OS7 - 145 Combination Immunotherapy for Glioma: Beyond PD 1 Inhibition
Bibliographic record
Abstract
Programmed Cell Death – 1 (PD1) inhibition activates tumor-specific T-lymphocytes and is an effective clinical therapy against some cancers. Preclinical data regarding immune checkpoint inhibitors against malignant glioma is scant, and interim analyses of clinical trials suggest modest effect in patients as single agents. We examined PD-1 inhibition in murine glioblastoma models in combination with other immunomodulatory agents. Methods – Syngeneic glioma tumors (GL261 and CT2A) were implanted intracranially in C57/Bl6 mice. In separate experiments, PD-1 inhibition was combined with antibody blockade of t-cell immunoglobulin and mucin protein (TIM3), ligation of OX40 on T-lymphocytes, or vaccination with irradiated GM-CSF expressing tumor cells. Systemic antitumor immunity and tumor infiltrating lymphocytes were analyzed by ELISPOT assay and flow cytometry, respectively. Results - In both syngeneic glioma models, day 3,6, and 9 systemic delivery of a monoclonal antibody against PD-1 led to increased survival vs. controls. In animals with GL261 intracranial tumors, survival was improved by combination of PD-1 blockade with subcutaneous injection of irradiated GM-CSF expressing GL261 tumor cells, with antibody blockade of t-cell immunoglobulin and mucin protein 3 (TIM3), or binding of OX40 on T-lymphocytes by an activating antibody. In most cases, ELISPOT analyses demonstrated enhanced Th1 immunity by combination immunotherapies. Vaccination was associated with an increased intratumoral CD8+ T lymphocyte / FoxP3+ T lymphocyte ratio. Conclusion –Blockade of PD-1 on T lymphocytes in glioma-bearing mice is active. Both antitumor immunity and survival can be enhanced by combination of PD-1 inhibition with agents that activate antitumor immunity by complementary mechanisms.
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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) | 0.001 | 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".