Quantitative Multiscale Imaging to Optimize Cancer Treatment Strategies

描述（由申请人提供）：靶向药物正在彻底改变癌症治疗。然而，重要的挑战依然存在。特别是，即使在具有相同已知突变的患者中，他们对特定靶向治疗敏感，对治疗的反应范围也很大，从无反应（疾病进展）到完全反应（肿瘤体积减少100%）。导致这种反应变异性的原因尚不清楚，对治疗的反应通常是事后确定的。此外，肿瘤总是对治疗产生耐药性并复发。在治疗过程的早期识别患者是否会对给定的治疗方案产生反应，并预测反应的持久性，这将对临床有巨大的好处：除了限制患者接触与不成功治疗相关的毒性外，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，而且更多的方案正在开发中，因此在治疗过程的早期切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，因为它们需要长时间的临床观察，随之而来的不适，经济负担以及无法寻求替代方案。该项目的总体目标是整合定量的体外和体内成像测量，以在癌基因靶向治疗过程的早期预测患者的最大肿瘤反应，从而实现替代治疗选择，最大限度地减少或防止耐药表型的出现。实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模式是可行的：二维培养，其中药物反应可以通过自动显微镜准确和动态地量化；3D生物反应器，更接近体内模拟，可通过显微镜和磁共振（MR）成像寻址；大鼠脑肿瘤异种移植是一种适合于磁共振成像的临床前药物治疗模型。这三个水平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Aim 1中，我们将优化从厄洛替尼应答（PC9-DS9）和耐药（PC9-BR1）人肺癌细胞系的2D和3D显微镜和MR成像数据中提取参数，这些细胞系是癌基因依赖性肺癌的充分研究模型。根据这些数据，我们将建立一个将2D显微镜和3D生物反应器MR估计相关联的增殖和死亡率的“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，量化厄洛替尼处理的DS9/BR1混合培养物在3D生物反应器中的肿瘤生长动力学。在Aim 3中，我们将通过整合体内MRI数据和显微镜数据并对其建模以监测耐药表型的时空外观，测试预测DS9/BR1混合物的肿瘤异种移植物对癌基因定向治疗的急性耐药。

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.