Quantitative analysis of chromosomal CGH in human breast tumors associates copy number abnormalities with p53 status and patient survival

Abstract

We present a general method for rigorously identifying correlations between variations in large-scale molecular profiles and outcomes and apply it to chromosomal comparative genomic hybridization data from a set of 52 breast tumors. We identify two loci where copy number abnormalities are correlated with poor survival outcome (gain at 8q24 and loss at 9q13). We also identify a relationship between abnormalities at two loci and the mutational status of p53. Gain at 8q24 and loss at 5q15-5q21 are linked with mutant p53. The 9q and 5q losses suggest the possibility of gene products involved in breast cancer progression. The analytical techniques are general and also are applicable to the analysis of array-based expression data.T echniques for in vitro genomic and proteomic analysis are generating vast amounts of quantitative biological data. As an example, high-density DNA microarrays are capable of producing 30,000 measurements from a single sample of RNA (1). In the area of cancer, such data offer fertile ground for systematic computational analyses to help identify new cancer targets or potential therapeutics. Chromosomal comparative genomic hybridization (CGH) has been applied extensively to human tumor specimens, and array-based CGH methods are beginning to generate higher-density data (2, 3). For such techniques to be most useful, computational methods must generate conclusions that are supportable quantitatively in a rigorous statistical sense, and not provide just a means of visualization.The challenge arises when the ratio between the number of measurements to the number of experimental samples is high. In this case, false patterns often emerge. For example, suppose we measure expression levels for several thousand mRNAs in 10 cell lines, 5 of which exhibit phenotype A and 5 that exhibit phenotype B. The expression ratios for each gene will show some variation regardless of correlation with phenotype. The apparent correlation to cell-line phenotype resulting from a naïve computation of correlation over all genes will be distributed approximately normally, and some genes may show an apparently significant correlation. In fact, because there are only 252 [10!͞(5!(10-5)!)] ways of labeling 10 cell lines with 5 each of phenotypes A and B, it is extremely likely that many genes of the several thousand will show an apparently perfect correlation with phenotype, even if there is no true relationship between any observed genes' expression and phenotype.We present a method for rigorously identifying correlations between large-scale multivariate measurements and outcomes and apply it to chromosomal CGH data from a set of 52 human breast tumors. We identify two loci (8q24 and 9q13) where copy number abnormalities are correlated with poor survival outcome and also identify a relationship between two loci (8q24 and 5q15-5q21) and the mutational status of p53. The techniques are applicable generally and also are used easily in the analysis of array-based expression data. Materials and MethodsTumor Specimens. Fifty-two s...

Identification of molecular apocrine breast tumours by microarray analysis

“…In our view, the best way to make sense of subdivisions within a particular cell type will be to identify signature oncogenic defects. For example, MYC amplification at 8q24 is associated with poor survival (Jain et al, 2001). The low proliferation group in the luminal cluster in our data overlaps with a large cluster of coordinately regulated genes on chromosome 8q (Supplementary Figure 5 panel 2).…”

RNA-mediated epigenetic regulation of DNA copy number

“…copy number variation | genome rearrangement D NA copy number profoundly influences evolution (1-4), permitting changes in gene and protein expression (5)(6)(7) that may be acted upon by positive selection (7), permit adaptation (8)(9)(10)(11), or have links to human genetic disease (12) and disease susceptibility (13)(14)(15), including autism-spectrum disorders (16) and several cancers (5)(6)(17)(18)(19)(20)(21)(22).…”

High-Resolution Analysis of DNA Copy Number Using Oligonucleotide Microarrays

“…Comparative genomic hybridization (CGH; Kallioniemi et al 1992) has been used extensively to document gains and losses of genomic DNA in diseases such as cancer (Albertson et al 2000;Jain et al 2001) and mental retardation (Ghaffari et al 1998;Veltman et al 2002). The recent development of CGH using arrays of either genomic (Pinkel et al 1998) or cDNA clones (Pollack et al 1999) has improved the resolution of these analyses, allowing better detection and mapping of localized changes such as gene amplification or homozygous deletions.…”