External Validation of Plasma Glycosaminoglycans as Biomarkers to Improve Lung Cancer Risk Stratification
Davies, Michael P.A. ; Field, John K. and Gatto, Francesco LU
(2025)
In Cancer Epidemiology Biomarkers and Prevention
34(7).
p.1219-1225
- Abstract
Background: Lung cancer screening excludes individuals not considered at an increased risk for lung cancer, as predicted by risk models like the Liverpool Lung Project version 3 (LLPv3). In this study, we sought to validate whether plasma glycosaminoglycan profiles (GAGomes) could predict lung cancer independent of LLPv3 and other prespecified comorbidities. Methods: In this retrospective cohort-based case-control study, we included patients who were suspected of having lung cancer at baseline and were either diagnosed with lung cancer (cases) or remained cancer-free for 5 years after baseline (controls). Plasma GAGomes were measured at baseline and used to compute a prespecified GAGome score to discriminate lung cancer from controls.... (More)
Background: Lung cancer screening excludes individuals not considered at an increased risk for lung cancer, as predicted by risk models like the Liverpool Lung Project version 3 (LLPv3). In this study, we sought to validate whether plasma glycosaminoglycan profiles (GAGomes) could predict lung cancer independent of LLPv3 and other prespecified comorbidities. Methods: In this retrospective cohort-based case-control study, we included patients who were suspected of having lung cancer at baseline and were either diagnosed with lung cancer (cases) or remained cancer-free for 5 years after baseline (controls). Plasma GAGomes were measured at baseline and used to compute a prespecified GAGome score to discriminate lung cancer from controls. We then applied multivariable Bayesian logistic regression to evaluate the likelihood that 7 LLPv3 predictors or 14 comorbidities had an effect on the GAGome score. We tested the independence of the GAGome score from LLPv3-predicted 5-year risk using the likelihood ratio test and assessed whether it improved lung cancer risk prediction in a set equivalent to an LLPv3-predicted 5-year risk of ≥1.51%. Results: We included 653 lung cancer and 653 controls. The AUC of the GAGome score was 0.63 (95% confidence interval, 0.62-63). None of the LLPv3 predictors or comorbidities were compatible with a significant effect on the score. The GAGome score was independent of LLPv3 (P < 0.001) and improved its sensitivity (72% vs. 69%) and specificity (61% vs. 59%). Conclusions: Plasma GAGomes identified additional lung cancer cases beyond those predicted by LLPv3 alone. Impact: GAGomes could improve risk-stratified lung cancer if validated in a screening population.
(Less)
- author
- Davies, Michael P.A.
; Field, John K.
and Gatto, Francesco
LU
- organization
- publishing date
- 2025-07-01
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Cancer Epidemiology Biomarkers and Prevention
- volume
- 34
- issue
- 7
- pages
- 7 pages
- publisher
- American Association for Cancer Research
- external identifiers
-
- scopus:105009803246
- pmid:40243499
- ISSN
- 1055-9965
- DOI
- 10.1158/1055-9965.EPI-24-1537
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: ©2025 The Authors; Published by the American Association for Cancer Research.
- id
- 289b5595-17cb-4727-85ba-ca1727a0af45
- date added to LUP
- 2025-12-15 15:32:11
- date last changed
- 2026-01-12 18:34:24
@article{289b5595-17cb-4727-85ba-ca1727a0af45,
abstract = {{<p>Background: Lung cancer screening excludes individuals not considered at an increased risk for lung cancer, as predicted by risk models like the Liverpool Lung Project version 3 (LLPv3). In this study, we sought to validate whether plasma glycosaminoglycan profiles (GAGomes) could predict lung cancer independent of LLPv3 and other prespecified comorbidities. Methods: In this retrospective cohort-based case-control study, we included patients who were suspected of having lung cancer at baseline and were either diagnosed with lung cancer (cases) or remained cancer-free for 5 years after baseline (controls). Plasma GAGomes were measured at baseline and used to compute a prespecified GAGome score to discriminate lung cancer from controls. We then applied multivariable Bayesian logistic regression to evaluate the likelihood that 7 LLPv3 predictors or 14 comorbidities had an effect on the GAGome score. We tested the independence of the GAGome score from LLPv3-predicted 5-year risk using the likelihood ratio test and assessed whether it improved lung cancer risk prediction in a set equivalent to an LLPv3-predicted 5-year risk of ≥1.51%. Results: We included 653 lung cancer and 653 controls. The AUC of the GAGome score was 0.63 (95% confidence interval, 0.62-63). None of the LLPv3 predictors or comorbidities were compatible with a significant effect on the score. The GAGome score was independent of LLPv3 (P < 0.001) and improved its sensitivity (72% vs. 69%) and specificity (61% vs. 59%). Conclusions: Plasma GAGomes identified additional lung cancer cases beyond those predicted by LLPv3 alone. Impact: GAGomes could improve risk-stratified lung cancer if validated in a screening population.</p>}},
author = {{Davies, Michael P.A. and Field, John K. and Gatto, Francesco}},
issn = {{1055-9965}},
language = {{eng}},
month = {{07}},
number = {{7}},
pages = {{1219--1225}},
publisher = {{American Association for Cancer Research}},
series = {{Cancer Epidemiology Biomarkers and Prevention}},
title = {{External Validation of Plasma Glycosaminoglycans as Biomarkers to Improve Lung Cancer Risk Stratification}},
url = {{http://dx.doi.org/10.1158/1055-9965.EPI-24-1537}},
doi = {{10.1158/1055-9965.EPI-24-1537}},
volume = {{34}},
year = {{2025}},
}