Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools

Xiaoshuang Feng, Wendy Yi Ying Wu, Justina Ucheojor Onwuka, Zahra Haider, Karine Alcala, Karl Smith-Byrne, Hana Zahed, Florence Guida, Renwei Wang, Julie K. Bassett, Victoria Stevens, Ying Wang, Stephanie Weinstein, Neal D. Freedman, Chu Chen, Lesley Tinker, Therese Haugdahl Nøst, Woon Puay Koh, David Muller, Sandra M. Colorado-YoharRosario Tumino, Rayjean J. Hung, Christopher I. Amos, Xihong Lin, Xuehong Zhang, Alan A. Arslan, Maria Jose Sánchez, Elin Pettersen Sørgjerd, Gianluca Severi, Kristian Hveem, Paul Brennan, Arnulf Langhammer, Roger L. Milne, Jian Min Yuan, Beatrice Melin, Mikael Johansson, Hilary A. Robbins, Mattias Johansson

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

BACKGROUND: We 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. METHODS: We 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. RESULTS: The 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. CONCLUSION: Circulating proteins showed promise in predicting incident lung cancer and outperformed a standard risk prediction model and the commercialized EarlyCDT-Lung.
Original languageEnglish (US)
Pages (from-to)1050-1059
Number of pages10
JournalJournal of the National Cancer Institute
Volume115
Issue number9
DOIs
StatePublished - Sep 7 2023
Externally publishedYes

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