FAK inhibitor treatment systemically regulates the glioblastoma immune environment via blocking monocyte trafficking
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common primary malignant brain tumour in adults with dismal survival rates, and current therapies, including most immunotherapies, are not efficacious due to the highly immunosuppressive microenvironment. Studies in other solid cancers report that impairment of the integrin effector pathway involving focal adhesion kinase (FAK) can promote anti-tumour immune responses. Therefore, we set out to address whether, and if so how, suppressing FAK function may influence GBM by using both tumour cell-specific FAK gene deletion and systemic delivery of a clinically relevant FAK kinase inhibitor (FAKi) VS-4718 in an orthotopic murine stem cell model of GBM. We found that treatment with the FAKi, but not tumour cell-specific FAK gene deletion, resulted in GBM clearance and improved survival. This was dependent on adaptive immunity, and tumour-infiltrating T cells in FAKi-treated tumours displayed increased cytotoxic potential and reduced exhaustion. We also found a significant reduction in immuno-suppressive peripherally-derived macrophages and FAKi treatment caused sequestration of inflammatory monocytes within the bone marrow, resulting in impaired monocyte trafficking to tumours as judged by adoptive transfer. This is due to suppression of key adhesion and migration signalling through α4β1 integrin and CX3CR1 in peripheral monocytes. Our work here describes a previously unidentified role for FAK in trafficking of peripheral suppressive macrophages to GBM tumours, reducing T cell exhaustion and promoting anti-tumour immunity. This highlights a new way in which systemic FAK inhibitors can be used to provide a beneficial immune modulatory strategy for the treatment of GBM.
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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".