Stochastic inference of clonal dominance in gene therapy studies
Abstract
Clonal 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.