Stochastic inference of clonal dominance in gene therapy studies

Abstract

AbstractClonal dominance is a wake-up-call for adverse events in gene therapy applications. This phenomenon has mainly been observed as a consequence of a malignancy progression, and, in some rare cases, also during normal haematopoiesis. We propose here a random-effects stochastic model that allows for a quick detection of clonal expansions that possibly occur during a gene therapy treatment.Starting from the Ito-type equation, the dynamics of cells duplication, death and differentiation at clonal level without clonal dominance can be described by a local linear approximation. The parameters of the base model, which are inferred using a maximum likelihood approach, are assumed to be shared across the clones. In order to incorporate the possibility of clonal dominance, we extend the base model by introducing random effects for the clonal parameters. This extended model is estimated using a tailor-made expectation maximization algorithm. The main idea of this paper is to compare the base and the extended models in high dimensional clonal tracking datasets by means of Akaike Information Criterion in order to detect the presence of clonal dominance. The method is evaluated using a simulation study, and is applied to investigating the dynamics of clonal expansion in a in-vivo model of rhesus macaque hematopoiesis.Author summaryPreventing or quickly detecting clonal dominance is an important aspect in gene therapy applications. Over the past decades, clonal tracking has proven to be a cutting-edge analysis capable to unveil population dynamics and hierarchical relationships in vivo. For this reason, clonal tracking studies are required for safety and long-term efficacy assessment in preclinical and clinical studies. In this work we propose a random-effects stochastic framework that allows to investigate events of clonal dominance using high-dimensional clonal tracking data. Our framework is based on the combination between stochastic reaction networks and mixed-effects generalized linear models. We have shown in a simulation study and in a real world application that our method is able to detect the presence of clonal expansions. Our tool can provide statistical support to biologists in gene therapy surveillance analyses.