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Hautphenne, S.

Publications and source records attributed to Hautphenne, S..

2 recordsLinked to original sources

Statistical inference of the cellular origin of chronic myeloid leukemia using a discrete-parameter ABC-PMC framework

Chronic myeloid leukemia (CML) arises from the BCR::ABL1 fusion gene, but the exact stage of cellular differentiation at which the first leukemic cell emerges remains uncertain. We develop a stochastic 27-compartment model of hematopoiesis (blood cell development) using a continuous-time multitype branching process to capture the dynamics of both healthy and cancer cells. To infer the origin of CML, we develop a discrete-parameter Approximate Bayesian Computation - Population Monte Carlo (ABC-PMC) algorithm, tailored to estimate the posterior distribution for the stage of differentiation at which the first cancer cell appeared. Applied to patient data, our method consistently identifies the stem cell compartment as the most likely source of CML. These findings improve understanding of disease initiation and demonstrate the power of discrete-parameter ABC-PMC for statistical inference in complex biological systems. Author summaryChronic myeloid leukemia is a blood cancer that begins when a genetic change called the BCR::ABL1 fusion gene appears in one cell. Although this disease has been widely studied, some questions remain, particularly about the exact stage of blood cell development at which the first cancer cell arises. In our study, we build a stochastic model based on a biological hematopoiesis model that represents how blood cells grow and mature through many stages, from stem cells to fully developed white blood cells. Using this mathematical model, we develop a statistical approach that can infer from patient data where in this hierarchy the disease most likely began. When we apply the method to clinical data from patients with chronic myeloid leukemia, it consistently points to the stem cell stage as the most probable origin. By linking biological data with mathematical modelling, our work offers new insight into how this cancer starts and shows how quantitative approaches can help answer questions that are difficult to test experimentally.

systems biology↗

Linking micro and macroevolution in the presence of migration

1. The process of speciation is of key importance in evolutionary biology because it shapes macroevolutionary patterns. This process starts at the microevolutionary level, for instance, when two subpopulations evolve towards different phenotypic optima. The speed at which these optima are reached is controlled by the degree of stabilising selection, which pushes a mean trait towards an optimum within subpopulations, and ongoing migration that pulls the mean phenotype away from that optimum. Traditionally, macro phenotypic evolution with selection has been modelled by Ornstein-Uhlenbeck (OU) processes, but these models have ignored the role of migration within species.\n\n2. Here, our goal is to reconcile the processes of micro and macroevolution by modelling migration during speciation. More precisely, we introduce an OU model where migration happens between two subpopulations within a branch of a phylogeny and this migration decreases over time as it happens during speciation. We then use this model to study the evolution of trait means along a phylogeny, as well as the way phenotypic disparity between species changes with successive epochs.\n\n3. We show that ignoring the effect of migration in sampled time-series data leads to a significant underestimation of the selective forces acting upon it. We also show that migration decreases the expected phenotypic disparity between species and we show the effect of migration in the particular case of niche filling. We further introduce a method to jointly estimate selection and migration from time-series data.\n\n4. Our model extends standard results of interactions selection-migration in a microevolutionary time frame across multiple speciation events at a macroevolutionary scale. Our results further proof that not accounting for gene flow has important consequences in inferences at both the micro and macroevolutionary scale.

evolutionary biology↗