Long-Term Survival in Human Epidermal Growth Factor Receptor 2-Positive Bone-Only Metastatic Breast Cancer: Trastuzumab, Denosumab, and Potential Synergistic Effects
Bibliographic record
Abstract
Breast cancer is the second most common cancer worldwide. There are four main subtypes of breast cancer, one of which involves positivity for human epidermal growth factor receptor 2 (HER2). Here, we present a case series of unusually long survival in three patients with HER2-positive metastatic breast cancer. All cases involved post-menopausal women with bone-only metastases undergoing treatment with the HER2-targeted therapy trastuzumab and the receptor activator of nuclear factor kappa-Β ligand (RANK-L) inhibitor denosumab. Our three patients survived for 17, 13, and 11 years, respectively, from the time of metastasis. The patients who survived for 17 and 13 years both presented with metastatic disease at diagnosis, while the patient who survived for 11 years with metastatic disease was known to have non-metastatic breast cancer for four years prior. We also report the development of foot fractures from minor trauma, as low as walking, despite a bone density reported as normal in the patient with 17 years of treatment. These unusually long survival times and the unusual location of the fractures are questioned to be secondary to the long duration of treatment with HER2-targeted therapy and RANK-L inhibitor therapy. Our case series is the first to describe the use of trastuzumab and denosumab in HER2-positive metastatic breast cancer. All three reported cases had no clinical or radiographic disease progression at the time of reporting. Furthermore, our case of survival for 17 years represents the longest survival time reported yet, raising the possibility of a synergistic relationship between RANK-L inhibitors and HER2-targeted therapy in the long-term control of HER2-positive metastatic breast cancer. This manuscript discusses evidence from primary studies on HER2 and receptor activator of nuclear factor kappa-Β (RANK) signalling and drug responses and hypothesizes on possible mechanisms of synergism. Given that treatment of HER2-positive breast cancer has historically not involved RANK-L inhibition, this study may outline future areas of research in improving treatment algorithms, especially for bone-only metastatic disease.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".