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Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools.
Feng, Xiaoshuang; Wu, Wendy Yi-Ying; Onwuka, Justina Ucheojor; Haider, Zahra; Alcala, Karine; Smith-Byrne, Karl; Zahed, Hana; Guida, Florence; Wang, Renwei; Bassett, Julie K; Stevens, Victoria; Wang, Ying; Weinstein, Stephanie; Freedman, Neal D; Chen, Chu; Tinker, Lesley; Nøst, Therese Haugdahl; Koh, Woon-Puay; Muller, David; Colorado-Yohar, Sandra M; Tumino, Rosario; Hung, Rayjean J; Amos, Christopher I; Lin, Xihong; Zhang, Xuehong; Arslan, Alan A; Sánchez, Maria-Jose; Sørgjerd, Elin Pettersen; Severi, Gianluca; Hveem, Kristian; Brennan, Paul; Langhammer, Arnulf; Milne, Roger L; Yuan, Jian-Min; Melin, Beatrice; Johansson, Mikael; Robbins, Hilary A; Johansson, Mattias.
Afiliação
  • Feng X; Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
  • Wu WY; Department of Radiation Sciences, Oncology, Umea University, Umea, Sweden.
  • Onwuka JU; Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
  • Haider Z; Department of Radiation Sciences, Oncology, Umea University, Umea, Sweden.
  • Alcala K; Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
  • Smith-Byrne K; Cancer Epidemiology Unit, University of Oxford, Oxford, UK.
  • Zahed H; Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
  • Guida F; Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
  • Wang R; Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA.
  • Bassett JK; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.
  • Stevens V; Rollins School of Public Health, Emory University, Atlanta, GA, USA.
  • Wang Y; American Cancer Society, Atlanta, GA, USA.
  • Weinstein S; Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
  • Freedman ND; Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
  • Chen C; Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
  • Tinker L; Women's Health Initiative Clinical Coordinating Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
  • Nøst TH; Department of Community Medicine, University of Tromsø, The Arctic University of Norway, Tromsø, Norway.
  • Koh WP; Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
  • Muller D; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore.
  • Colorado-Yohar SM; Division of Genetic Medicine, Imperial College London School of Public Health, London, UK.
  • Tumino R; Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain.
  • Hung RJ; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
  • Amos CI; Research Group on Demography and Health, National Faculty of Public Health, University of Antioquia, Medellín, Colombia.
  • Lin X; Hyblean Association for Epidemiological Research, AIRE ONLUS Ragusa, Ragusa, Italy.
  • Zhang X; Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Canada.
  • Arslan AA; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
  • Sánchez MJ; Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA.
  • Sørgjerd EP; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Severi G; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Hveem K; Department of Statistics, Harvard University, Cambridge, MA, USA.
  • Brennan P; Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Langhammer A; Department of Population Health, New York University School of Medicine, New York, NY, USA.
  • Milne RL; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
  • Yuan JM; Escuela Andaluza de Salud Pública (EASP), Granada, Spain.
  • Melin B; Instituto de Investigación Biosanitaria ib, Granada, Spain.
  • Johansson M; Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain.
  • Robbins HA; HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway.
  • Johansson M; Inserm, Université Paris-Saclay, Villejuif, France.
J Natl Cancer Inst ; 115(9): 1050-1059, 2023 09 07.
Article em En | MEDLINE | ID: mdl-37260165
ABSTRACT

BACKGROUND:

We sought to develop a proteomics-based risk model for lung cancer and evaluate its risk-discriminatory performance in comparison with a smoking-based risk model (PLCOm2012) and a commercially available autoantibody biomarker test.

METHODS:

We designed a case-control study nested in 6 prospective cohorts, including 624 lung cancer participants who donated blood samples at most 3 years prior to lung cancer diagnosis and 624 smoking-matched cancer free participants who were assayed for 302 proteins. We used 470 case-control pairs from 4 cohorts to select proteins and train a protein-based risk model. We subsequently used 154 case-control pairs from 2 cohorts to compare the risk-discriminatory performance of the protein-based model with that of the Early Cancer Detection Test (EarlyCDT)-Lung and the PLCOm2012 model using receiver operating characteristics analysis and by estimating models' sensitivity. All tests were 2-sided.

RESULTS:

The area under the curve for the protein-based risk model in the validation sample was 0.75 (95% confidence interval [CI] = 0.70 to 0.81) compared with 0.64 (95% CI = 0.57 to 0.70) for the PLCOm2012 model (Pdifference = .001). The EarlyCDT-Lung had a sensitivity of 14% (95% CI = 8.2% to 19%) and a specificity of 86% (95% CI = 81% to 92%) for incident lung cancer. At the same specificity of 86%, the sensitivity for the protein-based risk model was estimated at 49% (95% CI = 41% to 57%) and 30% (95% CI = 23% to 37%) for the PLCOm2012 model.

CONCLUSION:

Circulating proteins showed promise in predicting incident lung cancer and outperformed a standard risk prediction model and the commercialized EarlyCDT-Lung.
Assuntos

Texto completo: 1 Coleções: 01-internacional Temas: Geral / Tipos_de_cancer / Pulmao Base de dados: MEDLINE Assunto principal: Proteômica / Neoplasias Pulmonares Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Humans Idioma: En Revista: J Natl Cancer Inst Ano de publicação: 2023 Tipo de documento: Article País de afiliação: França

Texto completo: 1 Coleções: 01-internacional Temas: Geral / Tipos_de_cancer / Pulmao Base de dados: MEDLINE Assunto principal: Proteômica / Neoplasias Pulmonares Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Humans Idioma: En Revista: J Natl Cancer Inst Ano de publicação: 2023 Tipo de documento: Article País de afiliação: França