This study explores the roles of genome copy number abnormalities (CNAs) in breast cancer pathophysiology by identifying associations between recurrent CNAs, gene expression, and clinical outcome in a set of aggressively treated early-stage breast tumors. It shows that the recurrent CNAs differ between tumor subtypes defined by expression pattern and that stratification of patients according to outcome can be improved by measuring both expression and copy number, especially high-level amplification. Sixty-six genes deregulated by the high-level amplifications are potential therapeutic targets. Nine of these (FGFR1, IKBKB, ERBB2, PROCC, ADAM9, FNTA, ACACA, PNMT, and NR1D1) are considered druggable. Low-level CNAs appear to contribute to cancer progression by altering RNA and cellular metabolism.

High-density oligonucleotide microarrays enable simultaneous monitoring of expression levels of tens of thousands of transcripts. For accurate detection and quantitation of transcripts in the presence of cellular mRNA, it is essential to design microarrays whose oligonucleotide probes produce hybridization intensities that accurately reflect the concentration of original mRNA. We present a model-based approach that predicts optimal probes by using sequence and empirical information. We constructed a thermodynamic model for hybridization behavior and determined the influence of empirical factors on the effective fitting parameters. We designed Affymetrix GeneChip probe arrays that contained all 25-mer probes for hundreds of human and yeast transcripts and collected data over a 4,000-fold concentration range. Multiple linear regression models were built to predict hybridization intensities of each probe at given target concentrations, and each intensity profile is summarized by a probe response metric. We selected probe sets to represent each transcript that were optimized with respect to responsiveness, independence (degree to which probe sequences are nonoverlapping), and uniqueness (lack of similarity to sequences in the expressed genomic background). We show that this approach is capable of selecting probes with high sensitivity and specificity for high-density oligonucleotide arrays.design ͉ modeling ͉ microarray design H igh-density oligonucleotide arrays (1, 2) have revolutionized the study of gene expression. The technology enables researchers to detect and quantify tens of thousands of transcripts in a single experiment and has become a standard for the discovery of gene functions, drug evaluation, pathway dissection, and classification of clinical samples (3). With the availability of mRNA sequences for a large subset of the draft of the human genome (4, 5) microarrays provide the potential to simultaneously monitor the whole expressed human genome. The expression profile of the whole human genome will allow a detailed and comprehensive view of cellular processes, responses, and their functional consequences.Quantitative detection of transcripts requires that microarray probes exhibit a sensitive and predictable response to concentrations of specific targets of the probes. This response must occur in the presence of a complex mixture of nonspecific targets. The previous probe selection method for GeneChip expression microarrays was to select candidate probes based on a set of heuristic rules (1). The rules act as filters to remove extreme sequence features that were known to degrade probe performance. However, probes passing the filters were treated as being of equal quality. To select optimal probe sets, it is essential to establish a continuous metric that distinguishes superior probes from merely good probes. Several theoretical studies of microarray probe selection (6, 7) were based on solution thermodynamics. No experimental data were produced to demonstrate that the theoretical predictions for hybr...

The transforming protein of Kirsten murine sarcoma virus (Ki-MuSV) is a virally encoded 21-kilodalton protein called p21 kis. The sequences encoding p21 kis were genetically localized to a 1.3-kilobase segment near the 5' end of the viral genome by assaying the capacity of a series of defined deletion mutants of molecularly cloned Ki-MuSV DNA to induce focal transformation of mouse cells. Nucleotide sequencing of a portion of this region has led to the identification of an open reading frame of 567 nucleotides coding for p21 kis protein.

Analysis of microarray data often involves extracting information from raw intensities of spots or cells and making certain calls. Rank-based algorithms are powerful tools to provide probability values of hypothesis tests, especially when the distribution of the intensities is unknown. For our current gene expression arrays, a gene is detected by a set of probe pairs consisting of perfect match and mismatch cells. The one-sided upper-tail Wilcoxon's signed rank test is used in our algorithms for absolute calls (whether a gene is detected or not), as well as comparative calls (whether a gene is increasing or decreasing or no significant change in a sample compared with another sample). We also test the possibility to use only perfect match cells to make calls. This paper focuses on absolute calls. We have developed error analysis methods and software tools that allow us to compare the accuracy of the calls in the presence or absence of mismatch cells at different target concentrations. The usage of nonparametric rank-based tests is not limited to absolute and comparative calls of gene expression chips. They can also be applied to other oligonucleotide microarrays for genotyping and mutation detection, as well as spotted arrays.

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