Biology subjects

VILLEMIN, J.-P.

Publications and source records attributed to VILLEMIN, J.-P..

2 recordsLinked to original sources

bioRxiv2024

Differential predictive value of resident memory CD8+T cell subpopulations in non-small-cell lung cancer patients treated by immunotherapy

A high density of resident memory T cells (TRM) in tumors correlates with improved clinical outcomes in immunotherapy-treated patients. However, in preclinical models, only some subpopulations of TRM are associated with cancer vaccine efficacy. We identified two main TRM subpopulations in tumor-infiltrating lymphocytes derived from non-small cell lung cancer (NSCLC) patients: one co-expressing CD103 and CD49a (DP), and the other expressing only CD49a (MP); both exhibiting additional TRM surface markers like CD69. DP TRM exhibited greater functionality compared to MP TRM. Analysis of T-cell receptor (TCR) repertoire and of the stemness marker TCF-1 revealed shared TCRs between populations, with the MP subset appearing more progenitor-like phenotype. In two NSCLC patient cohorts, only DP TRM predicted PD-1 blockade response. Multivariate analysis, including various biomarkers (CD8, TCF1+CD8+T cells, and PD-L1) associated with responses to anti-PD(L)1, showed that only intra-tumoral infiltration by DP TRM remained significant. This study highlights the non-equivalence of TRM populations and emphasizes the importance of distinguishing between them to better define their role in antitumor immunity and as a biomarker of response to immunotherapy.

immunology↗

bioRxiv2022

Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR

The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different bulk datasets, including new Visium liver metastasis ST data, with experimental validation of selected interactions. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.

bioinformatics↗