Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins.
There 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 mode...
Published in: | JAMA Oncology |
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Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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2018
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Online Access: | http://hdl.handle.net/10668/12706 https://doi.org/10.1001/jamaoncol.2018.2078 https://jamanetwork.com/journals/jamaoncology/articlepdf/2687371/jamaoncology_guida_2018_br_180010.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6233784/pdf |
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ftsspa:oai:www.repositoriosalud.es:10668/12706 |
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Open Polar |
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Sistema Sanitario Público de Andalucía (SSPA): Repositorio |
op_collection_id |
ftsspa |
language |
English |
topic |
Aged 80 and over Biomarkers Tumor CA-125 Antigen Carcinoembryonic Antigen Female Humans Keratin-19 Lung Neoplasms Male Mass Screening Membrane Proteins Middle Aged Non-Smokers Prospective Studies Protein Precursors Proteolipids ROC Curve Risk Assessment Risk Factors Tomography Scanners X-Ray Computed |
spellingShingle |
Aged 80 and over Biomarkers Tumor CA-125 Antigen Carcinoembryonic Antigen Female Humans Keratin-19 Lung Neoplasms Male Mass Screening Membrane Proteins Middle Aged Non-Smokers Prospective Studies Protein Precursors Proteolipids ROC Curve Risk Assessment Risk Factors Tomography Scanners X-Ray Computed Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer Guida, Florence Sun, Nan Bantis, Leonidas E Muller, David C Li, Peng Taguchi, Ayumu Dhillon, Dilsher Kundnani, Deepali L Patel, Nikul J Yan, Qingxiang Byrnes, Graham Moons, Karel G M Tjønneland, Anne Panico, Salvatore Agnoli, Claudia Vineis, Paolo Palli, Domenico Bueno-de-Mesquita, Bas Peeters, Petra H Agudo, Antonio Huerta, Jose M Dorronsoro, Miren Barranco, Miguel Rodriguez Ardanaz, Eva Travis, Ruth C Byrne, Karl Smith Boeing, Heiner Steffen, Annika Kaaks, Rudolf Hüsing, Anika Trichopoulou, Antonia Lagiou, Pagona La Vecchia, Carlo Severi, Gianluca Boutron-Ruault, Marie-Christine Sandanger, Torkjel M Weiderpass, Elisabete Nøst, Therese H Tsilidis, Kostas Riboli, Elio Grankvist, Kjell Johansson, Mikael Goodman, Gary E Feng, Ziding Brennan, Paul Johansson, Mattias Hanash, Samir M Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins. |
topic_facet |
Aged 80 and over Biomarkers Tumor CA-125 Antigen Carcinoembryonic Antigen Female Humans Keratin-19 Lung Neoplasms Male Mass Screening Membrane Proteins Middle Aged Non-Smokers Prospective Studies Protein Precursors Proteolipids ROC Curve Risk Assessment Risk Factors Tomography Scanners X-Ray Computed |
description |
There 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 ... |
format |
Article in Journal/Newspaper |
author |
Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer Guida, Florence Sun, Nan Bantis, Leonidas E Muller, David C Li, Peng Taguchi, Ayumu Dhillon, Dilsher Kundnani, Deepali L Patel, Nikul J Yan, Qingxiang Byrnes, Graham Moons, Karel G M Tjønneland, Anne Panico, Salvatore Agnoli, Claudia Vineis, Paolo Palli, Domenico Bueno-de-Mesquita, Bas Peeters, Petra H Agudo, Antonio Huerta, Jose M Dorronsoro, Miren Barranco, Miguel Rodriguez Ardanaz, Eva Travis, Ruth C Byrne, Karl Smith Boeing, Heiner Steffen, Annika Kaaks, Rudolf Hüsing, Anika Trichopoulou, Antonia Lagiou, Pagona La Vecchia, Carlo Severi, Gianluca Boutron-Ruault, Marie-Christine Sandanger, Torkjel M Weiderpass, Elisabete Nøst, Therese H Tsilidis, Kostas Riboli, Elio Grankvist, Kjell Johansson, Mikael Goodman, Gary E Feng, Ziding Brennan, Paul Johansson, Mattias Hanash, Samir M |
author_facet |
Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer Guida, Florence Sun, Nan Bantis, Leonidas E Muller, David C Li, Peng Taguchi, Ayumu Dhillon, Dilsher Kundnani, Deepali L Patel, Nikul J Yan, Qingxiang Byrnes, Graham Moons, Karel G M Tjønneland, Anne Panico, Salvatore Agnoli, Claudia Vineis, Paolo Palli, Domenico Bueno-de-Mesquita, Bas Peeters, Petra H Agudo, Antonio Huerta, Jose M Dorronsoro, Miren Barranco, Miguel Rodriguez Ardanaz, Eva Travis, Ruth C Byrne, Karl Smith Boeing, Heiner Steffen, Annika Kaaks, Rudolf Hüsing, Anika Trichopoulou, Antonia Lagiou, Pagona La Vecchia, Carlo Severi, Gianluca Boutron-Ruault, Marie-Christine Sandanger, Torkjel M Weiderpass, Elisabete Nøst, Therese H Tsilidis, Kostas Riboli, Elio Grankvist, Kjell Johansson, Mikael Goodman, Gary E Feng, Ziding Brennan, Paul Johansson, Mattias Hanash, Samir M |
author_sort |
Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer |
title |
Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins. |
title_short |
Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins. |
title_full |
Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins. |
title_fullStr |
Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins. |
title_full_unstemmed |
Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins. |
title_sort |
assessment of lung cancer risk on the basis of a biomarker panel of circulating proteins. |
publishDate |
2018 |
url |
http://hdl.handle.net/10668/12706 https://doi.org/10.1001/jamaoncol.2018.2078 https://jamanetwork.com/journals/jamaoncology/articlepdf/2687371/jamaoncology_guida_2018_br_180010.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6233784/pdf |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_relation |
http://hdl.handle.net/10668/12706 30003238 doi:10.1001/jamaoncol.2018.2078 2374-2445 PMC6233784 https://jamanetwork.com/journals/jamaoncology/articlepdf/2687371/jamaoncology_guida_2018_br_180010.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6233784/pdf |
op_rights |
open access |
op_doi |
https://doi.org/10.1001/jamaoncol.2018.2078 |
container_title |
JAMA Oncology |
container_volume |
4 |
container_issue |
10 |
container_start_page |
e182078 |
_version_ |
1782338196935278592 |
spelling |
ftsspa:oai:www.repositoriosalud.es:10668/12706 2023-11-12T04:23:25+01:00 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, Florence Sun, Nan Bantis, Leonidas E Muller, David C Li, Peng Taguchi, Ayumu Dhillon, Dilsher Kundnani, Deepali L Patel, Nikul J Yan, Qingxiang Byrnes, Graham Moons, Karel G M Tjønneland, Anne Panico, Salvatore Agnoli, Claudia Vineis, Paolo Palli, Domenico Bueno-de-Mesquita, Bas Peeters, Petra H Agudo, Antonio Huerta, Jose M Dorronsoro, Miren Barranco, Miguel Rodriguez Ardanaz, Eva Travis, Ruth C Byrne, Karl Smith Boeing, Heiner Steffen, Annika Kaaks, Rudolf Hüsing, Anika Trichopoulou, Antonia Lagiou, Pagona La Vecchia, Carlo Severi, Gianluca Boutron-Ruault, Marie-Christine Sandanger, Torkjel M Weiderpass, Elisabete Nøst, Therese H Tsilidis, Kostas Riboli, Elio Grankvist, Kjell Johansson, Mikael Goodman, Gary E Feng, Ziding Brennan, Paul Johansson, Mattias Hanash, Samir M 2018-10-11 http://hdl.handle.net/10668/12706 https://doi.org/10.1001/jamaoncol.2018.2078 https://jamanetwork.com/journals/jamaoncology/articlepdf/2687371/jamaoncology_guida_2018_br_180010.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6233784/pdf en eng http://hdl.handle.net/10668/12706 30003238 doi:10.1001/jamaoncol.2018.2078 2374-2445 PMC6233784 https://jamanetwork.com/journals/jamaoncology/articlepdf/2687371/jamaoncology_guida_2018_br_180010.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6233784/pdf open access Aged 80 and over Biomarkers Tumor CA-125 Antigen Carcinoembryonic Antigen Female Humans Keratin-19 Lung Neoplasms Male Mass Screening Membrane Proteins Middle Aged Non-Smokers Prospective Studies Protein Precursors Proteolipids ROC Curve Risk Assessment Risk Factors Tomography Scanners X-Ray Computed research article VoR 2018 ftsspa https://doi.org/10.1001/jamaoncol.2018.2078 2023-10-29T17:32:28Z There 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 ... Article in Journal/Newspaper Northern Sweden Sistema Sanitario Público de Andalucía (SSPA): Repositorio JAMA Oncology 4 10 e182078 |