Hang Phuong Pham

Background: A high density of resident memory T cells (T_RM) in tumors correlates with improved clinical outcomes in immunotherapy-treated patients. In most clinical studies, T_RM are defined by the CD103 marker. However, it is clearly established that not all T_RM express CD103, but can be defined by other markers (CD49a, CD69, etc). The frequency of these subpopulations of T_RM expressing or not CD103 varies according to the location of the cancer. Little is known about their functionality and their predictive impact on response to immunotherapy. In preclinical models, only some subpopulations of T_RM are associated with cancer vaccine efficacy. Methods: Multiparametric cytometry analyses were used to demonstrate the presence of T_RM subpopulations in the lung in mice after vaccination and in fresh ex vivo human non-small cell lung cancer (NSCLC). An analysis of the T-cell repertoire of these T_RM was conducted to search for their relationships. Multiplex immunofluorescence techniques were used to quantify intratumor infiltration of T_RM subpopulations in two cohorts of patients with NSCLC. The impact on the clinical outcome of the T_RM tumor infiltration was also investigated. Results: We identified two main T_RM subpopulations in tumor-infiltrating lymphocytes derived from patients with NSCLC: one co-expressing CD103 and CD49a (double positive (DP)), and the other expressing only CD49a (simple positive (SP)); both exhibiting additional T_RM surface markers like CD69. Despite higher expression of inhibitory receptors, DP T_RM exhibited greater functionality compared with SP T_RM. Analysis of T-cell receptor (TCR) repertoire and expression of the stemness marker TCF1 revealed shared TCRs between populations, with the SP subset appearing more progenitor-like phenotype. In the training cohort, PD-L1 (Programmed Death-Ligand 1) and TCF1⁺CD8⁺T cells predict response to anti-PD-1. In patient with NSCLC validation cohorts, only DP T_RM predicted PD-1 blockade response. Multivariate analysis, including various biomarkers associated with responses to anti-PD-(L)1, such as total CD8, TCF1⁺CD8⁺T cells, and PD-L1, showed that only intratumoral infiltration by DP T_RM remained significant. Conclusions: This study highlights the non-equivalence of T_RM subpopulations. The population of T_RM co-expressing CD103 and CD49a appears to be the most functional and has the most significant capacity for predicting response to immunotherapy in multivariate analysis in patients with NSCLC.

Background and aims: Faecalibacterium prausnitzii , a highly abundant bacterium in the human gut microbiota, has been linked to overall health and is decreased in several pathological conditions, such as Inflammatory Bowel Disease (IBD). F. prausnitzii has shown anti-inflammatory properties in human and mouse models, notably through the induction of IL-10 signaling. Here, we investigated which cell types from human blood and intestinal tissue are responsible for producing IL-10 induced by F. prausnitzii , and providing the first mechanistic insights. Methods: Immune cells isolated from human blood and intestinal lamina propria of patients with IBD and non-inflamed controls, were stimulated with either F. prausnitzii EXL01 strain or Escherichia coli lipopolysaccharide (LPS) and analysed by Legendplex, ELISA, flow cytometry, RNA-sequencing (RNAseq), and Seahorse technology. Results: F. prausnitzii EXL01 strain induced the direct and dose-dependent production of IL-10 in CD14 ⁺ monocytes from the systemic circulation and intestinal tissue of IBD patients and non-inflamed controls, without inducing a pro-inflammatory response as compared to LPS stimulation. RNAseq analysis corroborated these results and revealed that F. prausnitzii EXL01 strain differentially affects cell energy metabolism compared to LPS. The anti-inflammatory response induced by F. prausnitzii in monocytes was dependent on mitochondrial respiration. Conclusion: F. prausnitzii induces an anti-inflammatory response and rewires energy metabolism in human monocytes, which might explain its beneficial impact on intestinal inflammation and human health in general. These results provide new insight into the mechanisms underlying the anti-inflammatory effects of F. prausnitzii and are crucial for a better understanding of its potential use in the treatment of IBD.

The analysis of bulk adaptive immune receptor repertoires (AIRR) enables the understanding of immune responses in both normal and pathological conditions. However, the complexity of AIRR calls for advanced, specialized methods to extract meaningful biological insights. These sophisticated approaches often present challenges for researchers with limited bioinformatics expertise, hindering access to comprehensive immune system analysis. To address this challenge, we developed AnalyzAIRR, an AIRR-compliant R package enabling advanced bulk AIRR sequencing data. The tool integrates state-of-the-art statistical and visualization methods applicable at various levels of granularity. It offers a platform for general data exploration, filtering and manipulation, and in-depth cross-comparisons of AIRR datasets, aimed at answering specific biological questions. We illustrate AnalyzAIRR functionalities using of a published murine dataset of 18 T-cell receptor repertoires from three diferrent T cell subsets. We first detected and removed a major contaminant in a group of samples, before proceeding with to the comparative analysis. Subsequent cross-sample analysis revealed differences in repertoire diversity that aligned with the respective cell phenotypes, and in repertoire convergence among the studied subsets. AnalyzAIRR’s set of analytical metrics is integrated into a Shiny web application and complemented with a tutorial to help users in their analytical strategy, making it user-friendly for biologists with little or no background in bioinformatics.