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Blockade of LAG-3 and PD-1 leads to co-expression of cytotoxic and exhaustion gene modules in CD8+ T cells to promote antitumor immunity

GSE225063 Homo sapiens (human) RNA-seq (gene expression) 256 samples Submitted 2024/08/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 256 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Topic
MelanomaThis series is part of our curated briefing on it.
Samples
256 samples: 256 samples, each with a distinct title — no replicated groupsGroups 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.
Paper
Blockade of LAG-3 and PD-1 leads to co-expression of cytotoxic and exhaustion gene modules in CD8(+) T cells to promote antitumor immunityCell · 2024
Summary (written by the submitters)
Relatlimab (rela; anti-LAG-3) plus nivolumab (nivo; anti-PD-1) is safe and effective for treatment of advanced melanoma. We designed a trial (NCT03743766) where advanced melanoma patients received rela, nivo, or rela+nivo to interrogate the immunologic mechanisms of rela+nivo. Analysis of biospecimens from this ongoing trial demonstrated that rela+nivo led to enhanced capacity for CD8+ T cell receptor signaling and altered CD8+ T cell differentiation, leading to heightened cytotoxicity despite the retention of an exhaustion profile.
Read the full summary ↓
Co-expression of cytotoxic and exhaustion signatures was driven by PRDM1, BATF, ETV7, and TOX. Effector function was upregulated in clonally expanded CD8+ T cells that emerged after rela+nivo. A rela+nivo intratumoral CD8+ T cell signature was associated with a favorable prognosis. This intratumoral rela+nivo signature was validated in peripheral blood as an elevated frequency of CD38+TIM3+CD8+ T cells. Overall, we demonstrated that cytotoxicity can be enhanced despite the retention of exhaustion signatures, which will inform future therapeutic strategies.
Published in
Blockade of LAG-3 and PD-1 leads to co-expression of cytotoxic and exhaustion gene modules in CD8(+) T cells to promote antitumor immunity
Cillo AR, Cardello C, Shan F et al. · Cell 2024 · PMID 39121849 · doi:10.1016/j.cell.2024.06.036
This dataset
Open GSE225063 in finder → Find more human RNA-seq datasets → Melanoma 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 GSE225063_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA933637 and SRA study SRP421890. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39121849) ↗
Study design
256 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 256 sample titles: 256 distinct titles with little repetition. 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

+ 216 more — browse all 256 samples with per-sample file links →

More Melanoma datasets

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.