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Publications (2)8.28 Total impact

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    ABSTRACT: Non-small cell lung cancer (NSCLC) is characterized by a multitude of genetic aberrations with unknown clinical impact. In this study, we aimed to identify gene copy number changes that correlate with clinical outcome in NSCLC. To maximize the chance to identify clinically relevant events, we applied a strategy involving two prognostically extreme patient groups. Short-term (<20 month; n = 53) and long-term survivors (>58 month; n = 47) were selected from a clinically well-characterized NSCLC patient cohort with available fresh frozen tumor specimens. The samples were analyzed using high-resolution single-nucleotide polymorphism array technology to assess gene copy number variations and array-based gene expression profiling. The molecular data were combined with information on clinical parameters. Genetic aberrations were strongly associated with tumor histology. In adenocarcinoma (n = 50), gene copy number gains on chromosome 8q21-q24.3 (177 genes) were more frequent in long-term than in short-term survivors. In squamous cell carcinoma (n = 28), gains on chromosome 14q23.1-24.3 (133 genes) were associated with shorter survival, whereas losses in a neighboring region, 14q31.1-32.33 (110 genes), correlated with favorable outcome. In accordance with copy number gains and losses, messenger RNA expression levels of corresponding genes were increased or decreased, respectively. Comprehensive tumor profiling permits the integration of genomic, histologic, and clinical data. We identified gene copy number gains and losses, with corresponding changes in messenger RNA levels that were associated with prognosis in adenocarcinoma and squamous cell carcinoma of the lung.
    Journal of thoracic oncology: official publication of the International Association for the Study of Lung Cancer 11/2011; 6(11):1833-40. · 4.55 Impact Factor
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    ABSTRACT: Technologies based on DNA microarrays have the potential to provide detailed information on genomic aberrations in tumor cells. In practice a major obstacle for quantitative detection of aberrations is the heterogeneity of clinical tumor tissue. Since tumor tissue invariably contains genetically normal stromal cells, this may lead to a failure to detect aberrations in the tumor cells. Using SNP array data from 44 non-small cell lung cancer samples we have developed a bioinformatic algorithm that accurately models the fractions of normal and tumor cells in clinical tumor samples. The proportion of normal cells in combination with SNP array data can be used to detect and quantify copy number neutral loss-of-heterozygosity (CNNLOH) in the tumor cells both in crude tumor tissue and in samples enriched for tumor cells by laser capture microdissection. Genome-wide quantitative analysis of CNNLOH using the CNNLOH Quantifier method can help to identify recurrent aberrations contributing to tumor development in clinical tumor samples. In addition, SNP-array based analysis of CNNLOH may become important for detection of aberrations that can be used for diagnostic and prognostic purposes.
    PLoS ONE 02/2009; 4(6):e6057. · 3.73 Impact Factor