Predicting Treatment-Free Remission in Chronic Myeloid Leukemia with an Integrated Tumor-Immune Model

Together with our guest scientist Artur Fassoni, Ingo Roeder and Ingmar Glauche have developed a novel computational model aimed at predicting treatment-free remission (TFR) outcomes in patients with Chronic Myeloid Leukemia (CML). The study with the titled “Predicting treatment-free remission outcomes in Chronic Myeloid Leukemia patients using an integrated model of tumor-immune dynamics,” highlights the crucial role of tumor-immune interactions in cancer treatment and is now available as a preprint at bioRxiv.

The research leverages classical ecological modeling and integrates time-course data on natural killer (NK) cells, their function, and tumor-induced suppression. This approach allows our team to better understand the dynamics between residual leukemia cells and the immune system, providing insights into why some patients maintain remission after stopping treatment, while others experience relapse.

While key parameters for predicting treatment outcomes remain uncertain, the model’s incorporation of immune cell dynamics and response to tyrosine kinase inhibitor (TKI) dose adjustments offers promising avenues for more accurate predictions. This work is another step forward in personalized medicine, showing potential not only for improving CML treatment but also for broader applications in adaptive cancer therapies.