Feedback, lineages and cancer: A multidisciplinary approach.

反馈、谱系和癌症：多学科方法。

• 批准号: 7855432
• 负责人: Arthur D Lander
• 金额: $ 108.66万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7855432
• 关键词: Affect; Air; Aircraft; Algorithms; Altitude; Animal Model; Animals; Apoptotic; Architecture; Area; Arts; Behavior; Biology; Cancer Diagnostics; Cancer Patient; Cell Count; Cell Cycle; Cell Fraction; Cell Lineage; Cell Proliferation; Cells; Characteristics; Classification; Collaborations; Complex; Computer Simulation; Computers; Data; Diagnosis; Discipline; Disease; Engineering; Environment; Equilibrium; Evolution; Exhibits; Feedback; Force of Gravity; Goals; Growth; Helicopter; Heterogeneity; Image; Imaging Techniques; Individual; Interdisciplinary Study; Learning; Life; Lifting; Machine Learning; Malignant Neoplasms; Maps; Mathematics; Medicine; Methodology; Methods; Minority; Modeling; Natural History; Natural regeneration; Nature; Normal tissue morphology; Organ; Patients; Population; Process; Proliferating; Property; Research; Research Personnel; Scientific Advances and Accomplishments; Scientist; Services; Shapes; Solid Neoplasm; Staging; Stem cells; Surgical Flaps; System; Systems Biology; Testing; Therapeutic; Therapeutic Effect; Therapeutic Intervention; Time; Tissues; Transplantation; United States National Institutes of Health; Validation; Visual; Wing; Work; analytical method; base; cancer care; cancer stem cell; cell growth; complex biological systems; computer science; insight; interdisciplinary approach; malignant breast neoplasm; mathematical model; method development; mouse model; multidisciplinary; neoplastic cell; novel strategies; outcome forecast; prognostic; public health relevance; response; self-renewal; simulation; spatiotemporal; tumor; tumor growth; tumor initiation

DESCRIPTION (provided by applicant): Cancer is a disorder of unrestrained cell proliferation, but increasingly it seems that not all proliferating cells in a tumor matter equally. As with cells in normal tissues, tumor cells appear to progress through lineage stages, in which the capacity for unlimited self-renewal is, at some point, lost. The cancer stem cell hypothesis states that cancer diagnostic, prognostic and therapeutic efforts need to be focused on that population of cells-often a small minority-that undergoes long-term self-renewal. While this hypothesis acknowledges the existence of lineage progression in cancers, it is silent on the function that lineages normally serve. We recently found, through experimental and theoretical work, that a likely raison-d'etre for lineages is to provide a framework for powerful feedback control of growth and regeneration, through mechanisms that target the differentiation decisions of individual cells. For cancer to develop, such feedback control must be disrupted, and the natural history of most tumors suggests that it becomes disrupted progressively over time. Our studies indicate that what happens in a tissue when feedback is compromised can be very complex, yet still understandable and predictable. We argue, therefore, that from the details of how a tumor develops over time-size, shape, growth rate, stem cell fraction, etc.-one ought to be able to infer specific information about the kinds of control processes that operate (or recently operated) within the tumor and its surrounding environment. Such information can both provide insight into how different types of tumors develop, as well as patient-specific information about prognosis and the effects of therapy. The proposed project focuses on learning how to obtain such information from the observable properties of tumors. Three-dimensional mathematical models that incorporate various types of lineage progression, feedback, evolutionary processes, and therapeutic interventions will first be created, analyzed, and used to generate large numbers of simulations of solid tumor growth and progression. From these results, mappings from tumor properties to feedback and lineage architectures will be found through state-of-the art machine-learning algorithms. The ability of these mappings to reproduce and predict the behaviors of real tumors will be assessed using established animal models of breast cancer, in which luminescent and fluorescent imaging techniques are used to follow tumors, and their stem cells, over time. This will enable the validation of particular model architectures, or suggest methods for their refinement, and allow the determination of control strategies at work in tumors that can be exploited to provide a leap forward in both personalized medicine and cancer care. What makes this project a "grand opportunity" is the pursuit of rapid progress through a highly multidisciplinary team that will draw on new advances in the areas of cell lineage behaviors, cancer stem cells, three-dimensional mathematical and computational modeling, and machine-learning. PUBLIC HEALTH RELEVANCE: Tumors arise when the feedback control of cell growth breaks down. We hypothesize that, within the details of how a tumor grows lie important clues about the nature of feedback processes-including those that still remain or may be re-activated. By describing how such clues can be found, we will be defining a new approach for predicting how individual tumors behave in cancer patients, and how they respond to different kinds of therapy.

描述（由申请人提供）：癌症是一种不受约束的细胞增殖疾病，但越来越多的肿瘤似乎并非所有肿瘤中的增殖细胞都平等。与正常组织中的细胞一样，肿瘤细胞似乎通过谱系阶段进展，其中无限自我更新的能力在某些时候会丢失。癌症干细胞假设指出，癌症的诊断，预后和治疗努力需要集中于那些通常少数少数细胞的人群 - 长期自我更新。尽管该假设承认癌症中的谱系进展存在，但它对谱系通常使用的功能保持沉默。我们最近通过实验和理论工作发现，谱系可能存在的理由是通过针对单个细胞的分化决策的机制，为对生长和再生的强大反馈控制提供了一个框架。为了使癌症发展，这种反馈控制必须被破坏，并且大多数肿瘤的自然病史表明它会随着时间的流逝而逐渐破坏。我们的研究表明，当反馈受到损害时，组织中发生的事情可能非常复杂，但仍然可以理解和可预测。因此，我们认为，从肿瘤如何在肿瘤及其周围环境中运行（或最近操作）的控制过程（或最近操作）的特定信息中推断出有关特定的控制过程的特定信息。这些信息都可以提供有关不同类型肿瘤的发展以及有关预后和治疗作用的患者特定信息的见解。拟议的项目着重于学习如何从可观察到的肿瘤特性中获取此类信息。将首先创建，分析和用于生成大量的实体肿瘤生长和进展模拟。从这些结果来看，将通过最先进的机器学习算法找到从肿瘤特性到反馈和谱系架构的映射。这些映射的繁殖和预测实际肿瘤行为的能力将使用已建立的乳腺癌动物模型进行评估，其中使用发光和荧光成像技术跟随肿瘤及其干细胞随着时间的流逝。这将实现特定模型架构的验证，或提出其完善方法的方法，并允许确定可以利用的肿瘤工作中的控制策略，这些策略可以被利用，以在个性化医学和癌症护理中提供飞跃。使该项目成为“巨大机会”的原因是通过一个高度多学科的团队追求快速进步，该团队将借鉴细胞谱系行为，癌症干细胞，三维数学和计算建模以及机器学习领域的新进步。 公共卫生相关性：当细胞生长的反馈控制破裂时，会出现肿瘤。我们假设，在肿瘤如何生长的细节中，关于反馈过程的性质 - 包括仍然存在或可能被重新激活的反馈过程的性质重要。通过描述如何找到这些线索，我们将定义一种预测单个肿瘤在癌症患者中的行为以及他们如何应对不同疗法的新方法。