Concise Review: Bullseye: Targeting Cancer Stem Cells to Improve the Treatment of Gliomas by Repurposing Disulfiram
Bibliographic record
Abstract
Cancer stem cells (CSCs) are thought to be at the root of cancer recurrence because they resist conventional therapies and subsequently reinitiate tumor cell growth. Thus, targeting CSCs could be the bullseye to successful cancer therapeutics in the future. Brain tumors are some of the most challenging types of cancer to treat and the median survival following the initial diagnosis is 12-18 months. Among the different types of brain tumors, glioblastoma (GBM) is considered the most aggressive and remains extremely difficult to treat. Despite surgery, radiation, and chemotherapy, most patients develop refractory disease. Temozolomide (TMZ) is a chemotherapy used to treat GBM however resistance develops in most patients. The underlying mechanisms for TMZ resistance (TMZ-resistant) involve the expression of DNA repair gene O(6)-methylguanine-DNA methyltransferase. CSC genes such as Sox-2, BMI-1, and more recently Y-box binding protein-1 also play a role in resistance. In order to develop novel therapies for GBM, libraries of small interfering RNAs and off-patent drugs have been screened. Over the past few years, several independent laboratories identified disulfiram (DSF) as an off-patent drug that kills GBM CSCs. Reportedly DSF has several modes of action including its ability to inhibit aldehyde dehydrogenases, E3 ligase, polo-like kinase 1, and NFkB. Due to the fact that GBM is a disease of heterogeneity, chemotherapy with multitargeting properties may be the way of the future. In broader terms, DSF kills CSCs from a range of different cancer types further supporting the idea of repurposing it for "target practice."
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".