Endocrine therapy plays a preeminent role in the management of the majority (about two thirds) of women with breast cancer whose tumors have the target, the estrogen receptor (ER). A recent meta-analysis showed that aromatase inhibitors (AIs) were superior to tamoxifen as adjuvant therapy in early-stage disease, but despite this superiority, about one-fifth of women had recurrence by 10 years. In addition to variability in outcomes, there is also a marked variability in tolerance, which can adversely impact adherence to treatment. The mechanism of action of AIs is inhibition of aromatase, thereby suppressing estrogen synthesis and reducing the ligand for the ER. The assumption is that all AIs produce sufficient estrogen suppression. However, our Preliminary Data showed marked variation in estradiol and estrone levels before and while on treatment with the AI anastrozole. However, it remains unknown whether the degree of estrogen suppression, or how to best quantify it, is related to degree of clinical benefit of these agents. We also have Preliminary Data from GWAS, using germline DNA from patients receiving AIs, showing that the variability in AI-related adverse events and outcomes is related to variation in host (germline) genetics. Furthermore, our preliminary findings with SNPs and genes identified to be associated with estrogen suppression by AIs and breast events (recurrences) provide a strong rational to test the hypothesis that genetic variation plays an important role in AI response, and this effect might be through the regulation of estrogen suppression, the mechanism of AI action. Of particular interest is that our functional studies of these genetic variants and genes also showed a SNP- and individual AI-dependent regulation of the expression of the aromatase gene. This novel finding has potentially important implications for precision AI treatment. Therefore, in this current proposal, we propose to take advantage of the extensive resources and Preliminary Data we have obtained. These include three major multi-center clinical trials involving all three third-generation AIs (anastrozole, exemestane, letrozole) for which we already have genome-wide genotyping available: 1) our own M3 study of anastrozole alone with estrogen levels pre and on anastrozole and anastrozole and anastrozole metabolite concentrations, 2) the MA.27 trial, comparing anastrozole and exemestane, from which we have published two GWAS relating to adverse events, and 3) the PreFace, a single-arm letrozole trial. MA.27 and PreFace have clinical follow-up data as well as biospecimens before and on AI treatment that would allow us to determine if the degree of estrogen suppression correlates with clinical AI treatment outcomes and, with genotyping data, the common or AI specific SNPs associated with these two phenotypes. We will then test the SNPs related to estrogen suppression in a prospective trial. Of crucial importance, we will also perform functional studies of those SNPs to elucidate mechanisms by which they might affect estrogen levels and AI response. These findings would have direct implications for the majority of women with breast cancer and would contribute to precision endocrine therapy with the AIs.

Public Health Relevance

Endocrine therapy with aromatase inhibitors (AIs) plays a central role in the management of women with early breast cancer. We have found substantial variability in estrogen suppression with AI therapy raising the possibility that a patient's genetic make-up (her germline) plays a role in this variability. The results of this study would have direct and important applicability to the majority of women with breast cancer and be a prime example of precision endocrine therapy.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center (P50)
Project #
5P50CA116201-12
Application #
9340079
Study Section
Special Emphasis Panel (ZCA1)
Project Start
Project End
Budget Start
2017-09-01
Budget End
2018-08-31
Support Year
12
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905
Ho, Ming-Fen; Lummertz da Rocha, Edroaldo; Zhang, Cheng et al. (2018) TCL1A, a Novel Transcription Factor and a Coregulator of Nuclear Factor ?B p65: Single Nucleotide Polymorphism and Estrogen Dependence. J Pharmacol Exp Ther 365:700-710
Horne, Hisani N; Oh, Hannah; Sherman, Mark E et al. (2018) E-cadherin breast tumor expression, risk factors and survival: Pooled analysis of 5,933 cases from 12 studies in the Breast Cancer Association Consortium. Sci Rep 8:6574
Reese, Jordan M; Bruinsma, Elizabeth S; Nelson, Adam W et al. (2018) ER?-mediated induction of cystatins results in suppression of TGF? signaling and inhibition of triple-negative breast cancer metastasis. Proc Natl Acad Sci U S A 115:E9580-E9589
Lilyquist, Jenna; Ruddy, Kathryn J; Vachon, Celine M et al. (2018) Common Genetic Variation and Breast Cancer Risk-Past, Present, and Future. Cancer Epidemiol Biomarkers Prev 27:380-394
Yu, Jia; Qin, Bo; Moyer, Ann M et al. (2018) DNA methyltransferase expression in triple-negative breast cancer predicts sensitivity to decitabine. J Clin Invest 128:2376-2388
Kannan, Nagarajan; Eaves, Connie J (2018) Macrophages stimulate mammary stem cells. Science 360:1401-1402
Guidugli, Lucia; Shimelis, Hermela; Masica, David L et al. (2018) Assessment of the Clinical Relevance of BRCA2 Missense Variants by Functional and Computational Approaches. Am J Hum Genet 102:233-248
Kurmi, Kiran; Hitosugi, Sadae; Wiese, Elizabeth K et al. (2018) Carnitine Palmitoyltransferase 1A Has a Lysine Succinyltransferase Activity. Cell Rep 22:1365-1373
Goetz, Matthew P; Sangkuhl, Katrin; Guchelaar, Henk-Jan et al. (2018) Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6 and Tamoxifen Therapy. Clin Pharmacol Ther 103:770-777
Baheti, Saurabh; Tang, Xiaojia; O'Brien, Daniel R et al. (2018) HGT-ID: an efficient and sensitive workflow to detect human-viral insertion sites using next-generation sequencing data. BMC Bioinformatics 19:271

Showing the most recent 10 out of 473 publications