Background: Li–Fraumeni syndrome (LFS) is associated with germline TP53 mutations and a very high lifetime cancer risk. Algorithms that assess a patient’s risk of inherited cancer predisposition are often used in clinical counseling. The existing LFS criteria have limitations, suggesting the need for an advanced prediction tool to support clinical decision making for TP53 mutation testing and LFS management.
Methods: Based on a Mendelian model, LFSPRO estimates TP53 mutation probability through the Elston–Stewart algorithm and consequently estimates future risk of cancer. With independent datasets of 1,353 tested individuals from 867 families, we evaluated the prediction performance of LFSPRO.
Results: LFSPRO accurately predicted TP53 mutation carriers in a pediatric sarcoma cohort from MD Anderson Cancer Center in the United States, the observed to expected ratio (OE) = 1.35 (95% confidence interval, 0.99–1.80); area under the receiver operating characteristic curve (AUC) = 0.85 (0.75–0.93); a population-based sarcoma cohort from the International Sarcoma Kindred Study in Australia, OE = 1.62 (1.03–2.55); AUC = 0.67 (0.54–0.79); and the NCI LFS study cohort, OE = 1.28 (1.17–1.39); AUC = 0.82 (0.78–0.86). LFSPRO also showed higher sensitivity and specificity than the classic LFS and Chompret criteria. LFSPRO is freely available through the R packages LFSPRO and BayesMendel.
Conclusions: LFSPRO shows good performance in predicting TP53 mutations in individuals and families in varied situations. Impact: LFSPRO is more broadly applicable than the current clinical criteria and may improve clinical management for individuals and families with LFS.