Abstract 1428: DNA methylation predicts early onset of primary tumor in patients with Li-Fraumeni syndrome

Abstract Background: Li-Fraumeni syndrome (LFS) is an autosomal dominant cancer predisposition syndrome. Approximately 80% of individuals with LFS harbor a germline TP53 pathogenic variant rendering them susceptible to a wide spectrum of early-onset malignancies. A comprehensive surveillance regimen termed the ‘Toronto Protocol’, has recently been adopted for early tumor detection, demonstrating significant improvement in survival among TP53 pathogenic variant carriers. However, the protocol’s “one-size-fits-all” approach fails to consider an individual patient's risk of cancer. We built a machine learning model that predicts early-onset of primary tumors in LFS by estimating the probability of cancer onset before the age of six years, leveraging patient peripheral blood leukocyte methylation profiles. Methods: We built a gradient-boosted tree model to predict the probability of cancer onset before the age of six using methylation data from 288 TP53 pathogenic variant carriers. An external test set of 82 TP53 pathogenic variant carriers was used to validate our model. To increase the signal-to-noise ratio, methylation probes associated with TP53 status were retained and probes associated with aging were removed. In our study, we were primarily interested in minimizing the false negative rate (i.e. to reduce the number of patients who developed cancer before the age of six but were undetected by our algorithm. Findings: We correctly predicted whether the first tumor will occur before the age of six with an accuracy of 79% in our external test set. Importantly, our model classified 90% of the patients that developed cancer prior to the age of six correctly. In addition, 81% of the individuals without cancer in the external test set were predicted correctly. Interpretation: Our tool provides additional value to clinicians in stratifying patients into low- or high-risk groups of developing early-onset malignancies, and helps inform rational use of clinical surveillance tools for early cancer detection, with the ultimate aim to improve overall patient outcomes. Citation Format: Vallijah Subasri, Benjamin Brew, Lauren Erdman, Tanya Guha, Jordan R. Hansford, Elizabeth Cairney, Carol Portwine, Christine Elser, Jonathan L. Finlay, Kim E. Nichols, Wendy Kohlmann, Noa Alon, Ana Novokmet, Ledia Brunga, Anita Villani, Kelvin C. de Andrade, Payal P. Khincha, Sharon A. Savage, Joshua D. Schiffman, David Malkin, Anna Goldenberg. DNA methylation predicts early onset of primary tumor in patients with Li-Fraumeni syndrome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1428.