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）：目标药物正在彻底改变癌症治疗。但是，仍然存在重要的挑战。特别是，即使在具有相同已知突变的患者中，它们对特定的靶向疗法敏感，从没有反应（进行性疾病）到完全反应（E100％肿瘤量减少），对治疗的反应很大。驱动这种响应变异性的原因很少，并且对治疗的反应通常是事实后确定的。此外，肿瘤总是会产生对治疗和复发性的抗性。在治疗患者的过程中，对特定的治疗方案做出反应并预测反应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疗法相关的毒性外，它还可以使患者有机会切换到潜在的有效治疗。由于有许多治疗方案，并且还开发了更多治疗方案，因此在治疗过程中切换治疗是一种非常真实的选择，但只有在确定可靠的早期反应的可靠方法时，才可以选择。不幸的是，现有的确定反应和进展的方法不足，因为它们需要长时间的临床观察时间，因此不适，经济负担以及无法追求替代选择。该项目的总体目标是整合体外和体内成像测量值，以预测靶向致癌基因的疗法的早期患者肿瘤反应，以实现替代治疗方案，以最大程度地减少或防止抗性表型的出现。该目标的一个主要障碍是缺乏定量数据，该数据在细胞水平上动态连接基础反应的临床肿瘤反应T。初步研究表明，在三种生物量表上结合成像方式的可行性：2D培养物，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，更紧密地在体内模拟，并且可通过显微镜和磁共振（MR）成像进行解决；大鼠脑肿瘤异种移植物，一种适合MR成像的出色临床前药物治疗模型。这三个级别将通过包含可量化参数的数学模型和适合体内验证的数学模型进行集成。在AIM 1中，我们将优化从2D和3D显微镜的参数提取，以及Erlotinib反应性（PC9-DS9）和抗性（PC9-BR1）人类肺癌细胞系的MR成像数据，用于致癌基因肺癌的肺癌模型。从这些数据中，我们将建立一个连接2D显微镜和3D生物反应器MR估计值的增殖和死亡率的“查找表”。在AIM 2中，我们将通过初始化和约束基于图像的模型来量化经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力学。在AIM 3中，我们将测试通过将体内MRI数据与显微镜数据整合到DS9/BR1混合物的脑肿瘤异种移植物中对癌基因的急性耐药性，并模拟它们以监测抗药性表型的时空外观。