Focused Ultrasound Delivers Targeted Immune Cells to Metastatic Brain Tumors
Bibliographic record
Abstract
Natural killer (NK) cells are cytotoxic lymphocytes involved in innate immunity. NK-92, a human NK cell line, may be targeted to tumor-associated antigens in solid malignancies where it exhibits antitumor efficacy, but its clinical utility for treating brain tumors is limited by an inability to cross the blood-brain barrier (BBB). We investigated the potential for focused ultrasound (FUS) to deliver targeted NK-92 cells to the brain using a model of metastatic breast cancer. HER2-expressing human breast tumor cells were implanted into the brain of nude rats. The NK-92-scFv(FRP5)-zeta cell line expressing a chimeric HER2 antigen receptor was transfected with superparamagnetic iron oxide nanoparticles before intravenous injection, before and following BBB disruption using focused ultrasound (551.5 kHz focused transducer, 0.33 MPa average peak rarefaction pressure) in the presence of a microbubble contrast agent. Baseline and posttreatment 1.5T and 7T MR imaging was done, and histology used to identify NK-92 cells post-mortem. Contrast-enhanced MRI showed reproducible and consistent BBB disruption. 7T MR images obtained at 16 hours posttreatment revealed a significant reduction in signal indicating the presence of iron-loaded NK-92 cells at the tumor site. The average ratio of NK-92 to tumor cells was 1:100 when NK cells were present in the vasculature at the time of sonication, versus 2:1,000 and 1:1,000 when delivered after sonication and without BBB disruption, respectively. Our results offer a preclinical proof-of-concept that FUS can improve the targeting of immune cell therapy of brain metastases.
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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 | 0.000 |
| 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 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".