GEO series

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

GSE179994 Homo sapiens (human) Single-cell RNA-seq 47 samples Submitted 2021/10/06 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 47 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Topic: Lung cancerThis series is part of our curated briefing on it.
Samples: 47 samples
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.

Summary (written by the submitters)

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.

Read the full summary ↓

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.

This dataset

Open GSE179994 in finder → Find more human RNA-seq datasets → Lung cancer landscape →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE179994_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA746236. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 35121991) ↗

Samples in this study

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+ 47 more — browse all 47 samples with per-sample file links →

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