Targeting MYC dependency in ovarian cancer through inhibition of CDK7 and CDK12/13
Bibliographic record
Abstract
oncogene occurs in nearly half of tumors. We demonstrate that ovarian cancer cells highly depend on MYC for maintaining their oncogenic growth, indicating MYC as a therapeutic target for this difficult-to-treat malignancy. However, targeting MYC directly has proven difficult. We screen small molecules targeting transcriptional and epigenetic regulation, and find that THZ1 - a chemical inhibiting CDK7, CDK12, and CDK13 - markedly downregulates MYC. Notably, abolishing MYC expression cannot be achieved by targeting CDK7 alone, but requires the combined inhibition of CDK7, CDK12, and CDK13. In 11 patient-derived xenografts models derived from heavily pre-treated ovarian cancer patients, administration of THZ1 induces significant tumor growth inhibition with concurrent abrogation of MYC expression. Our study indicates that targeting these transcriptional CDKs with agents such as THZ1 may be an effective approach for MYC-dependent ovarian malignancies.
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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 | 0.000 |
| 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 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".