High-throughput screen identifies disulfiram as a potential therapeutic for triple-negative breast cancer cells: Interaction with IQ motif-containing factors
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it