ArticleJournal of the National Cancer Institute2023
Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools.
Article in Journal of the National Cancer Institute, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Risk prediction for lung cancer screening: a systematic review and meta-regression.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Circulating proteins associated with histological subtypes of lung cancer from genetic and population-based perspectives.PLoS genetics · 2025Pooled it
- A systematic review and meta-analysis of lung cancer risk prediction models.Acta oncologica (Stockholm, Sweden) · 2025Pooled it
- Plasma proteomic profiles associated with low-dose computed tomography findings and incident lung cancer: a prospective pilot study.Translational lung cancer research · 2026Article
- A high-resolution circulating metabolic atlas of lung adenocarcinoma progression supports early and accurate diagnosis.Cell reports. Medicine · 2026Article
- Blood-based DNA methylation marker model for short-term and long-term lung cancer risk prediction.BMC medicine · 2026Article
- A smoking-related plasma protein score and smoking-related cancer risk and mortality in the Atherosclerosis Risk in Communities study.Journal of the National Cancer Institute · 2026Article
- Next-generation proteomics improves lung cancer risk prediction.Molecular oncology · 2026Article
- From evidence to action: the IARC's role in strengthening cancer prevention and early detection.Journal of the National Cancer Institute. Monographs · 2026Review
- Multi-omics analysis reveals differential benefits of immunotherapy±chemotherapy based on detailed smoking history in advanced non-small cell lung cancer.Journal for immunotherapy of cancer · 2025Article
- T-cell Receptor Profiling of Blood to Detect Lung Cancer.Cancer immunology research · 2025Article
- SPAG11B, a potential biomarker for rheumatoid arthritis: a two-sample bidirectional Mendelian randomization analysis.BMC rheumatology · 2025Article
- Society for Immunotherapy of Cancer (SITC) consensus statement on essential biomarkers for immunotherapy clinical protocols.Journal for immunotherapy of cancer · 2025Article
- LcProt: Proteomics-based identification of plasma biomarkers for lung cancer multievent, a multicentre study.Clinical and translational medicine · 2025Article
- Pitfalls in interpreting calibration in comparative evaluations of risk models for precision lung cancer screening.NPJ precision oncology · 2024Article
- Protein Biomarkers in Lung Cancer Screening: Technical Considerations and Feasibility Assessment.Archivos de bronconeumologia · 2024Review
- Prognostic value of circulating proteins at diagnosis among patients with lung cancer: a comprehensive analysis by smoking status.Translational lung cancer research · 2024Article
- Data Resource Profile: The HUNT Biobank.International journal of epidemiology · 2024Article
- Proteome-wide Mendelian randomization identifies causal plasma proteins in lung cancer.iScience · 2024Article
- Multiplex analysis for the identification of plasma protein biomarkers for predicting lung cancer immunotherapy response.Therapeutic advances in medical oncology · 2024Article
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Authors and funding
38 authors.
Funding
Abstract
backgroundWe sought to develop a proteomics-based risk model for lung cancer and evaluate its risk-discriminatory performance in comparison with a smoking-based risk model (PLCOm2012) and a commercially available autoantibody biomarker test.
methodsWe designed a case-control study nested in 6 prospective cohorts, including 624 lung cancer participants who donated blood samples at most 3 years prior to lung cancer diagnosis and 624 smoking-matched cancer free participants who were assayed for 302 proteins. We used 470 case-control pairs from 4 cohorts to select proteins and train a protein-based risk model. We subsequently used 154 case-control pairs from 2 cohorts to compare the risk-discriminatory performance of the protein-based model with that of the Early Cancer Detection Test (EarlyCDT)-Lung and the PLCOm2012 model using receiver operating characteristics analysis and by estimating models' sensitivity. All tests were 2-sided.
resultsThe area under the curve for the protein-based risk model in the validation sample was 0.75 (95% confidence interval [CI] = 0.70 to 0.81) compared with 0.64 (95% CI = 0.57 to 0.70) for the PLCOm2012 model (Pdifference = .001). The EarlyCDT-Lung had a sensitivity of 14% (95% CI = 8.2% to 19%) and a specificity of 86% (95% CI = 81% to 92%) for incident lung cancer. At the same specificity of 86%, the sensitivity for the protein-based risk model was estimated at 49% (95% CI = 41% to 57%) and 30% (95% CI = 23% to 37%) for the PLCOm2012 model.
conclusionCirculating proteins showed promise in predicting incident lung cancer and outperformed a standard risk prediction model and the commercialized EarlyCDT-Lung.
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