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

Cancer comprehensive genomic profiling tests are increasingly used, but drug response rates to matched therapies remain limited, indicating a gap between genomic data and clinical benefit. However, existing cancer evolution simulations focus mainly on basic biology and rarely provide patient‑specific predictions for therapy response. We present tugMedi, a cancer‑cell evolution simulator designed for cancer genome medicine. Integrating patient‑specific genomic data from next‑generation sequencing and radiological imaging, tugMedi reconstructs tumor features and growth, enabling real‑time predictions of clonal dynamics under drug interventions. It explicitly models copy number alterations and SNVs on parental chromosomes with recessive/dominant modes, capturing intra‑tumor heterogeneity and loss‑of‑heterozygosity to yield precise variant allele frequencies and tumor purity estimates. Synthetic tests confirmed recovery of ground‑truth tumor parameters and trajectories. For TCGA samples, tugMedi provided ensemble forecasts of drug response, tumor shrinkage, and recurrence times under specific drug conditions, demonstrating the feasibility of simulation‑driven genome medicine using patient‑derived virtual tumors.