Plasma RANKL levels are not associated with breast cancer risk in <i>BRCA1</i> and <i>BRCA2</i> mutation carriers
Bibliographic record
Abstract
// Tasnim Zaman 1, 2 , Ping Sun 2 , Steven A. Narod 2, 3 , Leonardo Salmena 1, 2, 4 and Joanne Kotsopoulos 2, 3 1 Department of Pharmacology and Toxicology, University of Toronto, ON, M5S 1A8, Canada 2 Women’s College Research Institute, Women’s College Hospital, Toronto, ON, M5S 1B2, Canada 3 Dalla Lana School of Public Health, University of Toronto, ON, M5T 3M7, Canada 4 Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 2M9, Canada Correspondance to: Joanne Kotsopoulos, email: joanne.kotsopoulos@wchospital.ca Keywords: receptor activator of nuclear factor κB (RANKL); breast cancer; biomarker; BRCA Received: December 12, 2018 Accepted: January 19, 2019 Published: March 29, 2019 ABSTRACT Background: Aberrant progesterone/receptor activator of nuclear factor κβ (RANK) signaling has been implicated in BRCA1 breast cancer development. Furthermore, lower circulating RANKL has been reported among women with a BRCA mutation compared to non-carriers; however, there have been no reports of plasma RANKL levels and subsequent breast cancer risk. We prospectively evaluated the relationship between plasma RANKL and breast cancer risk among women with a BRCA1 or BRCA2 mutation. Methods: An enzyme-linked immunosorbent assay was used to quantify plasma RANKL levels in 184 BRCA mutation carriers. Women were stratified into high vs. low RANKL based on the median levels of the cohort (5.24 pg/ml). Kaplan-Meier survival analysis was used to estimate the cumulative incidence of breast cancer by baseline plasma RANKL and cox proportional hazards models were used to estimate the adjusted hazard ratios (HRs) and 95% confidence intervals (CI) for the association between plasma RANKL and risk. Results: Over a mean follow-up of 6.3 years (0.02-19.24), 15 incident breast cancers were identified. The eight-year cumulative incidence was 10% in the low RANKL group and 12% in the high RANKL group ( P -log-rank = 0.85). There was no significant association between plasma RANKL levels and breast cancer risk (multivariate HR high vs. low = 1.06; 95%CI 0.34-3.28; P- trend = 0.86). Conclusions: These findings suggest that circulating RANKL levels are not associated with breast cancer among BRCA mutation carriers. Pending validation in a larger sample, these findings suggest that RANKL is likely not a biomarker of breast cancer risk among BRCA mutation carriers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".