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

使用高分辨率、多组学方法定义和靶向肺癌祖细胞生态位

• 批准号: 10493155
• 负责人: Daniel Charytonowicz
• 金额: $ 4.52万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-07 至 2025-09-06
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10493155
• 关键词: ALCAM gene; Address; Aftercare; Apoptotic; Automobile Driving; Bar Codes; Biological Assay; Biopsy; CD44 gene; Cancer Etiology; Cancer Patient; Cell Fraction; Cell Line; Cells; Chemotherapy and/or radiation; Clinical; Coculture Techniques; Critical Pathways; Cryoultramicrotomy; Custom; Cytotoxic Chemotherapy; DNA Repair; Data; Disease Progression; Down-Regulation; Drug Efflux; Drug resistance; Elements; Equilibrium; Excision; Exhibits; Exposure to; Fibroblasts; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Generations; Genetic; Genetic Transcription; Genomics; Goals; Growth; Heterogeneity; Human; Immune signaling; In Vitro; Ionizing radiation; Lentivirus Vector; Lung; Lung Neoplasms; Malignant Neoplasms; Malignant neoplasm of lung; Mediating; Modeling; Molecular; Neoplasm Metastasis; Non-Small-Cell Lung Carcinoma; Outcome; Pathway interactions; Patients; Pharmaceutical Preparations; Phenotype; Play; Population; Population Growth; Prognostic Marker; Property; Protocols documentation; RNA; Regulation; Regulator Genes; Research; Resistance; Resolution; Role; Sampling; Selection for Treatments; Serial Passage; Signal Pathway; Signal Transduction; Sorting - Cell Movement; Specificity; Stimulus; Stromal Cells; Surface; Therapeutic; Time; Time Series Analysis; Tissue-Specific Gene Expression; Translating; Tumor Tissue; Tumor-Derived; United States; Up-Regulation; Validation; Variant; Woman; base; cancer cell; cancer drug resistance; cancer stem cell; cancer therapy; computerized tools; drug sensitivity; experience; high dimensionality; improved; in vivo; informatics tool; insight; lung cancer cell; matrigel; men; mortality; multimodality; multiple omics; neoplastic cell; new therapeutic target; next generation sequencing; novel; pressure; progenitor; pyrolytic carbon; receptor; response; self-renewal; single-cell RNA sequencing; stem cells; targeted biomarker; targeted treatment; therapy resistant; transcriptome sequencing; transcriptomics; tumor; tumor growth; tumor heterogeneity; tumor microenvironment; tumor progression; tumorigenesis

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.

项目概要 尽管治疗方案有所进步，非小细胞肺癌的 5 年总生存期 (OS) (NSCLC) 患者仍占 20% 左右[1]。肿瘤起始细胞 (TIC) 亚群 占整个肿瘤群体的<1.5%，表现出自我更新的能力， 耐药性，并被认为会导致疾病进展[2]。虽然表面标记 包括 CD133、CD44、CD166 和 EPCAM 已被提议用于分离肺部 TIC，结果为 不一致。 Micro-heterogeneity within the tumor microenvironment (TME) is believed to regulate balance between progenitor-like and differentiated tumor cell phenotypes, and consequently 支持异质药物反应。这项拟议的研究试图明确地 表征 TIC 的表达谱并研究其关系 药物反应背景下肿瘤微环境和 TIC 动态之间的关系， 目标是确定介导向 类祖状态。目标 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，一小部分肿瘤细胞往往会驱动肿瘤细胞的生长 总人口。 Having already established a protocol using cell line models, I will transfect 具有 RNA 表达条形码的患者来源的 NSCLC 细胞并分析不断增长的群体 using serial passaging assays under normal and drug-treated conditions.使用 时间序列单细胞 RNA 测序 (scRNA-Seq) 数据的转录分析 与定制计算工具相结合，我的目标是识别基因表达谱 和类祖亚克隆所特有的表面标记，可推动种群增长 治疗选择压力。目标 2 - TIC 依赖于来自异质性的利基信号 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例 并影响药物敏感性。 I will first develop an in-vitro spheroid culture 结合克隆条形码的患者来源肿瘤和暴露于细胞毒性的基质细胞的平台 therapy, processing them with the 10X Genomics Spatial Transcriptomics platform.这个数据 will enable assessment of essential TME crosstalk signalling and its impact on spatial cancer 从目标 1 定义的类似 projenitor 的转录签名。我们将确认这些见解 通过整合来自原始和 用于目标 1 表征的治疗后患者肺部样本 患者特定的 TIC 利基。通过对 TIC 转录的稳健分析 使用多模式测序方法进行概况及其相关的微环境，我们希望 潜在地确定新的靶标或预后生物标志物来帮助治疗非小细胞肺癌。