For individuals with Li-Fraumeni Syndrome (LFS), a rare hereditary condition caused by inherited TP53 mutations, cancer risk is extraordinarily high and begins in early childhood. While families often endure years of intensive imaging, access to specialized whole-body MRI programs varies widely, and false positives, anxiety, and missed interval cancers remain persistent problems. Around the world, many patients have no reliable surveillance system at all, despite facing a near-certain lifetime cancer risk. Recent advances suggest that traces of tumor- and immune-derived DNA circulating in blood or cerebrospinal fluid could flag cancer activity long before symptoms arise, creating an opportunity for more frequent, less burdensome monitoring. However, fragmented testing approaches and inconsistent data analysis limit their clinical reach. There is a pressing global need for an integrated, scalable, and equitable platform that brings high-quality cancer surveillance for every LFS carrier, regardless of geography or resources.
This project will build a unified surveillance framework by combining genome-wide methylation profiling of cell-free DNA, immune-repertoire sequencing, and machine-learning–enhanced whole-body MRI interpretation. Using extensive longitudinal biobanks assembled by several international partners, the team will evaluate whether enzymatic methylation sequencing can simultaneously resolve copy-number shifts, mutational patterns, and tissue-of-origin signals in a single assay. Parallel analyses of T-cell receptor sequences will map emergent anticancer immune responses that may precede radiologic or cell-free DNA evidence of disease. These multi-layered features will feed into a cloud-based analytics platform capable of harmonizing imaging, molecular, and clinical data for real-time risk prediction. This work could redefine lifelong surveillance for LFS carriers and establish a blueprint for precision monitoring across additional hereditary cancer syndromes.