Enhanced glioblastoma immunotherapy via SMAC mimetic dose escalation and TGFβ blockade
Bibliographic record
Abstract
Background: Glioblastoma (GBM) is the most common primary brain tumor with an overall survival under 21 months. Despite extensive research effort, patient outcomes have improved minimally over the past several decades. The Inhibitor of Apoptosis (IAP) proteins are critical survival factors implicated in both immune regulation and gliomagenesis. Small molecule IAP antagonists called SMAC mimetic compounds (SMCs) are under investigation as cancer therapeutics across multiple malignancies, including GBM. SMCs induce GBM cell death in the presence of inflammatory cytokines, synergize with immune checkpoint inhibitors (ICI), and induce death of microglia and macrophages. Although SMCs show significant efficacy in murine models, complete eradication is not achieved. Here, we aimed to understand the limitations of SMCs in murine GBM and identify strategies to enhance efficacy of combination treatment with ICIs with the goal of informing future translational efforts. Methods: We use animal models, co-culture systems, flow cytometry, and multiplex immunohistochemistry to optimize SMC dosing and delivery, uncovering resistance mechanisms that address key unmet research needs. Results: We demonstrate that although GBM cells are immunologically recognizable, their location within the central nervous system (CNS) limits effective anti-GBM immunity following SMC and ICI combination therapy. Increasing SMC dose potently improves overall survival, which is associated with reduced intratumoral macrophage content, increased microglial involvement, and peripheral immunoactivation. Given the immunosuppressive role of TGFβ, the incorporation of TGFβ blockade further enhances survival outcomes. Conclusion: We comprehensively outline how SMCs can be used in conjunction with ICIs to treat GBM and propose strategies to maximize SMC efficacy.
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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 | 0.001 |
| 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".