Curative timed NK cell-based immunochemotherapy aborts brain tumour recurrence driven by mesenchymal glioma stem cells
Bibliographic record
Abstract
High grade gliomas (HGG) are incurable brain cancers, where inevitable disease recurrence is driven by tumour-initiating glioma stem cells (GSCs). GSCs survive and expand in the brain after surgery, radiation and temozolomide (TMZ) chemotherapy, amidst weak immune and natural killer (NK) cell surveillance. The present study was designed to understand how to enhance the contribution of innate immunity to post TMZ disease control. Strikingly, molecular subtypes of HGG impacted the repertoire of NK cell sensitivity markers across human HGG transcriptomes, and in a panel of GSCs with either proneural (PN-GSC) or mesenchymal (MES-GSC) phenotypes. Indeed, only MES-GSCs (but not PN-GSCs) were enriched for NK cell ligands and sensitive to NK-mediated cytotoxicity in vitro. While NK cells alone had no effect on HGG progression in vivo, the post-chemotherapy (TMZ) recurrence of MES-GSC-driven xenografts was aborted by timed intracranial injection of live or irradiated NK (NK92MI) cells, resulting in long term survival of animals. This curative effect declined when NK cell administration was delayed relative to TMZ exposure pointing to limits of the immune control over resurging residual tumour stem cell populations that survived chemotherapy. Overall, these results suggest that chemotherapy-dependent tumour depopulation may create a unique window of opportunity for NK-mediated intervention with curative effects restricted to a subset of HGGs driven by mesenchymal brain tumour initiating cells.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".