Project 1: Organismal Evolution

项目1摘要 在生物进化中，癌症一直是一种重要的选择压力，在生物进化过程中， 癌症发病率存在于不同物种之间。为什么不同物种对癌症的易感性不同？机制是什么？ 要对此负责？生活史理论(LHT)可以为癌症发病率变化提供一个理论框架。LHT 是一种进化和生态方法，侧重于在生物体水平上权衡生长、 维护和繁殖。癌症抑制是躯体维持的一个方面，我们的模型 已经表明，促黄体生成素因子可以对癌症抑制的最佳水平产生显著影响。在目标1中，我们 建议扩展我们的促黄体生成素模型，以包括更多的促黄体生成素参数来预测癌症死亡率和躯体 动物的变异率很高。我们将用一个高度精选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费率。此外，我们假设，随着生物体进化出更大的 身体和更长的寿命，有选择地加强癌症防御。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 肿瘤抑制基因的选择、漂移和突变的特征。与项目2、目标3合作，将 从实验上验证了我们的比较细胞培养分析中的基因组学结果。 在目标3中，我们将把抑制癌症的生物进化(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的计算模型。由此产生的结果 模型可以预测Evo-ECO肿瘤分类的频率。 我们的研究团队使用跨学科的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、定量建模和动物健康。 我们的工作将包括对跨物种癌症发病率的最大量化研究，并揭示 在哺乳动物近1亿年的进化过程中，癌症的风险。通过鉴定抗癌能力 对于物种，我们已经确定了包含许多抗癌参数的生物“模拟”。使用 通过比较基因组学的方法，我们可以开始识别哪些已知参数(即DNA修复) 潜在地更可用于人类癌症的预防和治疗。最后，翻译有机体 进化论和生态学对肿瘤的进化和微环境可以提供对肿瘤的新见解 分类，这可以引导临床医生走向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.