Instability of Cancer Cell States in Tumor progression (ICCS)

项目概要 ICCS2020-A1 这个多 PI 项目进行实验来研究肿瘤细胞中的细胞状态不稳定性，其动机是 “关键转变”（CT）。 CT 是复杂非线性系统行为的突然转变，之前是 系统状态不稳定。癌细胞群代表细胞的统计集合，其中每个细胞都是 非线性随机动力系统。后者由基因调控网络（GRN）和细胞体现 通常处于稳定吸引子状态。我们假设小病变中的癌细胞可能处于 要么保持休眠，要么退出休眠（“逃脱”），这种二元决策就是 CT。这意味着要 处于这种平衡状态时，细胞状态必然会变得不稳定。因此，检测细胞状态不稳定性，表现在 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。很多 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” 动力学，通过 CT 的不稳定性，癌细胞群可以跳跃到稳定生长的状态。 具体目标。拟议的研究是实验性的，但有理论依据：细胞状态不稳定性表现在 我们从理论中得出的 IC 数量的增加需要人口中的单细胞 (sc) 转录组 计算关系（= GRN 统计集合的动态）。目标 1（体外）使用大型微系统集合 培养物（=癌细胞群）以定量显示生长行为的不稳定和分歧。 目标 2（体内）以新方案重新评估旧的小鼠肿瘤模型，该方案揭示了二元决策（dor- mancy 与“tumor-take”）来检验临床休眠逃逸之前细胞状态不稳定的假设。 方法：在目标 1 中，使用大规模并行微培养物、批量 RNASeq 和 scRNAseq，我们检查了 erto 无法区分癌细胞的生长模式，并测量双稳定性作为细胞密度的函数（休眠 与“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 通过滴定创建休眠肿瘤中的接种细胞数量来识别，一些小鼠表现出稳定的休眠状态 尽管初始条件相同，曼西等人仍表现出强劲的肿瘤摄取能力。这一发现表明一种平静的状态 定义了双稳态状态。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，以揭示 双稳态行为和 Ic 根据 scRNAseq 数据计算。我们预计处于不稳定休眠状态的肿瘤已做好准备 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sc转录组 还将揭示驱动 CT 的基因以及它们如何与即将发生的休眠逃逸风险相关。 意义：虽然这项一流的研究分析的是抽象原理而不是具体的分子，但它的意义在于： 潜在影响是有形的：它以新的方式预测惰性肿瘤的命运轨迹，补充了当前的 通过在单细胞分辨率细胞中进行检测，寻求分子特征以按预后组对肿瘤进行分类 人口数据表明不稳定的迹象预示着即将到来的 CT 或休眠逃逸的“临界点”。 这项工作还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。

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.