Biology subjects

Leblay, N.

Publications and source records attributed to Leblay, N..

3 recordsLinked to original sources

bioRxiv2025

Plasma cell identity escape drives resistance to anti-BCMA T-cell-redirecting therapy in multiple myeloma

Chimeric antigen receptor T-cell (CART) and T-cell engager (TCE) therapies targeting B-cell maturation antigen (BCMA) are transforming the treatment landscape for relapsed multiple myeloma (MM). However, despite impressive initial response rates, most patients eventually relapse. To investigate this unmet medical need, we applied whole-genome sequencing (WGS) to MM cells from cohorts of 102 relapsed patients treated with anti-BCMA CART and TCE therapies. Several genomic alterations were associated with clinical outcomes, particularly primary refractoriness, including high genomic complexity and mutations in genes regulating plasma cell identity, which predicted resistance to therapy. Single-cell RNA sequencing further revealed that MM cells from refractory patients exhibited high proliferation signatures and reduced expression of TNFRSF17 (encoding BCMA), while were less enriched for plasma cell-associated transcriptional programs, a phenomenon we term "plasma cell identity escape." This profile was strongly associated with immune dysregulation of CD8 T cells including increased activation and exhaustion. This evolution of MM toward a more proliferative and lineage-divergent state, refractory to the anti-BCMA T-cell redirecting therapies, was functionally validated in preclinical MM mouse models. Collectively, our results comprehensively define the cellular and molecular mechanisms underlying primary resistance to anti-BCMA therapies.

cancer biology↗

bioRxiv2024

Microbial metabolite-guided CAR T cell engineering enhances anti-tumor immunity via epigenetic-metabolic crosstalk

The microbiome is a complex host factor and key determinant of the outcome of antibody-based and cellular immunotherapy. Its postbiotics are a blend of soluble commensal byproducts that are released into the host environment and have been associated with the regulation of immune homeostasis, particularly through impacts on epigenetics and cell signaling. In this study, we show that the postbiotic pentanoate is metabolized to citrate within the TCA cycle via both the acetyl- and succinyl-CoA entry points, a feature uniquely enabled by the chemical structure of the C5 aliphatic chain. We identified ATP-citrate lyase as the crucial factor that redirects pentanoate-derived citrate from the succinyl-CoA route to the nucleus, thereby linking metabolic output and histone acetylation. This epigenetic-metabolic crosstalk mitigated T cell exhaustion and promoted naive-like differentiation in pentanoate-programmed chimeric antigen receptor (CAR) T cells. The predictive and therapeutic potential of pentanoate was corroborated in two independent patient cohorts and three syngeneic models of CAR T adoptive therapy. Our data demonstrate that postbiotics are integrated into mitochondrial metabolism and subsequently incorporated as epigenetic imprints. This bridge between microbial and mammalian interspecies communication can ultimately impact T cell differentiation and efficacy.

immunology↗

bioRxiv2019

Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data

The emergence of Next-Generation Sequencing (NGS) has revolutionized the way of reaching a genome sequence, with the promise of potentially providing a comprehensive characterization of DNA variations. Nevertheless, detecting somatic mutations is still a difficult problem, in particular when trying to identify low abundance mutations such as subclonal mutations, tumour-derived alterations in body fluids or somatic mutations from histological normal tissue. The main challenge is to precisely distinguish between sequencing artefacts and true mutations, particularly when the latter are so rare they reach similar abundance levels as artefacts. Here, we present needlestack, a highly sensitive variant caller, which directly learns from the data the level of systematic sequencing errors to accurately call mutations. Needlestack is based on the idea that the sequencing error rate can be dynamically estimated from analyzing multiple samples together. We show that the sequencing error rate varies across alterations, illustrating the need to precisely estimate it. We evaluate the performance of needlestack for various types of variations, and we show that needlestack is robust among positions and outperforms existing state-of-the-art method for low abundance mutations. Needlestack, along with its source code is freely available on the GitHub plateform: https://github.com/IARCbioinfo/needlestack.

bioinformatics↗