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

项目摘要 尽管治疗选择取得了进展，但非小细胞肺癌的5年总生存期（OS） 非小细胞肺癌患者仍占20%左右[1]。肿瘤起始细胞（TIC）亚群 占总肿瘤群体的<1.5%显示出自我更新的能力， 耐药性，并被认为是推动疾病进展[2]。虽然表面标记 包括CD 133、CD 44、CD 166和EPCAM，已经提出分离肺TIC，结果是 前后矛盾肿瘤微环境（TME）中的微异质性被认为调节了肿瘤的生长。 祖细胞样和分化的肿瘤细胞表型之间的平衡， 支持异质性药物反应。这项研究试图明确 表征TIC的表达谱，并研究其关系。 肿瘤微环境和药物反应背景下的TIC动力学之间的关系， 目的是确定介导向一个 祖细胞样状态目的1 -谱系追踪研究表明TIC表现出克隆性 在培养中占主导地位，其中一小部分肿瘤细胞倾向于驱动肿瘤细胞的生长。 总人口。已经建立了一个使用细胞系模型的协议，我将检查 具有RNA表达条形码的患者来源的NSCLC细胞，并分析生长群体 在正常和药物处理条件下使用连续传代测定。使用 时间序列单细胞RNA测序（scRNA-Seq）数据的转录分析， 结合自定义计算工具，我的目标是确定基因表达谱 和表面标志物独特的祖细胞样亚克隆，推动人口增长下， 治疗选择压力。目的2 -TIC依赖于来自异质性细胞的小生境信号传导。 肿瘤微环境（TME）以支持祖细胞表型。我们假设 TME内的微异质性调节祖细胞与分化的肿瘤细胞的比率 并影响药物敏感性。我将首先建立一个体外球状体培养 组合暴露于细胞毒性的克隆条形码化的患者来源的肿瘤和基质细胞的平台 治疗，用10 X Genomics Spatial Transcriptomics平台处理它们。该数据 将能够评估必要的TME串扰信号及其对空间癌症的影响 从Aim 1定义的projenitor样转录签名。我们将证实这些见解 通过整合scRNA-Seq和空间转录组学数据， 用于目标1表征的治疗后患者源性肺样本 患者特异性TIC小生境。通过对TIC转录的强大分析， 使用多模式测序方法，我们希望 可能识别新的靶点或预后生物标志物，以帮助治疗NSCLC。

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.