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Issue 335: Our recent work using digital twins shows how personalized chemotherapy regimens can be used to improve outcomes for individual triple negative breast cancer patients. We wanted to highlight the synergy that the digital twins concept provides in cancer, by visualizing the link between clinical measurements and mathematical simulations. The study provides a methodology for balancing a range of potentially conflicting aspects of personalized medicine. This includes the push and pull between response vs. toxicity, clinical time frames vs. model accuracy/complexity, and uncertainty vs. reliability; these features are addressed with constrained optimization, reduced order modeling, and Bayesian calibration, respectively. The modeling study showed that patient responses can be improved with an equivalent toxicity, or toxicity can be reduced with an equivalent response, all of which was assessed with data from breast cancer patients.