Expanded CD56superbrightCD16+ NK Cells from Ovarian Cancer Patients Are Cytotoxic against Autologous Tumor in a Patient-Derived Xenograft Murine Model
Bibliographic record
Abstract
Abstract Natural killer (NK) cells are useful for cancer immunotherapy and have proven clinically effective against hematologic malignancies. However, immunotherapies for poor prognosis solid malignancies, including ovarian cancer, have not been as successful due to immunosuppression by solid tumors. Although rearming patients' own NK cells to treat cancer is an attractive option, success of that strategy is limited by the impaired function of NK cells from cancer patients and by inhibition by self-MHC. In this study, we show that expansion converts healthy donor and immunosuppressed ovarian cancer patient NK cells to a cytotoxic CD56superbrightCD16+ subset with activation state and antitumor functions that increase with CD56 brightness. We investigated whether these expanded NK cells may overcome the limitations of autologous NK cell therapy against solid tumors. Peripheral blood- and ascites-derived NK cells from ovarian cancer patients were expanded and then adoptively transferred into cell-line and autologous patient-derived xenograft models of human ovarian cancer. Expanded ovarian cancer patient NK cells reduced the burden of established tumors and prolonged survival. These results suggest that CD56bright NK cells harbor superior antitumor function compared with CD56dim cells. Thus, NK cell expansion may overcome limitations on autologous NK cell therapy by converting the patient's NK cells to a cytotoxic subset that exerts a therapeutic effect against autologous tumor. These findings suggest that the value of expanded autologous NK cell therapy for ovarian cancer and other solid malignancies should be clinically assessed. Cancer Immunol Res; 6(10); 1174–85. ©2018 AACR.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 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".