Abstract 1689: Targeting PARP to enhance sensitivity to MEK inhibitors in low-grade serous ovarian cancer
Bibliographic record
Abstract
Abstract Low-grade serous ovarian cancer (LGSOC) presents therapeutic challenges due to its unique characteristics including a distinct genetic profile and high chemoresistance. Up to 40% of cases involve RAS-MAPK pathway mutations, driving cell growth and metabolic dysregulation. MEK inhibitors, such as trametinib and selumetinib, hold therapeutic potential, but their efficacy is often limited by inherent or acquired resistance linked to dysregulated lipid metabolism and ferroptosis. PARP1 has also been implicated in detoxifying phospholipid peroxidation, independent of its DNA repair function. This study explores the combination of MEK inhibitors with olaparib, an FDA-approved PARP inhibitor, to enhance therapeutic efficacy by co-targeting lipid metabolism and ferroptosis, leveraging their complementary mechanisms. We tested a panel of LGSOC cell lines with diverse mutations in KRAS (PM-LGSOC-01, HCC5075, HOC-7, VOA-7681), NRAS (VOA-6406), or with WT RAS (VOA3448). We also used a murine LGSOC cell line (PKOSE10) isolated from a mouse with the Ptenfl/fl -KrasG12D/+Amhr2-Cre genotype which had developed an LGSOC with peritoneal metastases. Cells were treated with MEKi ± PARPi and drug sensitivity was measured via ATP-based bioluminescence. Synergy was quantified using SynergyFinder Plus (Loewe additivity synergy scoring model). Reverse Phase Protein Array (RPPA) was conducted to confirm ferroptosis and explore whether additional pathways might be involved. The combination of trametinib and olaparib displayed robust synergy, enhancing trametinib efficacy in 5 of 7 cell lines, increasing cytotoxicity, reducing colony formation, and potentiating cell death. Effects were observed regardless of RAS mutation status, though the synergistic magnitude varied. In contrast, selumetinib combined with olaparib showed only additive cytotoxicity in 3 of 7 LGSOC cell lines. Trametinib and olaparib combination treatment induced ferroptosis, as evidenced by the downregulation of GPX4, SLC7A11, and NRF2, alongside increased iron-dependent phospholipid peroxidation. Elevated ROS levels and mitochondrial iron accumulation further indicated a transition to a ferroptosis-sensitive state, driven by reduced expression of antioxidant enzymes. RPPA analysis reinforced these findings, revealing changes in key signaling proteins, including 4EBP-1, FTO, WTAP, Vimentin, Yap, LRP6, Connexin-43, and YTHFD3, highlighting the interactions between epithelial-mesenchymal transition (EMT) pathways and ferroptosis. In conclusion, treatment with the PARP inhibitor olaparib substantially enhances the efficacy of MEK inhibition in LGSOC by inducing ferroptosis, with synergy between olaparib and trametinib. This approach offers a promising strategy to extend clinical benefits, potentially address treatment resistance, and broaden the therapeutic landscape for LGSOC patients with limited options. Citation Format: Gamze Bildik, Rumeysa Ozyurt, Weiqun Mao, Marta Llaurado-Fernandez, Hannah Kim, Olivier De Wever, Kwong K. Wong, Mark S. Carey, Robert C. Bast, Zhen Lu. Targeting PARP to enhance sensitivity to MEK inhibitors in low-grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1689.
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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) | 0.003 | 0.001 |
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".