Identification of Key Transcription Factor Target Interactions That Regulate Prostate Cancer Metastasis
Bibliographic record
Abstract
Prostate cancer remains the most commonly diagnosed cancer in U.S. males, and ranks second in mortality with over 28,000 deaths per year. The standard of care for patients with recurrent, aggressive prostate cancer is androgen deprivation therapy (ADT), but the benefits from ADT are typically short‐lived. Recurrent disease following ADT treatment is termed castration‐resistant prostate cancer (CRPC), and is generally incurable after progression to metastatic disease. Therefore, understanding the mechanisms underlying CRPC and subsequent progression to metastatic disease is critical. To gain insights into how transcriptional networks change in response to ADT and lead to metastasis, we have identified the relationships between transcription factors and corresponding gene targets in matched pre‐ADT and post‐ADT tissue samples, as well as matched primary and metastatic lesions. First, we generated gene expression data by sequencing RNA from 24 formalin‐fixed paraffin‐embedded patient‐matched pre‐ADT needle core biopsies and corresponding post‐ADT radical prostatectomy prostate cancer samples. For generating metastatic networks, we used publicly available expression data from matched primary prostate and metastatic tumors. Next, we integrated mRNA expression, protein‐protein interaction, and DNA binding motif data using the PANDA algorithm to reverse engineer transcriptional networks. We identified key transcription factors with significant gains or losses of interactions with target genes specifically in metastatic networks. We also identified putative novel key prostate cancer specific transcription factor interactions that share multiple gene targets, such as ETS1 and GATA2, possibly revealing relationships that directly contribute to increased metastatic potential. By comparing changes in post‐ADT and metastatic transcriptional networks, we may identify critical transcription factor‐target gene interactions that are essential for the progression of CRPC to metastatic disease. This study may thus provide insights into novel therapeutic approaches for treatment of CRPC and prevention of metastasis, as well as prognosis of patients with poor outcomes. Support or Funding Information Movember Foundation
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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 | 0.000 | 0.000 |
| 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".