# Project 1: Organismal Evolution

> **NIH NIH U54** · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · 2020 · $421,665

## Abstract

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.

## Key facts

- **NIH application ID:** 9900763
- **Project number:** 5U54CA217376-03
- **Recipient organization:** ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
- **Principal Investigator:** Carlo Maley
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $421,665
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9900763

## Citation

> US National Institutes of Health, RePORTER application 9900763, Project 1: Organismal Evolution (5U54CA217376-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9900763. Licensed CC0.

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