Immune involvement of the contralateral hemisphere in a glioblastoma mouse model
Bibliographic record
Abstract
BACKGROUND: Glioblastoma (GBM) is the most common and deadliest form of brain cancer in adults. Standard treatment, consisting of surgery and radiochemotherapy, only provides a modest survival benefit and is incapable of combating infiltrating GBM cells in other parts of the brain. New therapies in clinical trials, such as anti-programmed cell death 1 immunotherapy, have so far shown limited success in GBM. Moreover, it is unclear how the growth of GBM suppresses the immune system locally at the site of the brain tumor or if distant sites of tumor cell migration are also involved. Invasive GBM cells in brain tissue beyond the primary tumor limit the use of surgery, thus immunotherapy could be beneficial if activated/suppressed immune cells are present in the contralateral hemisphere. METHODS: Here, we used a syngeneic orthotopic GL26 GBM mouse model and multiparameter fluorescence-activated cell sorting analysis to study the phenotype of resident and infiltrating immune cells in both the brain tumor hemisphere and contralateral hemisphere. RESULTS: tumor-infiltrating lymphocytes (TILs) are present in the tumor and are characterized by a tolerogenic phenotype based on high immune checkpoint expression. Massive infiltration of myeloid cells is observed, expressing immune checkpoint ligands, suggesting an immune-dependent coinhibitory axis limiting TIL responses. Surprisingly, these phenotypes are paralleled in the contralateral hemisphere, showing that infiltrating immune cells are also present at distant sites, expressing key immune checkpoints and immune checkpoint ligands. CONCLUSION: Whole-brain analysis indicates active immune involvement throughout the brain, both at the site of the primary tumor and in the contralateral hemisphere. Using the right combination and timing, immune checkpoint blockade could have the potential to activate immune cells at the site of the brain tumor and at distant sites, thereby also targeting diffusely infiltrating GBM 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 | 0.000 |
| 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) | 0.000 | 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".