Abstract: |
Understanding the molecular basis of cancer requires characterization of its genetic defects. DNA microarray technologies can provide detailed raw data about chromosomal aberrations in tumor samples. Computational analysis is needed (1) to deduce from raw array data actual amplification or deletion events for chromosomal fragments and (2) to distinguish causal chromosomal alterations from functionally neutral ones. We present a comprehensive computational approach, RAE, designed to robustly map chromosomal alterations in tumor samples and assess their functional importance in cancer. To demonstrate the methodology, we experimentally profile copy number changes in a clinically aggressive subtype of soft-tissue sarcoma, pleomorphic liposarcoma, and computationally derive a portrait of candidate oncogenic alterations and their target genes. Many affected genes are known to be involved in sarcomagenesis; others are novel, including mediators of adipocyte differentiation, and may include valuable therapeutic targets. Taken together, we present a statistically robust methodology applicable to high-resolution genomic data to assess the extent and function of copy-number alterations in cancer. © 2008 Taylor et al. |
Keywords: |
controlled study; human tissue; human cell; major clinical study; single nucleotide polymorphism; genetics; polymorphism, single nucleotide; methodology; neoplasm; neoplasms; phenotype; gene targeting; cluster analysis; gene amplification; biological model; gene expression profiling; gene function; cell differentiation; carcinogenesis; poisson distribution; gene expression regulation; chromosome aberration; oncogene; gene expression regulation, neoplastic; models, statistical; human genome; soft tissue sarcoma; genomics; malignant neoplastic disease; models, genetic; chromosome aberrations; gene dosage; chromosome analysis; statistical model; mathematical computing; genome, human; liposarcoma; disease activity; adipocyte; chromosome disorder; chromosome number; chromosome duplication
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