Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosis

• Type of Material: Article
• Authors: Xiaoshuang Feng, International Agency for Research on Cancer; David C Muller, Imperial College London; Hana Zahed, International Agency for Research on Cancer, Lyon, France; Karine Alcala, International Agency for Research on Cancer, Lyon, France; Florence Guida, International Agency for Research on Cancer; Karl Smith-Byrne, University of Oxford; Jian-Min Yuan, UPMC Hillman Cancer Centre; Woon-Puay Koh, National University of Singapore; Renwei Wang, UPMC Hillman Cancer Centre, Pittsburgh, PA, USA; Roger Milne, Cancer Council Victoria; Julie K Bassett, Cancer Council Victoria; Arnulf Langhammer, NTNU Norwegian University of Science and Technology; Kristian Hveem, NTNU Norwegian University of Science and Technology; Victoria Stevens, Emory University; Ying Wang, Emory University; Mikael Johansson, Umea University; Anne Tjønneland, Danish Cancer Society Research Center; Rosario Tumino, AIRE ONLUS Ragusa, Italy; Mahdi Sheikh, International Agency for Research on Cancer, Lyon, France; Mattias Johansson, International Agency for Research on Cancer, Lyon, France; Hilary A Robbins, International Agency for Research on Cancer, Lyon, France
• Language: English
• Date: 2023-05-24
• Publisher: ELSEVIER
• Publication Version: Final Published Version
• Copyright Statement: © 2023 World Health Organization
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.ebiom.2023.104623
• Title of Journal or Parent Work: EBIOMEDICINE
• Volume: 92
• Start Page: 104623
• End Page: 104623
• Keywords: CDCP1; MANAGEMENT; 8TH EDITION; Science & Technology; Medicine, General & Internal; Lung cancer survival; Lung cancer; Research & Experimental Medicine; ENDOTHELIAL GROWTH-FACTOR; Lung cancer prognosis; Medicine, Research & Experimental; Life Sciences & Biomedicine; MARKERS; General & Internal Medicine; STAGE GROUPINGS; VALIDATION; TNM CLASSIFICATION; Protein biomarkers
• Research Categories: Health Sciences, Public Health; Health Sciences, Epidemiology

Abstract

Background: To evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis. Methods: We measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3 years prior to lung cancer diagnosis. We used Cox proportional hazards models to identify proteins associated with overall mortality after lung cancer diagnosis. To evaluate model performance, we used a round-robin approach in which models were fit in 5 cohorts and evaluated in the 6th cohort. Specifically, we fit a model including 5 proteins and clinical parameters and compared its performance with clinical parameters only. Findings: There were 86 proteins nominally associated with mortality (p < 0.05), but only CDCP1 remained statistically significant after accounting for multiple testing (hazard ratio per standard deviation: 1.19, 95% CI: 1.10–1.30, unadjusted p = 0.00004). The external C-index for the protein-based model was 0.63 (95% CI: 0.61–0.66), compared with 0.62 (95% CI: 0.59–0.64) for the model with clinical parameters only. Inclusion of proteins did not provide a statistically significant improvement in discrimination (C-index difference: 0.015, 95% CI: −0.003 to 0.035). Interpretation: Blood proteins measured within 3 years prior to lung cancer diagnosis were not strongly associated with lung cancer survival, nor did they importantly improve prediction of prognosis beyond clinical information. Funding: No explicit funding for this study. Authors and data collection supported by the US National Cancer Institute ( U19CA203654), INCA (France, 2019-1-TABAC-01), Cancer Research Foundation of Northern Sweden ( AMP19-962), and Swedish Department of Health Ministry.