Physical Dynamics of Cancer Response to Chemotherapy in 3D Microenvironments

 描述(申请人提供)：这些研究旨在开发一种新的计算驱动平台，利用芯片上器官微流控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。恶性乳腺肿瘤在其细胞组成、不同程度的氧合、酸度和营养方面具有高度的异质性。 因为细胞外基质的局部变化。此外，肿瘤组织和肿瘤微环境属性不仅可以在肿瘤生长过程中动态演变，而且在进行抗癌治疗时也可以动态演变。尽管如此，几乎所有的药物疗效和最佳剂量的临床前评估都是使用与体内肿瘤的细胞、代谢和物理特征不同的同质2D细胞培养进行的。这种方法受到过于简单化的体外/体外研究的影响，可能不能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，将芯片上器官3D组织生物反应器的功能与3DMultiCel数学模型相结合，该平台旨在包括非均匀的完全可控的物理和化学微环境，并允许对实验验证的基线周围的广泛微环境组合进行预测性测试。为了定量地实现这一目标，我们成立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，他们将开发一个基于物理科学原理的个性化抗癌治疗实验平台。我们的长期目标是为来自患者肿瘤活检组织的3D器官培养提供一个计算驱动的“芯片上实验室”平台，该平台将暴露在完全受控但动态变化的微环境中，用于优化个性化治疗，有效地引发乳腺癌的消退，并将对周围正常组织的有害副作用降至最低。这项研究的成果将是：(I)改进的实验平台，它结合了肿瘤有机化合物的3D培养和经过验证的预测数学模型，用于在现实微环境中预测人类乳腺肿瘤有机化合物的生长和反应；以及(Ii)使人们能够使用数学建模来评估乳腺肿瘤有机化合物的发展和对抗肿瘤治疗的反应的定量方法。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乳腺有机物的动态及其对治疗的敏感性的影响；2.在TTB-G反应堆内构建和验证在计算机模型指导下建立的复杂的空间和时间微环境梯度，并评估乳腺肿瘤有机物对化疗的反应。3.应用我们的集成计算/工程方法来指导治疗和预测体内外的治疗反应。

 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.