Spatial profiling of ovarian carcinoma and tumor microenvironment evolution under neoadjuvant chemotherapy.

Elisa Yaniz-Galende, Qinghe Zeng, Juan Francisco Grau-Bejar, Christophe Klein, Félix Blanc-Durand, Audrey Le Formal, Eric Pujade-Lauraine, Laure Chardin, Elodie Edmond, Virginie Marty, Isabelle Ray-Coquard, Florence Joly, Gwenaël Ferron, Patricia Pautier,Dominique Berton-Rigaud,Alain Lortholary,Nadine Dohollou,Christophe Desauw,Michel Fabbro,Emmanuelle Malaurie,Nathalie Bonichon-Lamichhane, Diana Bello Roufai, Justine Gantzer,Etienne Rouleau, Catherine Genestie, Alexandra Leary

Clinical cancer research : an official journal of the American Association for Cancer Research(2024)

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摘要
PURPOSE:Immune tumor microenvironment (iTME) determines ovarian cancer development. This study investigates changes in HLA-I expression, CD8+/Foxp3 ratio, CD8+ cells and coregulators density at diagnosis and upon neoadjuvant chemotherapy (NACT), correlating changes with clinical outcomes. EXPERIMENTAL DESIGN:Multiplexed immune profiling and cell clustering analysis was performed on paired matched OC samples to characterize the iTME at diagnosis and under NACT from patients enrolled in the CHIVA trial (NCT01583322). RESULTS:Several immune cells (IC) subsets and immune coregulators were quantified pre-/post-NACT. At diagnosis, patients with higher CD8+ T cells and HLA-1+ enriched tumors were associated with -better outcome. The CD8+/Foxp3+ ratio increased significantly post-NACT in favor of increased immune surveillance and the influx of CD8+ T cells predicted better outcomes. Clustering analysis stratified pre-NACT tumors into 4 subsets: high Binf, enriched in B clusters; high Tinf, low Tinf, according to their CD8+ density; and desert clusters. At baseline, these clusters were not correlated with patient outcomes. Under NACT, tumors segregated into 3 clusters: high BinfTinf, low Tinf and desert. The high BinfTinf, more diverse in IC composition encompassing T, B and NK cell, correlated with improved survival. PD-L1 was rarely expressed, while TIM-3, LAG- and IDO-1 were more prevalent. CONCLUSIONS:Several iTMEs exist during tumor evolution and NACT impact on iTME is heterogeneous. Clustering analysis of patients, unravels several IC subsets within OC and can guide future personalized approaches. Targeting different checkpoints such as TIM-3, LAG-3 and IDO-1, more prevalent than PD-L1, could more effectively harness anti-tumor immunity in this anti-PD-L1 resistant malignancy.
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