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JAMA Oncol. 2018 Oct 1;4(10):e182078. doi: 10.1001/jamaoncol.2018.2078. Epub 2018 Oct 11.

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

Author information

1
Genetic Epidemiology Group, International Agency for Research on Cancer, Lyon, France.
2
Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston.
3
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston.
4
Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, United Kingdom.
5
Laboratory of Population Health, Max Planck Institute for Demographic Research, Rostock, Germany.
6
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston.
7
Environment and Radiation Section, International Agency for Research on Cancer, Lyon, France.
8
Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, Netherlands.
9
Unit of Diet, Genes, and Environment, Danish Cancer Society Research Center, Copenhagen.
10
Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy.
11
Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
12
Molecular and Genetic Epidemiology Unit, Human Genetics Foundation, Torino, Italy.
13
Cancer Risk Factors and Life-Style Epidemiology Unit, Cancer Research and Prevention Institute-Istituto per lo Studio e la Prevenzione Oncologica, Florence, Italy.
14
Department for Determinants of Chronic Diseases, National Institute for Public Health and the Environment, Bilthoven, Netherlands.
15
Unit of Nutirition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain.
16
Department of Epidemiology, Murcia Regional Health Council, Biomedical Research Institute of Murcia (IMIB-Arrixaca), Murcia, Spain.
17
Centro de Investigación Biomédica en Red Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
18
Public Health Direction and Biodonostia Research Institute-CIBERESP, San Sebastian, Spain.
19
Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria, Granada, Spain.
20
Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain.
21
Epidemiology, Prevention, and Promotion Health Service, Navarra Public Health Institute, Pamplona, Spain.
22
Instituto de Investigación Sanitaria de Navarra (IdiSNA), Navarra Institute for Health Research, Pamplona, Spain.
23
Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
24
Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke.
25
Divison of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg.
26
Hellenic Health Foundation, Athens, Greece.
27
World Health Organization Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
28
Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts.
29
Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milano, Italy.
30
Université Paris-Saclay, Université Paris-Sud, Université de Versailles Saint-Quentin-en-Yvelines, Centre de Recherche en Epidémiologie et Santé des Populations, National Institute for Health and Medical Research (INSERM), Villejuif, France.
31
Department of Community Medicine, Universtiy of Tromsø, Arctic University of Norway, Tromsø.
32
Department of Research, Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway.
33
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
34
Genetic Epidemiology Group, Folkhälsan Research Center, Helsinki, Finland.
35
Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece.
36
Department of Medical Biosciences, Clinical Chemistry, Umeå University, Umeå, Sweden.
37
Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden.
38
Public Health Sciences Division, Program in Epidemiology, Fred Hutchinson Cancer Research Center, Seattle, Washington.

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.

PMID:
30003238
PMCID:
PMC6233784
[Available on 2019-07-12]
DOI:
10.1001/jamaoncol.2018.2078

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