Naturally Killing the Silent Killer: NK Cell-Based Immunotherapy for Ovarian Cancer
Bibliographic record
Abstract
Ovarian cancer (OC) is diagnosed in ~22,000 women in the US each year and kills 14,000 of them. Often, patients are not diagnosed until the later stages of disease, when treatment options are limited, highlighting the urgent need for new and improved therapies for precise cancer control. An individual's immune function and interaction with tumor cells can be prognostic of the response to cancer treatment. Current emerging therapies for OC include immunotherapies, which use antibodies or drive T cell-mediated cancer recognition and elimination. In OC, these have been limited by adverse side effects and tumor characteristics including inter- and intra-tumoral heterogeneity, lack of targetable antigens, loss of tumor human leukocyte antigen expression, high levels of immunosuppressive factors, and insufficient immune cell trafficking. Natural killer (NK) cells may be ideal as primary or collateral effectors to these nascent immunotherapies. NK cells exhibit multiple functions that combat immune escape and tumor relapse: they kill targets and elicit inflammation through antigen-independent pathways and detect loss of HLA as a signal for activation. NK cells are efficient mediators of tumor immune surveillance and control, suppressed by the tumor microenvironment and rescued by immune checkpoint blockade. NK cells are regulated by a variety of activating and inhibitory receptors and already known to be central effectors across an array of existing therapies. In this article, we highlight interactions between NK cells and OC and their potential to change the immunosuppressive tumor microenvironment and participate in durable immune control of OC.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".