Quantitative Multiscale Imaging to Optimize Cancer Treatment Strategies

定量多尺度成像优化癌症治疗策略

• 批准号: 8703365
• 负责人: Vito Quaranta
• 金额: $ 62.15万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8703365
• 关键词: Acute; Adverse effects; Appearance; Biological; Biological Markers; Bioreactors; Brain; Brain Neoplasms; Cancer Biology; Cancer cell line; Cell Culture Techniques; Cell Death; Cell division; Cells; Clinical; Clinical Oncology; Clinical Trials; Coculture Techniques; Complex; Computer Simulation; Data; Death Rate; Detection; Development; Drug Delivery Systems; Early identification; Early treatment; Erlotinib; Exposure to; Goals; Human; Image; In Vitro; In complete remission; Individual; Link; Magnetic Resonance; Magnetic Resonance Imaging; Malignant neoplasm of lung; Measurement; Methods; Microscopy; Modeling; Monitor; Mutate; Mutation; Oncogenes; Outcome; Patients; Pharmaceutical Preparations; Phenotype; Progressive Disease; Rattus; Regimen; Relapse; Resistance; Resistance development; Simulate; Source; Study models; System; Testing; Therapeutic; Time; Toxic effect; Translations; Treatment Efficacy; Tumor Volume; Validation; Vision; Work; Xenograft Model; alternative treatment; base; cancer cell; cancer therapy; cost; data acquisition; data modeling; imaging modality; in vivo; in vivo imaging; innovation; mathematical model; pre-clinical; predictive modeling; prevent; programs; public health relevance; research study; resistance mechanism; response; spatiotemporal; time use; tool; treatment response; treatment strategy; tumor; tumor growth; tumor xenograft

DESCRIPTION (provided by applicant): Targeted agents are revolutionizing cancer treatment. However, important challenges remain. In particular, even among patients with the same known mutation that sensitizes them to a particular targeted therapy, there is a significant range of responses to treatment, from no response (progressive disease) to complete response (e100% tumor volume reduction). What drives this response variability is poorly understood, and response to treatment is generally determined after the fact. In addition, tumors invariably develop resistance to treatment and recur. Identifying-early in the course of therapy-patients that will or will not respond to a given therapeutic regimen and predicting the durability of response would be of enormous clinical benefit: In addition to limiting patients' exposure to the toxicities associated with unsuccessful therapies, it would allow patients the opportunity to switch to a potentially more efficacious treatment. As there are many therapeutic regimens available, and many more being developed, switching treatment early in the course of therapy is a very real option-but only if a reliable method to determine early response were available. Unfortunately, existing methods of determining response and progression are inadequate, as they require long clinical observation times with consequent discomfort, financial burden as well as inability to pursue alternative options. The overall goal of this project is to integrate quantitative in vitro and in vivo imaging measurements to predict the maximum patient tumor response early in the course of oncogene-targeted therapy, in order to enable alternative treatment options that minimize or prevent the emergence of the resistant phenotype. A major barrier to this goal is the lack of quantitative data dynamically linking clinical tumor response t underlying response at the cellular level. Preliminary studies show the feasibility of combining imaging modalities at three biological scales: 2D culture, where drug response can be quantified accurately and dynamically by automated microscopy; 3D bioreactor, more closely simulating in vivo and addressable both by microscopy and magnetic resonance (MR) imaging; rat brain tumor xenografts, an excellent preclinical drug treatment model suitable to MR imaging. The three levels will be integrated by mathematical models incorporating quantifiable parameters and suitable to in vivo validation. In Aim 1 we will optimize extraction of parameters from 2D and 3D microscopy and MR imaging data of the erlotinib-responsive (PC9-DS9) and resistant (PC9-BR1) human lung cancer cell lines, well-studied models for oncogene-addicted lung cancer. From these data we will establish a "look up table" of proliferation and death rates linking 2D microscopy and 3D bioreactor MR estimates. In Aim 2 we will quantify tumor growth dynamics of erlotinib-treated DS9/BR1 mixed cultures in the 3D bioreactor, by initializing and constraining an image-based model. In Aim 3 we will test predicting acute resistance to oncogene directed therapy in brain tumor xenografts of DS9/BR1 mixtures, by integrating in vivo MRI data with microscopy data and model them to monitor the spatiotemporal appearance of the resistant phenotype.

描述（由申请人提供）：靶向药物正在彻底改变癌症治疗。然而，重要的挑战仍然存在。特别是，即使在具有相同已知突变（使他们对特定靶向治疗敏感）的患者中，对治疗的反应也有很大范围，从无反应（疾病进展）到完全反应（e100%肿瘤体积缩小）。人们对导致这种反应变异性的原因知之甚少，对治疗的反应通常是事后确定的。此外，肿瘤总是会对治疗产生耐药性并复发。在治疗过程的早期识别对给定治疗方案有反应或没有反应的患者并预测反应的持久性将具有巨大的临床益处：除了限制患者接触与不成功治疗相关的毒性之外，它还将使患者有机会转向可能更有效的治疗。由于有许多治疗方案可供选择，而且还有更多治疗方案正在开发中，因此在治疗过程的早期转换治疗是一个非常现实的选择，但前提是有可靠的方法来确定早期反应。不幸的是，现有的确定反应和进展的方法是不够的，因为它们需要较长的临床观察时间，从而带来不适、经济负担以及无法寻求替代选择。该项目的总体目标是整合定量体外和体内成像测量，以预测癌基因靶向治疗过程早期患者肿瘤的最大反应，以便提供替代治疗方案，最大限度地减少或防止耐药表型的出现。这一目标的主要障碍是缺乏将临床肿瘤反应与细胞水平的潜在反应动态联系起来的定量数据。初步研究表明在三个生物尺度上结合成像方式的可行性：2D 培养，可以通过自动显微镜准确、动态地量化药物反应； 3D生物反应器，更接近地模拟体内，并且可以通过显微镜和磁共振（MR）成像进行寻址；大鼠脑肿瘤异种移植，一种适合磁共振成像的优秀临床前药物治疗模型。这三个级别将通过包含可量化参数并适合体内验证的数学模型进行整合。在目标 1 中，我们将优化从 2D 和 3D 显微镜以及厄洛替尼反应性 (PC9-DS9) 和耐药性 (PC9-BR1) 人肺癌细胞系的 MR 成像数据中提取参数，这是经过充分研究的癌基因成瘾性肺癌模型。根据这些数据，我们将建立一个连接 2D 显微镜和 3D 生物反应器 MR 估计的增殖率和死亡率的“查找表”。在目标 2 中，我们将通过初始化和约束基于图像的模型来量化 3D 生物反应器中厄洛替尼处理的 DS9/BR1 混合培养物的肿瘤生长动态。在目标 3 中，我们将测试预测 DS9/BR1 混合物脑肿瘤异种移植物对癌基因定向治疗的急性耐药性，方法是将体内 MRI 数据与显微镜数据相结合，并对它们进行建模以监测耐药表型的时空表现。