Abstract 4771: A novel immunotherapy for metastatic prostate cancer: A monoclonal antibody that can bind both STEAP1 and STEAP2
Bibliographic record
Abstract
Prostate cancer (PCa) is the most common cancer among Canadian men, with metastatic PCa (mPCa) patients having a 5-year survival rate of only 30%. However, the prostate-specific membrane antigen, a key target in PCa therapy, is expressed in many normal tissues and shows heterogeneity among PCa patients. Recently, Six Transmembrane Epithelial Antigens of the Prostate 1 and 2 (STEAP1 and STEAP2) have gained attention as promising therapeutic targets due to their high expression in most PCa cases compared to normal tissues and essential organs. This study aimed to develop a novel immunotherapy targeting STEAP1/2 to elicit PCa-specific immune responses in cellular and animal mPCa models. Monoclonal antibodies were generated against the extracellular region of STEAP1, which shares 60% sequence similarity with STEAP2 but has minimal homology with other human proteins. The specificity and binding strength of these antibodies were validated through techniques such as immunofluorescence, flow cytometry, immunoprecipitation, and ELISA. Results from immunofluorescence and flow cytometry using STEAP1 overexpression and knockdown PCa cell lines demonstrated the specificity of certain clones for human STEAP1. Furthermore, one antibody clone successfully pulled down STEAP2 in immunoprecipitation-mass spectrometry experiments and showed binding to STEAP2 in cell lines overexpressing the antigen. ELISA results revealed binding affinities in the low nanomolar range for STEAP1 and the low micromolar range for STEAP2.These findings highlight the potential of the monoclonal STEAP1/2 antibody developed in this study, providing a promising platform for future immunotherapy strategies and expanding treatment options for mPCa patients. Citation Format: Minzhi Sheng, Boyang Su, Amanda Sparkes, Gobi Thillainadesan, Esther Matus, Jessica Wright, Stanley Liu, Jean Gariepy, Hon S. Leong. A novel immunotherapy for metastatic prostate cancer: A monoclonal antibody that can bind both STEAP1 and STEAP2 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4771.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".