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Patient-derived organoid – immune cell co-culture platform for therapeutics discovery in high-grade endometrial cancer

GSE307568 Homo sapiens (human) RNA-seq (gene expression) 189 samples Submitted 2026/02/14 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 189 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
Endometrial cancerThis series is part of our curated briefing on it.
Samples
189 samples: 135 distinct titles, 30 of them repeatedGroups 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
Ancestrally Diverse Autologous Patient-Derived Organoid-Immune Cell Coculture Platform for Addressing Immunotherapeutic Outcome Disparities in High-Grade Endometrial CancerCancer research communications · 2026
Summary (written by the submitters)
We established a biobank of 85 patient-derived organoids (PDOs) generated from 44 individuals, encompassing a broad range of endometrial cancer (EC) subtypes, including a majority of high-grade endometrial cancer (HGEC) PDOs from African American patients. From these same patients, we isolated autologous immune cells, enabling the creation of PDO–immune cell co-cultures that reflect each patient’s unique characteristics without evoking allogeneic immune responses.
Read the full summary ↓
To dissect the roles of antigen presentation pathways and evaluate proof-of-principle immunotherapeutic interventions, we performed bulk RNA sequencing, flow cytometry, and live-cell imaging.
Published in
Ancestrally Diverse Autologous Patient-Derived Organoid-Immune Cell Coculture Platform for Addressing Immunotherapeutic Outcome Disparities in High-Grade Endometrial Cancer
Chung C, Yueh B, Subhash S et al. · Cancer research communications 2026 · PMID 41709679 · doi:10.1158/2767-9764.CRC-25-0193
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 GSE307568_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 PRJNA1322299 and SRA study SRP618670. Searching any of these in the dataset finder brings you back here.

Study design
135 distinct titles, 30 of them repeated
Patient-derived Organoid pid9 ×6 Patient-derived Organoid pid10 ×6 Patient-derived Organoid pid18 ×5 Organoid from matched normal or biopsy pid6 ×4 Organoid from matched normal or biopsy pid11 ×4 Organoid from matched normal or biopsy pid14 ×4 Organoid from matched normal or biopsy pid3 ×3 Organoid from matched normal or biopsy pid5 ×3 +22 more

Supports a case/control comparison: 98 samples read as cases, 91 as controls.

How we read this: Read from 189 sample titles: 135 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

+ 149 more — browse all 189 samples with per-sample file links →

More Endometrial cancer 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.