Tumor Subtyping: Making Sense of Heterogeneity with a Goal Toward Treatment
Bibliographic record
Abstract
BACKGROUND: Bladder cancers have high total mutation burdens resulting in genomic diversity and intra- and inter-tumor heterogeneity that may impact the diversity of gene expression, biologic aggressiveness, and potentially response to therapy. To compare bladder cancers among patients, an organizational structure is necessary that describes the tumor at the histologic and molecular level. These "molecular subtypes", or "expression subtypes" of bladder cancer were originally described in 2010 and continue to evolve secondary to next generation sequencing (NGS) and an increasing public repository of well-annotated cohorts. OBJECTIVE: To review the history and methodology of expression-based subtyping of non-muscle invasive (NMIBC) and muscle invasive bladder cancer (MIBC). METHODS: A literature review was performed of primary papers from PubMed that described subtyping methods and their descriptive feature including search terms of "subtype", and "bladder cancer". RESULTS: 21 papers were identified for review. Tumor subtyping developed from N = 2 to N = 6 subtyping schemes with most subtypes comprised of at least luminal and basal tumors. Most NMIBCs are luminal cancers and luminal MIBCs may be associated with less aggressive features, while one study of basal tumors identified a better clinical outcome with systemic chemotherapy. Tumors with a P53-like may have intrinsic resistance to chemotherapy. The heterogeneity of tumors, which is likely derived from stromal components and immune cell infiltration, affect subtype calls. CONCLUSION: Subtyping, while still evolving, is ready for testing in clinical trials. Improved patient selection with tumor subtyping may help with tumor classification and potentially match patient or tumor to therapy.
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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 | 0.022 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".