MVL-PLA2, a Snake Venom Phospholipase A2, Inhibits Angiogenesis through an Increase in Microtubule Dynamics and Disorganization of Focal Adhesions

Integrins are essential protagonists of the complex multi-step process of angiogenesis that has now become a major target for the development of anticancer therapies. We recently reported and characterized that MVL-PLA2, a novel phospholipase A2 from Macrovipera lebetina venom, exhibited anti-integrin activity. In this study, we show that MVL-PLA2 also displays potent anti-angiogenic properties. This phospholipase A2 inhibited adhesion and migration of human microvascular-endothelial cells (HMEC-1) in a dose-dependent manner without being cytotoxic. Using Matrigel™ and chick chorioallantoic membrane assays, we demonstrated that MVL-PLA2, as well as its catalytically inactivated form, significantly inhibited angiogenesis both in vitro and in vivo. We have also found that the actin cytoskeleton and the distribution of αvβ3 integrin, a critical regulator of angiogenesis and a major component of focal adhesions, were disturbed after MVL-PLA2 treatment. In order to further investigate the mechanism of action of this protein on endothelial cells, we analyzed the dynamic instability behavior of microtubules in living endothelial cells. Interestingly, we showed that MVL-PLA2 significantly increased microtubule dynamicity in HMEC-1 cells by 40%. We propose that the enhancement of microtubule dynamics may explain the alterations in the formation of focal adhesions, leading to inhibition of cell adhesion and migration

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome associated with COVID-19: An Emulated Target Trial Analysis.

RATIONALE: Whether COVID patients may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. OBJECTIVES: To estimate the effect of ECMO on 90-Day mortality vs IMV only Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO vs. no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 <80 or PaCO2 ≥60 mmHg). We controlled for confounding using a multivariable Cox model based on predefined variables. MAIN RESULTS: 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability at Day-7 from the onset of eligibility criteria (87% vs 83%, risk difference: 4%, 95% CI 0;9%) which decreased during follow-up (survival at Day-90: 63% vs 65%, risk difference: -2%, 95% CI -10;5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand, and when initiated within the first 4 days of MV and in profoundly hypoxemic patients. CONCLUSIONS: In an emulated trial based on a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and in regions with ECMO capacities specifically organized to handle high demand. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Metronomic Maintenance for High-Risk Pediatric Malignancies: One Size Will Not Fit All

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Targeted therapy with propranolol and metronomic chemotherapy combination: sustained complete response of a relapsing metastatic angiosarcoma

International audienceWe report here a case of a 69-year-old woman with a relapsing metastatic angiosarcoma treated with a combination of metronomic chemotherapy and propranolol. The beta blockers were added since the tumour was positive for betaadrenergic receptor. A complete response was quickly obtained and lasted for 20 months. With this case, the combination of metronomic chemotherapy and propranolol in angiosarcoma warrants additional studies and illustrates the potential of metronomics to generate innovative yet inexpensive targeted therapies for both high-income and low-/middle-income countries

Metronomics: Intrinsic Anakoinosis Modulator?

International audienceNo abstract availabl

Metronomic Chemotherapy: Direct Targeting of Cancer Cells after all?

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Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data

International audienceDrug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least 2-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic