HER3 Is an Actionable Target in Advanced Prostate Cancer
Bibliographic record
Abstract
Abstract It has been recognized for decades that ERBB signaling is important in prostate cancer, but targeting ERBB receptors as a therapeutic strategy for prostate cancer has been ineffective clinically. However, we show here that membranous HER3 protein is commonly highly expressed in lethal prostate cancer, associating with reduced time to castration resistance (CR) and survival. Multiplex immunofluorescence indicated that the HER3 ligand NRG1 is detectable primarily in tumor-infiltrating myelomonocytic cells in human prostate cancer; this observation was confirmed using single-cell RNA sequencing of human prostate cancer biopsies and murine transgenic prostate cancer models. In castration-resistant prostate cancer (CRPC) patient-derived xenograft organoids with high HER3 expression as well as mouse prostate cancer organoids, recombinant NRG1 enhanced proliferation and survival. Supernatant from murine bone marrow–derived macrophages and myeloid-derived suppressor cells promoted murine prostate cancer organoid growth in vitro, which could be reversed by a neutralizing anti-NRG1 antibody and ERBB inhibition. Targeting HER3, especially with the HER3-directed antibody–drug conjugate U3-1402, exhibited antitumor activity against HER3-expressing prostate cancer. Overall, these data indicate that HER3 is commonly overexpressed in lethal prostate cancer and can be activated by NRG1 secreted by myelomonocytic cells in the tumor microenvironment, supporting HER3-targeted therapeutic strategies for treating HER3-expressing advanced CRPC. Significance: HER3 is an actionable target in prostate cancer, especially with anti-HER3 immunoconjugates, and targeting HER3 warrants clinical evaluation in prospective trials.
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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) | 0.002 | 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".