Instability of Cancer Cell States in Tumor progression (ICCS)

肿瘤进展过程中癌细胞状态的不稳定性 (ICCS)

• 批准号: 10491691
• 负责人: Amy Brock
• 金额: $ 47.94万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-21 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10491691
• 关键词: Animal Model; Animals; Awareness; Behavior; Biological; Biological Markers; Cell Count; Cell Culture Techniques; Cell Density; Cell physiology; Cells; Characteristics; Climate; Clinical; Complement; Complex; Data; Dependence; Detection; Diagnosis; Ecosystem; Event; Exhibits; Exposure to; Genes; Genetic; Goals; Growth; In Vitro; Indolent; Lesion; Link; Logistics; Malignant Neoplasms; Mathematics; Measurable; Measures; Microscopic; Modeling; Molecular Profiling; Mus; Mutation; Pathway interactions; Patients; Pattern; Phase; Phenotype; Play; Population; Population Dynamics; Population Growth; Population Process; Pre-Clinical Model; Recurrence; Regulator Genes; Regulatory Pathway; Relapse; Research Personnel; Resolution; Risk; Role; Scheme; Screening for cancer; Signal Transduction; Stochastic Processes; Stratification; System; Systems Theory; Testing; Translating; Tumor Escape; Work; austin; base; cancer cell; cancer genome; cancer invasiveness; cell dimension; design; driver mutation; dynamic system; experimental study; gene regulatory network; genome sequencing; high dimensionality; in vivo; indexing; mouse model; mutant; neoplastic cell; non-genetic; novel strategies; prognostic; single cell analysis; theories; transcriptome; transcriptome sequencing; tumor; tumor progression

PROJECT SUMMARY ICCS2020-A1 This multi-PI project conducts experiments to study cell state instability in tumor cells, motivated by the theory of “critical transitions” (CT). CTs are abrupt shifts of behavior of a complex non-linear system and are preceded by system state destabilization. A cancer cell population represents a statistical ensemble of cells, each of which is a nonlinear stochastic dynamical system. The latter is embodied by the gene regulatory network (GRN) and cells are normally in stable attractor states. We hypothesize that cancer cells in small lesions can be poised between either staying dormant or exiting dormancy (“escape”) and that this binary decision is a CT. This implies that to be in such a poised state, the cell state has to be destabilized. Thus, detecting cell state instability, manifest in the cell transcriptomes, can discern if a small tumor is safely in a stable state or poised in the above sense. Many an observation suggests that cell density of the dormant tumor may be a “bifurcation parameter” that drives GRN dynamics, via instability toward the CT, at which a cancer cell population can jump to the state of steady growth. SPECIFIC AIMS. The proposed study is experimental but grounded in theory: Cell state instability is manifest in an increase of the quantity IC that we derived from theory and requires single-cell (sc) transcriptomes in a popu- lation to compute (=dynamics of a statistical ensemble of GRNs). Aim 1 (in vitro) uses large ensembles of micro- cultures (=cancer cell populations) to quantitatively show destabilization and bifurcations of growth behaviors. Aim 2 (in vivo) reevaluates old mouse tumor models in a new scheme that exposes the binary decision (dor- mancy vs. “tumor-take”) to test the hypothesis that clinical dormancy escape is preceded by cell state instability. APPROACH: In Aim 1, using massively-parallel micro-cultures, bulk RNASeq and scRNAseq, we examine hith- erto undistinguished growth modes of cancer cells and measure bistability as a function of cell density (dormancy vs. “take-off”). In Aim 2 we examine our intriguing observations in many mouse models: under specific condi- tions, identified by titrating inoculum cell numbers in creating dormant tumors, some mice exhibit stable dor- mancy and others a robust tumor-take despite same initial conditions. This finding suggests a poised state and defines a bistable regime. Tumor models using cells studied in Aim 1 will be evaluated in our scheme to expose bistable behaviors and Ic computed from scRNAseq data. We anticipate that tumors in unstable dormancy poised to take-off display higher cell state instability (higher IC) than the stably dormant tumors. But sc-transcriptomes will also reveal the genes that drive the CT and how they are linked to the risk of impending dormancy escape. SIGNIFICANCE: While this first-in-its-class study analyzes abstract principles rather than specific molecules, its potential impact is tangible: It predicts the fate trajectory of indolent tumors in a new way, complementing current quest for molecular signatures to classify tumors by prognostic groups, by detecting in single-cell resolution cell population data signs of destabilization that herald an approach to the CT or “tipping point” of dormancy escape. This work also raises awareness of non-linear behaviors for the design of more relevant animal tumor models.

项目摘要 ICCS2020-A1 这个多PI项目进行实验，研究肿瘤细胞中的细胞状态不稳定性，其动机是 “临界过渡”（CT）。CT是复杂的非线性系统的行为的突然转变，并且在其之前是 系统状态不稳定。癌细胞群体代表细胞的统计学集合，每个细胞是 非线性随机动力系统。后者通过基因调控网络（GRN）和细胞来体现 通常处于稳定的吸引子状态。我们假设，小病灶中的癌细胞可以平衡在 或者保持休眠或者退出休眠（“逃逸”），并且该二元决策是CT。这意味着， 在这种平衡状态下，细胞状态必须不稳定。因此，检测细胞状态不稳定性， 细胞转录组可以辨别小肿瘤是否安全地处于稳定状态或处于上述意义上的平衡状态。许多 一项观察表明，休眠肿瘤的细胞密度可能是驱动GRN的“分叉参数”， 动态，通过对CT的不稳定性，癌细胞群体可以跳跃到稳定生长的状态。 具体目标。这项研究是实验性的，但有理论基础：细胞状态的不稳定性表现在 我们从理论上推导出的IC数量的增加，需要在一个流行的单细胞（sc）转录组， 要计算的lation（= GRN统计系综的动力学）。Aim 1（体外）使用大量的微- 培养物（=癌细胞群体），以定量显示生长行为的不稳定性和分叉。 目的2（体内）重新评估旧的小鼠肿瘤模型在一个新的计划，暴露了二元决策（或- 休眠与“肿瘤摄取”）来检验临床休眠逃逸之前是细胞状态不稳定性的假设。 方法：在目标1中，使用平行的微培养物，批量RNASeq和scRNAseq，我们检查了hith- 从而确定癌细胞的无差别生长模式，并测量作为细胞密度（休眠）函数的双稳态 vs.“起飞”）。在目标2中，我们研究了我们在许多小鼠模型中有趣的观察结果：在特定条件下， 通过滴定接种物细胞数量来确定，一些小鼠表现出稳定的dor， Mancy和其他人尽管初始条件相同，但仍具有强大的肿瘤发生能力。这一发现表明，一个平静的状态， 定义了一个新的政权。使用目标1中研究的细胞的肿瘤模型将在我们的方案中进行评估，以暴露 根据scRNAseq数据计算的生物学行为和Ic。我们预计处于不稳定休眠状态的肿瘤 与稳定休眠的肿瘤相比，起飞显示出更高的细胞状态不稳定性（更高的IC）。但是sc转录组 还将揭示驱动CT的基因，以及它们如何与即将到来的休眠逃逸的风险相关联。 意义：虽然这项一流的研究分析了抽象的原则，而不是具体的分子， 潜在的影响是有形的：它以一种新的方式预测惰性肿瘤的命运轨迹， 通过在单细胞分辨率细胞中检测，寻求通过预后组对肿瘤进行分类的分子特征 种群数据不稳定的迹象，预示着接近CT或休眠逃逸的“临界点”。 这项工作也提高了人们对非线性行为的认识，以设计更相关的动物肿瘤模型。