Implications of using whole genome sequencing to test unselected populations for high risk breast cancer genes: a modelling study

Abstract

Background

The decision to test for high risk breast cancer gene mutations is traditionally based on risk scores derived from age, family and personal cancer history. Next generation sequencing technologies such as whole genome sequencing (WGS) make wider population testing more feasible. In the UK's 100,000 Genomes Project, mutations in 16 genes including BRCA1 and BRCA2 are to be actively sought regardless of clinical presentation. The implications of deploying this approach at scale for patients and clinical services are unclear. In this study we aimed to model the effect of using WGS to test an unselected UK population for high risk BRCA1 and BRCA2 gene variants to inform the debate around approaches to secondary genomic findings.

Methods

We modelled the test performance of WGS for identifying pathogenic BRCA1 and BRCA2 mutations in an unselected hypothetical population of 100,000 UK women, using published literature to derive model input parameters. We calculated analytic and clinical validity, described potential health outcomes and highlighted current areas of uncertainty. We also performed a sensitivity analysis in which we re-ran the model 100,000 times to investigate the effect of varying input parameters.

Results

In our models WGS was predicted to identify correctly 93 pathogenic BRCA1 mutations and 151 BRCA2 mutations in 120 and 200 women respectively, resulting in an analytic sensitivity of 75.5-77.5 %. Of 244 women with identified pathogenic mutations, we estimated that 132 (range 121–198) would develop breast cancer, so could potentially be helped by intervention. We also predicted that breast cancer would occur in 41 women (range 36–62) incorrectly identified with no pathogenic mutations and in 12,460 women without BRCA1 or BRCA2 mutations. There was considerable uncertainty about the penetrance of mutations in people without a family history of disease and the appropriate threshold of absolute disease risk for clinical action, which impacts on judgements about the clinical utility of intervention.

Conclusions

This simple model demonstrates the need for robust processes to support the testing for secondary genomic findings in unselected populations that acknowledge levels of uncertainty about the clinical validity and clinical utility of testing positive for a cancer risk gene.

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