bioRxivImmunology
Differential predictive value of resident memory CD8+T cell subpopulations in non-small-cell lung cancer patients treated by immunotherapy
Léa Paolini, Thi Tran, Stéphanie Corgnac, Jean-Philippe Villemin, Marie WisleziD, Jennifer Arrondeau, Ludger Johannes, Jonathan Ulmer, Louis-Victorien Vieillard, Joséphine Pineau, Alain Gey, Valentin QuiniouiD, Pierre BarennesiD, Hang Phuong Pham, Nadège GrueliD, Milena HasaniD, Valentina LibriiD, Sebastien Mella, Sixtine De Percin, Pascaline Boudou-Rouquette, Aziza Caidi, Isabelle CremeriD, Hélène Blons, Karen LeroyiD, Pierre Laurent-Puig, , Eric TartouriD
Abstract
A high density of resident memory T cells (TRM) in tumors correlates with improved clinical outcomes in immunotherapy-treated patients. However, in preclinical models, only some subpopulations of TRM are associated with cancer vaccine efficacy. We identified two main TRM subpopulations in tumor-infiltrating lymphocytes derived from non-small cell lung cancer (NSCLC) patients: one co-expressing CD103 and CD49a (DP), and the other expressing only CD49a (MP); both exhibiting additional TRM surface markers like CD69. DP TRM exhibited greater functionality compared to MP TRM. Analysis of T-cell receptor (TCR) repertoire and of the stemness marker TCF-1 revealed shared TCRs between populations, with the MP subset appearing more progenitor-like phenotype. In two NSCLC patient cohorts, only DP TRM predicted PD-1 blockade response. Multivariate analysis, including various biomarkers (CD8, TCF1+CD8+T cells, and PD-L1) associated with responses to anti-PD(L)1, showed that only intra-tumoral infiltration by DP TRM remained significant. This study highlights the non-equivalence of TRM populations and emphasizes the importance of distinguishing between them to better define their role in antitumor immunity and as a biomarker of response to immunotherapy.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
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