GEO series

Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance [ATAC-seq]

GSE97680 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 17 samples Submitted 2017/04/13 Platform GPL18573

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 17 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)
Samples: 17 samples: 17 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)

Therapies targeting signaling molecules mutated in cancers can often have striking short-term effects, but the emergence of resistant cancer cells is a major barrier to full cures. Resistance can sometimes result from a secondary mutations in rare cells, but other times, there is no clear genetic cause, raising leaving the possibility of non-genetic rare cell variability.

Read the full summary ↓

Here, we show that melanoma cells can display profound transcriptional variability at the single cell level that predicts which cells will ultimately resist drug treatment. This variability involves semi-coordinated transcription of a number of resistance markers at high levels in a very small percentage of cells. The addition of drug then induces an epigenetic reprogramming in these cells, converting the transient transcriptional state to a stably resistant state. This reprogramming begins withis a progressive process consisting of a loss of SOX10-mediated differentiation followed by activation of new signaling pathways, partially mediated by activity of Jun-AP-1 and TEAD. Our work reveals the multistage nature of the acquisition of drug resistance and provides a framework for understanding resistance dynamics. We find that other cell types also exhibit sporadic expression of many of these same marker genes, suggesting the existence of a general rare-cell expression program.

This dataset

Open GSE97680 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE97680_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 PRJNA382753 and SRA study SRP103827. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 28607484) ↗

Study design

17 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 17 sample titles: 17 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

GSM2575288 EGFR-p9-noDrug_ATAC-seq
GSM2575289 EGFR-p9-week1_ATAC-seq
GSM2575290 EGFR-p9-week4_ATAC-seq
GSM2575291 EGFR-p14-noDrug-v2_ATAC-seq
GSM2575292 EGFR-p14-week1-v2_ATAC-seq
GSM2575293 EGFR-p14-week4-v2_ATAC-seq
GSM2575294 EGFR-week5-v2_ATAC-seq
GSM2575295 mix-p9-noDrug_ATAC-seq
GSM2575296 mix-p9-week1_ATAC-seq
GSM2575297 mix-p9-week4_ATAC-seq
GSM2575298 mix-p14-noDrug-v2_ATAC-seq
GSM2575299 mix-p14-week1-v2_ATAC-seq
GSM2575300 mix-p14-week4-v2_ATAC-seq
GSM2575301 mix-week5-v2_ATAC-seq
GSM2575302 neg-p9-week4_ATAC-seq
GSM2575303 neg-p14-week4-v2_ATAC-seq
GSM2575304 neg-week5-v2_ATAC-seq

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GSE308846 Reprogramming lineage features promotes epithelial behavior in carcinoma cells [ATAC-seq] 6 samples shares reprogramming, atac-seq
GSE291147 Mapping convergent regulators of melanoma drug resistance by PerturbFate 320 samples shares resistance, drug
GSE300628 HER2 heterogeneous breast cancer models reveal novel therapeutic targets and subclonal dynamics during evolution to resistance to HER2-targeted therapies 60 samples shares resistance, mode
GSE254366 Epigenome reprogramming through H3K27 and H3K4 trimethylation as a resistance mechanism to DNA methylation inhibition in BRAFV600E-mutated CRC. 36 samples shares reprogramming, resistance
GSE282423 Imaging-based drug screening combined with moleuclar profiling identifies signatures and drivers of therapy resistance in pediatric AML [RNA-seq] 28 samples shares resistance, drug
GSE326661 Reprogramming engineered autologous T cells to overcome resistance in Merkel cell carcinoma [scRNA-seq] 22 samples shares reprogramming, resistance
GSE308647 Drug tolerant persisters and immunotherapy persister cells exhibit cross-resistance and share common survival mechanisms 15 samples shares resistance, drug

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.