Disulfiram when Combined with Copper Enhances the Therapeutic Effects of Temozolomide for the Treatment of Glioblastoma
Bibliographic record
Abstract
PURPOSE: Glioblastoma is one of the most lethal cancers in humans, and with existing therapy, survival remains at 14.6 months. Current barriers to successful treatment include their infiltrative behavior, extensive tumor heterogeneity, and the presence of a stem-like population of cells, termed brain tumor-initiating cells (BTIC) that confer resistance to conventional therapies. EXPERIMENTAL DESIGN: To develop therapeutic strategies that target BTICs, we focused on a repurposing approach that explored already-marketed (clinically approved) drugs for therapeutic potential against patient-derived BTICs that encompass the genetic and phenotypic heterogeneity of glioblastoma observed clinically. RESULTS: Using a high-throughput in vitro drug screen, we found that montelukast, clioquinol, and disulfiram (DSF) were cytotoxic against a large panel of patient-derived BTICs. Of these compounds, disulfiram, an off-patent drug previously used to treat alcoholism, in the presence of a copper supplement, showed low nanomolar efficacy in BTICs including those resistant to temozolomide and the highly infiltrative quiescent stem-like population. Low dose DSF-Cu significantly augmented temozolomide activity in vitro, and importantly, prolonged in vivo survival in patient-derived BTIC models established from both newly diagnosed and recurrent tumors. Moreover, we found that in addition to acting as a potent proteasome inhibitor, DSF-Cu functionally impairs DNA repair pathways and enhances the effects of DNA alkylating agents and radiation. These observations suggest that DSF-Cu inhibits proteasome activity and augments the therapeutic effects of DNA-damaging agents (temozolomide and radiation). CONCLUSIONS: DSF-Cu should be considered as an adjuvant therapy for the treatment of patients with glioblastoma in both newly diagnosed and recurrent settings. Clin Cancer Res; 22(15); 3860-75. ©2016 AACR.
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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 | 0.000 |
| 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".