Comprehensive characterization of 412 muscle invasive urothelial carcinomas: Final analysis of The Cancer Genome Atlas (TCGA) project.
Bibliographic record
Abstract
405 Background: We reported the integrated molecular analysis of 131 tumors in 2014 (Nature 507:315, 2014) and now report on the entire cohort of 412 tumors from the TCGA project in chemotherapy-naïve muscle invasive urothelial bladder cancer. Methods: Following strict clinical and pathologic quality control, tumors were analyzed for DNA copy number variants, somatic mutations (WES), DNA methylation, mRNA, microRNA and (phospho-) protein expression, transcript splicing, gene fusions, viral integration, pathway perturbation, clinical correlates, and histopathology Results: There was a high overall somatic mutation rate (8.0/Mb), as previously reported, with a median of 245 and mean of 348 coding region mutations per sample. There were 54 significantly mutated genes (SMGs) (MutSig_2CV), increased from 32 in the original report. TP53 remained the most commonly mutated gene, and chromatin-modifying genes (MLL2, ARID1A, KDM6A) were also frequently mutated. KRAS, ERBB2, RB1, and ELF3 showed significant increased frequencies of mutation. High mutation burden was associated with improved outcome (p = 0.0004). APOBEC mutagenesis explained 70% of the mutation burden and also was associated with survival. 167 genes were silenced by promoter DNA hypermethylation in at least 5% of tumors. Several SMGs were also epigenetically silenced at various frequencies (ZNF773, CDKN2A, FAT1, CASP8). The previously identified four mRNA subtypes were predicted on the larger set identifying a similar proportion of samples in each subtype. We identified 5 expression subtypes based on reverse-phase protein arrays(344 tumors). Five miR expression subtypes were strongly associated with 4 mRNA-based subtypes (P = 3.8E-37) and associated with overall survival (P = 0.05). Conclusions: This integrated analysis of 412 TCGA patient samples validates and extends observations from the first 131 patients and significantly increases our power to detect additional low-frequency aberrations. The results provide a robust basis for functional studies to further our understanding of the biology of bladder cancer, and aid in the development of more precisely targeted therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".