2, CDKN2A); individuals who test negative; and individuals who do not test. Screening was assumed to take place if LE gains were achievable. We simulated multiple screening approaches, defined by starting age and frequency. Sensitivity analysis evaluated changes in results given varying model assumptions. ResultsFor women, 92% of mutation carriers had projected LE gains from screening for PDAC, if screening strategies (start age, frequency) were optimized. Among carriers, LE gains ranged from 0.1 days (ATM+ women screened once at age 70) to 510 days (STK11+ women screened annually from age 40). For men, LE gains were projected for all mutation carriers, ranging from 0.2 days (BRCA1+ men screened once at age 70) to 620 days (STK11+ men screened annually from age 40). For men and women who did not undergo genetic testing, or for whom testing showed no variant, screening yielded small LE benefit (0–2.1 days). ConclusionsGenetic testing of FDRs can inform targeted PDAC screening by identifying which FDRs may benefit.

Development of PCR assays to detect signature circulating tumor DNA methylation markers and KRas mutations for pancreatic ductal adenocarcinoma (PDAC).

524 Background: PDAC is a cancer of high mortality. Accurate and cost-effective PCR assays detecting diagnostic and prognostic PDAC markers in blood are desirable for conducting PDAC screening in high-risk populations, evaluating treatments for patients and surveying for post-treatment relapse. Methods: We combined PDAC-diagnosing ctDNA methylation markers with PDAC-driving Kras mutations as targets to develop said PCR tests. Methylation markers were previously validated to classify PDAC plasma at a high accuracy by a targeted methylation sequencing assay. Seven known PDAC driver mutations in KRas exon2 were selected. Taqman-based quantitative methylation-specific PCR (MSP) and ARMS PCR were designed for methylation markers and KRas mutations, respectively, and were validated for technical performances. A step of pre-amplification of input DNA by conventional PCR was included prior to quantitative PCR to improve sensitivity. Over 200 clinical PDAC and control tissue and plasma samples were used to determine their analytical performances in classifying PDAC plasma. Results: All targets were detected at a limit-of-detection of 0.25% or better on standards DNA in PCR. To test clinical plasma samples, methylation markers were first filtered by comparing their levels in PDAC-, para-tumor tissues and whole blood cells. Twenty-four most discriminatory markers for PDAC tissues were selected and used to classify 110 PDAC plasma samples from equal number of normal samples. They were evaluated for their sensitivity at a pre-set specificity of 90%. The 4 best-performing markers were selected to build and cross-validate a general-linear-model (GLM) PDAC classifier, which achieved a sensitivity of 66% at 90% specificity (AUC = 0.829) in cross-validation. Adding status of KRas mutations further improved prediction accuracy by increasing sensitivity to 75% (AUC = 0.89), demonstrating the methylation markers and KRas mutations complement each other in detection PDAC plasma. Conclusions: Our results suggest that a small number of our DNA methylation markers can classify PDAC plasma at a reasonably high accuracy by MSP. Incorporating KRas driver mutations further improved classification accuracy. Together they are promising to be further translated into diagnostics for PDAC early screening, treatment assessment and postoperative surveillance.

Blood levels of adiponectin and IL-1Ra distinguish type 3c from type 2 diabetes: Implications for earlier pancreatic cancer detection in new-onset diabetes.

SummaryBackgroundScreening for pancreatic ductal adenocarcinoma (PDAC) in populations at high risk is recommended. Individuals with new-onset type 2 diabetes mellitus (NOD) are the largest high-risk group for PDAC. To facilitate screening, we sought biomarkers capable of stratifying NOD subjects into those with type 2 diabetes mellitus (T2DM) and those with the less prevalent PDAC-related diabetes (PDAC-DM), a form of type 3c DM commonly misdiagnosed as T2DM.MethodsUsing mass spectrometry- and immunoassay-based methodologies in a multi-stage analysis of independent sample sets (n=443 samples), blood levels of 264 proteins were considered using Ingenuity Pathway Analysis, literature review and targeted training and validation.FindingsOf 30 candidate biomarkers evaluated in up to four independent patient sets, 12 showed statistically significant differences in levels between PDAC-DM and T2DM. The combination of adiponectin and interleukin-1 receptor antagonist (IL-1Ra) showed strong diagnostic potential, (AUC of 0.91; 95% CI: 0.84-0.99) for the distinction of T3cDM from T2DM.InterpretationAdiponectin and IL-1Ra warrant further consideration for use in screening for PDAC in individuals newly-diagnosed with T2DM.FundingNorth West Cancer Research, UK, Cancer Research UK, Pancreatic Cancer Action, UK.

Cost-Effectiveness of a Risk-Tailored Pancreatic Cancer Early Detection Strategy Among Patients With New-Onset Diabetes

Screening for pancreatic ductal adenocarcinoma (PDAC) in asymptomatic adults is not recommended, however, patients with new-onset diabetes (NoD) have an 8 times higher risk of PDAC than expected. A novel risk-tailored early detection strategy targeting high-risk NoD patients might improve PDAC prognosis. We sought to evaluate the cost effectiveness of this strategy. We compared PDAC early detection strategies targeting NoD individuals age 50 years and older at various minimal predicted PDAC risk thresholds vs standard of care in a Markov state-transition decision model under the health care sector perspective using a lifetime horizon. At a willingness to pay (WTP) threshold of $150,000 per quality-adjusted life-year, the early detection strategy targeting patients with a minimum predicted 3-year PDAC risk of 1% was cost effective (incremental cost-effectiveness ratio, $116,911). At a WTP threshold of $100,000 per quality-adjusted life-year, the early detection strategy at the 2% risk threshold was cost effective (incremental cost-effectiveness ratio, $63,045). The proportion of PDACs detected at local stage, costs of treatment for metastatic PDAC, utilities of local and regional cancers, and sensitivity of screening were the most influential parameters. Probabilistic sensitivity analysis confirmed that at a WTP threshold of $150,000, early detection at the 1.0% risk threshold was favored (30.6%), followed by the 0.5% risk threshold (20.4%) vs standard of care (1.7%). At a WTP threshold of $100,000, early detection at the 1.0% risk threshold was favored (27.3%) followed by the 2.0% risk threshold (22.8%) vs standard of care (2.0%). A risk-tailored PDAC early detection strategy targeting NoD patients with a minimum predicted 3-year PDAC risk of 1.0% to 2.0% may be cost effective.

Prediction of Pancreatic Cancer in Diabetes Patients with Worsening Glycemic Control.

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. 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. 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%. Sex-specific models for PDAC can estimate risk of PDAC at the time of progression of diabetes. Our models can identify diabetes patients who would benefit from PDAC screening.