Background:The effectiveness and cost-effectiveness of genetic testing for hereditary breast and ovarian cancer largely rely on the identification and clinical management of individuals with a pathogenic variant prior to developing cancer. Simulation modelling is commonly utilised to evaluate genetic testing strategies due to its ability to synthesise collections of data and extrapolate over long time periods and large populations. Existing genetic testing simulation models use simplifying assumptions for predictive genetic testing and risk management uptake, which could impact the reliability of their estimates. Our objective was to develop a microsimulation model that accurately reflects current genetic testing and subsequent care in Australia, directly incorporating the dynamic nature of predictive genetic testing within families and adherence to cancer risk management recommendations.Methods:The populatioN gEnEtic testing MOdel (NEEMO) is a population-level microsimulation that incorporates a detailed simulation of individuals linked within five-generation family units. The genetic component includes heritable high- and moderate-risk monogenic gene variants, as well as polygenic risk. Interventions include clinical genetic services, breast screening, and risk-reducing surgery. Model validation is described, and then to illustrate a practical application, NEEMO was used to compare clinical outcomes for four genetic testing scenarios in patients newly diagnosed with breast cancer (BC) and their relatives: (1) no genetic testing, (2) current practice, (3) optimised referral for genetic testing, and (4) genetic testing for all BC.Results:NEEMO accurately estimated genetic testing utilisation according to current practice and associated cancer incidence, pathology, and survival. Predictive testing uptake in first- and second-degree relatives was consistent with known prospective genetic testing data. Optimised genetic referral and expanded testing prevented up to 9.3% of BC and 4.1% of ovarian cancers in relatives of patients with BC. Expanding genetic testing eligibility to all BC patients did not lead to improvement in life-years saved in at-risk relatives compared to optimised referral of patients eligible for testing under current criteria.Conclusions:NEEMO is an adaptable and validated microsimulation model for evaluating genetic testing strategies. It captures the real-world uptake of clinical and predictive genetic testing and recommended cancer risk management, which are important considerations when considering real-world clinical and cost-effectiveness.
背景：遗传性乳腺癌和卵巢癌基因检测的有效性与成本效益，很大程度上取决于在个体患癌前识别出致病性变异并实施临床管理。模拟建模因其能够整合多源数据并推演长期、大规模人群特征，常被用于评估基因检测策略。现有基因检测模拟模型对预测性基因检测及风险管理采纳率采用了简化假设，这可能影响其评估结果的可靠性。我们的目标是开发一个能准确反映澳大利亚当前基因检测及后续照护实践的微观模拟模型，直接纳入家族内预测性基因检测的动态特征以及对癌症风险管理建议的依从性。 方法：人群基因检测模型（NEEMO）是一个包含五代家族关联个体精细模拟的人群层面微观模型。其遗传学模块涵盖可遗传的高风险与中风险单基因变异以及多基因风险。干预措施包括临床遗传服务、乳腺筛查和风险降低手术。本文描述了模型验证过程，并通过实际应用案例展示NEEMO在四种基因检测场景下对新诊断乳腺癌患者及其亲属临床结局的比较：（1）不进行基因检测，（2）现行临床实践，（3）优化基因检测转诊流程，（4）对所有乳腺癌患者进行基因检测。 结果：NEEMO准确估算了现行实践下的基因检测实施率及相关癌症发病率、病理特征和生存率。一级和二级亲属的预测性检测采纳率与已知的前瞻性基因检测数据一致。优化基因转诊和扩大检测范围可预防高达9.3%的乳腺癌患者亲属罹患乳腺癌，以及4.1%罹患卵巢癌。与按现行标准优化符合条件患者的转诊相比，将基因检测资格扩大至所有乳腺癌患者并未显著提升高危亲属的挽救生命年数。 结论：NEEMO是一个经过验证且适应性强的微观模拟模型，适用于评估基因检测策略。该模型捕捉了临床及预测性基因检测在真实世界中的采纳情况，以及推荐癌症风险管理措施的实施效果，这些都是在评估真实世界临床效益与成本效益时需重点考量的因素。
肿瘤(癌症)患者之家