Increased ErbB2 Signaling Is an Early Adaptation to Androgen Signaling Inhibition and Persists in Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
PURPOSE: ErbB2 activity is increased in a subset of prostate cancer, but the gene is rarely amplified. This study sought to identify mechanisms driving increased ErbB2 activity and their role in progression to castration-resistant prostate cancer (CRPC). EXPERIMENTAL DESIGN: ErbB2 signaling was interrogated with a combination of immunohistochemistry, reverse-phase protein array, and RNA sequencing in cell lines, xenografts, and clinical tumors at various stages of castration resistance. Sensitivity to ErbB2 inhibitors was tested in vitro and in vivo. RESULTS: ErbB2 activation, identified by IHC with an antibody against phosphorylated ErbB2, was present in ∼26% of residual tumors in radical prostatectomies after neoadjuvant androgen signaling inhibition (ASI) and in advanced CRPC. The ErbB3/ErbB2-activating ligand NRG1 was found by IHC in ∼75% of neoadjuvant-treated tumors. NRG1 mRNA was rapidly increased by ASI in prostate cancer cells and xenografts and was increased in post-ASI data sets. Overexpression of an active ERBB2 splice variant (d16ERBB2) was also increased rapidly after ASI in prostate cancer cells and was found in a subset of CRPC. ErbB2 signaling in all models remained sensitive to the covalent ErbB2 inhibitor neratinib, which enhanced responses to castration and suppressed the growth of castration-resistant xenografts with ErbB2 activation. CONCLUSIONS: Increased ErbB2 signaling is a rapid adaptation to ASI and contributes to castration resistance. Increases in NRG1 and d16ERBB2 contribute to increased ErbB2 signaling. Potent ErbB2 antagonists may enhance responses to ASI in castration-sensitive prostate cancer, and ErbB2 phosphorylation may be a biomarker for tumors that will respond in CRPC.
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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 | 0.001 |
| 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".