Dynamic rewiring of the androgen receptor protein interaction network correlates with prostate cancer clinical outcomes
Bibliographic record
Abstract
The androgen receptor (AR) is a ligand-inducible transcription factor, a member of the nuclear receptor superfamily, which plays an important role in the development and progression of prostate cancer (CaP). The transformation to CaP has been linked to several somatic AR gene mutations and changes in AR protein complex formation, which in turn increase the potential activity of the receptor. Thus, to address the mechanism of AR-mediated neoplastic transformation, we developed in vitro methodology to isolate and characterize, via mass spectrometry, AR complexes of three AR genetic variants: wild type-AR, and two somatic gain-of-function AR prostatic mutants (T877A-AR and 0CAG-AR isoforms). To fully characterize the significance of our large raw data set, we employed a sophisticated systems biology approach to create an integrative protein-interaction network profile for each AR isoform. Our comparative analysis identified subnetwork cluster profiles for AR isoforms (WT, T877A, and 0CAG) that segregated AR isoforms on the basis of androgen stimulation conditions and mutant aggressiveness. Furthermore, results from additional correlative gene microarray analysis studies of all three AR isoform (WT, T877A, 0CAG) subnetwork clusters were assessed and found to be significantly enriched in tumor versus normal prostate tissues. We also identified two AR-interaction clusters, containing 21 and 30 proteins, respectively, that showed unfavourable prognosis outcome of recurrent cancers, on the basis of PSA, Gleason score and combined PSA/Gleason score. In conclusion, we have characterized a large panel of novel AR-interacting proteins, through a combined proteomics/systems biology screen, that are of clinical relevance and could potentially serve as novel markers for diagnosis and prognosis of CaP.
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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 | 0.001 | 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".