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Assessment of lung cancer risk based on a biomarker panel of circulating proteins

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Guida_etal_LungProteins_JamaOncology_Briefreport_revised.docxAccepted version76.25 kBMicrosoft WordView/Open
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Title: Assessment of lung cancer risk based on a biomarker panel of circulating proteins
Authors: Guida, F
Sun, N
Bantis, L
Muller, DC
Li, P
Taguchi, A
Dhillon, D
Kundnani, D
Patel, N
Yan, Q
Byrnes, G
Moons, K
Tjonneland, A
Panico, S
Agnoli, C
Vineis, P
Palli, D
Bueno-de-Mesquita, HB
Peeters, P
Agudo, A
Huerta, J
Dorronsoro, M
Rodriguez-Barranco, M
Ardanaz, E
Travis, R
Smith Byrne, K
Boeing, H
Steffen, A
Kaaks, R
Husing, A
Trichoploulo, A
Lagiou, P
La Vecchia, C
Severi, G
Boutron-Ruault, M-C
Sandanger, T
Weiderpass, E
Nøst, T
Tsilidis, K
Riboli, E
Grankvist, K
Johansson, M
Goodman, G
Feng, Z
Brennan, P
Johansson, M
Hanash, S
Item Type: Journal Article
Abstract: Importance: There is an urgent need to improve lung cancer risk assessment as current screening criteria miss a large proportion of cases. Objective: To determine if a panel of selected circulating protein biomarkers can contribute to lung cancer risk assessment and outperform current US screening criteria. Design, Setting and Participants: Pre-diagnostic samples from ever-smoking cases diagnosed within one year of blood collection and smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk-score based on 4 proteins (CA125, CEA, CYFRA 21-1 and Pro-SFTPB). The biomarker score was subsequently validated blindly using absolute risk-estimates in ever-smoking cases diagnosed within one year of blood collection and matched controls from two large European population-based cohorts; the European Prospective Investigation into Cancer and nutrition (EPIC) study and the Northern Sweden Health and Disease Study (NSHDS). Main Outcome and Measures: Model validity in discriminating between future lung cancer cases and controls. Discrimination estimates were weighted to reflect the background populations of EPIC and NSHDS validation studies (area under receiver-operating characteristics curve [AUC], sensitivity and specificity). Results: In the validation study, an integrated risk-prediction model combining smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI: 0.76-0.90) compared to 0.73 (95% CI: 0.64-0.82) for a model based on smoking exposure alone (P=0.003 for difference in AUC). At an overall specificity of 0.83 based on the USPSTF screening criteria, the sensitivity of the integrated risk-prediction model (biomarker) model was 0.63 compared to 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42 (USPSTF), the integrated risk-prediction model yielded a specificity of 0.95 compared to 0.86 for the smoking model. Conclusions and Relevance: This study provided a proof-of-principle in demonstrating that a panel of circulating protein biomarkers can improve lung cancer risk assessment and may be used to define eligibility for CT-screening.
Issue Date: 11-Oct-2018
Date of Acceptance: 12-Apr-2018
URI: http://hdl.handle.net/10044/1/59051
DOI: https://dx.doi.org/10.1001/jamaoncol.2018.2078
ISSN: 2374-2445
Publisher: American Medical Association
Journal / Book Title: JAMA Oncology
Volume: 4
Issue: 10
Copyright Statement: © 2018 American Medical Association. All rights reserved.
Keywords: Science & Technology
Life Sciences & Biomedicine
Oncology
TUMOR-MARKER
PREDICTION
MODELS
Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer
Publication Status: Published online
Article Number: e182078
Online Publication Date: 2018-07-12
Appears in Collections:School of Public Health