Modeling the SCLC Phenotypic Space

SCLC 表型空间建模

• 批准号: 10375422
• 负责人: Vito Quaranta
• 金额: $ 51.56万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-13 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10375422
• 关键词: Address; Automobile Driving; Bar Codes; Bioinformatics; Biological; Biological Markers; Biological Models; Biology; Biopsy; Cancer Patient; Cancer cell line; Cells; Chemoresistance; Clinic; Clinical; Copy Number Polymorphism; Correlation Studies; Cytometry; DNA; DNA Sequence Alteration; Data; Diagnosis; Disease; Enzymes; Experimental Models; Gene Cluster; Gene Expression; Gene Mutation; Genes; Genetically Engineered Mouse; Genomics; Genotype; Goals; Growth; Heterogeneity; Human; Human Cell Line; Human Engineering; Information Theory; Inter-tumoral heterogeneity; Knock-out; Lead; Link; Lung; MYC Family Genes; MYC Gene Amplification; Maps; Measurable; Modeling; Mus; Mutation; Neoplasm Metastasis; Neurosecretory Systems; Oncogenes; Ontology; Operative Surgical Procedures; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacology; Pharmacotherapy; Phenotype; Plasma; Population; Primary Neoplasm; Proxy; Recurrence; Resistance; Role; Specimen; Stratification; System; TP53 gene; Techniques; Testing; Treatment outcome; Tumor Subtype; Tumor-Derived; base; cancer subtypes; cancer type; cell free DNA; cell type; chemoradiation; chromatin remodeling; epigenomics; experimental study; genetic manipulation; insight; liquid biopsy; longitudinal analysis; lung cancer cell; lung small cell carcinoma; mouse model; notch protein; novel; patient derived xenograft model; patient response; protein biomarkers; response; single-cell RNA sequencing; standard of care; stem cell niche; stem cells; transcriptomics; translational model; treatment response; treatment strategy; tumor

SUMMARY – PROJECT 1 Small cell lung cancer (SCLC) is a highly aggressive, incurable tumor. SCLC phenotypic heterogeneity has been associated with disease aggressiveness, yet there have been no clinical advances based on patient tumor stratification, and the uniform standard-of-care, based on combination chemo-radiation therapy unchanged for over half a century, remains largely ineffective. Recently, several groups including ourselves have independently identified phenotypic cell subpopulations in SCLC across a variety of experimental systems including human cell lines, patient-derived xenografts and primary tumors, as well as tumors from SCLC genetically engineered mouse models (GEMMs). Yet, there is no global understanding of SCLC phenotypic diversity across systems that could enable integration of findings, leverage GEMMs for translational purposes, and produce insights into its impact on treatment evasion. In this Project, we propose to address this challenge by developing a global blueprint of SCLC phenotypic space, clarifying the bias imposed to this space by genomic alterations, and understanding phenotype transition or selection dynamics in response to drugs. In Aim 1 we develop a workflow to infer SCLC phenotypic heterogeneity from bulk-level transcriptomics data, which we then validate experimentally at the single-cell level. We define a gene ontology metric to identify biological similarities and differences between phenotypes across model systems. The resulting phenotype map will inform studies aimed at connecting model systems to patients. In Aim 2, we propose to link the SCLC phenotypic heterogeneity space to genomic alterations, by statistical correlations validated with experiments that mechanistically induce cells to switch phenotypes through gene manipulation. Since in the clinic SCLC biopsies or surgery are rarely performed beyond initial diagnosis, we then propose liquid biopsies of circulating, cell-free DNA as a clinical proxy for the primary tumor, allowing a connection between these genomic alterations and phenotypic diversity of SCLC tumors. By bridging this gap, predictions about patient response to specific treatments could eventually be made. In Aim 3, we investigate the relative role of transitions vs. selection in supporting SCLC phenotypic plasticity and drug treatment evasion. To this end, we use DNA barcoding and information theory techniques to quantify rates of diversification of SCLC phenotypes in response to drug treatment. Specifically, we map trajectories of cells within the SCLC phenotype space as cells adapt and evade treatment. In summary, we propose to develop a comprehensive view of SCLC phenotypic heterogeneity, linking transcriptomic, genomic, and functional features of SCLC cells across diverse experimental model systems and patient primary tumor specimens. We will link these observations to clinically measurable variables, and develop a unified map of phenotypic response dynamics in response to therapy, providing possible novel avenues to SCLC treatment strategies.

摘要 - 项目1 小细胞肺癌（SCLC）是一种高度侵略性，无法治愈的肿瘤。 SCLC表型异质性具有 与疾病的侵略性有关，但基于患者没有临床进展 肿瘤分层和均匀的护理标准，基于联合化疗治疗 半个多世纪以来不变，在很大程度上无效。最近，包括我们在内的几个小组 已经在SCLC中独立鉴定了在多种实验的SCLC中的表型细胞亚群 包括人类细胞系，患者衍生的Xenograptic和原发性肿瘤以及肿瘤在内的系统 SCLC通用工程的鼠标模型（GEMM）。但是，对SCLC没有全球的理解 跨系统的表型多样性，可以使发现的整合，利用宝石进行翻译 目的，并深入了解其对逃避治疗的影响。在这个项目中，我们建议解决这个问题 通过开发SCLC表型空间的全球蓝图来挑战，从而确定对此空间施加的偏见 通过基因组改变以及理解对药物的反应的表型过渡或选择动力学。 AIM 1我们开发一个工作流程，以从批量转录组数据中推断SCLC表型异质性， 然后，我们在单细胞水平上实验验证。我们定义一个基因本体学指标来识别 模型系统跨模型的表型之​​间的生物相似性和差异。由此产生的表型 MAP将为旨在将模型系统连接到患者的研究提供信息。在AIM 2中，我们建议将SCLC联系起来 通过实验验证的统计相关性，表型异质性空间用于基因组改变 这种机械诱导细胞通过基因操纵切换表型。因为在诊所SCLC 活检或手术很少在初始诊断之外进行，然后我们提出循环的液体活检， 无细胞的DNA作为原发性肿瘤的临床代理，允许这些基因组之间的联系 SCLC肿瘤的改变和表型多样性。通过弥合此差距，关于患者反应的预测 最终可以对特定的治疗进行。在AIM 3中，我们研究了过渡VS的相对作用。 在支持SCLC表型可塑性和逃避药物治疗方面的选择。为此，我们使用DNA 条形码和信息理论技术来量化SCLC表型多样化的速率 对药物治疗的反应。具体而言，我们将SCLC表型空间内细胞的轨迹映射为 细胞适应和逃避治疗。总而言之，我们建议建立SCLC的全面观点 SCLC细胞的转录组，基因组和功能特征连接表型异质性 各种实验模型系统和患者原发性肿瘤标本。我们将将这些观察结果与 临床上可测量的变量，并为响应 治疗，为SCLC治疗策略提供可能的新途径。