Predicting the Heterogeneity of Cell-Fate Decisions

描述（由申请人提供）：在癌症中，遗传异质性是许多研究的焦点，因为它通过驱动表型多样性在肿瘤进展和耐药性中发挥重要作用。在这里，我们考虑另一种类型的异质性，其中在遗传上相同的哺乳动物细胞中蛋白质水平的自然细胞间变异性引起相同的刺激 从而产生不同的细胞命运，例如生或死。我们称这种现象为“自然表型分化”（NPD）。NPD可以表现为例如细胞的持续抗癌药物抗性亚群，并且理解它对于预测癌症治疗功效是重要的。 然而，从基于细胞的实验中预测NPD的方法尚未开发，并且是该提案的主题。我们假设NPD可以通过表征多变量内源性蛋白质表达噪声如何通过信号网络非线性传播来调节细胞命运来预测。它是信号网络内多种蛋白质水平的内源性表达和降解噪音，共同表现为NPD。我们将通过结合实验和计算方法来检验这一假设，以检查基于NPD的非转化MCF10A细胞增殖。这种增殖由表皮生长因子、胰岛素和皮质醇的组合诱导，并由ERK、Akt、JNK和SGK途径的激活介导。首先，在实验上，我们将使用活细胞成像方法与FRET探针来测量实时信号网络的动态和增殖的同时。虽然我们一次只能测量一个途径，但我们随后使用的计算，动态模块化反应分析理论使我们能够重建这些途径如何以刺激特异性方式动态相互作用，以控制随机增殖的命运。其次，我们将建立一个基于化学动力学的随机计算模型，模拟NPD背后的蛋白质表达变异性如何传播到信号动力学异质性。该模型的分析将提出一组关键蛋白质，其集体的多元波动对基于NPD的增殖有很大的影响。最后，我们将测量单个活细胞中这些关键蛋白质水平的波动，使用我们的计算模型来预测这些细胞是否应该增殖或不响应定义的扰动，并通过观察这些细胞中的实际增殖决定来测试预测。我们将测试这样的预测不仅在标准的2D细胞培养模型，而且在3D文化腺泡形成的背景下。如果成功的话，这将是第一次证明可以基于扰动之前存在的生物标志物来预测单个活细胞的随机命运。这将是为个体患者确定生物标志物集和制定个性化治疗策略的重要一步。

DESCRIPTION (provided by applicant): In cancer, genetic heterogeneity is the focus of many investigations as it plays important roles in tumor progression and drug resistance by driving phenotypic diversity. Here, we consider another type of heterogeneity, one where natural cell-to-cell variability in protein levels in genetically-identical mammalian cells causes the same stimuli to yield different cell fates, such as life or death. We term this phenomenon "natural phenotypic divergence" (NPD). NPD can manifest as, for example, a persistent anticancer drug resistant subpopulation of cells, and understanding it is important for predicting cancer treatment efficacy. However, means to predict NPD from cell-based experiments have not been developed and are the subject of the proposal. We hypothesize that NPD can be predicted by characterizing how multivariate, endogenous protein expression noise is propagated non-linearly through signaling networks to regulate cell fate. It is the endogenous expression and degradation noise in the levels of multiple proteins within a signaling network that collectively manifest as NPD. We will test this hypothesis by combining experimental and computational approaches to examine NPD-based proliferation of non-transformed MCF10A cells. This proliferation is induced by combinations of epidermal growth factor, insulin, and cortisol and mediated by activation of the ERK, Akt, JNK, and SGK pathways. First, experimentally, we will use live-cell imaging approaches with FRET probes to measure real-time signaling network dynamics and proliferation simultaneously. Although we can only measure one pathway at a time, our subsequent use of computational, dynamic modular response analysis theory allows us to reconstruct how these pathways dynamically interact in a stimulus-specific fashion to control stochastic proliferation fates. Second, we will build a chemical kinetics-based, stochastic computational model that simulates how the protein expression variability underlying NPD propagates into signaling dynamics heterogeneity. Analysis of this model will suggest sets of key proteins whose collective, multivariate fluctuations have a large influence on NPD-based proliferation. Finally, we will measure fluctuations in the levels of these key proteins in single ive cells, use our computational models to predict whether these cells should proliferate or not in response to defined perturbations, and test the predictions by observing the actual proliferation decision in those same cells. We will test such predictions not only in standard 2D cell culture models, but also in the context of 3D culture acini formation. If successful, this would be the firt demonstration that the stochastic fates of individual live cells could be predicted based on biomarkers present prior to perturbation. This would be an important step towards identifying biomarker sets for individual patients and fashioning personalized therapeutic strategies.