The myth of genetic destiny
There has been a recent explosion of genetic knowledge, and people are placing a great deal of faith in it. But these findings don’t tell the whole story. For truly predictive healthcare, we must find ways to bridge the gap between genomic research and clinical information.
There is a prevalent misconception: If you have a gene associated with a disease, you will get that disease; If you don’t have it, you’re safe. It turns out that simply isn’t true. This limited view doesn’t account for genetic cause and effect.
Other genetic information contributes to your likelihood to develop inherited conditions. For example, studies have shown that there are regulator genes and proteins that “turn on” or “turn off” other genes. Other studies show that having a gene may not mean anything unless it is transcribed. Research is also identifying variants of all kinds, but we don’t yet know what they mean.
Genomic information is only half of the equation. Clinical information – including research and individual patient data – is the other half. We must make associations between these vast bodies of information for it to truly impact patient care. Today, with the capabilities of big data, we are in a better position to do that than ever before.
Double-teaming heart disease with clinical and genomic information
History provides examples of how combining clinical with genomic information can have a positive impact on patient care. Let’s take a historical look at heart disease, for example.
In 1948, researchers launched efforts to identify the causes of heart disease, known as the Framingham Heart Study. They recruited thousands of participants and followed them for generations and delivered a truly monumental clinical study.
Among the findings, Framingham researchers determined that cholesterol was a major marker for heart disease. The first drugs developed to address the condition succeeded in lowering cholesterol levels, but people didn’t live any longer. A new class of drugs, statins, helped some people live a lot longer, but other people didn’t respond well to statins at all.
About the same time statins were gaining popularity, genetic research determined that PCSK9 is a gene that contributes to developing high cholesterol. It took a long time to piece it together, but patients who have the PCSK9 variant are among those who do not respond well to statins.
Recently a new class of drugs, called PCSK9 inhibitors, have been found to be very effective to treat this condition in the familial form. They can be expensive, so it’s important to prescribe for patients that will respond best to them.
At the point of care, if physicians have clinical-genomic information within the electronic health record (EHR) workflow, treatment plans for patients with high cholesterol can be much more targeted. Doctors can identify patients that might benefit from this new class of drugs, such as patients who have not responded to medications to date.
2bPrecise collaboration with Mayo Clinic
To bridge the clinical-genomic research divide, we must build new relationships. 2bPrecise recently announced a technology license agreement and collaboration with Mayo Clinic (Rochester, Minnesota, U.S.A.), a leading medical practice and research group. Together, we will work to develop and apply genomics-based clinical decision support tools for patients with genetic cardiovascular disease.
Using Mayo Clinic’s electronic phenotyping algorithms for familial hypercholesterolemia, 2bPrecise will incorporate new clinical protocols, and apply them in research to measure outcomes. Mayo Clinic has strong subject matter expertise in clinical genomics and a desire to share care algorithms more broadly with the medical community.
It’s these types of partnerships that will advance genomic science and help make it more clinically actionable.