Computational tools for assessing gene therapy under branching process models of mutation
Timothy C Stutz, Janet S. Sinsheimer, Mary Sehl, Jason Xu

TL;DR
This paper develops a three-type branching process model to study mutation accumulation in stem cells post-gene therapy, focusing on the emergence of double-mutants with leukemogenic potential, and explores how mutation dynamics influence risks.
Contribution
It introduces a novel three-type branching process model for mutation accumulation in stem cells, providing quantitative tools to assess leukemia risk after gene therapy.
Findings
Increasing single-mutant numbers raises double-mutant probability.
Single-mutants can be proliferative without significantly increasing risk if initial insertion avoids mutants.
Model behavior varies with birth rates and initial population sizes.
Abstract
Multitype branching processes are ideal for studying the population dynamics of stem cell populations undergoing mutation accumulation over the years following transplant. In such stochastic models, several quantities are of clinical interest as insertional mutagenesis carries the potential threat of leukemogenesis following gene therapy with autologous stem cell transplantation. In this paper, we develop a three-type branching process model describing accumulations of mutations in a population of stem cells distinguished by their ability for long-term self-renewal. Our outcome of interest is the appearance of a double-mutant cell, which carries a high potential for leukemic transformation. In our model, a single-hit mutation carries a slight proliferative advantage over a wild-type stem cells. We compute marginalized transition probabilities that allow us to capture important…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCancer Genomics and Diagnostics · Single-cell and spatial transcriptomics · Evolution and Genetic Dynamics
