IMMU-62. INVIGORATING EFFECTOR IMMUNE CELLS WITH HIGHLY SELECTIVE IL-2R AGONISTS AND POTENTIAL SYNERGY WITH TUMOR TARGETING THERAPEUTICS FOR TREATMENT OF GLIOBLASTOMAS
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is an aggressive brain tumor with a median overall survival (mOS) of 14 months. Treatment options are limited with no new approved therapies over the past 3 decades and no standard of care for a majority of patients (> 90%) who experience recurrence. GBM has a highly immune suppressive tumor microenvironment (TME) comprising of myeloid derived suppressor cells (MDSCs) and tumor associated macrophages (TAMs) that are capable of suppressing the activity of anti-cancer CD8+ T and NK cells. We have engineered highly selective IL-2 superkines (IL-2SK) that preferentially expand and activate CD8+ T and NK cells with limited effects on immune suppressive regulatory T cells (i.e., Tregs). MDNA11 is an IL-2SK -albumin fusion protein designed to increase half-life and promote tumor accumulation. MDNA223 is a bi-functional anti-PD1-IL-2SK designed to stimulate effector immune cells while preventing immune exhaustion. MDNA11 and MDNA223 extended survival of mice harboring orthotopic GBM tumors with accompanying increase in CD8+ T and NK cells within the TME. When patient-derived GBM tumor explants were treated with MDNA11 and MDNA223 ex vivo, there was clear evidence of activation among resident CD8+ T cells characterized by increased levels of intra-cellular Granzyme B responsible for tumor cell killing. There was also increased release of soluble Fas ligand and granulysin, consistent with an activated anti-tumor immune response within the TME. Ongoing studies include in depth immune profiling to further understand the mechanism of MDNA11 and MDNA223 in GBM as well as to test potential synergy with tumor targeting therapeutics and other treatment modalities capable of eliciting immunogenic cell death.
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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".