Extracellular matrix collagen remodeling predicts resistance to immune checkpoint blockade in mismatch repair-deficient endometrial cancer
8 affiliations
- 1Tumor Genetics Department, Gustave Roussy Institute, Villejuif, France
- 2INSERM U981, Prédicteurs moléculaires et nouvelles cibles en oncologie, Gustave Roussy Institute, Villejuif, France
- 3Else Kroener Fresenius Center for Digital Health, Dresden University of Technology, Dresden, Germany
- 4Biognosys AG, Schlieren, Switzerland
- 5Université Paris-Saclay, Gif-sur-Yvette, France
- 6INSERM US23, CNRS UAR 3655, AMMICa, Experimental and Translational Pathology Platform, Institut Gustave-Roussy, Villejuif, France
- 7Chimie Physique et Chimie du Vivant (CPCV), Département de Chimie, Ecole normale superieure - PSL, Paris, France
- 8CNRS, Inserm, École polytechnique, Institut Polytechnique de Paris, Laboratory for Optics and Biosciences, Palaiseau, France
Abstract
Background Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic paradigm for mismatch repair-deficient endometrial cancer (MMRd EC). However, a considerable subset of patients fails to achieve durable clinical benefit. This pronounced interpatient variability underscores the urgent unmet need for mechanistic predictive biomarkers beyond MMRd status alone to guide clinical decision-making.Methods We performed a multiproteomic mass spectrometry analysis on tumor specimens from an advanced or recurrent MMRd EC cohort stratified into ICI responders (ICI-R) and non-responders (ICI-NR). Proteomic signatures were validated spatially and structurally using whole-section Masson’s Trichrome digital imaging and polarization-resolved second-harmonic generation microscopy. Machine learning algorithms were constructed to integrate extracellular matrix (ECM) structural metrics with immune parameters to build a multiparametric predictive model of ICI resistance.Results ECM remodeling emerged as the dominant proteomic program characterizing ICI resistance. Quantitative spatial imaging revealed that increased intratumoral collagen density and peripheral fiber disorganization were significantly associated with impaired CD8+ T-cell infiltration and immunotherapy failure. Integrating collagen structural metrics with immune parameters yielded a predictive score that showed promising discrimination between ICI-Rs and ICI-NRs in our cohort, providing a framework for future validation in independent cohorts.Conclusions Our findings implicate ECM remodeling as a key determinant of immune exclusion and ICI resistance in MMRd EC. These findings support developing collagen-based spatial biomarkers and stromal-targeted approaches, though the clinical utility of this predictive score requires further validation in larger, independent cohorts.
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BibTeX
@article{rouleau2026extracellular,
title = {{Extracellular matrix collagen remodeling predicts resistance to immune checkpoint blockade in mismatch repair-deficient endometrial cancer}},
author = {Etienne Rouleau and Alexandra Leary and Jakob Nikolas Kather and Catherine Genestie and Yuehan Feng and Martin Mehnert and Juan Francisco Grau Bejar and Elisa Yaniz Galende and Qinghe Zeng and Audrey Le Formal and Elodie Edmond and Carole Aimé and Vaky Abdelsayed and Marie-Claire Schanne-Klein and Antoine A Lachaud and Gaël Latour},
journal = {Journal for ImmunoTherapy of Cancer},
year = {2026},
month = oct,
doi = {10.1136/jitc-2026-016463},
url = {https://doi.org/10.1136/jitc-2026-016463},
note = {CC BY},
}
RIS
TY - JOUR
TI - Extracellular matrix collagen remodeling predicts resistance to immune checkpoint blockade in mismatch repair-deficient endometrial cancer
AU - Etienne Rouleau
AU - Alexandra Leary
AU - Jakob Nikolas Kather
AU - Catherine Genestie
AU - Yuehan Feng
AU - Martin Mehnert
AU - Juan Francisco Grau Bejar
AU - Elisa Yaniz Galende
AU - Qinghe Zeng
AU - Audrey Le Formal
AU - Elodie Edmond
AU - Carole Aimé
AU - Vaky Abdelsayed
AU - Marie-Claire Schanne-Klein
AU - Antoine A Lachaud
AU - Gaël Latour
PY - 2026
DA - 2026/10/23
JO - Journal for ImmunoTherapy of Cancer
DO - 10.1136/jitc-2026-016463
UR - https://doi.org/10.1136/jitc-2026-016463
AB - Background Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic paradigm for mismatch repair-deficient endometrial cancer (MMRd EC). However, a considerable subset of patients fails to achieve durable clinical benefit. This pronounced interpatient variability underscores the urgent unmet need for mechanistic predictive biomarkers beyond MMRd status alone to guide clinical decision-making.Methods We performed a multiproteomic mass spectrometry analysis on tumor specimens from an advanced or recurrent MMRd EC cohort stratified into ICI responders (ICI-R) and non-responders (ICI-NR). Proteomic signatures were validated spatially and structurally using whole-section Masson’s Trichrome digital imaging and polarization-resolved second-harmonic generation microscopy. Machine learning algorithms were constructed to integrate extracellular matrix (ECM) structural metrics with immune parameters to build a multiparametric predictive model of ICI resistance.Results ECM remodeling emerged as the dominant proteomic program characterizing ICI resistance. Quantitative spatial imaging revealed that increased intratumoral collagen density and peripheral fiber disorganization were significantly associated with impaired CD8+ T-cell infiltration and immunotherapy failure. Integrating collagen structural metrics with immune parameters yielded a predictive score that showed promising discrimination between ICI-Rs and ICI-NRs in our cohort, providing a framework for future validation in independent cohorts.Conclusions Our findings implicate ECM remodeling as a key determinant of immune exclusion and ICI resistance in MMRd EC. These findings support developing collagen-based spatial biomarkers and stromal-targeted approaches, though the clinical utility of this predictive score requires further validation in larger, independent cohorts.
SN - 2051-1426
ER -