Abstract 2556: Expanded ovarian cancer patient NK cells represent a novel, cytotoxic phenotype and reduce autologous tumor in a patient-derived xenograft ovarian cancer model
Bibliographic record
Abstract
Abstract Introduction: Ex vivo expanded NK cells are emerging as a promising cancer immunotherapy and have recently shown clinical effectiveness in treating hematological malignancies. NK cell therapy has encountered challenges against solid tumors, such as ovarian cancer, due to the immunosuppressive tumor environment which inhibits NK cell function. In addition, autologous NK cell therapy has been limited due to the diminished function of cancer patient NK cells and inhibition by self-MHC. In the present study, we assessed whether expansion of ovarian cancer patient NK cells overcomes limitations of autologous NK cell therapy against high grade serous ovarian cancer (HGSOC). Experimental Procedures: NK cells were expanded ex vivo from peripheral blood (PB) and ascites of HGSOC patients or PB of healthy donors using K562-mb-IL-21 feeder cell expansion. We compared expanded and unexpanded NK cell cytotoxicity against ovarian cancer cells in vitro. Given recent evidence that CD56brightCD16- NK cells have greater anti-tumor potential than CD56dimCD16+ NK cells, we assessed relative expression of CD56 and CD16 on expanded NK cells. To study autologous NK cell therapy, we established a translational patient-derived xenograft (PDX) model of ovarian cancer. We adoptively transferred expanded ovarian cancer patient PB- and ascites-NK cells into cell-line xenograft and autologous PDX ovarian cancer models. Tumor burden, ascites progression, and survival of xenograft mice were assessed. Results: We report that expanded NK cells express a novel CD56bright/brightCD16+ phenotype and have greater in vitro cytotoxicity than unexpanded NK cells against ovarian cancer cells. Adoptive transfer of expanded ovarian cancer patient PB- and ascites-NK cells reduced tumor burden of well-established ovarian cancer in cell-line xenograft mice to levels comparable to no tumor controls. Furthermore, expanded NK cells improved median survival by 3.4-5.2 times compared to untreated xenograft mice. Importantly, expanded ovarian cancer patient NK cells exerted a therapeutic effect in autologous PDX mice: expanded NK cells delayed ascites progression and inhibited development of peritoneal tumors. Conclusions: Our results support an emerging concept that CD56bright NK cells have greater anti-tumor capacities than CD56dim NK cells. The therapeutic effects of expanded ovarian cancer patient NK cells on tumor burden and survival indicate that expansion converts immunosuppressed cancer patient NK cells to a cytotoxic subset that remains highly functional in an autologous tumor environment. The effectiveness of expanded ascites-NK cells identifies tumor-associated NK cells as a potential NK cell source for therapy. This study indicates that expanded autologous NK cells may be a promising therapy for ovarian cancer and other solid tumors. Citation Format: Sophie M. Poznanski, Tina Nham, Marianne V. Chew, Amanda J. Lee, Isabella Y. Fan, Dean A. Lee, Hal Hirte, Ali A. Ashkar. Expanded ovarian cancer patient NK cells represent a novel, cytotoxic phenotype and reduce autologous tumor in a patient-derived xenograft ovarian cancer model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2556.
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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) | 0.003 | 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".