Bayesian estimation of a semiparametric recurrent event model with applications to the penetrance estimation of multiple primary cancers in Li-Fraumeni Syndrome

TL;DR

This paper introduces a Bayesian recurrent event model to estimate the risk of multiple primary cancers in Li-Fraumeni Syndrome, incorporating family genetic data and addressing bias in rare disease studies.

Contribution

It develops a novel Bayesian non-homogeneous Poisson process model with family-wise likelihood for penetrance estimation in LFS, accounting for genetic inheritance and bias.

Findings

Reliable penetrance estimates for multiple primary cancers in LFS.

Model validated through internal and external studies.

First report of such estimates in LFS literature.

Abstract

A common phenomenon in cancer syndromes is for an individual to have multiple primary cancers at different sites during his/her lifetime. Patients with Li-Fraumeni syndrome (LFS), a rare pediatric cancer syndrome mainly caused by germline TP53 mutations, are known to have a higher probability of developing a second primary cancer than those with other cancer syndromes. In this context, it is desirable to model the development of multiple primary cancers to enable better clinical management of LFS. Here, we propose a Bayesian recurrent event model based on a non-homogeneous Poisson process in order to obtain penetrance estimates for multiple primary cancers related to LFS. We employed a family-wise likelihood that facilitates using genetic information inherited through the family pedigree and properly adjusted for the ascertainment bias that was inevitable in studies of rare diseases by using an inverse probability weighting scheme. We applied the proposed method to data on LFS, using a family cohort collected through pediatric sarcoma patients at MD Anderson Cancer Center from 1944 to 1982. Both internal and external validation studies showed that the proposed model provides reliable penetrance estimates for multiple primary cancers in LFS, which, to the best of our knowledge, have not been reported in the LFS literature.