Boosting γδ T cell-mediated antibody-dependent cellular cytotoxicity by PD-1 blockade in follicular lymphoma.
Cédric RossiPauline GravelleEmilie DecaupJulie BordenaveMary PoupotMarie TosoliniDon-Marc FranchiniCamille LaurentRenaud MorinJean-Michel LagardeLoïc YsebaertLaetitia LigatChristine JeanAriel SavinaChristiane NeumannAlba Matas CéspedesPatricia Perez-GalanJean-Jacques FourniéChristine BezombesPublished in: Oncoimmunology (2018)
Follicular lymphoma (FL) is a common non Hodgkin's lymphoma subtype in which immune escape mechanisms are implicated in resistance to chemo-immunotherapy. Although molecular studies point to qualitative and quantitative deregulation of immune checkpoints, in depth cellular analysis of FL immune escape is lacking. Here, by functional assays and in silico analyses we show that a subset of FL patients displays a 'high' immune escape phenotype. These FL cases are characterized by abundant infiltration of PD1+ CD16+ TCRVγ9Vδ2 γδ T lymphocytes. In a 3D co-culture assay (MALC), γδ T cells mediate both direct and indirect (ADCC in the presence of anti-CD20 mAbs) cytolytic activity against FL cell aggregates. Importantly, PD-1, which is expressed by most FL-infiltrating γδ T lymphocytes with ADCC capacity, impairs these functions. In conclusion, we identify a PD1-regulated γδ T cell cytolytic immune component in FL. Our data provide a treatment rational by PD-1 blockade aimed at boosting γδ T cell anti-tumor functions in FL.
Keyphrases
- end stage renal disease
- chronic kidney disease
- high throughput
- ejection fraction
- photodynamic therapy
- single cell
- squamous cell carcinoma
- transcription factor
- diffuse large b cell lymphoma
- mass spectrometry
- molecular docking
- optical coherence tomography
- hodgkin lymphoma
- machine learning
- cell therapy
- big data
- cancer therapy
- deep learning
- nk cells
- replacement therapy
- rectal cancer
- case control
- artificial intelligence