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ATACseq of TNBC cell lines treated with EZH2i (tazemetostat) and/or AKTi (ipatasertib)

GSE252382 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 84 samples Submitted 2024/07/08 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 84 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
Breast cancerThis series is part of our curated briefing on it.
Samples
84 samples: 84 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)
Triple negative breast cancer (TNBC) is the most aggressive breast cancer subtype and has the highest rate of recurrence1. The predominant standard of care for advanced TNBC is systemic chemotherapy with or without immunotherapy, however responses are typically short-lived1,2. Thus, there is an urgent need to develop more effective treatments.
Read the full summary ↓
PI3K pathway components represent plausible therapeutic targets, as approximately 70% of TNBCs have PIK3CA/AKT1/PTEN alterations3–6. However, unlike hormone receptor-positive tumors, it is still unclear if or how PI3K pathway inhibitors will be effective in triple-negative disease7. Here we identify a promising AKT inhibitor-based therapeutic combination for TNBC. Specifically, we show that AKT inhibitors potently synergize with agents that suppress the histone methyltransferase, EZH2, and promote robust tumor regression in multiple TNBC models in vivo. AKT and EZH2 inhibitors exert these effects by first cooperatively driving basal-like TNBC cells into a more differentiated, luminal-like state, which cannot be effectively induced by either agent alone. Once differentiated, these agents kill TNBCs by hijacking signals that normally drive mammary gland involution. Finally, using machine learning approach, we developed a classifier that can be used for patient selection. Together these findings identify a promising therapeutic strategy for this highly aggressive tumor type and illustrate how deregulated epigenetic enzymes can insulate tumors from oncogenic vulnerabilities. These studies also reveal how developmental tissue-specific cell death pathways may be co-opted for therapeutic benefit.
This dataset
Open GSE252382 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Breast 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 GSE252382_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 PRJNA1060317 and SRA study SRP480948. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
84 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 84 sample titles: 84 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

+ 44 more — browse all 84 samples with per-sample file links →

More Breast 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.