Biology subjects

Vande Velde, S.

Publications and source records attributed to Vande Velde, S..

2 recordsLinked to original sources

bioRxiv2025

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↗

bioRxiv2024

Interferon-γ driven differentiation of monocytes into PD-L1+ and MHC II+ macrophages and the frequency of Tim-3+ tumor-reactive CD8+ T cells within the tumor microenvironment predict a positive response to anti-PD-1-based therapy in tumor-bearing mice

While immune checkpoint inhibitors have demonstrated durable responses in various cancer types, a significant proportion of patients do not exhibit favourable responses to these interventions. To uncover potential factors associated with a positive response to immunotherapy, we established a bilateral tumor model using P815 mastocytoma implanted in DBA/2 mice. In this model, only a fraction of tumor-bearing mice responds favourably to anti-PD-1 treatment, thus providing a valuable model to explore the influence of the tumor microenvironment (TME) in determining the efficacy of immune checkpoint blockade (ICB)-based immunotherapies. Moreover, this model allows for the analysis of a pretreatment tumor and inference of its treatment outcome based on the response observed in the contralateral tumor. Here, we demonstrated that tumor-reactive CD8+ T cell clones expressing high levels of Tim-3 were associated to a positive anti-tumor response following anti-PD-1 administration. Our study also revealed distinct differentiation dynamics in tumor-infiltrating myeloid cells in responding and non-responding mice. An IFN{gamma}-enriched TME appeared to promote the differentiation of monocytes into PD-L1pos MHC IIhigh cells in mice responding to immunotherapy. Monocytes present in the TME of non-responding mice failed to reach the same final stage of differentiation trajectory, suggesting that an altered monocyte to macrophage route may hamper the response to ICB. These insights will direct future research towards a temporal analysis of TAMs, aiming to identify factors responsible for transitions between differentiation states within the TME. This approach may potentially pave the way to novel strategies to enhance the efficacy of PD-1 blockade.

immunology↗