Few genomic testing technologies have reached routine clinical practice or been incorporated into clinical guidelines to date.(1
) Nonetheless, a multitude of genomic tests are marketed to consumers and physicians, and genome-wide assays are available to consumers for several hundred dollars.(5
) These assays, coupled with the rapid growth of somatic gene expression profiling in oncology, present a significant challenge to clinicians and policy makers seeking to establish clinical practices that maximize benefit for patients while minimizing harm.
The efficient and appropriate translation of genomic discoveries into clinical practice is particularly challenging due to an interrelated combination of factors.(6
) First, there is a notable lack of comparative effectiveness data for genomic applications due to regulatory and reimbursement policies that neither require nor incentivize investment in such studies.(7
) Consequently, while randomized trials have been initiated for select genomic applications such as CYP2C9/VKORC1
testing with warfarin therapy,(10
testing with antidepressant use,(12
) and gene expression profiling in breast cancer treatment,(13
) there are generally few prospective comparative genomic tests evaluations planned or underway.(14
Second, the ease of market access for genomic tests makes the aforementioned lack of evidence more problematic.(15
) For example, when investigators from the National Institute of Mental Health reported an association between two genetic variants and suicidal ideation in patients taking citalopram, (17
) within a week a genomic testing company announced plans to offer testing to “help to reduce a recently announced spike in suicide rates among US youth”.(18
) This situation is partly related to regulatory policy, but is also related to the fact that providing information about genomic susceptibilities does not require specialized medical facilities or training, and involves very little direct risk of immediate harm to patients.
Lastly, there is a lack of consensus on evidence requirements or thresholds for genomic test evaluation . Some stakeholders accept the findings of retrospective analyses and clinical plausibility, while others expect controlled clinical trial data.(20
) For example, in the case of the anticoagulant warfarin, variants of the genes CYP2C9
are clearly associated with lower dose requirements, but no study to date has definitively demonstrated that using this information improves patient outcomes.(22
) Alternatively, warfarin patients concomitantly taking amiodarone also require lower warfarin dosing (due to inhibition of CYP2C9
), and doing so is considered standard of care.(23
) This lack of consistency in evidence requirements, in addition to the other factors outlined above, creates a roadblock on the translational pathway for genomic tests.
The Secretary’s Advisory Committee on Genetics, Health, and Society (SACGHS) recently issued a report (15
) emphasizing the importance of assessing and weighing potential harm against potential benefit, so that patients do not inadvertently forgo real benefit because of small or hypothetical harms. Additionally, regulatory authorities have shown heightened interest in the use of quantitative approaches to assess risk-benefit tradeoffs for pharmaceuticals.(24
) A recent Institute of Medicine (IOM) study advised that FDA “develop and continually improve a systematic approach to risk-benefit analysis.”(32
) FDA is currently evaluating various approaches to incorporate risk-benefit analyses into their assessment processes. Although approaches have been developed to incorporate indirect evidence (e.g., non-comparative data) in a semi-quantitative fashion, and decision-analytic techniques are beginning to be applied in the assessment of genomic tests,(4
) quantitative assessment of risk-benefit tradeoffs, and the uncertainty surrounding them, have not been explicitly included in genomic test evidence recommendations to date.
We believe there is a significant opportunity to use existing decision modeling methods to synthesize genomic, clinical, epidemiological, and patient outcome data to explicitly evaluate risk and benefit trade-offs of genomic tests, and the uncertainty surrounding their utility. The objective of this study was to develop a systematic and comprehensive approach to help clinicians and policy-makers estimate health outcomes of genomic testing in the absence of definitive data. The novel aspect of the risk-benefit framework described in this manuscript is the synthesis of approaches from a variety of fields in order to systematically and quantitatively evaluate the risk-benefit profile of genomic tests – the use of decision modeling, the projection of multiple clinical outcomes (including quality-adjusted life-years, QALYs, as a summary measure of clinical utility), and a recommendation framework that enables utilization of the information generated. These estimates are intended to help guide decisions about clinical test use and coverage, and provide a framework for encouraging practice-based evidence development for tests with plausible net health benefit.