Next-generation sequencing (NGS) allows the interrogation of genomes and transcriptomes at unparalleled resolution. NGS is becoming a powerful tool to identify cancer mutations that will eventually be translated to the clinic.
Further, second-generation RNA-Seq technology permits the simultaneous evaluation of gene expression and transcript structure at a high level of accuracy and at a single-nucleotide level. RNA-Seq has been called a revolutionary tool for transcriptomics. It works by utilizing NGS high-throughput technology to characterize cDNAs representative of the cell’s transcriptome.
RNA-Seq can be a valuable analytical tool for a variety of applications, notes Erik K. Flemington, Ph.D., professor of pathology, Tulane Health Sciences Center. “In my laboratory, we have utilized this technology to identify and analyze the transcriptomes of infectious viral organisms, to characterize tumor microbiomes, and for microRNA (miRNA) target analysis studies.”
Dr. Flemington says that often viruses have a high gene density, making it difficult to discriminate overlapping transcripts using RNA-Seq. “Newer RNA-Seq methodologies are allowing us to overcome those challenges. Using these methods, we are finding out that the old dogma that there are only a small number of transcripts isn’t true. In fact, we have identified an abundance of previously unannotated and/or undescribed transcripts in viromes.”
As an example, Dr. Flemington and colleagues studied Epstein-Barr virus (EBV), a human pathogen that causes malignancies such as Burkitt lymphoma and Hodgkin disease. “We used second-generation RNA-Seq pipeline tools and developed new tools to customize the approaches for the analysis of viromes in the context of their host.
“Among other things, these new strategies allowed for the identification of new viral genes and transcript isoforms important for EBV to establish infection. Overall, these studies allowed us to identify a whole new set of transcripts that are potentially related to such processes as cell fate determination and inflammatory events.”
Another use of RNA-Seq is to characterize tumor microbiomes. “Clinical samples may contain exogenous agents such as viruses. This is important to know because some of these contribute to tumor development. By assessing clinical samples with RNA-Seq we can discover if the tumor has viruses associated with it.
“An example is the analysis of stomach cancers. The identification of viruses in clinical samples is highly tractable, and instead of needing to perform numerous assays looking for each virus one at a time, RNA-Seq allows identification in one assay alone. As this technology is used more and more in clinical samples, we may be able to better determine which viruses are associated with which tumors and what the clinical significance of these interactions is.”
Dr. Flemington also employs RNA-Seq for miRNA-targeting studies. “The regulation of gene expression by miRNAs is a fundamental mechanism for controlling a number of biological processes. We used RNA-Seq to study, for example, miRNA-155. The gene encoding miRNA-155 was classified as an oncogene long before it was identified as an miRNA. It is now implicated in a wide variety of cancers.
“Previous studies have utilized microarrays to assess miRNA-mediated decreases in target RNA. But this approach suffers from technical limitations. We employed RNA-Seq because of its high level of accuracy, broad dynamic range, ability to assess transcript structure, and because it can sensitively assess transcriptome alterations. Using this approach, we were able to identify a large inferred targetome, and more interestingly, we could readily study the role that transcript structure plays in microRNA targeting.”
The ability of RNA-Seq to generate millions of reads has presented new challenges to data analysis and interpretation, notes Han Liang, Ph.D., assistant professor, department of bioinformatics and computational biology, University of Texas MD Anderson Cancer Center. “We are studying the molecular underpinnings of gastric cancer using an RNA-Seq approach. The huge amount of data generated required us to develop creative in-house ways to interpret and analyze it.”
In a recent study, Dr. Liang and colleagues profiled the transcriptomes of gastric tumor and noncancerous samples from the Asian population. “Gastric cancer is the most common cancer in developing countries and the second leading cause of cancer death in the world.
“Traditional approaches to study gastric cancer have utilized hybridization microarrays, including miRNA expression microarrays and exon microarrays, but those approaches only characterize some part of the transcriptomes. We chose RNA-Seq to perform this analysis and generated 680 million informative short reads of these transcriptomes. This included profiling mRNA and miRNA simultaneously.”
Dr. Liang and collaborators applied a SOLiD™ RNA-Seq (Life Technologies) approach and developed a multilayer and integrative approach for characterization. “We utilized two complementary protocols for generation of a target fragment library that ranged from 50–150 nucleotides in length as well as shorter reads from 18–40 nucleotides. In this way we generated reads on the entire population of transcribed molecules.”
The next challenge was to analyze the data. “We performed a multilayer and integrative analysis on the data and identified different types of transcriptional aberrations that were associated with different stages of gastric cancer. We used a combination of commercially available software and our own in-house algorithms. In order to integrate expression data of mRNA and miRNA, we developed algorithms to quantify and compare gene-expression patterns.”
Their analyses pinpointed a potentially functional target. “We identified the central metabolic regulator AMP-activated protein kinase (AMPK)α2. Thus, this gene is a potential therapeutic target for early-stage gastric cancer in Asian patients.”
Dr. Liang plans to utilize this system and NGS for gastric cancer patients in other populations. “Ultimately, we hope to identify the most important biomarkers in gastric cancer. Using RNA-Seq we can start with a more global dataset and then narrow that down in each population, eventually in each patient.”