From a research perspective, the goal of NGS is to obtain as much sequence data as possible—to find that SNP, translocation, or other variant that correlates with disease, but “we ask the opposite question as the researcher,” says Gitte Pedersen, CEO of Genomic Expression. To use RNA-seq in the clinical setting, for example, we want to know “what is the minimum amount of information you need to extract from your sample in order to identify and quantify all of the RNAs.” RNA-seq by itself is not a clinical application, because depending on how the test is analyzed, and who analyzes it, the results can be different, asserts Pedersen.
To reduce the time, cost, data burden, and complexity of RNA-seq applications to make them faster, easier, and more amenable for use in the development of a companion diagnostic for instance, Genomic Expression produced kits and accompanying software for performing automated RNA-seq that are platform-agnostic and work on existing and emerging NGS instruments. Based on a bait-free target filtering sample preparation method, the process from sample prep to results takes less than a week and generates a data file of 30 MB that can be attached to an electronic medical record.
Pedersen describes how pharma companies can use RNA-seq to develop companion diagnostics for stratifying patient populations for clinical testing of oncology drugs, for example. As data is collected it can be used to perfect the evolving algorithms and “optimize the definition of the target population based on data points.”
This exercise to link a companion diagnostic to a cancer therapeutic may have required an array of different markers and multiple testing platforms, but RNA-seq can turn that complexity “into a math exercise with a very high probability of success on one platform with one assay,” Pedersen adds.
“With the approval of the Illumina MiSeq for clinical applications, we are 90% there, the platforms are there; the last 10% is to develop the algorithms. Our business model is to partner around the last 10%—the content, leveraging our access to fully annotated clinical samples.”
Pedersen emphasizes the scalability of the Genomic Expression technology, and its broad range of potential applications. How sample- or disease-specific it will be remains to be seen. “We know it works in breast cancer,” she says, and have demonstrated that it could also work in a number of other cancers. Other promising application areas are organ transplantation rejection, cardiovascular and central nervous system disorders, and autoimmune diseases.