Multiplexed targeted proteomic assay to assess coagulation factor concentrations and thrombosis-associated cancer
Bibliographic record
Abstract
The plasma levels of pro- and anticoagulant proteins are important markers for venous thrombosis (VT) risk and can be affected by both genetic and acquired factors, including cancer. Generally, these markers are measured using activity- or antibody-based assays. Targeted proteomics with stable-isotope-labeled internal standards has proven adept at the rapid, multiplex, and precise quantification of proteins in complex biological samples such as plasma. We used liquid chromatography coupled to multiple reaction monitoring (MRM) mass spectrometry to evaluate the concentrations of 31 coagulation- and fibrinolysis-related proteins in plasma from 25 healthy controls, 25 patients with VT, and 25 patients with VT who were also diagnosed with cancer. The concentration level of 1 to 3 proteotypic peptides per protein was determined, and all samples were previously characterized using traditional antibody- or activity-based methods. When comparing the conventional and the MRM strategies, the mean Pearson correlation for the 13 proteins (covered by 36 target peptides) shared between the 2 approaches was 0.77, indicating a good correlation. Additionally, MRM offers higher sensitivity (mean regression slope, 0.81), higher multiplicity in a single run, and good ability to leverage all measurements to discriminate groups using unsupervised clustering, which identified vitamin K antagonist users as well as patients with VT and cancer. The data collected using MRM show that the combination of coagulation factor levels yields signature information on VT and cancer, which was not obvious from a single measurement. These results encourage the further validation and investigation of MRM in profiling protein signature of disease.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".