DMG-06. CD155 regulates tumor growth and immune evasion in diffuse midline glioma
Bibliographic record
Abstract
Abstract There is an unmet need for effective treatment strategies for diffuse midline glioma (DMG), a devastating pediatric brain tumor. Clinical trials with immune checkpoint inhibitors that are efficacious in other tumors have failed to show a survival benefit for DMG patients. In this study, we analyzed the expression of several known immune checkpoint molecules in human and murine DMG cells by flow cytometry and found that the poliovirus receptor CD155 (an adhesion molecule that interacts with CD96 and TIGIT on CD8+ T cells) is highly expressed in all DMG lines. This finding was confirmed in primary pediatric brain tumor samples. To test whether CD155 regulates susceptibility to CD8+ T cell killing, we cultured murine DMG cells with CD8+ T cells. shRNA mediated silencing of CD155 led to a marked increase in T cell-mediated killing in vitro. Intriguingly, CD155 also exerted cell-autonomous effects on tumor cells: silencing of CD155 led to induction of apoptosis of DMG cells even in the absence of T cells. In vivo, silencing of CD155 led to delayed tumor growth in immunocompromised mice and strikingly, CD155-deficient DMG cells failed to grow at all in immunocompetent mice, while depletion of CD8+ T cells allowed these tumors to grow. Transcriptomic analyses identified the transcription factor FOXM1 as a key target that is silenced in CD155-deficient cells. FOXM1 silencing also led to reduced proliferation of DMG cells in vitro and in vivo, and treatment of DMG-bearing mice with Thiostrepton, a FOXM1-targeting antibiotic, delayed tumor growth and prolonged survival. These studies demonstrate that CD155 functions as a modulator of tumor cell sensitivity to T cells and also regulates tumor cell survival in a T cell-independent manner. Our studies suggest that targeting CD155 or its downstream mediators could be a valuable double-pronged therapeutic strategy for this devastating disease.
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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".