Adjusting for Ascertainment Bias in Meta-Analysis of Penetrance for Cancer Risk

TL;DR

This paper introduces a Bayesian meta-analysis method that adjusts for ascertainment bias in estimating cancer risk from genetic studies, improving accuracy and inclusivity of data.

Contribution

The paper develops a novel Bayesian approach to correct for ascertainment bias in meta-analyses of cancer penetrance, enabling inclusion of biased studies for more reliable risk estimates.

Findings

The method produces more accurate penetrance estimates than unadjusted analyses.

Adjusted estimates for ATM and PALB2 show significant age-specific breast cancer risks.

Including biased studies increases the robustness of meta-analytic results.

Abstract

Multi-gene panel testing allows efficient detection of pathogenic variants in cancer susceptibility genes including moderate-risk genes such as ATM and PALB2. A growing number of studies examine the risk of breast cancer (BC) conferred by pathogenic variants of such genes. A meta-analysis combining the reported risk estimates can provide an overall age-specific risk of developing BC, i.e., penetrance for a gene. However, estimates reported by case-control studies often suffer from ascertainment bias. Currently there are no methods available to adjust for such ascertainment bias in this setting. We consider a Bayesian random-effects meta-analysis method that can synthesize different types of risk measures and extend it to incorporate studies with ascertainment bias. This is achieved by introducing a bias term in the model and assigning appropriate priors. We validate the method through a simulation study and apply it to estimate BC penetrance for carriers of pathogenic variants of ATM and PALB2 genes. Our simulations show that the proposed method results in more accurate and precise penetrance estimates compared to when no adjustment is made for ascertainment bias or when such biased studies are discarded from the analysis. The estimated overall BC risk for individuals with pathogenic variants in (1) ATM is 5.77% (3.22%-9.67%) by age 50 and 26.13% (20.31%-32.94%) by age 80; (2) PALB2 is 12.99% (6.48%-22.23%) by age 50 and 44.69% (34.40%-55.80%) by age 80. The proposed method allows for meta-analyses to include studies with ascertainment bias resulting in a larger number of studies included and thereby more robust estimates.