Breast cancer at bone metastatic sites: recent discoveries and treatment targets
Bibliographic record
Abstract
Breast carcinoma is the most common cancer of women. Bones are often involved with breast carcinoma metastases with the resulting morbidity and reduced quality of life. Breast cancer cells arriving at bone tissues mount supportive microenvironment by recruiting and modulating the activity of several host tissue cell types including the specialized bone cells osteoblasts and osteoclasts. Pathologically activated osteoclasts produce osteolytic lesions associated with bone pain, pathological fractures, cord compression and other complications of metastatic breast carcinoma at bone. Over the last decade there has been enormous growth of knowledge in the field of osteoclasts biology both in the physiological state and in the tumor microenvironment. This knowledge allowed the development and implementation of several targeted therapeutics that expanded the armamentarium of the oncologists dealing with the metastases-associated osteolytic disease. While the interactions of cancer cells with resident bone cells at the established metastatic gross lesions are well-studied, the preclinical events that underlie the progression of disseminated tumor cells into micrometastases and then into clinically-overt macrometastases are just starting to be uncovered. In this review, we discuss the established information and the most recent discoveries in the pathogenesis of osteolytic metastases of breast cancer, as well as the corresponding investigational drugs that have been introduced into clinical development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".