Examining the evidence for immune checkpoint therapy in high-grade serous ovarian cancer
Bibliographic record
Abstract
The 5-year survival rate for ovarian cancer has remained relatively static over the past number of years, which can be attributed in part to the lack of new therapeutic strategies to target this disease. Although numerous other cancer types have benefited from the success of immune checkpoint inhibitors, their use in clinical trials targeting ovarian cancer has shown limited efficacy. Most clinical trials have focused on PD-1/PD-L1 immune checkpoint blockade, either as a monotherapy or in combination with chemotherapies, however inhibiting other pathways may potentially be more efficacious in treating ovarian cancer. For example, drugs targeting some emerging immune checkpoints (such as LAG-3, TIM-3, TIGIT and PVRIG), are entering into clinical trials, which could show improved success for ovarian cancer patients. Similarly, predictive biomarkers that have been approved for use with immune checkpoint inhibitors, such as PD-L1 expression, are limited, as only the presence or absence of PD-L1 is assessed. However, the development of next generation predictive biomarkers, which assesses density and location of tumour infiltrating lymphocytes, could be more beneficial for this heterogenous cancer. In this review we discuss the use of immune checkpoint inhibitors in ovarian cancer, with a focus on high-grade serous disease, and delve into what the future may hold for immunotherapy in this cancer type.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
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".