Investigator

Laurence Zitvogel

Gustave Roussy

LZLaurence Zitvogel
Papers(1)
Harnessing lipid-driv…
Collaborators(5)
Sven ZukunftChenqi XuDaniel SchraivogelFrederik MarméIngrid Fleming
Institutions(5)
Institut Gustave Rous…Medizinische Fakultät…National Center for P…European Molecular Bi…Arbeitsgemeinschaft G…

Papers

Harnessing lipid-driven immunometabolic pathways in omental metastases to enhance immunotherapy in patients with ovarian cancer

Abstract Immunotherapy with immune checkpoint blockade (ICB) in epithelial ovarian carcinoma (EOC) shows limited clinical benefit only for a small subset of patients. Overall response rates are low, so that overcoming immunotherapy resistance and improved stratification are key. In this study, we investigated the immunometabolic landscape of EOC with a focus on omental metastases, identifying lipid-laden macrophages as central elements for actionable therapeutic vulnerabilities and giving rise to biomarkers for improved patient stratification. Using patient-derived explants, we demonstrated a functional dichotomy inside the typically lipid-rich microenvironment of omental metastases: augmented maintenance of effector T cell function, while lipid uptake and processing by tumor-associated macrophages (TAMs) induces oxidative stress–dependent signaling programs, which drive macrophage dysfunction and immune suppression. Pharmacological modulation of lipid-driven signaling pathways through CCR5 inhibition (inflammation modulation through maraviroc) or blockade of the lipid scavenger receptor CD36 reprograms TAMs, restores T cell activity, and enhances antitumor immune responses within lipid-rich tumor niches. Mechanistically, studies in humanized mouse models reveal that maraviroc-mediated CCR5 inhibition induces transcriptional programs associated with immune activation in stressed, lipid-laden human TAMs. Consistent with these mechanistic insights, we demonstrated that the specific immunometabolic niche in omental metastases is clinically associated with responsiveness to ICB. We propose a non-invasive radiomics and machine-learning–based analysis of imaging data to assess omental involvement for patient stratification.

542Works
1Papers
5Collaborators

Positions

Researcher

Gustave Roussy

Researcher

Université Paris-Saclay

Researcher

INSERM

Country

FR