In the beginning, there was RNA – the first genetic molecule.
In the primordial soup of chemicals that represented the beginning of life, ribonucleic acid (RNA) had the early job of storing information, likely with the support of peptides. Today, RNA’s cousin – deoxyribonucleic acid – or DNA, has taken over most of the responsibilities of passing down genetic information from cell-to-cell, generation-to-generation. As a result, most early health technologies were developed to analyze DNA. But, RNA is a powerful force. And its role in storing information, while different from its early years, has no less of an impact on human health and is gaining more mindshare in our industry.
RNA is often considered a messenger molecule, taking the information coded in our DNA and transcribing it into cellular directives that result in downstream biological signals and proteins-level changes. And for this reason, RNA is becoming known not only as a drug target but perhaps more importantly, as a barometer of health.
How and why is RNA so useful?
First, RNA is labile — changing in both sequence and abundance in response to genetic and epigenetic changes, but also external factors such as disease, therapy, exercise, and more. This is in contrast to DNA, which is generally static, changing little after conception.
Next, RNA is a more accurate snapshot of disease progression. When mutations do occur at the DNA level, these do not always result in downstream biological changes. Often, the body is able to compensate by repairing the mutation or overcome it by using redundancies in the pathway in which the gene resides. By instead evaluating RNA, we get one step closer to understanding the real impact disease is imparting on our body.
Finally, RNA is abundant. In most human cells, while only two copies of DNA are present, hundreds of thousands of mRNA molecules are present, representing more than 10,000 different species of RNA. Because even rare transcripts are present in multiple copies, biological signals can be confidently detected in RNA when the right technology is used.
We often talk about revolutionary technologies — like next-generation sequencing (NGS) — specifically in the context of enabling DNA and genome projects. The ever-climbing depth of sequencing we can now achieve, paired with the plummeting costs, allows us to now finally leverage all the benefits and unique qualities that are encoded and buried deep in the rich data represented by RNA. What’s more, adding technology such as machine learning to this breadth of RNA data has provided a new tool for maximizing the information embedded in this dynamic molecule.
How, then, are these qualities and technologies being leveraged to make the most of RNA’s unique characteristics? Health barometers, as it turns out, are more commonly referred to as diagnostics. As a result, we are seeing a new generation of diagnostics being built on RNA. A key area where diagnostics have made an impact in precision medicine is matching patients with the right therapies. And with the advent of immune-oncology, characterizing the patient’s immune system has become imperative for predicting treatment efficacy. Using RNA to characterize immune response has resulted in a new discipline, coined Predictive Immune Modeling.
Predictive Immune Modeling sits squarely in this predictive diagnostic space. Using machine learning, it distills down the excess of data generated from RNA into meaningful, actionable signals. This is most often accomplished by building RNA models that represent facets of biology, or cohorts of patients. This includes cell types and states, such as macrophage polarization or T cell exhaustion, in combination with other RNA signals such as immune escape or co-stimulatory gene expression. Importantly, researchers and clinicians are building RNA models for patient cohorts that include these important facets of immune response from very little tumor tissue, and these models are proving to be more powerful than historical approaches. Because of the advances in next-generation sequencing, this is all accomplished even with the most degraded samples from clinical archives, allowing us to build models from a large cohort of historic samples that can then be validated to develop a diagnostic to impact a patient’s treatment decision today.
And so, while RNA might have taken a backseat to DNA for some time, it’s certainly emerged as a key player in precision medicine today. By harnessing the combined power of NGS, machine learning and the dynamic nature of RNA we’re able to accurately measure the dynamic immune response and capture a more comprehensive picture of what’s happening at the site of the solid tumor. There’s no other molecule more suited to take us into this new era of predictive diagnostics to improve treatment decisions and most importantly, patient outcomes.
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