New Dual Monoclonal ELISA for Measuring Plasma Osteopontin as a Biomarker Associated with Survival in Prostate Cancer: Clinical Validation and Comparison of Multiple ELISAs
Bibliographic record
Abstract
BACKGROUND: A previously developed monoclonal/polyclonal ELISA (Mono/Poly) to detect plasma concentrations of osteopontin (OPN) was shown to provide prognostic information in breast, prostate, and other cancers. Here we describe the clinical validation of a new dual monoclonal (Dual Mono) assay. We compared both assays with 4 assays that recognize defined regions of OPN protein (dual polyclonal systems 5-1, 4-1, 4-3 and polyclonal-monoclonal system 1-3). METHODS: OPN sequences recognized by the monoclonal antibodies that make up the Dual Mono ELISA were identified by Pepscan CLIPS analysis. Using the 6 ELISAs, we measured OPN in plasma from 66 patients with castration-resistant prostate cancer, and we assessed the ability of each assay to predict patient survival. RESULTS: The assays varied in measured plasma OPN concentrations, with median values ranging from 112 to 1740 mug/L, and ability to predict patient survival. By Cox univariable regression of survival by tertiles of OPN, the Mono/Poly and Dual Mono ELISAs had the highest log-rank chi(2) values. After adjustment for risk factors independently associated with survival in our samples, OPN remained associated with survival only for the Mono/Poly and Dual Mono systems. CONCLUSIONS: OPN plasma values varied significantly depending on the assay used. Only the Mono/Poly and Dual Mono systems were independently associated with survival in a population of men with castration-resistant prostate cancer. The availability of a clinically validated, dual monoclonal-based ELISA will provide consistent reagents for studies of OPN plasma concentrations in cancer and other pathologies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".