Thalidomide Enhances the Anti-Tumor Activity of Standard Chemotherapy in a Human Melanoma Xenotransplatation Model
Bibliographic record
Abstract
It has been demonstrated that thalidomide's anti-angiogenic properties result in clear anti-tumor activity in a number of human malignancies. We studied thalidomide in a human melanoma severe combined immunodeficiency mouse xenotransplantation model. Thalidomide as a single agent showed a significant tumor reduction of 46% compared with the control group. Thalidomide combined with dacarbazine treatment markedly enhanced the anti-tumor effect of chemotherapy and showed a significant tumor reduction relative to the dacarbazine-only group (61%) and even more tumor reduction (74%) compared with the control group. We also measured clearly reduced levels of tumor necrosis factor-alpha in the thalidomide-treated group. A significantly lower microvessel density was encountered in the thalidomide treatment groups (thalidomide alone or combined with DTIC), underscoring the anti-angiogenic effect of thalidomide as a single agent as well as in combination with chemotherapy in this model. In line with these results, we observed a nearly 3-fold increase of apoptosis for the combination of thalidomide and DTIC compared with the rate of apoptotic cells in DTIC-only-treated melanoma xenotransplants. These data underline the rationale for combining dacarbazine--a cytotoxic agent--and thalidomide--an anti-angiogenic cytostatic agent--as a promising strategy for the treatment of melanoma.
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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 | 0.000 | 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) | 0.001 | 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".