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Optimal Predictors of Response to Trastuzumab

曲妥珠单抗反应的最佳预测因子

基本信息

批准号：
7647622
负责人：
Lyndsay Norine Harris
金额：
$ 58.79万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-08-05 至 2011-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7647622
关键词：
Adjuvant Adoption Age Alternative Therapies Biological Assay Cancer Patient Cancer and Leukemia Group B Cessation of life Clinical Trials Code Companions Computer Simulation Custom DNA DNA Fingerprinting Data Data Collection Detection Diagnostic Diagnostic tests Dimerization Disease Disseminated Malignant Neoplasm ERBB2 gene Enrollment Europe Event Family Funding Future Gene Expression Gene Expression Profile Gene Expression Profiling Generations Genes Goals Grant HercepTest Heterodimerization Homo Homodimerization In Situ In complete remission Lead Left Licensing Measurable Measurement Medicine Messenger RNA Methods Modality Modeling Outcome Paclitaxel Pathologic Pathway interactions Patient Selection Patients Pharmaceutical Preparations Phase III Clinical Trials Prediction of Response to Therapy Proteins RNA Recurrence Risk Roche brand of trastuzumab Sampling Series Signal Pathway Signaling Protein Specificity Specimen Statistical Models Subgroup Techniques Technology Testing Therapeutic Tissues Training Translating Trastuzumab Triage Validation base cancer therapy chemotherapy cohort density follow-up lapatinib malignant breast neoplasm novel novel diagnostics outcome forecast patient population predictive modeling prospective protein expression response tumor

项目摘要

Recently, trials of trastuzumab in the adjuvant setting have been completed in both the US (NCCTG/NSABP) and in Europe (the HERA trial) that have shown this drug can decrease the recurrence rate in patients with HER2 positive breast cancer. However in both trials, there are still a significant percentage of patients that recur on the drug. Similarly, numerous earlier trials in patients with metastatic cancer also showed a lack of response even though they had HER2 expressing tumors. These facts, combined with the facts that new drugs are now available that target HER family signaling pathways, suggest that new, more specific, companion diagnostics could be developed for trastuzumab that increase the specificity of selection of patients for this therapy. The underlying hypothesis of this proposal is that an optimal predictor of outcome for patients on trastuzumab can be achieved by combining multiple markers which predict response. We propose that using a set of novel techniques to interrogate HER2 tumors being treated with trastuzumab and chemotherapy, we can define the optimal predictor and validate this classifier in a prospective trial. The techniques will include assessment of DNA, RNA and protein to determine the best modality for optimal prediction. Protein will be assessed using multiplexed immunofluoresence, RNA will be assessed using transcriptional microarray profiling and DNA will be assessed in the form of copy number analysis using high density SNP arrays. Each of these assays has the potential to be translated into a usable companion diagnostic assay for breast cancer patients. In this revised 2 year version. of this grant we propose to keep the analysis of all 3 modalities, but to only complete the training set aspects of the grant. We envision a follow-up submission in 18-24 months that shows the model resulting for the efforts of this project, then proposing validation of the model(s) on the CALGB 40601 cohort or similar. The scaled aims include: AIM 1) To develop an optimal multiplexed predictive model that uses HER pathway related proteins, downstream signaling proteins and hetero- or homodimerization state of HER2. This aim will use the quantitative multiplexing technology (called AQUA) for accurate in situ measurement of protein expression on a series of 10-25 HER2 pathway related proteins to construct a series of models that predict response to trastuzumab in the CALGB 9840 trial (A trial of taxol and trastuzumab in in the first line metastatic setting) AIM 2) To use Ilumina DASL-based Gene Expression Profiling to develop an optimal multiplexed predictive model. This aim will assess gene expression using a custom gene set on the Illumina DASL custom array (1536 genes) Platform in the CALGB 9840 cohort to identify candidate predictors for the multiplexed predictive model. Specifically, candidate amplicons, and genes associated with these candidate amplicons, which are associated with response to trastuzumab will be identified for inclusion in the model. AIM 3) To do computational modeling of the combined data from Aim I and 2 to discover the best 3-5 models that fit the training set (CALGB 9840) data. This aim will create a series of optimal models that best select responders from non-responders in the CALGB training set. Validation of the models will be done by Leave One Out Cross Validation methods in anticipation of future more robust validation in an independent cohort in a subsequent study.

最近，曲妥珠单抗在辅助治疗中的试验已于美国（NCCTG/NSABP）和欧洲（HERA 试验）均表明该药物可以降低 HER2 阳性乳腺癌患者的复发率。然而，在这两项试验中，仍有很大比例的患者在服用该药物后复发。同样，许多早期针对转移性癌症患者的试验也显示出缺乏反应，即使他们患有表达 HER2 的肿瘤。这些事实，再加上现在有针对 HER 家族信号通路的新药的事实，表明可以为曲妥珠单抗开发新的、更具体的伴随诊断，从而提高选择该疗法患者的特异性。该提案的基本假设是结果的最佳预测因子对于接受曲妥珠单抗治疗的患者，可以通过组合多种预测反应的标志物来实现。我们建议使用一组新技术来询问正在接受曲妥珠单抗和化疗治疗的 HER2 肿瘤，我们可以定义最佳预测因子并在前瞻性试验中验证该分类器。这些技术将包括对 DNA、RNA 和蛋白质的评估，以确定最佳预测的最佳方式。蛋白质将使用多重免疫荧光进行评估，RNA 将使用转录微阵列分析进行评估，DNA 将使用高密度 SNP 阵列以拷贝数分析的形式进行评估。这些测定中的每一种都有可能转化为乳腺癌患者可用的伴随诊断测定。在这个修订后的 2 年版本中。对于这笔赠款，我们建议保留对所有 3 种模式的分析，但仅完成赠款的训练集方面。我们预计在 18-24 个月内提交后续文件，展示该项目的成果模型，然后建议在 CALGB 40601 队列或类似队列上验证模型。规模化目标包括：目标 1) 使用 HER 通路相关蛋白、下游信号蛋白和 HER2 异二聚或同二聚状态开发最佳多重预测模型。该目标将使用定量多重技术（称为 AQUA）对一系列 10-25 HER2 通路相关蛋白的蛋白表达进行准确的原位测量，以构建一系列模型来预测 CALGB 9840 试验（在一线转移环境中进行紫杉醇和曲妥珠单抗的试验）对曲妥珠单抗的反应。目标 2) 使用基于 Ilumina DASL 的基因表达谱分析来开发最佳的多重预测模型。该目标将使用 CALGB 9840 队列中 Illumina DASL 定制阵列（1536 个基因）平台上的定制基因集评估基因表达，以确定多重预测模型的候选预测因子。具体而言，候选扩增子以及与这些候选扩增子相关的基因（其与曲妥珠单抗的反应相关）将被鉴定以包含在模型中。目标 3) 对目标 I 和 2 的组合数据进行计算建模，以发现适合训练集 (CALGB 9840) 数据的最佳 3-5 个模型。这一目标将创建一系列最佳模型，在 CALGB 训练集中从无响应者中最好地选择响应者。模型的验证将通过留一交叉验证方法来完成，以期在后续研究中的独立队列中进行更强大的验证。