DIPG-17. CD155 regulates cell growth and immune evasion in diffuse intrinsic pontine glioma
Bibliographic record
Abstract
Abstract There is an unmet need for more effective treatment strategies for diffuse intrinsic pontine glioma (DIPG), a devastating brain tumour arising in children and young adults. While immunotherapy is emerging as a powerful approach to treatment of other cancers, clinical trials with immune checkpoint inhibitors have failed to show a survival benefit for DIPG patients. In this study, we analysed the expression of immune checkpoint molecules on the surface of human and murine DIPG cells by flow cytometry and identified CD155 and B7-H3 as the most highly expressed checkpoint molecules, with minimal expression of PD-L1, PD-L2, Galectin-9, CEACAM-1, CD86, CD252 and CD137. These findings were confirmed in primary patient samples from pediatric brain tumours, including high-grade gliomas, medulloblastomas and ependymomas. To test whether CD155 inhibition increases susceptibility to CD8+ T cell killing in vitro, we cultured DIPG cells expressing ovalbumin (OVA) with CD8+ T cells from OT-I mice, which express T cell receptors specific for OVA. Addition of CD155 blocking antibodies to these cultures increased expression of T cell activation markers (CD25, CD44 and CD69) as well as T cell-mediated tumour killing, supporting the notion that CD155 can function as an immune checkpoint in DIPG. In addition to its effects on T cells, CD155 also exerted direct effects on tumour cells: treatment with anti-CD155 antibodies led to impaired cell viability, and shRNA-mediated knockdown of CD155 resulted in reduced cell proliferation in vitro. Strikingly, knockdown of CD155 also led to reduced growth of DIPG cells in vivo, and mice transplanted with the CD155-deficient cells had a clear survival benefit compared to mice transplanted with wild type cells. These studies demonstrate that CD155 functions as an immune checkpoint and as a regulator of tumor growth in DIPG, and suggest that targeting CD155 could be a valuable therapeutic strategy for this devastating disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".