Quantifying the sources and dynamics of tumor growth variability using Tuba-seq

使用 Tuba-seq 量化肿瘤生长变异性的来源和动态

基本信息

批准号：
10304318
负责人：
Christopher Dennis McFarland
金额：
$ 24.9万
依托单位：
CASE WESTERN RESERVE UNIVERSITY
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：
2020-12-01 至 2023-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10304318
关键词：
Address Affect Bar Codes Benign CRISPR/Cas technology Cell division Cells Clinical Clinical Treatment DNA Data Data Set Disease Environment Event Evolution Future Genetic Genetic Predisposition to Disease Genomics Genotype Goals Growth Human Hyperplasia KRAS oncogenesis Lead Lung Neoplasms Malignant Neoplasms Measurement Measures Mediating Mentors Methodology Methods Modeling Mus Mutation Neoplasm Metastasis Oncogene Activation Organ Outcome Patients Process Prognosis Property Recurrence Risk Factors Sampling Shapes Source Statistical Methods TP53 gene Time Transplantation Tumor Suppressor Genes Tumor Suppressor Proteins Variant Work cancer genetics cancer genome cancer therapy carcinogenesis causal variant deep sequencing design driving force exhaust genome editing improved innovation mathematical model mouse model multidisciplinary neoplastic cell new technology non-genetic novel theories time use tumor tumor barcoding and sequencing tumor growth tumor progression tumorigenesis tumorigenic vector

项目摘要

PROJECT SUMMARY The causes of the incredible variability in tumor growth are poorly understood, confound cancer treatments, and complicate the prognosis of patients with early disease. Theory predicts that the variability is caused by a number of stochastic factors. These stochastic factors include the random accumulation of different mutations, variation in the local environment of the tumor, and differences in properties of the cell of origin (including its level of differentiation, replication potential, and somatic alterations). Mouse models have been used to understand tumorigenesis and to characterize tumorigenic mutations within the natural environment; however, creating these models is technically challenging and tumor growth measurements are imprecise. To overcome these limitations, we previously developed an innovative new method to create thousands of tumors and accurately measure their growth in parallel via DNA barcoding and deep-sequencing (Tuba-seq). Strikingly, Tuba-seq uncovered that isogenic lung tumors within the same mouse will diverge in size by more than one thousand-fold after only a few months of growth. This was unexpected from current models of tumorigenesis. Here, I will characterize the emergence of tumor growth variability and the underlying forces that drive this variability using Tuba-seq. Because different stochastic forces predict different dynamics by which tumor growth variability emerges, I will track the growth of millions of Kras-initiated lung tumors in mice for one year (Aim 1). Next, because we previously found that loss of different tumor suppressors lead to different levels of growth variability, I will track the growth dynamics of Kras-initiated tumors with over twenty different combinations of secondary tumor suppressor loses over time (Aim 2). This will be possible by virtue of our previous work that paired Tuba-seq with CRISPR/Cas9-mediated inactivation of targeted tumor suppressor genes using a high-throughout, multiplexed pool. Lastly, I will transplant thousands of DNA barcoded tumor cells across mice and track their growth in experimental condition designed to uncover the relative contributions of the local tumor environment and finite replicative potential of the cell of origin to growth variability (Aim 3). Collectively, by characterizing the forces governing tumor growth variability, we will improve models of carcinogenesis, which will affect our understanding of the risk factors, genetics, and vulnerabilities of the disease. Additionally, this project will create unique datasets and extensive tumor samples that will be critical for my future independent work modeling tumorigenesis and characterizing the genomic and cellular events that drive tumor growth.

项目摘要肿瘤生长令人难以置信的变异性的原因很少，人们混淆了癌症治疗，并使早期疾病患者的预后复杂化。理论预测可变性是由随机因素的数量。这些随机因素包括不同突变的随机积累，肿瘤的局部环境的变化以及原点细胞的性质差异（包括分化水平，复制潜力和躯体变化）。鼠标模型已用于了解肿瘤发生并表征自然环境内的肿瘤性突变；然而，创建这些模型在技术上具有挑战性，肿瘤生长测量值不精确。克服这些局限性，我们以前开发了一种创新的新方法来创建数千个肿瘤和通过DNA条形码和深层测量（Tuba-Seq）准确地测量其生长。令人惊讶的是， tuba-seq发现了同一小鼠内的等源性肺肿瘤的大小会差异超过一个经过几个月的增长，一千倍。这是当前肿瘤发生模型的意外。在这里，我将表征肿瘤生长变异性的出现以及推动这一驱动的潜在力量使用tuba-seq的变异性。因为不同的随机力预测了肿瘤的不同动力学出现生长可变性，我将跟踪一年中小鼠发起的数百万KRAS发射的肺部肿瘤的生长（目标1）。接下来，因为我们以前发现不同肿瘤抑制剂的损失导致不同水平生长差异，我将跟踪Kras引发的肿瘤的生长动力学，具有二十多种不同随着时间的推移，二次肿瘤抑制剂的组合会失去（AIM 2）。这是可能的以前的工作将tuba-seq与CRISPR/CAS9介导的靶向肿瘤抑制剂失活使用高通道的多重池的基因。最后，我将移植数千个DNA条形码肿瘤小鼠跨小鼠的细胞在实验条件下跟踪其生长，旨在发现相对局部肿瘤环境的贡献和原产细胞对生长的有限复制潜力可变性（目标3）。总体而言，通过表征控制肿瘤生长可变性的力，我们将改善致癌的模型，这将影响我们对风险因素，遗传学和脆弱性的理解疾病。此外，该项目将创建独特的数据集和广泛的肿瘤样本对我未来的独立工作至关重要，建模肿瘤发生并表征基因组和细胞驱动肿瘤生长的事件。