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Phase II proof-of-concept study of durvalumab and cediranib with and without olaparib in recurrent ovarian cancer

GSE318308 Homo sapiens (human) RNA-seq (gene expression) 75 samples Submitted 2026/02/06 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 75 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
75 samples: 75 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.
Summary (written by the submitters)
We report the efficacy and translational research findings of durvalumab, olaparib, and cediranib (D+O+C) and durvalumab plus cediranib (D+C) from the recurrent epithelial ovarian cancer (EOC) cohort within a single center, multi-arm, non-randomized, multi-cohort phase I/II trial (NCT02484404). Sixty-eight patients were enrolled (39 in D+O+C, 29 in D+C).
Read the full summary ↓
The objective response rate (ORR) was 19.4% (95% CI, 9.5-43.5) for D+O+C and 29.6% (95% CI, 13.8-46.9) for D+C. Median progression-free survival (PFS) was 4.5 months in both arms, with each arm including four exceptional responders (PFS ≥ 12 months). Pre- and on-treatment biopsies and blood samples were collected for pre-specified transcriptomic and immunophenotypic profiling; signature analyses and preclinical studies were exploratory. We identified distinct transcriptional signatures associated with clinical outcomes. Baseline tumors from exceptional responders and patients with clinical benefit (partial response or stable disease [SD] with PFS ≥ 4 months) demonstrated strong enrichment of immune activation and metabolic pathways. Conversely, tumors with no clinical benefit (NCB; progressive disease or SD with PFS < 4 months) exhibited upregulation of vascular adaptation and cytoskeletal remodeling pathways. The correlation between immune, metabolic, and NCB signatures and clinical outcomes within each arm was independent of prior platinum response. These findings support proof-of-concept clinical activity of D+O+C and D+C and identify molecular signatures with potential predictive value in subsets of recurrent EOC.
This dataset
Open GSE318308 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 GSE318308_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 PRJNA1418576 and SRA study SRP674494. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
75 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 75 sample titles: 75 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

+ 35 more — browse all 75 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.