Precision medicine is a form of medicine that uses information about a person’s own genes or proteins to prevent, diagnose, or treat disease.1 When we think of precision medicine, we often focus on cancer and the recent efforts to specifically target treatment based on the genetic composition of a tumor (somatic) or the person’s germline. While it is early days, the health gains of this approach are quite significant with better outcomes shown in multiple studies2 and even data showing lower overall health costs.3 However, the point of precision medicine is not only to optimize treatment for cancer, but also to target health interventions generally for all conditions. In fact, we see the application of precision medicine to high volume diseases such as diabetes and heart disease and even lifestyle interventions such as diet and exercise.
Nowhere is this more challenging than for patients suffering from a rare disease. Ironically it is common to have a rare disease. More than 25 million Americans and more than 400 million people worldwide suffer from one of over 7000 rare conditions, defined as those conditions having an incidence of 1 in 200,000 or less. Precision medicine has focused on big data approaches to studying more common conditions, thereby leaving out those with rare conditions. Lacking a diagnosis, most of these patients and their families are on a “diagnostic odyssey.” These patients typically spend more than five years seeking accurate diagnosis and might see up to eight doctors, often receiving many misdiagnoses and differing opinions on their journey. Along the way, they may be exposed to harmful treatments and invasive testing. It is clear that comprehensive genetic testing gives the best possibility of getting to the exact diagnosis efficiently. Still, historically, genetic data have been available to a minority of patients: only those referred to a clinical geneticist for testing.
It was terrific to see this paper by our long time collaborator Arindam Bhattacharjeeon the use of NGS as a second-tier test for Pompe Disease (PD). This is part of an important diagnostic trend of earlier (even to the point of first-line for selected infants) use of NGS as a diagnostic tool that can dramatically improve the newborn’s projected health outcome. Pompe Disease is a perfect example of how this can work.
PD is one of several glycogen storage diseases with variable timing of onset and rates of progressions. According to NORD, “Pompe disease is a rare multisystem disorder caused by pathogenic variations in the GAA gene containing the information for production and function of a protein called acid alpha-glucosidase (GAA). Because of the shortage of this protein (an enzyme), a complex sugar named ‘glycogen’ cannot be degraded to a simple sugar like glucose. This causes the glycogen to accumulate in all kinds of tissues, but primarily in skeletal muscle, smooth muscle, and cardiac muscle, where it causes damage to tissue structure and function. Pompe disease is inherited as an autosomal recessive genetic trait.” Early diagnosis and initiation of treatment are of paramount importance at nolater than two weeks of age to minimize muscle damage and avoid significant negative impact on the quality of life.
Next-Generation Sequencing (NGS) testing is experiencing tremendous growth driven at a high level by the promise of precision medicine and the life-changing power of applications in preventive genetic screening, somatic testing, and rare disease diagnosis. In all of these use cases, we see important clinical advances. Preventive genetic screening for risk factors such as BRCA mutations allows people to take preventative measures that save lives every day. Somatic mutation analysis allows for highly targeted therapies, and rare disease diagnosis is improving outcomes for babies in the NICU and providing hope for the 400 million people worldwide suffering from a rare disease.
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The power and cost-effectiveness of AI are calling into question many of our assumptions about healthcare. The most important dichotomy proving to be false is that providing the latest and most thorough diagnostic technology to optimize clinical outcomes is more expensive. When we use AI to more comprehensively analyze cases we benefit from Moore’s law rapidly and continuously reducing costs. By contrast, hospital-based care, especially when in an intensive setting such as the NICU is continuously increasing in cost. It is not surprising that when more extensive testing produces clinically actionable results that actually decrease hospital days we can accomplish the holy grail — better care and less expensive simultaneously…