Studying the Cell Cycle's Role in the Transdifferentiation of Prostate Cancer
Bibliographic record
Abstract
Prostate cancer (PC) is the second most common cancer in men worldwide, affecting 58 men daily in Canada. As treatment options continue to evolve, disease management and overall patient outcomes have improved. While effective, these treatments can sometimes pressure PC cells to transdifferentiate into a more aggressive, treatment resistant type of PC, known as neuroendocrine prostate cancer (NEPC). Treatment options for NEPC are very limited as it is resistant to all current therapies, leading overall prognosis to remain very poor with an estimated survival of less than one year. Further, the mechanism behind the progression of disease to NEPC remains limited, with few markers being used to study progression. Our lab has identified a class of cell cycle regulatory proteins elevated in NEPC, with evidence supporting that these proteins have the potential to drive progression to this drug-resistant form of disease. This project aims to establish a PC to NEPC platform of disease progression to study the specific role of these regulatory proteins during PC transdifferentiation. Further, we will utilize drugs that can block these proteins and test whether these drugs can treat and/or prevent the progression of disease to NEPC. This work will be completed using in vitro and in vivo models, including cells, animal, and human samples. Preventing the progression of disease to NEPC and identifying markers of NEPC remains one of the greatest challenges in this field, and we have strong rationale and data to support this being a promising direction that could make a meaningful impact.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".