Dasatinib + Gefitinib, a non platinum-based combination with enhanced growth inhibitory, anti-migratory and anti-invasive potency against human ovarian cancer cells
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer is the leading cause of death for gynecological cancers and the 6th cause of women cancer death in developed countries. The late stage detection, the peritoneal dissemination and the acquisition of resistance against carboplatin are the main reasons to explain this poor prognosis and strengthen the need of alternative treatments to improve the management of ovarian cancer and/or to sensitize tumors to platinum salts. Epidermal growth factor receptor (EGFR), hepatocyte growth factor receptor (Met) and cellular Src kinase (c-Src) are crucial kinases implied in ovarian tumor growth, survival, invasion and resistance to carboplatin. Their expression is increased in advanced ovarian cancers and is correlated with poor prognosis. Despite a clear potential in inhibiting these proteins in ovarian cancer, as a single agent or in combination with a carboplatin treatment, we need to target kinases in tandem because of their capacity to trigger compensatory pathways that synergize to promote drug resistance. METHODS: Here we target EGFR, c-Src and Met individually or in combination with carboplatin, using Gefitinib, Dasatinib and Crizotinib respectively, in a panel of carboplatin-sensitive (OVCAR-3, IGROV-1 and A2780) and carboplatin-resistant cells (SKOV-3 and EFO-21). We studied the ability of the most potent combination to induce apoptosis, regulate migration, invasion and to modulate the activation of proliferation and survival proteins. RESULTS: Crizotinib, Dasatinib and Gefitinib, alone or in combination with carboplatin, showed a cell-specific cytotoxic synergy in ovarian cancer cells. The Dasatinib plus Gefitinib combination was synergistic in OVCAR-3, SKOV-3 and, in IGROV-1 cells (high concentrations). This combination was unable to induce apoptosis but suppressed cell migration, invasion and the activation of EGFR, Erk, c-Src and Akt compared to single treatments. CONCLUSIONS: Combining carboplatin with kinase inhibitors lead to synergistic interactions in a cell-specific manner. Unlike platinum-based combinations, mixing Dasatinib with Gefitinib led to cytotoxic activity, inhibition of cell migration and invasion. Thus, the Dasatinib + Gefitinib combination presents anti-tumour properties that are superior to those of platinum-based combinations, indicating that it may well represent a promising new treatment modality to be tested in the clinic.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".