Virtual Clinical Trial Reveals Significant Clinical Potential of Targeting Tumor‐Associated Macrophages and Microglia to Treat Glioblastoma
Bibliographic record
Abstract
Glioblastoma is the most aggressive primary brain tumor, with a median survival of 15 months with treatment. Standard-of-care (SOC) consists of resection, radio- and chemotherapy. Clinical trials involving PD-1 inhibition with nivolumab combined with SOC failed to increase survival. A quantitative understanding of the interactions between the tumor and its immune environment that drive treatment outcomes is currently lacking. As such, we developed a mathematical model of tumor growth that considers CD8+ T cells, pro- and antitumoral tumor-associated macrophages and microglia (TAMs), SOC, and nivolumab. Using our model, we studied five TAM-targeting strategies currently under investigation for solid tumors. 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. 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 tumor burden. In particular, restoring antitumoral TAM phagocytic activity through anti-CD47 treatment in combination with SOC was predicted to nearly eradicate the tumor. 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 tumor growth. 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 modeling can uncover the mechanisms driving treatment outcomes and explore the potential of novel treatment strategies for hard-to-treat tumors 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".