Multifactor risk scores with susceptibility gene mutations to enhance risk assessment of pancreatic cancer

PROJECT SUMMARY / ABSTRACT We propose to improve risk assessment of pancreatic ductal adenocarcinoma (PDAC), a major lethal cancer for which no early detection strategy exists. We hypothesize that a novel enhanced multifactor risk score (eMRS) will improve risk stratification for moderate to high genetic risk strata for PDAC, by incorporating carrier

status of hereditary cancer syndrome pathogenic variants. We will develop a new eMRS using large Mayo Clinic datasets of PDAC cases recruited since 2002 and primary care controls in the Mayo Clinic Biobank with recently completed whole exome sequencing (WES) by Regeneron Genetics Center (RGC) to first assess the

limited number of existing published models. We will then construct our eMRS with these data and perform independent validation using the UK Biobank. We will test the value of the model in a dataset of at-risk first- degree relatives (FDRs) of Mayo Clinic PDAC patients. Our new eMRS for PDAC will for the first time

comprehensively accommodate inherited cancer syndrome pathogenic variants to stratify risk of PDAC in identically processed sequencing and informatics workflows. The study team includes leaders in gene identification and genetic epidemiology of PDAC, having led the GWAS that identified the risk SNPs that will be

included in risk model testing and development. By leveraging the three existing major genomic datasets and one family dataset with accompanying epidemiologic and disease covariates, our specific aims are: (1) To use uniquely coordinated datasets on Mayo Clinic PDAC cases and Mayo Biobank controls to validate published

polygenic risk score (PRS) and MRS models. We will assemble and analyze Mayo Clinic’s 4,552 prospectively recruited cases and 53,229 controls to evaluate the heretofore unvalidated models. (2) To build and validate a novel, enhanced eMRS for PDAC that incorporates cancer germline pathogenic variants. (2a) We will use the

datasets built for cases and controls in Aim 1 and include additional epidemiologic risk factors and germline pathogenic variant status for hereditary cancer genes, which have not been previously available in public datasets. (2b) We will validate our new eMRS with an independent dataset of genotype and risk factor data

extracted from 2,371 PDAC cases and the remaining 497,629 non-PDAC UK Biobank participants whose germline DNA will have also been sequenced by RGC. (3) To determine the extent that PRS, MRS or eMRS can further refine PDAC risk in first degree relatives of PDAC cases. We will compare standardized incidence

ratios (SIRs) in a dataset of personal cancer histories of 23,739 relatives assembled from Mayo Clinic PDAC cases’ family history questionnaires to quantify and assess impact. This project will result in several useful genetic risk stratification models to provide more precise risk assessment for PDAC. The results are

anticipated to have a translational impact on risk assessment, genetic counseling, and early detection of PDAC through better, potentially targeted, identification of individuals at increased risk who in the future could be offered minimally invasive surveillance or other options for early-stage detection and prevention.