Numerous laboratory and epidemiologic studies strongly implicate endogenous and exogenous estrogens in the etiology of breast cancer. Data summarized herein suggest that the ACI rat model of 17β-estradiol (E2)-induced mammary cancer is unique among rodent models in the extent to which it faithfully reflects the etiology and biology of luminal types of breast cancer, which together constitute ~70% of all breast cancers. E2 drives cancer development in this model through mechanisms that are largely dependent upon estrogen receptors and require progesterone and its receptors. Moreover, mammary cancer development appears to be associated with generation of oxidative stress and can be modified by multiple dietary factors, several of which may attenuate the actions of reactive oxygen species. Studies of susceptible ACI rats and resistant COP or BN rats provide novel insights into the genetic bases of susceptibility and the biological processes regulated by genetic determinants of susceptibility. This review summarizes research progress resulting from use of these physiologically relevant rat models to advance understanding of breast cancer etiology and prevention.

Pituitary adenoma is a common intracranial neoplasm that is observed in approximately 10% of unselected individuals at autopsy. Prolactin-producing adenomas, i.e., prolactinomas, comprise approximately 50% of all pituitary adenomas and represent the most common class of pituitary tumor. Multiple observations suggest that estrogens may contribute to development of prolactinoma; however, direct evidence for a causal role of estrogens in prolactinoma etiology is lacking. Rat models of estrogen-induced prolactinoma have been utilized extensively to identify the factors, pathways and processes that are involved in pituitary tumor development. The objective of this study was to localize to high resolution Ept7 (Estrogen-induced pituitary tumor), a quantitative trait locus (QTL) that controls lactotroph responsiveness to estrogens and was mapped to rat chromosome 7 (RNO7) in an intercross between BN and ACI rats. Data presented and discussed herein localize the Ept7 causal variant(s) to a 1.91 Mb interval of RNO7 that contains two protein coding genes, A1bg and Myc, and Pvt1, which yields multiple non-protein coding transcripts of unknown function. The Ept7 orthologous region in humans is located at 8q24.21 and has been linked in genome wide association studies to risk of 8 distinct epithelial cancers, including breast, ovarian, and endometrial cancers; 3 distinct types of B cell lymphoma; multiple inflammatory and autoimmune diseases; and orofacial cleft defects. In addition, the Ept7 locus in humans has been associated with variation in normal hematologic and development phenotypes, including height. Functional characterization of Ept7 should ultimately enhance our understanding of the genetic etiology of prolactinoma and these other diseases.

Inherited genetic variants are estimated to account for 30-35% of overall breast cancer risk. A few rare, highly penetrant, genetic determinants of risk in humans have been well defined. However, the actions of many common weakly penetrant breast cancer risk loci remain uncharacterized. Additionally, it is well established that endocrine factors in general, and estrogens in particular, influence breast cancer etiology. We are using the ACI rat model of 17β-estradiol (E2)-induced mammary cancer to parse the contributions of individual genetic risk variants to breast cancer susceptibility in a physiologically relevant context. ACI females develop mammary carcinomas at an incidence approaching 100% when exposed to physiological levels of E2, and these carcinomas share many features with luminal-type breast cancers in humans. In contrast, Brown Norway (BN) rats are highly resistant to E2-induced mammary cancer. Linkage analyses of progeny from intercrosses between susceptible ACI and resistant BN rats led to the identification of multiple quantitative trait loci for E2-induced mammary cancer. One such locus, Estrogen-induced mammary cancer 4 (Emca4), is the focus of the current investigation. We generated a series of novel congenic rat strains which carry BN alleles at distinct regions of interest across the Emca4 locus, introgressed onto the ACI genetic background. Characterization of mammary cancer phenotypes in the congenic strains facilitated fine resolution mapping of the Emca4 locus. These studies revealed that Emca4 is a complex locus harboring at least four interacting genetic determinants of risk, designated Emca4.1 – Emca4.4, and is orthologous to the 8q24.21 breast cancer risk locus in humans. To assess the relevance of the rat genetic data to human populations, novel machine learning methods were employed to generate risk prediction models using data from a human cohort. Genotype data for 76 SNPs located in the regions of the human genome orthologous to Emca4.1 – 4.4 were obtained from the Cancer Genetics Markers of Susceptibility case control population. Models generated from this data set were optimized with novel algorithms to identify a subset of 16 variants that significantly influenced the risk models. The best model distinguished breast cancer cases from controls with a remarkably high degree of accuracy for a model based on genotype (AUC = 0.6, P < 10-11 relative to random guessing). It is worth noting that the predictive power of this model arose from interactions between human SNPs. Our data show that Emca4 is a complex locus containing multiple interacting determinants of risk; variants in the orthologous 8q24.21 breast cancer risk locus in human interact to influence breast cancer risk as predicted by the rat model; and accounting for interactions between variants achieves a predictive power beyond what is observed with individual SNPs. We have demonstrated, for the first time, the ability to develop a multi-component genetic model in rats and test it in a human population. This illustrates the power of the rat model to elucidate the complex mechanisms through which common, weakly penetrant variants influence breast cancer risk in humans. Citation Format: Dennison KL, Chack A, Escanilla NS, Page D, Shull JD. A laboratory/machine learning based comparative genetics model accurately predicts breast cancer in a human cohort [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-05-02.