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）：癌症是一种不受限制的细胞增殖的疾病，但越来越多地发现肿瘤中并非所有增殖细胞都同等重要。与正常组织中的细胞一样，肿瘤细胞似乎经历了谱系阶段，其中无限自我更新的能力在某些时候会丧失。癌症干细胞假说指出，癌症诊断、预后和治疗工作需要集中于经历长期自我更新的细胞群（通常是一小部分）。虽然这一假说承认癌症中存在谱系进展，但它对谱系通常发挥的功能却没有提及。我们最近通过实验和理论工作发现，谱系存在的一个可能的目的是通过针对单个细胞分化决策的机制，为生长和再生的强大反馈控制提供框架。对于癌症的发展，这种反馈控制必须被破坏，大多数肿瘤的自然史表明它会随着时间的推移而逐渐被破坏。我们的研究表明，当反馈受到损害时，组织中发生的情况可能非常复杂，但仍然是可以理解和预测的。因此，我们认为，从肿瘤如何随时间发展的细节——大小、形状、生长速度、干细胞分数等——人们应该能够推断出有关运行的控制过程类型的具体信息（或最近进行过手术）在肿瘤及其周围环境内。这些信息既可以深入了解不同类型的肿瘤如何发展，也可以提供有关预后和治疗效果的患者特定信息。拟议的项目重点是学习如何从肿瘤的可观察特性中获取此类信息。首先将创建、分析并使用包含各种类型的谱系进展、反馈、进化过程和治疗干预的三维数学模型来生成大量实体瘤生长和进展的模拟。根据这些结果，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。这些映射复制和预测真实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，其中发光和荧光成像技术用于随着时间的推移跟踪肿瘤及其干细胞。这将能够验证特定模型架构，或提出改进方法，并确定肿瘤中的控制策略，这些策略可用于实现个性化医疗和癌症护理的飞跃。该项目之所以成为“巨大机遇”，是因为通过高度跨学科的团队追求快速进展，该团队将利用细胞谱系行为、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展。 公共卫生相关性：当细胞生长的反馈控制崩溃时，肿瘤就会出现。我们假设，在肿瘤生长的细节中存在着关于反馈过程性质的重要线索——包括那些仍然存在或可能被重新激活的反馈过程。通过描述如何找到这些线索，我们将定义一种新方法来预测癌症患者个体肿瘤的表现，以及它们对不同类型治疗的反应。