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Super-enhancer–mediated SMAD3–NR2F2–ID3 transcriptional complex orchestrates Osimertinib resistance in non-small cell lung cancer [CUT&Tag]

GSE308412 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 15 samples Submitted 2026/07/15 Platform GPL34284
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 15 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
15 samples: 15 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
A Non-Canonical Core Transcriptional Regulatory Circuit Orchestrates Chromatin Reprogramming to Drive Osimertinib Resistance in Non-Small Cell Lung CancerAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
Summary (written by the submitters)
Osimertinib resistance remains a major challenge in the treatment of non–small cell lung cancer (NSCLC). Here, we identify an unconventional super-enhancer–driven transcriptional module, composed of SMAD3, NR2F2, and ID3, in which ID3 functions as a transcriptional co-factor to sustain resistance. Mechanistically, ID3 directly interacts with SMAD3 and NR2F2, inducing conformational rearrangements that enhance their DNA-binding affinity to enhancer regions, thereby increasing transcriptional activity.
Read the full summary ↓
This ID3-SMAD3/NR2F2 complex selectively activates EPAS1, which in turn promotes neuroendocrine differentiation and suppresses ferroptosis, thereby maintaining the resistant phenotype. Disruption of this complex abrogates EPAS1 expression and restores Osimertinib sensitivity in preclinical models. Our findings uncover a previously unrecognized role of ID3 as a transcriptional co-factor and highlight the super-enhancer–driven SMAD3–NR2F2–ID3–EPAS1 axis as a promising therapeutic target to overcome Osimertinib resistance in NSCLC.
Published in
A Non-Canonical Core Transcriptional Regulatory Circuit Orchestrates Chromatin Reprogramming to Drive Osimertinib Resistance in Non-Small Cell Lung Cancer
Liu A, Liu Z, Jiang L et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2026 · PMID 42160004 · doi:10.1002/advs.75765
This dataset
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.

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 GSE308412_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1330364 and SRA study SRP621365. Searching any of these in the dataset finder brings you back here.

Study design
15 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 15 sample titles: 15 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
More Lung cancer datasets

Search all human ChIP / ATAC / CUT&Tag 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.