tugMedi: simulator of cancer-cell evolution for personalized medicine based on the genomic data of patients

Abstract

Cancer comprehensive genomic profiling tests are increasingly used, but drug response rates remain limited. Simulations forecasting cancer progression could aid targeted therapies; however, existing simulations focus mainly on basic biology. We present tugMedi, a cancer-cell evolution simulator designed for cancer genome medicine. By integrating patient-specific genomic and imaging data, tugMedi reconstructs each tumors genomic features and growth behavior, enabling real-time predictions of clonal dynamics under virtual drug regimens. tugMedi explicitly models copy number alterations and SNVs on parental chromosomes in recessive and dominant modes, capturing loss-of-heterozygosity and yielding precise variant allele frequencies and tumor contents. It handles mutations in cancer-related genes with real exon-intron structures by a fast algorithm. For TCGA samples, it provided ensemble predictions of clonal dynamics, allowing extraction of drug response, tumor-size shrinkage rates, and time to recurrence under specific drug conditions. tugMedi represents a first step toward simulation-driven genome medicine based on a patient-derived virtual tumor.