Esr1 but Not CYP19A1 Overexpression in Mammary Epithelial Cells during Reproductive Senescence Induces Pregnancy-Like Proliferative Mammary Disease Responsive to Anti-Hormonals.

Molecular-level analyses of breast carcinogenesis benefit from vivo disease models. Estrogen receptor 1 (Esr1) and cytochrome P450 family 19 subfamily A member 1 (CYP19A1) overexpression targeted to mammary epithelial cells in genetically engineered mouse models induces largely similar rates of proliferative mammary disease in prereproductive senescent mice. Herein, with natural reproductive senescence, Esr1 overexpression compared with CYP19A1 overexpression resulted in significantly higher rates of preneoplasia and cancer. Before reproductive senescence, Esr1, but not CYP19A1, overexpressing mice are tamoxifen resistant. However, during reproductive senescence, Esr1 mice exhibited responsiveness. Both Esr1 and CYP19A1 are responsive to letrozole before and after reproductive senescence. Gene Set Enrichment Analyses of RNA-sequencing data sets showed that higher disease rates in Esr1 mice were accompanied by significantly higher expression of cell proliferation genes, including members of prognostic platforms for women with early-stage hormone receptor-positive disease. Tamoxifen and letrozole exposure induced down-regulation of these genes and resolved differences between the two models. Both Esr1 and CYP19A1 overexpression induced abnormal developmental patterns of pregnancy-like gene expression. This resolved with progression through reproductive senescence in CYP19A1 mice, but was more persistent in Esr1 mice, resolving only with tamoxifen and letrozole exposure. In summary, genetically engineered mouse models of Esr1 and CYP19A1 overexpression revealed a diversion of disease processes resulting from the two distinct molecular pathophysiological mammary gland-targeted intrusions into estrogen signaling during reproductive senescence.

Overexpression of Estrogen Receptor α in Mammary Glands of Aging Mice Is Associated with a Proliferative Risk Signature and Generation of Estrogen Receptor α-Positive Mammary Adenocarcinomas.

Age is a risk factor for human estrogen receptor-positive breast cancer, with highest prevalence following menopause. While transcriptome risk profiling is available for human breast cancers, it is not yet developed for prognostication for primary or secondary breast cancer development utilizing at-risk breast tissue. Both estrogen receptor α (ER) and aromatase overexpression have been linked to human breast cancer. Herein, conditional genetically engineered mouse models of estrogen receptor 1 (Esr1) and cytochrome P450 family 19 subfamily A member 1 (CYP19A1) were used to show that induction of Esr1 overexpression just before or with reproductive senescence and maintained through age 30 months resulted in significantly higher prevalence of estrogen receptor-positive adenocarcinomas than CYP19A1 overexpression. All adenocarcinomas tested showed high percentages of ER+ cells. Mammary cancer development was preceded by a persistent proliferative transcriptome risk signature initiated within 1 week of transgene induction that showed parallels to the Prosigna/Prediction Analysis of Microarray 50 human prognostic signature for early-stage human ER+ breast cancer. CYP19A1 mice also developed ER+ mammary cancers, but histology was more divided between adenocarcinoma and adenosquamous, with one ER- adenocarcinoma. Results demonstrate that, like humans, generation of ER+ adenocarcinoma in mice was facilitated by aging mice past the age of reproductive senescence. Esr1 overexpression was associated with a proliferative estrogen pathway-linked signature that preceded appearance of ER+ mammary adenocarcinomas.

Mouse Mammary Gland Whole Mount Density Assessment across Different Morphologies Using a Bifurcated Program for Image Processing.

Mammographic density is associated with increased breast cancer risk. Conventional visual assessment of murine mouse models does not include quantified total density analysis. A bifurcated method was sufficient to obtain relative density scores on a broad range of two-dimensional whole mount images that contained both normal and abnormal findings. Image processing techniques, including a ridge operator and a gaussian denoising method, were used to isolate background away from mammary epithelium and use mean pixel intensity to represent mammary density on genetically engineered mouse models for breast cancer in mice 4 to 29 months of age. The bifurcated method allowed for application of an optimal image processing approach for the structural elements present in the whole mount images. Gaussian denoising was the optimal approach when more dense lobular growth and tertiary branching dominate and a ridge operator when epithelial growth was more sparse and secondary branching was the more dominant structural feature. The two processing approaches were combined in a single experimental flow program using an initial image density measurement as the decision point between the two approaches. Higher density was associated with lobular growth, tertiary branching, fibrotic stroma, and presence of cancer. The significance of the study is development of a readily accessible program for digital assessment of mammary gland whole mount density across a range of mammary gland morphologies.