Combinatorial Anticancer Drug Screen Identifies Off-Target Effects of Epigenetic Chemical Probes
Bibliographic record
Abstract
Anticancer drug response is determined by genetic and epigenetic mechanisms. To identify the epigenetic regulators of anticancer drug response, we conducted a chemical epigenetic 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, GSK864, A-196, UNC1999, SGC-CBP30, and PFI-4 (and its related analogues 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, these epigenetic probes exerted their potentiation by inhibiting the efflux transporter ATP-binding cassette subfamily G member 2 (ABCG2) without inducing significant changes in the ubiquitylation pathways or ABCG2 expression levels. As assessed by docking analysis, the identified probes could potentially interact with ABCG2. 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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".