Beroukhim R, Getz G, Nghiemphu L, Barretina J, Hsueh T, Linhart D et al.. Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc Natl Acad Sci USA 104: 20007-20012

Broad Institute, Massachusetts Institute of Technology and Harvard University, 7 Cambridge Center, Cambridge, MA 02142, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 01/2008; 104(50):20007-12. DOI: 10.1073/pnas.0710052104
Source: PubMed


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 approximately 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.

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Available from: John R Prensner, Mar 16, 2014
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    • "ISAR ISAR is based on the G score metric, a significance measure of the aberration for each marker, which was originally defined in GISTIC (Beroukhim et al., 2007). Specifically, the G score for a marker m is the summation of the copy number across samples that surpass an aberration threshold q. "
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    ABSTRACT: Identifying driver genes in cancer remains a crucial bottleneck in therapeutic development and basic understanding of the disease. We developed Helios, an algorithm that integrates genomic data from primary tumors with data from functional RNAi screens to pinpoint driver genes within large recurrently amplified regions of DNA. Applying Helios to breast cancer data identified a set of candidate drivers highly enriched with known drivers (p < 10(-14)). Nine of ten top-scoring Helios genes are known drivers of breast cancer, and in vitro validation of 12 candidates predicted by Helios found ten conferred enhanced anchorage-independent growth, demonstrating Helios's exquisite sensitivity and specificity. We extensively characterized RSF-1, a driver identified by Helios whose amplification correlates with poor prognosis, and found increased tumorigenesis and metastasis in mouse models. We have demonstrated a powerful approach for identifying driver genes and how it can yield important insights into cancer. Copyright © 2014 Elsevier Inc. All rights reserved.
    Cell 11/2014; 159(6). DOI:10.1016/j.cell.2014.10.048 · 32.24 Impact Factor
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    • "A subset of 693,000 high-quality single nucleotide polymorphisms was selected for all analyses (Supplemental Figure 1, Supplemental Digital Content 2, A gene was considered copy number amplified if the calculated copy number in a sample was more than or equal to 4, and a gene was considered copy loss if the copy number in a sample was 0. Recurrent genomic regions with DNA copy gain and loss were identified using GISTIC, version 2.0.14,15 "
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    • "Furthermore, GISTIC2.0 is geared towards the identification of genes. Consequently, this method prioritizes amplifications and homozygous deletions over recurrent single CNAs [27] [35]. Genomic regions without a known target or infrequent focal CNAs may not be identified. "
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