Phenotype Transitions in Small Cell Lung Cancer

摘要 肺癌是癌症相关死亡的主要原因。在其最致命的形式，小细胞肺癌（SCLC）， 异质性与侵袭性相关，但是没有区分SCLC亚型的驱动突变， 被识别。SCLC的另一个特点是它对初始治疗反应良好，但很快复发， 抗性，表明表型可塑性。在这个基础项目中，我们将研究转录的作用， 以及促进SCLC表型异质性和塑性状态转变的信号传导机制， 导致攻击性和快速复发。我们的初步结果表明，SCLC异质性是 比典型的神经内分泌（NE）和间质样（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转变， 混合状态，暗示他们在抵抗。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，并且由于表型可塑性， 这些状态之间的转换是SCLC中治疗逃避的关键机制。为了验证这个假设， 我们将结合联合收割机计算和实验来描述SCLC表型的全球景观， 定义表型转变对抗性的影响。在Aim 1中，我们将鉴定一种调节转录因子 (TF)控制SCLC细胞分化为NE、ML和混合表型状态的网络;验证 模型预测表型并量化其药物敏感性;以及，定义药物重编程途径， 敏感国家。我们的方法管道由表型聚类和基因共表达网络组成 分析SCLC肿瘤和细胞系数据，模拟基于逻辑的TF网络模型，以确定TF靶点的优先级 用于重编程，以及体外和体内模型预测的实验验证。在目标2中，我们将量化 对化疗的表型敏感性和对信号扰动的可塑性;识别扰动 促进表型转换;以及，测试最佳药物/干扰剂组合， 在治疗中死亡。表型和信号通路将通过流式细胞术和质谱法确定。SCLC 将使用随机表型转变来量化响应扰动的克隆动态， 优先考虑药物/干扰剂组合用于实验验证。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增殖率指标进行评估， 时间序列单细胞流式细胞术或质谱细胞术。该项目的成功将产生以下转化影响： 为寻找靶向治疗提供了动力，靶向治疗可以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， 这种方法将在其他癌症类型中有用，为基于癌症治疗的新范式打开大门。 关于表观遗传肿瘤重编程

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.