Computational Analysis of Subclonal Evolution in Chronic Lymphocytic Leukemia

 描述(由申请人提供)：肿瘤内基因和转录的异质性是不同癌症类型的共同特征，包括。CLL是一种特殊的癌症，在患者中表现出遗传和转录的异质性，以及高度可变的病程，目前仍知之甚少。先前的研究已经证实，CLL中特定亚克隆突变的存在可能与不良的临床结果有关，并且这些亚克隆突变会随着时间的推移而改变，以应对治疗。因此，这些亚克隆群体的基因和转录特征对于能够针对亚克隆驱动因素和消除侵袭性亚群从而改善临床结果的精准药物和协同治疗组合至关重要。虽然过去大量的测量和分析提供了对癌症生物学、病因学和预后的关键见解，但这种方法并没有提供对于理解相同环境和遗传背景中不同遗传事件之间的相互作用以驱动转移性疾病、耐药性和疾病进展至关重要的解决方案。单细胞测量是唯一能够明确地解开和连接这些关系的方法。然而，从同一单个细胞中同时提取DNA和RNA目前并不可靠。因此，需要新的统计方法和计算方法来仅使用单细胞转录数据来识别和解析遗传亚群。在这项拟议的研究中，我将开发统计方法和计算软件来分析来自CLL患者样本的单细胞RNA-SEQ数据。具体地说，我将开发方法来识别遗传异质性的各个方面，例如单细胞中是否存在小的单核苷酸突变和拷贝数变异区域。然后，我将重建遗传亚克隆结构，并表征已识别的亚克隆群体的基因表达谱。拟议的工作将产生创新的统计方法，以便能够识别和表征癌症中的亚克隆群体，并产生可定制并应用于不同癌症类型的开源软件。最终，将这些开发的方法应用于CLL将有助于更好地理解CLL的发展和进步。

 DESCRIPTION (provided by applicant): Intratumor genetic and transcriptional heterogeneity is a common feature across diverse cancer types, including. CLL is a particular cancer that exhibits genetic and transcriptional heterogeneity along with a highly variable disease course among patients that remains poorly understood. Previous research has established that the presence of particular subclonal mutations in CLL can be linked with adverse clinical outcomes and that these subclonal mutations change over time in response to therapy. Therefore, genetic and transcriptional characterization of these subclonal populations will be paramount to enabling precision medicine and synergistic treatment combinations that target subclonal drivers and eliminate aggressive subpopulations thereby improving clinical outcome. While bulk measurements and analysis has provided key insights into cancer biology, etiology, and prognosis in the past, this approach does not provide the resolution that is critical for understanding the interactions between different genetic events within the same environmental and genetic backgrounds to drive metastatic disease, drug resistance and disease progression. Single cell measurements are uniquely able to definitively unravel and connect these relationships. However, simultaneous extraction of DNA and RNA from the same single cells is currently not reliable. Therefore, new statistical methods and computational approaches are needed to identify and resolve genetic subpopulations using single cell transcriptional data alone. In this proposed research, I will develop statistical methods and computational software to analyze single cell RNA-seq data derived from CLL patient samples. Specifically, I 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. I will then reconstruct the genetic subclonal architecture and characterize the gene expression profiles of identified subclonal populations. The proposed work will yield innovative statistical methods to enable the identification and characterization of subclonal populations in cancer and yield opensource 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.