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Whole genome characterisation of chemoresistant ovarian cancer [Illumina_450K_Methylation]

GSE65820 Homo sapiens (human) DNA methylation 121 samples Submitted 2015/05/19 Platform GPL13534
At a glance

DNA methylation · human · 121 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Samples
121 samples: 99 distinct titles, 15 of them repeatedGroups 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.
Summary (written by the submitters)
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 GSE65820 in finder → Find more human methylation datasets →
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 (GSE65820_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 PRJNA275137. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 26017449) ↗
Study design
99 distinct titles, 15 of them repeated
AOCS ×7 AOCS_139_ICGC_DBDE_20130729 ×3 AOCS_167_ICGC_DBDE_20130904 ×3 AOCS_034_ICGC_DBPC_20130205 ×2 AOCS_064_ICGC_DBPC_20130205 ×2 AOCS_086_ICGC_DBPC_20130205 ×2 AOCS_088_ICGC_DBPC_20130701 ×2 AOCS_091_ICGC_DBPC_20130205 ×2 +7 more

How we read this: Read from 121 sample titles: 99 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

+ 81 more — browse all 121 samples with per-sample file links →

Datasets on a similar topic

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