Project 1: Organismal Evolution

项目1摘要 癌症一直是生物进化中一个重要的选择压力， 癌症发病率是跨物种的。为什么物种对癌症的易感性不同， 负责？生活史理论（LHT）可以为癌症发病率的变化提供一个理论框架。LHT 是一种进化和生态学的方法，专注于生物体水平的生长， 维持和繁殖。癌症抑制是体细胞维持的一个方面，我们的模型 已经表明LH因子可以对癌症抑制的最佳水平产生显著影响。目标1： 我建议扩展我们的LH模型，以包括额外的LH参数来预测癌症死亡率和体细胞死亡率。 动物间的突变率。我们将用一个高度策划的癌症死亡率数据集来验证这个模型 我们收集的病理报告中的比率。此外，我们假设，随着生物体进化得更大， 身体和更长的寿命，有选择增加癌症防御。在目标2中，我们建议测试 哺乳动物的癌症防御机制。使用比较基因组学方法，我们将测试 肿瘤抑制基因中的选择、漂移和突变的特征。与项目2合作，目标3将 实验验证了我们在来自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体进化（目标1）与细胞水平的进化联系起来 （项目2和3）通过创建计算模型的生态和进化的肿瘤。结果从这个 模型可以预测evo-eco肿瘤分类的频率。 我们的研究团队已经在这些基本问题上取得了进展，使用跨学科的方法， 涵盖进化生物学、癌症生物学、比较基因组学、定量建模和动物健康。 我们的工作将包括跨物种癌症发病率的最大定量研究，并阐明 癌症风险在近1亿年的哺乳动物进化过程中。通过识别癌症抵抗 物种，我们已经确定了包含许多抗癌参数的生物“模拟”。使用 比较基因组学方法，我们可以开始鉴定哪些已知参数（即，DNA修复） 更有可能用于人类癌症的预防和治疗。最后，翻译有机 生物进化和生态学对肿瘤进化和微环境的研究可以为肿瘤的研究提供新的视角 分类，这可以引导临床医生采用更个性化的方法来治疗肿瘤。

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.