Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosisResearch in context
Xiaoshuang Feng,
David C. Muller,
Hana Zahed,
Karine Alcala,
Florence Guida,
Karl Smith-Byrne,
Jian-Min Yuan,
Woon-Puay Koh,
Renwei Wang,
Roger L. Milne,
Julie K. Bassett,
Arnulf Langhammer,
Kristian Hveem,
Victoria L. Stevens,
Ying Wang,
Mikael Johansson,
Anne Tjønneland,
Rosario Tumino,
Mahdi Sheikh,
Mattias Johansson,
Hilary A. Robbins
Affiliations
Xiaoshuang Feng
Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France; Corresponding author. Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), 25 Avenue Tony Garnier, Lyon CEDEX 07, France.
David C. Muller
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Department of Epidemiology and Biostatistics, School of Public Health, MRC-PHE, Centre for Environment and Health, Imperial College London, London, United Kingdom
Hana Zahed
Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
Karine Alcala
Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
Florence Guida
Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
Karl Smith-Byrne
Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, United Kingdom
Jian-Min Yuan
UPMC Hillman Cancer Centre, Pittsburgh, PA, USA; Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
Woon-Puay Koh
Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A∗STAR), Singapore
Renwei Wang
UPMC Hillman Cancer Centre, Pittsburgh, PA, USA
Roger L. Milne
Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Australia; School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
Julie K. Bassett
Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
Arnulf Langhammer
HUNT Research Center, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Levanger, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
Kristian Hveem
HUNT Research Center, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Levanger, Norway; Department of Public Health and Nursing, K.G. Jebsen Centre for Genetic Epidemiology, Norwegian University of Science and Technology, Trondheim, Norway
Victoria L. Stevens
Rollins School of Public Health, Emory University, Atlanta, GA, USA
Ying Wang
American Cancer Society, Atlanta, GA, USA
Mikael Johansson
Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
Anne Tjønneland
Danish Cancer Society Research Center, Copenhagen, Denmark; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
Rosario Tumino
Hyblean Association for Epidemiological Research, AIRE ONLUS Ragusa, Italy
Mahdi Sheikh
Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
Mattias Johansson
Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
Hilary A. Robbins
Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France; Corresponding author. Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), 25 Avenue Tony Garnier, Lyon CEDEX 07, France.
Summary: 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.
