Cancer-Testis Antigens: Expression and Correlation with Survival in Human Urothelial Carcinoma
Bibliographic record
Abstract
PURPOSE: Vaccination against human cancer is a promising therapeutic approach but the optimal antigen or antigens remain undefined. Cancer-testis antigens (CTA), a family of tumor-associated antigens, have both potent immunogenicity and restricted expression patterns in normal adult tissues, highly desirable characteristics for targets of anticancer vaccines. These antigens were evaluated for both the degree of expression and prognostic value in cancer of the urothelium. EXPERIMENTAL DESIGN: The expression patterns of nine CTAs (NY-ESO-1, LAGE-1, MAGE-A1, MAGE-A3, MAGE-A4, MAGE-A10, CT7, CT10, and GAGE) were examined by immunohistochemistry and reverse transcription-PCR in a panel of high-grade urothelial carcinomas of the urinary bladder. Also assessed were correlations between the expression of CTAs by immunohistochemistry and both disease-free and overall survival. RESULTS: At least one CTA was expressed in 77% of samples and 61% of these tumors expressed more than one CTA. Additionally, patients with CT10-positive tumors had an improved disease-free survival (P=0.008) and overall survival (P=0.037) compared with patients with CT10-negative tumors. CONCLUSIONS: These findings establish CTAs as potential prognostic markers and as target candidates for vaccine development for patients with urothelial carcinoma.
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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".