Treatment of Ovarian Cancer Beyond PARP Inhibition: Current and Future Options
Bibliographic record
Abstract
Ovarian cancer is the leading cause of gynecological cancer death. Improved understanding of the biologic pathways and introduction of poly (ADP-ribose) polymerase inhibitors (PARPi) during the last decade have changed the treatment landscape. This has improved outcomes, but unfortunately half the women with ovarian cancer still succumb to the disease within 5 years of diagnosis. Pathways of resistance to PARPi and chemotherapy have been studied extensively, but there is an unmet need to overcome treatment failure and improve outcome. Major mechanisms of PARPi resistance include restoration of homologous recombination repair activity, alteration of PARP function, stabilization of the replication fork, drug efflux, and activation of alternate pathways. These resistant mechanisms can be targeted to sensitize the resistant ovarian cancer cells either by rechallenging with PARPi, overcoming resistance mechanism or bypassing resistance pathways. Augmenting the PARPi activity by combining it with other targets in the DNA damage response pathway, antiangiogenic agents and immune checkpoint inhibitors can potentially overcome the resistance mechanisms. Methods to bypass resistance include targeting non-cross-resistant pathways acting independent of homologous recombination repair (HRR), modulating tumour microenvironment, and enhancing drug delivery systems such as antibody drug conjugates. In this review, we will discuss the first-line management of ovarian cancer, resistance mechanisms and potential strategies to overcome these.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".