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Assessing the significance of chromosomal aberrations in cancer: Methodology and application to glioma

GSE9635 Homo sapiens (human) Genome variation profiling by SNP array; SNP genotyping by SNP array 374 samples Submitted 2007/12/11 Platform GPL2005Platform GPL2004
At a glance

human · 374 samples

Organism
Homo sapiens (human)
Samples
374 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI.
Summary (written by the submitters)
Comprehensive knowledge of the genomic alterations that underlie cancer is a critical foundation for diagnostics, prognostics and targeted therapeutics. Systematic efforts to analyze cancer genomes are underway, but the analysis is hampered by the lack of a statistical framework to distinguish meaningful events from random background aberrations.
Read the full summary ↓
Here, we describe a systematic method called Genomic Identification of Significant Targets in Cancer (GISTIC) designed for analyzing chromosomal aberrations in cancer. We use it to study chromosomal aberrations in 141 gliomas and compare the results with two prior studies. Traditional methods highlight hundreds of altered regions with little concordance between studies. The new approach reveals a highly concordant picture involving ~35 significant events, including 16-18 broad events near chromosome-arm size and 16-21 focal events. About half of these events correspond to known cancer-related genes, only some of which have been previously tied to glioma. We also show that superimposed broad and focal events may have different biological consequences. Specifically, gliomas with broad amplification of chromosome 7 have different properties than those with overlapping focal EGFR amplification: the broad events act in part through effects on MET and its ligand HGF and correlate with MET dependence in vitro. Our results support the feasibility and utility of systematic characterization of the cancer genome. Keywords: SNP analysis, gliomas, chromosomal aberrations
This dataset
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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 (GSE9635_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 ↓

Also filed as BioProject PRJNA103491. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 18077431) ↗
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