Comparative Modeling: Informing Breast Cancer Control Practice & Policy

描述(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。新的发现导致了被广泛接受的观点，即乳腺癌是一种具有分子上可区分的形态亚型的异质性疾病。这一认识正在推动乳腺癌预防、早期发现和临床管理的新模式的发展。然而，关于这些新的癌症控制方法对人群的影响的数据非常有限。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它们对美国乳腺癌死亡率的净影响来填补这一空白。 过去九年来，国际癌症研究中心乳房工作组一直在合作，应用独立的人口模型来评估癌症控制做法，并使用结果为临床和公共卫生指南提供信息。这项提议充分利用了对这些模式的投资，并提供了这一高生产率群体的连续性和凝聚力。建模小组包括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(S)和威斯康星-哈佛(W)。对于这一应用，我们将通过对具有不同风险因素(例如，乳房密度、HRT)的女性人群进行建模来扩展我们的工作，以发展特定的乳腺癌分子亚型(基于ER和HER2)。 我们的具体目标是利用这些调整后的模型：1)比较观察到的实践模式与基于风险因素和分子亚型的新筛查和辅助治疗模式的利弊；2)探讨改进获得新服务的机会的影响；3)进行类似信息价值的分析，以评估新筛查测试的性能特征(例如血液生物标记物)与其对乳腺癌死亡率、治疗方法的使用和过度诊断的影响之间的关系；4)使用网络平台将结果传达给最终用户。这项工作将通过明确捕捉乳腺癌的分子属性来推进建模领域，并通过这样做，建立一种强大的能力，为关于癌症控制干预的“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.