Rembrandt: Helping Personalized Medicine Become a Reality Through Integrative Translational Research

By Subha Madhavan, J. Zenklusen, Yuri Kotliarov, Himanso Sahni, H. Fine, K. Buetow. Molecular Cancer Research, 2009

416 citations recorded by the source corpus.

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 ∼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. (Mol Cancer Res 2009;7(2):157–67)

Subfields: Biophysics, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f7868b179d4c5bd11280a5630265bd870dc2d342

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Citations in the corpus

Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. (2010, methodology): “This data set included cohorts from the Rembrandt set (Madhavan et al., 2009) as well as other sources and did not include TCGA data (given that TCGA data were used to derive the gene list).”
Pathway-based personalized analysis of cancer (2013, methodology): “On the REMBRANDT dataset Pathi ﬁ er discovered 187 survival-related pathways and the linear version only 54 (8 of which were not identi ﬁ ed by Pathi ﬁ er).”