Physical Dynamics of Cancer Response to Chemotherapy in 3D Microenvironments

 描述（由申请人提供）：这些研究旨在开发一种新的计算驱动平台，以利用器官芯片微流体生物反应器技术结合肿瘤生长和治疗反应的预测数学模型来检查复杂的物理和化学微环境。恶性乳腺肿瘤在细胞组成、不同水平的氧合、酸性和营养方面具有高度异质性， 细胞外基质的局部变化。此外，肿瘤组织和肿瘤微环境特性不仅可以在肿瘤生长期间动态演变，而且可以在施用抗癌治疗时动态演变。尽管如此，几乎所有药物疗效和最佳剂量的临床前评估都是使用均质2D细胞培养物进行的，这些细胞培养物与体内肿瘤中表现出的细胞、代谢和物理特征不相似。这种过度简化的离体/体外研究方法可能无法完全概括癌症的复杂性，特别是物理和化学微环境。为了解决这些问题，我们建议开发一个集成的定量平台，该平台结合了器官芯片3D组织生物反应器的功能，该生物反应器被开发为包括非均匀的完全受控的物理和化学微环境，以及3DMultiCel数学模型，该模型允许预测测试实验验证的基线周围的广泛的微环境组合。为了以定量的方式实现这一目标，我们组建了一个由癌症生物学家，生物医学工程师和数学家组成的跨学科团队，他们将根据物理科学原理开发个性化抗癌治疗的实验平台。我们的长期目标是为来自患者肿瘤活检的3D器官型培养物提供一个计算驱动的“芯片实验室”平台，这些培养物将暴露于完全受控但动态可变的微环境中，这些微环境将用于优化个性化治疗，有效地促进乳腺肿瘤消退，同时最大限度地减少对周围正常组织的有害副作用。这项研究的结果将是：（i）一个改进的实验平台，结合肿瘤类器官的3D培养，以及经验证的预测数学模型，用于人类乳腺肿瘤类器官在现实微环境中的生长和反应;和（ii）定量方法，允许人们使用数学建模评估乳腺肿瘤类器官发育的动态和对抗肿瘤治疗的反应。我们的目标是：1.开发一种预测方法，以评估定义的微环境对正常和致瘤性乳腺类器官的动力学及其对治疗药物的敏感性的影响; 2.构建并验证在TTb-G反应器内建立的计算机模型引导的复杂空间和时间微环境梯度，并评估乳腺肿瘤类器官对化疗药物的反应。3.应用我们的集成计算/工程方法指导治疗并预测离体和体内的治疗反应。

 DESCRIPTION (provided by applicant): These studies aim to develop a new computationally driven platform to examine complex physical and chemical microenvironments utilizing organ-on-chip microfluidic bioreactor technology coupled with a predictive mathematical model of tumor growth and therapeutic response. Malignant breast tumors are highly heterogeneous in terms of their cellular composition, varying levels of oxygenation, acidity, and nutrients, as well as local changes in the extracellular matrix. Furthermore, tumor tissue and tumor microenvironment properties can dynamically evolve not only during tumor growth but also when anticancer treatments are administered. Despite this, nearly all pre-clinical assessments of drug efficacy and optimal dosing are performed using homogeneous 2D cell cultures that do not resemble the cellular, metabolic, and physical features manifest in tumors in vivo. Such approach suffered from overly reductionist ex vivo / in vitro studies may not fully recapitulate th complexity of cancers especially the physical and chemical microenvironment. To address these issues we propose to develop an integrated quantitative platform that combines the power of organ-on-chip 3D tissue bioreactor, developed to include non-uniform fully controlled physical and chemical microenvironments, together with a 3DMultiCel math model that allows predictive testing of a broad range of microenvironmental combinations around the experimentally validated baseline. To achieve this goal in a quantitative way we have formed a transdisciplinary team consisting of cancer biologists, biomedical engineers and mathematicians, who will develop an experimental platform for individualized anticancer treatment based on physical science principles. Our long-term goal is to provide a computationally driven "lab-on-chip" platform for 3D organotypic cultures derived from patients' tumor biopsies that will be exposed to fully controlled but dynamically variable microenvironments that will be used to optimize personalized therapeutic treatments that effectively provoke breast tumor regression with minimized harmful side effects for surrounding normal tissue. Outcomes of this study will be: (i) an improved experimental platform that combines 3D culture of tumor organoids coupled with validated predictive mathematical models for the growth and response of human breast tumor organoids within realistic microenvironments; and (ii) quantitative methods that allow one to assess the dynamics of breast tumor organoid development and response to anti-tumor treatments, using mathematical modeling. Our aims are: 1. Develop a predictive methodology to assess effects of defined microenvironments on the dynamics of normal and tumorigenic breast organoids and their sensitivity to therapeutics; 2. Construct and validate in silico model-guided complex spatial and temporal microenvironmental gradients established within TTb-G reactor, and assess breast tumor organoids response to chemotherapeutics. 3. Apply our integrated computational/engineering approach to guide therapy and predict therapeutic response ex vivo and in vivo.