Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosisResearch in context
Summary: Background: To evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis. Methods: We measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3...
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Online Access: | https://doi.org/10.1016/j.ebiom.2023.104623 https://doaj.org/article/ad5ce7e1861f4b77bc42b53f8ccbc532 |
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ftdoajarticles:oai:doaj.org/article:ad5ce7e1861f4b77bc42b53f8ccbc532 2023-06-11T04:15:24+02:00 Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosisResearch in context Xiaoshuang Feng David C. Muller Hana Zahed Karine Alcala Florence Guida Karl Smith-Byrne Jian-Min Yuan Woon-Puay Koh Renwei Wang Roger L. Milne Julie K. Bassett Arnulf Langhammer Kristian Hveem Victoria L. Stevens Ying Wang Mikael Johansson Anne Tjønneland Rosario Tumino Mahdi Sheikh Mattias Johansson Hilary A. Robbins 2023-06-01T00:00:00Z https://doi.org/10.1016/j.ebiom.2023.104623 https://doaj.org/article/ad5ce7e1861f4b77bc42b53f8ccbc532 EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S2352396423001883 https://doaj.org/toc/2352-3964 2352-3964 doi:10.1016/j.ebiom.2023.104623 https://doaj.org/article/ad5ce7e1861f4b77bc42b53f8ccbc532 EBioMedicine, Vol 92, Iss , Pp 104623- (2023) Lung cancer Lung cancer survival Protein biomarkers Lung cancer prognosis Medicine R Medicine (General) R5-920 article 2023 ftdoajarticles https://doi.org/10.1016/j.ebiom.2023.104623 2023-05-28T00:35:53Z Summary: Background: To evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis. Methods: We measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3 years prior to lung cancer diagnosis. We used Cox proportional hazards models to identify proteins associated with overall mortality after lung cancer diagnosis. To evaluate model performance, we used a round-robin approach in which models were fit in 5 cohorts and evaluated in the 6th cohort. Specifically, we fit a model including 5 proteins and clinical parameters and compared its performance with clinical parameters only. Findings: There were 86 proteins nominally associated with mortality (p < 0.05), but only CDCP1 remained statistically significant after accounting for multiple testing (hazard ratio per standard deviation: 1.19, 95% CI: 1.10–1.30, unadjusted p = 0.00004). The external C-index for the protein-based model was 0.63 (95% CI: 0.61–0.66), compared with 0.62 (95% CI: 0.59–0.64) for the model with clinical parameters only. Inclusion of proteins did not provide a statistically significant improvement in discrimination (C-index difference: 0.015, 95% CI: −0.003 to 0.035). Interpretation: Blood proteins measured within 3 years prior to lung cancer diagnosis were not strongly associated with lung cancer survival, nor did they importantly improve prediction of prognosis beyond clinical information. Funding: No explicit funding for this study. Authors and data collection supported by the US National Cancer Institute (U19CA203654), INCA (France, 2019-1-TABAC-01), Cancer Research Foundation of Northern Sweden (AMP19-962), and Swedish Department of Health Ministry. Article in Journal/Newspaper Northern Sweden Directory of Open Access Journals: DOAJ Articles Inca ENVELOPE(-59.194,-59.194,-62.308,-62.308) eBioMedicine 92 104623 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Lung cancer Lung cancer survival Protein biomarkers Lung cancer prognosis Medicine R Medicine (General) R5-920 |
spellingShingle |
Lung cancer Lung cancer survival Protein biomarkers Lung cancer prognosis Medicine R Medicine (General) R5-920 Xiaoshuang Feng David C. Muller Hana Zahed Karine Alcala Florence Guida Karl Smith-Byrne Jian-Min Yuan Woon-Puay Koh Renwei Wang Roger L. Milne Julie K. Bassett Arnulf Langhammer Kristian Hveem Victoria L. Stevens Ying Wang Mikael Johansson Anne Tjønneland Rosario Tumino Mahdi Sheikh Mattias Johansson Hilary A. Robbins Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosisResearch in context |
topic_facet |
Lung cancer Lung cancer survival Protein biomarkers Lung cancer prognosis Medicine R Medicine (General) R5-920 |
description |
Summary: Background: To evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis. Methods: We measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3 years prior to lung cancer diagnosis. We used Cox proportional hazards models to identify proteins associated with overall mortality after lung cancer diagnosis. To evaluate model performance, we used a round-robin approach in which models were fit in 5 cohorts and evaluated in the 6th cohort. Specifically, we fit a model including 5 proteins and clinical parameters and compared its performance with clinical parameters only. Findings: There were 86 proteins nominally associated with mortality (p < 0.05), but only CDCP1 remained statistically significant after accounting for multiple testing (hazard ratio per standard deviation: 1.19, 95% CI: 1.10–1.30, unadjusted p = 0.00004). The external C-index for the protein-based model was 0.63 (95% CI: 0.61–0.66), compared with 0.62 (95% CI: 0.59–0.64) for the model with clinical parameters only. Inclusion of proteins did not provide a statistically significant improvement in discrimination (C-index difference: 0.015, 95% CI: −0.003 to 0.035). Interpretation: Blood proteins measured within 3 years prior to lung cancer diagnosis were not strongly associated with lung cancer survival, nor did they importantly improve prediction of prognosis beyond clinical information. Funding: No explicit funding for this study. Authors and data collection supported by the US National Cancer Institute (U19CA203654), INCA (France, 2019-1-TABAC-01), Cancer Research Foundation of Northern Sweden (AMP19-962), and Swedish Department of Health Ministry. |
format |
Article in Journal/Newspaper |
author |
Xiaoshuang Feng David C. Muller Hana Zahed Karine Alcala Florence Guida Karl Smith-Byrne Jian-Min Yuan Woon-Puay Koh Renwei Wang Roger L. Milne Julie K. Bassett Arnulf Langhammer Kristian Hveem Victoria L. Stevens Ying Wang Mikael Johansson Anne Tjønneland Rosario Tumino Mahdi Sheikh Mattias Johansson Hilary A. Robbins |
author_facet |
Xiaoshuang Feng David C. Muller Hana Zahed Karine Alcala Florence Guida Karl Smith-Byrne Jian-Min Yuan Woon-Puay Koh Renwei Wang Roger L. Milne Julie K. Bassett Arnulf Langhammer Kristian Hveem Victoria L. Stevens Ying Wang Mikael Johansson Anne Tjønneland Rosario Tumino Mahdi Sheikh Mattias Johansson Hilary A. Robbins |
author_sort |
Xiaoshuang Feng |
title |
Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosisResearch in context |
title_short |
Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosisResearch in context |
title_full |
Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosisResearch in context |
title_fullStr |
Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosisResearch in context |
title_full_unstemmed |
Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosisResearch in context |
title_sort |
evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosisresearch in context |
publisher |
Elsevier |
publishDate |
2023 |
url |
https://doi.org/10.1016/j.ebiom.2023.104623 https://doaj.org/article/ad5ce7e1861f4b77bc42b53f8ccbc532 |
long_lat |
ENVELOPE(-59.194,-59.194,-62.308,-62.308) |
geographic |
Inca |
geographic_facet |
Inca |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_source |
EBioMedicine, Vol 92, Iss , Pp 104623- (2023) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S2352396423001883 https://doaj.org/toc/2352-3964 2352-3964 doi:10.1016/j.ebiom.2023.104623 https://doaj.org/article/ad5ce7e1861f4b77bc42b53f8ccbc532 |
op_doi |
https://doi.org/10.1016/j.ebiom.2023.104623 |
container_title |
eBioMedicine |
container_volume |
92 |
container_start_page |
104623 |
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