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Whole genome characterisation of chemoresistant ovarian cancer

GSE65821 Homo sapiens (human) DNA methylation 356 samples Submitted 2015/05/19 Platform GPL16104Platform GPL18952Platform GPL19765Platform GPL13534
At a glance

DNA methylation · human · 356 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Topic
Ovarian cancerThis series is part of our curated briefing on it.
Samples
356 samples: 99 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed expression table (series matrix) and the array files, from NCBI.
SuperSeries — this record groups several sub-series.
Summary (written by the submitters)
This SuperSeries is composed of the SubSeries listed below. Patients with high-grade serous ovarian cancer (HGSC) have experienced little improvement in overall survival, and standard treatment has not advanced beyond platinum-based combination chemotherapy, during the past 30 years. To understand the drivers of clinical phenotypes better, here we use whole-genome sequencing of tumour and germline DNA samples from 92 patients with primary refractory, resistant, sensitive and matched acquired resistant disease.
Read the full summary ↓
We show that gene breakage commonly inactivates the tumour suppressors RB1, NF1, RAD51B and PTEN in HGSC, and contributes to acquired chemotherapy resistance. CCNE1 amplification was common in primary resistant and refractory disease. We observed several molecular events associated with acquired resistance, including multiple independent reversions of germline BRCA1 or BRCA2 mutations in individual patients, loss of BRCA1 promoter methylation, an alteration in molecular subtype, and recurrent promoter fusion associated with overexpression of the drug efflux pump MDR1.
This dataset
Open GSE65821 in finder → Find more human datasets → Ovarian cancer landscape →
Download · which file do I need?

Direct links to NCBI, 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.

All supplementary files ↗
3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE65821_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

What the files usually look like: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA275136. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 26017449) ↗
Study design
99 conditions, mostly in triplicate
AOCS ×14 AOCS_139_ICGC_DBDE_20130729 ×9 AOCS_167_ICGC_DBDE_20130904 ×9 AOCS_092_ICGC_DBPC_20130205 ×6 AOCS_093_ICGC_DBPC_20130205 ×6 AOCS_094_ICGC_DBPC_20130205 ×6 AOCS_064_ICGC_DBPC_20130205 ×6 AOCS_091_ICGC_DBPC_20130205 ×6 +91 more

Supports a between-group comparison across 356 samples.

How we read this: 99 replicated groups read from 356 sample titles; they account for 356 of them. 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

+ 316 more — browse all 356 samples with per-sample file links →

More Ovarian cancer datasets

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