IMMU-25. PROGRAMMED CELL DEATH-LIGAND 1 (PD-L1) IS NOT EXPRESSED IN DIFFUSE INTRINSIC PONTINE GLIOMA (DIPG) TUMOR CELLS
Bibliographic record
Abstract
Diffuse intrinsic pontine glioma (DIPG) is a pediatric high grade, infiltrative tumor that arises in the pons. It is the leading cause of brain tumor related death in children. Despite increased understanding of the genetics and epigenetics of DIPG, development of in vivo research models, and several clinical trials, standard of care and survival has not improved. One avenue that has yet to be explored in DIPG is the use of immune checkpoint inhibitors such as the anti-PD-1 and PD-L1 monoclonal antibodies, Nivolumab and Pembrolizumab, respectively. PD-1/PD-L1 checkpoint inhibition is currently being used to treat certain solid tumors, including melanoma and non-small cell lung cancer. In addition, two pediatric glioblastoma multiforme cases secondary to bi-allelic mismatch repair deficiency responded to treatment with Nivolumab. Evidence of immune infiltration and PD-1/PD-L1 expression was seen in these two tumors. Similarly, there may be a subset of DIPG that may respond to inhibiting the PD-1/PD-L1 axis. To test this hypothesis, immunohistochemistry was performed for PD-L1 (SP142 clone), CD4 and CD8 in DIPG tumor samples using formalin-fixed paraffin-embedded tissues from autopsy, and appropriate positive and negative controls. PD-L1 (n=31) was evaluated as percentage of positive tumor cells. CD4 and CD8 (n=20) expression was assessed from 0 to 3+ based on average number of positive lymphocytes in four high power fields. Membranous PD-L1 expression was negative in each of the 31 cases characterized. There was low expression of CD4 (1+) in 11 out of 20 cases and CD8 was moderately expressed in 19 cases (1–2+). Targeting the PD-1/PD-L1 axis may have limited efficacy against DIPG. However, since tumor infiltrating lymphocytes are present, alternate immune evasion mechanisms, such as IDO and CTLA-4, should be evaluated in order to further characterize the immune microenvironment and assess the potential of immunotherapy against DIPG.
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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) | 0.002 | 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".