An MRM‐Based Cytokeratin Marker Assay as a Tool for Cancer Studies: Application to Lung Cancer Pleural Effusions
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: The goal of this work was to develop an LC-MRM assay for the quantitative analysis of a set of established and diagnostically important cytokeratin (CK) markers used in cancer diagnosis, prognosis, and therapy monitoring. Second, the potential of this assay in lung cancer diagnosis through pleural effusion (PE) analysis was examined. EXPERIMENTAL DESIGN: A multiplexed MRM assay was developed for 17 CKs and their select caspase-cleaved fragments. Isotope-labeled standard peptides were used for high assay specificity and absolute peptide quantitation; with robust standard-flow LC coupled to a latest-generation triple-quadrupole instrument for high sensitivity. The potential clinical applicability was demonstrated by the analysis of 118 PE samples. RESULTS: The MRM assay was evaluated for endogenous detection, linearity, precision, upper and lower limits of quantification, selectivity, reproducibility and peptide stability, and is generally applicable to any epithelial cancer study. A set of 118 patients with known pathologies allowed us to define the range of CK levels in clinical PE samples. Specific CKs were able to differentiate cancer-related PEs from those caused by benign ailments. In addition, they allowed to differentiate between PEs from subjects with small cell lung cancer versus non-small cell lung carcinoma, and to further differentiate the latter into its two subtypes, adenocarcinoma and squamous cell carcinoma. CONCLUSION AND CLINICAL RELEVANCE: An MRM-based CK assay for carcinoma studies can differentiate between the three lung cancer histological types using less-invasive PE sampling providing potential therapy-guiding information on patients that are inoperable.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it