The Role of Prolactin in Bone Metastasis and Breast Cancer Cell–Mediated Osteoclast Differentiation
Bibliographic record
Abstract
BACKGROUND: Metastasis to the bone is a deleterious aspect of breast cancer and is a preferred site that results in bone loss. Hormones such as prolactin (PRL) have not yet been studied for their role in modulating the secondary tumor bone microenvironment. METHODS: We used quantitative immunohistochemistry with 134 samples of human primary breast cancer and 17 matched primary breast cancers and bone metastases. A Cox proportional hazards regression model was fitted to evaluate the associations between high prolactin receptor (PRLR) expression and time to bone metastasis, adjusting for estrogen receptor status, lymph node status, and chemotherapy status. We assessed osteoclast differentiation, osteoclast size, and measured pit formation in dentine slices. Statistical tests were two-sided. RESULTS: High PRLR expression in the primary breast tumor was associated with a shorter time to metastasis that includes bone (PRLRAQUA Max-per 100 unit hazard ratio = 1.04, 95% confidence interval = 1.00 to 1.07, P = .03). We observed the PRLR in rare samples of bone metastases and matched primary breast cancer. PRL treatment of breast cancer cells induced osteoclast differentiation and bone lysis via secreted factors and was abrogated by a PRLR antagonist (delta1-9-G129R-hPRL). We demonstrated that sonic hedgehog is a PRL-regulated cytokine in breast cancer cells and part of the mechanism that induces osteoclast differentiation. CONCLUSIONS: Our evidence indicates that PRL-PRLR can escalate the impact of breast cancer on bone metastasis and that the presence of the PRLR in the tumor microenvironment of breast cancer bone metastasis has the potential to modulate the microenvironment to induce lytic osteoclast formation.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".