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TKI dose reduction in CML
of proliferating LCs which potentially stimulate a patient’s immune response.
Although TKI treatment response is still substantially heterogeneous with respect to many clinical parameters, the well-defined CML phenotype and the accessibility of kinetic data on treatment response has made CML a pri- mary target for mathematical model approaches in oncol- ogy.4-6,22 It has also been recognized that the regulation of stem cell quiescence under continuing TKI therapy affects the kinetics of disease eradication.5,22 Here, we consider a conservative scenario, which only assumes a direct cyto- toxic TKI effect on proliferating LCs, but does not alter LSCs quiescence. If assuming an additional TKI-depen- dent reduction of the activation rate 𝑝𝑋𝑌, as previously dis- cussed,5,17,35-37 any favorable dose de-escalation would lead to an even better long-term response (Online Supplementary Figure S3). Recently, using an analytical approach very similar to our formulation, Werner et al. developed a mathematical model that allows an estima- tion of LSC fractions to be made from longitudinal measurements of tumor load.38 However, this model assumed no direct therapeutic effect on LSCs, which are, therefore, increasing even during treatment.
In summary, we show that the systematic assessment of available clinical data by means of mathematical models has direct clinical implications, but also reveals underlying disease and treatment mechanisms. Our results are sub- stantiated by and support the interim findings of the ongo-
ing DESTINY trial, thereby suggesting a change in current clinical practice and the consideration of TKI dose de-esca- lation strategies in maintenance therapy. As with any the- oretical prediction, our results represent hypotheses that need to be validated in clinical trials. However, modeling approaches can substantially support the design of inform- ative trials. We consider this simulation study as a proof- of-concept for the use of systems medicine to optimize treatment efficacy and to minimize health care costs.
Acknowledgments
Our analysis is based on previously published data from the German cohort of the IRIS trial and the CML IV study. We would like to acknowledge the PIs, study groups and reference labs involved in these studies for generating and providing the data. In particular, we thank Andreas Hochhaus, Rüdiger Hehlman and Martin Mueller.
Funding
The research of ACF was supported by the Excellence Initiative of the German Federal and State Governments (Dresden Junior Fellowship) and by CAPES/Pós-Doutorado no Exterior Grant number 88881.119037/2016-01. This work was further sup- ported by the German Federal Ministry of Education and Research (www.bmbf.de/en/), Grant number 031A424 “HaematoOpt” to IR and Grant number 031A315 “MessAge” to IG. We acknowledge support from the Open Access Publication Funds of the SLUB/TU Dresden.
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