Biology subjects

Maucort-Boulch, D.

Publications and source records attributed to Maucort-Boulch, D..

4 recordsLinked to original sources

bioRxiv2022

An in vivo avian model of human melanoma to perform rapid and robust preclinical studies

Metastatic melanoma patients carrying a BRAFV600 mutation can be treated with BRAF inhibitors (BRAFi), in combination with MEK inhibitors (MEKi), but innate and acquired resistance invariably occurs. Resistance can involve transcriptional- and epigenetic-based phenotypic adaptations, as yet unpredictable. Predicting patient response to targeted therapies is crucial to guide clinical decision. We describe here the development of a highly efficient patient-derived xenograft model adapted to patient melanoma biopsies, using the avian embryo as a host (AVI-PDX). In this in vivo paradigm, we depict a fast and reproducible tumor engraftment of patient samples within the embryonic skin, preserving key molecular and phenotypic features. We show that sensitivity and resistance to BRAFi/MEKi targeted therapies can be reliably modeled in these AVI-PDX, as well as synergies with other drugs, such as HDACi. We further provide proof-of-concept that the AVI-PDX models the diversity of responses of melanoma patients to BRAFi/MEKi, within days, hence positioning it as a valuable tool for the design of personalized medicine assays and for the evaluation of novel combination strategies.

cancer biology↗

bioRxiv2022

Fusion-negative Rhabdomyosarcoma 3D-organoids as an innovative model to predict resistance to cell death inducers

Rhabdomyosarcoma (RMS) is the main form of soft-tissue sarcoma in children and adolescents. For 20 years, and despite international clinical trials, its cure rate has not really improved, and remains stuck at 20% in case of relapse. The definition of new effective therapeutic combinations is hampered by the lack of reliable models, which complicate the transposition of promising results obtained in pre-clinical studies into efficient solutions for young patients. Inter-patient heterogeneity, particularly in the so-called fusion-negative group (FNRMS), adds an additional level of difficulty in optimizing the clinical management of children and adolescents with RMS. Here, we describe an original 3D-organoid model derived from relapsed FNRMS and show that it finely mimics the characteristics of the original tumor, including inter- and intra-tumoral heterogeneity. Moreover, we have established the proof-of-concept of their preclinical potential by re-evaluating the therapeutic opportunities of targeting apoptosis in FNRMS from a streamlined approach based on the exploitation of bulk and single-cell omics data.

cancer biology↗

bioRxiv2019

Modeling individual disability evolution in multiple sclerosis patients based on longitudinal multimodal imaging and clinical data

BackgroundThe individual disease evolution of multiple sclerosis (MS) is very different from one patient to another. Therefore, the prediction of long-term disability evolution is difficult based on only clinical information. Magnetic resonance imaging (MRI) provides a very efficient tool to distinguish between healthy and abnormal brain tissue, monitor disease evolution, and help decision-making for personalized treatment of MS patients.\n\nObjectiveWe aim to develop a patient-specific model to predict individual disease evolution in MS, using demographic, clinical, and imaging data that were collected at study onset.\n\nMethodsThe study included 75 patients tracked over 5 years. The latent class linear mixed model was used to consider individual and unobserved subgroup variability. First, the clinical model was established with demographic and clinical variables to predict clinical disease evolution. Second, the imaging model was built using the multimodal imaging variables. Third, the imaging variables were added one by one, two by two, and all three together to investigate their contribution to the clinical model. The clinical disability is measured with the Expanded Disability Status Scale (EDSS). The performances of the clinical, imaging, and the combined models were compared mainly using the Bayesian Information Criterion (BIC). The mean of the posterior probabilities was also given as the secondary performance evaluation criterion.\n\nResultsThe clinical model gave higher BIC value than imaging and any combined models. The means of the posterior probabilities given by the three models were over 0.94. The clinical model clustered the patients into two latent classes: stable evolution class (n=6, 88%) and severe evolution class (n=9, 12%).\n\nConclusionThe latent class linear mixed model may provide a well-fitted prediction for the disability evolution in MS patients, thus giving further information for personalized treatment decisions after thorough validation with a larger and independent dataset.

neuroscience↗

bioRxiv2019

Comparison of classification methods for tissue outcome after ischemic stroke

In acute ischemic stroke, identifying brain tissue at high risk of infarction is important for clinical decision-making. This tissue may be identified with suitable classification methods from magnetic resonance imaging (MRI) data. The aim of the present study was to assess comparatively the performance of five popular classification methods (Adaptive Boosting (ADA), Logistic Regression (LR), Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machine (SVM)) in identifying tissue at high risk of infarction on human voxel-based brain imaging data. The classification methods were used with eight MRI parameters including diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) obtained in 55 patients. Sensitivity, specificity, the area under the receiver operating curve (ROC) as well as the area under the precision-recall curve criteria were used to compare the method performances. The methods performed equally in terms of sensitivity and specificity while the results of the area under the ROC were significantly better for ADA, LR, ANN and RF. However, there was no statistically significant difference between the performances of these five classification methods regarding the area under the precision-recall curve, which was the main comparison metric.

neuroscience↗