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Assessment of lung cancer risk based on a biomarker panel of circulating proteins
File | Description | Size | Format | |
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Guida_etal_LungProteins_JamaOncology_Briefreport_revised.docx | Accepted version | 76.25 kB | Microsoft Word | View/Open |
Supplementary Material.docx | Supporting information | 552.43 kB | Microsoft Word | View/Open |
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 |