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CDK10 suppression sensitizes cells to CDK12/13 blockade

GSE307213 Homo sapiens (human) RNA-seq (gene expression) 48 samples Submitted 2026/06/09 Platform GPL28038
At a glance

RNA-seq (gene expression) · human · 48 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)
Samples
48 samples: 24 conditions, mostly in duplicateGroups 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.
Paper
CDK10 loss primes tumor cells for transcriptional vulnerability and sensitizes to CDK12/13 inhibitionCell death & disease · 2026
Summary (written by the submitters)
A CRISPR screen with a CDK12/13 inhibitor identified CDK10 as a modulator of CDK12/13 blockade. CDK10 KO cells in OVCA and BRCA showed higher sensitivity to CDK12/13 inhibitors and increased apoptosis, compared to wild type cells. To investigate the effects on transcription, CDK10 KO cells with or without CDK12/13 inhibitor treatment were analyzed by RNA-sequencing.
Published in
CDK10 loss primes tumor cells for transcriptional vulnerability and sensitizes to CDK12/13 inhibition
Islam S, Tarumoto Y, Takano M et al. · Cell death & disease 2026 · PMID 42270604 · doi:10.1038/s41419-026-08973-x
This dataset
Open GSE307213 in finder → Find more human RNA-seq 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 GSE307213_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 PRJNA1314771 and SRA study SRP616953. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42270604) ↗
Study design
24 conditions, mostly in duplicate
OVCAR8_gCDK10-1_0nM ×2 OVCAR8_gCDK10-1_10nM ×2 OVCAR8_gCDK10-1_30nM ×2 OVCAR8_gCDK10-1_90nM ×2 OVCAR8_gCTRL_0nM ×2 OVCAR8_gCTRL_10nM ×2 OVCAR8_gCTRL_30nM ×2 OVCAR8_gCTRL_90nM ×2 +16 more

Supports a between-group comparison across 48 samples.

How we read this: 24 replicated groups read from 48 sample titles; they account for 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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