← BioTransfer GEO Dataset Finder
GEO series

Blood immunomap for prediction of responses to anti-PD-1 immunotherapy in metastatic non-small cell lung cancer

GSE295969 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/05/31 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 18 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
Lung cancerThis series is part of our curated briefing on it.
Samples
18 samples: 18 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
Blood immunomap for prediction of responses to anti-PD-1 immunotherapy in metastatic non-small cell lung canceriScience · 2025
Summary (written by the submitters)
The identification of circulating predictors of response to ICB therapy is vital as very few of them meet the demands of the clinic. Herein, by using high-dimensionality mass cytometry, we designed a blood immunomap in metastatic NSCLC individuals who underwent anti-PD-1 treatment. We identified heightened frequencies of CD8+PD-L1+ T cells in non-responders compared responders.
Read the full summary ↓
Notably, Imaging Mass Cytometry data revealed that CD8+PD-L1+ T cells were enriched in tumor biopsies, pleural infusions and BAL of early-stage NSCLC individuals, proposing this cells subset as candidate not only for the advanced but also for early disease detection. Transcriptomic analysis unveiled that CD8+PD-L1+ T cells displayed an exhausted phenotype related to their increased frequencies to non-responders to immunotherapy, and gene signatures correlated with the overall survival of an independent cohort. Overall, our study pinpoints immune-related events which may benefit the quest for detection of predictive biomarkers of immunotherapy responses.
Published in
Blood immunomap for prediction of responses to anti-PD-1 immunotherapy in metastatic non-small cell lung cancer
Semitekolou M, Paschalidis N, Lo Tartaro D et al. · iScience 2025 · PMID 40860257 · doi:10.1016/j.isci.2025.112804
This dataset
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.

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 GSE295969_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1256890 and SRA study SRP582236. Searching any of these in the dataset finder brings you back here.

Study design
18 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 18 sample titles: 18 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
More Lung cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.