Project description:Tumor formation is in part driven by copy number alterations (CNAs), which can be measured using array Comparative Genomic Hybridization (aCGH). Identifying regions of DNA that are gained or lost in a significant fraction of tumor samples can facilitate identification of genes possibly related to the development of cancer. Until now, no method has been described that provides a statistical framework in which these regions can be identified without prior discretization of the aCGH data. Kernel Convolution - a Statistical Method for Aberrant Region deTection (KC-SMART) is a new approach which inputs continuous aCGH data to identify regions that are significantly aberrant across an entire tumor set. KC-SMART uses kernel convolution to generate a Kernel Smoothed Estimate (KSE) of CNAs across the genome, aggregated over all tumors. By varying the width of the kernel function, a scale space is created which enables the detection of aberrations of varying size. In an analysis of 89 human sporadic breast tumors KC-SMART performs better than a previously published method, STAC. Our method not only identified aberrations that are strongly associated with clinical parameters, but also showed stronger enrichment for known cancer genes in the detected regions. Furthermore, KC-SMART identifies 18 aberrant regions in mammary tumors from p53 conditional knock-out mice. These regions, combined with gene expression micro-array data, point to known cancer genes and novel candidate cancer genes. Keywords: Comparative Genomic Hybridization, aCGH
Project description:Tumor formation is in part driven by copy number alterations (CNAs), which can be measured using array Comparative Genomic Hybridization (aCGH). Identifying regions of DNA that are gained or lost in a significant fraction of tumor samples can facilitate identification of genes possibly related to the development of cancer. Until now, no method has been described that provides a statistical framework in which these regions can be identified without prior discretization of the aCGH data. Kernel Convolution - a Statistical Method for Aberrant Region deTection (KC-SMART) is a new approach which inputs continuous aCGH data to identify regions that are significantly aberrant across an entire tumor set. KC-SMART uses kernel convolution to generate a Kernel Smoothed Estimate (KSE) of CNAs across the genome, aggregated over all tumors. By varying the width of the kernel function, a scale space is created which enables the detection of aberrations of varying size. In an analysis of 89 human sporadic breast tumors KC-SMART performs better than a previously published method, STAC. Our method not only identified aberrations that are strongly associated with clinical parameters, but also showed stronger enrichment for known cancer genes in the detected regions. Furthermore, KC-SMART identifies 18 aberrant regions in mammary tumors from p53 conditional knock-out mice. These regions, combined with gene expression micro-array data, point to known cancer genes and novel candidate cancer genes. 19 mouse mammary tumors samples were measured against spleen-derived DNA from the same animal on our in-house aCGH platform. Goal of the study was to assess recurrent genomic aberrations in these tumors. This is a tissue specific knockout of p53. Experiments were perfomed in dye-swap
Project description:Pilocytic astrocytomas (PAs), WHO Grade I, are one of the most frequently occurring childhood brain tumors. We have used microarray comparative genomic hybridization (aCGH) at 1Mb resolution to study copy number changes in a series of PAs (n=44). Keywords: Comparative Genomic Hybridization, aCGH
Project description:We have used microarray comparative genomic hybridization (aCGH) at 1Mb resolution to study copy number changes in a series of WHO Grade II Astrocytomas (n=21). We have used Illumina arrays to study genome-wide expression patterns in a series of WHO Grade II Astrocytomas (n=10). Keywords: Array Comparative Genomic Hybridization (aCGH), Expression microarray
Project description:aCGH analysis of murine transgenic liver tissues affected with HCC, hybridized with age (12 months) and sex matched alb cre mice. Keywords: Array comparative genomic hybridization analysis (aCGH).
Project description:Comparison of normal versus tumor DNA (hepatocellular carcinoma). Whole-genome screening of DNA-copy number changes by array-based or matrix comparative genomic hybridization (aCGH). Tumor DNA labeled in Cy3 and pooled DNA of lymphocytes from healthy donors labeled in Cy5. Keywords: Genetic modification
Project description:Pilocytic astrocytomas (PAs), WHO Grade I, are one of the most frequently occurring childhood brain tumors. We have used microarray comparative genomic hybridization (aCGH) to study copy number changes on chromosome 7 in a series of PAs (n=44). Keywords: Comparative Genomic Hybridization
Project description:We have used microarray comparative genomic hybridization (aCGH) at 1Mb resolution to study copy number changes in a series of WHO Grade II Astrocytomas (n=21). We have used Illumina arrays to study genome-wide expression patterns in a series of WHO Grade II Astrocytomas (n=10). Keywords: Array Comparative Genomic Hybridization (aCGH), Expression microarray [aCGH]: Single hybridization per case. 21 astrocytomas WHO grade II were analyzed. Target (tumor) labelled with Cy5 and reference with Cy3. Mixture of 20 normal male or female genomic DNA was used in sex-mismatched hybridization. [mRNA]: Single hybridization per case. 10 astrocytomas WHO grade II were analyzed (5 adult, 5 pediatric)