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Genomic analysis to identify correlates of the inflamed tumor phenotype in patients with advanced malignancies receiving immunotherapy

GSE285619 Homo sapiens (human) RNA-seq (gene expression) 60 samples Submitted 2025/09/09 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 60 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)
Samples
60 samples: 38 × YZHA vs 22 × YZGroups 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
PKCδ Germline Variants and Genetic Deletion in Mice Augment Antitumor Immunity through Regulation of Myeloid CellsCancer immunology research · 2025
Summary (written by the submitters)
This biomarker study will analyze tissues from patients with cancer who are receiving immunomodulatory treatment (or therapy that could be hypothesized to modulate the immune response), to correlate molecular/genetic features of the tumor cells, the host, and the intestinal microbiome with the immunologic phenotype of the tumor microenvironment.
Read the full summary ↓
Recent data have suggested that patients with pre-existing T cell infiltration in the tumor microenvironment and/or an “inflamed” gene expression profile in tumor sites have better clinical outcome following treatment with several immunotherapeutic approaches. However, the mechanism by which this spontaneous anti-tumor immune response develops in only a subset of patients is not understood. Potential correlations between mutational patterns in the tumor, germline genetic polymorphisms, peripheral blood immune response parameters, and the composition of intestinal microbiota and the presence or absence of a T cell-infiltrated tumor microenvironment will be sought. Understanding these mechanisms will have important implications for future patient selection for treatment with immunotherapies and highlight new potential pathways for therapeutic development. Mechanisms of secondary resistance to immunotherapies also will be interrogated using tissues obtained at time of recurrence.
Published in
PKCδ Germline Variants and Genetic Deletion in Mice Augment Antitumor Immunity through Regulation of Myeloid Cells
Cron KR, Sivan A, Aquino-Michaels K et al. · Cancer immunology research 2025 · PMID 39808445 · doi:10.1158/2326-6066.CIR-23-0999
This dataset
Open GSE285619 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 GSE285619_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 PRJNA1204974 and SRA study SRP554708. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39808445) ↗
Study design
38 × YZHA vs 22 × YZ

Supports a between-group comparison across 60 samples.

How we read this: 2 replicated groups read from 60 sample titles; they account for 60 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

+ 20 more — browse all 60 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.