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A Patient-Derived Xenograft Model Identifies AK1 as a Key Regulator of Metastatic Potential in Primary Triple-Negative Breast Cancer

GSE334033 Homo sapiens (human) Single-cell RNA-seq 105 samples Submitted 2026/06/30 Platform GPL24676Platform GPL11154
At a glance

Single-cell RNA-seq · human · 105 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
105 samples: 8 conditions, mostly with about 11 replicates eachGroups 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)
Metastasis remains the major cause of mortality in triple-negative breast cancer (TNBC), yet the metastatic potential of primary tumors is difficult to predict at diagnosis. Here, we leveraged patient-derived xenograft (PDX) models as a functional readout of primary tumor metastatic propensity. We first performed integrated genomic and transcriptomic profiling of primary breast tumors and their matched PDX models and found that key driver mutations, recurrent copy number alterations, and molecular subtype features were largely conserved between patient tumors and xenografts.
Read the full summary ↓
These findings support the utility of matched PDX models as clinically relevant platforms for studying tumor-intrinsic features associated with breast cancer progression. We next focused on TNBC PDX models with reproducible metastatic phenotypes across biological replicates and classified primary tumors into metastatic and non-metastatic groups based on their matched PDX behavior. Single-cell transcriptomic profiling of these tumors revealed that metastatic samples were characterized by hypoxia-associated metabolic reprogramming, with coordinated activation of hypoxia and glycolysis pathways. This metabolic phenotype was associated with poor clinical outcome in independent breast cancer cohorts. Network-based prioritization identified adenylate kinase 1 (AK1) as a candidate regulator of this metastatic program. Functionally, AK1 promoted metastatic potential in primary tumor by supporting glycolytic ATP production, resistance to hypoxic and oxidative stress, and promoting trans-endothelial migration. Together, our findings establish primary tumor–matched PDX models as a clinically relevant discovery platform for metastatic potential, overcoming the challenge that future metastatic events are not readily predictable from primary tumors at diagnosis. Using reproducible PDX metastasis as a functional readout, we identify AK1-mediated metabolic stress adaptation as a key mechanism underlying metastatic potential in primary TNBC.
This dataset
Open GSE334033 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 GSE334033_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1473615 and SRA study SRP706028. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly with about 11 replicates each
MNT ×22 MT ×21 MX ×20 NT ×11 T ×11 X ×11 231_shCTL_# ×3 231_shAK1_# ×3 +3 more

Supports a between-group comparison across 102 samples.

How we read this: 8 replicated groups read from 105 sample titles; they account for 102 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

+ 65 more — browse all 105 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.