An integrated proteomic portrait of prostate cancer progression
Bibliographic record
Abstract
Cancer forms a local tumor that subsequently metastasizes to distant organs. In prostate cancer, the latter part of the trajectory is influenced by the inhibition of the androgen receptor (AR). The study of proteomic changes along disease progression may reveal insights into how prostate cancer evolves and open new therapeutic avenues. Here, we profile changes in protein abundance and post-translational modifications (PTMs) along the disease trajectory in patient-derived xenograft models. Our results suggest new therapeutic opportunities, such as USP1 inhibition and a key early involvement of the receptor tyrosine kinase (RTK)-RAS-mitogen-activated protein kinase (MAPK) pathway during disease progression. We highlight multiple alterations within the latter, including the tumor suppressors NF1 and ERF. Specific PTMs suggest changes in mitochondrial ATP synthesis, proteasomal activity, gene splicing, and transforming growth factor beta (TGF-β) signaling. Finally, we show how different transcription factors engage with disease progression. A web resource is provided, enabling the investigation of proteomic resources.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".