GEO series

Early downmodulation of tumor glycolysis predicts response to fasting-mimicking diet in triple-negative breast cancer patients

GSE279219 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2024/10/15 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 24 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: 24 samples: 24 samples, each with a distinct title — no replicated groupsGroups 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: Early downmodulation of tumor glycolysis predicts response to fasting-mimicking diet in triple-negative breast cancer patientsCell metabolism · 2025

Summary (written by the submitters)

In preclinical experiments, cyclic fasting-mimicking diets (FMDs) showed broad anticancer effects in combination with chemotherapy. Among different tumor types, triple-negative breast cancer (TNBC) is exquisitely sensitive to FMD. However, the antitumor activity and efficacy of cyclic FMD in TNBC patients remain unclear.

Read the full summary ↓

Here, we show that a severely calorie-restricted, triweekly, 5-day FMD regimen results in excellent pathologic complete response (pCR) rates (primary endpoint) and long-term clinical outcomes (secondary endpoints) when combined with preoperative chemotherapy in 30 patients with early-stage TNBC enrolled in the phase II trial BREAKFAST. Bulk and single-cell RNA-sequencing analysis revealed that highly glycolytic cancer cells, myeloid cells and pericytes from tumors achieving pCR undergo a significant, early downmodulation of pathways related to glycolysis and pyruvate metabolism. Our findings pave the wave for conducting larger clinical trials to investigate the efficacy of cyclic FMD in early-stage TNBC patients, and to validate early changes of intratumor glycolysis as a predictor of clinical benefit from nutrient restriction. ClinicalTrials.gov Identifier: NCT04248998

Published in

Early downmodulation of tumor glycolysis predicts response to fasting-mimicking diet in triple-negative breast cancer patients

Ligorio F, Vingiani A, Torelli T et al. · Cell metabolism 2025 · PMID 39694040 · doi:10.1016/j.cmet.2024.11.004

This dataset

Open GSE279219 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 GSE279219_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 PRJNA1171406 and SRA study SRP537773. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39694040) ↗

Study design

24 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 24 sample titles: 24 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

GSM8564809 BRF14,scRNAseq
GSM8564810 BRF15,scRNAseq
GSM8564811 BRF15.C1,scRNAseq
GSM8564812 BRF16,scRNAseq
GSM8564813 BRF16.C1,scRNAseq
GSM8564814 BRF18,scRNAseq
GSM8564815 BRF18.C1,scRNAseq
GSM8564816 BRF19.C1,scRNAseq
GSM8564817 BRF22,scRNAseq
GSM8564818 BRF22.C1,scRNAseq
GSM8564819 BRF23,scRNAseq
GSM8564820 BRF23.C1,scRNAseq
GSM8564821 BRF24,scRNAseq
GSM8564822 BRF24.C1,scRNAseq
GSM8564823 BRF3,scRNAseq
GSM8564824 BRF3.C1,scRNAseq
GSM8564825 BRF31,scRNAseq
GSM8564826 BRF31.C1,scRNAseq
GSM8564827 BRF32,scRNAseq
GSM8564828 BRF32.C1,scRNAseq
GSM8564829 BRF33,scRNAseq
GSM8564830 BRF33.C1,scRNAseq
GSM8564831 BRF4,scRNAseq
GSM8564832 BRF5,scRNAseq

More Breast cancer datasets

GSE104193 Hypoxia-mediated translational activation of ITGB3 in breast cancer cells enhances TGF-β signalling and malignant features in vitro and in vivo 32 samples same disease briefing · shares breast
GSE104675 Role for the Transcriptional Activator ZRF1 in Breast Cancer Progression and Endocrine Resistance 6 samples same disease briefing · shares breast
GSE10886 A Supervised Risk Predictor of Breast Cancer Based on Biological Subtypes 226 samples same disease briefing · shares breast
GSE11121 The humoral immune system has a key prognostic impact in node-negative breast cancer 200 samples same disease briefing · shares breast
GSE10885 An integromic analysis reveals that metaplastic carcinomas are distinct from basal-like tumors. 244 samples same disease briefing
GSE268851 Immunogenomic Diversity of Triple-Negative Breast Cancers in Obese and Non-obese Black and White Women 253 samples shares triple-negative, breast
GSE334033 A Patient-Derived Xenograft Model Identifies AK1 as a Key Regulator of Metastatic Potential in Primary Triple-Negative Breast Cancer 105 samples shares triple-negative, breast
GSE234324 CRISPR-Enabled Profiling of Enhancer RNA-Producing Super-Enhancers in Triple-Negative Breast Cancer Identifies Drivers of Tumor Progression [RNA-Seq] 104 samples shares triple-negative, breast

Search all human RNA-seq datasets in GEO →

Share this dataset

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.