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Targeting G1–S-checkpoint-compromised cancers with cyclin A/B RxL inhibitors

GSE291451 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2025/05/29 Platform GPL24676
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
Small cell lung cancerThis series is part of our curated briefing on it.
Samples
21 samples: 6 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
Targeting G1-S-checkpoint-compromised cancers with cyclin A/B RxL inhibitorsNature · 2025
Summary (written by the submitters)
Cancer cell proliferation requires precise control of E2F1 activity; excess activity promotes apoptosis. We developed cell-permeable and bioavailable macrocycles that selectively kill small cell lung cancer (SCLC) cells with inherent high E2F1 activity by blocking RxL-mediated interactions of cyclin A and cyclin B with select substrates. This work uncovers gain-of-function mechanisms by which cyclin A/Bi induce apoptosis in cancers with high E2F activity, and suggests cyclin A/Bi as a therapeutic strategy for SCLC and other cancers driven by high E2F activity.
Published in
Targeting G1-S-checkpoint-compromised cancers with cyclin A/B RxL inhibitors
Singh S, Gleason CE, Fang M et al. · Nature 2025 · PMID 40836083 · doi:10.1038/s41586-025-09433-w
This dataset
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.

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 GSE291451_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1233782 and SRA study SRP568967. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in duplicate
DFCI393 ×11 H1048 ×2 H1048_05 ×2 H1048_04 ×2 H1048_14 ×2 H1048_PF ×2

Supports a between-group comparison across 21 samples.

How we read this: 6 replicated groups read from 21 sample titles; they account for 21 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 Small cell lung cancer datasets

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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.