Citations

ABSTRACT Background Family history of pancreatic cancer (PC), pathogenic germline variants (PGVs), and increased intrapancreatic fat (IPF) are individually associated with increased PC risk. Whether IPF is elevated in high‐risk individuals (HRIs) with PC family history or PGVs is unknown. Although MRI is the preferred modality for IPF measurement, region‐of‐interest–based approaches are prone to sampling bias because pancreatic fat is heterogeneously distributed. We aimed to compare IPF among HRIs and matched non‐HRI controls using an Artificial intelligence (AI)‐based tool for whole pancreatic IPF measurement applied to magnetic resonance imaging (MRI). Methods We developed and validated a deep‐learning pipeline for IPF measurement that incorporates automated pancreas segmentation on T1‐weighted MRI images registered to the corresponding iterative decomposition of water and fat with echo asymmetry and least squares estimation images to compute IPF using proton density fat fraction (PDFF). This pipeline was used to quantify IPF and assess IPF distribution patterns in 85 HRIs and 170 non‐HRIs matched 1:2 for age, sex, BMI, and diabetes status. A surface erosion approach was developed to minimize the impact of peripancreatic fat on IPF measurement. Linear models adjusting for matching variables were used to assess associations. Results Model‐expert agreement for whole‐pancreas PDFF was excellent (intraclass correlation coefficient 0.99; 95% CI, 0.98–0.99 mean absolute error 1.4%). Mean PDFF did not differ between HRIs and controls (25.8% [11.5] vs. 26.9% [11.4]; p = 0.32). PDFF increased with age ( p = 0.015), male sex ( p < 0.001), presence of diabetes ( p = 0.009), BMI ( p < 0.001). IPF distribution patterns did not differ by risk group. Conclusion An AI‐based approach provides accurate, scalable, and objective assessment of IPF quantity and distribution. IPF quantity and spatial distribution do not appear to differ based on germline or familial predisposition to PC.

Abstract Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 ( CLPTM1L , rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24×10 -8 ), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors. Statement of Significance To understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.

Abstract Purpose: The Pancreatic Cancer Detection Consortium (PCDC) performed a blinded Early Detection Research Network–defined phase II biomarker bakeoff study of blood-based biomarker panels. The aims were to evaluate panel performance, to compare the panels’ performance with that of cancer antigen 19-9 (CA19-9) alone, and to evaluate the performance of new combinations of the individual biomarkers. Experimental Design: Ten biomarkers representing eight biomarker panels and CA19-9 were evaluated using plasma, serum, and germline DNA from 140 stage I to IV pancreatic ductal adenocarcinoma (PDAC) cases and 140 controls from three tertiary care institutions, with controls frequency matched to cases on age and sex. LASSO regression was employed to explore new biomarker combinations. The primary metric was area under the receiver operating characteristic curve (AUC). Results: The study population was 51% female, with median age 67.3 (minimum: 45, maximum: 90) years. Biomarker panel AUCs ranged from 89.9 to 96.3; the AUC for serum CA19-9 alone was 91.7 [95% confidence interval (CI), 87.8–95.6]. Two panels had significantly higher AUCs than serum CA19-9 alone, the CA19-9/FUT2/3 panel (AUC = 96.3, P = 0.002), and the tissue factor pathway inhibitor/tenascin C (TFPI/TNC-FNIII-C) panel (AUC = 95, P = 0.01). Exploratory models to recombine biomarkers retained all but two biomarkers [optimism-corrected AUC = 96.4 (94–98.9)]. Conclusions: The CA19-9/FUT2/3 panel was the best performing panel in this biomarker bakeoff. Its evaluation in larger studies is warranted. Biomarker bakeoffs are an effective strategy for comparing the performance of promising biomarkers for pancreatic cancer early detection, and the PCDC is well poised to conduct such studies. Recommendations for performing such studies are provided.

Abstract Purpose: Pancreatic ductal adenocarcinoma (PDAC) is typically detected too late for useful therapeutic interventions; hence, we sought blood biomarkers to detect early-stage disease. Experimental Design: Using mass spectrometry and ELISA on plasma pools from the University of Pennsylvania (Penn) and the Mayo Clinic (Mayo), we identified aminopeptidase N (ANPEP) and polymeric immunoglobulin receptor (PIGR) as increased in early-stage (stage I/II) PDAC plasma compared with controls. We tested ANPEP and PIGR, along with prior data for thrombospondin 2 (THBS2) and carbohydrate antigen 19-9 (CA19-9), in retrospective phase II studies using separate cohorts of PDAC plasmas at different stages versus healthy or nonmalignant disease controls (DC) from Penn (n = 135) and Mayo (n = 537). Results: Comparing healthy controls with stage I/II PDAC, we obtained area under the receiver-operating characteristic curves (AUC) of 0.78 [95% confidence interval (CI), 0.68–0.86]/0.80 (95% CI, 0.74–0.85; ANPEP) and 0.81 (95% CI, 0.70–0.88)/0.86 (95% CI, 0.82–0.90; PIGR) for the Penn/Mayo phase II studies, respectively. In multivariable models, CA19-9/THBS2/ANPEP, CA19-9/THBS2/PIGR, and CA19-9/THBS2/ANPEP/PIGR elicited AUC of 0.94 to 0.96 for Penn and 0.97 for Mayo. Notably, the four-marker panel elicited AUC of 0.87 for the Mayo stage I/II versus DC and 0.91 for stages I to IV versus DC. At a specificity of 95%, a plasma biomarker panel composed of CA19-9 (≥35 U/mL), THBS2 (≥42 ng/mL), ANPEP (≥2,995 ng/mL), and PIGR (≥1,800 ng/mL) yielded a sensitivity of 91.9% for PDAC stages I to IV and 87.5% for PDAC stage I/II. Conclusions: Adding ANPEP and PIGR to a plasma biomarker panel of CA19-9 and THBS2 enhances the detection of early-stage PDAC when comparing cancer versus healthy or nonmalignant DC. Given the concordance of our data in two retrospective phase II studies, assessments in prediagnostic cases are warranted.

Abstract Background The associations between different types of diabetes, characterized by distinct pathophysiology and genetic architecture, and pancreatic ductal adenocarcinoma (PDAC) risk are not understood. Methods We investigated associations of genetic susceptibility to type 2 diabetes (T2D), 8 T2D mechanistic clusters, type 1 diabetes (T1D), and maturity-onset diabetes of the young (MODY) with PDAC risk. We used genome-wide association study (GWAS) summary-level statistics for T2D (242 283 cases, 1 569 734 controls), T1D (18 942 cases, 501 638 controls), and PDAC (10 244 cases and 360 535 controls) in individuals of European ancestry. Results Two-sample Mendelian randomization (MR) using the Robust Adjusted Profile Score (MR-RAPS) method indicated that genetically predicted T2D was associated with PDAC risk (OR = 1.10; 95% CI = 1.05 to 1.15), particularly the T2D obesity (OR = 1.28; 95% CI = 1.15 to 1.42) and lipodystrophy (OR = 1.25; 95% CI = 1.03 to 1.51) clusters. No association was observed for T1D with PDAC risk (OR = 1.01; 95% CI = 0.99 to 1.02). Pathway/gene-set analysis using the summary-based Adaptive Rank Truncated Product (sARTP) method revealed a significant association between the MODY gene-sets and PDAC risk (P = 1.5 × 10−8), which remained after excluding 20 known PDAC GWAS loci (P = 7.6 × 10−4). HNF1A, FOXA3, and HNF4A were the top contributing genes after excluding the previously identified GWAS loci regions. Conclusions Our results from this genetic association study support that T2D, particularly the obesity and lipodystrophy mechanistic clusters, and MODY genomic susceptibility regions play a role in the etiology of PDAC.

Abstract Objective: The Pancreatic Cancer Detection Consortium (PCDC) performed a blinded EDRN defined phase 2 Biomarker Bakeoff study of eight promising biomarker panels using pancreatic cancer ductal adenocarcinoma (PDAC) case and general population healthy control samples from three institutions. The scientific questions were to determine whether panels could discriminate PDAC cases from controls with Receiver Operating Characteristic Area Under the Curve (AUC) &gt; 0.8, compare panel performance head-to-head via 95% confidence intervals (CI), and determine whether the panel improved upon serum CA19-9 alone. A secondary goal was to perform modeling to discover a new panel consisting of individual biomarkers. Methods: Eligible biomarkers were required to have had a prior EDRN defined phase 2 validation study published and be ready for validation in a sample set from multiple institutions. Three institutions submitted a total of 140 cases and 140 controls frequency matched to cases on age and sex. Four consortium member institutions submitted for validation eight panels consisting of ten plasma, serum and DNA genotyping biomarkers in addition to CA19-9. Locked panel formulas were submitted prior to specimen distribution. The primary metric was AUC with bootstrapped 95% CIs. LASSO penalized regression was used for exploratory panel building. Nested cross validation and internal-external validation were performed, and optimism-corrected performance estimates were computed. Results: The overall sample (N=280) was 51% female, median age of 67 years, and 94% were of non-Hispanic white race and ethnicity. Controls had median BMI 27.3, 56% never smokers, and 12% type II diabetes mellitus (DM2); cases (staged I to IV) had median BMI 26.6, 42% never smokers, and 26% DM2. Overall panel AUC estimates ranged from 89.9 to 96.3 and all were significantly larger than the null hypothesis value of 0.8 (p&lt;0.05). The apparent serum CA19-9 AUC was 91.7 (87.8, 95.6). Two panels were significantly different from serum CA19-9, a panel with FUT2/3 genotype with serum CA19-9 (p=0.002) and a panel containing tissue factor pathway inhibitor (TFPI), tenascin C (TNC), and plasma CA19-9 (p=0.01). In subset analyses by PDAC stage, no panel improved upon the null hypothesis value of 0.8 in stage I (p&gt;0.05), all panels improved upon the null hypothesis value of 0.8 in stage II and III (p&lt;0.05), and only serum CA19-9 did not improve upon the null hypothesis value of 0.8 in stage IV. All biomarkers but two microRNAs were retained in a LASSO model that had optimism corrected AUC=96.4 (95% CI: 94.0, 98.9). Conclusions: All submitted panels performed well overall in this blinded study, with the panel containing FUT2/3 genotype performing the best. Panel discovery improved upon serum CA19-9, though optimism corrected AUC 95% CIs overlap with those of apparent CA19-9 performance. Further work is needed to prioritize promising biomarkers and to improve detection of early stage PDAC. Citation Format: Ann L. Oberg, William R. Bamlet, Grant Izmirlian, Seetharaman Balasenthil, Surinder K. Batra, Masataka Hayashi, Daniel Herman, Michael A. Hollingsworth, Maneesh Jain, Ann M. Killary, Brianna M. Krusen, Suyu Liu, Gopalakrishnan Natarajan, Subrata Sen, Lynette M. Smith, Sudhir Srivastava, Brian M. Wolpin, Kenneth S. Zaret, Michael G. Goggins. Pancreatic Cancer Detection Consortium biomarker bake-off: A consortium Phase 2 blinded biomarker validation and panel discovery study [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B070.

Abstract Pancreatic neuroendocrine neoplasm (panNEN) is a rare malignancy and the second most common type of pancreatic cancer after pancreatic ductal adenocarcinoma (PDAC), but its etiology is poorly understood. We investigated whether the risk factors of panNEN are concordant with those known for PDAC. We performed the largest case-control study to date on panNENs, comprising 927 sporadic nonfunctional panNEN cases and 1807 frequency-matched controls, using data from the Mayo Clinic Biospecimen Resource for Pancreas Research. We assessed associations for obesity, first-degree family history of pancreatic cancer, cigarette smoking, overall type II diabetes mellitus (T2DM), new-onset T2DM (&lt;1 year before panNEN diagnosis), longstanding T2DM (≥5 years), alcohol intake and aspirin use. Multivariable logistic regression was used to calculate odds ratios and 95% confidence intervals (CIs). Our results show that overall T2DM (OR = 1.71, 95% CI: 1.37–2.14) and new-onset T2DM (OR = 2.65, 95% CI: 1.92–3.69) are associated with higher odds of panNEN, but not longstanding T2DM (OR = 1.29, 95% CI: 0.94–1.75). A non-significant elevated odds of panNEN was observed among participants with a positive family history of pancreatic cancer (OR = 1.44, 95% CI: 0.96–2.14). Alcohol use was inversely related to panNEN (OR = 0.52, 95% CI: 0.42–0.66, ever-vs-never). No association was observed for smoking, obesity or aspirin use. These findings indicate that overall T2DM and new-onset T2DM are associated with higher odds of panNEN. Unlike PDAC, alcohol use was inversely related to panNEN, and we found no associations for cigarette smoking, obesity or aspirin use. These results indicate differences in the risk factor profiles of panNEN and PDAC.

ABSTRACT Lymphoma is one of the leading causes of cancer and cancer deaths and yet has not been amenable to population screening. The role of methylated DNA markers (MDMs) in the detection of lymphoma has not been extensively studied. We aimed to discover, validate, and test tissue–derived MDMs of lymphoma in archival plasma specimens. Reduced representation bisulfite sequencing (RRBS) was performed on a discovery set of frozen tissues. MDMs identified were converted to methylation–specific PCR assays and validated on independent formalin–fixed, paraffin‐embedded (FFPE) tissues. Target enrichment long–probe quantitative‐amplified signal (TELQAS) assays were developed and assayed in plasma–extracted, bisulfite‐converted DNA from independent treatment‐naïve lymphoma patients and healthy controls. Prediction of cancer status was modeled using random forest model with in silico cross‐validation. After discovery and validation in tissue, 16 TELQAS assays ( ZNF503 , VWA5B1 , HOXA9 , GABRG3 , ITGA5 , MAX.chr17.7190 , BNC1 , CDK20 , MAX.chr4.4069 , TPBG , DNAH14 , SYT6 , CACNG8 , FAM110B , ADRA1D , and NRN1 ) were selected for testing in plasma. These detected 78% (95% CI, 74%–82%) of lymphoma cases at 90% specificity. Excluding marginal zone and T‐cell lymphomas, sensitivity increased to 84% (80%–88%). MDMs in plasma show promise to detect lymphoma and are candidates for inclusion in multi‐cancer detection studies.

Abstract Aberrant activation of GLI transcription factors has been implicated in the pathogenesis of different tumor types including pancreatic ductal adenocarcinoma. However, the mechanistic link with established drivers of this disease remains in part elusive. In this study, using a new genetically engineered mouse model overexpressing constitutively active mouse form of GLI2 and a combination of genome-wide assays, we provide evidence of a novel mechanism underlying the interplay between KRAS, a major driver of pancreatic ductal adenocarcinoma development, and GLI2 to control oncogenic gene expression. These mice, also expressing KrasG12D, show significantly reduced median survival rate and accelerated tumorigenesis compared with the KrasG12D only expressing mice. Analysis of the mechanism using RNA sequencing demonstrate higher levels of GLI2 targets, particularly tumor growth–promoting genes, including Ccnd1, N-Myc, and Bcl2, in KrasG12D mutant cells. Furthermore, chromatin immunoprecipitation sequencing studies showed that in these cells KrasG12D increases the levels of trimethylation of lysine 4 of the histone 3 (H3K4me3) at the promoter of GLI2 targets without affecting significantly the levels of other major active chromatin marks. Importantly, Gli2 knockdown reduces H3K4me3 enrichment and gene expression induced by mutant Kras. In summary, we demonstrate that Gli2 plays a significant role in pancreatic carcinogenesis by acting as a downstream effector of KrasG12D to control gene expression.

Abstract Background: Pancreatic cancer is a leading cause of cancer-related death globally. Risk factors for pancreatic cancer include common genetic variants and potentially heavy alcohol consumption. We assessed if genetic variants modify the association between heavy alcohol consumption and pancreatic cancer risk. Methods: We conducted a genome-wide interaction analysis of single-nucleotide polymorphisms (SNP) by heavy alcohol consumption (more than three drinks per day) for pancreatic cancer in European ancestry populations from genome-wide association studies. Our analysis included 3,707 cases and 4,167 controls from case–control studies and 1,098 cases and 1,162 controls from cohort studies. Fixed-effect meta-analyses were conducted. Results: A potential novel region of association on 10p11.22, lead SNP rs7898449 (interaction P value (Pinteraction) = 5.1 × 10−8 in the meta-analysis; Pinteraction = 2.1 × 10−9 in the case–control studies; Pinteraction = 0.91 in the cohort studies), was identified. An SNP correlated with this lead SNP is an expression quantitative trait locus for the neuropilin 1 gene. Of the 17 genomic regions with genome-wide significant evidence of association with pancreatic cancer in prior studies, we observed suggestive evidence that heavy alcohol consumption modified the association for one SNP near LINC00673, rs11655237 on 17q25.1 (Pinteraction = 0.004). Conclusions: We identified a novel genomic region that may be associated with pancreatic cancer risk in conjunction with heavy alcohol consumption located near an expression quantitative trait locus for neuropilin 1, a protein that plays an important role in the development and progression of pancreatic cancer. Impact: This work can provide insights into the etiology of pancreatic cancer, particularly in heavy drinkers.