Comparison of two multiplexed technologies for profiling >1,000 serum proteins that may associate with tumor burden
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background:</ns3:bold> In this pilot study, we perform a preliminary comparison of two targeted multiplex </ns3:p> <ns3:p>proteomics technologies for discerning serum protein concentration changes that may correlate to tumor burden in ovarian cancer (OC) patients.</ns3:p> <ns3:p> <ns3:bold>Methods</ns3:bold> : Using the proximity extension assay (PEA) and Quantibody® Kiloplex Array (QKA), we measured >1,000 proteins in the pre-surgical and post-surgical serum from nine OC patients (N=18 samples). We expect that proteins that have decreased significantly in the post-surgical serum concentration may correlate to tumor burden in each patient. Duplicate sera from two healthy individuals were used as controls (N=4 samples). We employed in-house ELISAs to measure five proteins with large serum concentration changes in pre- and post-surgical sera, from four of the original nine patients and the two original controls. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> Both platforms showed a weak correlation with clinical cancer antigen 125 (CA125) data. The two multiplexed platforms showed a significant correlation with each other for >400 overlapping proteins. PEA uncovered 15 proteins, while QKA revealed 11 proteins, with more than a two-fold post-surgical decrease in at least six of the nine patients. Validation using single enzyme-linked immunosorbent assays (ELISAs) showed at least a two-fold post-surgical decrease in serum concentration of the same patients, as indicated by the two multiplex assays. </ns3:p> <ns3:p> <ns3:bold>Conclusion:</ns3:bold> Both methods identified proteins that had significantly decreased in post-surgical serum concentration, as well as recognizing proteins that had been implicated in OC patients. Our findings from a limited sample size suggest that novel targeted proteomics platforms are promising tools for identifying candidate serological tumor-related proteins. However further studies are essential for the improvement of accuracy and avoidance of false results. </ns3:p>
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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 | 0.000 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".