Instability of Cancer Cell States in Tumor progression (ICCS)

项目总结 ICCS2020-A1 这个多PI项目进行了实验，以研究肿瘤细胞的细胞状态不稳定性，动机是 “关键转变”(CT)。CTS是复杂非线性系统行为的突变，前面有 系统状态不稳定。癌细胞群体代表了细胞的统计集合，每个细胞都是 一个非线性随机动力系统。后者体现为基因调控网络(GRN)和细胞 通常处于稳定的吸引子状态。我们假设小病变中的癌细胞可以稳定在 要么保持休眠状态，要么退出休眠状态(“逃逸”)，这一二元决策是一种CT。这意味着要 处于如此稳定的状态，细胞状态必须不稳定。因此，检测细胞状态不稳定性，在 细胞转录本，可以识别一个小的肿瘤是安全地处于稳定状态还是在上面的意义上稳定。许多 一项观察表明，潜伏期肿瘤的细胞密度可能是驱动GRN的一个“分叉参数” 动力学，通过对CT的不稳定，在这种情况下，癌细胞群体可以跳到稳定增长的状态。 明确的目标。这项拟议的研究是实验性的，但有理论基础：细胞状态的不稳定性在 我们从理论上推导出的数量IC的增加，并要求在一个人口中有单细胞转录本。 计算(=GRN统计系综的动力学)。目标1(体外)使用了大量的微型 培养(=癌细胞群体)，以定量显示生长行为的不稳定和分叉。 AIM 2(体内)在一个新的方案中重新评估了旧的小鼠肿瘤模型，该方案揭示了二元决策(DOR-DOR-1)。 Mancy vs“Tumor-Take”)，以检验临床休眠逃脱在细胞状态不稳定之前的假设。 方法：在目标1中，使用大规模平行微培养、Bulk RNAseq和scRNAseq，我们检查了Hith- ERTO无法区分癌细胞的生长模式，并测量作为细胞密度(休眠)的函数的双稳态 对“起飞”)。在目标2中，我们在许多小鼠模型中检查了我们有趣的观察结果：在特定条件下- 通过滴定接种细胞数来鉴定在产生潜伏期肿瘤方面，一些小鼠表现出稳定的死亡。 Mancy和其他人尽管初始条件相同，但仍有很强的肿瘤摄取力。这一发现表明了一种镇定的状态 定义了一种双稳制度。使用AIM 1中研究的细胞的肿瘤模型将在我们的计划中进行评估，以揭示 由scRNAseq数据计算的双稳态行为和IC。我们预计处于不稳定休眠状态的肿瘤 起飞的肿瘤细胞状态的不稳定性(IC)高于稳定期休眠的肿瘤。但sc-转录本 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脱风险联系在一起。 意义：虽然这项首创的研究分析的是抽象原理而不是具体的分子，但它的 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，补充目前的情况 寻找分子特征，通过在单细胞分辨细胞中检测，按预后分组对肿瘤进行分类 人口数据显示出不稳定的迹象，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，为设计更相关的动物肿瘤模型奠定了基础。

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.