A combinatorial anticancer drug screen identifies off-target effects of epigenetic chemical probes
Bibliographic record
Abstract
Abstract Anticancer drug response is determined by genetic and epigenetic mechanisms. To identify the epigenetic regulators of anticancer drug response, we conducted a chemical epigenetics screen using chemical probes that target different epigenetic modulators. In this screen, we tested 31 epigenetic probes in combination with 14 mechanistically diverse anticancer agents and identified 8 epigenetic probes that significantly potentiate the cytotoxicity of TAK-243, a first-in-class ubiquitin-activating enzyme (UBA1) inhibitor evaluated in several solid and hematologic malignancies. These probes are TP-472, GSK-864, A-196, UNC1999, SGC-CBP30 and PFI-4 (and its related analogs GSK6853 and GSK5959), and they target BRD9/7, mutant IDH1, SUV420H1/2, EZH2/1, p300/CBP and BRPF1B, respectively. In contrast to epigenetic probes, negative control compounds did not have a significant impact on TAK-243 cytotoxicity. Potentiation of TAK-243 cytotoxicity was associated with reduced ubiquitylation and induction of apoptosis. Mechanistically, epigenetic probes exerted their potentiation by inhibiting the efflux transporter ABCG2 without inducing significant changes in the ubiquitylation pathways or ABCG2 expression levels. The identified probes shared chemical scaffold similarities with TAK-243 and could potentially interact with ABCG2 as assessed by docking analysis. Based on these data, we have developed a cell-based assay that can quantitatively evaluate ABCG2 inhibition by drug candidates. In conclusion, our study identifies epigenetic chemical probes that profoundly potentiate TAK-243 cytotoxicity through off-target ABCG2 inhibition. We also provide experimental evidence that several negative control compounds cannot exclude a subset of off-target effects of chemical probes. Finally, potentiation of TAK-243 cytotoxicity can serve as a quantitative measure of ABCG2-inhibitory activity.
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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 | 0.001 | 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) | 0.002 | 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".