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Lagarde, N.

Publications and source records attributed to Lagarde, N..

3 recordsLinked to original sources

Vascularized tumor organoids enable immunotherapy testing in tumor-remodeled stroma

Accurately predicting whether a new molecule will be safe and effective in humans remains a fundamental challenge in biomedical research, requiring models that can reliably inform clinical outcomes. In oncology, drug attrition rates remain disproportionately high compared to other therapeutic areas, largely due to the poor translatability of conventional preclinical models that fail to capture the complexity of human immune-tumor-stroma interactions (1, 2). Here, we describe human vascularized organoids (VascO), formed through self-assembly of an immune cell perfused capillary network and stromal compartment, interfacing with patient-derived colorectal cancer organoids that enable the combined safety and efficacy assessment of tumor-targeted immunotherapies. VascO generates functional and integrated vascular networks within tumor organoids that sustain organoid growth and recapitulate the morphology and functionality of tumor vasculature. Single-cell transcriptomics reveals that the tumor organoids-educated VascO niche reprograms otherwise healthy fibroblasts into canonical cancer-associated fibroblast states, adopting my-ofibroblastic or inflammatory signatures, and faithfully mirroring donor-specific tumor cues. In parallel, VascO tumor vessels display an expanded tip-cell program, aberrant morphology, and VEGF/ANG2-driven permeability, recapitulating defining hallmarks of tumor vasculature. Immune perfusion with a tumor-targeted T cell bispecific antibody induces dynamic immune trafficking, infiltration and engagement of effector cells with the tumor tissue, leading to its effective killing. A protease-activated variant restricts T cell activation to the tumor microenvironment, retaining potent tumoricidal activity while sparing donor-matched healthy colon organoids, and thereby improving the therapeutic index. VascO thus models a patient-specific, tumor-educated microenvironment that is amenable to assessing the combined safety and efficacy of new drug candidates and provides a scalable platform to improve the preclinical assessment of innovative cancer immunotherapies.

bioengineering↗

PLO3S : Protein LOcal Surficial Similarity Screening

The study of protein molecular surfaces enable to better understand and predict protein interactions. Different methods have been developed in computer vision to compare surfaces that can be applied to protein molecular surfaces. The present work proposes a method using the the Wave Kernel Signature : Protein LOcal Surficial Similarity Screening (PLO3S). The descriptor of the PLO3S method is a local surface shape descriptor projected on a unit sphere mapped onto a 2D plane and called Surface Wave Interpolated Maps (SWIM). PLO3S allows to rapidly compare protein surface shapes through local comparisons to filter large protein surfaces datasets in protein structures virtual screening protocols.

bioinformatics↗

Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions

Protein-protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein-protein interactions. Cross-docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross-docking simulations of 358 proteins with two different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity-sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross-docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, i.e. partners not included in the original cross-docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.\n\nAbbreviationsANOVA: ANalysis Of Variance; AUC: Area Under the Curve; Best Interface: BI; CAPRI: Critical Assessment of Prediction of Interactions; CC-D: Complete Cross-Docking; DNA: DesoxyriboNucleic Acid; FDR: False Discovery Rate; FRIres(type): Fraction of each Residue type in the Interface; FP: False Positives; GI: Global Interface; HCMD: Help Cure Muscular Dystrophy; JET: Joint Evolutionary Tree; MAXDo: Molecular Association via Cross Docking; NAI: Nucleic Acid Interface; NPV: Negative Predicted Value; PDB: Protein Data Bank; PIP: Protein Interface Propensity; PiQSi: Protein Quaternary Structure investigation; PPIs: Protein-Protein Interactions; PPV: Positive Predicted Value; Prec.: Precision; PrimI: Primary Interface; RNA: RiboNucleic Acid; ROC: Receiver Operating Characteristic; SecI: Secondary Interface; Sen.: Sensitivity; Spe.: Specificity; TN: True Negatives; TP: True Positives; WCG: World Community Grid.

bioinformatics↗