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March 01, 2015 (Vol. 35, No. 5)

Could RNA-Seq Become the Workhorse of Precision Medicine?

Plowing through Transcriptional Variations by Harnessing the Powerful Next-Gen Technique

  • Click Image To Enlarge +
    Sequencing of RNA molecules, like the one pictured here, is a powerful laboratory discovery tool and has the potential to play a major role in clinical diagnostics. [petarg/Shutterstock]

    Advances in genomics over the past several years have had a profound impact on our grasp of molecular biology and genetics. In the laboratory, next-generation sequencing (NGS) has been applied to identifying novel genomes for an array of organisms, DNA resequencing, transcriptome sequencing, and epigenetics. Within clinical settings, NGS is beginning to cut its teeth and is being rapidly embraced as an invaluable diagnostic tool. Specifically, the ability to interpret the genetic mechanisms that underlie variations in human gene expression through the direct analysis of the transcriptome makes RNA sequencing (RNA-Seq) an attractive method to clinical diagnosticians. “RNA-Seq provides a very specific and sensitive genomic signature that can be useful in many clinical situations,” said Gary Schroth, Ph.D., distinguished scientist at Illumina.

    RNA-Seq examines the dynamic nature of the cell’s transcriptome, the portion of genome that is actively transcribed into RNA molecules. While DNA remains relatively unchanged throughout an individual’s lifespan, RNA, in the form of transcriptional elements, can vary dramatically due to influences on epigenetic regulators, alternative spliced variants, or post-transcriptional modifications.

    Through the study of transcriptomes, researchers hope to determine when and where genes are turned on or off in a variety of cell types. Methods such as RNA-Seq are quantifiable and provide insights to the level of gene activity or expression within a cell. For instance, transcript information could reveal the gene expression profile changes that are associated with cancer. Moreover, careful analysis of the transcriptome may provide a comprehensive snapshot of what genes are active during various stages of development.

  • RNA-Seq vs. Microarrays

    In many important ways RNA-Seq has surpassed microarrays and is becoming the preferred platform for transcriptome analysis. Though, since its inception, RNA-Seq has had to contend with the devotees of microarray technology as a means of generating transcriptional information, understandably so as microarrays were an important technological breakthrough for the genomics field. When used to analyze gene transcripts, arrays were faster and capable of observing a much broader range of transcripts than RT-PCR, which was the gold standard for decades.

    RNA-Seq is an ideal platform for discovery-based experiments as it has the capacity for unbiased detection of novel transcripts, which means it does not require species or transcript-specific probes. This affords RNA-Seq a level of adaptability that’s not possible for microarrays; once a chip is produced it is not able to be modified as new information becomes available. However, since capital equipment cost and price per sample currently favor the use of microarrays, the technology will not quietly slip into obsolescence, as many industry experts hypothesized several years ago.

    From a clinical perspective, RNA-Seq delivers a low signal-to-noise ratio and is capable of detecting rare and low-abundance transcripts with increased specificity and sensitivity. This is important as clinicians are parsimonious with their samples, trying to maximize data acquisition from the smallest allowable size, as they are often difficult to obtain. Furthermore, with its complete measurement of RNA transcripts and ability to detect single nucleotide polymorphisms, RNA-Seq has the advantage to provide quantitative data in a clinically relevant timeframe. For example, the ability to compare the transcriptomes of tumor and normal cells and look for copy number alterations or alternative spliced variants are valuable methodologies that are unavailable to microarray users.

    Although they have their differences, RNA-Seq and microarrays are not mutually exclusive. In many instances the two technologies can be combined for a more accurate assessment of the transcriptome. Specifically, since many of the RNA-Seq protocols have not been standardized, researchers will often use microarray results to validate the output from sequencing.

    There are still several areas of RNA-Seq that need to be addressed before the technology is ready to handle the large workload of a clinical laboratory. Steve Siembieda, vice president of business development for Advanced Analytical Technologies, identified “extraction of single cell materials, library prep of small samples, accurate quantification, and working with FFPE samples,” as key areas in need of attention for clinical RNA-Seq development to move forward.

  • Establishing Standards

    For RNA-Seq to transition from a purely analytical research discovery method to a clinically useful tool, scientists and regulatory officials must adopt standard analysis methodology and benchmark datasets for their level of accuracy and reproducibility. Although, there have been a number of publications and conferences recently that tackle the topics of assessing sequencing platforms, specific laboratory protocols, and data analysis software, consensus is far from being unanimous. However, after notifying the U.S. Congress about the details of the draft guidances for the regulation of laboratory developed tests (LDTs) and companion diagnostics, which would include RNA-Seq, the FDA posted the information on its website in October of 2014. This is a positive step forward and indicates that the RNA-Seq field is moving closer to being established as an important clinical diagnostic tool.

    Additionally, recent comparisons of RNA-Seq with conventionally employed clinical methodologies for the analysis of differential expression were found to be similar between RNA-Seq, qPCR, and microarrays. In general, RNA-Seq has provided increased detection sensitivity and allowed for new research opportunities in transcriptome analyses, such as the study of gene fusions, allele-specific expression, and novel alternative transcripts.

    “Currently, fusion gene detection is the most clinically relevant application of RNA-Seq. Longer term, miRNA (microRNA) sequencing has the potential to become a clinical tool,” said Jonathan Arnold, senior director of marketing for NGS at Qiagen. Dr. Schroth is in agreement, stating that “We are finding that, especially in the case of fusion transcripts, which are very specific biomarkers for many different types of cancers, more and more labs are turning to RNA sequencing to find and characterize these valuable targets.”

  • The Clinical Future of RNA-Seq

    While next-generation DNA sequencing is beginning to become well entrenched at the front lines of clinical diagnostics, RNA-Seq is still undergoing its boot camp training. That’s not to say that clinical researchers aren’t excited about transcriptome sequencing technology, to the contrary, many clinical institutions have been actively establishing unified protocols for using RNA-Seq more readily.

    Data analysis could be the limiting factor that has the potential to restrain RNA-Seq from becoming a clinical diagnostic tool in a timely manner. Sensing that, some institutions have begun to address underlying issues of analyzing the huge amounts of data that are obtained from RNA-Seq. For instance, the Mayo Clinic has recently developed an analysis pipeline called MAP-RSeq, which their websites states “integrates a suite of open source bioinformatics tools along with in-house developed methods to analyze paired-end RNA-Seq data.” This is an important step in establishing a comprehensive database to identify, annotate and prioritize expressed single nucleotide variants (eSNVs) from the accumulated RNA-Seq data.

    Since RNA-Seq has such a wide dynamic range the clinical pathologies that would benefit from its sequencing capabilities are almost limitless, but most scientists and publications would point to various types of cancers as being atop the list of potential primary candidates for clinical RNA-Seq usage. “Fusion gene detection is quickly becoming a standard for several types of cancers such as NSCLC (non-small cell lung cancer) and hematological disorders,” stated Arnold.

    If the laboratory discovery phase is indicative of the breadth of disease states that RNA-Seq could address, than the era of precision medicine should begin to expand exponentially within the next few years. RNA-Seq is well positioned to handle the large clinical workload if scientists can institute the appropriate practices and procedures that are essential for precision clinical medicine.

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