Project 1: Organismal Evolution

项目1概述

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.