Comparative Modeling: Informing Breast Cancer Control Practice & Policy

描述（由申请人提供）：乳腺癌仍然是美国女性癌症发病率和死亡率的第二大原因。新的发现导致了广泛接受的观点，即乳腺癌是一种异质性疾病，具有分子上可区分的形态亚型。这种认识正在推动乳腺癌预防、早期发现和临床管理新模式的发展。然而，关于这些新的癌症控制方法的群体效应的数据非常有限。人口模型是一种独特的比较有效性范式，通过将实验室和临床试验的进展转化为了解其对美国乳腺癌死亡率的净影响来填补这一空白。 CISNET乳腺工作组在过去九年中开展合作，应用独立的人口模型来评估癌症控制做法，并利用结果为临床和公共卫生指南提供信息。该提案充分利用了对这些模式的投资，并为这一高生产力群体提供了连续性和凝聚力。模特团队包括Dana Farber（D）。伊拉斯谟MC（E），乔治敦-爱因斯坦（G），MD安德森（M），斯坦福大学（S）和威斯康星-哈佛大学（W）。对于这个应用程序，我们将通过对具有不同风险因素（例如，乳腺密度，HRT）用于乳腺癌的特定分子亚型（基于ER和HER 2）的发展。 我们的具体目标是使用这些适应性模型：1）比较观察到的实践模式的影响，以基于风险因素和分子亚型的新筛查和辅助治疗模式为目标的利弊; 2）探索改善获得新服务的影响; 3）进行信息价值分析，以评估新筛选测试的性能特征之间的关系（例如基于血液的生物标志物）及其对乳腺癌死亡率、治疗利用率和过度诊断的影响;以及4）使用基于网络的平台将结果传达给最终用户。这项工作将通过明确捕获乳腺癌的分子属性来推进建模领域，并在此过程中建立强大的能力，为有关癌症控制干预措施的“最佳实践”的辩论提供信息。

DESCRIPTION (provided by applicant): Breast cancer remains the second leading cause of cancer morbidity and mortality among women in the US. New discoveries have resulted in the widely accepted view that breast cancer is a heterogeneous disease with molecularly distinguishable morphological subtypes. This awareness is driving the development of new paradigms for the prevention, early detection and clinical management of breast cancer. However, there are very limited data on the population effects of these novel cancer control approaches. Population modeling is a unique comparative effectiveness paradigm to fill this gap by translating advances from the laboratory and clinical trials to understanding their net effects on US breast cancer mortality. The CISNET Breast Working Group has collaborated over the past nine years to apply independent population models to evaluate cancer control practices and use results to inform clinical and public health guidelines. This proposal leverages the investment in these models and provides the continuity and cohesion of this highly productive group. The modeling groups include Dana Farber (D). Erasmus MC (E), Georgetown-Einstein (G), MD Anderson (M), Stanford (S) and Wisconsin-Harvard (W). For this application, we will extend our work by modeling populations of women with varying risk factors (e.g., breast density, HRT) for the development of specific molecular subtypes of breast cancer (based on ER and HER2). Our specific aims are to use these adapted models to: 1) compare the impact of observed practice patterns to the benefits and harms of targeting new screening and adjuvant therapy modalities based on risk factors and molecular subtypes; 2) explore the impact of improving access to new services; 3) conduct value-of information-like analyses to evaluate the relationship between performance characteristics of a new screening test (e.g. blood based biomarker) and its impact on breast cancer mortality, utilization of treatments and over-diagnosis; and 4) communicate results to end-users using a web-based platform. This work will advance the field of modeling by explicitly capturing molecular attributes of breast cancer, and in so doing, build a robust capacity to inform debates about "best practices" for cancer control interventions.