Recent technological advances make large-scale genetic testing possible. Current guidelines recommend testing in certain clinically affected individuals or those with strong family history (1). The successful use for high-risk groups has led many to consider extending genetic testing to the general population. In this issue, Manchanda and colleagues present a cost-effectiveness analysis of population-based testing for high-penetrance mutations associated with an increased risk of breast and ovarian cancer (2). They conclude that population-based testing of women age 30 years and older for a limited set of genes is more cost-effective for preventing death than genetic testing in women with a personal or a family history. This conclusion differs from a previous cost-effectiveness analysis that did not find population-based BRCA1/2 testing to be cost-effective (3). The previous results were based on a decision-analysis model developed for an Ashkenazi Jewish population that was applied to the general population (4), while the new model was developed using general population estimates (2). We discuss opportunities and challenges of population-based genetic testing and emphasize where additional evidence is needed. Several parameters are important when considering population-based genetic testing (Figure 1): first, how many cancers are related to the mutations under consideration for testing? This indicates the maximum number of cases that could possibly be prevented or detected early through genetic testing. It is estimated that 5% to 10% of all breast cancers (5) and up to 15% of all ovarian cancers (6) are related to germline mutations in BRCA1 and BRCA2 mutations with lower contributions of the intermediate risk genes RAD51C, RAD51D, BRIP1, and PALB2 considered in the current panel. Figure 1. View largeDownload slide Population-wide genetic testing vs testing of high-risk individuals. The figure highlights differences between population genetic testing and genetic testing of women with a family history of a specific cancer using a hypothetical example. In this example, the population is divided into four groups, described here in order of the population prevalence. Most women do not have a mutation and do not develop cancer in their lifetime (gray). The second group includes women who do not have a mutation, but who will develop cancer in their lifetime (yellow). The third group includes women who do have a mutation but do not develop cancer (blue). The fourth group includes women who have a mutation and who develop cancer during their lifetime (red). The subset of women with a family history of cancer is indicated by the dashed box. In this hypothetical example, testing based on family history would be restricted to one-fifth of the population. Population-wide genetic testing would detect two additional cancers, but six additional women who tested negative for mutations would develop cancer, and five additional women would undergo unnecessary interventions compared with genetic testing of women with a family history. The penetrance estimate for the subgroup of women with a family history would be 71%, compared with 50% for the complete population. Accurate estimates of these proportions are important when considering population-wide genetic testing. Figure 1. View largeDownload slide Population-wide genetic testing vs testing of high-risk individuals. The figure highlights differences between population genetic testing and genetic testing of women with a family history of a specific cancer using a hypothetical example. In this example, the population is divided into four groups, described here in order of the population prevalence. Most women do not have a mutation and do not develop cancer in their lifetime (gray). The second group includes women who do not have a mutation, but who will develop cancer in their lifetime (yellow). The third group includes women who do have a mutation but do not develop cancer (blue). The fourth group includes women who have a mutation and who develop cancer during their lifetime (red). The subset of women with a family history of cancer is indicated by the dashed box. In this hypothetical example, testing based on family history would be restricted to one-fifth of the population. Population-wide genetic testing would detect two additional cancers, but six additional women who tested negative for mutations would develop cancer, and five additional women would undergo unnecessary interventions compared with genetic testing of women with a family history. The penetrance estimate for the subgroup of women with a family history would be 71%, compared with 50% for the complete population. Accurate estimates of these proportions are important when considering population-wide genetic testing. Second is the population frequency of the mutations that might be considered for testing. The authors discuss their prior modeling work with testing in Ashkenazim as evidence that broad population-based testing might be effective (2,3). While the frequency of BRCA1 and BRCA2 carrier status is well understood in Ashkenazim, the estimates of gene mutation frequency in the general population in their model are mostly based on small studies for the proposed genes (7,8). Third, what is the risk of cancer (or penetrance) in a mutation carrier? The penetrance drives clinical decision-making and has important implications for benefits and harms. The higher the risk, the more aggressive clinical interventions (such as prophylactic surgery) may be justified, but there are no absolute risk thresholds supporting specific clinical actions. In the proposed panel, BRCA1 and BRCA2 are high-penetrance gene mutations associated with both breast and ovarian cancer. The cost-effectiveness model assumes 64% breast cancer penetrance and 20% ovarian cancer penetrance for both genes combined, but prevalence and penetrance vary strongly by gene, individual mutation, and the tested population (9). Current penetrance estimates for most gene mutations are based on genetic testing of subjects with a family history or a predicted high risk for mutations. It has been argued that many mutations occur in individuals without family history that would be missed by the classic “disease first” approach. We have been learning, however, that penetrance estimates from family studies cannot necessarily be applied to the general population. Recent studies of population-based germline sequencing data have reported much higher prevalence of DICER1 and TP53 mutations than expected from family studies (10,11). Importantly, a lot of these mutation carriers had no disease phenotype, implying that current penetrance estimates may overestimate risk in the general population. Overall, criteria used for inclusion of genes in the panel were based on small studies, and the estimates may change when more data become available, possibly affecting the outcomes of the cost-effectiveness analysis. New population-based resources will allow pursuit of a “gene-first” approach that can provide more reliable penetrance estimates for these mutations (12). Genetic testing to identify women at very high risk of breast or ovarian cancer is relevant only when it leads to clinical actions that improve the morbidity and mortality of these cancers. There are fortunately many options available for prevention and screening. Prophylactic bilateral mastectomies lower the risk of breast cancer. The hazard rate for breast cancer after risk-reducing salpingo-oophorectomies (RRSO) alone is stated by Manchanda et al. as 0.49 (95% confidence interval = 0.37 to 0.65) (2). This is a conservative estimate that varies depending on age and hormone use (13). Prophylactic RRSO is highly effective in lowering the risk of ovarian cancer, and in some studies lowering breast cancer risk as well, with up to a 77% decrease in all-cause mortality by age 70 years with RRSO (14). Screening for breast cancer in the general population relies on mammography, while high-risk women may be tested with magnetic resonance imaging. Ovarian cancer screening is not available. On the other hand, identifying women with mutations that do not cause cancer could lead to unnecessary anxiety, diagnostic procedures, surgeries with potential major complications, and cost. Apart from surgery complications, oophorectomy can have other consequences like cardiovascular diseases and is associated with increased mortality (15). Importantly, the lower the penetrance for a mutation included in a panel, the more subjects in the general population may suffer from these harms (16). Clinical decision models and cost-effectiveness models are complex and need to account for numerous factors related to benefits, harms, and costs of health interventions. Unavoidably, there are areas of uncertainty that need to be accounted for in the models and that need to be communicated to users of cost-effectiveness models (15). Manchanda et al. (2) employed established approaches for evaluating the robustness of their model by conducting multiple one-way sensitivity analyses and a probabilistic sensitivity analysis that allows one to vary different parameters jointly and evaluate the impact on the results. The results were robust with regard to assumptions on a variety of factors, including mutation prevalence. However, the penetrance of the mutations for breast and ovarian cancer was varied only over a limited range. Based on the experience from TP53 and DICER1 population testing, the penetrance of mutations found in the general population could be much lower, which may impact the conclusions. Thus, the prevalence and penetrance of the targeted mutations need to be confirmed in larger population-based studies before implementation of population-based genetic testing. Manchanda et al. (2) only briefly mention variants of unknown significance (VUS). Handling these could be quite complex and may require registries to follow women with these variants to assess outcomes and, for counseling and tracking, if a variant is later identified as clinically significant. For example, National Institute for Health and Care Excellence (NICE) requires information on variants of uncertain significance to be recorded along with known pathogenic mutations in a searchable electronic database (16). The analysis by Manchanda et al. is at an exciting confluence of genetics, risk prediction, and decision modeling accounting for health care cost (2). The findings are provocative and will further advance the discussion of population-wide genetic testing. It is crucial to scrutinize all the parameters going into decision models and critically evaluate areas of greatest uncertainty. If the underlying assumptions are confirmed in larger studies, particularly the mutation prevalence, penetrance, and cost that account for management of VUS and genetic counseling, the cost-effectiveness model shows that lives can be saved with population-wide genetic testing. Yet, most women in the general population would not benefit and could be falsely reassured by negative testing, as more than 98% of breast cancers and more than 95% of ovarian cancers would not be prevented according to the model (2). Notes The authors have no conflicts of interest to disclose. We wish to thank Dr. David Ransohoff for his input on this editorial. References 1 US Preventive Services Task Force . USPSTF final recommendation statement BRCA-related cancer: Risk assessment, genetic counseling, and genetic testing. https://www.uspreventiveservicestaskforce.org/Page/Document/RecommendationStatementFinal/brca-related-cancer-risk-assessment-genetic-counseling-and-genetic-testing . Accessed November 16, 2017. 2 Manchanda RSP , Gordeev VS , Antoniou AC et al. , . Cost-effectiveness of population-based BRCA1, BRCA2, RAD51C, RAD51D, BRIP1, PALB2 mutation-testing in unselected general-population women . J Natl Cancer Inst. 2018 ; 110 7 : 714 – 725 . 3 Long EF , Ganz PA. Cost-effectiveness of universal BRCA1/2 screening: Evidence-based decision making . JAMA Oncol. 2015 ; 1 9 : 1217 – 1218 . http://dx.doi.org/10.1001/jamaoncol.2015.2340 4 Manchanda R , Legood R , Burnell M et al. , . Cost-effectiveness of population screening for BRCA mutations in Ashkenazi jewish women compared with family history-based testing . J Natl Cancer Inst. 2015 ; 107 1 : 380 . 5 Campeau PM , Foulkes WD , Tischkowitz MD. Hereditary breast cancer: New genetic developments, new therapeutic avenues . Hum Genet. 2008 ; 124 1 : 31 – 42 . http://dx.doi.org/10.1007/s00439-008-0529-1 6 Pal T , Permuth-Wey J , Betts JA et al. , . BRCA1 and BRCA2 mutations account for a large proportion of ovarian carcinoma cases . Cancer. 2005 ; 104 12 : 2807 – 2816 . http://dx.doi.org/10.1002/cncr.21536 7 Antoniou A , Pharoah PD , Narod S et al. , . Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case Series unselected for family history: A combined analysis of 22 studies . Am J Hum Genet. 2003 ; 72 5 : 1117 – 1130 . http://dx.doi.org/10.1086/375033 8 Chen S , Iversen ES , Friebel T et al. , . Characterization of BRCA1 and BRCA2 mutations in a large United States sample . J Clin Oncol. 2006 ; 24 6 : 863 – 871 . http://dx.doi.org/10.1200/JCO.2005.03.6772 9 BRCA Exchange http://brcaexchange.org/factsheet. Accessed November 16, 2017. 10 de Andrade KC , Mirabello L , Stewart DR et al. , . Higher-than-expected population prevalence of potentially pathogenic germline TP53 variants in individuals unselected for cancer history . Hum Mutat. 2017 ; 38 12 : 1723 – 1730 . http://dx.doi.org/10.1002/humu.23320 11 Kim J , Field A , Schultz KAP , Hill DA , Stewart DR. The prevalence of DICER1 pathogenic variation in population databases . Int J Cancer. 2017 ; 141 10 : 2030 – 2036 . http://dx.doi.org/10.1002/ijc.30907 12 Dewey FE , Murray MF , Overton JD et al. , . Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study . Science. 2016 ; 354 6319 : ii . 13 Rebbeck TR , Kauff ND , Domchek SM. Meta-analysis of risk reduction estimates associated with risk-reducing salpingo-oophorectomy in BRCA1 or BRCA2 mutation carriers . J Natl Cancer Inst. 2009 ; 101 2 : 80 – 87 . http://dx.doi.org/10.1093/jnci/djn442 14 Finch AP , Lubinski J , Moller P et al. , . Impact of oophorectomy on cancer incidence and mortality in women with a BRCA1 or BRCA2 mutation . J Clin Oncol. 2014 ; 32 15 : 1547 – 1553 . http://dx.doi.org/10.1200/JCO.2013.53.2820 15 Parker WH , Feskanich D , Broder MS et al. , . Long-term mortality associated with oophorectomy compared with ovarian conservation in the nurses' health study . Obstet Gynecol. 2013 ; 121 4 : 709 – 716 . http://dx.doi.org/10.1097/AOG.0b013e3182864350 16 Wentzensen N , Wacholder S. From differences in means between cases and controls to risk stratification: A business plan for biomarker development . Cancer Discov. 2013 ; 3 2 : 148 – 157 . http://dx.doi.org/10.1158/2159-8290.CD-12-0196 17 Wentzensen N , Clarke MA. From clinical epidemiology to practice recommendations: Knowledge gaps and uncertainty in the management of anal precancers . Cancer . In press. 18 National Institute for Health and Care Excellence . https://www.nice.org.uk/guidance/cg1642017. Accessed November 16, 2017. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
JNCI: Journal of the National Cancer Institute – Oxford University Press
Published: Feb 1, 2018
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