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The activity of tertiary lymphoid structures in high grade serous ovarian cancer is governed by site, stroma, and cellular interactions

GSE276935 Homo sapiens (human) RNA-seq (gene expression) 417 samples Submitted 2024/10/10 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 417 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
Ovarian cancerThis series is part of our curated briefing on it.
Samples
417 samples: 417 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
The activity of tertiary lymphoid structures in high grade serous ovarian cancer is governed by site, stroma, and cellular interactionsCancer cell · 2024
Summary (written by the submitters)
Most high grade serous ovarian cancers (HGSOC) originate in the fallopian tube but spread to the ovary and peritoneal cavity, highlighting the need to understand antitumor immunity across HGSOC sites. Using spatial analyses, we discover that tertiary lymphoid structures (TLS) within ovarian tumors are less developed compared with TLS in fallopian tube or omental tumors.
Read the full summary ↓
We reveal transcriptional differences across a spectrum of lymphoid structures, demonstrating that immune cell activity increases when residing in more developed TLS and produce a prognostic, spatially derived TLS signature from HGSOC tumors. We interrogate TLS-adjacent stroma and assess how normal mesenchymal stem cells MSCs (nMSCs) may support B cell function and TLS, contrary to cancer-educated MSCs (CA-MSCs) which negate the prognostic benefit of our TLS signature, suggesting that pro-tumorigenic stroma could limit TLS formation.
Published in
The activity of tertiary lymphoid structures in high grade serous ovarian cancer is governed by site, stroma, and cellular interactions
MacFawn IP, Magnon G, Gorecki G et al. · Cancer cell 2024 · PMID 39393357 · doi:10.1016/j.ccell.2024.09.007
This dataset
Open GSE276935 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 GSE276935_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 PRJNA1159767 and SRA study SRP532077. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39393357) ↗
Study design
417 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 the first 400 of 417 sample titles: 400 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

+ 377 more — browse all 417 samples with per-sample file links →

More Ovarian 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.