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@article{Guida:2018:10.1001/jamaoncol.2018.2078,
author = {Guida, F and Sun, N and Bantis, L and Muller, DC and Li, P and Taguchi, A and Dhillon, D and Kundnani, D and Patel, N and Yan, Q and Byrnes, G and Moons, K and Tjonneland, A and Panico, S and Agnoli, C and Vineis, P and Palli, D and Bueno-de-Mesquita, HB and Peeters, P and Agudo, A and Huerta, J and Dorronsoro, M and Rodriguez-Barranco, M and Ardanaz, E and Travis, R and Smith, Byrne K and Boeing, H and Steffen, A and Kaaks, R and Husing, A and Trichoploulo, A and Lagiou, P and La, Vecchia C and Severi, G and Boutron-Ruault, M-C and Sandanger, T and Weiderpass, E and Nøst, T and Tsilidis, K and Riboli, E and Grankvist, K and Johansson, M and Goodman, G and Feng, Z and Brennan, P and Johansson, M and Hanash, S},
doi = {10.1001/jamaoncol.2018.2078},
journal = {JAMA Oncology},
title = {Assessment of lung cancer risk based on a biomarker panel of circulating proteins},
url = {http://dx.doi.org/10.1001/jamaoncol.2018.2078},
volume = {4},
year = {2018}
}

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TY  - JOUR
AB  - 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-prin
AU  - Guida,F
AU  - Sun,N
AU  - Bantis,L
AU  - Muller,DC
AU  - Li,P
AU  - Taguchi,A
AU  - Dhillon,D
AU  - Kundnani,D
AU  - Patel,N
AU  - Yan,Q
AU  - Byrnes,G
AU  - Moons,K
AU  - Tjonneland,A
AU  - Panico,S
AU  - Agnoli,C
AU  - Vineis,P
AU  - Palli,D
AU  - Bueno-de-Mesquita,HB
AU  - Peeters,P
AU  - Agudo,A
AU  - Huerta,J
AU  - Dorronsoro,M
AU  - Rodriguez-Barranco,M
AU  - Ardanaz,E
AU  - Travis,R
AU  - Smith,Byrne K
AU  - Boeing,H
AU  - Steffen,A
AU  - Kaaks,R
AU  - Husing,A
AU  - Trichoploulo,A
AU  - Lagiou,P
AU  - La,Vecchia C
AU  - Severi,G
AU  - Boutron-Ruault,M-C
AU  - Sandanger,T
AU  - Weiderpass,E
AU  - Nøst,T
AU  - Tsilidis,K
AU  - Riboli,E
AU  - Grankvist,K
AU  - Johansson,M
AU  - Goodman,G
AU  - Feng,Z
AU  - Brennan,P
AU  - Johansson,M
AU  - Hanash,S
DO  - 10.1001/jamaoncol.2018.2078
PY  - 2018///
SN  - 2374-2445
TI  - Assessment of lung cancer risk based on a biomarker panel of circulating proteins
T2  - JAMA Oncology
UR  - http://dx.doi.org/10.1001/jamaoncol.2018.2078
UR  - http://hdl.handle.net/10044/1/59051
VL  - 4
ER  -

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