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Integrative multi-omics identifies AP-1 transcription factor as a targetable mediator of acquired osimertinib resistance in non-small cell lung cancer [RNA_SEQ_FOSL1 and JUN KO]

GSE278409 Homo sapiens (human) RNA-seq (gene expression) 10 samples Submitted 2025/05/27 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 10 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
10 samples: 5 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
Integrative multi-omics identifies AP-1 transcription factor as a targetable mediator of acquired osimertinib resistance in non-small cell lung cancerCell death & disease · 2025
Summary (written by the submitters)
Osimertinib, a third-generation EGFR tyrosine kinase inhibitor (EGFR-TKI), has dramatically transformed the treatment landscape for patients with EGFR-mutant NSCLC. However, the long-term success of this therapy is often compromised by the onset of acquired resistance, with non-genetic mechanisms increasingly recognized as pivotal contributors.
Read the full summary ↓
Here, we exploit a multi-omics approach to profile genome-wide chromatin accessibility and transcriptional landscapes between drug sensitive and resistant EGFR-mutant cells. Our findings reveal a robust concordance between epigenetic regulome and transcriptomic changes that characterize the osimertinib resistant state. Using CRISPR-based functional genomics screen targeting epigenetic regulators and transcription factors, we uncover a critical regulatory network featuring key members of the NuRD and PRC2 complexes that mediate resistance. Most critically, our screen identifies FOSL1 and JUN, two subunits of the AP-1 transcription factor within this network, as the most significant hits. Mechanistically, we demonstrate that cis-regulatory elements exhibiting altered chromatin accessibility in the resistant state are enriched for cognate AP-1 binding motifs, enabling AP-1 to orchestrate a gene expression program that underpins the druggable MEK/ERK signaling axis, potentially enhancing cell viability and fitness of resistant cells. Importantly, genetic depletion or pharmacological inhibition of AP-1 reinstates cellular and molecular sensitivity and reverts resistance-associated phenotypes, such as epithelial-to-mesenchymal transition, upon anti-EGFR rechallenge by repressing AKT and ERK signaling. The findings provide novel insights into the epigenetic and transcriptional control of osimertinib resistance in EGFR-mutant NSCLC, highlighting AP-1 as a targetable vulnerability of resistance-related hallmarks and offering a promising avenue for developing resistance reversal strategies.
Published in
Integrative multi-omics identifies AP-1 transcription factor as a targetable mediator of acquired osimertinib resistance in non-small cell lung cancer
Dayanc B, Eris S, Gulfirat NE et al. · Cell death & disease 2025 · PMID 40414926 · doi:10.1038/s41419-025-07711-z
This dataset
Open GSE278409 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 GSE278409_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 PRJNA1167201 and SRA study SRP535708. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40414926) ↗
Study design
5 conditions, mostly in duplicate
Resistant HCC827 FOSL1 g1 knockout cells, (HC… ×2 Resistant HCC827 FOSL1 g2 knockout cells, (HC… ×2 Resistant HCC827 JUN g1 knockout cells, (HCC8… ×2 Resistant HCC827 JUN g2 knockout cells, (HCC8… ×2 Resistant HCC827 Renilla knockout cells, (HCC… ×2

Supports a between-group comparison across 10 samples.

How we read this: 5 replicated groups read from 10 sample titles; they account for 10 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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