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Lung cancer-enriched p53 mutants occupy canonical p53 target genes without activating transcription, revealing a distinct loss-of-function behavior [ChIP-Seq]

GSE318292 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 26 samples Submitted 2026/02/04 Platform GPL30173
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 26 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
26 samples: 26 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.
Summary (written by the submitters)
Lung cancer is the most common cause of cancer-related death in the U.S. and globally. Cigarette smoking remains the leading risk factor for lung cancer, in part by inducing loss-of-function mutations in tumor suppressor genes, including TP53. While most cancers share a set of common “hotspot” mutations in p53, lung cancer exhibits an additional, distinct cluster of hotspot mutations.
Read the full summary ↓
This cluster is typified by the missense mutations TP53:p.V157F and TP53:p.R158L. While canonical hotspot mutations cause broad misfolding of p53 or eliminate specific DNA contact residues, mechanistic studies of the lung cancer mutants reported here demonstrate that they retain the ability to bind the same genomic sites as wild-type p53. Despite actively binding to traditional p53 target genes, the lung cancer mutants are defective in activating transcription. To our knowledge, this represents the first demonstration of functional inactivation of the p53 tumor suppressor at a point after DNA binding, but prior to target gene activation. Relevant to the sequential inactivation of each p53 allele during cancer progression, the lung cancer mutants block the activity of a wild-type p53 allele when co-expressed in a dominant negative manner. Identification of this loss-of-function mechanism has key implications for therapeutic strategies aimed at restoring p53 function in lung cancer.
This dataset
Open GSE318292 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Lung 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 GSE318292_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1418532 and SRA study SRP674524. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
26 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 26 sample titles: 26 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.