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Distinct and convergent effects of SF3B1 mutations in human breast cancer

GSE304350 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2025/09/30 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 30 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
30 samples: 28 distinct titles, 1 of them repeatedGroups 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)
Tumor genomic profiling has uncovered many cancer drivers whose implications in terms of tumor biology and therapeutic actionability remain understudied. Hotspot mutations in SF3B1 induce widespread transcriptomic alterations and occur across multiple cancer types. Despite this, the biological and clinical consequences of SF3B1 mutations remain elusive.
Read the full summary ↓
Characterization of the largest SF3B1 mutant breast cancer clinical cohort to date identifies SF3B1 driver mutations in approximately 2.5% of HR+ HER2- breast cancer, with strong enrichment of K700E substitutions, substantial variation in variant allele fraction, and significantly improved overall survival. In-vitro and in-vivo studies in representative cell models suggest several of the most prevalent SF3B1 mutations have deleterious effects on cell growth, leading to selective loss of the mutation over time, providing a rationale for the low frequency and low VAF of SF3B1 mutations in breast cancer. Though all introduced hotspot mutations limit growth and are reverted to wildtype over time, mutations at position R625 have a more pronounced phenotype than K700E, providing an explanation for the clinically observed skew in mutation frequencies. RNA and DNA sequencing analyses were used to identify characteristic pathway-level transcriptomic changes in SF3B1-mutant cells and identify copy number alterations as a mechanism of both improved mutation tolerability and a means to eliminate the mutation over time. These data suggest that while SF3B1 mutations are enriched in some clinical contexts, their role in breast tumorigenesis is highly complex and dependent on secondary events that overcome their deleterious effects on cell growth and survival.
This dataset
Open GSE304350 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 GSE304350_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 PRJNA1300440 and SRA study SRP605887. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
28 distinct titles, 1 of them repeated
MCF10AK666N ×3

How we read this: Read from 30 sample titles: 28 distinct titles with little repetition. 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.