Abstract 73: Molecular characterization of senescence induced by PARPi in preclinical xenograft mouse models of ovarian cancer
Bibliographic record
Abstract
Abstract Introduction: Ovarian cancer (OC) is amongst the most lethal cancers in Canada. High-grade serous ovarian cancer (HGSC) usually responds to initial therapy, however resistance to drugs often develops over time. We have described a new two-step OC combo-therapy: 1st, a PARP inhibitor is used to induce a senescence proliferation arrest in cancer cells followed by, 2nd, the induction of senolysis in senescent cancer cells using the Bcl-2 family inhibitor ABT263, which redirects senescent cells towards apoptosis. Our hypothesis is that therapy-induced senescence (TIS) could be beneficial in the clinic, but questions remain on its exact function in this context. Our objective is to characterize the spatiotemporal evolution of senescence during OC treatment using xenograft mouse models to define the senescence peak in tissues and optimize the targeting of senolysis approaches. Methods-results: We generate a tumor biobank consisting of OV1946, OV4453, TOV21G and MDAMB231 preclinical xenograft models treated with combinations of the PARP inhibitor Olaparib and ABT263. In all models, PARPi-ABT263 resulted in a significant reduction in tumor growth compared to each drug alone. To explore spatiotemporal cell fate decisions including senescence, a time course analysis was performed by gene expression analysis, SA-βgal staining and COMET multiplexing. COMET is an immunofluorescence technology that allows up to 40 molecular biomarkers (vasculature, epithelium, senescence phenotypes, DNA damage response) at single-cell resolution on paraffin embedded tumors. Conclusion: We propose that characterizing the cell fate decisions within the tumor including senescence can be used to improve drug administration patterns in combo therapies and reduce resistance. For example, this should refine targets and timing to improve chemotherapies that aim to manipulate senescence. Citation Format: Sarah Saoudaoui, Nicolas Malaquin, Isabelle Clément, Tibila Kientega, Erwan Goy, Kim Leclerc-Desaulniers, Anne Marie Mes-Masson, Francis Rodier. Molecular characterization of senescence induced by PARPi in preclinical xenograft mouse models of 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 73.
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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 | 0.001 | 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".