Applied Biosystems to Study Mechanisms of Cancer Development Using SOLiD™ System
News Feb 10, 2009
A team of researchers from the Mayo Clinic has announced preliminary data that demonstrates the advantages of sequencing-based approaches for associating structural variants with RNA expression profiles, to identify potential biomarkers in head and neck cancer. The data is the result of an ongoing scientific collaboration formed in 2008 between the Mayo Clinic and Applied Biosystems, a division of Life Technologies Corporation.
Large-scale genomic rearrangements or structural variations are a hallmark of most cancers, because they contribute to genetic instability reportedly involved in carcinogenesis. It is widely believed that mutations in DNA sequence are transcribed to messenger RNA (mRNA), and ultimately translate into a functional protein. An emerging hypothesis suggests that mRNA may regulate processes such as alternative splicing, RNA editing and a variety of cellular functions.
Researchers from the Mayo Clinic are using digital gene expression capabilities of the SOLiD™ System to associate genetic variation at the transcript level with structural variants. A key to understanding structural variation is the ability to visualize chromosomal rearrangements, and other changes to large segments of DNA such as copy number variations, inversions, translocations, insertions, and deletions.
“This collaboration is enabling the discovery of genetic variation that may ultimately uncover clues that reveal the underlying mechanisms of cancer development,” said Kip Miller, President of Life Technologies’ Genetic Systems Division. “This project further demonstrates how the SOLiD System is an advanced genomic analysis platform that is paving the way for future advances in the diagnosis, prognosis and treatment of a host of complex diseases.”
Traditionally, the most widely used method to analyze global patterns of RNA expression is the DNA microarray. However, microarray technology offers limited sensitivity, requires greater sample input and is unable to detect novel RNAs. Alternatively, a sequencing-based approach to RNA expression analysis allows researchers to detect low levels of expression invisible on microarray platforms and perform a hypothesis-neutral analysis of gene expression profiles, enabling the detection of all known and novel RNAs present in biological samples, with no bias toward known RNA molecules as with probe-based array technologies.
Sequencing-based RNA expression analysis enabled the researchers to establish directionality of expressed transcripts which is significant because DNA is transcribed in two different directions. Establishing directionality of expressed transcripts allows researchers to more easily determine which RNA transcripts are coding and non-coding. Non-coding RNAs play an increasingly important role in regulating biological processes involved in cancer differentiation and development.
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