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Published on: September 19, 2019
Modeling constrained tumor evolution through hybrid Ornstein-Uhlenbeck and branching dynamics
1Department of Biology, Fisher College, Boston, MA, USA.
Iscience
|August 7, 2026
Summary
This study introduces a new computational model to understand how pediatric leukemia evolves under treatment. The Ornstein-Uhlenbeck-Branching model better explains tumor progression trajectories in children with KMT2A-rearranged acute leukemia.
Area of Science:
- Computational Biology
- Cancer Evolution
- Mathematical Oncology
Background:
- Pediatric leukemias exhibit complex evolution under developmental and therapeutic pressures.
- Understanding lineage diversification and phenotypic constraints is crucial for targeted therapies.
Purpose of the Study:
- To evaluate a hybrid Ornstein-Uhlenbeck (OU)-Branching framework for modeling pediatric leukemia evolution.
- To assess if stabilizing attraction improves the description of longitudinal tumor-state trajectories compared to Brownian motion.
Main Methods:
- Developed and applied a hybrid Ornstein-Uhlenbeck (OU)-Branching framework coupling continuous-state dynamics with discrete lineage events.
- Benchmarked OU dynamics against Brownian diffusion and other models using likelihood-based case-level comparison.
- Analyzed longitudinal variant-allele-frequency trajectories from pediatric KMT2A-rearranged acute leukemia using targeted sequencing.
Main Results:
- The OU-Branching model, OU diffusion, and Brownian diffusion were preferred in 6, 6, and 4 of 16 patients, respectively.
- Results indicate patient-level heterogeneity in evolutionary dynamics, including constrained mean reversion and branching-like reconfiguration.
- The Ornstein-Uhlenbeck (OU)-Branching framework demonstrated utility in describing tumor evolution.
Conclusions:
- Patient-level heterogeneity exists in pediatric leukemia evolution, encompassing constrained mean reversion, branching, and drift.
- The OU-Branching framework provides an interpretable scaffold for evolution-aware precision oncology.
- This modeling approach aids in understanding and potentially targeting pediatric leukemia progression.
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