Mathematical Oncology Comes to the Clinic: A Data-Driven Treatment for Financial Toxicity?

Over the past two decades, progress in tumor immunology and targeted therapy has reshaped oncology, and in many cases, reshaped the course of once-intractable diseases. Yet the cost of its clinical manifestation has created a disease of its own: financial toxicity, the burden of drugs such as imatinib, where a year's supply can easily cost as much as a house. Equally rapid progress in mathematical oncology over this time period has often come in the form of fundamental, rather than applied, advances. However, in new work by Hahnel and colleagues, we can see the outlines of a viable treatment for financial toxicity: precise, dynamic, clinically validated, and immune-aware models, able to accurately identify patients who remain disease-free in the months and years after discontinuing effective, but pricey, targeted therapies.