Cancer as a Complex Adaptive System

癌症作为一个复杂的适应系统

• 批准号: 9553661
• 负责人: Alexander Robertson Allan Anderson
• 金额: $ 232.48万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-23 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9553661
• 关键词: Acidosis; Affect; African American; Biopsy Specimen; Blood flow; Bone Marrow; Bone marrow biopsy; Buffers; Cancer Biology; Cancer Center; Caucasians; Clinical; Clinical Data; Clinical Trials; Clinical/Radiologic; Complex; Computer Simulation; Computers; Consensus; Data; Development; Ecology; Environmental Risk Factor; Evolution; Funding; Glioblastoma; Goals; Growth; Habitats; Heterogeneity; Hormonal; Hormones; Iatrogenesis; Image Analysis; Immune response; Immunotherapy; In Vitro; Intervention; Investigation; Knowledge; Learning; Length; MRI Scans; Malignant Neoplasms; Malignant neoplasm of prostate; Mathematics; Methods; Microfluidics; Modeling; Molecular; Molecular Analysis; Multiple Myeloma; Neoplasm Circulating Cells; Neoplasm Metastasis; Non-linear Models; Pathologic; Patients; Phase; Precision therapeutics; Proliferating; Property; Recurrence; Relapse; Research Personnel; Resistance; Role; Scientist; Source; System; Testing; Time; Tissues; Translating; Tumor Immunity; Validation; Variant; Work; abiraterone; advanced disease; analytical method; base; bench to bedside; blood treatment; cancer cell; cancer therapy; carcinogenesis; chemotherapy; clinical application; clinical imaging; clinical translation; cost; data acquisition; deprivation; design; dynamic system; effective therapy; fitness; hormone therapy; host neoplasm interaction; improved; in vivo; individual patient; interdisciplinary approach; mathematical model; men; method development; multidisciplinary; novel; novel therapeutics; oncology; outcome prediction; physical science; pilot trial; pre-clinical; predict clinical outcome; predictive modeling; programs; prospective; prospective test; public health relevance; response; spatiotemporal; therapy outcome; therapy resistant; treatment optimization; treatment strategy; trial design; tumor; virtual

 DESCRIPTION (provided by applicant): Moffitt PSOC Here we focus on two deeply interconnected physical science questions: "How do we study, quantify, integrate, and model the complexity of cancer biology and treatment across multiple length and time scales that form the tumor ecology?" and "Can the evolutionary dynamics of therapeutic resistance be exploited through dynamic spatio-temporal models to optimize treatments and improve the lives of patients with cancer?" We propose that cancer must be investigated and treated as a complex adaptive systems in which the underlying first principles are Darwinian. We view intratumoral evolution as a dynamical interaction between environmental selection forces and tumor adaptive strategies to maximize fitness. A critical property of cancer complex system is that it is open and thus can be perturbed by host response and iatrogenic interventions. Thus, the multi-scale (e.g. molecular, cellular and tissue scales) spatio-temporal variations within and between cancers (i.e. the "ecology" of cancer) is dependent in large part on the open components of the system such as alterations in blood flow that affect local environmental conditions and subsequent cellular adaptive strategies. Similarly, the Darwinian response to therapy will vary within each habitat within the tumor ecology and must be understood to design consistently effective therapies. We approach these questions in two different ways: In project 1 we focus on fundamental principles - the cancer cell evolutionary dynamics and molecular mechanisms that permit adaptation to host-generated perturbations including blood flow and treatment strategies. Here the focus will be on sophisticated in-vitro and in-vivo experimental methods integrated with Darwinian-based mathematical models. A key deliverable from Project 1 is identification of novel therapeutic strategies that can exploit these evolutionary dynamics and molecular mechanism to improve clinical therapy. In Project 2 we will focus on developing computational models that use first principles and available clinical data to: 1. understand the patient-specific dynamics that govern response and resistance and 2. develop computational models that predict the outcomes of different therapies (e.g. multidrug chemotherapy, immunotherapy, and hormone therapy ) in individual patients. In the longer term our goal is to increase the scope of these models to permit design of patient-specific therapy to optimize overall survival. The deliverable from Project 2, therefore, include development of methods to extract maximum amounts of information from clinically available data and development of computational models to optimize clinical therapy using often sparse dynamic data. Both Projects will interact extensively with a core focused on developing computational models and applying sophisticated analytic methods to extract maximum knowledge from available molecular, pathological, and radiological clinical data.

 描述（由申请人提供）：Moffitt PSOC在这里，我们重点关注两个相互关联的物理科学问题：“我们如何在形成肿瘤生态的多个长度和时间尺度上研究、量化、整合和建模癌症生物学和治疗的复杂性？以及“能否通过动态时空模型利用治疗耐药性的进化动力学来优化治疗并改善癌症患者的生活？”“我们认为，癌症必须作为一个复杂的适应性系统来研究和治疗，其中的基本原则是达尔文主义。我们认为肿瘤内的进化是环境选择力和肿瘤适应策略之间的动态相互作用，以最大限度地提高适应性。癌症复杂系统的一个关键特性是它是开放的， 因此可能受到宿主反应和医源性干预的干扰。因此，癌症内和癌症之间的多尺度（例如分子、细胞和组织尺度）时空变化（即癌症的“生态学”）在很大程度上取决于系统的开放组件，例如影响局部环境条件的血流改变和随后的细胞适应策略。类似地，达尔文对治疗的反应在肿瘤生态中的每个栖息地内都会有所不同，必须理解以设计一致有效的治疗方法。 我们以两种不同的方式处理这些问题：在项目1中，我们专注于基本原则-癌细胞进化动力学和分子机制，允许适应宿主产生的扰动，包括血流和治疗策略。在这里，重点将是复杂的体外和体内实验方法与基于达尔文主义的数学模型相结合。项目1的一个关键成果是确定新的治疗策略，可以利用这些进化动力学和分子机制来改善临床治疗。在项目2中，我们将专注于开发使用第一原理和可用临床数据的计算模型：1。了解控制反应和阻力的患者特异性动力学; 2.开发计算模型，预测不同疗法（如多药化疗、免疫疗法和激素疗法）在个体患者中的结果。从长远来看，我们的目标是增加这些模型的范围，以允许设计患者特异性治疗，以优化总生存率。因此，项目2的可交付成果包括开发从临床可用数据中提取最大量信息的方法，以及开发使用通常稀疏的动态数据优化临床治疗的计算模型。这两个项目将与一个专注于开发计算模型和应用复杂的分析方法的核心广泛互动，以从可用的分子，病理和放射临床数据中提取最大的知识。