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

MOTIVATION: Investigating cell differentiation under a genetic disorder offers the potential for improving current gene therapy strategies. Clonal tracking provides a basis for mathematical modelling of population stem cell dynamics that sustain the blood cell formation, a process known as haematopoiesis. However, many clonal tracking protocols rely on a subset of cell types for the characterization of the stem cell output, and the data generated are subject to measurement errors and noise. RESULTS: We propose a stochastic framework to infer dynamic models of cell differentiation from clonal tracking data. A state-space formulation combines a stochastic quasi-reaction network, describing cell differentiation, with a Gaussian measurement model accounting for data errors and noise. We developed an inference algorithm based on an extended Kalman filter, a nonlinear optimization, and a Rauch-Tung-Striebel smoother. Simulations show that our proposed method outperforms the state-of-the-art and scales to complex structures of cell differentiations in terms of nodes size and network depth. The application of our method to five in vivo gene therapy studies reveals different dynamics of cell differentiation. Our tool can provide statistical support to biologists and clinicians to better understand cell differentiation and haematopoietic reconstitution after a gene therapy treatment. The equations of the state-space model can be modified to infer other dynamics besides cell differentiation. AVAILABILITY AND IMPLEMENTATION: The stochastic framework is implemented in the R package Karen which is available for download at https://cran.r-project.org/package=Karen. The code that supports the findings of this study is openly available at https://github.com/delcore-luca/CellDifferentiationNetworks.

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 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.

Hematopoietic reconstitution dynamics of mobilized- and bone marrow-derived human hematopoietic stem cells after gene therapy.

Mobilized peripheral blood is increasingly used instead of bone marrow as a source of autologous hematopoietic stem/progenitor cells for ex vivo gene therapy. Here, we present an unplanned exploratory analysis evaluating the hematopoietic reconstitution kinetics, engraftment and clonality in 13 pediatric Wiskott-Aldrich syndrome patients treated with autologous lentiviral-vector transduced hematopoietic stem/progenitor cells derived from mobilized peripheral blood (n = 7), bone marrow (n = 5) or the combination of the two sources (n = 1). 8 out of 13 gene therapy patients were enrolled in an open-label, non-randomized, phase 1/2 clinical study (NCT01515462) and the remaining 5 patients were treated under expanded access programs. Although mobilized peripheral blood- and bone marrow- hematopoietic stem/progenitor cells display similar capability of being gene-corrected, maintaining the engineered grafts up to 3 years after gene therapy, mobilized peripheral blood-gene therapy group shows faster neutrophil and platelet recovery, higher number of engrafted clones and increased gene correction in the myeloid lineage which correlate with higher amount of primitive and myeloid progenitors contained in hematopoietic stem/progenitor cells derived from mobilized peripheral blood. In vitro differentiation and transplantation studies in mice confirm that primitive hematopoietic stem/progenitor cells from both sources have comparable engraftment and multilineage differentiation potential. Altogether, our analyses reveal that the differential behavior after gene therapy of hematopoietic stem/progenitor cells derived from either bone marrow or mobilized peripheral blood is mainly due to the distinct cell composition rather than functional differences of the infused cell products, providing new frames of references for clinical interpretation of hematopoietic stem/progenitor cell transplantation outcome.