||5R01CA207369-04 Interpret this number
||University Of Massachusetts Amherst
||Endogenous Hormones and Postmenopausal Breast Cancer: Etiologic Insights and Improving Risk Prediction
We propose to continue our research group’s work to identify and validate hormonal markers that predict risk
of invasive breast cancer in postmenopausal women. Although the estrogen pathway plays a critical role in
breast carcinogenesis, accruing evidence suggests measurement of the classic estrogens only, as done in
prior epidemiologic studies (including our own) may miss a sizeable component of estrogenic activity that is
etiologically relevant to risk. Further, the insulin pathway is another critical, but relatively understudied,
hormonal pathway that may impact breast cancer risk. Using a prospective nested case-control design, we
plan to analyze blood samples collected from the 32,826 participants in the Nurses' Health Study (NHS) who
provided a blood sample in 1989-90 and, for 18,743 of these women, a second sample in 1999-2000.
Specifically, we propose to evaluate the independent associations of 27-hydroxycholesterol (27HC; a recently
identified endogenous selective estrogen receptor modulator) as well as an estrogen bioassay (that assesses
estrogen pathway activation) in relation to breast cancer risk. We also will evaluate several new aspects of the
relationship between plasma c-peptide (reflecting insulin secretion) and breast cancer risk. With 26 years of
follow-up and two blood samples in a subset of women, we will evaluate the importance of timing of c-peptide
exposure in breast carcinogenesis. Further, we will assess whether the c-peptide/breast cancer relationship
varies by breast tumor expression of insulin receptor or several markers of PI3k/Akt/mTOR pathway activation.
Previous work, by not accounting for these tumor molecular characteristics, may have underestimated the
etiologic importance of insulin in breast cancer risk and hence opportunities for prevention. We also will
evaluate if c-peptide (and secondarily the novel estrogen related markers) improve the Gail and Rosner-Colditz
risk prediction models, and validate the addition of multiple biomarkers (i.e., the plasma hormones, a genetic
risk score and mammographic density) to these models in the prospective Mayo Mammography Health Study
(MMHS) and Melbourne Collaborative Cohort Study (MCCS). The MMHS and MCCS have risk factor and
biomarker information that is comparable to that in the NHS. In addition to providing one or two blood samples,
NHS cohort members have provided detailed exposure and disease information biennially (including data on
most known or probable breast cancer risk factors) since 1976, and, for about 70% of breast cancer cases, we
have collected formalin-fixed paraffin embedded tumor tissue, thus allowing molecular analyses of the tumor.
The recently funded NHS UM1 grant will provide cohort follow-up, breast cancer documentation, and important
covariate data, increasing the cost-effectiveness of the proposed project. This project, by working across the
translational research spectrum from discovery of novel hormonal biomarkers to practical application of well
confirmed biomarkers in risk prediction models, should add considerable insight into breast cancer etiology and
ways to better identify high risk women who may benefit from risk reduction efforts (e.g., chemoprevention).
Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction.
, Kasi C.D.
, Scott C.G.
, Brandt K.R.
, Jensen M.R.
, Hruska C.B.
, Wu F.F.
, Norman A.D.
, Conant E.F.
, Winham S.J.
, et al.
Radiology, 2020 Jul; 296(1), p. 24-31.