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

项目总结 肿瘤生长的惊人差异的原因人们知之甚少，令人困惑的癌症治疗， 并使早期疾病患者的预后复杂化。理论预测，这种可变性是由 随机因素的数量。这些随机因素包括不同突变的随机累积， 肿瘤局部环境的差异，以及起源细胞(包括其 分化程度、复制潜力和体细胞变化)。小鼠模型已经被用来 了解肿瘤发生并表征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，而且肿瘤生长测量也不精确。要克服 这些局限性，我们之前开发了一种创新的新方法来制造数千个肿瘤和 通过DNA条形码和深度测序(Tuba-Seq)准确地并行测量它们的生长。令人惊讶的是， Tuba-Seq发现，同一小鼠体内的同基因肺肿瘤将在大小上分化不止一个。 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意想不到的。 在这里，我将描述肿瘤生长变异性的出现以及驱动这一变化的潜在力量 使用Tuba-Seq的可变性。因为不同的随机力预测不同的肿瘤动力学 生长变异性的出现，我将跟踪数百万Kras发起的小鼠肺癌的生长一年 (目标1)。接下来，因为我们之前发现，不同的肿瘤抑制基因的缺失会导致不同程度的 生长的可变性，我将跟踪Kras启动的肿瘤的生长动力学，有20多种不同的 随着时间的推移，继发性肿瘤抑制因子的组合会失去作用(目标2)。这将是可能的凭借我们的 将Tuba-Seq与CRISPR/Cas9介导的靶向抑癌基因失活配对的研究 使用高通量、多路复用池的基因。最后，我将移植数千个DNA条形码肿瘤 并在实验条件下跟踪它们的生长，以揭示其亲缘关系 局部肿瘤环境和来源细胞有限的复制能力对生长的贡献 可变性(目标3)。总而言之，通过表征控制肿瘤生长可变性的力量，我们将改进 致癌模型，这将影响我们对癌症的危险因素、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， 对我未来的独立工作至关重要，建模肿瘤发生和描述基因组和细胞 推动肿瘤生长的事件。

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.