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Published on: October 26, 2017
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Next-generation proteomics improves lung cancer risk prediction.
Megha Bhardwaj1, Clara Frick1,2, Ben Schöttker1
1Clinical Epidemiology of Early Cancer Detection, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Molecular Oncology
|November 20, 2025
Summary
A new blood test using four protein markers can identify individuals at high risk for lung cancer (LC). This blood-based protein marker model improves selection for low-dose computed tomography (LDCT) screening, enhancing overall efficacy.
Area of Science:
- Oncology
- Biomarker Discovery
- Medical Diagnostics
Background:
- Low-dose computed tomography (LDCT) screening reduces lung cancer (LC) mortality in heavy smokers.
- Identifying individuals who benefit most from LDCT screening is crucial for cost-effectiveness and requires improved risk stratification.
- Current risk assessment models may not optimally select high-risk populations for screening.
Purpose of the Study:
- To develop and validate a blood-based protein marker model for enhanced lung cancer risk stratification.
- To improve the selection of high-risk individuals for LDCT screening.
- To compare the performance of the novel protein marker model against established lung cancer screening criteria.
Main Methods:
- A two-stage study design using a derivation set (UK Biobank, n=18,868) and an independent validation set.
- Analysis of 2025 protein markers using proximity extension assays.
- Development of a risk prediction algorithm using least absolute shrinkage and selection operator (LASSO) regression with bootstrapping.
Main Results:
- The developed protein marker model, comprising CEACAM5, CXCL17, MMP12, and WFDC2, demonstrated strong discriminatory performance (AUCs of 0.814 in both sets).
- The model outperformed the PLCOm2012 model and improved risk prediction when added to it.
- The protein marker model significantly increased sensitivity for identifying future LC cases compared to LDCT trial criteria.
Conclusions:
- A novel blood-based protein marker model effectively identifies individuals at high risk for lung cancer.
- This model enhances the selection accuracy for low-dose computed tomography (LDCT) screening.
- The findings suggest improved lung cancer screening efficacy through better patient stratification.
