Predictive Immune Modeling using RNA Provides New Context
In this week’s Q&A Cofactor Genomic’s CEO, Jarret Glasscock, explains Predictive Immune Modeling, Health Expression Models, and the advantages over isolated, single-analyte biomarkers.
Q: When will RNA be used in the clinic more often than DNA?
A: To understand that, we have to understand the strengths of each of those types of molecules. If we listen to the industry including directors from the NIH, they believe in a very short time, every child that is born will have their DNA sequenced and it’ll become a natural part of their clinical record. The information that DNA really tells is the risks for disease throughout an individual’s life time. In many diseases, DNA is very informative; providing a risk statement. When there is actual onset of disease is another question. For those types of questions, you need molecules which are dynamic in nature. Ideally they would be proteins, but proteins are difficult to analyze on a high-throughput scale. And, you can catch much of the same information in RNA transcripts. With those, you have the opportunity to do three things. First, RNA is being used as a tool to identify the onset of disease as early as possible. Second is when a disease is detected, RNA is being used to further subtype that disease and understand who is a candidate for a given type of treatment and who’s not. Third I would say is monitoring. After treatment, RNA is being used in creative new ways to monitor that disease and whether the individual is getting better, getting worse, or if there’s been no change in state as a result of the treatment paradigm. When you think about this in the context of a single individual, they may have their DNA sequenced once, but there’s multiple opportunities to sequence RNA for the individual depending on which of those three categories an individual’s needs fall into.
Q: What are Health Expression Models?
A: Health Expression Models are a move away from RNA-seq in that RNA-seq is very much focused on individual gene observations. Although RNA-seq captures many times tens of thousands of genes, the analysis and the insight is usually done at an individual gene level. Health expression models are modeling some defined aspect of biology, whether that is a cell type in the case of Cofactor’s work with immune cell types or a response to therapy or a given tissue type. Transcriptional data and RNA in general is known to be extremely variable with expression level distributions, so when you take a more global view of the data type, you’re able to see signal where it could be very difficult to see if you’re just looking at a single gene with lots of noise.
Q: How can people access Cofactor’s Predictive Immune Modeling platform?
A: Cofactors Predictive Immune Modeling platform is what is represented by our product, ImmunoPrism™. ImmunoPrism™ can be accessed through three avenues. Two of those avenues are centralized, or within our own facilities. The third avenue is distributed, meaning that that the analysis happens outside our own facilities. Under the centralized model, you can choose to either have us process or characterize the samples under a CAP/CLIA pathway and if that’s not necessary for your work you can choose to have them done under our RUO or research use only pathway. The third pathway, being distributed, means that you can use Cofactor’s own kit – the same exact kit that we use in our laboratory – to process your samples within your own laboratory.
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