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Inference of tumor growth dynamics using genomic data

使用基因组数据推断肿瘤生长动态

基本信息

批准号：
10158455
负责人：
Subhajyoti De
金额：
$ 18.32万
依托单位：
RBHS -CANCER INSTITUTE OF NEW JERSEY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-15 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10158455
关键词：
Address Adopted Age Animal Model Architecture Area Back Biological Models Biopsy Cancer cell line Cell Lineage Cells Characteristics Clinical Clinical Management Clonal Evolution DNA Sequence Alteration DNA sequencing Data Detection Development Diagnosis Disease Disease Progression Emerging Technologies Event Evolution Genetic Genomic approach Genomics Growth Heterogeneity Human In Vitro Individual Institutional Review Boards Investigation KRAS2 gene KRASG12D Malignant Neoplasms Measurement Methods Modeling Morbidity - disease rate Motivation Mutate Mutation Natural Selections Neoplasm Metastasis Oncogenic Pancreatic Ductal Adenocarcinoma Pathologic Patients Pharmaceutical Preparations Population Population Sizes Property Research Personnel Resected Resistance Resolution Resources Risk Sampling TP53 gene Technical Expertise Time Variant Work base cancer cell cancer type clinically relevant driver mutation experimental study fitness genome-wide genomic data in vivo individual patient innovation liquid biopsy molecular clock mortality mutant neoplastic cell non-genetic novel pancreatic cancer cells personalized management precision medicine predictive modeling senescence single cell analysis single-cell RNA sequencing tumor tumor growth tumor progression

项目摘要

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.

摘要异质性和进化性是癌症的标志。到检测时，典型的肿瘤包括数十亿个属于多个不同亚克隆细胞群的恶性细胞，这些细胞追踪它们的进化谱系回到单个肿瘤起始细胞。亚克隆在肿瘤发生过程中的不同时间点出现其种群规模随着时间的推移而增长（或在某些情况下缩小）。定量评估肿瘤的亚克隆生长速率可以指示疾病进展的模式，预测出现的风险耐药性，可以合理指导精准医疗环境中患者的临床管理。它目前尚不清楚异质肿瘤中遗传上不同的亚克隆是否倾向于具有主要的体内适应性和生长率存在差异，或者大多数亚克隆的生长情况相当，正如中性预测的那样进化模型。这是由于许多技术挑战造成的。活检的病理基因组分析和切除的肿瘤只能提供有限且不完整的癌症进展情况；肿瘤进化的大部分其中的克隆生长动态仍未观察到。病理学评估可以提示整体肿瘤的增殖特征，但不能将其归因于个体亚克隆和致癌驱动因素其中的突变。用于描绘肿瘤或遗传和非克隆结构的基因组方法遗传异质性也不能提供亚克隆生长速率的直接、定量估计。不正确肿瘤内亚克隆特性的测量导致了对肿瘤进化和多次引发争议——强调迫切需要开发可靠的该领域的资源。为了解决这一未满足的需求，本提案旨在开发一个新颖的框架使用新兴的基因组方法估计人类肿瘤的亚克隆生长率，然后进行验证在应用该框架之前对它们进行实验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争论，并有助于完善肿瘤进展的临床相关预测模型以产生可检验的假设。