Inference of tumor growth dynamics using genomic data

提取 异质性和可进化性是癌症的特征。在发现的时候，一个典型的肿瘤包括 数十亿的恶性细胞属于多个不同的亚克隆细胞群体，这些细胞追溯到他们的 进化谱系可以追溯到单一的肿瘤启动细胞。亚克隆出现在肿瘤发生的不同时间点 随着时间的推移，它们的种群规模会增长(或在某些情况下会缩小)。定量评估 肿瘤的亚克隆生长率可以指示疾病的进展方式，预测出现的风险 可合理指导精准医疗环境下患者的临床管理。它 目前尚不清楚异质肿瘤中遗传上截然不同的亚克隆是否倾向于具有主要的 体内适合度和生长率的差异，或者说大多数亚克隆的生长速度与中性人预测的一样 进化模型。这是由于一些技术挑战造成的。活检组织的病理基因组图谱和 切除的肿瘤提供了有限和不完整的癌症进展的快照；大部分肿瘤的演变 而其中的克隆生长动态仍未观察到。病理评估可提示总体情况 肿瘤的增殖特征，但不能将其归因于单个亚克隆和致癌驱动因素 其中的突变。描述肿瘤或遗传性和非遗传性肿瘤克隆构筑的基因组方法 遗传异质性也不能提供对亚克隆生长率的直接、定量估计。不正确 对肿瘤内亚克隆属性的测量导致了对肿瘤进化和 在多个场合引发争议-突出了开发可靠的 这一地区的资源。为了解决这一未得到满足的需求，本提案旨在开发一个新的框架，以 使用新兴的基因组方法估计人类肿瘤的亚克隆生长率，然后验证 在应用该框架来估计由 个体患者肿瘤进展过程中的致癌驱动因素。本提案中开发的资源将 使我们能够重新审视正在进行的关于癌症中性进化和选择的辩论，并帮助完善 临床相关的肿瘤进展预测模型，以产生可检验的假说。

ABCTRACT Heterogeneity and evolvability are hallmarks of cancer. By the time of detection, a typical tumor comprises of billions of malignant cells that belong to multiple distinct subclonal cell populations, which trace their evolutionary lineage back to a single tumor initiating cell. Subclones arise at different time-points during tumor progression, and their population sizes grow (or in some cases shrink) with time. Quantitative assessment of subclonal growth rates of tumors can indicate the mode of disease progression, predict the risk of emergence of resistance, and can rationally guide clinical management of the patients in the Precision Medicine setting. It remains unclear whether the genetically distinct subclones in heterogeneous tumors tend to have major differences in fitness and growth rates in vivo, or most subclones grow comparably, as predicted by the neutral evolution model. This is due to a number of technical challenges. Patho-genomic profiling of biopsies and resected tumors provide limited and incomplete snapshots of cancer progression; much of the tumor evolution and clonal growth dynamics therein remain unobserved. Pathological assessment can indicate overall proliferative characteristics of a tumor but cannot attribute them to individual subclones and oncogenic driver mutations therein. Genomic approaches for delineating clonal architectures in tumors, or genetic and non- genetic heterogeneity also do not provide direct, quantitative estimates of subclonal growth rates. Incorrect measurements of intra-tumor subclonal properties have led to biased inference about tumor evolution and fueled controversies on multiple occasions - highlighting the immediate need for development of reliable resource in this area. To address this unmet need, this proposal aims to develop a novel framework to estimate subclonal growth rates in human tumors using emerging genomic approaches, and then validate them experimentally before applying the framework to estimate the selective advantage conferred by oncogenic drivers during tumor progression in individual patients. The resources developed in this proposal will enable us to revisit the ongoing debate about the neutral evolution and selection in cancer, and also help refine clinically relevant predictive models of tumor progression to generate testable hypotheses.