Adjuvant bisphosphonate treatment for breast cancer: Where are we heading and can the pre-clinical literature help us get there?
Bibliographic record
Abstract
Bisphosphonates have demonstrated anti-tumour activity in preclinical studies of bone metastatic disease, thus it was natural to transition these agents into the adjuvant cancer therapy setting. Surprisingly, the results of adjuvant breast cancer trials have shown either modest to no benefit or even harm. We sought to explore whether the preclinical results supporting bisphosphonate use provided clues to help explain the current clinical data. Interestingly, the majority of preclinical data suggested that bisphosphonate treatment was more efficacious when administered after the establishment of osseous metastases. This is similar to the findings of one clinical study whereby patients with biopsy evidence of osseous micrometastases derive greater survival benefit from bisphosphonate treatment. Another clinical study found bisphosphonates were associated with increased incidence of visceral metastases, similar to what has been previously published in preclinical models using "preventative" dosing strategies. While the current clinical data suggest bisphosphonates may be more efficacious in post-menopausal or oestrogen depleted patients, or those with hormone receptor positive tumours, to date no appropriately designed preclinical studies have evaluated these effects. Furthermore, putative mechanisms that regulate response to bisphosphonates in other tumour types remain to be evaluated in breast cancer. Despite the initial optimism regarding adjuvant bisphosphonate therapy, the conflicting clinical results from large trials suggest that we should return to the bench to further investigate factors that may influence response to bisphosphonate treatment or identify appropriate characteristics that would indicate the sub-groups of patients most likely to benefit from bisphosphonate treatment.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".