Modeling the Impact of Targeted Therapy Based on Breast Cancer Subtypes

描述(申请人提供)：几十年来，我们对乳腺癌的生物学和临床认识一直基于三个治疗预测生物标志物：雌激素(ER)、孕酮(PR)受体和人类表皮生长因子受体-2(HER2)。今天，我们认识到乳腺癌的生物学更加复杂；此外，临床肿瘤学家经常使用额外的生物标记物和基因表达标记(例如Ki-67/IHC4、MammaPrint或Oncotype-Dx)来推荐乳腺癌治疗。尽管人们对乳腺癌生物学有了更深入的了解，生物驱动的乳腺癌疗法的临床应用也越来越多，但我们缺乏基于人群的估计，无法估计这些日益昂贵的针对乳腺癌亚型的诊断和治疗方法在多大程度上实际上降低了乳腺癌死亡率(BCM)，提高了生活质量(QOL)，或证明了其他方面的成本效益。为了满足这一需求并克服癌症干预和监测建模网络(CISNET)目前正在推进的横断面美国人口建模工作带来的挑战性限制，我们将对与生物学相关的预后和预测性生物标记物的扩展库进行建模。我们将采用一个在美国没有的独特的纵向人口数据集：已有40多年历史的斯德哥尔摩乳腺癌登记系统，目前跟踪一段时间内约40,000名乳腺癌患者，并对筛查、肿瘤生物标记物、治疗和结果进行注释，它可以通过以下方式链接到斯德哥尔摩乳房摄影登记系统 唯一的识别符，为建模提供无与伦比的纵向人口数据集。为了模拟更现代的预测生物标记物和量身定制的辅助疗法对人群的好处，我们将利用我们获得的另外两个独特的乳腺癌随机试验：斯德哥尔摩-1和i-spy临床试验数据集。斯德哥尔摩-1试验由729名妇女组成，随机接受他莫昔芬治疗和不接受系统治疗，随访时间为30年；I-SPY试验完全以生物标记物驱动的途径靶向药物试验为特征，包括对治疗的反应和无事件的生存结果。最后，我们将更新CISNet模型，以估计更具生物针对性的治疗和筛查方法的人口水平效益(BCM和成本效益)。本研究的具体目的包括：目的1.利用瑞典纵向人口数据开发和编程一个桥接模型，以确定基于生物学亚型分配处理的影响。然后，该模型将根据美国人口使用有偏抽样来反映SEER特征。目的2.使用目标1中的模型来评估个体化治疗对乳腺癌死亡率的总体影响，使用具有高度特征性的数据集，这些数据集具有来自生物标记物驱动的结果和/或治疗的生存益处和/或应答率。目的3.评估生物靶向治疗的人群水平成本效益。

DESCRIPTION (provided by applicant): For decades our biological and clinical understanding of breast cancer has been based on three therapeutically predictive biomarkers: estrogen (ER), progesterone (PR) receptors and the human epidermal growth factor receptor-2 (HER2). Today, we recognize that breast cancer biology is more complex; as well, clinical oncologists routinely use additional biomarkers and gene expression signatures (e.g. Ki-67/IHC4, MammaPrint or Oncotype-Dx) to recommend breast cancer treatments. Despite this deeper understanding of breast cancer biology and increasing clinical use of biology-driven breast cancer therapeutics, we lack population-based estimates of the extent that these ever more costly breast cancer subtype-targeted diagnostics and therapeutics actually reduce breast cancer mortality (BCM), improve quality of life (QOL), or otherwise prove cost-effective. To address this need and overcome the challenging constraints imposed by cross-sectional US population modeling efforts now being advanced by the Cancer Intervention and Surveillance Modeling Network (CISNET), we will model an expanded repertoire of prognostic and predictive biomarkers linked to biology. We will employ a unique longitudinal population dataset not available in the US: the 40+ year old Stockholm Breast Cancer Registry, which currently tracks ~40,000 individual breast cancer patients over time and is annotated for screening, tumor biomarkers, treatments and outcomes, and which can be linked to the Stockholm Mammography Registry through unique identifiers, providing an unparalleled longitudinal population dataset for modeling. To model the population benefits of more modern predictive biomarkers and tailored adjuvant therapies, we will utilize our access to two other unique breast cancer randomized trials: the Stockholm-1 and I-SPY clinical trial datasets. Stockholm-1 consists of 729 women randomized to tamoxifen vs. no systemic therapy with 30-year follow-up; and the I-SPY trials are fully characterized biomarker-driven trials of pathway targeted agents that include response to therapy and event-free survival outcomes. Finally, we will update the CISNET model to estimate the population level benefits (BCM and cost effectiveness) of a more biologically targeted approach to treatment and screening. The specific aims for this study include: Aim 1. Develop and program a bridging model using longitudinal Swedish population data to determine the impact of assigning treatments on the basis of biological subtypes. This model will then be tailored to the US population using biased sampling to reflect SEER characteristics. Aim 2. Use the model in Aim 1 to evaluate the population effects on breast cancer mortality of tailored therapy employing highly characterized data sets with survival benefits and/or response rates from biomarker-driven outcomes and or/treatment. Aim 3. Estimate the population level cost effectiveness of biologically targeted therapy.