Phenotype Transitions in Small Cell Lung Cancer

摘要 肺癌是癌症相关死亡的主要原因。最致命的形式是小细胞肺癌(SCLC)， 异质性与侵袭性相关，然而，没有区分SCLC亚型的驱动突变 已被确认身份。小细胞肺癌的另一个奇怪之处是，它对最初的治疗反应良好，但很快就复发到 抗性，表明表型可塑性。在这个基础项目中，我们将研究转录因子的作用 以及促进小细胞肺癌表型异质性和塑性状态转变的信号机制， 导致攻击性和迅速复发。我们的初步结果表明，SCLC的异质性是 比典型的神经内分泌(NE)和间充质样(ML)亚型更广泛，包括 多个混合状态。最重要的是，我们发现药物治疗导致表型向 混血状态，将它们牵连到抵抗中。基于这些数据，我们的中心假设是SCLC是 NE、ML和杂交表型状态的异质混合，以及由于表型可塑性， 这些状态之间的转换是小细胞肺癌逃避治疗的关键机制。为了检验这一假设， 我们将结合计算和实验来描述小细胞肺癌表型的全球景观，以及 确定表型转换对抗性的影响。在Aim1中，我们将鉴定一个调节性转录因子 (TF)控制SCLC细胞分化为NE、ML和混合表型状态的网络；验证 建模预测的表型并量化它们的药物敏感性；并定义重新编程的药物途径- 敏感的州。我们的方法流水线由表型聚类和基因共表达网络组成 分析小细胞肺癌肿瘤和细胞系数据，模拟基于逻辑的转移因子网络模型以确定转移因子靶点的优先顺序 用于重新编程，并在体外和体内对模型预测进行实验验证。在AIM2中，我们将量化 表型对化疗的敏感性和对信号扰动的可塑性；识别扰动 促进表型转换；并测试使小细胞肺癌细胞最大化的最佳药物/干扰素组合 在治疗中杀人。表型和信号通路将通过流式细胞术和质量细胞术来确定。小细胞肺癌 将使用随机表型转换来量化对扰动的响应的克隆动力学 确定药物/干扰剂组合的优先顺序，以进行实验验证。小细胞肺癌的药物敏感性和可塑性 表型将用我们最近描述的药物诱导的增殖率指标进行评估，以及 时间序列单细胞流式细胞术或质量细胞术。该项目的成功将通过以下方式产生翻译影响 支持对靶向治疗的搜索，将耐药细胞重新编程为药物敏感细胞， 我们预计这将显著改善小细胞肺癌患者的预后。我们进一步预计，这将是 该方法将对其他癌症类型有用，为基于 关于表观遗传肿瘤的重新编程。

Abstract Lung cancer is the leading cause of cancer related deaths. In its most lethal form, small-cell lung cancer (SCLC), heterogeneity correlates with aggressiveness, however no driver mutations distinguishing SCLC subtypes have been identified. Another singularity of SCLC is that it responds well to initial treatment but quickly relapses into resistance, suggesting phenotypic plasticity. In this basic project, we will investigate the role of transcriptional and signaling mechanisms in promoting SCLC phenotypic heterogeneity and plastic state transitions, leading to aggressiveness and rapid relapse. Our preliminary results indicate that SCLC heterogeneity is more extensive than the canonical neuroendocrine (NE) and mesenchymal-like (ML) subtypes, and includes multiple hybrid states. Most significantly, we found that drug treatment results in phenotypic transitions toward the hybrid states, implicating them in resistance. Based on these data, our central hypothesis is that SCLC is a heterogeneous mix of NE, ML and hybrid phenotypic states and that, due to phenotypic plasticity, transitions between these states is a key mechanism of treatment evasion in SCLC. To test this hypothesis, we will combine computation and experiments to characterize the global landscape of phenotypes in SCLC, and define the impact of phenotypic transitions on resistance. In Aim1, we will identify a regulatory transcription factor (TF) network that controls the differentiation of SCLC cells into NE, ML, and hybrid phenotypic states; validate model predicted phenotypes and quantify their drug sensitivity; and, define reprogramming pathways to drug- sensitive states. Our approach pipeline is comprised of phenotypic clustering and gene co-expression network analysis on SCLC tumor and cell line data, simulations of logic-based TF network models to prioritize TF targets for reprogramming, and experimental validation of model predictions in vitro and in vivo. In Aim2, we will quantify phenotype sensitivity to chemotherapy and plasticity in response to signaling perturbations; identify perturbations that promote phenotype switching; and, test optimal drug/perturbagen combinations that maximize SCLC cell killing under treatment. Phenotypes and signaling pathways will be defined by flow and mass cytometry. SCLC clonal dynamics in response to perturbations will be quantified using a stochastic phenotype transition to prioritize drug/perturbagen combinations for experimental validation. Drug sensitivity and plasticity of SCLC phenotypes will be assessed with the drug-induced proliferation rate metric, which we recently described, and time series single-cell flow or mass cytometry. Success of this project will have translational impact by empowering searches for targeted therapies that reprogram drug-resistant cells toward drug-sensitive cells, which we anticipate will lead to significantly improved patient outcomes in SCLC. We further anticipate that this approach will be useful in other cancer types, opening the doors to a new paradigm of cancer treatment based on epigenetic tumor reprogramming.