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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".