CAR-T cell immunotherapy for ovarian cancer: hushing the silent killer
Bibliographic record
Abstract
cause of cancer-related mortality in women, with a high percentage of the patients being diagnosed at late stages of the disease and a five-year survival of ~ 30%. Ovarian cancer patients conventionally undergo surgery for tumor removal followed by platinum- or taxane-based chemotherapy; however, a high percentage of patients experience tumor relapse. Cancer immunotherapy has been regarded as a silver lining in the treatment of patients with various immunological or oncological indications; however, mirvetuximab soravtansine (a folate receptor α-specific mAb) and bevacizumab (a VEGF-A-specific mAb) are the only immunotherapeutics approved for the treatment of ovarian cancer patients. Chimeric antigen receptor T-cell (CAR-T) therapy has achieved tremendous clinical success in the treatment of patients with certain B-cell lymphomas and leukemias, as well as multiple myeloma. In the context of solid tumors, CAR-T therapies face serious obstacles that limit their therapeutic benefit. Such hindrances include the immunosuppressive nature of solid tumors, impaired tumor infiltration, lack of qualified tumor-associated antigens, and compromised stimulation and persistence of CAR-Ts following administration. Over the past years, researchers have made arduous attempts to apply CAR-T therapy to ovarian cancer. In this review, we outline the principles of CAR-T therapy and then highlight its limitations in the context of solid tumors. Ultimately, we focus on preclinical and clinical findings achieved in CAR-T-mediated targeting of different ovarian cancer-associated target antigens.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".