Combinational expression of tumor testis antigens NY-ESO-1, MAGE-A3, and MAGE-A4 predicts response to immunotherapy in mucosal melanoma patients
Bibliographic record
Abstract
PURPOSE: Immunotherapy using immune checkpoint inhibitors (ICI) has revolutionized cancer treatment in recent years, particularly in melanoma. While response to immunotherapy is associated with high tumor mutational burden (TMB), PD-L1 expression, and microsatellite instability in several cancers, tumors lacking these biomarkers can still respond to this treatment. Especially, mucosal melanoma, commonly exhibiting low TMB compared to cutaneous melanoma, may respond to immunotherapy with immune checkpoint inhibitors. Therefore, the aim of our study was to investigate novel biomarkers in mucosal melanoma that predict response to combined ipilimumab and nivolumab. METHODS: We investigated 10 tumor samples from 10 patients (three responders, seven non-responders) before treatment and six tumor samples from five patients after progression using a targeted Next Generation Sequencing (NGS) gene expression panel. The findings were corroborated with an independent method (i.e., immunohistochemical staining) on the same 10 tumor samples before treatment and, to increase the cohort, in addition on three tumor samples before treatment of more recent patients (one responder, two non-responders). RESULTS: With the targeted gene expression panel, we found the three tumor testis antigens CTAG1B (NY-ESO-1), MAGE-A3, and MAGE-A4 to be predominantly expressed in responding tumors. This marker panel was either not or not completely expressed in non-responders (p < 0.01). Using immunohistochemistry for all three markers, we could confirm the elevated expression in tumors responding to the ipilimumab/nivolumab combination therapy. CONCLUSION: In conclusion, these three biomarkers await validation in a larger patient cohort and could be easily used in future routine diagnostics to predict the outcome of ipilimumab/nivolumab combination therapy in mucosal melanoma patients.
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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".