Majumder, Shounak

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 Background and Objectives Occult intra‐abdominal metastases are identified in approximately 20% of patients with radiographically localized pancreatic ductal adenocarcinoma (PDAC) through staging laparoscopy, as peritoneal cytology is limited in sensitivity. Methylated DNA markers (MDMs) have demonstrated high diagnostic accuracy for PDAC in tumor tissue, blood, and pancreatic secretions. This study assesses the feasibility and diagnostic performance of MDMs in peritoneal lavage fluid collected during staging laparoscopy, comparing their performance to mutant KRAS (m KRAS ) detection. Methods DNA from peritoneal lavage fluid of PDAC patients was analyzed for m KRAS mutations (codons 12, 13, and 61) using droplet‐digital PCR and for 13 MDMs and a reference gene (B3GALT6) using quantitative methylation‐specific PCR. An MDM score was generated from the average signal of the 13 MDMs and evaluated for its ability to detect intra‐abdominal metastases via receiver operator characteristic (ROC) analysis. Results Among 48 patients, 15 (31%) had intra‐abdominal metastases. The MDM score showed 87% sensitivity and 90% specificity (AUC = 0.98), outperforming m KRAS , which showed 40% sensitivity and 85% specificity (AUC = 0.62; p < 0.0001). Conclusions MDMs in peritoneal lavage fluid may enable highly accurate molecular staging of PDAC and warrant validation in larger studies.

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 Pancreatic Cancer Detection Consortium (PCDC) is an NCI-sponsored initiative to improve detection of early-stage pancreatic ductal adenocarcinoma (PDAC). A key strategy in this initiative is to develop prospective, multi-center, longitudinal biospecimen collections from individuals without PDAC who are at high-risk of PDAC due to family history, germline variants, or the presence of pancreatic cystic lesions. The biospecimen collections, linked to clinically relevant data, will enable Phase 3 biomarker validation studies. Methods: The PCDC currently includes 10 funded Research Units (RUs): City of Hope/TGen, Dana Farber Cancer Institute, Johns Hopkins Medical Institute, Mayo Clinic/University of Pennsylvania, MD Anderson Cancer Center, Oregon Health and Science University, University of Nebraska, University of Pittsburgh Medical Center, University of California (UC) San Diego, UC San Franciso. Non-protected patient health information and biospecimen data is logged into a central Medidata Rave® database managed by a Management and Data Coordination Unit (MDCU) at Mayo Clinic (NCT06271291). Biospecimens collected include blood and pancreatic cyst fluid. Two options for blood collections exist: 1) 20 mL EDTA, 10 mL no additive if samples can be frozen within 4 hours of collection; 2) 10 mL EDTA, 10 mL Streck, 10 mL no additive if samples cannot be frozen within 4 (but less than 12) hours. Cyst fluid (up to 1000 uL), if available as clinical residual, will be obtained at the time of clinically indicated operative resection or endoscopic ultrasound guided aspiration procedures. All samples must be processed per standardized methods outlined in the lab manual and aliquoted into 500 uL (blood) or 100 uL (cyst fluid) units and frozen (-70oC). Long-term biospecimen storage will be set up at NCI Frederick National Laboratory (FNL). Single Institutional Review Board (sIRB) oversight is provided by Mayo Clinic. A collaborative research agreement has been executed to allow sharing of data and biospecimens between and within PCDC RU members, the MDCU, NCI and NCI FNL. Results: The enrollment of patients began February 7, 2025 with additional sites joining as necessary sIRB reliance documents and training is completed. The protocol is approved for up to 30,000 patients, age 18 years and older. 14 have been enrolled from 1 site as of July 10, 2025. Study endpoints include diagnosis of PDAC, death or complete pancreatectomy for non-PDAC reasons. Biospecimens and questionnaires will be collected annually. Summary and Future Plans: The PCDC has designed a Phase 3 biobank following EDRN study design principles. Biospecimens will be stored at NCI FNL. Access to the biobank resource will be overseen by the PCDC Steering Committee. We anticipate this being a rich Phase 3 biospecimen resource for the pancreatic cancer early detection research community. Citation Format: Janet E. Olson, Shounak Majumder, Jennifer J. Brooks, Emily J. Kirsch, Bridget H. Schmidt, Bill R. Bamlet, Erin E. Carlson, Ann L. Oberg. On the development of the Pancreatic Cancer Detection Consortium (PCDC) Prospective Cohorts biobank [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 B069.

Abstract Purpose: Germline pathogenic variants (PV) in ATM increase the risk of pancreatic ductal adenocarcinoma (PDAC), but the underlying tumor biology of PDAC associated with germline PV in ATM has not been adequately explored. Experimental Design: Whole-genome, whole-exome, and RNA sequencing were performed on PDAC tumors from 25 germline ATM PV carriers diagnosed at Mayo Clinic between 2007 and 2017. Somatic and copy-number alterations, mutational signatures, transcriptomic subtypes, and the immune landscape were evaluated. Results: High-quality whole-exome and whole-genome sequencing were obtained from 21 and 15 tumors, respectively. Biallelic inactivation of ATM was observed in 87%, KRAS PV in 90%, CDKN2A homozygous loss in 60%, and TP53 alterations in &lt;10% of these tumors. A predominant clock-like mutational signature was present in all samples. Whole-transcriptome analysis identified that the aberrantly differentiated endocrine exocrine subtype accounted for 18% of PDAC and was consistently associated with &gt;5-year overall survival. In addition, a 28-gene expression–based signature associated with overall survival was identified and further validated in The Cancer Genome Atlas cohort. Immune landscape analysis through CODEX identified enriched CD4 T-helper cell/tumor interactions and reduced B7H3-high cell/tumor interactions in ATM PV carriers compared with noncarriers. Conclusions: The observed absence of TP53 PV and enrichment for CDKN2A alterations in ATM tumors, along with differences in the mutational signatures, transcriptomic subtypes and immune landscape, improve our understanding of the mechanistic pathways involved in PDAC development in germline ATM PV carriers and help identify potential targeted therapeutic strategies.

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the U.S. mainly because of late-stage diagnosis. Heritable genetic information can improve risk stratification for individuals with moderate to high-risk of PDAC for targeted screening and early detection. We aimed to develop an enhanced polygenic risk score (PRS) for PDAC using a Bayesian approach, LDpred2, that reweights risk estimates attributed to millions of polymorphisms (SNPs) estimated from genome-wide association studies (GWASs). We then compared the performance of LDpred2 with the commonly used genetic risk score (GRS) approach that is based only on genome-wide significant SNPs. Methods: GWAS summary statistics were obtained from the Pancreatic Cancer Cohort Consortium and the Pancreatic Cancer Case Control Consortium for 9040 cases and 12496 controls and were used to compute weights for a GRS (17 SNPs) using a p-value threshold of 5e-8 and weights for an enhanced PRS (1.3 million SNPs) using LDpred2. These weights were then used to compute risk scores in an independent validation sample of 2616 cases and 45407 controls from the Mayo Clinic. We used logistic regression to test for associations of the GRS and PRS with PDAC risk, adjusting for age, sex, and the first 10 principal components of ancestry. Results: The validation sample was of mostly European ancestries (&gt;95%). Cases were slightly older than controls (median age: 66 yrs. vs. 63 yrs.) and had a higher frequency of men than controls (57% vs. 42%). Comparisons between the GRS and the PRS are shown in Table 1. DeLong’s test comparing the AUCs between the two genetic scores showed the PRS significantly outperformed the GRS (AUC = 0.68 vs. 0.67; p = 7.6e-7). Conclusions: The LDpred2 PRS model that used a larger set of SNPs significantly improved prediction of PDAC. Comparing the top percentile of the PRS versus the rest showed that the PRS is a clinically significant risk score (OR &gt; 3) for PDAC and could inform clinical decision making and genetic counseling of individuals identified as having a high risk of PDAC. Citation Format: Samuel O. Antwi, Brandon J. Coombes, Nicholas B. Larson, Erin E. Carlson, Kari G. Rabe, Hunter J. Atkinson, Shounak Majumder, Jun Zhong, David McKean, Pancreatic Cancer Cohort & Case Control Consortia (PanScan and PanC4), Brian M. Wolpin, Rachael Z. Stolzenberg-Solomon, Alison P. Klein, Laufey T. Amundadottir, Ann L. Oberg. Enhanced polygenic risk score for pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB331.