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）是一种高度侵袭性的、不可治愈的肿瘤。SCLC表型异质性 与疾病的侵袭性有关，但没有基于患者的临床进展 肿瘤分层和统一的标准治疗，基于联合放化疗 半个多世纪来一直没有改变，基本上仍然无效。最近，包括我们在内的几个团体 在各种实验中独立鉴定了SCLC中的表型细胞亚群， 包括人细胞系、患者来源的异种移植物和原发性肿瘤，以及来自 SCLC基因工程小鼠模型（GEMM）。然而，对SCLC没有全球性的理解 跨系统的表型多样性，可以整合发现，利用GEMM进行翻译 目的，并深入了解其对治疗逃避的影响。在本项目中，我们建议解决这一问题 通过制定SCLC表型空间的全球蓝图，澄清对该空间的偏见， 通过基因组改变，了解表型转换或选择动态响应药物。在 目的1：我们开发了一个工作流程，从批量水平的转录组学数据推断SCLC表型异质性， 然后我们在单细胞水平上进行实验验证。我们定义了一个基因本体度量来识别 跨模型系统的表型之间的生物学相似性和差异。由此产生的表型 地图将为旨在将模型系统与患者连接的研究提供信息。在目标2中，我们建议将SCLC 表型异质性空间与基因组改变，通过实验验证的统计相关性 通过基因操作机械地诱导细胞转换表型。因为在诊所SCLC 活检或手术很少进行超出初步诊断，我们建议液体活检循环， 无细胞DNA作为原发性肿瘤的临床代表，允许这些基因组之间的连接， SCLC肿瘤的改变和表型多样性。通过弥合这一差距，预测患者的反应， 具体的治疗方法最终可以实现。在目标3中，我们研究了过渡与 选择支持SCLC表型可塑性和药物治疗逃避。为此，我们使用DNA 条形码和信息理论技术来量化SCLC表型的多样化率， 对药物治疗的反应。具体来说，我们将SCLC表型空间内的细胞轨迹绘制为 细胞适应并逃避治疗。总之，我们建议发展一个全面的看法SCLC 表型异质性，将SCLC细胞的转录组学，基因组学和功能特征联系起来， 不同的实验模型系统和患者原发性肿瘤标本。我们将把这些观察结果与 临床可测量的变量，并制定一个统一的表型反应动态图，以应对 治疗，为SCLC治疗策略提供了可能的新途径。