TMIC-70. Integrative spatiotemporal proteomic and metabolomic characterization of immune infiltration following intracranial injection of oncolytic immunotherapy in glioblastoma patients
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most aggressive and lethal primary brain tumor in adults, characterized by rapid proliferation, an immunologically inert microenvironment, diffuse tumor cell infiltration and therapy resistance. Progress in developing effective glioblastoma treatments is impeded by a lack of mechanistic insight into how GBM cells and the surrounding microenvironment respond to therapeutic interventions. To address this prominent gap, we developed an innovative on-therapy, longitudinal, and multi-regional tissue sampling approach in patients receiving rQNestin34.5v.2, an engineered oncolytic biologic, based on herpes simplex virus 1, to investigate the dynamic evolution of tumor and immune ecosystems during treatment. Using targeted spatial proteomics (CyCIF) alongside spatial metabolomics (MALDI-MSI), we analyzed over 100 samples from six patients, profiling immune, stromal, and malignant cells across both space and time. Our CyCIF data revealed that T and B cells infiltrate and colocalize within rQnestin34.5v.2–positive regions, forming clusters that reflect active immune interactions. These immune-rich zones were inversely correlated with SOX2⁺ and OLIG2⁺ tumor cells. Notably, patients with longer survival showed a progressive increase in CD3⁺ T cells, driven by expanding cytotoxic T cell populations alongside a reduction in CD4⁺ T cells. Despite increased immune infiltration, post-trial tissues displayed an immunosuppressive environment characterized by few cytotoxic T cells and reduced tumor cells. Integrating spatial proteomics and metabolomics, we observed elevated purine metabolites—including ATP, GTP, and xanthine—in regions of heightened immune infiltration. Extracellular ATP and GTP likely facilitate T cell activation and migration through purinergic signaling and GTPase pathways. These metabolic patterns suggest that purine metabolism plays a key role in shaping immunologically active tumor microenvironments following treatment. Together, these findings provide novel mechanistic insights into the evolving tumor-immune landscape during rQnestin34.5v.2 therapy in glioblastoma, highlighting purine metabolism as a potential driver of immune activation within the tumor microenvironment. This integrated spatial approach offers a powerful framework to guide the development of more effective immunotherapeutic strategies for this challenging malignancy.
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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".