Abstract 128: Comprehensive molecular characterization of 412 muscle-invasive urothelial bladder carcinomas: final analysis of The Cancer Genome Atlas (TCGA) project
Bibliographic record
Abstract
Abstract Introduction: In 2014, TCGA's Bladder Cancer Working Group presented a preliminary integrated molecular analysis of 131 muscle-invasive urothelial carcinomas (Nature 507:315, 2014). We now report on the entire cohort of 412 fresh-frozen, chemotherapy-naïve tumors. Included in the analysis were paired blood and/or tumor-adjacent tissue samples. This is the largest sequencing project on bladder cancer to date. After strict clinical and pathologic quality control, tumors were analyzed for DNA copy number variants, somatic mutations, DNA methylation, mRNA, microRNA and (phospho-) protein expression, transcript splicing, gene fusions, viral integration, APOBEC mutagenesis, pathway perturbation, clinical correlates, and histopathology. Results: There was a high overall somatic mutation rate (8.0/Mb), with a median of 245 and mean of 348 coding-region mutations per sample. That is the third highest mutation rate among the cancer types profiled by TCGA (after cutaneous melanoma and non-small cell lung cancers). We identified 54 genes as significantly mutated, compared with 32 in the original report on 131 tumors. TP53 mutations were the most common (49%), and also quite common were mutations in a number of chromatin-modifying genes, including MLL2 (29%), KDM6A (26%), ARID1A (25%), MLL3 (19%), EP300 (15%), CREBBP (12%), and MLL (11%). Other cancer-related genes showing frequent mutations included PIK3CA (22%), RB1 (17%), FGFR3 (14%), STAG2 (14%), ATM (14%), ELF3 (12%), FAT1 (12%), SPTAN1 (12%), ERBB2 (12%), ERBB3 (11%), ASXL2 (10%), ERCC2 (9%), CDKN1A (9%), TSC1 (8%), CDKN2A (7%), RHOB (6%), NFE2L2 (6%), PARD3 (6%), FAM47C (5%), RBM10 (5%),HRAS (5%), KRAS (4%), and PTEN (3%). High mutation burden was associated with improved outcome (p = 0.0004). APOBEC mutagenesis explained 70% of the mutation burden and was associated with survival. Gene silencing by promoter hypermethylation was identified in 167 genes with at least 5% frequency in the cohort. The previously identified four mRNA expression subtypes were again found in the complete set of 412 tumors, and the proportions of samples in each subtype were similar to the previous proportions. Reverse-phase proteomic array analysis of 344 of the samples revealed clusters associated with diagnostic subtype, pathological stage, and grade but not with smoking history or non-muscle invasive status. Conclusions: This integrated molecular analysis of 412 TCGA tumor samples largely validates and considerably extends observations from the initial cohort of 131 patients. The larger cohort significantly increased our power to detect lower-frequency aberrations that were not identified in the original cohort. The results provide a robust basis for further functional studies of bladder cancer biology and also provide additional incisive information for the identification of molecular targets for therapy. Citation Format: John N. Weinstein, Seth P. Lerner, David J. Kwiatkowski, Gad Getz, Jaegil Kim, Hikmat A. Al-ahmadie, Andrew D. Cherniack, Guangwu Guo, Rehan Akbani, Katherine A. Hoadley, William Y. Kim, Gordon Robertson, Andrew J. Mungall, Toshinori Hinoue, Peter W. Laird, Jonathan E. Rosenberg, Joaquim Bellmunt, Dean F. Bajorin, Margaret B. Morgan, Chad J. Creighton, Dmitry Gordenin, Joshua M. Stuart, Xiaoping Su, Michael C. Ryan, Jeffrey S. Damrauer, Wei Zhang, Yuexin Liu, Yiling Lu, Nikolaus Schultz, Raju Kucherlapati, Gordon B. Mills, Donna E. Hansel, Brian D. Robinson, Bodgen A. Czerniak, Victor E. Reuter. Comprehensive molecular characterization of 412 muscle-invasive urothelial bladder carcinomas: final analysis of The Cancer Genome Atlas (TCGA) project. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 128.
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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) | 0.001 | 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".