High throughput screening identifies dasatinib as synergistic with trametinib in low grade serous ovarian carcinoma
Bibliographic record
Abstract
BACKGROUND: Low grade serous ovarian carcinoma (LGSOC) is a distinct histotype of ovarian cancer characterised high levels of intrinsic chemoresistance, highlighting the urgent need for new treatments. High throughput screening in clinically-informative cell-based models represents an attractive strategy for identifying candidate treatment options for prioritisation in clinical studies. METHODS: We performed a high throughput drug screen of 1610 agents across a panel of 6 LGSOC cell lines (3 RAS/RAF-mutant, 3 RAS/RAF-wildtype) to identify novel candidate therapeutic approaches. Validation comprised dose-response analysis across 9 LGSOC models and 5 high grade serous comparator lines. RESULTS: 16 hits of 1610 screened compounds were prioritised for validation based on >50% reduction in nuclei counts in over half of screened cell lines at 1000 nM concentration. 11 compounds passed validation, and the four agents of greatest interest (dasatinib, tyrosine kinase inhibitor; disulfiram, aldehyde dehydrogenase inhibitor; carfilzomib, proteasome inhibitor; romidepsin, histone deacetylase inhibitor) underwent synergy profiling with the recently approved MEK inhibitor trametinib. Disulfiram demonstrated excellent selectivity for LGSOC versus high grade serous ovarian carcinoma comparator lines (P = 0.003 for IC50 comparison), while the tyrosine kinase inhibitor dasatinib demonstrated favourable synergy with trametinib across multiple LGSOC models (maximum zero interaction potency synergy score 46.9). The novel, highly selective Src family kinase (SFK) inhibitor NXP900 demonstrated a similar trametinib synergy profile to dasatinib, suggesting that SFK inhibition is the likely driver of synergy. CONCLUSION: Dasatinib and other SFK inhibitors represent novel candidate treatments for LGSOC and demonstrate synergy with trametinib. Disulfiram represents an additional treatment strategy worthy of 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".