Overcoming Platinum and PARP-Inhibitor Resistance in Ovarian Cancer
Bibliographic record
Abstract
Platinum chemotherapy remains the cornerstone of treatment for epithelial ovarian cancer (OC) and Poly (ADP-ribose) polymerase inhibitors (PARPi) now have an established role as maintenance therapy. The mechanisms of action of these agents is, in many ways, complementary, and crucially reliant on the intracellular DNA Damage Repair (DDR) response. Here, we review mechanisms of primary and acquired resistance to treatment with platinum and PARPi, examining the interplay between both classes of agents. A key resistance mechanism appears to be the restoration of the Homologous Recombination (HR) repair pathway, through BRCA reversion mutations and epigenetic upregulation of BRCA1. Alterations in non-homologous end-joint (NHEJ) repair, replication fork protection, upregulation of cellular drug efflux pumps, reduction in PARP1 activity and alterations to the tumour microenvironment have also been described. These resistance mechanisms reveal molecular vulnerabilities, which may be targeted to re-sensitise OC to platinum or PARPi treatment. Promising therapeutic strategies include ATR inhibition, epigenetic re-sensitisation through DNMT inhibition, cell cycle checkpoint inhibition, combination with anti-angiogenic therapy, BET inhibition and G-quadruplex stabilisation. Translational studies to elucidate mechanisms of treatment resistance should be incorporated into future clinical trials, as understanding these biologic mechanisms is crucial to developing new and effective therapeutic approaches in advanced OC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".