Synthetic lethality interaction between aurora kinases and CHEK1 inhibitors in ovarian cancer.
Bibliographic record
Abstract
e17089 Background: Ovarian cancer is characterized by frequent mutations at TP53. These tumors also harbor germline mutations at homologous recombination repair (HR) genes, so they rely on DNA-damage checkpoint proteins, like the Checkpoint kinase 1 (CHEK1) to induce G2 arrest. In our study, by using an in silico approach, we identified a synthetic lethality interaction between CHEK1 inhibitors and mitotic Aurora Kinase A and B (AURKA/B) inhibitors. Methods: Gene expression analyses were used for the identification of relevant biological functions. OVCAR3, OVCAR8, IGROV1 and SKOV3 were used for proliferation studies. Alisertib was tested as AURKA/B inhibitor and LY2603618 as CHEK1 inhibitor. Analyses of cell cycle and intracellular mediators were performed by flow cytometry and western-blot. Stem cell properties were evaluated with immunofluorescence. Results: Gene expression analyses followed by functional annotation identified cell cycle as a deregulated function. Identified druggable kinases within this function included AURKA/B, TTK kinase and CHEK1. CHEK1 and AURKA/B inhibitors showed a synergistic interaction in different cellular models. Combination of Alisertib and LY2603618 triggered apoptosis, reduced the stem cell population and increased the effect of taxanes and platinum compounds. AURKA and CHEK1 were amplified in 8.7% and 3.9% of ovarian cancers, respectively. Expression of these genes was linked with detrimental clinical outcome. Conclusions: Amplification of AURKA and CHEK1 was observed in more than 12% of ovarian tumors. Our data describes a synthetic lethality interaction between CHEK1 and AURKA/B inhibitors with potential translation to the clinic.
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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".