Risk Of Pancreatic Ductal Adenocarcinoma Research Articles

Survival benefit associated with screening of patients at elevated risk for pancreatic cancer.

All patients enrolled in screening at a High-Risk Pancreatic Cancer Clinic (HRC) from July 2013 to June 2020 were identified from a prospectively maintained institutional database and compared to patients evaluated at a Surgical Oncology Clinic (SOC) at the same institution during the same period. Clinical outcomes of patients selected for surgical resection, particularly clinicopathologic stage and overall survival, were compared. Among 826 HRC patients followed for a median (IQR) of 2.3 (0.8-4.2) years, 128 were selected for surgical resection and compared to 402 SOC patients selected for resection. Overall survival was significantly longer among HRC patients (median survival: not reached vs. 2.6 years, p < 0.001). Among 31 HRC and 217 SOC patients with a diagnosis of pancreatic ductal adenocarcinoma(PDAC), the majority of HRC patients were diagnosed with stage 0 disease (carcinoma in situ), while the majority of SOC patients were diagnosed with stage II disease (p < 0.001). Overall survival after resection of invasive PDAC was also significantly longer among HRC patients compared to SOC patients (median survival 5.5 vs. 1.6 years, p = 0.002). Patients at increased risk for PDAC and followed with guideline-based screening exhibited downstaging of disease and improved survival from PDAC in comparison to patients who were not screened.

Prediagnostic whole blood cadmium and molybdenum associated with pancreatic cancer in an American cohort.

Environmental exposures such as cadmium might be contributing to the increasing incidence of pancreatic cancer. Few prospective studies have examined the association between trace elements and pancreatic ductal adenocarcinoma (PDAC). We conducted a nested case-control study in participants aged 55-74 years at baseline from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial cohort to examine the association between 12 trace elements measured in predignostic whole blood and PDAC. From May 1998 through December 2014, 318 incident PDAC cases were identified during follow-up to 16.7 years. Two controls (n = 636) alive when each case was diagnosed were selected and matched by age (+ 5 years), sex, calendar date of blood draw (2-month blocks), and race and ethnic group. We used multivariable adjusted conditional logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI). Cadmium and molybdenum were associated with PDAC [highest compared to lowest quintile: cadmium OR=1.81; 95% CI: 01.12, 2.95; P-trend = 0.03; molybdenum OR=0.50; 95% CI: 0.32, 0.80; P-trend = 0.02]. The inverse molybdenum association was only observed among ever smokers (OR=0.31, 95% CI: 0.17, 0.58, P-trend= 0.003, P-interaction=0.03) with no association in never smokers. Lead, arsenic, and other trace elements were not associated with PDAC. Our results support that increasing prediagnostic whole blood cadmium increases while molybdenum reduces PDAC risk.

Two Japanese families with familial pancreatic cancer with suspected pathogenic variants of CDKN2A: a case report

BackgroundGermline mutations in CDKN2A result in Familial Atypical Multiple Mole Melanoma Syndrome (FAMMM) (OMIM #155,601), which is associated with an increased risk of pancreatic ductal adenocarcinoma and melanoma. FAMMM has been reported globally, but it is quite rare in Japan. We report two families with familial pancreatic cancer with suspected pathogenic variants of CDKN2A that were incidentally identified through comprehensive genomic profiling.Case presentationThe first case is a 74-year-old woman with a diagnosis of pancreatic carcinoma with multiple liver metastases. She had family histories of pancreatic cancer, but no personal or family history of malignant melanoma. Whole exon sequencing detected a germline CDKN2A variant evaluated as likely pathogenic. The results were disclosed to her daughters after she died, and the same CDKN2A variant was detected in one of the daughter. The daughter was referred to a nearby hospital for her clinical management. The second case is a 65-year-old man with pancreatic ductal adenocarcinoma. He had family histories of pancreatic cancer, but no personal or family history of malignant melanoma. He underwent a comprehensive genomic profiling test using pancreatic cancer tissue, and detected a presumed germline pathogenic variant of CDKN2A. Germline testing confirmed the same CDKN2A variant. Genetic analysis of his relatives produced negative results. Other blood relatives are scheduled for genetic analysis in the future. We report two families with familial pancreatic cancer with suspected pathogenic variants of CDKN2A that were incidentally identified through comprehensive genomic profiling.ConclusionsIn current Japanese precision medicine, comprehensive genetic analysis can reveal rare genetic syndromes and offer us the opportunity to provide health management for patients and their relatives. However, gene-specific issues are raised in terms of the evaluation of a variant’s pathogenicity and the extent of surveillance of the at-risk organs due to a lack of genetic and clinical data concerning CDKN2A variant carriers in Japan.

Blood type as a risk factor for pancreatic ductal adenocarcinoma.

10559 Background: Non-O blood type has been associated with increased risk of pancreatic ductal adenocarcinoma (PDAC), the third leading cause of cancer death in the United States (US), in largely white populations. We aimed to explore PDAC risk across blood types in the racially diverse Veteran’s Health Administration (VA), the largest integrated healthcare system in the US. Methods: We included all individuals in the VA from 2002 - 2022 with at least one inpatient or outpatient encounter and a non-missing race. We defined the index date as 1.5 years after the initial VA encounter and excluded individuals with prior PDAC. We defined the exposure by non-O (A, B, AB) or type O blood type. We defined PDAC based on a previously validated algorithm of ≥2 sources (cancer registry, hospitalizations, or outpatient oncology encounters) with 95% positive predictive value. Using time to PDAC as the outcome in a multivariable Cox regression, we evaluated the association between blood type and PDAC risk, adjusting for age, sex, BMI, smoking, alcohol use, Hepatitis C (HCV) and diabetes. We censored at time of death, 1.5 years after the last VA encounter, or the end of the study period. We performed a subgroup analysis to evaluate the association of blood type by race. Results: Of the 2.5 million individuals with an available blood type, 1.35 million (52.6%) had a non-O blood type (Table) and over a quarter of our cohort (28.2%, 725,878) were over baseline age 50. Median time to PDAC diagnosis was 8.6 years. There were 877 PDAC cases (0.03%), with 530 (60.4%) of those having a non-O blood type. Compared to those with type O blood, risk of PDAC for individuals with non-O blood type was higher overall [adjusted HR = 1.37 (95% CI 1.20-1.57)], and higher among Whites (n=1,689,092, HR 1.52, 95% CI 1.27-1.82) than Black patients (n=525,666, HR 1.15, 95% CI 0.90-1.48), though this was limited by smaller sample size. Conclusions: Non-O blood type was associated with higher risk of PDAC in a diverse population. Given the potential differences between races, we will further explore the impact of neighborhood-level socioeconomic data on PDAC risk. [Table: see text]

Development of a multi-analyte blood test for the early detection of pancreatic ductal adenocarcinoma (PDAC) and performance compared to CA19-9.

e16360 Background: Early detection of PDAC can improve survival but is hampered by the lack of recognizable symptoms in early disease. Imaging has been the mainstay for surveillance of high-risk individuals but has proven to be cumbersome and may lack sensitivity to detect small tumors. CA19-9 is the only available blood biomarker for PDAC, but it is neither sensitive nor specific enough for it to be recommended for high-risk surveillance. A sensitive and specific blood biomarker for PDAC could positively impact this situation. Methods: Serum samples from patients across multiple centers with stage I and II PDAC (n = 75) and matched controls at high risk for PDAC (genetic and familial) (n = 83) were assessed for more than 2,900 protein analytes using O-link multiplex technology and conventional immunoassays for selected protein candidates. Machine learning was used to combine the biomarker candidates to create 4-plex signatures discriminating PDAC and controls. Results: ROC (receiver operating characteristic) curve analysis of these revealed signatures with performance superior to CA19-9 and a previously commercially available assay, IMMRay PanCan-d (AUC of 0.97 and 0.94 vs. 0.87 for CA19-9 and 0.88 for IMMRay). A subset of these candidate biomarkers has now been transitioned to standardized ELISA assays which are being used to construct a final signature for clinical validation in an independent set of PDAC and high-risk control samples. Conclusions: A novel multi-analyte blood test utilizing newly-discovered PDAC biomarkers has the potential to improve early detection of PDAC in high-risk individuals.

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]

Prolactin receptor potentiates chemotherapy through miRNAs-induced G6PD/TKT inhibition in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive malignancies, with a notoriously dismal prognosis. As a competitive inhibitor of DNA synthesis, gemcitabine is the cornerstone drug for treating PDAC at all stages. The therapeutic effect of gemcitabine, however, is often hindered by drug resistance, and the underlying mechanisms remain largely unknown. It is unclear whether their response to chemotherapeutics is regulated by endocrine regulators, despite the association between PDAC risk and endocrine deregulation. Here, we show that prolactin receptor (PRLR) synergizes with gemcitabine in both invitro and invivo treatment of PDAC. Interestingly, PRLR promotes the expression of miR-4763-3p and miR-3663-5p, two novel miRNAs whose functions are unknown. Furthermore, the analysis of transcriptome sequencing data of tumors from lactating mouse models enriches the PPP pathway, a multifunctional metabolic pathway. In addition to providing energy, the PPP pathway mainly provides a variety of raw materials for anabolism. We demonstrate that two key enzymes of the pentose phosphate pathway (PPP), G6PD and TKT, are directly targeted by miR-4763-3p and miR-3663-5p. Notably, miR-4763-3p and miR-3663-5p diminish the nucleotide synthesis of the PPP pathway, thereby increasing gemcitabine sensitivity. As a result, PRLR harnesses these two miRNAs to suppress PPP and nucleotide synthesis, subsequently elevating the gemcitabine sensitivity of PDAC cells. Also, PDAC tissues and tumors from LSL-KrasG12D/+, LSL-Trp53R172H/+, and PDX1-cre (KPC) mice exhibit downregulation of PRLR. Bisulfite sequencing of PDAC tissues revealed that PRLR downregulation is due to epigenetic methylation. In this study, we show for the first time that the endocrine receptor PRLR improves the effects of gemcitabine by boosting two new miRNAs that block the PPP pathway and nucleotide synthesis by inhibiting two essential enzymes concurrently. The PRLR-miRNAs-PPP axis may serve as a possible therapeutic target to supplement chemotherapy advantages in PDAC.

The best linear unbiased prediction (BLUP) method as a tool to estimate the lifetime risk of pancreatic ductal adenocarcinoma in high-risk individuals with no known pathogenic germline variants

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the Western world. The number of diagnosed cases and the mortality rate are almost equal as the majority of patients present with advanced disease at diagnosis. Between 4 and 10% of pancreatic cancer cases have an apparent hereditary background, known as hereditary pancreatic cancer (HPC) and familial pancreatic cancer (FPC), when the genetic basis is unknown. Surveillance of high-risk individuals (HRI) from these families by imaging aims to detect PDAC at an early stage to improve prognosis. However, the genetic basis is unknown in the majority of HRIs, with only around 10–13% of families carrying known pathogenic germline mutations. The aim of this study was to assess an individual’s genetic cancer risk based on sex and personal and family history of cancer. The Best Linear Unbiased Prediction (BLUP) methodology was used to estimate an individual’s predicted risk of developing cancer during their lifetime. The model uses different demographic factors in order to estimate heritability. A reliable estimation of heritability for pancreatic cancer of 0.27 on the liability scale, and 0.07 at the observed data scale as obtained, which is different from zero, indicating a polygenic inheritance pattern of PDAC. BLUP was able to correctly discriminate PDAC cases from healthy individuals and those with other cancer types. Thus, providing an additional tool to assess PDAC risk HRI with an assumed genetic predisposition in the absence of known pathogenic germline mutations.

The Population-level Prevalence of Exocrine Pancreas Insufficiency and the Subsequent Risk of Pancreatic Cancer.

To study the prevalence of exocrine pancreas insufficiency (EPI) at a population level and the subsequent risk of pancreatic ductal adenocarcinoma (PDAC). Using TriNetX (a database of over 79 million US residents), we included patients ≥18 years with EPI (identified via ICD-10 codes) and continuous follow-up from 2016-2022. Patients with prior pancreas resection and PDAC before an EPI diagnosis were excluded. The primary outcome was EPI prevalence. Secondary outcomes included imaging utilization, PDAC risk and, pancreas enzyme replacement therapy (PERT) utilization. We performed 1:1 propensity score matching of patients with EPI vs. patients without an EPI diagnosis. Adjusted odds ratio (aOR) and hazard ratios (aHR) with 95% confidence intervals were reported. The population prevalence of EPI was 0.8% (n = 24,080) with a mean age of 55.6 years at diagnosis. After propensity score matching, PDAC risk among patients with EPI was twice as high compared to patients without EPI (AHR 1.97, 95% confidence interval [CI] 1.66-2.36). This risk persisted even after excluding patients with a history of acute or chronic pancreatitis (aOR: 4.25, 95% CI 2.99-6.04). Only 58% (n = 13, 390) of patients with EPI received PERT with a mean treatment duration of 921 days. No difference was observed in PDAC risk between patients with EPI treated with PERT vs. those that did not receive PERT (AHR 1.10, 95% CI 0.95-1.26, p = 0.17). Despite a low prevalence, patients with EPI may have a higher risk of PDAC and many of these patients with EPI were not on PERT. PERT did not appear to impact incident PDAC risk after an EPI diagnosis.

Understanding familial risk of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease that is the result of an accumulation of sequential genetic alterations. These genetic alterations can either be inherited, such as pathogenic germline variants that are associated with an increased risk of cancer, or acquired, such as somatic mutations that occur during the lifetime of an individual. Understanding the genetic basis of inherited risk of PDAC is essential to advancing patient care and outcomes through improved clinical surveillance, early detection initiatives, and targeted therapies. In this review we discuss factors associated with an increased risk of PDAC, the prevalence of genetic variants associated with an increased risk in patients with PDAC, estimates of PDAC risk in carriers of pathogenic germline variants in genes associated with an increased risk of PDAC. The role of common variants in pancreatic cancer risk will also be discussed.

A pleiotropy scan to discover new susceptibility loci for pancreatic ductal adenocarcinoma.

Pleiotropic variants (i.e., genetic polymorphisms influencing more than one phenotype) are often associated with cancer risk. A scan of pleiotropic variants was successfully conducted ten years ago in relation to pancreatic ductal adenocarcinoma susceptibility. However, in the last decade, genetic association studies performed on several human traits have greatly increased the number of known pleiotropic variants. Based on the hypothesis that variants already associated with a least one trait have a higher probability of association with other traits, 61,052 variants reported to be associated by at least one genome wide association study (GWAS) with at least one human trait were tested in the present study consisting of two phases (discovery and validation), comprising a total of 16,055 pancreatic ductal adenocarcinoma (PDAC) cases and 212,149 controls. The meta-analysis of the two phases showed two loci (10q21.1-rs4948550 (P=6.52×10-5) and 7q36.3-rs288762 (P=3.03×10-5) potentially associated with PDAC risk. 10q21.1-rs4948550 shows a high degree of pleiotropy and it is also associated with colorectal cancer risk while 7q36.3-rs288762 is situated 28,558 base pairs upstream of the Sonic Hedgehog (SHH) gene, which is involved in the cell differentiation process and PDAC etiopathogenesis. In conclusion, none of the single nucleotide polymorphisms (SNPs) showed a formally statistically significant association after correction for multiple testing. However, given their pleiotropic nature and association with various human traits including colorectal cancer, the two SNPs showing the best associations with PDAC risk merit further investigation through fine mapping and ad hoc functional studies.

Genetic and other risk factors for pancreatic ductal adenocarcinoma (PDAC).

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at an advanced stage, resulting in poor prognosis and low 5-year survival rates. While early evidence suggests increased long-term survival in those with screen-detected resectable cancers, surveillance imaging is currently only recommended for individuals with a lifetime risk of PDAC ≥ 5%. Identification of risk factors for PDAC provides opportunities for early detection, risk reducing interventions, and targeted therapies, thus potentially improving patient outcomes. Here, we summarize modifiable and non-modifiable risk factors for PDAC. We review hereditary cancer syndromes associated with risk for PDAC and their implications for patients and their relatives. In addition, other biologically relevant pathways and environmental and lifestyle risk factors are discussed. Future work may focus on elucidating additional genetic, environmental, and lifestyle risk factors that may modify PDAC risk to continue to identify individuals at increased risk for PDAC who may benefit from surveillance and risk reducing interventions.

Abstract 6818: Obesity-mediated perineural invasion promotes PDAC tumorigenesis

Abstract Obesity is a modifiable host factor that has been reported to increase the risk of pancreatic ductal adenocarcinoma (PDAC) by ~60% and the mortality by two-fold, however, the exact mechanisms by which obesity enhances PDAC aggressiveness is incompletely understood. Given that nearly 74% of U.S. adults are overweight or obese (US Center for Disease Control), resolving the complex association between obesity and PDAC may unveil new targets for prevention and/or intervention to decrease incidence and improve outcomes. The prevailing notion is that obesity-induced tumor in situ factors (i.e., oxidative, hypoxic, nutrient, and biochemical) play crucial roles in enhancing cancer cell fitness and survival. However, these concepts have yet to lead to the identification of any actionable target in patients, with obesity-related cancer in general and PDAC in particular. Prior studies and our preliminary data show the presence of a distinctive tumor immune profile in PDAC models under conditions of obesity, which encompasses immune suppressive myeloid and type 2 immune cells, namely M2-like tumor-associated macrophages (TAMs), and myeloid-derived suppressor cells (MDSCs), that inhibit effector CD8+ T cell expansion and function enabling PDAC progression. However, the genesis of this ‘type 2 immune cell’ dominant tumor-immune microenvironment (TIME) of obesity-driven PDAC remains to be fully understood. This led us to take a holistic approach utilizing multiple genetically engineered mouse models (GEMMs), pharmacological inhibitors, transcriptomic analysis (RNA-seq, spatial transcriptomic, and scRNA-seq), fresh PDAC patient samples, and imaging technologies (confocal microscopy) to understand the molecular mechanisms. Our novel mechanistic study using some of the above-mentioned tools shows that obesity-driven PDAC has significantly higher perineural invasion (PNI) that enhances PDAC progression. We found that sympathetic nerve terminals (of the PNI) via neurotransmitter release control the expression of integral cytokines that cause a dramatic remodeling of the TIME. Specifically, the PNI-mediated neuro-adrenergic signaling activates β2-adrenergic receptor (β2-AR) on PDAC cells, leading to increased tumor cell production/proliferation and secretion of the ‘alarmins’ IL-33 and IL-1β. Importantly, we showed that mitigation of adrenergic signaling using two separate approaches: (i) pharmacological antagonists of β2-ARs (β-blockers), and (ii) the use of a thermoneutral model leads to substantial abrogation of obesity-mediated tumor progression. These, and other key data raise the provocative question that reducing adrenergic signaling-dependent IL-33 expression and release favor a pro-inflammatory TIME and significantly increase patient survival in obesity-driven PDAC. Citation Format: Shyamananda Singh Mayengbam, Aftab Alam, Sharon Senchanthisai, Irmak Bektas, Prasenjit Dey. Obesity-mediated perineural invasion promotes PDAC tumorigenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6818.

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

ObjectivesScreening 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. MethodsWe 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. ResultsIn 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. ConclusionsRule-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.

Polymorphisms in transcription factor binding sites and enhancer regions and pancreatic ductal adenocarcinoma risk

Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10−8), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10−7), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10−6) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10−5). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.

Evidence for a causal link between intra-pancreatic fat deposition and pancreatic cancer: A prospective cohort and Mendelian randomization study

Prior observational studies suggest an association between intra-pancreatic fat deposition (IPFD) and pancreatic ductal adenocarcinoma (PDAC); however, the causal relationship is unclear. To elucidate causality, we conduct a prospective observational study using magnetic resonance imaging (MRI)-measured IPFD data and also perform a Mendelian randomization study using genetic instruments for IPFD. In the observational study, we use UK Biobank data (N= 29,463, median follow-up: 4.5 years) and find that high IPFD (>10%) is associated with PDAC risk (adjusted hazard ratio [HR]: 3.35, 95% confidence interval [95% CI]: 1.60-7.00). In the Mendelian randomization study, we leverage eight out of nine IPFD-associated genetic variants (p<5×10-8) from a genome-wide association study in the UK Biobank (N= 25,617) and find that genetically determined IPFD is associated with PDAC (odds ratio [OR] per 1-standard deviation [SD] increase in IPFD: 2.46, 95% CI: 1.38-4.40) in the Pancreatic Cancer Cohort Consortium I, II, III (PanScan I-III)/Pancreatic Cancer Case-Control Consortium (PanC4) dataset (8,275 PDAC cases and 6,723 non-cases). This study provides evidencefor a potential causal role of IPFD in the pathogenesis of PDAC. Thus, reducing IPFD may lower PDAC risk.

Abstract A017: Lipidomic biomarkers of usual alcohol use and their association with pancreatic ductal adenocarcinoma risk

Abstract There is suggestive epidemiologic evidence for an association between alcohol use and pancreatic ductal adenocarcinoma (PDAC). Previous studies reported alcohol use to be associated with high-density lipoprotein and fatty acyl ethyl esters. Lipidomic metabolites may improve assessment of self-reported alcohol use and provide mechanistic insights for the alcohol-PDAC association. We aimed to identify lipidomic signatures of self-reported alcohol use and evaluate their associations with PDAC risk using comprehensive lipidomic measures from nested case-control studies within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial cohorts (716 case-control sets). Controls were matched to cases (1:1) by age, sex, race, date of blood draw and cohort. Serum samples and self-reported alcohol use were collected at baseline up to 24 years before PDAC diagnosis. Absolute concentrations of 611 lipid species across 15 classes were measured on the Complex Lipid Panel (Lipidizer Sciex SelexIon-5500 QTRAP platform) and grouped into 161 class-specific fatty acids (FA) and 28 total FAs. We conducted cross-sectional analyses using linear regression to identify associations between log-transformed alcohol and 1-standard deviation (SD) increase in log-lipid concentrations among controls within each cohort. Conditional logistic regression and fixed-effects meta-analysis were used to calculate PDAC odds ratios (OR) per 1-SD increase in the log-concentration identified alcohol-lipidomic metabolites in common in both cohorts. All models were adjusted for age, smoking, BMI, height, diabetes, education, and diet quality. Alcohol was associated with 36 species, 13 class-specific FAs and 3 total FAs at Bonferroni thresholds &amp;lt;8.18 × 10−5, &amp;lt;3.11 × 10−4, and &amp;lt;1.79 × 10−3, respectively. These belonged to ceramides, cholesterylesters, diacylglycerols, hexosylceramides, lactosylceramides, lysophosphatidylcholines, phosphatidylcholines, phospatidylethanolamines, phosphatidylinositols, and triacylglycerols lipid classes, and mostly holding FA15:0 (pentadecanoic acid), FA17:0 (margaric acid) and FA18:2 (linoleic acid). Nine triacylglycerols (TAG(49:3-FA18:2), TAG(51:3-FA18:2), TAG(49:2-FA18:2), TAG(49:3-FA15:0), TAG(51:3-FA15:0), TAG(51:4-FA18:3), TAG(51:2-FA18:2), TAG(51:5-FA18:3), TAG(51:2-FA15:0)) and one phosphatidylcholine (PC(15:0/18:2)) lipid species and one total FA (FA15:0) showed significant inverse associations with PDAC with ORs ranging from 0.82 to 0.86 (FDR&amp;lt;0.10). There was no association between class-specific FAs and PDAC. Our results support the hypothesis that alcohol use is associated with serum lipid species from 10 lipid classes, mostly with FA15:0, FA17:0 and FA18:2. Nine triacylglycerols, one phosphatidylcholine and total FA15:0 showed inverse prospective associations with PDAC, the latter having been shown to decrease inflammation and regulate glucose metabolism in previous studies. These findings may help understand the inconsistent association between alcohol use and PDAC. Citation Format: Sabine Naudin, Paloma Mitra, Ting Zhang, Demetrius Albanes, Steven C. Moore, Rachael Stolzenberg-Solomon. Lipidomic biomarkers of usual alcohol use and their association with pancreatic ductal adenocarcinoma risk [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A017.

Abstract C052: Ketogenic diet enhanced nutrient metabolic pathways but did not inhibit tumor growth in an obesity-associated PDAC mouse model

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) has a dismal 5-year survival rate due to ineffective treatments. Obesity is a recognized risk factor for PDAC. Thus, developing effective weight management strategies is essential to decrease obesity, and potentially mitigate PDAC risk. The high-fat, low-carbohydrate ketogenic diet (KD) promotes weight loss and is a promising therapeutic strategy in some cancer preclinical models by altering tumor metabolism. Purpose: The KD will disrupt cancer metabolism, promote weight loss, and delay tumor growth in obesity-associated PDAC. Methods: C57BL/6 mice were fed an obesogenic high-fat diet (HFD) for 15 weeks to induce obesity, after which were randomized to a control diet (CD), a KD, or continued in the HFD for 7 weeks. After, mice were orthotopically injected with mouse KRas G12D/Trp53−/−/PDX-1-CRE (mKPC) cells containing the firefly luciferase gene. Tumor growth was monitored using luminescence through an In Vivo Imaging System. Body weight, glucose tolerance tests, and ketone measurements were performed before, during, and at the study end. Tumors metabolomics were analyzed by Liquid Chromatography Mass Spectrometry. Results: Mice gained weight after 15 weeks of an obesogenic diet. Mice that were switched to a KD or CD lost weight compared to mice that remained on the HFD following diet intervention. Mice fed a KD maintained increased ketone concentrations and increased glucose tolerance after tumors. However, none of the dietary interventions decreased tumor growth. Tumors in mice fed a KD showed significant enrichment in metabolic processes, such as, vitamin B6 and vitamin B5 metabolism, the citrate cycle, and arginine biosynthesis compared to mice that remained on a HFD. Conclusions: Although the KD induced weight loss and altered tumor metabolism, it was insufficient to decrease tumor growth in an obesogenic PDAC mouse model. Our data identified vitamin B6 and vitamin B5 metabolism, the citrate cycle, and arginine biosynthesis as important compensatory metabolic processes induced by the KD. Understanding the availability and functions of these metabolic processes could identify mechanisms of diet interventions for obesity-associated PDAC to decrease obesity, and potentially mitigate PDAC growth. Citation Format: Ericka Velez-Bonet, Kristyn Gumpper-Fedus, Fouad Choueiry, Zhu Jiangjiang, Zobeida Cruz-Monserrate. Ketogenic diet enhanced nutrient metabolic pathways but did not inhibit tumor growth in an obesity-associated PDAC mouse model [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C052.

Abstract C048: Obesity transforms the gut microbiome to invoke a cancer permissive state

Abstract Obesity – an established, modifiable risk factor for pancreatic ductal adenocarcinoma (PDAC) – has increased in prevalence worldwide yet the precise mechanisms underlying its propensity to promote cancer have remained an enigma. Alterations to the gut microbiota, the microorganisms that colonize the intestinal tract of humans and mice, may provide a link between obesity and PDAC, offering a previously untapped target to develop novel anti-cancer strategies in this highly lethal disease. To ascertain the importance of the obesity-associated microbiome in PDAC progression, we leveraged our previously established autochthonous model of obesity-associated PDAC (Pdx1-Cre; KrasLSL-G12D/+; LepOb/Ob) (KCO) which allows for rapid and reversible weight gain in a Kras-driven mouse tumor model that closely mimics the genetic and histologic features of human PDAC progression. Ablation of the gut microbiome via broad-spectrum antibiotics intercepted tumor development in KCO mice highlighting the importance of the gut microbiome in obesity-driven PDAC. By correlating microbial composition to tumor burden in antibiotic-treated and control mice via 16S rRNA sequencing, we are identifying microbial communities that may be causal for tumor development. In parallel studies, we performed 16S rRNA sequencing on feces serially collected from wild-type, LepOb/Ob ad libitum-fed mice (AL), and LepOb/Ob mice on calorie restriction (CR), which induces rapid weight loss. We found that obesity causes reversible changes to gut microbial composition including an increase in Proteobacteria, a phylum that holds the most known species to secrete histamine, a bioamine implicated in pancreatitis, a PDAC risk factor, and PDAC development itself. Concordant with these findings, Parallel Receptorome Expression and Screening via Transcriptional Output-SALSA (PRESTO-SALSA), a novel high-throughput system that allows for sensitive detection of microbial metabolites that exhibit mammalian GPCR agonism, feces from LepOb/Ob AL mice showed robust activation of histamine receptors (HRHs), while feces from obese mice subject to CR exhibited reduced Proteobacteria species and decreased capacity to activate mammalian HRHs, suggesting weight loss by CR mitigates microbe-derived histamine. Ongoing studies are testing whether microbe-derived histamine is necessary and sufficient for the tumor-promoting effects of obesity. Collectively, our results establish a dependency for the gut microbiome in obesity-driven PDAC, identify reversible obesity-induced microbial communities in the gut, and nominate microbial metabolites capable of activating pro-tumorigenic GPCR pathways as potential causative mechanisms. Citation Format: Christian F. Ruiz, Rylee McDonnell, Lauren Lawres, Yiyun Cao, Deguange Song, Nicole Sonnert, Noah Palm, Mandar D. Muzumdar. Obesity transforms the gut microbiome to invoke a cancer permissive state [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C048.

Prediction of pancreatic cancer risk in patients with new-onset diabetes using a machine learning approach based on routine biochemical parameters

ObjectiveTo develop a machine-learning model that can predict the risk of pancreatic ductal adenocarcinoma (PDAC) in people with new-onset diabetes (NOD). MethodsFrom a population-based sample of individuals with NOD aged >50 years, patients with pancreatic cancer-related diabetes (PCRD), defined as NOD followed by a PDAC diagnosis within 3 years, were included (n = 716). These PCRD patients were randomly matched in a 1:1 ratio with individuals having NOD. Data from Danish national health registries were used to develop a random forest model to distinguish PCRD from Type 2 diabetes. The model was based on age, gender, and parameters derived from feature engineering on trajectories of routine biochemical variables. Model performance was evaluated using receiver operating characteristic curves (ROC) and relative risk scores. ResultsThe most discriminative model included 20 features and achieved a ROC-AUC of 0.78 (CI:0.75–0.83). Compared to the general NOD population, the relative risk for PCRD was 20-fold increase for the 1 % of patients predicted by the model to have the highest cancer risk (3-year cancer risk of 12 % and sensitivity of 20 %). Age was the most discriminative single feature, followed by the rate of change in haemoglobin A1c and the latest plasma triglyceride level. When the prediction model was restricted to patients with PDAC diagnosed six months after diabetes diagnosis, the ROC-AUC was 0.74 (CI:0.69–0.79). ConclusionIn a population-based setting, a machine-learning model utilising information on age, sex and trajectories of routine biochemical variables demonstrated good discriminative ability between PCRD and Type 2 diabetes.