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000136726 0247_ $$2doi$$a10.1001/jamaoncol.2018.2078
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000136726 0247_ $$2ISSN$$a2374-2445
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000136726 1001_ $$aRisk, Integrative Analysis of Lung Cancer Etiology and$$b0$$eCollaboration Author
000136726 245__ $$aAssessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins.
000136726 260__ $$aChicago, Ill.$$bAmerican Medical Association$$c2018
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000136726 520__ $$aThere is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases.To investigate whether a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers can outperform a traditional risk prediction model and current US screening criteria.Prediagnostic samples from 108 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and samples from 216 smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk score based on 4 proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]). The biomarker score was subsequently validated blindly using absolute risk estimates among 63 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and 90 matched controls from 2 large European population-based cohorts, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS).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 the receiver-operating characteristics curve [AUC], sensitivity, and specificity).In the validation study of 63 ever-smoking patients with lung cancer and 90 matched controls (mean [SD] age, 57.7 [8.7] years; 68.6% men) from EPIC and NSHDS, an integrated risk prediction model that combined smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI, 0.76-0.90) compared with 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P = .003 for difference in AUC). At an overall specificity of 0.83, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 compared with 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the integrated risk prediction model yielded a specificity of 0.95 compared with 0.86 for the smoking model.This study provided a proof of principle in showing that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.
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000136726 7001_ $$aGuida, Florence$$b1
000136726 7001_ $$aSun, Nan$$b2
000136726 7001_ $$aBantis, Leonidas E$$b3
000136726 7001_ $$aMuller, David C$$b4
000136726 7001_ $$aLi, Peng$$b5
000136726 7001_ $$aTaguchi, Ayumu$$b6
000136726 7001_ $$aDhillon, Dilsher$$b7
000136726 7001_ $$aKundnani, Deepali L$$b8
000136726 7001_ $$aPatel, Nikul J$$b9
000136726 7001_ $$aYan, Qingxiang$$b10
000136726 7001_ $$aByrnes, Graham$$b11
000136726 7001_ $$aMoons, Karel G M$$b12
000136726 7001_ $$aTjønneland, Anne$$b13
000136726 7001_ $$aPanico, Salvatore$$b14
000136726 7001_ $$aAgnoli, Claudia$$b15
000136726 7001_ $$aVineis, Paolo$$b16
000136726 7001_ $$aPalli, Domenico$$b17
000136726 7001_ $$aBueno-de-Mesquita, Bas$$b18
000136726 7001_ $$aPeeters, Petra H$$b19
000136726 7001_ $$aAgudo, Antonio$$b20
000136726 7001_ $$aHuerta, Jose M$$b21
000136726 7001_ $$aDorronsoro, Miren$$b22
000136726 7001_ $$aBarranco, Miguel Rodriguez$$b23
000136726 7001_ $$aArdanaz, Eva$$b24
000136726 7001_ $$aTravis, Ruth C$$b25
000136726 7001_ $$aByrne, Karl Smith$$b26
000136726 7001_ $$aBoeing, Heiner$$b27
000136726 7001_ $$aSteffen, Annika$$b28
000136726 7001_ $$0P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a$$aKaaks, Rudolf$$b29
000136726 7001_ $$0P:(DE-He78)6519c85d61a3def7974665471b8a4f74$$aHüsing, Anika$$b30$$udkfz
000136726 7001_ $$aTrichopoulou, Antonia$$b31
000136726 7001_ $$aLagiou, Pagona$$b32
000136726 7001_ $$aLa Vecchia, Carlo$$b33
000136726 7001_ $$aSeveri, Gianluca$$b34
000136726 7001_ $$aBoutron-Ruault, Marie-Christine$$b35
000136726 7001_ $$aSandanger, Torkjel M$$b36
000136726 7001_ $$aVainio, Elisabete Weiderpass$$b37
000136726 7001_ $$aNøst, Therese H$$b38
000136726 7001_ $$aTsilidis, Kostas$$b39
000136726 7001_ $$aRiboli, Elio$$b40
000136726 7001_ $$aGrankvist, Kjell$$b41
000136726 7001_ $$aJohansson, Mikael$$b42
000136726 7001_ $$aGoodman, Gary E$$b43
000136726 7001_ $$aFeng, Ziding$$b44
000136726 7001_ $$aBrennan, Paul$$b45
000136726 7001_ $$aJohansson, Mattias$$b46
000136726 7001_ $$aHanash, Samir M$$b47
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