Statistical Methods for Characterizing Tumor Heterogeneity at the Single Cell Level

在单细胞水平表征肿瘤异质性的统计方法

• 批准号: 9898349
• 负责人: Jean Fan
• 金额: $ 9.07万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9898349
• 关键词: Aftercare; Architecture; Biological Process; Brain; Cancer Relapse; Cells; Chronic Lymphocytic Leukemia; Clinical; Communities; Computer software; Copy Number Polymorphism; Dana-Farber Cancer Institute; Data; Data Set; Development; Disease; Drug resistance; Evolution; Exhibits; Expression Profiling; Gene Expression; Genetic; Genetic Heterogeneity; Genetic Transcription; Growth; Heterogeneity; Individual; Investigation; Knowledge; Malignant Neoplasms; Measurement; Methods; Modeling; Morphologic artifacts; Mus; Mutation; Myeloid Cells; Nature; Noise; Nucleotides; Outcome; Pathway interactions; Patients; Phase; Phylogenetic Analysis; Population; Process; Relapse; Research; Research Project Grants; Research Proposals; Role; Sampling; Shapes; Statistical Methods; Statistical Models; T-Lymphocyte; Time; Work; base; cancer cell; cancer therapy; cancer type; chronic lymphocytic leukemia cell; computerized tools; genetic information; improved; innovation; insight; neoplastic cell; novel; open source; personalized cancer therapy; precision medicine; response; single cell technology; single-cell RNA sequencing; skills; therapy resistant; treatment response; treatment strategy; tumor; tumor heterogeneity; tumor microenvironment; tumor progression

Chronic lymphocytic leukemia (CLL) is a cancer that exhibits genetic and transcriptional heterogeneity along with a highly variable disease course among patients that remains poorly understood. Previous research has highlighted vast inter- and intra-patient genetic heterogeneity, with subclonal evolution commonly occurring in treatment settings leading to therapeutic resistance and relapse in many cases. In addition, our understanding of the role of co-existing non-cancer cells in the tumor-microenvironment remains limited. Therefore, characterization of these subclonal populations and their corresponding microenvironment will be paramount to enabling precision medicine and synergistic treatment combinations that target subclonal drivers and eliminate aggressive subpopulations thereby improving clinical outcome. In order to accurately dissect the genetic landscape and reconstruct the underlying subclonal architecture in CLL, measurements must be made on the single cell level. In the F99-phase of this proposed research, Jean Fan will continue developing statistical methods and computational software to analyze single cell RNA-seq data derived from CLL patient samples. Specifically, Jean will develop methods to identify aspects of genetic heterogeneity, such as the presence of small single nucleotide mutations and regions of copy number variation, in single cells. Jean will then reconstruct the genetic subclonal architecture and characterize the gene expression profiles of identified subclonal populations. In the K00-phase of this proposed research, Jean will characterize heterogeneity in the tumor-microenvironment and develop methods to assess potential reciprocal interactions between subclones and their microenvironment over time in response to therapy. The proposed work will yield innovative statistical methods to enable the identification and characterization of subpopulations in cancer and yield open-source software that can be tailored and applied to diverse cancer types. Ultimately, application of these developed methods to CLL will provide a better understanding of CLL development and progression.

慢性淋巴细胞白血病(CLL)是一种表现出遗传和转录异质性的癌症。 以及患者中高度可变的病程，目前仍知之甚少。以前的研究 突出了患者之间和患者内部的巨大遗传异质性，亚克隆进化经常发生 在治疗环境中，在许多情况下导致治疗抵抗和复发。另外，我们的理解是 共存的非肿瘤细胞在肿瘤微环境中的作用仍然有限。因此， 这些亚克隆种群及其相应的微环境的特征将至关重要 实现针对亚克隆驱动程序的精确药物和协同治疗组合，并消除 具有侵略性的亚群，从而改善临床结果。为了准确地解剖基因 景观和重建CLL中潜在的亚克隆结构，必须对 单细胞水平。 在这项拟议研究的F99阶段，Jean Fan将继续开发统计方法和 用于分析来自慢性淋巴细胞性白血病患者样本的单细胞rna-seq数据的计算软件。具体地说，Jean 将开发方法来识别遗传异质性的各个方面，例如小分子单核苷酸的存在 单细胞中拷贝数变异的突变和区域。然后Jean将重建基因亚克隆 构建并表征已识别的亚克隆种群的基因表达谱。 在这项拟议研究的K00阶段，Jean将描述肿瘤微环境的异质性。 并开发方法来评估亚克隆和它们的 随着时间的推移微环境对治疗的反应。 拟议的工作将产生创新的统计方法，以使识别和 癌症亚群的特征和收益可以定制和应用的开源软件 癌症类型多样。最终，将这些开发的方法应用于CLL将提供更好的 了解CLL的发展和进展。