Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins.

Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer, Guida F1, Sun N2, Bantis LE3, Muller DC4, Li P1,5, Taguchi A6, Dhillon D2, Kundnani DL2, Patel NJ2, Yan Q3, Byrnes G7, Moons KGM8, Tjønneland A9, Panico S10, Agnoli C11, Vineis P4,12, Palli D13, Bueno-de-Mesquita B4,14, Peeters PH8, Agudo A15, Huerta JM16,17, Dorronsoro M18, Barranco MR17,19,20, Ardanaz E17,21,22, Travis RC23, Byrne KS23, Boeing H24, Steffen A24, Kaaks R25, Hüsing A25, Trichopoulou A26,27, Lagiou P26,27,28, La Vecchia C26,29, Severi G12,30, Boutron-Ruault MC30, Sandanger TM31, Vainio EW31,32,33,34, Nøst TH31, Tsilidis K4,35, Riboli E4, Grankvist K36, Johansson M37, Goodman GE38, Feng Z3, Brennan P1, Johansson M1, Hanash SM2.

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Abstract

IMPORTANCE:

There is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases.

OBJECTIVE:

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.

DESIGN, SETTING, AND PARTICIPANTS:

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).

MAIN OUTCOMES 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 the receiver-operating characteristics curve [AUC], sensitivity, and specificity).

RESULTS:

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.

CONCLUSIONS AND RELEVANCE:

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.