Feasibility and Impact of Immunohistochemistry-based Molecular Subtyping for Muscle-invasive Bladder Cancer in Patients Treated with Radiation-based Therapy
Bibliographic record
Abstract
Background: Distinct molecular subtypes of muscle-invasive bladder cancer (MIBC) have been identified via gene expression profiling. Objective: We investigated the feasibility of a simple immunohistochemistry (IHC)-based Lund subtyping method and the association of MIBC subtypes with oncological outcomes for patients after bladder-preserving radiation-based therapy. Design setting and participants: Transurethral resected tumor tissues from 104 patients treated with radiation-based therapy were sampled on tissue microarray blocks. Outcome measurements and statistical analysis: The expression of KRT5, GATA3, and p16 proteins was scored via digital image analysis. Hierarchical clustering was used to classify tumors as the basal subtype or one of two luminal subtypes: genomically unstable (GU) or urothelial-like (URO). Subtypes were evaluated for association with complete response (CR), recurrence-free survival (RFS), and overall survival (OS). Results and limitations: < 0.05). IHC-based subtype classification was feasible in most patients (89%). The subtype was basal in 23.6%, GU in 14.0%, URO in 31.2%, and unclassified in 31.2% of patients. No significant differences in CR, RFS, or OS were observed between the molecular subtypes. Limitations include the retrospective design and relatively small sample size. Conclusions: IHC-based molecular MIBC subtyping using a three-antibody algorithm is feasible in most patients treated with radiation-based therapy. MIBC subtype was not associated with response or survival. Further prospective studies are warranted to confirm the lack of association between molecular subtype and survival in patients treated with trimodal therapy. Patient summary: For patients with invasive bladder cancer treated with radiation-based therapy, we classified tumors into different subtypes using just three molecular stains. This method is cheaper and more widely available than the usual approach. However, we did not find an association between different cancer subtypes and survival.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".