REDWOOD CITY, Calif.
Feb. 25, 2013
/PRNewswire/ -- Genomic Health, Inc. (Nasdaq: GHDX) today announced the results of two studies demonstrating that DNA strand-of-origin information can help further refine the identification of prognostic biomarkers, and that tumor specific gene mutations can be effectively examined using archival fixed paraffin embedded tumor (FPET) tissue, enabling an improved and more practical process of tumor analysis. These new findings were presented at the 14th Annual Advances in Genome Biology and Technology (AGBT) meeting in
Marco Island, Fla.
"Our continued research efforts demonstrate the biological and technical capabilities of our advanced next generation sequencing (NGS) methods for biomarker discovery and validation," said
Steven Shak, M.D
., chief medical officer and executive vice president for research and development at Genomic Health. "These findings will accelerate the development of future tests based on our ongoing clinical research that combines both whole transcriptome profiling and mutation analysis."
Novel Next Generation Sequencing and Bioinformatics Methods Enhance Biomarker Discovery
- Building on study results published in PLoS One -- where Genomic Health scientists carried out whole transcriptome RNA-Seq on FPET RNA from a cohort of 136 breast cancer patients -- this analysis evaluated the impact of DNA strand-of-origin information on the identification of prognostic biomarkers.
During DNA transcription, only one of the two double-stranded DNA molecules is used as a template for an RNA transcript and protein production. Analytic approaches that do not consider the strand-of-origin of RNA sequencing data can be limited in their accuracy in assigning reads to human genes. Therefore, to perform this study, Genomic Health scientists developed a proprietary NGS and bioinformatics approach to produce a more definite specification of the strand-of-origin of the RNA transcript. The application of this method enabled more precise detection of expressed genes that were significantly associated with breast cancer recurrence, and permitted the identification of 228 additional candidate genes associated recurrence risk in breast cancer.