Molecular profiling in muscle‐invasive bladder cancer: more than the sum of its parts
Bibliographic record
Abstract
Bladder cancers are biologically and clinically heterogeneous. Recent large-scale transcriptomic profiling studies focusing on life-threatening muscle-invasive cases have demonstrated a small number of molecularly distinct clusters that largely explain their heterogeneity. Similar to breast cancer, these clusters reflect intrinsic urothelial cell-type differentiation programs, including those with luminal and basal cell characteristics. Also like breast cancer, each cell-based subtype demonstrates a distinct profile with regard to its prognosis and its expression of therapeutic targets. Indeed, a number of studies suggest subtype-specific differential responses to cytotoxic chemotherapy and to therapies that inhibit a number of targets, including growth factors (EGFR, ERBB2, FGFR) and immune checkpoint (PD1, PDL1) inhibitors. Despite burgeoning evidence for important clinical implications, subtyping has yet to enter into routine clinical practice. Here we review the conceptual basis for intrinsic cell subtyping in muscle-invasive bladder cancer and discuss evidence behind proposed clinical uses for subtyping as a prognostic or predictive test. In deliberating barriers to clinical implementation, we review pitfalls associated with transcriptomic profiling and illustrate a simple immunohistochemistry (IHC)-based subtyping algorithm that may serve as a faster, less expensive alternative. Envisioned as a research tool that can easily be translated into routine pathology workflow, IHC-based profiling has the potential to more rapidly establish the utility (or lack thereof) of cell type profiling in clinical practice. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 0.001 |
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".