Language

English

Publication Date

5-8-2026

Journal

Journal of Thrombosis and Haemostasis

DOI

10.1016/j.jtha.2026.04.029

PMID

42107713

PMCID

PMC13197987

PubMedCentral® Posted Date

5-24-2026

PubMedCentral® Full Text Version

Author MSS

Abstract

Background: Existing risk models for cancer-associated thrombosis (CAT) show suboptimal performance in selective high-risk populations with cancer. Affinity-based plasma proteomics offers a novel approach for detecting CAT risk.

Objectives: To identify plasma biomarkers for CAT using proximity extension assays in an advanced cancer cohort.

Methods: We performed a nested case-control study using the Olink Explore HT panel. The final cohort included 57 patients with CAT and 113 matched control patients from 5 selected cancer types who had samples collected between cancer diagnosis and chemotherapy initiation. Random survival forest model was used to assess nonlinear associations with CAT in 5416 normalized protein expressions and 8 clinical variables. Evaluation metrics averaged across bootstrapped out-of-bag test sets included time-dependent receiver operating characteristic curve, calibration plot, and cumulative incidence in high- vs low-risk predicted groups. We used SHapley Additive exPlanations values for feature interpretability. We performed overrepresentation analysis and gene set enrichment analysis to assess biological pathway plausibility.

Results: Our internally validated model predicted early thrombotic events well (time-dependent receiver operation characteristic value of 0.83 at 30 days and 0.73 at 90 days), but the discrimination waned with follow-up time (0.67 at 180 days). Calibration followed a similar pattern. In overrepresentation analysis and gene set enrichment analysis, important proteins were observed in hemostatic pathways, including platelet activation, fibrin clot formation, and complement cascade regulation.

Conclusion: Affinity-based plasma proteomics can be used as a novel strategy to identify biomarkers of CAT. External validation with a larger sample size in a cohort setting is required for risk prediction models.

Keywords

Proteomics, Venous Thromboembolism, Biomarkers

Published Open-Access

yes

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