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Genomic Networks in Development and Cancer: Resolving Biomarkers and Therapeutic Targets from a Cloud of Data

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Air date: Tuesday, February 14, 2012, 1:00:00 PM
Time displayed is Eastern Time, Washington DC Local
Views: Total views: 566, (215 Live, 351 On-demand)
Category: Special
Runtime: 01:16:25
Description: Systems level approaches to construct abstract molecular networks can lead to predictions about genetic and biochemical functions in cells, organisms and in disease states. I will show examples of this approach from work in my laboratory. In one example we used an integrated experimental and computational approach to construct a large scale functional network in Drosophila melanogaster built around key transcription factors involved in the process of embryonic segmentation. Our network model is based on a combination of gene expression, transcription factor DNA binding site mapping, automated literature mining and protein-protein interaction mapping. We provide a strategy for reducing the dimensionality of the massive networks that result from such integrated whole genome analyses.

Using results from one factor in particular, we demonstrated that our approach can rapidly translate a finding in a model organism to the development of a therapeutic target in kidney cancer. In another example, we built a large scale network based on gene expression and genome-wide ChIP results for 40 transcription factors, including two dozen Nuclear Receptor (NR) class proteins. Using this NR network we identified novel prognostic signatures for breast cancer survival and recurrence, as well as new therapeutic leads.

Finally, if time permits I will talk about how we are mining The Cancer Genome Atlas along with data from the Chicago Cancer Genomes Project using the Bionimbus Cloud in order to identify new tumor suppressors and panels of genetic markers capable of classifying cancer subtypes that correspond to patient outcome.
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NLM Title: Genomic networks in development and cancer : resolving biomarkers and therapeutic targets from a cloud of data [electronic resource] / Kevin White.
Series: NCI Center for Cancer Genomics lecture
Author: White, Kevin.
National Institutes of Health (U.S.)
Publisher:
Other Title(s): NCI Center for Cancer Genomics lecture
Abstract: (CIT): Systems level approaches to construct abstract molecular networks can lead to predictions about genetic and biochemical functions in cells, organisms and in disease states. I will show examples of this approach from work in my laboratory. In one example we used an integrated experimental and computational approach to construct a large scale functional network in Drosophila melanogaster built around key transcription factors involved in the process of embryonic segmentation. Our network model is based on a combination of gene expression, transcription factor DNA binding site mapping, automated literature mining and protein-protein interaction mapping. We provide a strategy for reducing the dimensionality of the massive networks that result from such integrated whole genome analyses. Using results from one factor in particular, we demonstrated that our approach can rapidly translate a finding in a model organism to the development of a therapeutic target in kidney cancer. In another example, we built a large scale network based on gene expression and genome-wide ChIP results for 40 transcription factors, including two dozen Nuclear Receptor (NR) class proteins. Using this NR network we identified novel prognostic signatures for breast cancer survival and recurrence, as well as new therapeutic leads. Finally, if time permits I will talk about how we are mining The Cancer Genome Atlas along with data from the Chicago Cancer Genomes Project using the Bionimbus Cloud in order to identify new tumor suppressors and panels of genetic markers capable of classifying cancer subtypes that correspond to patient outcome.
Subjects: Genetic Techniques
Genomics--methods
Neoplasms--genetics
Publication Types: Lectures
Webcasts
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Caption Text: Download Caption File
NLM Classification: QZ 200
NLM ID: 101581446
CIT Live ID: 10995
Permanent link: http://videocast.nih.gov/launch.asp?17107