AMG 509 (Xaluritamig), an Anti-STEAP1 XmAb 2+1 T-cell Redirecting Immune Therapy with Avidity-Dependent Activity against Prostate Cancer
Bibliographic record
Abstract
The tumor-associated antigen STEAP1 is a potential therapeutic target that is expressed in most prostate tumors and at increased levels in metastatic castration-resistant prostate cancer (mCRPC). We developed a STEAP1-targeted XmAb 2+1 T-cell engager (TCE) molecule, AMG 509 (also designated xaluritamig), that is designed to redirect T cells to kill prostate cancer cells that express STEAP1. AMG 509 mediates potent T cell-dependent cytotoxicity of prostate cancer cell lines in vitro and promotes tumor regression in xenograft and syngeneic mouse models of prostate cancer in vivo. The avidity-driven activity of AMG 509 enables selectivity for tumor cells with high STEAP1 expression compared with normal cells. AMG 509 is the first STEAP1 TCE to advance to clinical testing, and we report a case study of a patient with mCRPC who achieved an objective response on AMG 509 treatment. SIGNIFICANCE: Immunotherapy in prostate cancer has met with limited success due to the immunosuppressive microenvironment and lack of tumor-specific targets. AMG 509 provides a targeted immunotherapy approach to engage a patient's T cells to kill STEAP1-expressing tumor cells and represents a new treatment option for mCRPC and potentially more broadly for prostate cancer. See related commentary by Hage Chehade et al., p. 20. See related article by Kelly et al., p. 76. This article is featured in Selected Articles from This Issue, p. 5.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".