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Abstract P036: Circulating osteoprotegerin levels and breast cancer risk among women with a <i>BRCA1</i> mutation: A prospective study

2023· article· en· W4313590276 on OpenAlexaff
Shana J. Kim, Tasnim Zaman, Aleksandra Uzelac, Ping Sun, Jan Lubiński, Steven A. Narod, Leonardo Salmena, Joanne Kotsopoulos

Bibliographic record

VenueCancer Prevention Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsBreast cancerMedicineOsteoprotegerinHazard ratioOncologyInternal medicineRANKLProspective cohort studyProportional hazards modelCancerBiomarkerConfidence intervalEndocrinologyReceptorBiologyGeneticsActivator (genetics)

Abstract

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Abstract Background: Upregulation of the receptor activator of nuclear factor κB (RANK) pathway has been implicated in the pathogenesis of BRCA1-associated breast cancer, and pharmacologic inhibition of the RANK pathway has been shown to suppress BRCA1-mammary tumorigenesis in animal studies. Osteoprotegerin (OPG) is the endogenous decoy receptor for RANK-ligand (RANKL) that inhibits RANK/RANKL-signaling. Lower levels of circulating OPG have been reported among women with a BRCA1 pathogenic variant (mutation). Thus, it is of interest to evaluate the association between circulating OPG and breast cancer as a potential novel biomarker of risk. Objective: To prospectively investigate the association between circulating OPG levels and breast cancer risk among women with a BRCA1 mutation. Methods: Eligible women from an on-going longitudinal study with a biobanked blood sample were included and had a confirmed BRCA1 mutation, no previous history of cancer, and no preventive bilateral mastectomy. Self-reported biennial questionnaires collected detailed information on key risk factors, screening, surgery, and cancer incidence. Serum OPG (pg/ml) was quantified using an enzyme-linked immunosorbent assay (ELISA). The exposure was dichotomized into high vs. low OPG level using the median OPG value in the entire cohort (low: ≤79.2 vs. high: &amp;gt;79.2 pg/ml) and continuously (per 10-unit increase). Cox proportional hazards models were used to estimate the hazard ratio (HR) and 95% confidence intervals (CI) of breast cancer by OPG level. The multivariable model adjusted for age, breastfeeding, smoking history, and coffee consumption. Results: There was a total of 662 BRCA1 mutation included in this prospective analysis with a mean age of 40.3 years (SD 12.1). Over a mean follow-up of 5.6 years (range 0.0-11.9), 49 incident breast cancers were diagnosed. Among women with low OPG, there were 31 incident cases compared to 18 incident cases among women with high OPG. Women with high OPG levels had a lower risk of developing breast cancer (HR 0.57; 95% CI 0.32-1.04; P value 0.07) compared to those with low OPG levels. Breast cancer risk decreased by a factor of 0.91 for every 10-unit increase in circulating OPG concentration (95% CI 0.84-1.00; P value 0.04). Conclusion: These findings suggest an inverse association between OPG levels and breast cancer risk among women with a BRCA1 mutation. Pending validation, circulating OPG levels may improve current risk prediction models and enhance the identification of women at the highest threshold of cancer risk. This may offer more personalized risk management strategies, and a potential for pharmacologic inhibition of the RANKL-signaling pathway as a novel approach to cancer prevention for high-risk women. Citation Format: Shana J. Kim, Tasnim Zaman, Aleksandra Uzelac, Ping Sun, Jan Lubinski, Steven A. Narod, Leonardo Salmena, Joanne Kotsopoulos. Circulating osteoprotegerin levels and breast cancer risk among women with a BRCA1 mutation: A prospective study. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P036.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.365
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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