Early treatment of HER2-amplified brain tumours with targeted nk-92 cells and focused ultrasound improves survival
Bibliographic record
Abstract
Background: Malignant brain tumors have a dismal prognosis, with residual after surgery necessitating adjuvant chemoradiotherapy. We previously demonstrated that targeted Natural Killer (NK-92) cells could be delivered to the brain using a combination of MRI-guided focused ultrasound and Definity microbubbles. Once in the CNS, they can track to malignant tissues without inflicting collateral damage. The HER2 receptor is expressed by epithelial tumours including both breast and glioblastoma; breast tumors with HER2-amplification have a higher risk of CNS metastasis, and poorer prognosis. Methods: We investigated whether multiple combined treatments of targeted NK-92 cells and focused ultrasound with microbubbles could slow tumour growth and improve survival in an orthotopic HER2-amplified rodent brain tumour model using a human breast cancer line as a prototype. Results: Early daily treatments with targeted NK-92 cells and ultrasound improved survival and decreased tumour volumes compared with bi-weekly treatments, or either treatment alone. The intensive treatment paradigm resulted in cure in 50% of subjects. Conclusions: Many tumour proteins could be exploited for targeted therapy with the NK-92 cell line, and combined with the mounting safety evidence for transcranial ultrasound, this may soon provide a non-invasive and highly targeted treatment option for patients with brain tumours.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".