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Little J, Wilson B, Carter R, et al. Multigene Panels in Prostate Cancer Risk Assessment. Rockville (MD): Agency for Healthcare Research and Quality (US); 2012 Jul. (Evidence Reports/Technology Assessments, No. 209.)

Discussion

The purpose of this review was to establish the evidence base behind using single nucleotide polymorphism-based panels in prostate cancer risk assessment, which includes risk stratification, screening for undiagnosed disease, and assessing prognosis. The high incidence of prostate cancer, the problems associated with current test methods (particularly prostate-specific antigen [PSA] screening in asymptomatic men), the difficulty of determining prognosis in many affected men, and the lack of clarity on the utility of different therapeutic approaches, mean that other avenues need to be explored with some energy. Even fairly modest improvements in risk classification could translate into large health gains in absolute terms.

It is of crucial conceptual importance to recognize that this review is based on a framework of risk prediction, as distinct from causal inference. In the situation of risk prediction, it is relevant to compare models that include standard risk factors with models that include the same risk factors together with single nucleotide polymorphisms (SNPs). This contrasts with the situation of causal inference in which the SNP status of an individual is “assigned” at birth (and is by definition unconfounded). In a clinically-oriented, test evaluation approach, such concerns are secondary to assessing performance as a predictor of a particular outcome.

The review was structured around the ACCE framework, which emphasizes technical assessment as well as clinical performance, although the intent was always to draw conclusions to guide current clinical practice. This was not achieved because of the dearth of evidence relating to most of the questions of interest.

We identified a number of SNP panels that we considered fulfilled the definition of “close to commercially available”. They were widely variable in their makeup, containing a range of different SNPs, many combined with other risk factor data in predictive algorithms. There was a lack of published data describing the technical protocols and analytical accuracies achieved for the specific SNPs by panel, and of information describing the laboratory protocols used to demonstrate the analytical validity of SNP panels used for clinical service testing. The limited data available suggest that the analytic validity of genotyping of the 5-SNP panel is high in research settings, but questions remain about potential errors which could influence test results in a clinical setting. This concern also applies to the other panels assessed, for which data were only available from single studies.

With regard to the clinical validity of the 5-SNP panel, the studies were predominantly done with participants of European origin, and so the generalizability of these findings to men of other ancestral or ethnic groups is limited. None of the analyses showed any substantial increment in AUC when the SNPs were added to other risk factors in the models evaluated. The AUCs with the inclusion of SNPs ranged between 63 and 73 percent, and would not in themselves be considered useful for individual risk prediction. In general, proposed tests with an AUC of 75 percent or less are unlikely to be clinically useful.228,229 In the single study of the 5-SNP panel that investigated mortality, there was no difference between SNP-based and non-SNP-based models. In the single study of the panel that addressed differences by Gleason score, and aggressive and nonaggressive disease, there was no association with scores derived from the 5-SNP panel.

There were only single studies of the other panels, almost all of which reported on panel development, with no information on internal or external validation. When AUC was reported, it was in the range of 62 to 74 percent, and would not in itself be considered useful for individual risk prediction. Any increase in AUC compared with models not incorporating the SNP combinations was small. In the few studies that investigated the distinction between clinically important and latent/asymptomatic prostate cancer or prognosis, no associations were observed with risk scores derived from the SNP panels.

Thus currently available or documented SNP panels proposed for prediction of risk for prostate cancer have poor discriminative ability. Only one of the panels was tested in data independent of the data in which the panel was developed, and by independent teams of investigators. None of the articles considered calibration, that is, the agreement between the proportion predicted to have the outcome and the proportion observed in the participants in which the panel was tested. Evaluation of calibration is important if predictions based on a test panel are used to inform those tested or health professionals in making decisions.230 Moreover, discrimination and calibration have limited usefulness for clinical decisionmaking. On the one hand, a panel with good discrimination in a research context may not be clinically useful if the threshold for clinical decision making is outside the range of predictions provided by the panel.230 On the other hand, a model with relatively poor discrimination may be clinically useful if there is little evidence or consensus to guide clinical choice between alternative managements; none of the studies use a decision-analytic approach.231

No evidence was found which addressed the important questions of clinical utility. This is unsurprising, given that this field is in the early stages of development.232,233 However, even if the review had identified more compelling evidence to support clinical validity (the ability to accurately predict or detect prostate cancer), this would not in itself provide any direct evidence of the value of SNP-based test panels in reducing morbidity and mortality.

Even if SNP-based panels were determined to be useful in improving prostate cancer screening (i.e., the detection of undiagnosed but clinically important cancer), the overall benefits would also depend on the consistent application of appropriate diagnostic strategies, which in turn would depend at least partly on clinicians' willingness to trust the results of initial screening. The most important limitation with PSA-based screening is its lack of specificity (i.e., high rate of false positives).88,102,103 Improving on this by using SNP-based panels would reduce unnecessary diagnostic investigations and their associated morbidity and costs. However, this will only be successful if patients are willing to trust in negative screen results, given a prevailing culture that seems to promote higher levels of screening as ‘better’ screening practice.234-239 Thus, SNP-based screening panels will need not only to demonstrate increased specificity, but may also need to demonstrate superior levels of sensitivity compared with PSA-based screening in order for patients and their physicians to have confidence in their use.

SNP-based panels may also have a role in stratifying future risk of prostate cancer in men who are currently unaffected. This would permit tailoring of surveillance strategies according to risk category: those at highest risk would presumably be offered more frequent screening and those at lowest risk could avoid unnecessary surveillance. However, this assumes that it would be possible to optimize surveillance strategies and ensure valid screening tests. It might also be assumed that men at higher risk would be more motivated to make positive lifestyle changes, although there is no evidence that this actually occurs from studies based on other forms of risk stratification (family history or genetic testing).240,241 It has also been argued that while the risk of a disease outcome varies between risk strata, the risk of harm from treatment is more uniform.242 Thus, some individuals could benefit more from treatment than others, but all would be at similar risk of harm.

It is also hoped that SNP-based panels may improve the overall tailoring of treatment so that only those men who are at risk of aggressive disease are offered radical surgical interventions. Evaluations of the prognostic accuracy of such panels would be a first step, but definitive evidence from rigorous trial would still be required to determine the overall utility of such an approach. To date, there is limited evidence from randomized controlled trials (RCTs) about the efficacy of radical prostatectomy compared with watchful waiting in men with clinically localized prostate cancer,70,71,81 and syntheses of observational evidence are significantly hampered by serious methodological issues.243 Two RCTs comparing watchful waiting with radical prostatectomy are ongoing, one in the U.K.,82 and one in the United States.84

Taken together, therefore, benefits from improvements in prostate cancer risk prediction, screening, and prognostic stratification will depend to a large extent on clearer evidence that surveillance, diagnostic, and treatment strategies in themselves lead to reductions in morbidity and mortality.

Applicability

At present it would be premature to apply the results of this review to a clinical population.

Cover of Multigene Panels in Prostate Cancer Risk Assessment
Multigene Panels in Prostate Cancer Risk Assessment.
Evidence Reports/Technology Assessments, No. 209.
Little J, Wilson B, Carter R, et al.

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