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

Abstract

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 , 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.

Scalable inference of cell differentiation networks in gene therapy clonal tracking studies of haematopoiesis

“…We tested our proposed method Karen and compared it with the state-of-the-art approaches, such as the generalized least squares (GLS) method ( Pellin et al 2019 ), the maximum likelihood method RestoreNet ( Del Core et al 2023 ), and the branchCorr method ( Xu et al 2019 ). The validation and comparisons were made in terms of robustness against (i) the sampling frequency τ , (ii) the fraction ζ of false-negatives, and (iii) the magnitude of the measurement noise parameters ρ 0 and ρ 1 .…”