Abstract A23: Plasma osteoprotegerin and breast cancer risk in BRCA1 and BRCA2 mutation carriers
Bibliographic record
Abstract
Abstract Background: There is emerging evidence to suggest that progesterone-mediated upregulation of the receptor activator of nuclear factor κ β (RANK)/RANK ligand (RANKL) signaling pathway plays a critical role in mammary gland epithelial cell proliferation, mammary stem cell expansion and carcinogenesis. Of relevance for women at a high risk of developing breast cancer due to an inherited BRCA mutation, are recent findings showing that circulating levels of osteoprotegerin (OPG) (an endogenous decoy receptor for RANKL and thus inhibitor of RANK/RANKL-mediated signaling) are lower in women with a BRCA1 or BRCA2 mutation compared to non-carriers. Whether low OPG concentrations contribute to the high breast cancer risk in this population is unknown. If so, a therapeutic intervention that mimics the action of OPG might be used for primary prevention. We evaluated the relationship between plasma OPG and breast cancer risk among women with a BRCA1 or BRCA2 mutation in a prospective study. Methods: Baseline blood samples were available from 206 BRCA mutation carriers with no previous history of cancer. Plasma OPG concentrations were measured using a commercial enzyme-linked immunosorbent assay (ELISA) and categorized dichotomously as high vs. low based on the median of the entire cohort. The cumulative incidence of breast cancer by baseline plasma OPG concentration was estimated using Kaplan-Meier survival analysis. Results: Over a mean follow-up period of 6.5 years (range 0.1-18.8 years), 18 incident cases of primary invasive breast cancer were observed in the cohort. Women who developed breast cancer had significantly lower mean baseline OPG concentrations (90.59 pg/ml [range 4.2-205.7 pg/ml]) compared to the OPG concentrations of women who did not develop breast cancer ((117.9 pg/ml [7.4-547.7]) (P = 0.04). BRCA mutation carriers with low baseline OPG concentrations (< 95 pg/ml) had a significantly higher risk of developing breast cancer compared to those with high baseline OPG concentrations (≥95 pg/ml). After ten years of follow-up, the cumulative incidence of breast cancer among women with low OPG concentrations was 21% compared to 9% among women with high OPG concentrations (P-log rank test = 0.046). There was no evidence of effect modification by menopausal status or BRCA mutation type. After multivariate adjustment, women with high plasma OPG had a significantly decreased risk of developing breast cancer, compared to women with low OPG (HR = 0.25; 95%CI 0.08-0.78; P = 0.02). Conclusions: Our preliminary data suggest that low OPG concentrations are associated with an increased risk of breast cancer in BRCA1 and BRCA2 mutation carriers. These data support the potential for targeting of the RANKL pathway as a plausible cancer prevention strategy among women with germline BRCA mutations. Additional analyses with a larger sample size are underway and may help inform strategies of personalized prevention. These findings will not only further our understanding of the progesterone/OPG/RANKL pathway in breast cancer development, but will improve our identification of high-risk populations that can be targeted by prevention options that are currently available (i.e., denosumab) to simultaneously prevent breast cancer development and maintain bone health (particularly after salpingo-oophorectomy). Citation Format: Leonardo Salmena, Lovisa Oden, Shana Kim, Mohammad Akbari, Ping Sun, Steven Narod, Joanne Kotsopoulos. Plasma osteoprotegerin and breast cancer risk in BRCA1 and BRCA2 mutation carriers. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A23.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".