Effective Treatment of Advanced Human Melanoma Metastasis in Immunodeficient Mice Using Combination Metronomic Chemotherapy Regimens
Bibliographic record
Abstract
PURPOSE: The development of effective therapeutic approaches for treatment of metastatic melanoma remains an immense challenge. Present therapies offer minimal benefit. Although dacarbazine chemotherapy remains the standard therapy, it mediates only low response rates, usually of short duration, even when combined with other chemotherapeutic agents. Thus, new therapeutic strategies are urgently needed. EXPERIMENTAL DESIGN: Using a newly developed preclinical model, we evaluated the efficacy of various doublet metronomic combination chemotherapy against established advanced melanoma metastasis and compared these with the standard maximum tolerated dose dacarbazine (alone or in combination with chemotherapeutic agents or vascular endothelial growth factor receptor-blocking antibody). RESULTS: Whereas maximum tolerated dose dacarbazine therapy did not cause significant improvement in median survival, a doublet combination of low-dose metronomic vinblastine and low-dose metronomic cyclophosphamide induced a significant increase in survival with only minimal toxicity. Furthermore, we show that the incorporation of the low-dose metronomic vinblastine/low-dose metronomic cyclophosphamide combination with a low-dose metronomic dacarbazine regimen also results in a significant increase in survival, but not when combined with maximum tolerated dose dacarbazine therapy. We also show that a combination of metronomic vinblastine therapy and a vascular endothelial growth factor receptor 2-blocking antibody (DC101) results in significant control of metastatic disease and that the combination of low-dose metronomic vinblastine/DC101 and low-dose metronomic dacarbazine induced a significant improvement in median survival. CONCLUSIONS: The effective control of advanced metastatic melanoma achieved by these metronomic-based chemotherapeutic approaches warrants clinical consideration of this treatment concept, given the recent results of a number of metronomic-based chemotherapy clinical trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".