A mixed-effects stochastic model reveals clonal dominance in gene therapy safety studies

Background Mathematical models of haematopoiesis can provide insights on abnormal cell expansions (clonal dominance), and in turn can guide safety monitoring in gene therapy clinical applications. Clonal tracking is a recent high-throughput technology that can be used to quantify cells arising from...

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Bibliographic Details
Published in:BMC bioinformatics Vol. 24; no. 1; pp. 228 - 19
Main Authors: Del Core, Luca, Pellin, Danilo, Wit, Ernst C., Grzegorczyk, Marco A.
Format: Journal Article
Language:English
Published: London BioMed Central 02.06.2023
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Algorithms
Analysis
Approximation
Bioinformatics
Biomedical and Life Sciences
Cell death
Cell differentiation
Clonal dominance
Clone Cells
Cloning
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer Simulation
Differential equations
Dominance
E-M algorithm
Gene therapy
Generalized linear models
Genes
Genetic aspects
Hematopoietic stem cells
Hemopoiesis
Heterogeneity
Learning models (Stochastic processes)
Life Sciences
Likelihood Functions
Mathematical models
Microarrays
Mixed-effects models
Models, Theoretical
Mutagenesis
Parameters
Population biology
Population dynamics
Safety
Safety analysis
Safety and security measures
Single-cell technologies
Statistical models
Stem cell research
Stem cells
Stochastic models
Stochastic Processes
Stochastic reaction networks
tau$$ τ -Leaping
Tracking
Transplantation
Netherlands
Stochastic reaction networks
Leaping
Gene therapy
Clonal dominance
Mixed-effects models
E-M algorithm
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background Mathematical models of haematopoiesis can provide insights on abnormal cell expansions (clonal dominance), and in turn can guide safety monitoring in gene therapy clinical applications. Clonal tracking is a recent high-throughput technology that can be used to quantify cells arising from a single haematopoietic stem cell ancestor after a gene therapy treatment. Thus, clonal tracking data can be used to calibrate the stochastic differential equations describing clonal population dynamics and hierarchical relationships in vivo. Results In this work we propose a random-effects stochastic framework that allows to investigate the presence of events of clonal dominance from high-dimensional clonal tracking data. Our framework is based on the combination between stochastic reaction networks and mixed-effects generalized linear models. Starting from the Kramers–Moyal approximated Master equation, the dynamics of cells duplication, death and differentiation at clonal level, can be described by a local linear approximation. The parameters of this formulation, which are inferred using a maximum likelihood approach, are assumed to be shared across the clones and are not sufficient to describe situation in which clones exhibit heterogeneity in their fitness that can lead to clonal dominance. In order to overcome this limitation, we extend the base model by introducing random-effects for the clonal parameters. This extended formulation is calibrated to the clonal data using a tailor-made expectation-maximization algorithm. We also provide the companion  package RestoreNet , publicly available for download at https://cran.r-project.org/package=RestoreNet . Conclusions Simulation studies show that our proposed method outperforms the state-of-the-art. The application of our method in two in-vivo studies unveils the dynamics of clonal dominance. Our tool can provide statistical support to biologists in gene therapy safety analyses.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-023-05269-1