Investigating Potential Therapeutic Synergism of MRT68921 and Afatinib in Three-Dimensional Models of Epithelial Ovarian Cancer
Bibliographic record
Abstract
Background/Objectives: Epithelial ovarian cancer (EOC) is often diagnosed at advanced stages, with metastasis driven by spheroid dissemination within the peritoneal cavity. We previously demonstrated that autophagy supports spheroid cell survival and suggest that it contributes to chemoresistance. Unc-51-like autophagy activating kinase 1 (ULK1), a key regulator of autophagy, has emerged as a promising therapeutic target. Here, we eval-uated the effects of ULK1 inhibition via MRT68921, alone and in combination with afatinib—a tyrosine kinase inhibitor (TKI) known to induce pro-survival autophagy—in EOC. Methods: High-grade serous (HGSOC) and ovarian clear cell carcinoma (OCCC) cell lines were cultured under ad-herent and spheroid conditions. Immunoblotting confirmed on-target effects and modulation of autophagy. Autophagic flux was assessed using mCherry-eGFP-LC3 reporter assays. We assessed 96 dose combinations of MRT68921 and afatinib using drug combination matrices, with synergy evaluated via Synergy Finder. Promising combinations were evaluated across multiple EOC spheroid models and patient ascites-derived organoids. Results: MRT68921 inhibited ULK1 activity and reduced autophagic flux in a con-text-dependent manner while afatinib alone induced autophagy. Their combination produced synergistic effects at select concentrations, impairing spheroid reattachment and viability. However, MRT68921 alone significantly reduced viability across multiple EOC models, including patient ascites-derived organoids. Conclusions: This study is the first to evaluate the combined effects of MRT68921 and afatinib in EOC. Our findings support ULK1 inhibition as a therapeutic strategy with potential enhanced efficacy when combined with afatinib. These results warrant further preclinical investigation to explore the translational potential of ULK1-targeted therapies alone and in combination with TKIs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".