Discovery and Characterization of SY-1365, a Selective, Covalent Inhibitor of CDK7
Bibliographic record
Abstract
Abstract Recent studies suggest that targeting transcriptional machinery can lead to potent and selective anticancer effects in cancers dependent on high and constant expression of certain transcription factors for growth and survival. Cyclin-dependent kinase 7 (CDK7) is the catalytic subunit of the CDK-activating kinase complex. Its function is required for both cell-cycle regulation and transcriptional control of gene expression. CDK7 has recently emerged as an attractive cancer target because its inhibition leads to decreased transcript levels of oncogenic transcription factors, especially those associated with super-enhancers. Here, we describe a selective CDK7 inhibitor SY-1365, which is currently in clinical trials in populations of patients with ovarian and breast cancer (NCT03134638). In vitro, SY-1365 inhibited cell growth of many different cancer types at nanomolar concentrations. SY-1365 treatment decreased MCL1 protein levels, and cancer cells with low BCL2L1 (BCL-XL) expression were found to be more sensitive to SY-1365. Transcriptional changes in acute myeloid leukemia (AML) cell lines were distinct from those following treatment with other transcriptional inhibitors. SY-1365 demonstrated substantial antitumor effects in multiple AML xenograft models as a single agent; SY-1365–induced growth inhibition was enhanced in combination with the BCL2 inhibitor venetoclax. Antitumor activity was also observed in xenograft models of ovarian cancer, suggesting the potential for exploring SY-1365 in the clinic in both hematologic and solid tumors. Our findings support targeting CDK7 as a new approach for treating transcriptionally addicted cancers. Significance: These findings demonstrate the molecular mechanism of action and potent antitumor activity of SY-1365, the first selective CDK7 inhibitor to enter clinical investigation.
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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) | 0.000 | 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) | 0.001 | 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".