Transcriptomics

Dataset Information

0

Temporal single cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer


ABSTRACT: Immune-checkpoint therapies have shown unprecedented clinical success in the treatment of non-small-cell lung cancer, but the underlying mechanisms of anti-PD-1–induced tumour rejection remain incompletely understood. Here, we performed temporal single-cell RNA and paired T cell receptor sequencing on 47 clinical tumour biopsies from 36 non-small-cell lung cancer patients before and during combination therapies of PD-1 blockade with chemotherapy. We found that the treatment in responsive tumours preferentially increased the precursors of exhausted T cells (Tex), characterized by the low expression of co-inhibitory molecules and high expression of GZMK. By contrast, non-responsive tumours failed to accumulate Tex precursors (Texp), suggestive of the critical role of such cells in PD-1-based immunotherapies. Although these post-treatment Texp cells shared clonotypes extensively with terminal Tex subset prior to treatment, our data suggested that they were not derived from the reinvigoration of terminal Tex cells; instead, such Texp cells were likely accumulated by (i) the expansion of pre-existing Texp cells, and (ii) the replenishment with peripheral T cells, a phenomenon we named clonal revival. Furthermore, post-treatment Texp cells expressed high level of CXCL13, which was exclusively expressed by terminal Tex subset in treatment-naïve tumours, implying that the emergence of CXCL13-expressing Texp cells resulted from the blockade of the transition to terminal Tex cells by PD-1 antibodies. Our study provides new insights into mechanisms underlying PD-1-based therapies, implicating both clonal revival and expansion of Texp cells as steps to enhance the clinical response of lung cancer patients.

ORGANISM(S): Homo sapiens

PROVIDER: GSE179994 | GEO | 2021/10/06

REPOSITORIES: GEO

Similar Datasets

2022-07-28 | GSE209965 | GEO
2024-01-17 | GSE237613 | GEO
2024-01-17 | GSE237611 | GEO
2016-10-31 | GSE86796 | GEO
2021-09-15 | GSE169246 | GEO
2024-01-29 | E-MTAB-13704 | biostudies-arrayexpress
2021-07-14 | BIOMD0000001014 | BioModels
2021-07-23 | BIOMD0000001013 | BioModels
2023-05-31 | GSE211410 | GEO
2023-05-31 | GSE211409 | GEO