GEO series

Colorectal cancer organoids drive patient-specific inflammation, cytotoxicity and tissue-residency programs in NK cells

GSE292020 Homo sapiens (human) RNA-seq (gene expression) 15 samples Submitted 2025/12/17 Platform GPL21697

At a glance

RNA-seq (gene expression) · human · 15 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: Colorectal cancerThis series is part of our curated briefing on it.
Samples: 15 samples: 6 conditions, mostly in duplicateGroups 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: Colorectal cancer organoids drive hypoxia, TGF-β, and patient-specific diversification of NK cell activation programsJournal for immunotherapy of cancer · 2025

Summary (written by the submitters)

Natural killer (NK) cells represent a promising strategy for cellular cancer immunotherapy, but it remains unclear which patients benefit by such therapies. Using co-cultures of primary human allogenic NK cells and patient-derived colon cancer organoids (PDOs), we could stratify PDOs into NK cell “highly susceptible” and “rather resistant” groups reflected by differential expression of NKG2D-ligands, MHC class I and CEACAM.

Read the full summary ↓

RNA-seq unveiled that hypoxia- and TGF-β-related gene signatures were induced in NK cells after PDO co-culture. Deletion of hypoxia inducible factor (HIF)-2a in primary NK cells or blocking of TGF-β-R1 empowered NK cell-mediated PDO killing. We further demonstrate that upon PDO killing, NK cells adopted an activation/inflammationhigh, cytotoxicitylow, tissue-residencyhigh program as evidenced by CXCR6 and CD49a expression coinciding with a “hot” cytokine/chemokine-rich microenvironment. Since PDO-exposed NK cells largely resemble features of NK cells from CRC patient tissues, our NK cell/PDO platform could be highly relevant for the optimization of NK cell products for personalized medicine.

Published in

Colorectal cancer organoids drive hypoxia, TGF-β, and patient-specific diversification of NK cell activation programs

von Kries A, Garcés-Lázaro I, Balzasch BM et al. · Journal for immunotherapy of cancer 2025 · PMID 41360426 · doi:10.1136/jitc-2025-012988

This dataset

Open GSE292020 in finder → Find more human RNA-seq datasets → Colorectal cancer 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 GSE292020_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 PRJNA1236336 and SRA study SRP570514. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41360426) ↗

Study design

6 conditions, mostly in duplicate

NK cells only D ×5 NK cells D2 from PDO ×2 NK cells D3 from PDO ×2 NK cells D5 from PDO ×2 NK cells D6 from PDO ×2 NK cells D1 from PDO ×2

Supports a between-group comparison across 15 samples.

How we read this: 6 replicated groups read from 15 sample titles; they account for 15 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

GSM8846551 NK cells only D2 NK cells only D
GSM8846552 NK cells only D3 NK cells only D
GSM8846553 NK cells only D5 NK cells only D
GSM8846554 NK cells only D6 NK cells only D
GSM8846555 NK cells only D1 NK cells only D
GSM8846556 NK cells D2 from PDO22 NK cells D2 from PDO
GSM8846557 NK cells D3 from PDO22 NK cells D3 from PDO
GSM8846558 NK cells D5 from PDO22 NK cells D5 from PDO
GSM8846559 NK cells D6 from PDO22 NK cells D6 from PDO
GSM8846560 NK cells D1 from PDO22 NK cells D1 from PDO
GSM8846561 NK cells D2 from PDO7 NK cells D2 from PDO
GSM8846562 NK cells D3 from PDO7 NK cells D3 from PDO
GSM8846563 NK cells D5 from PDO7 NK cells D5 from PDO
GSM8846564 NK cells D6 from PDO7 NK cells D6 from PDO
GSM8846565 NK cells D1 from PDO7 NK cells D1 from PDO

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GSE251958 Predicting patient outcome based on gene-expression biomarkers learned from colorectal cancer organoids and cell lines 21 samples shares colorectal, organoids

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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.