Multifactoral breast cancer risk prediction accounting for ethnic and tumor diversity

摘要 乳腺癌风险评估工具在临床实践中被广泛用于指导有关筛查的决策 时机和方式、生活方式干预、基因检测、预防性治疗和降低风险的手术。 虽然在实践中使用了一些工具，但它们面临着各种挑战，包括： 由于缺乏一个包含一整套风险因素的统一模式，歧视能力不足; ㈡ 不能产生亚型特异性风险，特别是考虑到乳腺癌的侵袭性亚型和/或 仅对激素受体阳性肿瘤有效的预防性内分泌治疗;（iii）缺乏数据， 为不同的种族群体建立模型;以及（iv）模型的验证不足，特别是在医疗保健方面 在实践中可以广泛传播模型的环境。在这个建议中，我们将吸收和分析 来自参加NCI队列联盟研究的大量不同女性样本的数据， 开发一种全面的工具，预测乳腺癌的风险，总体和亚型，在主要种族 在美国的团体。我们进一步建议在不同的临床环境中前瞻性地验证该模型，包括 风险分层筛选试验在目标1中，我们将开发一个综合模型，用于预测 多种族女性的总体乳腺癌，纳入家族史信息;多基因 风险评分（PRS）;人体测量、生活方式和生殖因素;激素生物标志物;以及 乳房摄影密度在目标2中，我们将针对特定的乳腺癌亚型定制这些风险模型，特别是 雌激素受体阴性和阳性癌症。在目标3中，我们将评估这些风险预测的有效性 综合卫生保健系统中的模型、乳房X光检查登记处和正在进行的基于风险的 在美国进行的乳房X光筛查试验。由此产生的模型可用于不同的临床环境， 指导预防性治疗或风险分层筛查计划，增加乳腺癌死亡人数 避免过度诊断和过度治疗。

Abstract Breast cancer risk assessment tools are widely used in clinical practice to guide decisions regarding screening timing and modality, life-style interventions, genetic testing, preventive therapy, and risk-reducing surgery. Although a number of tools are used in practice, they face various challenges including: (i) modest discriminatory ability due to lack of a unified model that incorporates a comprehensive set of risk-factors; (ii) inability to produce sub-type specific risk, especially considering aggressive subtypes of breast cancer and/or prophylactic endocrine therapy that is effective only for hormone receptor positive tumors; (iii) lack of data to build models for different ethnic populations; and, (iv) scant validation of models, especially in healthcare settings where models can be widely disseminated in practice. In this proposal, we will assimilate and analyze data on a large and diverse sample of women from studies participating in the NCI Cohort Consortium to develop a comprehensive tool that will predict breast cancer risk, overall and by sub-types, across major ethnic groups in the US. We further propose to prospectively validate the model in different clinical settings, including a risk-stratified screening trial. In Aim 1 we will develop a comprehensive model for predicting absolute risk of overall breast cancer for women from multiple ethnicities, incorporating information on family history; polygenic risk-scores (PRS); anthropometric, life-style and reproductive factors; hormonal biomarkers; and mammographic density. In Aim 2 we will tailor these risk models to specific breast cancer subtypes, notably estrogen receptor negative and positive cancers. In Aim 3 we will evaluate the validity of these risk prediction models in integrated health care systems, mammography registries, and an ongoing risk-based mammographic screening trial in the US. The resulting models could be used in diverse clinical settings to guide preventive therapy or risk-stratified screening programs, increasing the number of breast cancer deaths prevented while minimizing overdiagnosis and overtreatment.