Genomics-Empowered AI for Personalized Cancer Risk Assessment, Monitoring, and Prevention

About This Grant

Project Summary This proposed MAGen development site aims to develop genomics and multi-modal artificial intelligence (AI) models to transform personalized cancer risk assessment, monitoring, and prevention. A substantial gap exists between the theoretical potential of genomics-based AI predictions and their practical application in clinical and population healthcare settings. The clinical classification of genetic variants is hindered by insufficient data to classify ultra-rare variants, particularly those found in non-European populations. Moreover, despite significant advances in AI across fields, we lack AI models that can combine diverse streams of health data to accurately predict disease risk across a person’s life course. Finally, the real-world effectiveness of these AI models remains untested and their ethical, legal, and social implications (ELSI) are unclear. To address these challenges, our primary goal is to develop state-of-the-art (SOTA) AI models that can accurately identify pathogenic variants affecting DNA repair genes and predict cancer risks over the life course of high-risk carriers, thereby optimizing screening and prevention strategies in an ELSI-informed manner. Our multidisciplinary team comprises experts in computational genomics, AI/ML, health informatics, statistical genetics, medical genetics, population health, oncology, and ELSI research from Icahn School of Medicine at Mount Sinai (ISMMS), Boston Children’s Hospital/Harvard, and Columbia University, and has complementary and extensive experience in consortium and team science projects. In our proposed project for MAGen, Aim 1 will develop robust genomic AI models for identifying protein-disrupting missense variants that confer high cancer risks. Aim 2 will combine other genetic factors, including common and rare variant polygenic risk scores (PRS), and non-genetic factors, including EHR, longitudinal lab markers, SDoH, and digital pathology, to predict cancer risk over the life course and optimize screening recommendations for carriers. Aim 3 will cross-validate AI models in real-world population biobanks and determine their clinical impact. Aim 4 will construct an ELSI framework and conduct ELSI projects to evaluate the multi-faceted impacts of AI-driven genetic diagnostics. Aim 5 will disseminate AI model/predictions, cross-validation data, and ELSI recommendations. The completion of these Aims will bring genomics-based and multi-modal AI closer to the advancement of personalized medicine in real-world settings by more accurately classifying pathogenic variants, optimizing the timing of screening, and identifying key lifestyle and medical prevention strategies that could ultimately save lives from cancer.