Bayesian Semiparametric Estimation of Cancer-Specific Age-at-Onset Penetrance With Application to Li-Fraumeni Syndrome

ABSTRACT

Penetrance, which plays a key role in genetic research, is defined as the proportion of individuals with the genetic variants (i.e., genotype) that cause a particular trait and who have clinical symptoms of the trait (i.e., phenotype). We propose a Bayesian semiparametric approach to estimate the cancer-specific age-at-onset penetrance in the presence of the competing risk of multiple cancers. We employ a Bayesian semiparametric competing risk model to model the duration until individuals in a high-risk group develop different cancers, and accommodate family data using family-wise likelihoods. We tackle the ascertainment bias arising when family data are collected through probands in a high-risk population in which disease cases are more likely to be observed. We apply the proposed method to a cohort of 186 families with Li-Fraumeni syndrome identified through probands with sarcoma treated at MD Anderson Cancer Center from 1944 to 1982. Supplementary materials for this article are available online.

KEYWORDS:

• Cancer-specific age-at-onset penetrance
• Competing risk
• Family-wise likelihood
• Gamma frailty model
• Li-Fraumeni syndrome

Supplementary Material

Supplementary material includes an illustrative example of the peeling algorithm, a description of the carrier probability estimation based on family cancer history, additional simulation results for different baseline hazard models, penetrance of LFS estimated by various competing methods, prior sensitivity analysis, and cross-validated ROC curves at different ages.

Acknowledgments

The authors thank two reviewers, an associate editor, and the editor for their most thoughtful comments that improved their work substantially. The authors thank Gang Peng for providing the computer code to implement the peeling algorithm, and thank Lee Ann Chastain for her editorial assistance.

Additional information

Funding

Cancer Prevention and Research Institute of Texas [RP130090]; National Institutes of Health [P01CA34936, P30 CA016672].