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Protective effects of metformin against ponatinib-induced toxicity in iPSC-derived cardiomyocytes

GSE312602 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/06/02 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 3 × hiPSC-CMs treated with ponatinib biol vs 3 × hiPSC-CMs treated with vehicle biol vs 3 × hiPSC-CMs treated with metformin biol vs 3 × hiPSC-CMs co-treated with ponatinib and metformin biolGroups 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)
Aims: Ponatinib is currently considered the most effective treatment for patients with chronic myeloid leukemia (CML) or Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL), who develop resistance or intolerance to first- and second-line therapy, particularly those with T315I "gatekeeper" mutation in BCR-ABL. However, ponatinib is also one of the most cardiotoxic tyrosine kinase inhibitors (TKIs), with toxicity stemming from a combination of contractile dysfunction and inflammatory effects.
Read the full summary ↓
Our goal was to model and investigate this toxicity in human-induced pluripotent stem-cell derived cardiomyocytes (hiPSC-CMs) and to evaluate whether metformin, a clinically well-tolerated antidiabetic drug with known cardioprotective potential via AMPK pathway activation in various pathologies, can mitigate the damage.
This dataset
Open GSE312602 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 GSE312602_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 PRJNA1374434 and SRA study SRP651561. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × hiPSC-CMs treated with ponatinib biol vs 3 × hiPSC-CMs treated with vehicle biol vs 3 × hiPSC-CMs treated with metformin biol vs 3 × hiPSC-CMs co-treated with ponatinib and metformin biol

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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
Datasets on a similar topic

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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.