Abstract B56: Prolactin promotes breast cancer to bone metastasis and breast cancer cell-mediated osteoclast differentiation
Bibliographic record
Abstract
Abstract The hormones prolactin (PRL), estrogen and progesterone have long been studied for their role in the primary breast tumor but not yet in modulating the secondary tumor microenvironment of the bone. Metastasis to the bone is a debilitating aspect of many cancers, including breast cancer, where it is a preferred site of metastasis that results in bone loss. Breast cancer cells release osteolytic factors that induce the breakdown of bone, which releases growth factors and calcium that create a vicious cycle of metastatic tumor growth. Using quantitative immunohistochemistry (AQUA) (n=134), we determined that high PRL-receptor expression in the primary tumor was associated with a shorter time to bone metastasis (PRLR AQUA Max Hazard ratio=1.04, 95% Hazard Ratio confidence limits 1.00-1.07, p=0.03/multivariable Cox proportional hazards model), indicating their treatment failure may be related to the PRL-receptor. We also identified the PRL-receptor on rare samples of matched primary and bone metastases. In an analysis of advanced breast cancer patients, we also detected the PRL-receptor in circulating tumor cells of the blood. PRL treatment of breast cancer cells induced osteoclast differentiation and bone lysis via presumed secreted factors, and interestingly these effects were abrogated by a PRL-receptor-antagonist (delta1-9-G129R-hPRL). We identified sonic hedgehog as part of the molecular mechanism by which PRL and the PRL-receptor induce breast cancer cells to directly promote the differentiation of osteoclast cells capable of bone resorption. This molecular mechanism identifies key potential therapeutic targets to ameliorate the devastating effects of breast cancer to bone metastasis and potential predictive biomarkers. Citation Format: Amanda Forsyth, Ashley Sutherland, Yingying Cong, Laurel Grant, Tzu-Hua Juan, Jae K. Lee, Alexander Klimowicz, Stephanie K. Petrillo, Jinghui Hu, Angela Chan, Florence Boutillon, Vincent Goffin, Cay Egan, Patricia A. Tang, Li Cai, Don Morris, Anthony Magliocco, Carrie S. Shemanko. Prolactin promotes breast cancer to bone metastasis and breast cancer cell-mediated osteoclast differentiation. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr B56.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".