Biology subjects

Derangere, V.

Publications and source records attributed to Derangere, V..

3 recordsLinked to original sources

bioRxiv2024

Second generation of LNP based mRNA vaccine leads to a T-cell-inflamed tumor microenvironment favorable for improving PD-1/PD-L1 blocking therapy and long-term immunity in a cold tumor model

The delivery of mRNA-based cancer vaccines has demonstrated significant promise in triggering antitumor immune responses. With the aim of using them in combination with other immunotherapies already used in the clinical appropriately, the modifications of the intratumoral immune microenvironment needs to be deeply characterized. We have shown that the second generation of lipid nanoparticles (LNPs), nanostructured lipid carriers (so-called Lipidots(R)), are able to vector protein antigens and nucleic acids. Here, we test Lipidots(R) for the delivery of mRNA encoding OVA antigen and eliciting a specific antitumor immune response. We demonstrate in vitro that our LNPs deliver mRNA into dendritic cells (DCs), when complexed with mRNA, activate DCs through the TLR4/8 and ROS signaling pathways and induce specific CD4+ and CD8+ T cell activation. Our vaccinal strategy exhibits significant antitumor efficacy both in the context of tumor prevention and as a therapeutic vaccine in B16OVA and E.G7-OVA cold tumors. The LNP-Ova mRNA vaccine induces a profound intratumoral remodeling of the innate and adaptive immunity associated with an increase in the gene expression of chemokines (Cxcl10, Cxcl11, Cxcl9) involved in CD8+ T cell attraction. Additionally, the vaccine induces the establishment of an escape mechanism mediated by PD-1/PDL-1 axis, making it an adjuvant therapy for optimized responses to the blocking of this signaling pathway. Finally, the combination of vaccine and anti-PD-1 therapy achieves a much higher rate of complete responses and memory immune responses compared to monotherapies. Our work demonstrates the capability of Lipidots(R) as an effective platform for the development of preventive and therapeutic vaccines against cancer based on mRNA delivery and that combination with other immunotherapies such as immune checkpoint blockers could counter tumor resistance and promote long-term antitumor immunity.

immunology↗

bioRxiv2024

Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients.

Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of YAP1 and TEAD-family genes. Recent works have highlighted the role of YAP1/TEAD activity in several cancers and its potential therapeutic implications. Therefore, identifying patients with a dysregulated Hippo pathway is key to enhancing treatment impact. Although recent studies have derived RNAseq-based signatures, there remains a need for a reproducible and cost-effective method to measure the pathway activation. In recent years, deep learning applied to histology slides have emerged as an effective way to predict molecular information from a data modality available in clinical routine. Here, we trained models to predict YAP1/TEAD activity from H&E-stained histology slides in multiple cancers. The robustness of our approach was assessed in seven independent validation cohorts. Finally, we showed that histological markers of disease aggressiveness were associated with dysfunctional Hippo signaling.

bioinformatics↗

bioRxiv2022

A Deep Learning solution for triaging patient with cancer from their predicted mutational status using histopathological images

The presence of mutation in cancer can be associated with a response to a targeted cancer therapy. Therefore it has become an important information while it helps giving a more specialized and more efficient treatment for each patient. Detection of mutation is routinely made by DNA-sequencing diagnostic tests. Recently, a new strategy of deep learning based method of prediction of mutation from histopathological images has shown promising results. However it is still unknown whether these methods can be useful as screening tests aside from sequencing methods for efficient population diagnosis. Here we developed our own deep learning based prediction pipeline for the detection of mutation in breast, lung, colon and ovarian cancer and find clinically relevant genes that have easy-to-detect mutational status, and especially TP53 which can be detected in these four tumors. We then propose 3 potential screening strategies and show the effectiveness of our deep learning pipeline for optimizing diagnosis in the patient population.

bioinformatics↗