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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：
10394424
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/10394424
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 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 patient prognosis 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.

项目总结