A Systems Genetics Analysis of Metastatic Mammary Cancer Development in Mice Fed Varying Levels of Dietary Fat

Gordon, Ryan Robert

A Systems Genetics Analysis of Metastatic Mammary Cancer Development in Mice Fed Varying Levels of Dietary Fat Public Deposited

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March 19, 2019

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Gordon, Ryan Robert
- Affiliation: Gillings School of Global Public Health, Department of Nutrition

Abstract

High dietary fat intake and/or obesity may increase the risk of susceptibility to certain forms of cancer. To study the interactions of dietary fat, obesity, and metastatic mammary cancer, a population of F2 mice cosegregating obesity quantitative trait loci (QTL) and the MMTV-PyMT transgene was created. The mice were fed either a very high-fat or a matched-control-fat diet, and evaluated for growth, body composition, age at mammary tumor onset, tumor progression, and pulmonary metastases development. Single nucleotide polymorphism (SNP) genotyping across the genome facilitated analyses of QTL and QTL by diet interaction effects. To further investigate the complex genetic architecture that modifies mammary cancer and metastasis, expression profiles of axillary tumors were characterized with the Illumina Mouse-6 whole genome sentrix arrays. Using a systems-based analysis pipeline developed in R, we conducted a genome-wide expression QTL (eQTL) analysis was conducted. In addition, network and pathway QTL analyses for mammary tumors that have developed in the presence of varying degrees of obesity, and during exposure to high or normal fat diets. Results demonstrated that mice fed a high-fat diet are not only more likely to experience decreased mammary cancer latency but they also have increased tumor growth and occurrence of pulmonary metastases over an equivalent time. We identified 25 modifier loci for mammary cancer and pulmonary metastasis, likely representing 13 unique loci after accounting for pleiotropy, as well as novel QTL x diet interactions at a majority of these loci. Transciptome mapping revealed several candidate genes potentially underlying both tumor and metastasis QTL. These candidates were subsequently prioritized using multiple analytic approaches, including but not limited too causality testing, copy number variation analysis and database evaluations.

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