IM-07 * NK CELL IMMUNOTHERAPY FOR PEDIATRIC BRAIN TUMORS: OVERCOMING RESISTANCE TO EXPAND THERAPEUTIC SUCCESS
Bibliographic record
Abstract
Pediatric brain tumors including medulloblastomas and ATRTs are associated with significant mortality and morbidity. Much of this morbidity is due to conventional treatments: radiation and chemotherapy. Therefore, there is an urgent need to develop new therapeutic options to combat these devastating diseases. Immunotherapy has gained traction as a potential alternative to current treatments. Because many immunotherapies rely on the presence of tumor-associated antigens (TAAs) and pediatric brain tumors have poorly defined antigen profiles, we pursued the development of natural killer (NK) cells, which don't require TAAs for their activity, to treat these malignancies. Our work shows that most medulloblastoma and ATRT cell lines are sensitive to NK cell lysis in vitro. Furthermore, both intratumoral NK cell injections as well as infusion at a distant site limit the medulloblastoma growth in mouse orthotopic xenograft models, indicating NK cell trafficking through the brain. These results have provided the foundation for a FDA-approved Phase I clinical trial to infuse NK cells directly into the fourth ventricle of patients who have undergone re-resection of infratentorial tumors. The trial is scheduled to enroll patients in February 2015. Interestingly, our pre-clinical data has also identified overexpression of a novel tumor-secreted immunosuppressive molecule, which was originally shown to confer cardio-protection following myocardial ischemia, in human ATRT samples, and its involvement in promoting resistance to NK cell-mediated lysis. In conclusion, our findings have paved the way for a first in pediatrics brain tumor immunotherapy trial that merges two cutting edge technologies: immunotherapy and loco-regional therapeutics delivery. Our study also offers a novel biomarker for predicting patient response to NK therapy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".