Biology subjects

Barlesi, F.

Publications and source records attributed to Barlesi, F..

4 recordsLinked to original sources

bioRxiv2026

Specific dendritic cells spatial organization is associated to ICB Response in Non Small Cell Lung Cancer (NSCLC)

Dendritic cells (DCs) are central orchestrators of antitumor immunity. Several DC subsets--including conventional type 1 (cDC1), conventional type 2 (cDC2), plasmacytoid DCs (pDCs), and mature DC populations--play distinct roles in immune surveillance, tumor control, immunotherapy response and prognosis. Recent findings suggest that cDC1 are spatially closed to CD8 T-cell and contribute to tertiary lymphoid structure formation in lung cancer. However, how other DC subsets interact with cDC1 to shape the tumor microenvironment (TME) remains largely unknown. Here, we analyzed the spatial distribution of major DC subsets, including cDC1, cDC2, mature DC and pDC, together with CD8 T cells in a cohort of anti-PD1-treated NSCLC patients and we deciphered the corresponding immune microenvironment behavior by paired transcriptomic analysis. We found that, while other DC subsets populated the stroma, cDC2 were localized both in the stroma and in tumor nests. Moreover, unlike other DC subsets, cDC2 abundancy did not affect ICB response both at transcriptomic and in situ analysis. We described spatial organization of DCs in megaclusters characterized by distinct proportions of DC subsets. Patients enriched in megaclusters involving variable proportion of pDC, cDC1 and mature DC, exhibited pro-inflammatory transcriptomic programs while those enriched in cDC2-based megaclusters showed limited immune activation features. Globally, DC in lung cancer were structured around three distinct DC spatial patterns, namely cDC1-driven, cDC2-driven and DC-Scattered, each defined by unique compositions of DC megaclusters, immune features and pathways activation profiles. Among them, the cDC1-driven pattern was associated to prolonged anti-PD1 response in two independent cohorts.

cancer biology↗

bioRxiv2022

Anti-HVEM mAb therapy improves antitumoral immunity both in vitro and in vivo, in a novel transgenic mouse model expressing human HVEM and BTLA molecules challenged with HVEM expressing tumors

BackgroundTNFRF-14/HVEM is the ligand for BTLA and CD160 negative immune co-signaling molecules as well as viral proteins. Its expression is dysregulated with an overexpression in tumors and a connection with tumors of adverse prognosis. MethodsWe developed C57BL/6 mouse models co-expressing human huBTLA and huHVEM as well as antagonistic monoclonal antibodies (mAbs) that completely prevent the interactions of HVEM with its ligands. ResultsHere, we show that the anti-HVEM18-10 mAb increases primary human {beta}-T cells activity alone (CIS-activity) or in the presence of HVEM-expressing lung or colorectal cancer cells in vitro (TRANS-activity). Anti-HVEM18-10 synergizes with anti-PD-L1 mAb to activate T cells in the presence of PDL-1 positive tumors, but is sufficient to trigger T cell activation in the presence of PD-L1 negative cells. In order to better understand HVEM18-10 effect in vivo and especially disentangle its CIS and TRANS effects, we developed a knock-in (KI) mouse model expressing human BTLA (huBTLA+/+) and a KI mouse model expressing both human BTLA and human HVEM (huBTLA+/+ /huHVEM+/+ (DKI)). In vivo pre-clinical experiments performed in both mouse models showed that HVEM18-10 treatment was efficient to decrease human HVEM+ tumor growth. In the DKI model, anti-HVEM 18-10 treatment induces a decrease of exhausted CD8+ T cells and regulatory T cells and an increase of Effector memory CD4+ T cells within the tumor. Interestingly, mice which completely rejected tumors ({+/-} 20%) did not develop tumors upon re-challenge in both settings, therefore showing a marked T cell-memory phenotype effect. ConclusionsAltogether, our preclinical models validate anti-HVEM18-10 as a promising therapeutic antibody to use in clinics as a monotherapy or in combination with existing immunotherapies (anti-PD1/anti-PDL-1/anti-CTLA-4).

immunology↗

bioRxiv2019

Optimal Scheduling of Bevacizumab and Pemetrexed/Cisplatin Dosing in Non-Small Cell Lung Cancer

Bevacizumab-pemetrexed/cisplatin (BEV-PEM/CIS) is a first line therapeutic for advanced non-squamous non-small cell lung cancer (NSCLC). Bevacizumab potentiates PEM/CIS cytotoxicity by inducing transient tumor vasculature normalization. BEV-PEM/CIS has a narrow therapeutic window. Therefore, it is an attractive target for administration schedule optimization. The present study leverages our previous work on BEV-PEM/CIS pharmacodynamic modeling in NSCLC-bearing mice to estimate the optimal gap in the scheduling of sequential BEV-PEM/CIS. We predicted the optimal gap in BEV-PEM/CIS dosing to be 2.0 days in mice and 1.2 days in humans. Our simulations suggest that the efficacy loss in scheduling BEV-PEM/CIS at too great of a gap is much less than the efficacy loss in scheduling BEV-PEM/CIS at too short of a gap.

pharmacology and toxicology↗

bioRxiv2018

Computational modeling reveals dynamics of brain metastasis in non-small cell lung cancer and provides a tool for personalized therapy

Brain metastases (BMs) are associated with poor prognosis in non-small cell lung cancer (NSCLC), but are only visible when large enough. Therapeutic decisions such as whole brain radiation therapy would benefit from patient-specific predictions of radiologically undetectable BMs. Here, we propose a mathematical modeling approach and use it to analyze clinical data of BM from NSCLC.\n\nPrimary tumor growth was best described by a gompertzian model for the pre-diagnosis history, followed by a tumor growth inhibition model during treatment. Growth parameters were estimated only from the size at diagnosis and histology, but predicted plausible individual estimates of the tumor age (2.1-5.3 years). Multiple metastatic models were assessed from fitting either literature data of BM probability (n = 183 patients) or longitudinal measurements of visible BMs in two patients. Among the tested models, the one featuring dormancy was best able to describe the data. It predicted latency phases of 4.4 - 5.7 months and onset of BMs 14 - 19 months before diagnosis. This quantitative model paves the way for a computational tool of potential help during therapeutic management.

cancer biology↗