Fine analysis of spontaneous MAGE-C1/CT7–specific immunity in melanoma patients
Bibliographic record
Abstract
Cancer/testis (CT) antigens represent prime candidates for immunotherapy in cancer patients, because their expression is restricted to cancer cells and germ cells of the testis. MAGE-C1/CT7 is a CT antigen that is highly expressed in several types of cancers. Spontaneous occurrence of CT7-specific antibodies was previously detected by SEREX screen in a melanoma patient. However, naturally occurring CT7-specific T-cell responses have thus far not been detected. Peripheral blood mononuclear cells (PBMCs) from 26 metastatic melanoma patients expressing CT7 in their tumor lesions (CT7(+)) were analyzed for CT7-specific T-cell responses using overlapping peptides. CT7-specific CD4(+) T-cell responses were detected in three patients (11.5%). These CT7-specific CD4(+) T-cell responses were detectable in melanoma patients' PBMCs exclusively from preexisting CD45RA(-) memory CD4(+) T-cell pool. Additional CT7-specific memory CD4(+) T-cell responses were detected in CT7(+) melanoma patients after depletion of CD4(+)CD25high Treg cells showing that Treg cells impact on CT7-specific CD4(+) T cells in melanoma patients. CT7-specific CD4(+) T-cell clones were generated and used to define minimal epitopes, restriction elements, and confirm the recognition of naturally processed antigen. Surprisingly, these clones were able to secrete perforin and exert cytotoxicity. This study shows that CT7 can induce specific cellular immunity in melanoma patients. Based on these findings, CT7 will be further explored as a potential vaccine for melanoma immunotherapy.
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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".