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Detecting small cell transformation in patients with advanced EGFR mutant lung adenocarcinoma through epigenomic cfDNA profiling

GSE269746 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 128 samples Submitted 2024/06/20 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 128 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
128 samples: 128 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)
Histologic transformation to small cell lung cancer (SCLC) is an increasingly common resistance mechanism to EGFR tyrosine kinase inhibitors in EGFR mutant lung adenocarcinoma (LUAD) that is underdiagnosed in clinical practice due to the requirement for tissue biopsy. Early and accurate detection of transformed (t)SCLC has important prognostic and therapeutic implications.
Read the full summary ↓
To address this unmet need, we first comprehensively profiled the epigenomes of metastatic lung tumors finding widespread epigenomic reprogramming during histologic transformation from LUAD to SCLC. We then utilized a novel approach for epigenomic profiling of cell-free DNA, which discriminated patients with EGFR mutant tSCLC from patients with EGFR mutant LUAD with greater than 90% accuracy. This first demonstration of the ability to accurately, and non-invasively, detect small cell transformation in patients with EGFR mutant LUAD through epigenomic cfDNA profiling is a critical step towards a new paradigm of diagnostic and therapeutic precision for patients with advanced lung cancer.
This dataset
Open GSE269746 in finder → Find more human methylation 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 GSE269746_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 PRJNA1123604 and SRA study SRP517234. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
128 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 128 sample titles: 128 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

+ 88 more — browse all 128 samples with per-sample file links →

More Lung cancer datasets

Search all human methylation 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.