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CAN-Scan: a multi-omic phenotype-driven precision oncology platform identifies prognostic biomarkers of therapy response for colorectal cancer

GSE287956 Homo sapiens (human) RNA-seq (gene expression) 69 samples Submitted 2025/04/04 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 69 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
69 samples: 65 distinct titles, 1 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
CAN-Scan: A multi-omic phenotype-driven precision oncology platform identifies prognostic biomarkers of therapy response for colorectal cancerCell reports. Medicine · 2025
Summary (written by the submitters)
Application of machine learning (ML) on cancer-specific pharmacogenomic datasets shows immense promise for identifying predictive response-biomarkers to enable personalized treatment. We introduce CAN-Scan, a precision oncology platform, that applies ML on next-generation pharmacogenomic datasets generated from a freeze-viable biobank of patient-derived primary cell lines (PDCs).
Read the full summary ↓
These PDCs are screened against 84 FDA-approved drugs at clinically relevant doses (Cmax), focusing on colorectal cancer (CRC) as a model system. CAN-Scan uncovers prognostic biomarkers and alternative treatment strategies, particularly for patients unresponsive to first-line chemotherapy. Specifically, it identifies gene expression signatures linked to resistance against 5-Fluorouracil (5FU)-based drugs and a focal copy number gain on chromosome 7q, harbouring critical resistance-associated genes. CAN-Scan-derived response signatures accurately predict clinical outcomes across four independent, ethnically-diverse CRC cohorts. Notably, drug-specific ML models reveal Regorafenib and Vemurafenib as alternative treatments for BRAF-expressing, 5FU-insensitive CRC. Altogether, this approach demonstrates significant potential in improving biomarker-discovery and guiding personalized treatments.
Published in
CAN-Scan: A multi-omic phenotype-driven precision oncology platform identifies prognostic biomarkers of therapy response for colorectal cancer
Chia S, Wen Seow JJ, Peres da Silva R et al. · Cell reports. Medicine 2025 · PMID 40187357 · doi:10.1016/j.xcrm.2025.102053
This dataset
Open GSE287956 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 GSE287956_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 PRJNA1215353 and SRA study SRP559702. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40187357) ↗
Study design
65 distinct titles, 1 of them repeated
HNTM ×5

How we read this: Read from 69 sample titles: 65 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

+ 29 more — browse all 69 samples with per-sample file links →

More Colorectal 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.