Mathematical oncology comes to the clinic: A data-driven treatment for financial toxicity?

Abstract

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 H€ahnel 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.