Project 1: Organismal Evolution

项目1：有机体进化

基本信息

批准号：
10392867
负责人：
Carlo Maley
金额：
$ 39.57万
依托单位：
ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-12 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10392867
关键词：
Animals Apoptosis Arizona Autopsy Basal metabolic rate Biological Biological Assay Biology Body Size Cancer Biology Cell Culture Techniques Cell Proliferation Cell division Cell model Cells Classification Clinical Management Collaborations Collection Computer Models DNA Damage DNA Repair Data Data Set Defense Mechanisms Ecology Elephants Equilibrium Evolution Fibroblasts Frequencies Gene Amplification Genome Genomic approach Genomics Growth Health Human Immune Incidence LH Cell Lead Light Longevity Maintenance Malignant Neoplasms Mammals Measures Methods Modeling Mutation Natural Selections Neoplasms Oncogenes Oncology Organism Pathology Report Phenotype Population Sizes Predisposition Prevention Recording of previous events Reproduction Research Resistance Somatic Mutation TP53 gene Testing Time Tissues Translating Tumor Suppressor Genes Tumor Suppressor Proteins Variant Veterinarians Work anti-cancer base cancer prevention cancer risk cancer therapy cell injury comparative comparative genomics genetic testing genomic data indexing insight interdisciplinary collaboration life history mortality novel personalized approach pressure prevent refractory cancer reproductive response simulation theories tool trait tumor tumor progression

项目摘要

Project 1 Summary Cancer has been an important selective pressure in organismal evolution and a great deal of variation in cancer rates exist across species. Why do species vary in their susceptibility to cancer and what mechanisms are responsible? Life history theory (LHT) can provide a theoretical framework for why cancer rates vary. LHT is an evolutionary and ecological approach that focuses on organism-level tradeoffs between growth, maintenance and reproduction. Cancer suppression is one aspect of somatic maintenance, and our models have shown that LH factors can have dramatic effects on the optimal level of cancer suppression. In Aim 1, we propose to expand our LH models to include additional LH parameters to predict cancer mortality and somatic mutations rates across animals. We will validate this model with a highly curated dataset on cancer mortality rates from our collection of pathology reports. Additionally, we hypothesize that as organisms evolved larger bodies and longer lives, there was selection for increased cancer defenses. In Aim 2, we propose to test for the mechanisms of cancer defenses in mammals. Using a comparative genomics approach, we will test for signatures of selection, drift and mutation in tumor suppressor genes. In collaboration with Project 2, Aim 3, will experimentally validate the genomics findings in our comparative cell culture assays from primary fibroblasts. In Aim 3, we will connect the organismal evolution of cancer suppression (Aim 1) to cell level evolution (Projects 2 & 3) by creating computational model of the ecology and evolution of a neoplasm. Results from this model can predict the frequency of evo-eco tumor classifications. Our research team has made progress on these fundamental questions using a transdisciplinary approach that spans evolutionary biology, cancer biology, comparative genomics, quantitative modeling and animal health. Our work will comprise the largest quantitative study of cross-species cancer incidence, and shed light on cancer risk throughout nearly 100 million years of mammalian evolution. By identifying cancer resistant species, we have identified biological “simulations” that contain many anti-cancer parameters. Using a comparative genomics approach, we can begin to identify which known parameters (i.e., DNA repair) are potentially more exploitable for human cancer prevention and treatment. Lastly, translating organismal evolution and ecology to tumor evolution and microenvironment can provide new insights into tumor classifications, which can lead clinicians towards a more personalized approach to treating tumors.

项目1概要癌症一直是生物进化中重要的选择压力，并且在生物进化过程中存在着巨大的变异。不同物种之间存在癌症发病率。为什么物种对癌症的易感性存在差异以及机制是什么。生活史理论 (LHT) 可以为 LHT 发生率变化的原因提供理论框架。是一种进化和生态方法，重点关注生长之间的有机体水平权衡，癌症抑制是体细胞维持的一个方面，也是我们的模型的一个方面。已经表明，LH 因子可以对癌症抑制的最佳水平产生显着影响。在目标 1 中，我们。建议扩展我们的 LH 模型以包含额外的 LH 参数来预测癌症死亡率和躯体死亡率我们将使用精心策划的癌症死亡率数据集来验证该模型。此外，我们认为随着生物体进化得更大。在目标 2 中，我们建议测试增强的癌症防御能力。我们将使用比较基因组学方法来测试哺乳动物的癌症防御机制。肿瘤抑制基因的选择、漂移和突变特征将与项目 2、目标 3 合作。通过实验验证了我们在原代成纤维细胞的比较细胞培养测定中的基因组学发现。在目标 3 中，我们将把癌症抑制的生物进化（目标 1）与细胞水平的进化联系起来（项目 2 和 3）通过创建肿瘤的生态和进化计算模型得出结果。模型可以预测 evo-eco 肿瘤分类的频率。我们的研究团队采用跨学科方法在这些基本问题上取得了进展涵盖进化生物学、癌症生物学、比较基因组学、定量模型和动物健康。我们的工作将包括跨物种癌症发病率的最大定量研究，并揭示通过识别癌症抗性，了解哺乳动物近一亿年的进化过程中的癌症风险。物种，我们已经确定了包含许多抗癌参数的生物“模拟”。通过比较基因组学方法，我们可以开始确定哪些已知参数（即 DNA 修复）最后，转化生物学。进化和生态学对肿瘤进化和微环境的研究可以为肿瘤提供新的见解分类，这可以带来更加个性化的肿瘤治疗方法。