Proteomic profiling identifies muscle-invasive bladder cancers with distinct biology and responses to platinum-based chemotherapy
Bibliographic record
Abstract
Platinum-based neoadjuvant chemotherapy prior to radical cystectomy is the preferred treatment for muscle-invasive bladder cancer despite modest survival benefit and significant associated toxicities. Here, we profile the global proteome of muscle-invasive bladder cancers pre- and post-neoadjuvant chemotherapy treatment using archival formalin-fixed paraffin-embedded tissue. We identify four pre-neoadjuvant chemotherapy proteomic clusters with distinct biology and response to therapy and integrate these with transcriptomic subtypes and immunohistochemistry. We observe proteomic plasticity post-neoadjuvant chemotherapy that is associated with increased extracellular matrix and reduced keratinisation compared to pre-neoadjuvant chemotherapy. Post-neoadjuvant chemotherapy clusters appear to be differentially enriched for druggable proteins. For example, MTOR and PARP are over-expressed at the protein level in tumours identified as neuronal-like. In addition, we determine that high intra-tumoural proteome heterogeneity in pre-neoadjuvant chemotherapy tissue is associated with worse prognosis. Our work highlights aspects of muscle-invasive bladder cancer biology associated with clinical outcomes and suggests biomarkers and therapeutic targets based on proteomic clusters. The proteomic landscape of muscle-invasive bladder cancer (MIBC) in the context of platinum-based neoadjuvant chemotherapy (NAC) remains to be explored. Here, proteomic analysis of MIBC tumours pre- and matched post-NAC treatment identifies proteomic clusters with distinct biological and clinical features.
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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".