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

By Rameen Beroukhim, Gad Getz, L. Nghiemphu, J. Barretina, Teli Hsueh, David Linhart, I. Vivanco, Jeffrey C. Lee, Julie Huang, Sethu Alexander and 21 more. Proceedings of the National Academy of Sciences of the United States of America, 2007

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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. 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. Approximately 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 properties different from those with overlapping focalEGFR 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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1fdc7997ac25f343e955a80a0287b49126d32169

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Citations in the corpus

An integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR and NF1 (2010, methodology): “To confirm copy number events related to the subtypes, we used copy number data available for 43 samples in the validation set (Beroukhim et al., 2007).”
Glioblastoma: pathology, molecular mechanisms and markers (2015, background): “Large-scale molecular profiling of diffuse gliomas has taken place in individual laboratories [13, 123, 129], at the national level in the US by TCGA network [23], and at the international level within the International Cancer Genome Consortium (ICGC) [74].”
Integrated Genomic Analyses of Ovarian Carcinoma (2011, background): “Weinstein(22,61), Elizabeth Purdom(13), Andrew Berchuck(23,24), Michael Birrer(25,26), miRNA: D.”
An Integrated Approach to Uncover Drivers of Cancer (2010, methodology): “For example, GISTIC iden-tiﬁes regions of the genome that are aberrant more often than would be expected by chance and has been used to analyze a number of cancers (Beroukhim et al., 2007, 2009; Lin et al., 2008).”
The landscape of somatic copy-number alteration across human cancers (2010, methodology): “For this purpose, we calculated the genome-average ,andusedtheGISTIC(genomicidentificationofsignificant targetsincancer)algorithm 18 with improvements as described in Supplementary Methods.”
MODEL SELECTION FOR HIGH-DIMENSIONAL, MULTI-SEQUENCE CHANGE-POINT PROBLEMS (2012, background): “Most of the existing approaches for cross-sample modeling of tumors focus on finding significant “overlaps” between samples after segmentation (Newton et al. (1998); Newton and Lee (2000); Diskin et al. (2006); Beroukhim et al. (2007); Guttman et al. (2007); Rouveirol et al. (2006)).”
Focal Gains of Vascular Endothelial Growth Factor A and Molecular Classification of Hepatocellular Carcinoma (2008, methodology): “Euclidean distance between the log 2–transformed signal profiles (21).”
Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics (2013, background): “We also noted copyneutral loss of heterozygosity for TP53 (two cases), an important event for the inactivation of this gene (19).”
High-resolution mapping of copy-number alterations with massively parallel sequencing (2008, background): “A powerful way to find cancer genes is to identify genomic regions with recurrent copy-number alterations (gains and losses) in tumor genome”