Comprehensive targeted gene profiling to determine the genomic signature likely to drive progression of high-grade nonmuscle invasive bladder cancer to muscle invasive bladder cancer.
Bibliographic record
Abstract
568 Background: Bladder cancer is the fifth most common cancer and eighth leading cause of cancer related-death in North America. It can present as non-muscle invasive bladder cancer (NMIBC) and/or muscle invasive bladder (MIBC). Although genomic profiling studies have established that low-grade NMIBC and MIBC are genetically distinct, high-grade NMIBC can recur and progress to MIBC [ Knowles, M.A. and C.D. Hurst, 2015]. Low grade, non-invasive bladder cancers are characterized by activating mutations in fibroblast growth factor receptor 3 (FGFR3), HRAS or other pathways of receptor kinase activation. High-grade disease, which is often becomes invasive, is characterized by inactivation of TP53 and Rb pathways [Kim, J., et al.]. Finding a subtype of invasive carcinoma with FGFR3 mutation may suggest an alternate pathway by which low grade, non-invasive pathology could transform into invasive disease [Knowles, M.A. and C.D. Hurst, 2015]. Methods: In this study, using a total of 30 bladder cancer (NMIBC and MIBC) patient samples from Windsor Regional Hospital Cancer Program, we performed comprehensive targeted gene sequencing to identify single nucleotide variants, small insertions / deletions, copy number variants and splice variants in over 500 common tumor genes panel. Results: Preliminary data from our study correlates with previously published mutation landscape for NMIBC and MIBC, and includes mutations in EGFR, FGFR3, FGFR4, PIK3CA, CDK6, ALK, JAK, as well as RET. While mutations in AKT1, BRCA1, CCND1, ERBB2, FGFR1, FGFR2, HRAS, and MET appear to be prevalent in NMIBC, mutations in IDH1 and MAP2K2 appear to be more common in MIBC. Three of the samples used in the study are from patients who progressed from high-grade NMIBC to MIBC. Conclusions: Therefore, have the genomic profiling performed at these two stages, which provides a unique ability to identify the potential “genomic triggers” for the transition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".