Clinical Innovators | Breaking Ground in the Genetics of Cardiovascular Disease: An Interview with Rahul C. Deo, MD, PhD
Rahul Deo, MD, PhD, was born and raised in Canada and earned his medical degree from Cornell University Medical College and PhD in molecular biophysics from the Rockefeller University. He completed his internship and residency in internal medicine at Brigham and Women's Hospital and Cardiology Fellowship at the Massachusetts General Hospital, moving on to conduct postdoctoral research in human genetics and computational biology at Harvard Medical School. Dr. Deo's clinical and research interests involve bringing large-scale genetic and genomic data, and emerging computational approaches towards the goal of personalized diagnosis and therapy in cardiovascular medicine. He is currently an assistant professor of medicine at UCSF School of Medicine. In 2013 Dr. Deo was awarded the National Istituties of Health (NIH) Director's New Innovator Award for his research in the genetics of cardiovascular disease.
You were recently awarded the prestigious NIH Director's New Innovator Award for your research in "Resolving Incomplete Penetrance in the Cardiomyopathies and Channelopathies." What are some of your goals for this project?
One of the most challenging aspects about medicine is our lack of ability to provide individualized predictions on how severe a patient's disease course is likely to be. Although it was hoped that genetics would be able to help overcome this challenge, in most circumstances, this has not been the case. Surprisingly, even when we consider patients with Mendelian forms of cardiac disease, where the causal genetic variants have an incredibly powerful impact on the likelihood of disease development, we still are quite poor at making predictions of disease severity.
The primary goal of this research proposal is to improve such predictions in the inherited cardiomyopathies and channelopathies by integrating knowledge about how other genetic variants might modulate the effect of the primary mutation. My laboratory is combining genomics and machine-learning approaches to predict some modulation with the ultimate aim of making prognoses more accurate and personalized.
What should all clinicians know about the genetics of cardiomyopathies?
I think there are three main messages regarding inherited cardiomyopathy patients. The first is that in patients who have a cardiomyopathy but lack obvious risk factors sufficient to explain the condition, digging deeper for a family history is important but takes some effort, as very few of us have sufficient knowledge of the health of our relatives. The second is that in cases where there appears to be an inherited form of cardiomyopathy or channelopathy, careful clinical screening of relatives is easy to do, and might very well pick up early asymptomatic early manifestations of a deadly disease. The third is that in many cases genetic testing can help guide the approach to clinical screening, but there are nuances in how we interpret the genetic data. If possible, referral to a center that has experience with such inherited conditions should be attempted.
Some of your research in the past has highlighted differences in cardiovascular risk factors among races. Have you found a genetic basis for some of these differences?
From the past seven years of genome-wide association studies, we now have a lot of information on genetic variants that contribute to disease risk. What we're finding is that some of these risk variants are only found at appreciable levels in certain races. One striking example is a risk variant in a gene, SLC16A11, that is found commonly in Mexican, Latin, and Native American samples, but is very rare in European and African individuals. So extending genetic association studies to be more representative of worldwide populations appears to be paying off in terms of novel discoveries.
A harder question, related to the idea of personalized care, is whether certain genetic variants (or therapies) would only have substantial impact on disease risk in certain races because of differences in physiology. It isn't difficult to imagine that this could be the case, but will likely be very challenging to determine, given the sample sizes needed to make such conclusions.
What scientific breakthroughs in genomic research must we see to make progress towards personalized cardiovascular medicine?
I think we need to find a way to get a lot more training data to build models that will allow us to personalize disease risk. What I mean by this is we need to find a way to inexpensively collect a lot of genomic information and other types of phenotypic data on large numbers of individuals if we ever want to make truly novel discoveries on how diseases should be classified, or how to make accurate predictions. The computer processing speeds and statistical learning algorithms are more than adequate. We just need much, much more informative, multidimensional data on thousands to tens of thousands of patients to learn from for every disease we're interested in. This will require an overhaul in the way we conduct and pay for clinical research, including how we enroll participants, and how we collaborate effectively across institutions both nationally and internationally.
What are some of the challenges to using genetic research to guide screening and diagnostic approaches in cardiovascular disease?
One of the challenges is certainty concerning whether a mutation you discover is really responsible for causing a disease in a given family. If there is some other factor contributing in a substantial (necessary and sufficient) way, you can provide false reassurance, which is worse than not testing at all. The second has to do with this problem of whether knowledge of a mutation can give truly valuable information on classifying a disease and predicting therapeutic response and outcome. This is not a problem unique to geneticssince all of clinical medicine suffers from this failurebut somehow genetics is expected to serve as the ultimate crystal ball, when in reality it will only be one of many predictive features in the next generation of predictive models.
How will the use of large-scale genetic data and innovative computational approaches lead to improvements in clinical care in the next one-two decades?
One of the most daunting challenges related to diagnosis and treatment in medicine is the ability to get the right therapies to the right individual. It might be wishful thinking on my part, but this seems to me a problem in how we can best classify individuals into more homogeneous categories regarding likelihood of response to a particular therapy. Such an improved classification has implications at the level of successful clinical trials, and also in how we practice with the existing therapeutic tools.
My suspicion is that collecting large amounts of "unbiased data"genetic and genomic, as well as more conventional, rich phenotypic datawill be essential to achieve this classification system. By looking at thousands if not tens of thousands of individuals, we may make significant strides towards more accurate and personalized medicine.
Interview conducted by Katlyn Nemani, MD. Dr. Nemani is a physician at NYU.
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