Quantitative Multiscale Imaging to Optimize Cancer Treatment Strategies

定量多尺度成像优化癌症治疗策略

基本信息

  • 批准号:
    9131999
  • 负责人:
  • 金额:
    $ 15.28万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2014
  • 资助国家:
    美国
  • 起止时间:
    2014-08-01 至 2020-07-31
  • 项目状态:
    已结题

项目摘要

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,仍然存在重大挑战。特别是,即使在具有使其对特定靶向治疗敏感的相同已知突变的患者中,也存在对治疗的显著反应范围,从无反应(进行性疾病)到完全反应(e100%肿瘤体积减小)。是什么驱动了这种反应变异性知之甚少,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抗性并复发。在治疗过程的早期识别患者是否对给定的治疗方案有反应,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可能更有效的治疗。由于有许多治疗方案可用,而且还有更多正在开发中,因此在治疗过程的早期转换治疗是一个非常真实的选择-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,因为它们需要长的临床观察时间,随之而来的不适,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的最大患者肿瘤反应,从而实现最大限度地减少或防止耐药表型出现的替代治疗方案。实现这一目标的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。初步研究显示了在三个生物尺度上结合成像模式的可行性:2D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,更紧密地模拟体内并且通过显微镜和磁共振(MR)成像可寻址;大鼠脑肿瘤异种移植物,一种适合于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,并适用于体内验证。在目标1中,我们将优化从厄洛替尼应答(PC 9-DS 9)和耐药(PC 9-BR 1)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成瘾肺癌的研究模型。从这些数据中,我们将建立一个“查找表”的增殖和死亡率连接2D显微镜和3D生物反应器MR估计。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DS 9/BR 1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数据和显微镜数据来测试预测DS 9/BR 1混合物脑肿瘤异种移植物对癌基因定向治疗的急性抗性,并对其建模以监测抗性表型的时空外观。

项目成果

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Vito Quaranta其他文献

Vito Quaranta的其他文献

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{{ truncateString('Vito Quaranta', 18)}}的其他基金

Phenotype Heterogeneity and Dynamics in SCLC
SCLC 的表型异质性和动态
  • 批准号:
    9901484
  • 财政年份:
    2018
  • 资助金额:
    $ 15.28万
  • 项目类别:
Administrative Core
行政核心
  • 批准号:
    10375419
  • 财政年份:
    2018
  • 资助金额:
    $ 15.28万
  • 项目类别:
Phenotype Heterogeneity and Dynamics in SCLC
SCLC 的表型异质性和动态
  • 批准号:
    10375418
  • 财政年份:
    2018
  • 资助金额:
    $ 15.28万
  • 项目类别:
Modeling the SCLC Phenotypic Space
SCLC 表型空间建模
  • 批准号:
    10375422
  • 财政年份:
    2018
  • 资助金额:
    $ 15.28万
  • 项目类别:
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
  • 财政年份:
    2014
  • 资助金额:
    $ 15.28万
  • 项目类别:
Inhibition of proliferation by Laminin
层粘连蛋白抑制增殖
  • 批准号:
    8691542
  • 财政年份:
    2014
  • 资助金额:
    $ 15.28万
  • 项目类别:
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast Cancer
图像驱动的多尺度建模来预测乳腺癌的治疗反应
  • 批准号:
    8664820
  • 财政年份:
    2013
  • 资助金额:
    $ 15.28万
  • 项目类别:
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast Cancer
图像驱动的多尺度建模来预测乳腺癌的治疗反应
  • 批准号:
    8920097
  • 财政年份:
    2013
  • 资助金额:
    $ 15.28万
  • 项目类别:
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast Cancer
图像驱动的多尺度建模来预测乳腺癌的治疗反应
  • 批准号:
    8476896
  • 财政年份:
    2013
  • 资助金额:
    $ 15.28万
  • 项目类别:
Administration
行政
  • 批准号:
    8181597
  • 财政年份:
    2010
  • 资助金额:
    $ 15.28万
  • 项目类别:

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