Hybrid Ornstein–Uhlenbeck–Branching Modeling of Pediatric Leukemia Evolution: A Computational Extension and Cohort-Level Application
Abstract
Pediatric cancers evolve under developmental constraints that limit mutational diversity yet preserve adaptive potential. A computational extension of the Hybrid Ornstein–Uhlenbeck (OU)–Branching framework was developed to model clonal diversification and phenotypic stabilization in pediatric leukemia. The OU component captures mean-reverting dynamics representing developmental homeostasis, while the branching component introduces stochastic lineage bifurcation and extinction. Using de-identified clinical metadata from Ahlgren et al . ( Nature Communications , 2025; 16:8964), the model simulates patient-specific evolutionary trajectories across relapse categories and disease subtypes (B-ALL, T-ALL, MPAL, AML). Simulations reproduce observed clinical trends—rapid relapse and limited diversification in early or refractory KMT2A-r ALL, and slower, therapy-resistant relapses in AML. Group- and patient-level analyses demonstrate how the balance between stabilizing selection ( θ ) and diversification rate ( λ) determines clonal persistence and phenotypic drift. This computational implementation provides a quantitative framework for linking developmental constraint, clonal diversity, and therapeutic response in pediatric malignancies and establishes a tractable platform for model-driven hypothesis testing and translational oncology.
Statement of Relationship to Prior Work
This preprint represents a computational and cohort-level extension of the hybrid OU–Branching framework introduced in the author’s manuscript currently under review at Frontiers in Oncology (“A hybrid Ornstein–Uhlenbeck–branching framework unifies microbial and pediatric tumor evolution,” Manuscript ID 1727973). The Frontiers paper focuses on experimental validation and cross-domain analogies between microbial long-term evolution experiments (LTEE) and pediatric tumor evolution, emphasizing biological interpretation. In contrast, this preprint focuses on clinical modeling, patient specific simulations, and computational methods applied to pediatric KMT2A-rearranged leukemia. No data, text, or figures are duplicated from the in-review article. All code and simulations presented here are novel and will be released upon publication.
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