Rembrandt: Helping personalized medicine become a reality through integrative translational research Academic Article uri icon


MeSH Major

  • Brain Neoplasms
  • Computational Biology
  • Databases, Genetic
  • Gene Expression Profiling
  • Genome, Human
  • Oligonucleotide Array Sequence Analysis


  • Finding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set and the ability to integrate biomedical data from disparate sources enabling translation of therapies from bench to bedside. Hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed, and analyzed both within and across functional domains. Novel biomedical informatics infrastructure and tools are essential for developing individualized patient treatment based on the specific genomic signatures in each patient's tumor. Here, we present Repository of Molecular Brain Neoplasia Data (Rembrandt), a cancer clinical genomics database and a Web-based data mining and analysis platform aimed at facilitating discovery by connecting the dots between clinical information and genomic characterization data. To date, Rembrandt contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising approximately 566 gene expression arrays, 834 copy number arrays, and 13,472 clinical phenotype data points. Data can be queried and visualized for a selected gene across all data platforms or for multiple genes in a selected platform. Additionally, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-anomaly pairs to facilitate the discovery of novel biomarkers and therapeutic targets. We believe that Rembrandt represents a prototype of how high-throughput genomic and clinical data can be integrated in a way that will allow expeditious and efficient translation of laboratory discoveries to the clinic.

publication date

  • February 2009



  • Academic Article



  • eng

PubMed Central ID

  • PMC2645472

Digital Object Identifier (DOI)

  • 10.1158/1541-7786.MCR-08-0435

PubMed ID

  • 19208739

Additional Document Info

start page

  • 157

end page

  • 67


  • 7


  • 2