Modeling the Impact of Targeted Therapy Based on Breast Cancer Subtypes

几十年来，我们对乳腺癌的生物学和临床认识一直基于三种治疗预测性生物标志物：雌激素（ER）、孕激素（PR）受体和人表皮生长因子受体-2 （HER2）。今天，我们认识到乳腺癌生物学更为复杂；此外，临床肿瘤学家通常使用其他生物标志物和基因表达特征（例如Ki-67/IHC4， MammaPrint或Oncotype-Dx）来推荐乳腺癌治疗。尽管对乳腺癌生物学有了更深入的了解，生物学驱动的乳腺癌治疗方法的临床应用也越来越多，但我们缺乏基于人群的估计，这些更昂贵的针对乳腺癌亚型的诊断和治疗方法实际上降低了乳腺癌死亡率（BCM），提高了生活质量（QOL），或者证明了成本效益。为了满足这一需求并克服目前由癌症干预和监测建模网络（CISNET）推进的横断面美国人口建模工作所施加的挑战性限制，我们将对与生物学相关的预后和预测生物标志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：40年以上的斯德哥尔摩乳腺癌登记处，目前跟踪约40,000名乳腺癌患者，并对筛查，肿瘤生物标志物，治疗和结果进行了注释，并且可以通过链接到斯德哥尔摩乳房x光检查登记处

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.