GEO series

Multi-omic analysis of subtype evolution and heterogeneity in high-grade serous ovarian carcinoma

GSE154600 Homo sapiens (human) Single-cell RNA-seq 5 samples Submitted 2020/07/17 Platform GPL16791

At a glance

Single-cell RNA-seq · human · 5 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: 5 samples
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)

Multiple studies have identified transcriptome subtypes of high-grade serous ovarian carcinoma (HGSOC), but their interpretation and translation are complicated by tumor evolution and polyclonality accompanied by extensive accumulation of somatic aberrations, varying cell type admixtures, and different tissues of origin.

Read the full summary ↓

In this study, we examined the chronology of HGSOC subtype evolution in the context of these factors using a novel integrative analysis of absolute copy number analysis and gene expression in The Cancer Genome Atlas complemented by single-cell analysis of six independent tumors. Tumor purity, ploidy, and subclonality were reliably inferred from different genomic platforms, and these characteristics displayed marked differences between subtypes. Genomic lesions associated with HGSOC subtypes tended to be subclonal, implying subtype divergence at later stages of tumor evolution. Subclonality of recurrent HGSOC alterations was evident for proliferative tumors, characterized by extreme genomic instability, absence of immune infiltration, and greater patient age. In contrast, differentiated tumors were characterized by largely intact genome integrity, high immune infiltration, and younger patient age. Single-cell sequencing of 42,000 tumor cells revealed widespread heterogeneity in tumor cell type composition that drove bulk subtypes but demonstrated a lack of intrinsic subtypes among tumor epithelial cells. Our findings prompt the dismissal of discrete transcriptome subtypes for HGSOC and replacement by a more realistic model of continuous tumor development that includes mixtures of subclones, accumulation of somatic aberrations, infiltration of immune and stromal cells in proportions correlated with tumor stage and tissue of origin, and evolution between properties previously associated with discrete subtypes.

This dataset

Open GSE154600 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 GSE154600_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 PRJNA646754 and SRA study SRP272287. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 32747365) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 5 more — browse all 5 samples with per-sample file links →

More Ovarian cancer datasets

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GSE57477 Molecular subtyping of serous ovarian tumors reveals multiple connections to intrinsic breast cancer subtypes 72 samples same disease briefing · shares subtype, ovarian, serous
GSE311212 Transcriptomic profiling in human ovarian cancer cell line OVCAR-3 following CDK2 inhibitor treatment 6 samples same disease briefing · shares ovarian
GSE128098 TET1 reprograms the epithelial ovarian cancer epigenome and reveals casein kinase 2α as a therapeutic target 4 samples same disease briefing · shares ovarian
GSE314300 Rb CUT&RUN profiling in ovarian cancer cell lines following CDK2 inhibitor treatment 10 samples same disease briefing · shares ovarian
GSE135242 Hi-C identifies structural variation associated with resistance in paired patient-derived ovarian cancer models 8 samples same disease briefing · shares ovarian
GSE28330 Telomeric Allelic Imbalance Indicates Defective DNA Repair and Sensitivity to DNA-Damaging Agents 107 samples same disease briefing
GSE273637 Gene expression profiles of platinum-resistant high-grade serous ovarian carcinoma 20 samples shares high-grade, ovarian, serous

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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.