Cancer as a Complex Adaptive System

 描述(由申请人提供)：Moffitt PSOC这里我们关注两个相互关联的物理科学问题：“我们如何研究、量化、集成癌症生物学和治疗的复杂性，并对形成肿瘤生态的多个长度和时间尺度进行建模？”以及“能否通过动态时空模型利用抗药性的进化动态来优化治疗并改善癌症患者的生活？”我们认为，必须将癌症作为一个复杂的适应性系统来研究和对待，在这个系统中，潜在的第一原理是达尔文的。我们认为肿瘤内的进化是环境选择力量和肿瘤适应策略之间的动态相互作用，以最大限度地适应。癌症复杂系统的一个关键属性是它是开放的和 因此可以被宿主反应和医源性干预所干扰。因此，癌症内部和癌症之间的多尺度(如分子、细胞和组织尺度)时空差异(即癌症的“生态”)在很大程度上依赖于系统的开放组成部分，例如影响局部环境条件的血流变化和随后的细胞适应策略。同样，达尔文主义对治疗的反应在肿瘤生态内的每个栖息地都会有所不同，必须被理解为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，我们专注于基本原理--癌细胞进化动力学和允许适应宿主产生的扰动的分子机制，包括血流和治疗策略。在这里，重点将集中在复杂的体外和体内实验方法与达尔文的数学模型相结合。项目1的一个关键成果是确定新的治疗策略，这些策略可以利用这些进化动力学和分子机制来改进临床治疗。在项目2中，我们将重点开发使用第一原理和现有临床数据的计算模型，以：1.了解控制反应和耐药性的患者特定动态，以及2.开发预测不同治疗方法(例如，多药化疗、免疫治疗和激素治疗)在个别患者中的结果的计算模型。从长远来看，我们的目标是扩大这些模型的范围，以允许设计针对患者的治疗方法，以优化总体存活率。因此，项目2的成果包括开发从临床可用数据中提取最大量信息的方法，以及开发计算模型以使用通常稀疏的动态数据来优化临床治疗。这两个项目都将与一个核心广泛互动，该核心专注于开发计算模型和应用复杂的分析方法，从现有的分子、病理和放射临床数据中提取最大限度的知识。

 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.