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Immunotherapies that repolarize macrophages and regulatory T cells enhance the effect of chemotherapy in high-grade serous ovarian cancer (Human)

GSE224392 Homo sapiens (human) Single-cell RNA-seq 21 samples Submitted 2024/10/11 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 21 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
Ovarian cancerThis series is part of our curated briefing on it.
Samples
21 samples: 21 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
Immunotherapy that improves response to chemotherapy in high-grade serous ovarian cancerNature communications · 2024
Summary (written by the submitters)
We conducted single-cell RNA sequencing (scRNAseq) of tumor-infiltrating immune cells in high-grade serous ovarian cancer (HGSOC) omental biopsies to identify potential targets that could enhance response to neo-adjuvant chemotherapy (NACT). Analysis of 64,097 cells identified NACT-induced overexpression of stabilin-1 (clever-1) on macrophages and FOXP3 in Tregs and these were validated at the protein level.
Read the full summary ↓
STAB1 inhibition in vitro induced anti-tumor macrophages. FOXP3 anti-sense oligonucleotide (FOXP3-ASO), repolarised Tregs to an effector T cell phenotype. ScRNAseq on 69,781 cells from an HGSOC syngeneic mouse model recapitulated the patients’ data. Combining chemotherapy with anti-stabilin1 antibody and/or Foxp3-ASO significantly increased survival of mice with established peritoneal disease in two HGSOC syngeneic models and progression-free survival in a third model. Long-term survivors (300 days+) were resistant to tumor rechallenge. Anti-stabilin1 antibody enriched the tumors with CXCL9+ macrophages and Foxp3-ASO increased TBET cell infiltration. Our results suggest targeting these molecules may improve chemotherapy response in patients.
Published in
Immunotherapy that improves response to chemotherapy in high-grade serous ovarian cancer
Elorbany S, Berlato C, Carnevalli LS et al. · Nature communications 2024 · PMID 39578450 · doi:10.1038/s41467-024-54295-x
This dataset
Open GSE224392 in finder → Find more human RNA-seq datasets → Ovarian 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 GSE224392_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 PRJNA930763 and SRA study SRP420714. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39578450) ↗
Study design
21 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 21 sample titles: 21 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 Ovarian cancer datasets

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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.