GEO series

Coupling of response biomarkers between tumour and peripheral blood in patients undergoing chemoimmunotherapy II

GSE253173 Homo sapiens (human) Single-cell RNA-seq 100 samples Submitted 2024/10/31 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 100 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 100 samples: 100 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: Coupling of response biomarkers between tumor and peripheral blood in patients undergoing chemoimmunotherapyCell reports. Medicine · 2025

Summary (written by the submitters)

Platinum-based chemotherapy in combination with anti-PD-L1 antibodies has shown promising results in mesothelioma. However, the immunological mechanisms underlying its efficacy are not well understood and there are no predictive biomarkers to guide treatment decisions. Here, we combine time-course RNA sequencing of peripheral blood mononuclear cells with pre-treatment tumour transcriptome data from the 54-patient single arm phase II DREAM study.

Read the full summary ↓

Single cell RNA-seq and TCR-seq reveal CD8+ T effector memory (TEM) cells with stem-like properties are more abundant in peripheral blood of responders and this population expands upon treatment. These peripheral blood changes are linked to the transcriptional state of the tumour microenvironment. Combining information from both compartments, rather than individually, is most predictive of response. Our study highlights the complex, but predictive interactions between the tumour and immune cells in peripheral blood during the response to chemoimmunotherapy.

Published in

Coupling of response biomarkers between tumor and peripheral blood in patients undergoing chemoimmunotherapy

Chin WL, Cook AM, Chee J et al. · Cell reports. Medicine 2025 · PMID 39731918 · doi:10.1016/j.xcrm.2024.101882

This dataset

Open GSE253173 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 GSE253173_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 PRJNA1064153. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39731918) ↗

Study design

100 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 100 sample titles: 100 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

GSM8015524 PT34,Baseline
GSM8015525 PT35,Baseline
GSM8015526 PT36,C2D1
GSM8015527 PT21,C3D1
GSM8015528 PT18,Baseline
GSM8015529 PT27,C2D1
GSM8015530 PT5,C2D1
GSM8015531 PT32,C3D1
GSM8015532 PT43,Baseline
GSM8015533 PT36,Baseline
GSM8015534 PT22,C2D1
GSM8015535 PT41,C3D1
GSM8015536 PT10,Baseline
GSM8015537 PT26,C2D1
GSM8015538 PT18,C3D1
GSM8015539 PT51,C3D1
GSM8015540 PT49,Baseline
GSM8015541 PT44,Baseline
GSM8015542 PT15,C2D1
GSM8015543 PT34,C3D1
GSM8015544 PT32,Baseline
GSM8015545 PT6,C2D1
GSM8015546 PT55,C2D1
GSM8015547 PT6,C3D1
GSM8015548 PT15,Baseline
GSM8015549 PT2,Baseline
GSM8015550 PT9,C2D1
GSM8015551 PT9,C3D1
GSM8015552 PT26,Baseline
GSM8015553 PT46,C2D1
GSM8015554 PT27,C3D1
GSM8015555 PT54,C3D1
GSM8015556 PT24,Baseline
GSM8015557 PT21,C2D1
GSM8015558 PT13,C2D1
GSM8015559 PT22,C3D1
GSM8015560 PT37,Baseline
GSM8015561 PT29,Baseline
GSM8015562 PT29,C2D1
GSM8015563 PT46,C3D1

+ 60 more — browse all 100 samples with per-sample file links →

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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.