Quantitative Multiscale Imaging to Optimize Cancer Treatment Strategies

描述(由申请人提供)：靶向药物正在给癌症治疗带来革命性的变化。然而，重要的挑战依然存在。特别是，即使在具有使他们对特定靶向治疗敏感的相同已知突变的患者中，对治疗的反应也有很大的范围，从无反应(进展性疾病)到完全反应(e100%肿瘤体积缩小)。是什么驱动了这种反应的变异性，人们对此知之甚少，治疗的反应通常是在事实发生后确定的。此外，肿瘤总是对治疗和复发产生抗药性。在治疗过程的早期确定对给定治疗方案有反应或无反应的患者并预测反应的持久性将具有巨大的临床益处：除了限制患者暴露于与不成功的治疗相关的毒性之外，它还将使患者有机会转向潜在更有效的治疗。由于有许多治疗方案可用，而且还有更多的治疗方案正在开发中，在治疗过程中及早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，因为它们需要较长的临床观察时间，从而导致不适、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体外和体内的定量成像测量，以预测癌基因靶向治疗过程中早期患者肿瘤的最大反应，以便能够选择替代治疗方案，最大限度地减少或防止耐药表型的出现。这一目标的一个主要障碍是缺乏在细胞水平上将临床肿瘤反应与潜在反应动态联系起来的定量数据。初步研究表明，将三种生物尺度的成像模式结合在一起是可行的：2D培养，其中可以通过自动显微镜精确和动态地量化药物反应；3D生物反应器，更接近体内，并且可以通过显微镜和磁共振成像进行寻址；大鼠脑瘤异种移植，适合MR成像的优秀临床前药物治疗模型。这三个水平将通过包含可量化参数的数学模型进行整合，并适用于活体验证。在目标1中，我们将优化从二维和三维显微镜和磁共振成像数据中提取的埃洛替尼敏感(PC9-DS9)和耐药(PC9-BR1)人肺癌细胞系的参数提取，这两种细胞系是癌基因成瘾肺癌的良好研究模型。从这些数据中，我们将建立一个关于增殖和死亡率的“查询表”，将2D显微镜和3D生物反应器MR的估计联系起来。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生物反应器中经厄洛替尼处理的DS9/BR1混合培养物的肿瘤生长动力学。在目标3中，我们将通过整合体内MRI数据和显微镜数据，并对它们进行建模，以监测耐药表型的时空表现，以测试对DS9/BR1混合物的脑瘤移植瘤的急性耐药预测。

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.