Modeling the Impact of Targeted Therapy Based on Breast Cancer Subtypes

根据乳腺癌亚型模拟靶向治疗的影响

• 批准号: 9123567
• 负责人: DONALD A BERRY
• 金额: $ 42.05万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-18 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9123567
• 关键词: Address; Adjuvant Therapy; Advanced Malignant Neoplasm; Biological; Biological Markers; Biology; Breast; Breast Cancer Patient; Breast Cancer Treatment; Cancer Biology; Cancer Intervention and Surveillance Modeling Network; Characteristics; Clinical; Clinical Trials; Collection; Complex; Country; Data; Data Set; Databases; Diagnosis; Diagnostic; Disease; Disease-Free Survival; Drops; Epidermal Growth Factor Receptor; Estrogens; Gene Expression Profile; Genetic Crossing Over; Health; Human; In complete remission; Individual; Knowledge; Link; Longitudinal Studies; Malignant Neoplasms; Medicare; Modeling; Oncologist; Outcome; Pathologic; Pathway interactions; Patients; Pattern; Population; Population Database; Progesterone; Progesterone Receptors; Prognostic Marker; Quality of life; RNA; Randomized; Recording of previous events; Registries; Resources; Sampling Biases; Specific qualifier value; Sweden; Systemic Therapy; Tamoxifen; Therapeutic; Time; Treatment outcome; Tumor Markers; Update; Vitelliform macular dystrophy; Woman; base; biomarker-driven; breast cancer registry; cancer subtypes; cost; cost effective; cost effectiveness; data registry; follow-up; genetic signature; improved; longitudinal database; malignant breast neoplasm; mammography registry; model building; models and simulation; mortality; network models; novel; population based; predictive marker; programs; randomized trial; response; response biomarker; screening; standard care; statistics; survival outcome; targeted agent; targeted treatment; tumor

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.

描述（由申请人提供）：几十年来，我们对乳腺癌的生物学和临床理解一直基于三种治疗预测生物标志物：雌激素（ER）、孕酮（PR）受体和人表皮生长因子受体-2（HER 2）。今天，我们认识到乳腺癌生物学更加复杂;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/IHC 4，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，以评估这些成本更高的乳腺癌亚型靶向诊断和治疗方法实际上降低了乳腺癌死亡率（RR），改善了生活质量（QOL），或以其他方式证明具有成本效益。为了满足这一需求，并克服美国癌症干预和监测建模网络（CISNET）目前正在推进的横断面人口建模工作所带来的挑战性限制，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不可用的独特纵向人群数据集：40年以上的斯德哥尔摩乳腺癌登记处，该登记处目前随时间跟踪约40，000名个体乳腺癌患者，并对筛查、肿瘤生物标志物、治疗和结局进行了注释，并且可以通过以下方式与斯德哥尔摩乳腺X线摄影登记处链接： 独特的标识符，为建模提供了无与伦比的纵向人口数据集。为了模拟更现代的预测性生物标志物和定制的辅助治疗的人群益处，我们将利用我们对另外两个独特的乳腺癌随机试验的访问：斯德哥尔摩-1和I-SPY临床试验数据集。斯德哥尔摩-1由729名随机接受他莫昔芬与无全身治疗的妇女组成，随访30年; I-SPY试验是完全特征化的生物标志物驱动的通路靶向药物试验，包括对治疗的反应和无事件生存结局。最后，我们将更新CISNET模型，以估计更具生物靶向的治疗和筛查方法的人群水平获益（风险和成本效益）。本研究的具体目标包括：目标1。使用纵向瑞典人口数据开发和编程桥接模型，以确定基于生物亚型分配治疗的影响。然后，将使用偏倚抽样对美国人群量身定制该模型，以反映SEER特征。目标2.使用目标1中的模型，采用高度表征的数据集（具有生物标志物驱动结局和/或治疗的生存获益和/或缓解率），评价定制治疗对乳腺癌死亡率的人群影响。目标3.估计生物靶向治疗的群体水平成本效益。