Drug combinations screening using a Bayesian ranking approach based on dose-response models.

Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia.

Biosensing Extracellular Vesicle Subpopulations in Neurodegenerative Disease Conditions.

Extracellular vesicles (EVs) are secreted nanoparticles that are involved in intercellular communication and that modulate a wide range of biological processes in normal and disease conditions. However, EVs are highly heterogeneous in terms of origin in the cell, size, and density. As a result, complex protocols are required to identify and characterize specific EV subpopulations, limiting biomedical applications, notably in diagnostics. Here, we show that combining quartz crystal microbalance with dissipation (QCM-D) and nanoplasmonic sensing (NPS) provides a facile method to track the viscoelastic properties of small EVs. We applied this multisensing strategy to analyze small EVs isolated by differential ultracentrifugation from knock-in mouse striatal cells expressing either a mutated allele or wild-type allele of huntingtin (Htt), the Huntington's disease gene. Our results validate the sensing strategy coupling QCM-D and NPS and suggest that the mass and viscoelastic dissipation of EVs can serve as potent biomarkers for sensing the intercellular changes associated with the neurodegenerative condition.

FOXO3 targets are reprogrammed as Huntington's disease neural cells and striatal neurons face senescence with p16INK4a increase.

Neurodegenerative diseases (ND) have been linked to the critical process in aging-cellular senescence. However, the temporal dynamics of cellular senescence in ND conditions is unresolved. Here, we show senescence features develop in human Huntington's disease (HD) neural stem cells (NSCs) and medium spiny neurons (MSNs), including the increase of p16INK4a , a key inducer of cellular senescence. We found that HD NSCs reprogram the transcriptional targets of FOXO3, a major cell survival factor able to repress cell senescence, antagonizing p16INK4a expression via the FOXO3 repression of the transcriptional modulator ETS2. Additionally, p16INK4a promotes cellular senescence features in human HD NSCs and MSNs. These findings suggest that cellular senescence may develop during neuronal differentiation in HD and that the FOXO3-ETS2-p16INK4a axis may be part of molecular responses aimed at mitigating this phenomenon. Our studies identify neuronal differentiation with accelerated aging of neural progenitors and neurons as an alteration that could be linked to NDs.