Tracing specificity of immune landscape remodeling associated with distinct anticancer treatments

Immune cells within the tumor microenvironment impact cancer progression, resistance, response to treatments. Despite remarkable outcomes for some cancer patients, immunotherapies remain unsatisfactory for others. Here, we designed an experimental setting using the <i>Alb-R26</i> <sup><i>Met</i></sup> "inside-out" mouse model, faithfully recapitulating molecular features of liver cancer patients, to explore the effects of distinct anticancer targeted therapies on the tumor immune landscape. Using two treatments in clinical trials for different cancer types, Decitabine and MEK+BCL-XL blockage, we show their capability to trigger tumor regression in <i>Alb-R26</i> <sup><i>Met</i></sup> mice and to superimpose distinct profiles of immune cell types and immune-checkpoints, impacting immunotherapy response. A machine learning approach processing tumor imaging and immune profile data identified a putative signature predicting tumor treatment response in mice and patients. Outcomes exemplify how the tumor immune microenvironment is differentially reshaped by distinct anticancer agents and highlight the importance of measuring its modulation during treatment to optimize oncotherapy and immunotherapy combinations.

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Publication Details

Journal
iScience
Published
2025-02-20
DOI
https://doi.org/10.1016/j.isci.2025.112071
Citations
2
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
2.09

Funders

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article

Tracing specificity of immune landscape remodeling associated with distinct anticancer treatments

Paulo A. S. Veloso, Jean‐Paul Borg, Célia Sequera, C. Morel et al.
2 citations
iScience
Cancer Immunotherapy and Biomarkers
2.09
article

Tracing specificity of immune landscape remodeling associated with distinct anticancer treatments

Paulo A. S. Veloso, Jean‐Paul Borg, Célia Sequera, C. Morel, Sylvie Richelme, Yannick Boursier, Mehmet Fatih Kaya, Afef Cherni, Flavio Maina, Abdessamad El Kaoutari, Mathieu Dupont, Floriane Cannet, Melissa Methia
article en
2 citations

Abstract

Immune cells within the tumor microenvironment impact cancer progression, resistance, response to treatments. Despite remarkable outcomes for some cancer patients, immunotherapies remain unsatisfactory for others. Here, we designed an experimental setting using the <i>Alb-R26</i> <sup><i>Met</i></sup> "inside-out" mouse model, faithfully recapitulating molecular features of liver cancer patients, to explore the effects of distinct anticancer targeted therapies on the tumor immune landscape. Using two treatments in clinical trials for different cancer types, Decitabine and MEK+BCL-XL blockage, we show their capability to trigger tumor regression in <i>Alb-R26</i> <sup><i>Met</i></sup> mice and to superimpose distinct profiles of immune cell types and immune-checkpoints, impacting immunotherapy response. A machine learning approach processing tumor imaging and immune profile data identified a putative signature predicting tumor treatment response in mice and patients. Outcomes exemplify how the tumor immune microenvironment is differentially reshaped by distinct anticancer agents and highlight the importance of measuring its modulation during treatment to optimize oncotherapy and immunotherapy combinations.

iScienceVol. 28(3)
Centre National de la Recherche Scientifique (FR), Inserm (FR), Institut Universitaire de France (FR), Aix-Marseille Université (FR), Institut de Biologie du Développement Marseille (FR), Centre de Recherche en Cancérologie de Marseille (FR), Living Systems (United States) (US)
Association pour la Recherche sur le Cancer, Agence Nationale de la Recherche, Institut National de la Santé et de la Recherche Médicale, Fondation pour la Recherche Médicale, Fondation de France, Centre National de la Recherche Scientifique, Institut National Du Cancer, Association Française pour l'Etude du Foie, Canceropôle Provence-Alpes-Côte d’Azur, Groupement des Entreprises Françaises dans la lutte contre le Cancer, Société d'Accélération du Transfert de Technologies
Sustainable cities and communities
Openalex Percentile: Top 14%
Cancer Immunotherapy and Biomarkers
2.09
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