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Disruption of transcriptional programming by CDK9 inhibition suppresses triple-negative breast cancer and is enhanced by EGFR inhibition

GSE283178 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2025/09/30 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 21 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
21 samples: 7 conditions, mostly in triplicateGroups 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)
CDK9, in complex with cyclin T1 or T2, is essential for mRNA transcription by enabling paused RNA polymerase II to proceed into elongation. Increasing evidence highlights CDK9’s involvement in transcriptional addiction in cancer. In this study, we explored the efficacy of novel CDK inhibitors and demonstrated that targeting CDK9 significantly impairs proliferation and induces apoptosis in various triple-negative breast cancer (TNBC) cell lines.
Read the full summary ↓
Transcriptomic analysis revealed that CDK9 inhibition caused downregulation of genes governing transcription, cell cycle progression, and oncogenic signalling pathways, such as TGF-β and Wnt/β-catenin signalling. This disruption of transcriptional programs was further enhanced by combining CDK9 and EGFR inhibitors.
This dataset
Open GSE283178 in finder → Find more human RNA-seq 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 GSE283178_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1192281 and SRA study SRP548404. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
7 conditions, mostly in triplicate
Hs578T, D81, 0.1, ×3 Hs578T, DMSO, ×3 Hs578T, I73, 0.316, ×3 Hs578T, Lap, 3.16, ×3 Hs578T, Lap+I73, 0.1, ×3 Hs578T, LY3, 0.1, ×3 Hs578T, I73, 0.1, ×2 Hs578T, I73 0.1, ×1

Supports a between-group comparison across 20 samples.

How we read this: 7 replicated groups read from 21 sample titles; they account for 20 of them. 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 Breast cancer datasets

Search all human RNA-seq 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.