IMMU-67. On-Treatment Spatiotemporal Profiling of Glioblastoma During Oncolytic ImmunoTherapy
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common and deadliest primary brain tumor, with no effective therapies and a longstanding record of failed phase 3 clinical trials—often without mechanistic insight into why promising approaches fall short. To address this critical gap, we implemented a novel on-therapy, longitudinal, and multi-regional tissue sampling strategy in patients treated with rQNestin34.5v2, an engineered oncolytic biologic based on herpes simplex virus 1, to explore how tumor and immune ecosystems evolve during treatment. Using high-resolution spatial transcriptomics (Xenium) and targeted spatial proteomics (CyCIF), we profiled over 80 spatially and temporally distinct samples from six patients, capturing over half a million immune, stromal, and malignant cells. This unique dataset enabled unprecedented resolution of the dynamic interactions between tumor and immune compartments in situ over the course of therapy. Our preliminary analysis revealed discrete, virus-associated lymphoid-like structures that formed early after treatment initiation. These structures were enriched in T cells, B cells, and myeloid cells, and were associated with localized reductions in tumor cell density. Over time, we observed a shift from an initially inflamed microenvironment to a more immunosuppressive state. By the time of radiographic progression, these lymphoid structures had largely dissipated—underscoring the importance of on-treatment sampling in capturing transient, treatment-responsive immune architectures. While these findings are preliminary, they suggest a transient immune structure linked to early therapeutic response, whose collapse may contribute to treatment resistance. Our study illustrates how integrative, on-therapy spatial profiling can begin to reveal mechanisms of response and resistance in GBM, providing a framework for refining future immunotherapeutic strategies in brain tumors and other treatment-refractory cancers.
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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".