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The SRC Family Kinase, YES1, is a modulator of centrosome homeostasis, chromosomal stability, and therapeutic response in triple negative breast cancer

GSE226766 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2025/06/30 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 4 × MDA-MB-231, siNS, 48 hrs, vs 4 × MDA-MB-231, siYES1, 48 hrs, vs 4 × SUM159PT, siNS, 48 hrs, vs 4 × SUM159PT, siYES1, 48 hrs,Groups 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)
Triple negative breast cancer (TNBC) is a collection of heterogeneous disease with limited therapeutic options. One commonality of TNBCs is their distinct molecular profiles and increased chromosomal instability (CIN), both of which contribute to its difficulty to treat, but also offer points of leverage for therapeutic intervention. To capitalize on this vulnerability, we focused on Src Family Kinases (SFKs), a group comprised of 9-11 non-receptor tyrosine kinases that interact with upstream signaling partners to regulate cell phenotypes such as adhesion, motility, survival, and mitosis.
Read the full summary ↓
We found that treatment with pan-SFK inhibitors leads to abnormal nuclear phenotypes and DNA double strand breaks, both characteristics of CIN, in TNBC cell lines. However, previous clinical studies demonstrated that pan-SFK inhibition resulted in significant toxicities and minimal efficacy in TNBC patients. Therefore, we sought to identify which, if any, of the SFK members may be a specific vulnerability in TNBC that was essential for maintenance of genomic integrity, mitotic progression, and cell survival. The SFK, YES1, was identified to be one of the most highly expressed SFKs in basal breast cancer, with high YES1 expression being correlated with poor patient outcomes. Using genomic depletion and pharmacologic inhibition, we demonstrated that sustained YES1 activity was essential for bipolar spindle formation, regulation of centrosome number, and maintenance of chromosomal stability. Combination treatment utilizing CIN-inducing therapies have been considered to improve treatment efficacy or re-sensitize tumor cells to certain drugs by inducing profound mitotic defects, maladaptive CIN, and cell death. Thus, we evaluated YES1 inhibition in combination with Taxols as an innovative drug treatment regimen that improves TNBC patient outcomes. We found that paclitaxel and the selective YES1 inhibitor, CH695355, synergized in both in vitro and in vivo TNBC models. Moreover, use of this inhibitor re-sensitized Taxol resistant TNBC cells to paclitaxel, a common obstacle in TNBC treatment. Our studies demonstrate that YES1 is a previously understudied component of genomic stability-regulating pathways in TNBC. More broadly, our data suggests that YES1 may be a therapeutically targetable vulnerability with the ability to be paired with other DNA damaging or mitotic inhibitors to improve treatment efficacy in TNBC.
This dataset
Open GSE226766 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 GSE226766_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 PRJNA941348 and SRA study SRP425930. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × MDA-MB-231, siNS, 48 hrs, vs 4 × MDA-MB-231, siYES1, 48 hrs, vs 4 × SUM159PT, siNS, 48 hrs, vs 4 × SUM159PT, siYES1, 48 hrs,

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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 Breast cancer datasets

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