PancPRO: Risk Assessment for Individuals With a Family History of Pancreatic Cancer

TLDR

Pancreatic cancer has a high fatality rate largely because most cases are diagnosed at an advanced stage, making early detection of high‑risk individuals—especially those carrying major susceptibility genes—critical. This study aimed to develop and validate PancPRO, a Mendelian risk‑prediction model for individuals with familial pancreatic cancer, to identify those at high risk. PancPRO extends the BRCAPRO Bayesian framework, was trained on published data, and validated with prospective data from 961 families in the National Familial Pancreas Tumor Registry, including 26 incident cancer cases. The model, available as free software, achieved an observed‑to‑predicted ratio of 0.83 and an AUC of 0.75, making it the first pancreatic‑cancer risk predictor and demonstrating accurate risk assessment even when causative genes are unknown, underscoring the value of detailed family history.

Abstract

Purpose The rapid fatality of pancreatic cancer is, in large part, the result of an advanced stage of diagnosis for the majority of patients. Identification of individuals at high risk of developing pancreatic cancer is a first step towards the early detection of this disease. Individuals who may harbor a major pancreatic cancer susceptibility gene are one such high-risk group. The goal of this study was to develop and validate PancPRO, a Mendelian model for pancreatic cancer risk prediction in individuals with familial pancreatic cancer, to identify high-risk individuals. Methods PancPRO was built by extending the Bayesian modeling framework developed for BRCAPRO, trained using published data, and validated using independent prospective data on 961 families enrolled onto the National Familial Pancreas Tumor Registry, including 26 individuals who developed incident pancreatic cancer during follow-up. Results We developed a risk prediction model, PancPRO, and free software for the estimation of pancreatic cancer susceptibility gene carrier probabilities and absolute pancreatic cancer risk. Model validation demonstrated an observed to predicted pancreatic cancer ratio of 0.83 (95% CI, 0.52 to 1.20) and high discriminatory ability, with an area under the receiver operating characteristic curve of 0.75 (95% CI, 0.68 to 0.81) for PancPRO. Conclusion PancPRO is the first risk prediction model for pancreatic cancer. When we validated our model using the largest registry of familial pancreatic cancer, our model provided accurate risk assessment. Our findings highlight the importance of detailed family history for clinical cancer risk assessment and demonstrate that accurate genetic risk assessment is possible even when the causative genes are not known.