Quantitative targeted proteomics for occult cancer screening in patients with unprovoked venous thromboembolism: results from the prospective PLATO-VTE study
Bibliographic record
Abstract
Background About 5% of patients with unprovoked venous thromboembolism (VTE) have occult cancer. Despite standard cancer screening, 50% of cancers remain undetected. We used quantitative targeted proteomics to identify novel cancer biomarkers among patients with unprovoked VTE. Methods Patients ≥40 years with a first unprovoked VTE and without a malignancy in the preceding 5 years were invited to an international prospective cohort study. Plasma samples were collected within 10 days after VTE. The primary outcome was an adjudicated cancer diagnosis during 12-month follow-up. Concentrations of 269 plasma proteins, covering coagulation, complement, and cancer-associated pathways, were measured using quantitative mass spectrometry-based targeted proteomics. In a nested case-control study, protein profiles of patients with cancer were compared with those of randomly sampled unique control patients (ratio 3:1). Proteins with an unadjusted P-value <0.05 and fold change ≥15% were combined in a multivariable logistic regression model. To address the variability in the obtained model, the protein selection and model building approach was replicated in 250 bootstrap samples and an optimism-adjusted c-statistic was calculated. Results Of the 476 included participants, 28 (5.9%) were newly diagnosed with cancer. Plasma samples were available for 24 cases, which were compared with those of 75 control patients. Concentrations of P-selectin, β-2-microglobulin, complement C7, intracellular adhesion molecule 1, and lumican were higher in cases than in controls, whereas coagulation factors VII, X, and XII, β-Ala-His dipeptidase, and kalistatin were lower. The optimism-adjusted c-statistic of the multivariable logistic regression model including these proteins was 0.78 (95%CI, 0.70-0.87). Conclusions Ten differentially abundant proteins were identified in patients with occult cancer, suggesting potential of plasma proteomic tests as novel biomarker for occult cancer in patients with unprovoked VTE.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".