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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Published in:eBioMedicine
Main Authors: 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
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier 2023
Subjects:
R
Online Access:https://doi.org/10.1016/j.ebiom.2023.104623
https://doaj.org/article/ad5ce7e1861f4b77bc42b53f8ccbc532
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spelling 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
op_collection_id 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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