Feedback, lineages and cancer: A multidisciplinary approach.

描述(申请人提供)：癌症是一种细胞不受抑制的增殖障碍，但似乎并不是所有的增殖细胞在肿瘤中都有同等的作用。与正常组织中的细胞一样，肿瘤细胞似乎经历了谱系阶段，在这一阶段，无限自我更新的能力在某种程度上丧失了。癌症干细胞假说指出，癌症诊断、预后和治疗的努力需要集中在经历长期自我更新的细胞群体--通常是一小部分--。虽然这一假说承认了癌症中谱系进展的存在，但它对谱系通常所起的作用却只字不提。我们最近通过实验和理论工作发现，对于谱系来说，一个可能的理由是通过针对单个细胞的分化决定的机制，为生长和再生提供一个强大的反馈控制框架。对于癌症的发展，这种反馈控制必须被打乱，大多数肿瘤的自然历史表明，随着时间的推移，它会逐渐被打乱。我们的研究表明，当反馈被破坏时，组织中发生的事情可能非常复杂，但仍然可以理解和预测。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、干细胞比例等--应该能够推断出关于在肿瘤及其周围环境中操作(或最近操作)的控制过程的类型的具体信息。这些信息既可以提供对不同类型肿瘤如何发展的洞察，也可以提供关于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得此类信息。首先将创建、分析和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肿瘤生长和进展的模拟。从这些结果中，将通过最先进的机器学习算法找到从肿瘤属性到反馈和谱系结构的映射。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，在该模型中，随着时间的推移，发光和荧光成像技术被用于跟踪肿瘤及其干细胞。这将使验证特定的模型架构成为可能，或建议改进它们的方法，并允许确定在肿瘤中发挥作用的控制策略，这些策略可以被利用来在个性化药物和癌症护理方面实现飞跃。这个项目之所以成为一个“重大机遇”，是因为通过一个高度多学科的团队追求快速发展，这个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等领域的新进展。 与公共健康相关：当细胞生长的反馈控制失效时，肿瘤就会出现。我们假设，在肿瘤如何生长的细节中，隐藏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过程。通过描述如何找到这样的线索，我们将定义一种新的方法来预测癌症患者的个体肿瘤行为，以及它们对不同类型的治疗的反应。

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.