Bladder Cancer Tissue-Based Biomarkers
Bibliographic record
Abstract
This review aims to provide a practical update regarding the current role of tissue-based biomarkers in bladder cancer. Their prognostic and predictive role both in non-muscle-invasive (NMIBC) and in muscle-invasive disease (MIBC) has been reviewed with particular focus to their use in clinical practice. In summary, the literature on the prediction of disease recurrence in NMIBC is inconclusive, and there is little information on prediction of response to intravesical bacillus Calmette-Guerin (BCG). Concerning disease progression, external prospective validation studies suggest that FGFR3 mutation status and gene signatures may improve models that are based only on clinicopathologic information. In MIBC, tissue-based biomarkers are increasingly important, since they may predict the response to systemic chemotherapy and immunotherapy. In particular, the advent of molecular characterization promises to revolutionize the paradigm of decision-making in the treatment of MIBC. Molecular subtyping has been shown to improve the prediction of pathological stage at RC and to predict the response to systemic chemotherapy and immunotherapy. However, external and prospective validations are warranted to confirm these preliminary findings. Several different tissue-based biomarkers such as PD-1/PD-L1 expression, tumor mutational burden, and the analysis of tumor microenvironment, may in future play a role in selecting patients for systemic immunotherapy. However, to date, no pretreatment recommendations can be definitively made on the basis of any molecular predictors. In conclusion, despite the potential of tissue-based biomarkers, their use in bladder cancer should be limited to experimental settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".