Are there any shortcuts on the translation highway to genomic medicine?

Note to our readers: A modified version of this blog post has been published in JAMA.

Rapid advances in genomics have led to a new era of precision medicine, resulting in a dramatic increase in the number of genomic tests available for research and clinical practice. As of April 18, 2017, the Genetic Testing Registry, maintained and updated by National Institutes of Health, contained information on 49,500 tests conducted at 492 laboratories for 10731 disease conditions involving 16222 genes. These tests cover a wide variety of diseases, rare and common, for different types of applications (e.g., risk assessment, predictive, diagnostic, and prognostic).

For more than two decades, scientists, professional, and advisory groups have discussed the importance of a strong evidentiary foundation for genetic testing, and made a number of recommendations for research, policy and implementation. The Task Force on Genetic testing (1997), the Secretary’s Advisory Committee on Genetic Testing (2000), and the Secretary’s Advisory Committee on Genetics, Health and Society (2008) have published reports. Numerous commentaries by us (see example) and other authors (see a recent example) have done the same.

At stake are answers to a number of scientific questions that are relevant to establishing the analytical performance of these tests (the ability of tests to come up with the correct answer), their clinical validity (showing an association with disease endpoints), and their clinical utility (showing effectiveness in improving health outcomes).

So where does evidence-based genomic medicine stand? In April 2017, as part of a collaborative review led by Dr. Kathryn Philips from the University of California at San Francisco, we summarized findings of systematic reviews that compared the analytic and clinical validity and clinical utility of genomic tests, as compared to other alternative non-genetic tests? Of systematic reviews published in 2010 through 2015, more than half were cancer-related. All reviews identified potentially important clinical applications of genomics, but most had significant methodological weaknesses that precluded any conclusions about clinical utility. All in all, “we found a very limited body of evidence about the effect of using genomic tests on health outcomes.”

In March 2017, the National Academy of Sciences, Engineering and Medicine, released a study report entitled: “An evidence framework for genetic testing.” A special committee, composed of a multidisciplinary group of experts, examined the scientific literature to evaluate the evidence base for different types of genetic tests and “to develop a framework for decision making regarding the use of genetic tests in clinical care.” The committee focused on clinical applications and utility of genetic tests and examined how evidence is generated, evaluated, and synthesized.

The committee reviewed several available methods for assessing genetic tests’ analytic validity, clinical validity, and clinical utility. These included the CDC Office of Public Health Genomics-sponsored ACCE framework and the EGAPP working group. The committee developed a hybrid evaluation system for decision making, with seven components incorporated from existing methods. These recommendations are worthwhile repeating here:

1. Define genetic test scenarios on the basis of the clinical setting, the purpose of the test, the population, the outcomes of interest, and comparable alternative methods.
2. For each genetic test scenario, conduct an initial structured assessment to determine whether the test should be covered, denied, or subject to additional evaluation.
3. Conduct or support evidence-based systematic reviews for genetic test scenarios that require additional evaluation.
4. Conduct or support a structured decision process to produce clinical guidance for a genetic test scenario.
5. Publicly share resulting decisions and justification about evaluated genetic test scenarios, and retain decisions in a repository.
6. Implement timely review and revision of decisions on the basis of new data.
7. Identify evidence gaps to be addressed by research.

Of course, any system of evaluation is based on data collected by research studies. We have previously documented that this type of research constitutes less than 2% of the total published literature in genomics. To be sure, there are current applications in genomics that can save lives and prevent disease (see tier 1 applications in our online evidence table). However, there are no shortcuts to fulfill the ultimate promise of genomics and precision medicine in improving health and preventing disease. In order for genomics to provide health benefits to all of us, we need to acquire necessary data that document analytic validity, clinical validity, and clinical utility of genomic tests for their intended use in practice.

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