Identifying Molecular Markers of Progression to Muscle Invasive Bladder Cancer
Bibliographic record
Abstract
An estimated 9,000 Canadians are diagnosed with bladder cancer each year making it the 5thmost common cancer in Canada, the 12thmost common among women and the 4thamong men. Most patients are initially diagnosed with non-muscle invasive bladder cancer (NMIBC), which in some cases can progress to muscle invasive bladder cancer (MIBC). MIBC is associated with significantly poorer prognosis than NMIBC, and it is unclear why some progress to MIBC while others do not. Thus, a better understanding of the molecular progression of bladder cancer is needed. Through a collaboration with the computer sciences department, our team has applied a machine learning method that identifies copy number variations associated with muscle invasion and identified three target genes, TP53, MLL2 and DDR2, which are able to predict MIBC with 91% accuracy. This project aims to investigate the validity of these findings. A panel of bladder cancer cell lines ranging from low to high grade will be utilized. Expression of TP53, DDR2 and MLL2 will be examined across the panel of cell lines and correlated with proliferative and invasive potential. Manipulation of the genes through either overexpression or knockdown experiments will allow us to determine if altering expression of these genes contributes to the progression of NMIBC to MIBC. This work seeks to identify molecular markers which predict progression to MIBC thus identifying novel prognostic and therapeutic targets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".