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Induction of CDX2 as a differentiation therapy in colorectal cancers [CRC organoids]

GSE237623 Homo sapiens (human) RNA-seq (gene expression) 39 samples Submitted 2025/09/23 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 39 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
39 samples: 39 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
CANDiT: A machine learning framework for differentiation therapy in colorectal cancerCell reports. Medicine · 2025
Summary (written by the submitters)
Background: Lack of expression of the transcription factor CDX2 identifies a subset of poorly differentiated colorectal cancers (CRCs) that are associated with lower overall (OS) and disease-free (DFS) survival, independent of ethnicity, MSI status, or stage. CDX2 expression lowers risk of death and disease recurrence by ~50%. Induction of CDX2 as a therapeutic strategy is yet to be implemented in clinical practice.
Read the full summary ↓
Methods: We used a novel network-based computational model to identify and prioritize therapeutic targets that can robustly augment CDX2 expression and, consequently, differentiation. The top candidate with available clinical-grade pharmacophore was tested for efficacy in 3 models: CRC lines in vitro, xenografts in mice, and in prospective cohorts of CRC patient-derived organoids (PDOs; n = 23). Results:  Our network-derived gene clusters reliably classified healthy and CRC tissues in diverse publicly available gene expression datasets (>10,000 human and >250 mouse samples). The target showed potent anti-cancer activity, induced crypt differentiation signatures in all models tested, and shifted the network in predictable ways. Effective pairing of therapy (IC50 of the target) and CDX2 expression (companion biomarker) was confirmed in an independent cohort of CRC PDOs. A multivariate analysis revealed KRAS mutation and high-CDX2 as key determinants of resistance to the target. Conclusions: Induction of CDX2 expression by the target augments differentiation and shows potent anti-cancer effect on CRCs that lack CDX2 expression. This benefit of drug-biomarker pairing should be considered for confirmatory studies in randomized clinical trials.
Published in
CANDiT: A machine learning framework for differentiation therapy in colorectal cancer
Sinha S, Alcantara J, Perry K et al. · Cell reports. Medicine 2025 · PMID 41118768 · doi:10.1016/j.xcrm.2025.102421
This dataset
Open GSE237623 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 GSE237623_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 PRJNA996061 and SRA study SRP456660. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41118768) ↗
Study design
39 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 17 samples read as cases, 20 as controls.

How we read this: Read from 39 sample titles: 39 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
Datasets on a similar topic

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