MHC Class II Presentation of gp100 Epitopes in Melanoma Cells Requires the Function of Conventional Endosomes and Is Influenced by Melanosomes
Bibliographic record
Abstract
Many human solid tumors express MHC class II (MHC-II) molecules, and proteins normally localized to melanosomes give rise to MHC-II-restricted epitopes in melanoma. However, the pathways by which this response occurs have not been defined. We analyzed the processing of one such epitope, gp100(44-59), derived from gp100/Pmel17. In melanomas that have down-regulated components of the melanosomal pathway, but constitutively express HLA-DR*0401, the majority of gp100 is sorted to LAMP-1(high)/MHC-II(+) late endosomes. Using mutant gp100 molecules with altered intracellular trafficking, we demonstrate that endosomal localization is necessary for gp100(44-59) presentation. By depletion of the AP-2 adaptor protein using small interfering RNA, we demonstrate that gp100 protein internalized from the plasma membrane to such endosomes is a major source for gp100(44-59) epitope production. The gp100 trapped in early endosomes gives rise to epitopes that are indistinguishable from those produced in late endosomes but their production is less sensitive to inhibition of lysosomal proteases. In melanomas containing melanosomes, gp100 is underrepresented in late endosomes, and accumulates in stage II melanosomes devoid of MHC-II molecules. The gp100(44-59) presentation is dramatically reduced, and processing occurs entirely in early endosomes or stage I melanosomes. This occurrence suggests that melanosomes are inefficient Ag-processing compartments. Thus, melanoma de-differentiation may be accompanied by increased presentation of MHC-II restricted epitopes from gp100 and other melanosome-localized proteins, leading to enhanced immune recognition.
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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".