Illustration of a woman's profile made up of sequencing fragments.
Improving health and reducing the cost of care using predictive genomics involves leveraging genomic testing technologies such as microarrays.
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Building a Predictive Genomics Ecosystem to Improve Population Health and Lower Costs

Clinical labs are crucial for providing patients access to precision medicine

Kim Caple

Kim Caple is president of genetic analysis at Thermo Fisher Scientific, overseeing innovation in technologies, including microarrays, capillary electrophoresis, and human identification. She has more than 30 years of experience...

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Published:Nov 02, 2022
|3 min read
Photo portrait of Kim Caple
Kim Caple is president of genetic analysis at Thermo Fisher Scientific, overseeing innovation in technologies, including microarrays, capillary electrophoresis, and human identification. She has more than 30 years of experience in the clinical diagnostics and life sciences industry.

Efforts to use predictive genomics to improve health care and reduce costs are progressing around the world. Leading the way are government-run health systems, such as the NHS in the UK and Singapore’s public health service, as well as a handful of large US-based health care systems, including University of Pennsylvania Health System and Kaiser Permanente.

Improving health and reducing the cost of care using predictive genomics involves leveraging genomic testing technologies such as microarrays. Microarrays provide a reliable, cost-effective tool for assessing disease risk and enabling clinical genomics at the point of care. This includes identifying multiple biomarkers associated with complex diseases to develop polygenic risk scores (PRSs) that can guide early diagnosis and personalized therapeutic studies. It also includes pharmacogenomics (PGx) testing to determine how individual patients metabolize certain drugs to ensure they receive the right dose of the right medication to minimize drug reactions and costly trial and error prescribing.

The role of the clinical lab in predictive genomics

Many of the tools and technologies that a clinical laboratory would need to become an essential part of the predictive genomics ecosystem have been developed and improved over the past decade. Ongoing advances in sequencing technologies have made it possible to analyze rare gene variants to predict disease, and the introduction of targeted next-generation sequencing, or NGS, has helped reduce the cost and time it takes to analyze biomarkers of interest. However, microarrays remain the fastest and most cost-effective platform to screen broad populations. 

Programs such as the Qatar Genome Project, Finland’s FinnGen genetic population study, and the University of Pittsburgh Pharmacogenomics Center of Excellence are already demonstrating the value of preemptive genomic testing in routine clinical practice. Often, these projects start with an initial test to determine if they are feasible before scaling to broader applications. For instance, the University of Pittsburgh Medical Center started with testing a single gene to guide the use of antiplatelet medication that is used to prevent clotting after cardiac stenting. Now, that medical center is using a customized, multigene panel to guide individualized prescribing across more than 50 therapies.

How can clinical labs support predictive genomics efforts?

The clinical lab is a crucial element in the predictive genomics ecosystem, providing much more expertise than the processing of patient samples. Labs are expected to provide guidance on the collection of those samples, as well as protocols on how they should be processed. And they are also expected to help translate the data for health care providers and patients.

In general, there are three areas that clinical labs, including smaller operations, should develop to support a predictive genomics ecosystem:

  • The right set of tools, including a solid testing platform and decision support tools, such as software and artificial intelligence-based applications that can translate genetic data into analytical and prognostic information health care providers can use. For instance, a patient’s genetics may indicate that they would respond negatively to a specific opioid, so the right tool would identify that need and suggest alternative medications.
  • A leader, such as a pathologist or medical director who believes in predictive genomics and can effectively communicate the benefit of this type of proactive health care.
  • The ability to convince patients that there is a benefit to predictive genomics.

The changing paradigm

Health care payers have begun to see the benefit of predictive genomics and are supporting PGx testing, but it’s a slow process that requires the collective support of health counselors, the medical community, and clinical labs. These are broad initiatives that require serious commitment by those who see the value in health care cost reduction.

More studies demonstrating the benefits of predictive genomics are needed to support the decisions of payers and health care systems, including clinical laboratories, to invest in building the ecosystem necessary to incorporate predictive genomics into routine care. When predictive genomics becomes the standard of care, it will help drive a shift away from reactive health care to preemptive care that focuses on keeping people healthy.