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Differential T cell clonal dynamics underlie outcomes to frontline chemoimmunotherapy in advanced gastric cancer [paired scRNA/TCR-seq]

GSE315928 Homo sapiens (human) Single-cell RNA-seq 94 samples Submitted 2026/04/01 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 94 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)
Samples
94 samples: 28 conditions, mostly in duplicateGroups are read from the sample names, not from the paper. Check the list below.
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)
The addition of aPD1 to 5-FU/platinum in advanced gastric cancer (GC) yields variable responses. To understand cooperativity between chemotherapy and immunotherapy, we previously reported a phase II trial sequentially adding pembrolizumab to 5-FU/platinum. In this study, we use single-cell RNA- and TCR-sequencing to analyze 66,813 T cells from primary tumor biopsies pre-treatment, post-chemotherapy, and post-immunotherapy in 33 patients.
Read the full summary ↓
We observed greater abundance, persistence, and recruitment of T cells with predicted tumor-reactivity in patients with prolonged progression-free survival (slow progressors). Increased B cell abundance and predicted B cell to T cell interactions supported T cell memory and co-stimulation, providing a mechanism for increased abundance and persistence of progenitor-exhausted and tumor-reactive T cells in slow progressors. T cell clones emerging in the tumor after immunotherapy were in the blood before treatment only in slow progressors. Our study thus highlights pre-treatment and early chemotherapy-induced T cell dynamics and B cell to T cell interactions that may drive durable response to chemoimmunotherapy in GC.
This dataset
Open GSE315928 in finder → Find more human RNA-seq datasets →
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 GSE315928_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 PRJNA1399965. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
28 conditions, mostly in duplicate
E12_F ×2 E15_F ×2 E16_F ×2 E17_F ×2 E18_F ×2 E20_F ×2 E21_F ×2 E22_F ×2 +58 more

Supports a between-group comparison across 56 samples.

How we read this: 28 replicated groups read from 94 sample titles; they account for 56 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 54 more — browse all 94 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.