Quantification of a Proteotypic Peptide from Protein C Inhibitor by Liquid Chromatography–Free SISCAPA-MALDI Mass Spectrometry: Application to Identification of Recurrence of Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Biomarker validation remains one of the most challenging constraints to the development of new diagnostic assays. To facilitate biomarker validation, we previously developed a chromatography-free stable isotope standards and capture by antipeptide antibodies (SISCAPA)-MALDI assay allowing rapid, high-throughput quantification of protein analytes in large sample sets. Here we applied this assay to the measurement of a surrogate proteotypic peptide from protein C inhibitor (PCI) in sera from patients with prostate cancer. METHODS: A 2-plex SISCAPA-MALDI assay for quantification of proteotypic peptides from PCI and soluble transferrin receptor (sTfR) was used to measure these peptides in 159 trypsin-digested sera collected from 51 patients with prostate cancer. These patients had been treated with radiation with or without neoadjuvant androgen deprivation. RESULTS: Patients who experienced biochemical recurrence of prostate cancer showed decreased serum concentrations of the PCI peptide analyte within 18 months of treatment. The PCI peptide concentrations remained increased in the sera of patients who did not experience cancer recurrence. Prostate-specific antigen concentrations had no predictive value during the same time period. CONCLUSIONS: The high-throughput, liquid chromatography-free SISCAPA-MALDI assay is capable of rapid quantification of proteotypic PCI and sTfR peptide analytes in complex serum samples. Decreased serum concentrations of the PCI peptide were found to be related to recurrence of prostate cancer in patients treated with radiation with or without hormone therapy. However, a larger cohort of patients will be required for unequivocal validation of the PCI peptide as a biomarker for clinical use.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".