Tracing specificity of immune landscape remodeling associated with distinct anticancer treatments
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 Alb-R26 Met "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 Alb-R26 Met 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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BibTeX
@article{cannet2026tracing,
title = {{Tracing specificity of immune landscape remodeling associated with distinct anticancer treatments}},
author = {Floriane Cannet and Célia Sequera and Paula Michea Veloso and Abdessamad El Kaoutari and Melissa Methia and Sylvie Richelme and Muge Kaya and Afef Cherni and Mathieu Dupont and Jean-Paul Borg and Christian Morel and Yannick Boursier and Flavio Maina},
journal = {iScience},
year = {2026},
month = nov,
doi = {10.1016/j.isci.2025.112071},
url = {https://hal.science/hal-05856422v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Tracing specificity of immune landscape remodeling associated with distinct anticancer treatments
AU - Floriane Cannet
AU - Célia Sequera
AU - Paula Michea Veloso
AU - Abdessamad El Kaoutari
AU - Melissa Methia
AU - Sylvie Richelme
AU - Muge Kaya
AU - Afef Cherni
AU - Mathieu Dupont
AU - Jean-Paul Borg
AU - Christian Morel
AU - Yannick Boursier
AU - Flavio Maina
PY - 2026
DA - 2026/11/08
JO - iScience
DO - 10.1016/j.isci.2025.112071
UR - https://hal.science/hal-05856422v1
AB - 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 Alb-R26 Met "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 Alb-R26 Met 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.
SN - 2589-0042
ER -