Molecular profiling in muscle‐invasive bladder cancer: more than the sum of its parts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it