Performance Characteristics of the BluePrint® Breast Cancer Diagnostic Test.

The analytical performance of a multi-gene diagnostic signature depends on many parameters, including precision, repeatability, reproducibility and intra-tumor heterogeneity. Here we study the analytical performance of the BluePrint 80-gene breast cancer molecular subtyping test through determination of these performance characteristics. BluePrint measures the expression of 80 genes that assess functional pathways which determine the intrinsic breast cancer molecular subtypes (i.e. Luminal-type, HER2-type, Basal-type). Knowing a tumor's dominant functional pathway can help allocate effective treatment to appropriate patients. Here we show that BluePrint is a highly precise and highly reproducible test with correlations above 98% based on the generated index and subtype concordance above 99%. Therefore, BluePrint can be used as a robust and reliable tool to identify breast cancer molecular subtypes.

MammaPrint and BluePrint Molecular Diagnostics Using Targeted RNA Next-Generation Sequencing Technology.

Next-generation DNA sequencing is rapidly becoming an indispensable tool for genome-directed cancer diagnostics, but next-generation RNA sequencing (RNA-seq) is currently not standardly used in clinical diagnostics for expression assessment. However, multigene RNA diagnostic assays are used increasingly in the routine diagnosis of early-stage breast cancer. Two of the most widely used tests are currently available only as a central laboratory service, which limits their clinical use. We evaluated the use of RNA-seq as a decentralized method to perform such tests. The MammaPrint and BluePrint RNA-seq tests were found to be equivalent to the clinically validated microarray tests. The RNA-seq tests were highly reproducible when performed in different locations and were stable over time. The MammaPrint RNA-seq test was clinically validated. Our data demonstrate that RNA-seq can be used as a decentralized platform, yielding results substantially equivalent to results derived from the predicate diagnostic device.

Using hexamers to predict cis-regulatory motifs in Drosophila.

BACKGROUND: Cis-regulatory modules (CRMs) are short stretches of DNA that help regulate gene expression in higher eukaryotes. They have been found up to 1 megabase away from the genes they regulate and can be located upstream, downstream, and even within their target genes. Due to the difficulty of finding CRMs using biological and computational techniques, even well-studied regulatory systems may contain CRMs that have not yet been discovered. RESULTS: We present a simple, efficient method (HexDiff) based only on hexamer frequencies of known CRMs and non-CRM sequence to predict novel CRMs in regulatory systems. On a data set of 16 gap and pair-rule genes containing 52 known CRMs, predictions made by HexDiff had a higher correlation with the known CRMs than several existing CRM prediction algorithms: Ahab, Cluster Buster, MSCAN, MCAST, and LWF. After combining the results of the different algorithms, 10 putative CRMs were identified and are strong candidates for future study. The hexamers used by HexDiff to distinguish between CRMs and non-CRM sequence were also analyzed and were shown to be enriched in regulatory elements. CONCLUSION: HexDiff provides an efficient and effective means for finding new CRMs based on known CRMs, rather than known binding sites.