Mechanisms of bone metastasis (BM) growth in patients with metastatic breast cancer (MBC): An exploratory study
Bibliographic record
Abstract
1102 Background: The benefits of bisphosphonates (BPs) in reducing or delaying skeletal related events (SREs) in patients with BM have been attributed to their potent osteoclast (OC) inhibiting effect. However, despite the use of modern systemic anti-cancer therapy including potent BPs, many patients with BM continue to suffer from the consequences of their bone disease. An improved understanding of the basic mechanisms of bone destruction would allow further appropriate targeted treatment strategies to be developed. Methods: Archival paraffin embedded BM specimens from patients with MBC were examined for expression of OCs, receptor activator of nuclear factor kappa B (RANK), RANK Ligand (RANKL) and Osteoprotegerin (OPG). Histological specimens were also available for primary breast cancer, lymph node (LN) metastasis, normal breast and bone tissues for comparison. Results: BM specimens were available for 20 BP naïve pts and 2 pts treated with BP. OCs were significantly increased in the BM of the BP naive group compared to controls. There were no OCs seen in the BP treated group. RANK was expressed on tumor cells (TCs) in the both bone and nodal metastases but not on the primary cancer cells. It is also expressed on the OCs which were present in both BM and normal bone. While RANKL was absent in TCs, it was strongly expressed in all stromal cells (SCs) in all specimens and in osteoblasts. The OPG, while present in TCs of the BM and LN metastases, is not detected in the primary cancer. Conclusion: The mechanism of bone destruction in MBC are not fully understood and are clearly multifactorial. OCs may not be the singular obligatory factor for osteolysis in BM. Further investigation of various inhibitors of the RANK/RANKL/OPG pathways, may allow novel treatment strategies to be developed. No significant financial relationships to disclose.
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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 | 0.000 |
| 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) | 0.002 | 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 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".