Cancer as a Complex Adaptive System

癌症作为一个复杂的适应系统

基本信息

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
项目类别：
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

项目摘要

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 这里我们关注两个紧密相连的物理科学问题：“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？”和“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？”我们建议必须将癌症作为一个复杂的适应性系统进行研究和治疗，其中潜在的第一原则我们认为肿瘤内进化是环境选择力和肿瘤适应策略之间的动态相互作用，以最大化癌症复杂系统的一个关键特性是它是开放的和开放的。因此，癌症内部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”）在很大程度上取决于宿主反应和医源性干预。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性策略，类似地，达尔文对治疗的反应在肿瘤生态内的每个栖息地中都会有所不同，必须理解这一点才能设计一致有效的方案。我们以两种不同的方式解决这些问题：在项目 1 中，我们关注基本原理——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。项目 1 的一个关键成果是确定可以利用这些进化动力学和分子机制来改善临床治疗的新型治疗策略。开发使用第一原理的计算模型可用的临床数据可以： 1. 了解控制反应和耐药性的患者特异性动态； 2. 开发预测不同疗法（例如多药化疗、免疫疗法和激素疗法）在个体患者中的长期结果的计算模型。我们的目标是扩大这些模型的范围，以便设计针对患者的治疗方案，从而优化总体生存率，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。经常使用来优化临床治疗这两个项目将主要与专注于开发计算模型和应用复杂的分析方法从可用的分子、病理和放射临床数据中提取最大知识的核心进行交互。