CCNE1 amplification is synthetic lethal with PKMYT1 kinase inhibition
Bibliographic record
Abstract
Abstract Amplification of the CCNE1 locus on chromosome 19q12 is prevalent in multiple tumour types, particularly in high-grade serous ovarian cancer, uterine tumours and gastro-oesophageal cancers, where high cyclin E levels are associated with genome instability, whole-genome doubling and resistance to cytotoxic and targeted therapies 1–4 . To uncover therapeutic targets for tumours with CCNE1 amplification, we undertook genome-scale CRISPR–Cas9-based synthetic lethality screens in cellular models of CCNE1 amplification. Here we report that increasing CCNE1 dosage engenders a vulnerability to the inhibition of the PKMYT1 kinase, a negative regulator of CDK1. To inhibit PKMYT1, we developed RP-6306, an orally bioavailable and selective inhibitor that shows single-agent activity and durable tumour regressions when combined with gemcitabine in models of CCNE1 amplification. RP-6306 treatment causes unscheduled activation of CDK1 selectively in CCNE1- overexpressing cells, promoting early mitosis in cells undergoing DNA synthesis. CCNE1 overexpression disrupts CDK1 homeostasis at least in part through an early activation of the MMB–FOXM1 mitotic transcriptional program. We conclude that PKMYT1 inhibition is a promising therapeutic strategy for CCNE1 -amplified cancers.
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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 | 0.001 |
| 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".