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Roel G W Verhaak,
Katherine A Hoadley,
Elizabeth Purdom,
Victoria Wang, Yuan Qi,
Matthew D Wilkerson,
C Ryan Miller,
Li Ding,
Todd Golub,
Jill P Mesirov, [......],
Cameron Brennan,
Ari Kahn,
Paul T Spellman,
Richard K Wilson,
Terence P Speed,
Joe W Gray,
Matthew Meyerson,
Gad Getz,
Charles M Perou,
D Neil Hayes
[show abstract]
[hide abstract]
ABSTRACT: The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, and PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies.
Cancer cell 01/2010; 17(1):98-110. · 25.29 Impact Factor
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Christopher R Cabanski, Yuan Qi,
Xiaoying Yin,
Eric Bair,
Michele C Hayward,
Cheng Fan,
Jianying Li,
Matthew D Wilkerson,
J S Marron,
Charles M Perou,
D Neil Hayes
[show abstract]
[hide abstract]
ABSTRACT: Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray.
PLoS ONE 01/2010; 5(3):e9905. · 4.09 Impact Factor
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Roger McLendon,
Allan Friedman,
Darrell Bigner,
Erwin G Van Meir,
Daniel J Brat,
Gena Mastrogianakis,
Jeffrey J Olson,
Tom Mikkelsen,
Norman Lehman,
Ken Aldape, [......],
Martin L Ferguson,
Carl Schaefer,
Subhashree Madhavan,
Kenneth H Buetow,
Francis Collins,
Peter Good,
Mark Guyer,
Brad Ozenberger,
Jane Peterson,
Elizabeth Thomson
[show abstract]
[hide abstract]
ABSTRACT: Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas (TCGA) pilot project aims to assess the value of large-scale multi-dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas-the most common type of adult brain cancer-and nucleotide sequence aberrations in 91 of the 206 glioblastomas. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the phosphatidylinositol-3-OH kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of glioblastoma. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.
Nature 10/2008; · 36.28 Impact Factor
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Roger McLendon,
Allan Friedman,
Darrell Bigner,
Erwin G. Van Meir,
Daniel J. Brat,
Gena M. Mastrogianakis,
Jeffrey J. Olson,
Tom Mikkelsen,
Norman Lehman,
Ken Aldape, [......],
Martin L. Ferguson,
Carl Schaefer,
Subhashree Madhavan,
Kenneth H. Buetow,
Francis Collins,
Peter Good,
Mark Guyer,
Brad Ozenberger,
Jane Peterson,
Elizabeth Thomson
[show abstract]
[hide abstract]
ABSTRACT: Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas (TCGA) pilot project aims to assess the value of large-scale multi-dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas—the most common type of adult brain cancer—and nucleotide sequence aberrations in 91 of the 206 glioblastomas. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the phosphatidylinositol-3-OH kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of glioblastoma. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.
Nature 09/2008; 455(7216):1061-1068. · 36.28 Impact Factor
