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Mutated b-catenin Gene Signature to Identify CTNNB1-mutated and Immune Checkpoint Resistant Hepatocellular Cancers

GSE261316 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2024/10/09 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 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
Mus musculus (mouse)
Samples
15 samples: 5 conditions, mostly in triplicateGroups 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
Development of mutated β-catenin gene signature to identify CTNNB1 mutations from whole and spatial transcriptomic data in patients with HCCJHEP reports : innovation in hepatology · 2024
Summary (written by the submitters)
Background & Aims: Patients with beta-catenin (encoded by CTNNB1)-mutated hepatocellular carcinoma (HCC) have demonstrated limited clinical benefit to first-line immunotherapy (IO). Animal models of HCC expressing mutant-beta-catenin and additional aberrations via hydrodynamic tail vein injection with sleeping beauty transposon/transposase (SB-HDTVI) represent clinically relevant human HCC subsets.
Read the full summary ↓
Here, we perform transcriptomic analysis on multiple beta-catenin-mutated and non-mutated HCC animal models to identify Mutated beta-catenin Gene Signature (MBGS) for HCC patient stratification for CTNNB1-mutations and IO response. Methods: We co-expressed in mice mutant-NFE2L2 and hMET +- mutant-CTNNB1 via SB-HDTVI and monitored for HCC development. Bulk RNA-sequencing was assessed for transcriptional differences between various beta-catenin-mutated and non-mutated models. MBGS was generated for predictive ability of CTNNB1 mutations and IO resistance in multiple HCC patient cohorts. Results: Co-expression of S45Y-beta-catenin + G31A-NFE2L2 + hMet (beta-N-M) resulted in HCC development by 4.5 weeks while co-expression of G31A-NFE2L2 + hMet (N-M) led to HCC by 14 weeks with tumors being positive for expected targets by immunohistochemistry (IHC). Bulk RNA-sequencing comparing beta-catenin-driven versus non-beta-catenin driven models yielded 95 common upregulated genes. Differential gene expression analysis of the common genes comparing CTNNB1-mutated vs non-mutated TCGA patients narrowed the gene panel to 13-(or 10-) genes. This MBGS predicted CTNNB1-mutation status in TCGA (n=374) and French (n=398) patient cohorts (with ROC AUC of 0.90 and 0.94). High MBGS expression was also associated with no overall and progression-free survival benefit when comparing atezolizumab + bevacizumab versus sorafenib arms in IMbrave150 cohort implying fewer treatment effects. Conclusions: In an era of patient molecular stratification for HCC, MBGS may aid in optimally selecting patients for IO through diagnosis of a molecular subset which lacks optimal response.
Published in
Development of mutated β-catenin gene signature to identify CTNNB1 mutations from whole and spatial transcriptomic data in patients with HCC
Lehrich BM, Tao J, Liu S et al. · JHEP reports : innovation in hepatology 2024 · PMID 39583094 · doi:10.1016/j.jhepr.2024.101186
This dataset
Open GSE261316 in finder → Find more mouse 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 GSE261316_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 PRJNA1086482 and SRA study SRP494546. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39583094) ↗
Study design
5 conditions, mostly in triplicate
Wild-type FVB liver, JY ×3 S45Y-CTNNB1 + G31A-NFE2L2 + hMET, JY ×3 S45Y-CTNNB1 + G31A-NFE2L2, JY ×3 G31A-NFE2L2 + hMET, JY ×3 S45Y-CTNNB1 + hMET, JY ×2 S45Y-CTNNB1 + hMET, JY-112 ×1

Supports a between-group comparison across 14 samples.

How we read this: 5 replicated groups read from 15 sample titles; they account for 14 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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