With valid arguments rebutting the raised technical concerns, it may be time to consider how these discoveries may be further evaluated and then brought from the bench to the patient. For this we again use prostate cancer to highlight one possible translational research (TR) framework that will allow us to capitalize upon the exciting results from multi-SNP tests.
Contemporary notions of TR have extended this definition to include translation into the community, while also defining a series of intermediate phases that comprise TR [34
]. Genomic TR is essentially a progression through several stages; T1) confirms association and establishes clinical validity; T2) clinical utility; T3) practice-based implementation research; and finally T4) population/community wide outcomes assessment.
After initial discovery of candidate associations, T1 research verifies associations and assesses analytical and clinical validity. A fundamental goal of T1 is to minimize the possibility of spurious associations due to both statistical and clinical causes. Statistical causes are addressed by utilizing independent populations with large numbers of samples for confirmation analyses, reducing the possibility of false positives due to chance. Clinical sources of spurious association are difficult to address in case-control studies, as described above for PSA detection bias, and may be addressed by prospective studies in which potential bias is minimized. T1 research aims to answer questions such as, “are these SNPs truly associated with PCa, or rather with PSA levels that lead to the detection of most PCa cases”? By answering this type of question, we can establish the validity of associations.
T2 research addresses whether the valid associations from T1 have clinical utility. The necessary approaches in T2 include prospective studies, either observational or interventional (clinical trials), and comparative effectiveness research (CER). Unfortunately, very few of the initially promising associations are tested in prospective studies that can pave the way through the T2 phase [39
], in part because prospective studies are costly and require many years. One efficient approach is to utilize previously completed prospective studies, by examining predictors at baseline (e.g. clinical parameters and genotypes) in relation to outcome data. This approach is particularly appropriate for genetic studies in which genetic markers are practically blinded to patients and observers, reducing potential bias. CER is defined by the Institute of Medicine as, “The generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care” [41
]. By comparing genomics tests to existing clinical markers, CER gives clinical context to statistics. If T2 research is successful, then we have answered questions such as “how does the PPV of a combined SNP test for PCa risk compare to the PPV of family history or PSA”? Clear answers to these types of questions can inform the public discourse and the subsequent development of professional guidelines and public policy.
T3 research aims to maximize the utility that is established by T2 research, by examining the practical issues impacting clinical usage. These studies could examine physician motivation to offer tests, patient uptake of tests, patient interpretation of results, physician recommendations based on tests, and the downstream decisions of those receiving (or not) test results. T3 research may also explore the differential impact when testing is applied in various clinical settings (e.g. private practice versus specialty or academic centers) or implementation scenarios (e.g. population screening versus targeting to high risk families). Because T3 research is so wide-ranging, it may be necessary to direct research toward the most pressing issues in clinical implementation. Again, CER can be used to weigh a genomic test against existing methods. T3 research can ask, “do genomic test results for PCa risk alter perception and accordingly patterns of PSA screening or willingness to opt for chemoprevention”? Answers to these types of questions are intended to help capitalize on the potential positive health impacts of tests, and may further guide the development of public policy.
T4 research focuses on health outcomes amongst a wide community or population, following the introduction of a new intervention. Going beyond the well defined groups of patients typically studied in T3 research, T4 examines real world impact. For example, when new genomic tests are introduced, it is possible to monitor disease incidence using population based registries; if a decrease is observed, then this may be attributable to the test, particularly if evidence from T1 through T3 would predict the observed effect. Formal cost effectiveness analysis is also an important component of T4, utilizing real world data on cost, test usage, and outcomes. Questions addressed in T4 could include “following the widespread introduction of a new genomic risk assessment test, how many cases of PCa are prevented in a population, and at what financial cost”? By answering these questions, we can monitor whether the test is having the expected effects.
Currently, NCI-funded projects are heavily skewed toward T1, the early discovery phase of TR [40
]. If we are to reap the full benefit of the heavy investment in discovery approaches such as GWAS, then it is imperative that scientists and clinicians commit to carrying out T2, T3, and T4 research. Fortunately the NIH intends to promote translational research that is aimed at areas in which the FDA likewise intends to step up the regulation of tests, and this will promote research across the TR continuum [42