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Distinct Mutational Patterns of Infection and Non-Infection-Related Bile Duct Cancers Revealed by Exome Sequencing

GSE49656 Homo sapiens (human) DNA methylation 40 samples Submitted 2013/10/20 Platform GPL13534
At a glance

DNA methylation · human · 40 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Topic
CholangiocarcinomaThis series is part of our curated briefing on it.
Samples
40 samples
Data you can get
A processed expression table (series matrix) and the array files, from NCBI.
Summary (written by the submitters)
The impact of different carcinogenic exposures on the specific patterns of somatic mutations in human tumors remains unclear. To clarify this issue, we profiled 209 cholangiocarcinomas (CCAs) from Asia and Europe, including 108 cases caused by liver fluke Opisthorchis viverrini (OV)-infection and 101 cases due to non-OV etiologies. Whole-exome (N = 15) and prevalence screening (N = 194) revealed recurrent somatic mutations in BAP1 and ARID1A, neither of which has been previously reported to be mutated in CCA.
Read the full summary ↓
Comparisons between intrahepatic OV and non-OV CCAs demonstrated statistically significant different mutation patterns: BAP1 and IDH1/2 were more frequently mutated in non-OV CCAs, while TP53 displayed the reciprocal pattern. Functional studies demonstrated tumor suppressive roles of BAP1 and ARID1A, establishing the role of chromatin modulators in CCA pathogenesis. These findings indicate that different causative etiologies may induce distinct somatic alterations even within the same tumor type.
This dataset
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.

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 (GSE49656_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

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

Also filed as BioProject PRJNA214647. Searching any of these in the dataset finder brings you back here.

Samples in this study

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