False Biomarker Discovery due to Reactivity of a Commercial ELISA for CUZD1 with Cancer Antigen CA125
Bibliographic record
Abstract
BACKGROUND: By using proteomics and bioinformatics, we have previously identified a group of highly pancreas-specific proteins as candidate pancreatic ductal adenocarcinoma (PDAC) biomarkers. With the use of commercially available ELISAs, the performance of some of these candidates was initially evaluated in a relatively small serum cohort (n = 100 samples). This phase revealed that CUB and zona pellucida-like domains protein 1 (CUZD1) may represent a new, promising PDAC biomarker. METHODS: We performed detailed experiments to investigate the specificity of the commercial CUZD1 ELISA assay. CUZD1 was expressed in house in both bacteria and yeast expression systems. Recombinant CUZD1 and biological samples containing CUZD1, as well as commercial CUZD1 ELISA standards, were analyzed by Western blot, size exclusion HPLC, and mass spectrometry (LC-MS Orbitrap). RESULTS: We confirmed that instead of CUZD1, the commercial assay is recognizing a nonhomologous, known cancer antigen [cancer antigen 125 (CA125)]. CONCLUSIONS: We conclude that poor characterization of commercial ELISA assays is a factor that could lead to false biomarker discovery. To our knowledge, this is the first report documenting that a commercial ELISA marketed for one analyte (CUZD1) may, in fact, recognize a different, nonhomologous antigen (CA125).
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".