Abstract
Precision oncology aims to tailor cancer treatment to individual patients by leveraging molecular data and computational tools. Digital twin modeling, computational representations of a patient’s disease state, has emerged as a promising framework for predicting personalized treatment responses. In this work, we review the integration of multi-omics data (genomics, transcriptomics, proteomics, metabolomics) with mechanistic cancer models to create digital twins capable of predicting patient-specific responses to combination therapies. We highlight key methodological advances, data challenges, model validation strategies, and clinical translation pathways. We conclude by discussing ethical, regulatory, and technical considerations and propose future directions to accelerate clinical adoption.
