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Gene expression and mutation profiles in advanced-stage epithelial ovarian cancer (EOC)

GSE284400 Homo sapiens (human) RNA-seq (gene expression) 78 samples Submitted 2025/01/15 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 78 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
78 samples: 78 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
Neoadjuvant Chemotherapy with Dual Immune Checkpoint Inhibitors for Advanced-Stage Ovarian Cancer: Final Analysis of TRU-D Phase II Nonrandomized Clinical TrialClinical cancer research : an official journal of the American Association for Cancer Research · 2025
Summary (written by the submitters)
Purpose: This open-label, investigator-initiated, phase II study was conducted to evaluate the safety, survival and neoadjuvant outcomes of NAC combined with dual immune checkpoint inhibitors in advanced-stage EOC. Patients and methods: Between June 2019 and July 2021, 45 patients with unresectable stage III-IV EOC were enrolled. The patients received three cycles of NAC combined with durvalumab and tremelimumab.
Read the full summary ↓
All patients underwent interval debulking surgery and received three cycles of durvalumab and adjuvant chemotherapy, followed by 12 cycles of durvalumab as maintenance therapy. The primary endpoint was the 12-month progression-free survival (PFS) rate; the secondary endpoints were the objective response rate (ORR) after NAC, a chemotherapy response score (CRS), pathologic complete response (pCR), overall survival (OS), and safety. The pre-planned exploratory analyses assessed the lymphocyte infiltration, PD-L1 expression, and genomic profiles of pre-treatment tumors.
Published in
Neoadjuvant Chemotherapy with Dual Immune Checkpoint Inhibitors for Advanced-Stage Ovarian Cancer: Final Analysis of TRU-D Phase II Nonrandomized Clinical Trial
Park J, Joung JG, Lim MC et al. · Clinical cancer research : an official journal of the American Association for Cancer Research 2025 · PMID 40043003 · doi:10.1158/1078-0432.CCR-24-3753
This dataset
Open GSE284400 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 GSE284400_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 PRJNA1198945. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40043003) ↗
Study design
78 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 78 sample titles: 78 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

+ 38 more — browse all 78 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.