OBJECTIVES

: Around 20-25% of ovarian cancers and 5-10% of breast cancers are due to an inherited predisposition. The primary clinical and economic benefit of hereditary breast and ovarian cancer (HBOC) testing is from preventing cancers in unaffected relatives who present for predictive/cascade testing for a known pathogenic variant in the family. Population-based genetic testing (GT) as an alternative to current practice has been gaining momentum, as it enables identification of high-risk women before they develop cancer. Existing evaluations of population-based GT mostly rely on heavily simplified comparators, and are based on selected point estimates for probabilities of identifying relatives and cascade testing uptake. This likely overestimates the benefit of population-based testing.

METHODS

We developed a microsimulation model for evaluating HBOC testing that includes individuals linked within family structures (thus a more sophisticated cascade testing component). Family structures are modelled using an analysis of 11,143 pedigrees from an Australian genetics clinic. The genetic component includes Mendelian inheritance of pathogenic variants for eight high- and moderate-risk genes. HBOC risk associated with single nucleotide polymorphisms was incorporated through polygenic risk scores. Life histories for individuals are generated, considering their gender, age, and genetic risk.

RESULTS

: This microsimulation model, called NEEMO, more accurately reflects current practice using a simulated family-based approach. It has been validated for gene-specific cancer incidence, mortality, pathology, and uptake of cancer risk management. It has also been validated for genetic testing referral rates, and uptake of predictive GT in relatives.

CONCLUSIONS

: Modelling GT presents challenges due to the nature of inheritance and interaction between individuals. Clinical genetics is complex and highly specialised, with a commonly cited barrier to GT being a lack of awareness or understanding in non-genetics specialists around eligibility and management. The NEEMO model will be useful for evaluating changes in risk assessment, target GT populations, and modifying cancer prevention strategies.