Defining and targeting the lung cancer progenitor cell niche using a high-resolution, multi-omics approach

项目摘要 尽管在治疗方案方面取得了进展，但非小细胞肺癌的5年总生存率(OS) (非小细胞肺癌)患者保持在20%左右[1]。肿瘤起始细胞亚群(TICs) 占总肿瘤人口的1.5%，表现出自我更新的能力， 抗药性，并被认为是推动疾病进展的因素[2]。尽管表面标记 包括CD133、CD44、CD166和EpCAM已被提出用于分离肺部抽动，结果是 前后不一致。肿瘤微环境(TME)内的微观异质性被认为调节 祖细胞样和分化的肿瘤细胞表型之间的平衡，因此 支持不同种类的药物反应。这项拟议的研究试图明确地 表征TICS的表达谱，并研究它们之间的关系 在药物反应的背景下，肿瘤微环境和TIC动力学之间， 其目标是确定调解过渡到 像祖先一样的状态。目标1-血统追踪研究表明抽搐表现出克隆 在培养中的优势，其中一小部分肿瘤细胞倾向于驱动细胞的生长 总人口。在已经建立了使用细胞系模型的协议之后，我将 具有RNA表达条形码的患者来源的NSCLC细胞和分析不断增长的种群 在正常和药物处理条件下进行连续传代试验。vbl.使用 时间序列单细胞RNA测序(scRNA-Seq)数据的转录分析 结合定制的计算工具，我的目标是识别基因表达谱 和独特的表面标记，这些标记是类似祖先的亚克隆所特有的，它们在 治疗选择压力。AIM 2-TICS依赖于来自异类的利基信号 肿瘤微环境(TME)支持祖细胞表型。我们假设 TME内的微观异质性调节祖细胞与分化肿瘤细胞的比率 并影响药物敏感性。我将首先开发一种体外球状细胞培养 结合克隆条形码患者来源的肿瘤和暴露于细胞毒的基质细胞的平台 治疗，用10X基因组学空间转录学平台进行处理。此数据 将能够评估基本的TME串扰信号及其对空间癌症的影响 从AIM 1定义的监狱长样转录签名。我们将证实这些见解 通过整合来自NAIVE和AND的scRNA-Seq和空间转录数据 用于AIM 1的治疗后患者来源的肺样本用于表征 患者特定的TIC利基市场。通过对TIC转录的强大剖析 利用多模式测序方法，我们希望 有可能确定新的靶点或预后生物标志物，以帮助非小细胞肺癌的治疗。

Project Summary Despite advances in treatment options, 5-year overall survival (OS) for non-small cell lung cancer (NSCLC) patients remains around 20% [1]. Subpopulations of tumor initiating cells (TICs) representing <1.5% of the overall tumor population exhibit the capacity for self-renewal, drug-resistance, and are believed to drive disease progression [2]. Although surface markers including CD133, CD44, CD166, and EPCAM have been proposed to isolate lung TICs, results are inconsistent. Micro-heterogeneity within the tumor microenvironment (TME) is believed to regulate balance between progenitor-like and differentiated tumor cell phenotypes, and consequently supports heterogeneous drug responses. This proposed research attempts to definitively characterize expression profiles of TICs, and study the relationship between the tumor micro-environment and TIC dynamics in the context of drug response, with the goal of identifying critical pathways that mediate transitions to a progenitor-like state. Aim 1 - Lineage tracing studies suggest that TICs exhibit clonal dominance in culture, whereby a small fraction of tumor cells tend to drive outgrowth of the overall population. Having already established a protocol using cell line models, I will transfect patient-derived NSCLC cells with RNA-expressed barcodes and analyze growing populations using serial passaging assays under normal and drug-treated conditions. Using transcriptional analysis of time-series single-cell RNA Sequencing (scRNA-Seq) data in combination with custom computational tools, I aim to identify gene expression profiles and surface markers unique to progenitor-like subclones that drive population growth under treatment selection pressure. Aim 2 - TICs are dependent on niche signalling from a heterogeneous tumor microenvironment (TME) to support the progenitor phenotype. We hypothesize that micro-heterogeneity within the TME regulates the ratio of progenitor-to-differentiated tumor cells and influences drug sensitivity. I will first develop an in-vitro spheroid culture platform combining clonally barcoded patient-derived tumor and stromal cells exposed to cytotoxic therapy, processing them with the 10X Genomics Spatial Transcriptomics platform. This data will enable assessment of essential TME crosstalk signalling and its impact on spatial cancer projenitor-like transcriptional signatures defined from Aim 1. We will confirm these insights by integrating scRNA-Seq and Spatial Transcriptomics data from naive and post-treatment patient-derived lung samples used for Aim 1 to characterize patient-specific TIC niches. Through the robust profiling of the TIC transcriptional profile and its associated microenvironment using multimodal sequencing approaches, we hope to potentially identify new targets or prognostic biomarkers to aid in the treatment of NSCLC.