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Generation of a biliary tract cancer cell line atlas identifies molecular subtypes and therapeutic targets

GSE295371 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/04/30 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 12 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
CholangiocarcinomaThis series is part of our curated briefing on it.
Samples
12 samples: 11 × ICCGroups 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
Generation of a Biliary Tract Cancer Cell Line Atlas Identifies Molecular Subtypes and Therapeutic TargetsCancer discovery · 2025
Summary (written by the submitters)
Biliary tract cancers (BTCs) are aggressive malignancies encompassing intrahepatic and extrahepatic cholangiocarcinoma, gallbladder carcinoma, and ampullary carcinoma. Here, we report integrative analysis of 63 BTC cell lines via multi-omics and genome-scale CRISPR screens. We identify widespread EGFR dependency in BTC, alongside dependencies selective to anatomic subtypes.
Read the full summary ↓
Additionally, we delineate strategies to overcome therapeutic resistance, with combined EGFR inhibition potentiating targeting of KRAS-mutant and FGFR2-fusion-driven models, and SHP2 inhibition effective in the latter context. Clustering RNA/protein expression and dependencies data revealed functional relationships transcending single-gene alterations, with biliary, squamous, or dual biliary/hepatocyte lineage signatures stratifying BTC models. These subtypes exhibit distinct dependency profiles— including cell fate transcription factors GRHL2, TP63, and HNF1B, respectively— and demonstrate prognostic significance in patient samples. Potential subtype-specific targetable vulnerabilities include Integrin-a3 and the detoxification enzyme UXS1. This cell line atlas reveals therapeutic targets in molecularly-defined BTCs, unveils disease subtypes, and provides a resource for therapeutic development.
Published in
Generation of a Biliary Tract Cancer Cell Line Atlas Identifies Molecular Subtypes and Therapeutic Targets
Vijay V, Karisani N, Shi L et al. · Cancer discovery 2025 · PMID 40353839 · doi:10.1158/2159-8290.CD-24-1383
This dataset
Open GSE295371 in finder → Find more human RNA-seq datasets → Cholangiocarcinoma 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 GSE295371_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 PRJNA1254299 and SRA study SRP580603. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40353839) ↗
Study design
11 × ICC

Supports a between-group comparison across 11 samples.

How we read this: 1 replicated groups read from 12 sample titles; they account for 11 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
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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.