Virtual clinical trial reveals significant clinical potential of targeting tumour-associated macrophages and microglia to treat glioblastoma
Bibliographic record
Abstract
Abstract Glioblastoma is the most aggressive primary brain tumour, with a median survival of just fifteen months with treatment. Standard-of-care (SOC) for glioblastoma consists of resection followed by radio- and chemotherapy. Clinical trials involving PD-1 inhibition with nivolumab in combination with SOC failed to increase overall survival. A quantitative understanding of the interactions between the tumour and its immune environment driving treatment outcomes is currently lacking. As such, we developed a mathematical model of tumour growth that considers cytotoxic CD8+ T cells, pro- and antitumoral tumour-associated macrophages and microglia (TAMs), SOC, and nivolumab. Our results show that PD-1 inhibition fails due to a lack of CD8+ T cell recruitment during treatment explained by TAM-driven immunosuppressive mechanisms. Using our model, we studied five TAM-targeting strategies currently under investigation for solid tumours. Our model predicts that while reducing TAM numbers does not improve prognosis, altering their functions to counter their protumoral properties has the potential to considerably reduce post-treatment tumour burden. In particular, restoring antitumoral TAM phagocytic activity through anti-CD47 treatment in combination with SOC was predicted to nearly eradicate the tumour. By studying time-varying efficacy with the same half-life as the anti-CD47 antibody Hu5F9-G4, our model predicts that repeated dosing of anti-CD47 provides sustained control of tumour growth. Thus, we propose that targeting TAMs by enhancing their antitumoral properties is a highly promising avenue to treat glioblastoma and warrants future clinical development. Together, our results provide proof-of-concept that mechanistic mathematical modelling can uncover the mechanisms driving treatment outcomes and explore the potential of novel treatment strategies for hard-to-treat tumours like glioblastoma.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".