Multifactoral breast cancer risk prediction accounting for ethnic and tumor diversity

考虑种族和肿瘤多样性的多因素乳腺癌风险预测

基本信息

批准号：
10416066
负责人：
Nilanjan Chatterjee
金额：
$ 32.54万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-15 至 2023-04-07
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10416066
关键词：
Accounting Africa Asia Assessment tool Biological Markers Breast Cancer Detection Breast Cancer Model Breast Cancer Risk Assessment Tool Breast Cancer Risk Factor Calibration Cancer Etiology Categories Cessation of life Characteristics Clinical Clinical Trials Counseling Data Data Pooling Data Science Estrogen Receptor Status Estrogen receptor negative Estrogen receptor positive Ethics Ethnic Origin Ethnic group Europe Face Family Future Guidelines Health Surveys Hormonal Incidence Individual Integrated Health Care Systems Internet Intervention Latin America Life Style Malignant Neoplasms Mammographic Density Mammographic screening Meta-Analysis Methodology Methods Modality Modeling National Cancer Institute Operative Surgical Procedures Output Patients Pharmaceutical Preparations Physicians Population Preventive therapy Prothrombin Race Recording of previous events Registries Reproductive History Research Risk Risk Assessment Risk Factors Running Sampling Statistical Models System Translations Universities Update Validation Woman base cancer subtypes clinical application clinical practice cohort flexibility genetic testing genome wide association study health care delivery health care settings hormone receptor-positive hormone therapy lifestyle intervention malignant breast neoplasm mammography registry model building multi-ethnic neoplasm registry novel overtreatment polygenic risk score population based predictive modeling prevent prophylactic prospective risk prediction risk prediction model risk stratification screening screening program tool tumor tumor heterogeneity

项目摘要

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.

抽象的乳腺癌风险评估工具被广泛用于临床实践中，以指导有关筛查的决策时间和方式，生活方式的干预措施，基因检测，预防疗法和降低风险手术。尽管在实践中使用了许多工具，但它们面临着各种挑战，包括：（i）谦虚由于缺乏统一模型，该模型包含了一组全面的风险因素；（ii）无法产生亚型特异性风险，尤其是考虑到乳腺癌的积极亚型和/或预防性内分泌疗法仅对激素受体阳性肿瘤有效；（iii）缺乏数据为不同种族的人群建立模型；以及（iv）模型的验证很少，尤其是在医疗保健领域在实践中可以广泛传播模型的设置。在此提案中，我们将同化和分析来自参与NCI队列联盟的研究的大量妇女样本的数据开发一种全面的工具，可以在主要种族中预测整体和子类型的乳腺癌风险在美国。我们进一步建议在不同的临床环境中前景验证该模型，包括风险分层的筛查试验。在AIM 1中，我们将开发一个综合模型，以预测来自多个种族的女性的整体乳腺癌，结合了有关家族史的信息；多基因风险评分（PRS）；人体测量，生命风格和生殖因素；激素生物标志物；和乳房X线摄影密度。在AIM 2中，我们将针对特定的乳腺癌亚型量身定制这些风险模型，尤其是雌激素受体阴性和阳性癌症。在AIM 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，乳房X线摄影注册和持续基于风险的模型美国乳房X线诊断试验。由此产生的模型可以在不同的临床环境中使用指导预防治疗或风险分层筛查计划，增加乳腺癌死亡的数量在最小化过度诊断和过度治疗的同时阻止了。