Computational tools for assessing gene therapy under branching process models of mutation

Area of Science:

• Stochastic modeling
• Population dynamics
• Biomathematics

Background:

• Stem cell transplantation involves mutation accumulation, posing risks like leukemogenesis.
• Understanding insertional mutagenesis is crucial for gene therapy safety.
• Multitype branching processes model complex cell population dynamics.

Purpose of the Study:

• Develop a three-type branching process model for stem cell mutation dynamics.
• Quantify the risk of double-mutant cell formation, a precursor to leukemia.
• Analyze the impact of mutation rates and cell populations on leukemic transformation risk.

Main Methods:

• Constructed a three-type branching process model for stem cells with varying mutation states.
• Computed marginalized transition probabilities to track mutation accumulation.
• Performed numerical simulations varying initial cell populations and proliferation rates.

Main Results:

• Initial single-mutant stem cell populations strongly influence double-mutant cell appearance.
• Single-mutant cells with proliferative advantages are relatively safe if initial mutations are avoided.
• Model provides quantitative insights into mutation dynamics and leukemic transformation probability.

Conclusions:

• The developed branching process model effectively captures critical aspects of stem cell mutation dynamics.
• Findings inform strategies for safer gene therapy by managing mutation risks.
• The approach is applicable to various multi-stage population processes, including cancer modeling.