Variant‐Specific Mendelian Risk Prediction Model

ABSTRACT

Many pathogenic sequence variants (PSVs) have been associated with increased risk of cancers. Mendelian risk prediction models use Mendelian laws of inheritance, as well as specified PSV frequency and penetrance (age‐specific probability of developing cancer given genotype), to predict the probability of having a PSV based on family history. Most existing models assume that the penetrance is the same for all PSVs in a certain gene. However, for some genes (e.g., BRCA1/2), cancer risk has been shown to vary by PSV. We propose an extension of Mendelian risk prediction models that relaxes the assumption of homogeneous gene‐level risk by incorporating PSV‐specific penetrances and illustrate this extension on an existing Mendelian risk prediction model, Fam3PRO. We illustrate our proposed Fam3PRO‐variant model by incorporating variant‐specific BRCA1/2 PSVs through region classifications. Based on prior literature, we defined three cancer‐specific risk regions: The breast cancer clustering region (BCCR), the ovarian cancer clustering region (OCCR), and the “other” region. We conducted simulations to evaluate the performance of the proposed illustrative Fam3PRO‐variant model compared to the existing Fam3PRO model. Simulation results showed that the Fam3PRO‐variant model was well calibrated to predict region‐specific BRCA1/2 carrier status with high discrimination and accuracy. Importantly, our simulations also highlighted the impact of underreporting in family history data on model performance: While underreporting slightly reduced absolute calibration, the Fam3PRO‐variant model remained robust in discrimination and provided more accurate region‐specific PSV risk predictions than gene‐level models. We further evaluated Fam3PRO‐variant on two cohorts: 1897 families from the Cancer Genetics Network (CGN) and 25 671 families from the Clinical Cancer Genomics Community Research Network (CCGCRN). Results showed that our proposed model provides region‐specific PSV carrier probabilities with high accuracy, while the calibration, discrimination, and accuracy of gene‐specific PSV carrier probabilities were comparable to the existing gene‐specific model. Moreover, we assessed the clinical utility of Fam3PRO‐variant by evaluating positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity at clinically relevant thresholds (2.5%, 5%, and 10%), as recommended by NCCN guidelines. Fam3PRO‐variant performed comparably to Fam3PRO at the gene level across all metrics, with notably high specificity and NPV at the region‐specific level. These results suggest that, even in the presence of underreporting, Mendelian risk prediction models can be effectively extended to incorporate variant‐specific penetrances, providing more precise region‐specific PSV carrier probabilities and improving cancer prevention and risk prediction.