Novel Therapeutics in Ovarian Cancer: Expanding the Toolbox
Bibliographic record
Abstract
Despite high response rates to initial therapy, most patients with ovarian cancer will ultimately recur and go on to develop resistance to standard treatments. Novel therapies have been developed to overcome drug resistance and alter the tumor immune microenvironment by targeting oncogenic pathways, activating the innate immune response, and enhancing drug delivery. In this review, we discuss the current and future roles of chemotherapy, targeted agents such as poly (ADP-ribose) polymerase (PARP) inhibitors, bevacizumab, and mirvetuximab in the treatment of ovarian cancer. We explore the emerging role of therapeutic targets, including DNA repair pathway inhibitors and novel antibody-drug conjugates. Furthermore, we delve into the role of immunotherapeutic agents such as interleukins as well as immune-promoting agents such as oncolytic viruses and cancer vaccines. Innovative combination therapies using these agents have led to a rapidly evolving treatment landscape and promising results for patients with recurrent ovarian cancer.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".