Statistical Methods for Characterizing Tumor Heterogeneity at the Single Cell Level

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 沿着患者中高度可变的疾病过程，其仍然知之甚少。以前的研究 强调了患者间和患者内的巨大遗传异质性，亚克隆进化通常发生在 在许多情况下，在治疗环境中导致治疗抗性和复发。此外，我们的了解 共存的非癌细胞在肿瘤微环境中的作用仍然有限。因此，我们认为， 这些亚克隆种群及其相应的微环境的特征将是至关重要的， 使精准医疗和协同治疗组合能够靶向亚克隆驱动因子， 侵袭性亚群，从而改善临床结果。为了准确地剖析 景观和重建潜在的亚克隆建筑在CLL，测量必须对 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， 使用计算软件分析来源于CLL患者样品的单细胞RNA-seq数据。具体来说，Jean 将开发方法来确定遗传异质性的各个方面，例如小的单核苷酸的存在， 突变和拷贝数变异的区域。吉恩会重建基因亚克隆 构建和表征鉴定的亚克隆群体的基因表达谱。 在这项拟议研究的K 00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及其 微环境对治疗的反应。 拟议的工作将产生创新的统计方法，使识别和 癌症亚群的特征，并产生开源软件，可以定制和应用于 不同的癌症类型。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。

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.