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Novel ovarian cancer PD-1+ NK cell subsets, targetable by combined immune-checkpoint blockade

GSE255486 Homo sapiens Expression profiling by high throughput sequencing 12 samples 2025/09/03 GPL24676
Summary
Ovarian cancer (OC) is the fifth leading cause of cancer-related death and High-Grade Serous Ovarian Carcinoma (HGSOC) is its most common histotype. Current therapies are ineffective, and most patients develop a recurrence within a few years. Moreover, if outstanding results can be obtained with immune-checkpoint blockade strategies (mainly PD-1/PD-L axis) in immunotherapeutic protocols for several aggressive tumors, clinical trials didn’t show good results with HGSOC patients so far. Here we analysed, by multiparametric flow cytometry, NK cells derived from peripheral blood, peritoneal fluid, tumor tissue, and metastatic tissue of a large cohort of HGSOC patients, in order to define how NK cells could be leveraged to improve HGSOC immunotherapy. We identified new PD-1+NK subsets occurring in HGSOC patients, but absent in healthy donors, characterized by a CD56dimNKG2A+KIR+/-NKp46+CD57low phenotype and ineffective anti-tumor response against autologous HGSOC cells. This impairment could be rescued by treatment with a combination of anti-PD-1/PD-Ls, NKG2A, and KIRs mAbs. PD-1+ NK cells were enriched in the metastatic niche and high levels of tumor-infiltrating PD-1+ NK cells correlate with a worse outcome, suggesting a role for PD-1 in metastatic promotion/progression. Our data demonstrate the existence of novel tumor-infiltrating PD-1+ NK cell subsets in HGSOC patients, which are inversely related to patient outcome, showing the importance of these NK subsets. These subsets show indeed impaired anti-tumor activity, which can be rescued by combined ICs blockade, paving the way for more successful NK cell-based immunotherapy in OC patients.
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NCBI GEO page ↗ Paper (PMID 40877861) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human RNA-seq datasets →
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