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Rb1 deficiency induces synthetic lethality with ATR and PKMYT1 co-inhibition in breast cancer

GSE303648 Homo sapiens (human) RNA-seq (gene expression) 57 samples Submitted 2025/11/21 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 57 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
57 samples: 16 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.
Paper
Rb1 deficiency induces synthetic lethality with ATR and PKMYT1 coinhibition in breast cancer cell lines and patient-derived xenograftsScience translational medicine · 2025
Summary (written by the submitters)
Targeting cell cycle checkpoints has emerged as a promising strategy in cancer therapy, yet single-agent inhibitors often fail due to compensatory mechanisms. Here, we demonstrate that co-inhibition of ATR (RP-3500) and PKMYT1 (RP-6306) induces synthetic lethality in Rb1-deficient breast cancers by disrupting both S/G2 and G2/M checkpoints, resulting in replication stress, premature mitotic entry, and DNA damage accumulation.
Read the full summary ↓
In vitro, Rb1-deficient breast cancer cell lines exhibited marked apoptosis and loss of clonogenic potential, whereas Rb1-proficient models remained largely resistant to combination treatment. Genetic manipulation of Rb1 confirmed this dependency: Rb1 knockdown sensitized resistant models, and overexpression conferred protection. In vivo, patient-derived xenograft (PDX) models recapitulated these findings. Rb1-deficient tumors underwent complete regression, while Rb1-proficient tumors showed limited response with RP-3500/RP-6306 treatment. Combination therapy was well tolerated in vivo, without significant toxicity or weight loss. Biomarker analysis revealed increased γH2AX and reduced Ki67 staining exclusively in Rb1-deficient PDX models, underscoring the specificity of this response. Mechanistically, Rb1 loss impaired double-strand DNA repair by attenuating homologous recombination and non-homologous end joining, leading to replication fork collapse, chromosomal instability, and mitotic catastrophe. Proteogenomic analysis identified JNK/p38 stress response pathway activation as a key driver of apoptosis following ATR/PKMYT1 inhibition in Rb1-deficient cells. Clinically, analysis of stage IV breast cancer patient datasets revealed that Rb1-low tumors display reduced DNA repair pathway activity and are enriched in triple-negative and CDK4/6 inhibitor-resistant luminal breast cancers. These findings establish Rb1 loss as a functional biomarker for ATR/PKMYT1-targeted therapy, offering a precision treatment strategy for advanced breast cancers.
Published in
Rb1 deficiency induces synthetic lethality with ATR and PKMYT1 coinhibition in breast cancer cell lines and patient-derived xenografts
Jiang XT, Chen MK, Lee J et al. · Science translational medicine 2025 · PMID 41442499 · doi:10.1126/scitranslmed.adx6797
This dataset
Open GSE303648 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 GSE303648_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 PRJNA1295982 and SRA study SRP603408. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41442499) ↗
Study design
16 conditions, mostly in triplicate
BT549_D ×6 MCF7_D ×6 MCF7PR_D ×6 MB231_D ×3 MB231_Mi ×3 MB231_Ai ×3 MB231_C ×3 BT549_Mi ×3 +8 more

Supports a between-group comparison across 57 samples.

How we read this: 16 replicated groups read from 57 sample titles; they account for 57 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

+ 17 more — browse all 57 samples with per-sample file links →

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.