High-throughput screen identifies disulfiram as a potential therapeutic for triple-negative breast cancer cells: Interaction with IQ motif-containing factors
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) represents an aggressive subtype, for which radiation and chemotherapy are the only options. Here we describe the identification of disulfiram, an FDA-approved drug used to treat alcoholism, as well as the related compound thiram, as the most potent growth inhibitors following high-throughput screens of 3185 compounds against multiple TNBC cell lines. The average IC50 for disulfiram was ~300 nM. Drug affinity responsive target stability (DARTS) analysis identified IQ motif-containing factors IQGAP1 and MYH9 as direct binding targets of disulfiram. Indeed, knockdown of these factors reduced, though did not completely abolish, cell growth. Combination treatment with 4 different drugs commonly used to treat TNBC revealed that disulfiram synergizes most effectively with doxorubicin to inhibit cell growth of TNBC cells. Disulfiram and doxorubicin cooperated to induce cell death as well as cellular senescence, and targeted the ESA(+)/CD24(-/low)/CD44(+) cancer stem cell population. Our results suggest that disulfiram may be repurposed to treat TNBC in combination with doxorubicin.
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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) | 0.001 | 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".