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

项目摘要 肿瘤生长中令人难以置信的变异性的原因知之甚少，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，变异性是由 随机因素的数量。这些随机因素包括不同突变的随机累积， 肿瘤局部环境的变化，以及起源细胞（包括其 分化水平、复制潜力和体细胞改变）。小鼠模型已经被用于 了解肿瘤发生并描述自然环境中的致瘤突变;然而， 创建这些模型在技术上具有挑战性，并且肿瘤生长测量不精确。克服 这些限制，我们以前开发了一种创新的新方法来创建数千个肿瘤， 通过DNA条形码和深度测序（Tuba-seq）并行准确测量它们的生长。引人注目的是， Tuba-seq发现，同一只小鼠体内的同基因肺肿瘤大小会相差一个以上。 在短短几个月的增长之后，增长了数千倍。这在目前的肿瘤发生模型中是出乎意料的。 在这里，我将描述肿瘤生长变异性的出现和驱动这一变化的潜在力量 使用Tuba-seq.因为不同的随机力预测不同的动力学， 当生长变异性出现时，我将在一年内跟踪数百万Kras引发的小鼠肺肿瘤的生长情况。 (Aim 1）。其次，因为我们以前发现不同肿瘤抑制因子的缺失会导致不同水平的 生长变异性，我将跟踪Kras启动的肿瘤的生长动力学， 继发性肿瘤抑制因子随时间推移而丢失的组合（Aim 2）。这将是可能的凭借我们的 先前的工作将Tuba-seq与CRISPR/Cas9介导的靶向肿瘤抑制因子失活配对 使用高通量的多路复用池来检测基因。最后，我会移植上千个DNA条码肿瘤 在实验条件下跟踪它们的生长，旨在揭示相对于 局部肿瘤环境和起源细胞的有限复制潜力对生长的贡献 可变性（目标3）。总的来说，通过表征控制肿瘤生长变异性的力量，我们将提高 致癌作用的模型，这将影响我们对风险因素，遗传学和脆弱性的理解。 这种疾病此外，该项目将创建独特的数据集和广泛的肿瘤样本， 这对我未来的独立工作建模肿瘤发生和表征基因组和细胞的关键 导致肿瘤生长的事件。

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.