Plasma protein biomarkers for early prediction of lung cancerResearch in context
Summary: Background: Individual plasma proteins have been identified as minimally invasive biomarkers for lung cancer diagnosis with potential utility in early detection. Plasma proteomes provide insight into contributing biological factors; we investigated their potential for future lung cancer pr...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | EBioMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396423002517 |
| _version_ | 1797794032238198784 |
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| author | Michael P.A. Davies Takahiro Sato Haitham Ashoor Liping Hou Triantafillos Liloglou Robert Yang John K. Field |
| author_facet | Michael P.A. Davies Takahiro Sato Haitham Ashoor Liping Hou Triantafillos Liloglou Robert Yang John K. Field |
| author_sort | Michael P.A. Davies |
| collection | DOAJ |
| description | Summary: Background: Individual plasma proteins have been identified as minimally invasive biomarkers for lung cancer diagnosis with potential utility in early detection. Plasma proteomes provide insight into contributing biological factors; we investigated their potential for future lung cancer prediction. Methods: The Olink® Explore-3072 platform quantitated 2941 proteins in 496 Liverpool Lung Project plasma samples, including 131 cases taken 1–10 years prior to diagnosis, 237 controls, and 90 subjects at multiple times. 1112 proteins significantly associated with haemolysis were excluded. Feature selection with bootstrapping identified differentially expressed proteins, subsequently modelled for lung cancer prediction and validated in UK Biobank data. Findings: For samples 1–3 years pre-diagnosis, 240 proteins were significantly different in cases; for 1–5 year samples, 117 of these and 150 further proteins were identified, mapping to significantly different pathways. Four machine learning algorithms gave median AUCs of 0.76–0.90 and 0.73–0.83 for the 1–3 year and 1–5 year proteins respectively. External validation gave AUCs of 0.75 (1–3 year) and 0.69 (1–5 year), with AUC 0.7 up to 12 years prior to diagnosis. The models were independent of age, smoking duration, cancer histology and the presence of COPD. Interpretation: The plasma proteome provides biomarkers which may be used to identify those at greatest risk of lung cancer. The proteins and the pathways are different when lung cancer is more imminent, indicating that both biomarkers of inherent risk and biomarkers associated with presence of early lung cancer may be identified. Funding: Janssen Pharmaceuticals Research Collaboration Award; Roy Castle Lung Cancer Foundation. |
| first_indexed | 2024-03-13T02:56:58Z |
| format | Article |
| id | doaj.art-d26048ddc4f34b84b583103464e0fe85 |
| institution | Directory Open Access Journal |
| issn | 2352-3964 |
| language | English |
| last_indexed | 2024-03-13T02:56:58Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj.art-d26048ddc4f34b84b583103464e0fe852023-06-28T04:29:39ZengElsevierEBioMedicine2352-39642023-07-0193104686Plasma protein biomarkers for early prediction of lung cancerResearch in contextMichael P.A. Davies0Takahiro Sato1Haitham Ashoor2Liping Hou3Triantafillos Liloglou4Robert Yang5John K. Field6Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool L7 8TX, UKWorld Without Disease Accelerator, Johnson & Johnson, 10th Floor 255 Main St, Cambridge, MA 02142, USAWorld Without Disease Accelerator, Johnson & Johnson, 10th Floor 255 Main St, Cambridge, MA 02142, USAPopulation Analytics & Insights, Data Science, Janssen R&D, 1400 McKean Rd, Spring House, PA 19477, USAFaculty of Health, Social Care & Medicine, Edge Hill University, St Helens Road, Ormskirk, Lancashire L39 4QP, UKWorld Without Disease Accelerator, Johnson & Johnson, 10th Floor 255 Main St, Cambridge, MA 02142, USADepartment of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool L7 8TX, UK; Corresponding author. Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular & Integrative Biology, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, L7 8TX, Liverpool, UK.Summary: Background: Individual plasma proteins have been identified as minimally invasive biomarkers for lung cancer diagnosis with potential utility in early detection. Plasma proteomes provide insight into contributing biological factors; we investigated their potential for future lung cancer prediction. Methods: The Olink® Explore-3072 platform quantitated 2941 proteins in 496 Liverpool Lung Project plasma samples, including 131 cases taken 1–10 years prior to diagnosis, 237 controls, and 90 subjects at multiple times. 1112 proteins significantly associated with haemolysis were excluded. Feature selection with bootstrapping identified differentially expressed proteins, subsequently modelled for lung cancer prediction and validated in UK Biobank data. Findings: For samples 1–3 years pre-diagnosis, 240 proteins were significantly different in cases; for 1–5 year samples, 117 of these and 150 further proteins were identified, mapping to significantly different pathways. Four machine learning algorithms gave median AUCs of 0.76–0.90 and 0.73–0.83 for the 1–3 year and 1–5 year proteins respectively. External validation gave AUCs of 0.75 (1–3 year) and 0.69 (1–5 year), with AUC 0.7 up to 12 years prior to diagnosis. The models were independent of age, smoking duration, cancer histology and the presence of COPD. Interpretation: The plasma proteome provides biomarkers which may be used to identify those at greatest risk of lung cancer. The proteins and the pathways are different when lung cancer is more imminent, indicating that both biomarkers of inherent risk and biomarkers associated with presence of early lung cancer may be identified. Funding: Janssen Pharmaceuticals Research Collaboration Award; Roy Castle Lung Cancer Foundation.http://www.sciencedirect.com/science/article/pii/S2352396423002517Lung cancer predictionEarly-detectionPlasmaProteinsProteomics |
| spellingShingle | Michael P.A. Davies Takahiro Sato Haitham Ashoor Liping Hou Triantafillos Liloglou Robert Yang John K. Field Plasma protein biomarkers for early prediction of lung cancerResearch in context EBioMedicine Lung cancer prediction Early-detection Plasma Proteins Proteomics |
| title | Plasma protein biomarkers for early prediction of lung cancerResearch in context |
| title_full | Plasma protein biomarkers for early prediction of lung cancerResearch in context |
| title_fullStr | Plasma protein biomarkers for early prediction of lung cancerResearch in context |
| title_full_unstemmed | Plasma protein biomarkers for early prediction of lung cancerResearch in context |
| title_short | Plasma protein biomarkers for early prediction of lung cancerResearch in context |
| title_sort | plasma protein biomarkers for early prediction of lung cancerresearch in context |
| topic | Lung cancer prediction Early-detection Plasma Proteins Proteomics |
| url | http://www.sciencedirect.com/science/article/pii/S2352396423002517 |
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