TMIC-75. ICAM-1 PROMOTES AN IMMUNOSUPPRESSIVE LANDSCAPE IN GLIOBLASTOMAS BY REGULATING THE PHENOTYPIC POLARIZATION STATE OF TUMOR ASSOCIATED MACROPHAGES
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a fatal adult CNS tumor, with a median overall survival of 12-15 months. Intercellular adhesion molecule 1 (ICAM-1) is a cell adhesion molecule whose expression is significantly greater in the mesenchymal transcriptional subtype of GBMs, whereby it is prognostic. We have previously shown that when ICAM-1 is knocked out in the tumor microenvironment (TME) of immune-competent GBM-bearing mice, these mice showed prolonged overall survival and reduced tumor volume. It is yet to be determined how ICAM-1 expression affects infiltration and functional polarization of tumor-associated macrophages (TAMs) and other immune cells in GBM, and if we can suppress the infiltration of M2-like TAMs into the TME by inhibiting ICAM-1. We used a mouse glioma model with and without knockout (KO) of the ICAM-1 gene and intracranially injected GL261 cells. ICAM-1 KO mice survived longer and had reduced tumor volume relative to wildtype, with survival and volume differences being rescued upon ICAM-1 bone marrow reconstitution. Upon endpoint, we performed cytometry by time-of-flight, and found that ICAM-1 KO tumors harbored a reduction in helper T cells, Tregs, and cytotoxic T cells, but an increased proportion in double-negative T cells relative to ICAM-1 WT tumors. ICAM-1 KO tumors also demonstrated an increased proportion of M1-like TAMs relative to M2-like. These results were validated upon administration of ISIS-3082 to tumor models, an antisense oligonucleotide for which a human-specific form has been fast-track approved for other diseases, that targets ICAM-1 transiently in peripheral immune cells prior to TME infiltration. These findings suggest that ICAM-1 expression influences immune cell infiltration, potentially modulates the functional state of TAMs towards a M1-like state, and that ICAM-1 may be targeted in TAMs to ‘reeducate’ them before entering the TME. This may be translated into clinical practice and combined with standards-of-care to render GBMs more sensitive to treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".