Collaborative Research: A New Multiscale Methodology and Application to Tumor Growth Modeling

协作研究：一种新的多尺度方法及其在肿瘤生长建模中的应用

• 批准号: 1930583
• 负责人: Vittorio Cristini
• 金额: $ 15.91万
• 依托单位: The Methodist Hospital Research Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1930583&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Multiscale; Methodology

The complexity of tumor growth, which involves interactions within cells, among cells, and between cells and their environment, calls for development of mathematical and computational models that can connect processes from the cell, and sub-cell scales, to tissue level scales. These methods are needed to help tumor biologists gain further insight into the underlying mechanisms of the processes (e.g., proliferation, differentiation, and migration) involved in tumor development, at the scales which influence their behavior. Because of this complexity, it has been challenging to functionally link cell and tissue scale processes, the knowledge of which is key to development of predictive multiscale tumor models. However, current models typically use ad-hoc rules to bridge between scales, which limits their predictive capability. This project will address this challenge by developing a new multiscale method where directly measurable quantities at the cell-scale inform the model parameters at the continuum tissue scale through rigorous, mathematical upscaling techniques. The multiscale model will be tested and validated by comparing simulation results against experimentally obtained information about the overall growth rates and spatiotemporal behaviors of the different cells and tumors. The new multiscale method will be used to study pancreatic tumors to elucidate the transition of pancreatic lesions into invasive pancreatic ductal adenocarcinoma (PDAC). By integrating patient data analysis with quantitative tumor modeling, the project will develop reliable methods that can predict the likelihood of pancreatic cyst progression to PDAC using relatively non-invasive approaches. The project team will develop a new class of multiscale models that bridge these scales non-phenomenologically through application of rigorous upscaling techniques in order to close the continuum equations at the tissue scale and provide an accurate description of the processes across both cell and tissue scales. Specifically, stochastic agent-based models at the cell-scale and continuum partial differential equation models at the tissue-scale will be developed. Consistent functional relationships between the variables at the tissue-scale and measurements at the cell-scale will be found by upscaling the discrete models by using and extending the framework of dynamic density functional theory (DDFT) to obtain multi-cell scale continuum equations that account for correlations among cells as well as biological processes such cell birth and death. Further upscaling to the tissue scale will be done by identifying and deriving equations for slowly varying variables. The consistency of the different models in domains where the scales overlap will be tested and validated. The new multiscale method will be applied to model the progression of pancreatic neoplasms into invasive carcinomas in order to estimate the probability of this progression. Large-scale human patient datasets of pancreatic lesions, provided by our consultants through a separately funded project, will be used to validate and refine the models. The project will enhance the cross disciplinary training of students.

肿瘤生长的复杂性涉及细胞内部，细胞之间以及细胞与其环境之间的相互作用，呼吁开发数学和计算模型，这些模型可以将过程从细胞和子细胞尺度连接到组织水平尺度。需要这些方法来帮助肿瘤生物学家进一步深入了解参与肿瘤发展的过程的基本机制（例如，增殖，分化和迁移），这会影响其行为。由于这种复杂性，它在功能上将细胞和组织量表过程连接起来一直是具有挑战性的，而细胞和组织量表过程是预测性多尺度肿瘤模型的开发的关键。但是，当前模型通常使用临时规则在尺度之间桥接，这限制了其预测能力。该项目将通过开发一种新的多尺度方法来应对这一挑战，该方法在细胞尺度上直接测量的数量通过严格的数学上限提高技术来告知模型参数。将通过将模拟结果与实验获得的有关不同细胞和肿瘤的总体生长速率和时空行为的信息进行比较，对多尺度模型进行测试和验证。新的多尺度方法将用于研究胰腺肿瘤，以阐明胰腺病变向侵入性胰腺导管腺癌（PDAC）的过渡。通过将患者数据分析与定量肿瘤建模相结合，该项目将开发可靠的方法，可以使用相对非侵入性的方法来预测胰腺囊肿进展到PDAC的可能性。项目团队将开发一种新的多尺度模型，该模型通过应用严格的升级技术在组织尺度上关闭连续方程，并提供对细胞和组织量表的过程的准确描述，从而在非原子学上桥接这些量表。具体而言，将开发在细胞尺度和连续性部分微分方程模型的基于随机剂的模型。通过使用和扩展动态密度功能理论（DDFT）的框架来获得多个细胞量表连续方程，以占细胞之间的相关性以及生物学生物的相关性，可以找到组织尺度上的变量之间的一致功能关系，并且可以通过使用和扩展动态密度功能理论（DDFT）的框架来提高离散模型，并扩展细胞尺度的框架，从而找到离散模型，并扩展细胞尺度的框架，从而找到分散模型之间的一致功能关系。通过识别和得出缓慢变化的变量的方程，将进一步升级到组织量表。将测试和验证量表重叠的域中不同模型的一致性。新的多尺度方法将用于对胰腺肿瘤的进展建模为浸润性癌，以估计这种进展的概率。我们顾问通过单独资助的项目提供的胰腺病变的大规模人类患者数据集将用于验证和完善模型。该项目将增强学生的跨学科培训。

项目成果

Mathematical Modeling to Address Challenges in Pancreatic Cancer

• DOI: 10.2174/1568026620666200101095641
• 发表时间: 2020-01-01
• 期刊: CURRENT TOPICS IN MEDICINAL CHEMISTRY
• 影响因子: 3.4
• 作者: Dogra, Prashant;Ramirez, Javier R.;Rawat, Manmeet
• 通讯作者: Rawat, Manmeet

Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy

• DOI: 10.1126/sciadv.aay6298
• 发表时间: 2020-04-01
• 期刊: SCIENCE ADVANCES
• 影响因子: 13.6
• 作者: Butner, Joseph D.;Elganainy, Dalia;Cristini, Vittorio
• 通讯作者: Cristini, Vittorio

Tumor core biopsies adequately represent immune microenvironment of high-grade serous carcinoma

• DOI: 10.1038/s41598-019-53872-1
• 发表时间: 2019-11-26
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: Lara, Olivia D.;Krishnan, Santhoshi;Sood, Anil K.
• 通讯作者: Sood, Anil K.

A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery

• DOI: 10.1016/j.csbj.2020.02.014
• 发表时间: 2020-01-01
• 期刊: COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
• 影响因子: 6
• 作者: Dogra, Prashant;Butner, Joseph D.;Wang, Zhihui
• 通讯作者: Wang, Zhihui