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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".