Systems-level mechanisms of small cell lung cancer dynamics

项目摘要 小细胞肺癌（SCLC）是神经内分泌来源的肺癌，其特征在于侵袭性生长和 迅速蔓延。最初对治疗有反应，SCLC几乎总是复发并导致患者死亡 两年内。最近的研究表明，SCLC是一种高度异质性的疾病，包括多种肿瘤。 肿瘤内对药物治疗表现出不同敏感性的细胞亚型。然而，SCLC研究 迄今为止，主要集中在两个尚未统一的主要领域。细胞分子研究显示 肿瘤群体中的两种广泛的表型亚型，神经内分泌（NE）和非神经内分泌（非- NE）。已经发现了受体-配体相互作用，例如Notch受体与其配体DLL 4之间的相互作用 在诱导这些不同细胞类型之间的转化中发挥作用，例如从NE 细胞转化为非NE表型。单细胞RNA测序方法已经产生了转录数据 显示了在SCLC肿瘤细胞中活跃的调节途径，其驱动细胞向广泛的NE或非NE细胞生长。 流程.虽然细胞信号传导方法和转录方法都提供了有价值的 由于对SCLC表型亚型分化的深入了解，目前尚不清楚亚型的转录调控是如何进行的， 与肿瘤生长的细胞群体行为有关。SCLC肿瘤的系统水平图 如果不了解肿瘤中细胞间的动力学和细胞间的信号传导， 直接影响转录激活，以及转录激活如何导致亚型转换 影响细胞间的动力学我提出了一种研究这两种表型机制的方法 小细胞肺癌的亚型分化，使用群体动力学的计算建模来表征细胞-细胞 关于表型亚型的相互作用和肿瘤组成，以及布尔逻辑建模， 表征细胞内的转录信号通路，以提供关于不同转录 因子可导致分化成每种表型亚型。我也将研究这两个过程 在实验上，研究使每个亚型不稳定并导致肿瘤抑制的方法， 增长，同时使用结果来进一步完善我的数学模型。研究每个领域的背景下， 另一种则有望提高对肿瘤行为的认识。肿瘤亚型的增强表征 关于生长或表型转变的敏感性将导致新的小细胞肺癌治疗策略 这将对改善患者的治疗效果非常重要。该项目将产生一个系统- 水平理解转录网络和肿瘤亚型组成之间的对应关系， SCLC，揭示有针对性的治疗方案，以破坏系统的稳定。

PROJECT SUMMARY Small cell lung cancer (SCLC) is lung cancer of neuroendocrine origin, characterized by aggressive growth and rapid spread. Initially responsive to treatment, SCLC nearly invariably relapses and leads to patient death within 2 years. Recent work has shown that SCLC is a highly heterogeneous disease comprising multiple cellular subtypes within a tumor that exhibit differential sensitivity to drug treatments. However, SCLC research to date has focused on two major areas, which have not yet been unified. Cell-molecular studies have revealed two broad phenotypic subtypes within a tumor population, neuroendocrine (NE) and non-neuroendocrine (non- NE). Receptor-ligand interactions such as those between Notch receptor and its ligand DLL4 have been found to play a role in inducing transformations between these different cell types, such as the transition from NE cells into a non-NE phenotype. Single-cell RNA sequencing approaches have produced transcriptional data showing regulatory pathways active in SCLC tumor cells, which drive cells towards broadly NE or non-NE cell processes. While both cell-signaling approaches and transcriptional approaches have provided valuable insights into SCLC phenotypic subtype differentiation, it is not clear how transcriptional regulation of subtypes is linked to cell population behaviors that enable tumor growth. A systems-level picture of SCLC tumor behavior is not achievable without understanding how intercellular dynamics and cell-cell signaling in the tumor directly affects transcriptional activation, and how transcriptional activation leading to subtype transitions affects these intercellular dynamics. I present an approach to study both of these mechanisms of phenotypic subtype differentiation in SCLC, using computational modeling of population dynamics to characterize cell-cell interactions and tumor makeup with regard to phenotypic subtypes, and Boolean logic modeling to characterize transcriptional signaling pathways intracellularly to provide detail about how different transcription factors can result in differentiation into each phenotypic subtype. I will also study both of these processes experimentally, investigating means by which to destabilize each subtype and lead to inhibition of tumor growth, while using results to further refine my mathematical models. Studying each area in the context of the other is expected to improve knowledge of tumor behavior. Enhanced characterization of tumor subtype sensitivities with regard to growth or phenotypic transition will lead to new therapeutic strategies for SCLC patients, which will be extremely significant in improving patient outcomes. This project will result in a systems- level understanding of the correspondence between transcriptional networks and tumor subtype composition in SCLC, bringing to light targeted treatment options to destabilize the system.