High frequency of MAGE‐A4 and MAGE‐A9 expression in high‐risk bladder cancer
Bibliographic record
Abstract
Cancer-testis (CT) genes encode proteins that are ideal targets for cancer immunotherapy because of their restricted expression in normal tissues and frequent expression in cancers. We previously observed that MAGE-A9 was one of the CT genes most frequently expressed in bladder tumors. To confirm that observation and evaluate the potential prognostic value of MAGE-A9 protein, we analyzed its expression by immunohistochemistry in 493 primary bladder tumors and 33 lymph node metastases, in comparison with MAGE-A4 protein, also frequently expressed in bladder tumors. Overall, MAGE-A4 and MAGE-A9 were observed, respectively, in 38% and 63% of nonmuscle-invasive tumors, 48% and 57% of muscle-invasive tumors, 65% and 84% of carcinomas in situ and in 73% and 85% of lymph node metastases. Expression was associated with higher grade (MAGE-A4, p = 0.007; MAGE-A9, p = 0.012). In multivariate Cox regression analyses, expression of MAGE-A9 in pTa tumors was associated with recurrence (HR = 1.829; p = 0.010). In univariate analyses, MAGE-A4 expression in these same tumors was associated with progression to muscle-invasive cancer (HR = 7.417, p = 0.013). MAGE-A9 expression was even more predictive of progression as all tumors that progressed expressed this antigen. In muscle-invasive bladder tumors, no association was found between expression of either MAGE and bladder cancer-specific death. In conclusion, MAGE-A9 is a target of choice for bladder cancer immunotherapy as it is expressed in 60% of bladder tumors, predominantly high-grade tumors, and at higher frequency in pTis and metastatic tumors. Moreover, in pTa tumors, an immunotherapy targeting MAGE-A9 could be protective against recurrence and progression to more advanced cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".