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Spatial tumour-immune ecosystems shape the efficacy of anti-PD1 immunotherapy in primary cutaneous melanoma [RNAseq]

GSE300793 Homo sapiens (human) RNA-seq (gene expression) 57 samples Submitted 2026/06/23 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 57 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
57 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)
Intra-tumoral heterogeneity in melanoma arises from dynamic cancer cell plasticity and underlies various mechanisms of immune escape. Here, we combined high-plex immunofluorescence imaging with spatially resolved transcriptomics to map the architecture of melanoma cell states and their interactions with the immune microenvironment in primary cutaneous tumours prior to adjuvant anti-PD1 immune checkpoint inhibitor (ICI) treatment.
Read the full summary ↓
Computational analyses showed that melanoma cells organise into spatially restricted patches, with a preferential organisation of undifferentiated cells associated with poor ICI efficacy. Neighbouring immune cell composition varied according to cancer cell states, with a crucial involvement of specific subsets of tumour-associated macrophages, driven by signalling pathways involving tumour-derived and microenvironmental cues such as IFN-γ and hypoxia. Integrated spatial analyses further revealed tumour-immune ecosystems that stratify patient outcomes, delineating configurations either associated with ICI efficacy or metastatic relapse. These results uncover the spatial landscape of tumour ecosystems and identify signalling pathways as potential targets for improving the efficacy of ICI in melanoma.
This dataset
Open GSE300793 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 GSE300793_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 PRJNA1282115 and SRA study SRP594590. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
A cohort of 57 individual samples

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

How we read this: Sample titles run ADJIII-0003 … ADJIII-0169 and carry no group information.

Samples in this study

+ 17 more — browse all 57 samples with per-sample file links →

More Melanoma 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.