bioRxivImmunologyPreprint
CD39 defines a cytotoxic tumor-associated NK cell state responsive to NKG2A blockade in lung cancer
Clara SergeriD, Lucas RebuffetiD, Michael T. SandholzeriD, Wiebke RackwitziD, Irene FusiiD, Petra Herzig, Annalea Brüggemann, Carina A. Pawlow, Evgeny ChichelnitskiyiD, Daniel Hajnal, Nicole OelgarthiD, Sarp UzuniD, Christoph SchultheißiD, Andreas ZinggiD, Sofia TundoiD, Thuy T. LuuiD, Aljaž HojskiiD, Didier Lardinois, Lavinia NeubertiD, Mascha BinderiD, Kirsten MertziD, Marcel P. TrefnyiD, Nicole KirchhammeriD, Marina NatoliiD, Matthias S. Matter, , Alfred ZippeliusiD
Abstract
Natural killer (NK) cell-targeting immunotherapies are emerging, yet the differentiation and functional states of tumor-infiltrating NK cells remain poorly understood. Using matched single-nucleus RNA and ATAC sequencing of non-small cell lung cancer (NSCLC) specimens, we resolved the transcriptional and epigenetic landscape of intratumoral NK cells. We identified two tumor-associated NK (taNK) cell subsets marked by ITGAE (CD103) and ITGA1 (CD49a) that display features of tissue residency and dysfunction while preserving cytotoxic function. Trajectory and regulon analyses revealed an inflammation-driven transition from early GZMK NKs toward an ENTPD1+ (CD39) effector state characterized by interferon-stimulated gene (ISG) programs. Functional profiling established CD39 taNK as the dominant cytotoxic NK cell population with superior killing capacity that is further potentiated by NKG2A blockade. This study offers mechanistic insights into NK cell differentiation in NSCLC and establishes CD39 taNK cells as a targetable effector population for immunotherapy. One sentence summaryMultiomic analysis identifies CD39+ tumor-associated NK cells as a cytotoxic effector state in NSCLC responsive to NKG2A blockade.
- organism: human
- assay: scRNA-seq
- assay: ATAC-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 0
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