Tumor CD155 Expression Is Associated with Resistance to Anti-PD1 Immunotherapy in Metastatic Melanoma
Bibliographic record
Abstract
PURPOSE: Resistance to anti-PD1-based immune checkpoint blockade (ICB) remains a problem for the treatment of metastatic melanoma. Tumor cells as well as host myeloid cells can express the immune checkpoint ligand CD155 to regulate immune cell function. However, the effect of tumor CD155 on the immune context of human melanoma has not been well described. This observational study characterizes tumor CD155 ligand expression by metastatic melanoma tumors and correlates results with differences in immune cell features and response to ICB. EXPERIMENTAL DESIGN: Pretreatment tumor specimens, from 155 patients with metastatic melanoma treated with ICB and from 50 patients treated with BRAF/MEK-directed targeted therapy, were assessed for CD155 expression by IHC. Intratumor T-cell features were analyzed using multiplex-immunohistofluorescence for CD8, PD1, and SOX10. Correlations were made between CD155 tumor level and bulk tumor RNA sequencing results, as well as clinical RECIST response and progression-free survival. RESULTS: ) and poor response to anti-PD1 therapy. In PDL1 negative tumors, high CD155 tumor expression was associated with patients who had poor response to combination anti-PD1/CTLA4 therapy. CONCLUSIONS: T cells in anti-PD1 refractory melanoma tumors and, further, that targeting the CD155 pathway might improve response to anti-PD1 therapy for patients with metastatic melanoma.
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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 | 0.001 |
| 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) | 0.001 | 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".