Quantifying replication dynamics to predict clonal evolution and drug sensitivity in cancer cells using single-cell whole genome sequencing

使用单细胞全基因组测序量化复制动态以预测癌细胞的克隆进化和药物敏感性

• 批准号: 10603140
• 负责人: Adam Clayton Weiner
• 金额: $ 4.77万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-24 至 2026-02-23
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10603140
• 关键词: Address; BRCA1 gene; BRCA2 gene; Basic Science; Bayesian Modeling; Biological Markers; Cell Count; Cell Cycle; Cell Line; Cells; Clinical; Clinical Management; Clonal Evolution; Computing Methodologies; DNA; DNA Damage; DNA Repair; DNA biosynthesis; DNA replication fork; Data; Data Set; Development; Drug resistance; Ensure; Evolution; Exposure to; Fire - disasters; Genes; Genetic; Genome; Genomic Instability; Genomics; Goals; Grant; Heritability; Heterogeneity; Libraries; Literature; Malignant Neoplasms; Malignant neoplasm of ovary; Measures; Methods; Modeling; Mutation; Neoplasm Metastasis; Parents; Pathologist; Patients; Pattern; Phenotype; Phylogenetic Analysis; Platinum; Positioning Attribute; Preparation; Process; Proliferating; Property; Proxy; Research Personnel; Resolution; Role; S phase; S-Phase Fraction; Sampling; Series; Somatic Mutation; Stress; TP53 gene; Technology; Testing; Time; Work; cancer cell; cancer therapy; cell type; chemotherapy; computer framework; daughter cell; drug development; drug sensitivity; fitness; genetically modified cells; genome sequencing; improved; inhibitor; insight; malignant breast neoplasm; next generation; patient derived xenograft model; patient stratification; prospective; replication stress; response; stressor; targeted treatment; therapeutic development; treatment response; tumor; tumor heterogeneity; whole genome

Project Summary Recent developments in single-cell whole genome sequencing (scWGS) such as direct library preparation (DLP) have enabled rich interrogation into the genomic diversity of hard-to-treat breast and ovarian cancers. Studies from the Shah Lab have used DLP to infer clonal fitness from time-series sampling and assess the impact of mutational processes on structural genomic plasticity; however, these studies only looked at genomes from non-replicating cells. The proposed project leverages S-phase cells captured by DLP to prospectively predict clonal fitness and chemosensitivity using data from a single time point (Aim 1) and, separately, to measure the contribution of aberrant replication towards the accumulation of copy number aberrations (CNAs) across cancers of various genetic context (Aim 2). For my first aim, I propose a computational framework for assigning S-phase cells to phylogenetically inferred clones and subsequently calculating the S-phase fraction (SPF) within each clone. For my second aim, I propose a Bayesian model to estimate single-cell replication timing in a manner that is robust to somatic CNAs. I hypothesize that SPF will be a proxy for proliferation rate and thus high SPF clones will have higher treatment-naïve fitness and sensitivity to platinum-based chemotherapy than their low SPF counterparts. I further hypothesize that heterogeneous replication timing will correlate with the extent of replication stress in a particular DLP sample. Investigating both hypotheses will be important for extending the clinical utility of DLP since identifying which clones will respond to chemotherapy will help guide clinical management and identifying the presence of replication stress will help stratify patients for next-generation targeted therapies, such as ATR inhibitors, that selectively kill cancer cells undergoing replication stress. In summary, the proposed computational methods will extend the utility of scWGS technologies which capture replicating cells by connecting replication dynamics to clonal fitness, replication stress, CNA patterns, and drug sensitivities in genomically unstable cancers.

项目摘要 单细胞全基因组测序（scWGS）的最新进展，如直接文库制备 (DLP)已经能够对难以治疗的乳腺癌和卵巢癌的基因组多样性进行丰富的调查。 Shah实验室的研究使用DLP从时间序列采样中推断克隆适合度，并评估 突变过程对结构基因组可塑性的影响;然而，这些研究只着眼于 非复制细胞的基因组。拟议的项目利用DLP捕获的S期细胞， 使用单个时间点的数据前瞻性预测克隆适应度和化疗敏感性（目标1）， 分别测量异常复制对拷贝数累积的贡献 在各种遗传背景的癌症中的CNA（目标2）。我的第一个目标是， 用于将S期细胞分配给遗传学推断的克隆并随后 计算每个克隆内的S期分数（SPF）。对于我的第二个目标，我提出了一个贝叶斯模型， 以对体细胞CNA稳健的方式估计单细胞复制时间。我假设SPF会 作为增殖率的代表，因此高SPF克隆将具有更高的未处理适应性， 对铂类化疗的敏感性高于低SPF的患者。我进一步假设， 异质复制时机将与特定DLP样品中的复制应激程度相关。 研究这两种假设对于扩展DLP的临床效用非常重要，因为确定 克隆对化疗有反应将有助于指导临床管理和识别 复制压力将有助于对患者进行下一代靶向治疗，如ATR抑制剂， 选择性地杀死正在复制应激的癌细胞。总之，所提出的计算方法 将扩展scWGS技术的实用性，该技术通过连接复制动力学来捕获复制细胞 克隆适应性，复制压力，CNA模式，以及基因组不稳定癌症的药物敏感性。