Jeremy Berg: Precision medicine is using genomic information in addition to other factors to try to customize diagnosis and potentially treatment of a wide range of diseases.

 

Mylynda Massart: When I explain precision medicine to my patients or to my family, it’s really about taking all of the factors that lead up to our individual health. So, looking at our genetics and all of our environmental factors, such as our nutrition, our exposures in our local environments, even our traumas. 

 

 

 

JB: In terms of the technology, most of the direct-to-consumer tests are not whole-genome sequencing, but are a sampling of known variations across the human genome. So it’s a very incomplete picture of your whole genetic background. 

 

But more important than that is: All of this is really complicated. In some cases, there are variations that are so-called highly penetrant, [meaning they] are very predictive. If you have this [gene] variant, then you are certain or very likely to have a particular disease. The archetype of that is sickle cell anemia. You have two copies of each gene. If you have two copies [of a particular variation in a hemoglobin gene], you have sickle cell disease; if you have one copy, you have sickle cell trait. 

 

But for most other diseases or other traits, things are much more complicated, where there are lots of variations, and they all contribute a little bit [in addition to] other factors like environment and exposures and experience. 

 

And that complexity tends to get lost in the sort of cartoon version of the reports that come back. And one concern that I have is that consumers take it much more seriously or uncritically than they should for their own benefit. 

 

MM: I think that when the direct-to-consumer testing market first opened up, it was really fun, and that’s really what the key term was. It was engaging, the general society was learning more about genetics and genetics terminology, and they were having fun doing it. Now, the level of results are increasing in clinical utility. There are results coming out now on BRCA mutations [for breast cancer], genes that may contribute to risk of Alzheimer’s disease or Parkinson’s disease, and a lot about pharmacogenomics—how genetics affect the metabolism of everyday medications (which can result in adverse reactions, or toxicity levels, or drugs simply not working or being therapeutically effective at all). 

 

And to me, the part about that that’s so important is that while it’s still fun and interesting and engaging, these results now need to be brought into the clinical world and interpreted from a clinical perspective. And the doctors out there really need to recognize the limitations of these tests: How much is actually accurate? What is left that perhaps didn’t get tested? And what we call residual risk. How do you explain to a patient and the general population the limits of the capacity of this testing to really be informative?

 

And then on the flip side, a lot of these tests are done in [approved] labs and are probably very accurate. But the FDA has passed some pretty strict regulations that these tests, to be used clinically, need to be repeated and confirmed [in order to determine how to apply a result to] an individual’s care plan.

 

 

JB: One very fundamental aspect is the difference between absolute risk and relative risk. So if you have a test result, and it says you have a 10 times higher risk than that of the general population based on your genetic variation, it depends what the baseline risk is. If the absolute risk for the population is 1 percent, and you have a tenfold higher risk, it means you have a 10 percent absolute risk, which may be worrisome or not, depending on what the condition is and so on.

 

But tenfold sounds really scary! Ten percent, not so much. 

 

The other [aspect is that] the uncertainty in the models that leads to these risk predictions is pretty substantial. The models are based on some outrageously gross simplification of everything, because that’s the best we can do. Conveying the uncertainty in the risk is something that I think really gets lost in the shuffle.

 

MM: Even with BRCA, having one copy of that mutation does not guarantee that someone will ever develop breast cancer in their lifetime or ovarian cancer in their lifetime. It simply is, again, about risk, and having a general-population understanding of risk and where these risks fit in, what they mean, and what are the preventative interventions that could be put in place to reduce their risk.

 

JB: The tests are never going to be deterministic. Genetic determinism—meaning, if you knew your genome sequence, and we understood everything, you could predict your whole future life—is just absolutely false. And the studies that [disprove genetic determinism] go back decades and decades, looking at identical twins, where their genomes, for all intents and purposes, are identical. Some traits are closely shared, but a lot of other traits, including getting different diseases, aren’t.

 

MM: Right. And I think that’s where this greater concept of precision medicine comes into play. Taking the genetics component and recognizing what it does contribute as well as its limitations. And then really starting to understand, and using computer-assisted technology for all the other types of exposures and data and family histories, and then being able to better refine that predictive model. Even so, it’s unclear whether that will ever reach 100 percent, but it will increase in its predictive aspect as technology develops.

 

 

JB: Well, one of the obvious things is more data, and Mylynda can talk more about that. Then, the challenge is still substantial—to do the computer analysis to try to develop better risk models. 

 

A lot of genetic data originally were focused on people who had reason to believe that they have a risk for a particular disease. [For example], if you test people who think they have a [higher] risk for breast cancer because they have a family history of breast cancer, and you identify a gene. So you’ll identify particular variations in that population of patients. [But] then the question is: What’s the prevalence of that same genetic variation in a general population that doesn’t have any risk for breast cancer? And that’s the sort of thing that can now be done with these larger studies.

 

MM: The All of Us study is about putting together that million-person research cohort to gather a vast amount of data, to bring it together and organize it in a way that researchers can finally start looking at all those different layers that contribute to precision medicine. And there were a lot of things that had to line up to finally be able to do a study of this size. We had to have the computing technology, the ability to store [and analyze] all that data. And the cost of these analyses needed to come down. Now, someone can have their genome sequenced for $500 to $1,000. All of that had to line up to create this large project that’s being funded through the National Institutes of Health.

 

Also, one of the large emphases of the All of Us study is to collect that data from a very diverse population. We’re trying to understand the components that lead to strong health and to longevity, as well as the components that lead to disease and illness. We need to look at a vast population that’s very diverse to apply that to unique communities and have something that’s meaningful. 

 

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