Abstract 1969: Advancing metastatic prostate cancer diagnostics: STEAP1-based imaging flow cytometry for circulating tumor cell detection and characterization
Bibliographic record
Abstract
Abstract Prostate cancer (PCa) is the most common cancer among Canadian men, with metastatic PCa (mPCa) patients experiencing a low 5-year survival rate of just 30%. Liquid biopsies using circulating tumor cells (CTCs) offer a non-invasive approach to diagnosing and monitoring treatment efficacy. Six transmembrane epithelial antigens of the prostate 1 (STEAP1) have been identified as a promising biomarker due to its significant overexpression in most PCa cases compared to normal tissues. We have developed monoclonal antibodies specifically targeting STEAP1 and propose their application in imaging flow cytometry (imFC) for identifying CTCs in mPCa patients. Our CTC staining and imFC protocols were validated and optimized using benign prostate hyperplasia and PCa cell lines. We also compared two CTC isolation techniques: density-based separation using Ficoll-Paque and microfluidic isolation with the BioRad Genesis Cell Isolation System for the best CTC purity. Isolated peripheral blood mononuclear cells (PBMCs) were labelled with our STEAP1 antibodies, DAPI for nuclear staining, CD45 antibodies for hematopoietic cells, and EpCAM antibodies, a widely used marker for CTCs. Additionally, we developed analytical pipelines leveraging imaging software with built-in machine-learning capabilities to classify probe binding patterns and identify subtypes of CTCs and cancer-derived extracellular vesicles (EVs) in immune cells. These optimized protocols were then tested on blood samples from patients with localized and metastatic PCa. CTCs were successfully detected in mPCa patients based on their expression of PCa biomarkers and lack of immune cell markers. Furthermore, STEAP1-positive vesicle-like signals were observed in association with hematopoietic cells, suggesting the uptake of PCa EVs by immune cells. Our findings demonstrate that STEAP1 antibodies in imFC can effectively detect and characterize CTCs. This approach represents a next-generation liquid biopsy for mPCa, offering improved diagnostic accuracy and the potential to enhance long-term outcomes for mPCa patients. Citation Format: Minzhi Sheng, Shuyang Feng, Omar Alawamry, Urban Emmenegger, Kristin Cimolini, Danny Vesprini, Andrew Loblaw, Laurence Klotz, Christopher S. Lim, Stanley K. Liu, Hon S. Leong. Advancing metastatic prostate cancer diagnostics: STEAP1-based imaging flow cytometry for circulating tumor cell detection and characterization [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 1969.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".