Blood-based DNA methylation markers for lung cancer prediction
Objective Screening high-risk individuals with low-dose CT reduces mortality from lung cancer, but many lung cancers occur in individuals who are not eligible for screening. Risk biomarkers may be useful to refine risk models and improve screening eligibility criteria. We evaluated if blood-based DN...
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ftdoajarticles:oai:doaj.org/article:31adadfff44f48119b68988cdec01203 2024-09-15T18:26:13+00:00 Blood-based DNA methylation markers for lung cancer prediction Marc Chadeau-Hyam Paolo Vineis Caroline Relton Mattias Johansson Gianluca Severi Roger L Milne Melissa C Southey Pierre-Antoine Dugué Florence Guida Mikael Johansson Torkjel Sandanger Justina Ucheojor Onwuka Ryan Langdon Therese Haugdahl Nøst Hilary A. Robbins Matthew Suderman 2024-07-01T00:00:00Z https://doi.org/10.1136/bmjonc-2024-000334 https://doaj.org/article/31adadfff44f48119b68988cdec01203 EN eng BMJ Publishing Group https://bmjoncology.bmj.com/content/3/1/e000334.full https://doaj.org/toc/2752-7948 doi:10.1136/bmjonc-2024-000334 2752-7948 https://doaj.org/article/31adadfff44f48119b68988cdec01203 BMJ Oncology, Vol 3, Iss 1 (2024) Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 article 2024 ftdoajarticles https://doi.org/10.1136/bmjonc-2024-000334 2024-08-05T17:48:49Z Objective Screening high-risk individuals with low-dose CT reduces mortality from lung cancer, but many lung cancers occur in individuals who are not eligible for screening. Risk biomarkers may be useful to refine risk models and improve screening eligibility criteria. We evaluated if blood-based DNA methylation markers can improve a traditional lung cancer prediction model.Methods and analysis This study used four prospective cohorts with blood samples collected prior to lung cancer diagnosis. The study was restricted to participants with a history of smoking, and one control was individually matched to each lung cancer case using incidence density sampling by cohort, sex, date of blood collection, age and smoking status. To train a DNA methylation-based risk score, we used participants from Melbourne Collaborative Cohort Study-Australia (n=648) and Northern Sweden Health and Disease Study-Sweden (n=380) based on five selected CpG sites. The risk discriminative performance of the methylation score was subsequently validated in participants from European Investigation into Cancer and Nutrition-Italy (n=267) and Norwegian Women and Cancer-Norway (n=185) and compared with that of the questionnaire-based PLCOm2012 lung cancer risk model.Results The area under the receiver operating characteristic curve (AUC) for the PLCOm2012 model in the validation studies was 0.70 (95% CI: 0.65 to 0.75) compared with 0.73 (95% CI: 0.68 to 0.77) for the methylation score model (Pdifference=0.07). Incorporating the methylation score with the PLCOm2012 model did not improve the risk discrimination (AUC: 0.73, 95% CI: 0.68 to 0.77, Pdifference=0.73).Conclusions This study suggests that the methylation-based risk prediction score alone provides similar lung cancer risk-discriminatory performance as the questionnaire-based PLCOm2012 risk model. Article in Journal/Newspaper Northern Sweden Directory of Open Access Journals: DOAJ Articles BMJ Oncology 3 1 e000334 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 |
spellingShingle |
Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Marc Chadeau-Hyam Paolo Vineis Caroline Relton Mattias Johansson Gianluca Severi Roger L Milne Melissa C Southey Pierre-Antoine Dugué Florence Guida Mikael Johansson Torkjel Sandanger Justina Ucheojor Onwuka Ryan Langdon Therese Haugdahl Nøst Hilary A. Robbins Matthew Suderman Blood-based DNA methylation markers for lung cancer prediction |
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 |
description |
Objective Screening high-risk individuals with low-dose CT reduces mortality from lung cancer, but many lung cancers occur in individuals who are not eligible for screening. Risk biomarkers may be useful to refine risk models and improve screening eligibility criteria. We evaluated if blood-based DNA methylation markers can improve a traditional lung cancer prediction model.Methods and analysis This study used four prospective cohorts with blood samples collected prior to lung cancer diagnosis. The study was restricted to participants with a history of smoking, and one control was individually matched to each lung cancer case using incidence density sampling by cohort, sex, date of blood collection, age and smoking status. To train a DNA methylation-based risk score, we used participants from Melbourne Collaborative Cohort Study-Australia (n=648) and Northern Sweden Health and Disease Study-Sweden (n=380) based on five selected CpG sites. The risk discriminative performance of the methylation score was subsequently validated in participants from European Investigation into Cancer and Nutrition-Italy (n=267) and Norwegian Women and Cancer-Norway (n=185) and compared with that of the questionnaire-based PLCOm2012 lung cancer risk model.Results The area under the receiver operating characteristic curve (AUC) for the PLCOm2012 model in the validation studies was 0.70 (95% CI: 0.65 to 0.75) compared with 0.73 (95% CI: 0.68 to 0.77) for the methylation score model (Pdifference=0.07). Incorporating the methylation score with the PLCOm2012 model did not improve the risk discrimination (AUC: 0.73, 95% CI: 0.68 to 0.77, Pdifference=0.73).Conclusions This study suggests that the methylation-based risk prediction score alone provides similar lung cancer risk-discriminatory performance as the questionnaire-based PLCOm2012 risk model. |
format |
Article in Journal/Newspaper |
author |
Marc Chadeau-Hyam Paolo Vineis Caroline Relton Mattias Johansson Gianluca Severi Roger L Milne Melissa C Southey Pierre-Antoine Dugué Florence Guida Mikael Johansson Torkjel Sandanger Justina Ucheojor Onwuka Ryan Langdon Therese Haugdahl Nøst Hilary A. Robbins Matthew Suderman |
author_facet |
Marc Chadeau-Hyam Paolo Vineis Caroline Relton Mattias Johansson Gianluca Severi Roger L Milne Melissa C Southey Pierre-Antoine Dugué Florence Guida Mikael Johansson Torkjel Sandanger Justina Ucheojor Onwuka Ryan Langdon Therese Haugdahl Nøst Hilary A. Robbins Matthew Suderman |
author_sort |
Marc Chadeau-Hyam |
title |
Blood-based DNA methylation markers for lung cancer prediction |
title_short |
Blood-based DNA methylation markers for lung cancer prediction |
title_full |
Blood-based DNA methylation markers for lung cancer prediction |
title_fullStr |
Blood-based DNA methylation markers for lung cancer prediction |
title_full_unstemmed |
Blood-based DNA methylation markers for lung cancer prediction |
title_sort |
blood-based dna methylation markers for lung cancer prediction |
publisher |
BMJ Publishing Group |
publishDate |
2024 |
url |
https://doi.org/10.1136/bmjonc-2024-000334 https://doaj.org/article/31adadfff44f48119b68988cdec01203 |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_source |
BMJ Oncology, Vol 3, Iss 1 (2024) |
op_relation |
https://bmjoncology.bmj.com/content/3/1/e000334.full https://doaj.org/toc/2752-7948 doi:10.1136/bmjonc-2024-000334 2752-7948 https://doaj.org/article/31adadfff44f48119b68988cdec01203 |
op_doi |
https://doi.org/10.1136/bmjonc-2024-000334 |
container_title |
BMJ Oncology |
container_volume |
3 |
container_issue |
1 |
container_start_page |
e000334 |
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1810466668571262976 |