Steady states and cellular transitions associated with carcinogenesis and tumorprogression

项目总结/摘要 细胞转变是癌发生和肿瘤进展的许多步骤的基础。这种过渡 被广泛研究，但一般模型历来限于定性描述。此相反 物理系统中的相变，在物理学的背景下， 化学定律，并可以部分理解，在预测能力，使用简单，精确的模型，如 伊辛模型这样的模型是基于一个系统的相互作用的晶格网站。参数（例如， 温度）是变化的，并且随着系统的接近和 通过一个临界点。所有关键的系统特定细节都在 晶格位置，并且模型可以产生具体的、实验可验证的预测。Ising样计算机模型 已经指导了各种基因或蛋白质调控网络中的转换的理论研究，尽管结果 预测对于实验测试来说是具有挑战性的。 我们寻求一种通用的方法，其中实验输入是统计上大量的单细胞 测量，其中每个细胞定量测量许多蛋白质和代谢物分析物。根据这些数据，我们 捕获波动，从而确定分析物-分析物相关性。在伊辛模型中， 这样的测量定义了现场相互作用。这些输入允许直接的理论模型， 解析细胞稳定状态、稳定状态之间的转换，以及进行可测试的预测。研究 化学诱导的致癌转变提供了初步数据/概念验证。对于目标1， 使用整合的代谢和蛋白质组学单细胞测定， 多形性胶质母细胞瘤和黑素瘤的癌症模型。在目标2和目标3中，我们将这一方法扩展到两个 与靶向治疗耐药相关的明显细胞转变： 异质性脑癌对某些靶向抑制剂，以及药物诱导的细胞去分化 在黑色素瘤和其他肿瘤中观察到对免疫疗法和靶向抑制剂的反应。所有目标都是 实验/理论目标。目的2-3涉及预测的体内测试，以及外显子组测序， 全球RNA-seq动力学研究，以补充单细胞动力学分析。 预期成果的工作包括一个一般的，定量的方法来描述细胞 与癌症有关的转变。此外，我们建议挖掘这些描述的细胞过渡， 确定旨在达到驱动肿瘤生长的目标的治疗组合，以及那些 推动过渡（从而促进耐药性）提供了支持这一目标的初步数据。 此外，非连续治疗给药（例如，节拍或脉冲方案）的指南， 人们期望了解向耐药转变的动力学、障碍和可逆性

Project Summary/Abstract Cellular transitions are fundamental to many steps of carcinogenesis and tumor progression. Such transitions are broadly studied, but general models have been historically limited to qualitative descriptions. This contrasts with phase transitions in physical systems, which are well characterized within the context of the physico- chemical laws, and can be partially understood, in a predictive capacity, using simple, precise models such as the Ising model. Such models are based upon a system of interacting lattice sites. A parameter (e.g. Temperature) is varied, and the fluctuations of the lattice sites are analyzed as the system approaches and passes through a critical point. All critical system-specific details are captured in the interactions between the lattice sites, and the models can yield specific, experimentally verifiable predictions. Ising-like in silico models have guided theoretical studies of transitions in various gene or protein regulatory networks, although resultant predictions can be challenging to experimentally test. We seek a general approach where the experimental input is a statistically large number of single cell measurements, with many protein and metabolite analytes quantitatively measured per cell. From this data we capture the fluctuations and thereby determine the analyte-analyte correlations. In an Ising model analogy, such measurements define the site interactions. These inputs permit straightforward theoretic models for resolving cellular steady states, transitions between steady states, and for making testable predictions. Studies of the chemically-induced-carcinogenesis transition provide preliminary data/proof of concept. For Aim 1 we develop a picture of cancer cell steady states using integrated metabolic and proteomic single cell assays on cancer models of Glioblastoma Multiforme and Melanoma. In Aims 2 and 3 we expand this approach to two apparent cellular transitions associated with resistance against targeted therapies: the adaptation of heterogeneous brain cancers to certain targeted inhibitors, and a drug-induced cellular de-differentiation observed in melanomas and other tumors in response to immunotherapy and targeted inhibitors. All aims are joint experiment/theory aims. Aims 2-3 involve in vivo testing of predictions, as well as exome sequencing and global RNA-seq kinetic studies to complement the single cell kinetic analyses. Anticipated outcomes of the work include a general, quantitative approach towards describing cellular transitions associated with cancer. Further, we propose to mine those descriptions of cellular transitions to identify therapy combinations that are designed to hit targets that drive tumor growth, as well as those that drive the transition (and thus promote resistance) Preliminary data to support of this goal is provided. Additionally, guidance for non-continuous therapy dosing (e.g. metronomic or pulsatile regimens) that exploit knowledge of the kinetics, barriers, and reversibility of the transition to resistance is anticipated