Dissecting the interplay between aging, genotype and the microenvironment in lung cancer

项目摘要 癌症主要是老年人的疾病。虽然这部分是由于基因组的顺序获取， 随着年龄的增长，衰老也与一系列可能影响肿瘤发生和生长的变化有关。 这些“衰老的标志”涉及多种途径，这些途径影响癌发生并导致全身性疾病。 变化然而，尽管衰老和癌症之间有着非常密切的联系，或者也许正因为如此， 关于癌细胞内在的、微环境的和系统的年龄相关的变化如何影响肿瘤细胞， 癌症的发生和发展。基因工程小鼠模型独特地能够引入定义的 在确定的时间控制下，将基因改变转化为正常的成体细胞。人类肺癌的模型 使用基因工程小鼠模型，这些肿瘤重现了早期人类肿瘤的许多特征， 肺腺癌为了增加体内癌症建模的范围和精度，我们整合了 常规的基因工程小鼠模型，基于CRISPR/Cas9的体细胞基因组工程，以及 定量基因组学与统计方法。与CRISPR/Cas9介导的基因偶联的肿瘤条形码 灭活和高通量条形码测序（Tuba-seq）能够定量分析 大的基因组对肿瘤起始和自体肿瘤生长的各个方面的影响。这些模型可以 从而区分衰老和突变事件的影响，同时提供一定程度的精确度，使我们能够 检测不同年龄背景下肿瘤抑制功能的差异。在目标1中，我们将量化相互作用 年龄和肿瘤抑制基因功能之间的联系我们的体内实验将确定衰老是否会增加 或降低肿瘤起始的绝对效率，并揭示衰老对 不同的肿瘤抑制基因对肿瘤发生和生长的影响。在目标2中。我们将确定肺部肿瘤 微环境和肺癌细胞本身随年龄而变化。我们将阐明肿瘤的影响 基因型对整个年龄的微环境，并确定是否年龄依赖性变化的增长， 伴随着癌细胞状态的巨大差异。在目标3中，我们将解开细胞自主 肿瘤抑制功能驱动的年轻和老年小鼠肿瘤发展的差异 特别是通过局部组织和全身宿主环境的老化。这些实验将提供洞察力 年龄依赖性基因型特异性效应是否主要是癌细胞内在的，还是由癌细胞的变化驱动的。 微环境或整个生物体环境。通过改变癌细胞的年龄和基因型， 微环境和宿主年龄，我们将获得对这些因素的贡献的前所未有的理解 肺癌发生的多个方面。最终，这些发现可能对癌症有重要意义。 预防、检测和治疗。

PROJECT SUMMARY Cancer is primarily a disease of the old. While this is due in part to the sequential acquisition of genomic alterations, aging is also associated with a constellation of changes that could impact tumor initiation and growth. These “hallmarks of aging” involve diverse pathways that impinge on carcinogenesis and lead to systemic changes. However, despite the very close association between aging and cancer, or perhaps because of it, very little is known about how cancer cell-intrinsic, microenvironmental, and systemic age-related changes impact cancer initiation and growth. Genetically engineered mouse models uniquely enable the introduction of defined genetic alterations into normal adult cells with defined temporal control. Human lung cancer has been modeled using genetically engineered mouse models, and these tumors recapitulate many features of early-stage human lung adenocarcinoma. To increase the scope and precision of in vivo cancer modeling, we integrated conventional genetically engineered mouse models, CRISPR/Cas9-based somatic genome engineering, and quantitative genomics with statistical approaches. Tumor barcoding coupled with CRISPR/Cas9-mediated gene inactivation and high-throughput barcode sequencing (Tuba-seq) enables quantitative analysis of the effects of large panels of genes on tumor initiation and various facets of autochthonous tumor growth. These models can thus distinguish the effects of aging from mutational events while affording a level of precision that allows us to detect differences in tumor suppressor function across age contexts. In Aim 1, we will quantify the interaction between age and tumor suppressor gene function. Our in vivo experiments will define whether aging increases or decreases the absolute efficiency of tumor initiation and uncover the impact of aging on the importance of diverse tumor suppressor genes on tumor initiation and growth. In Aim 2. we will determine how the lung tumor microenvironment and lung cancer cells themselves change with age. We will elucidate the impact of tumor genotype on the microenvironment across age and determine whether age-dependent changes in growth are accompanied by dramatic differences in cancer cell state. In Aim 3, we will disentangle cell-autonomous differences in tumors developing in young and aged mice from effects on tumor suppressor function driven specifically by aging of the local tissue and systemic host environments. These experiments will provide insight into whether age-dependent genotype-specific effects are largely cancer cell-intrinsic or driven by the shifts in the microenvironment or whole organism environment. By permuting cancer cell age and genotype, as well as microenvironment and host age, we will gain an unprecedented understanding of the contribution of these factors to multiple aspects of lung carcinogenesis. Ultimately, these findings could have important implication for cancer prevention, detection, and treatment.