Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women

Kelechi Njoku; Andrew Pierce; Bethany Geary; Amy E. Campbell; Janet Kelsall; Rachel Reed; Alexander Armit; Rachel Da sylva; Liqun Zhang; Heather Agnew; Ivona Baricevic-Jones; Davide Chiasserini; Anthony D. Whetton; Emma J. Crosbie

doi:10.1038/s41416-022-02139-0

Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women

Kelechi Njoku, Andrew Pierce, Bethany Geary, Amy E. Campbell, Janet Kelsall, Rachel Reed, Alexander Armit, Rachel Da sylva, Liqun Zhang, Heather Agnew, Ivona Baricevic-Jones, Davide Chiasserini, Anthony D. Whetton, Emma J. Crosbie

Research output: Contribution to journal › Article › peer-review

Abstract

Background
A non-invasive endometrial cancer detection tool that can accurately triage symptomatic women for definitive testing would improve patient care. Urine is an attractive biofluid for cancer detection due to its simplicity and ease of collection. The aim of this study was to identify urine-based proteomic signatures that can discriminate endometrial cancer patients from symptomatic controls.

Methods
This was a prospective case–control study of symptomatic post-menopausal women (50 cancers, 54 controls). Voided self-collected urine samples were processed for mass spectrometry and run using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning techniques were used to identify important discriminatory proteins, which were subsequently combined in multi-marker panels using logistic regression.

Results
The top discriminatory proteins individually showed moderate accuracy (AUC > 0.70) for endometrial cancer detection. However, algorithms combining the most discriminatory proteins performed well with AUCs > 0.90. The best performing diagnostic model was a 10-marker panel combining SPRR1B, CRNN, CALML3, TXN, FABP5, C1RL, MMP9, ECM1, S100A7 and CFI and predicted endometrial cancer with an AUC of 0.92 (0.96–0.97). Urine-based protein signatures showed good accuracy for the detection of early-stage cancers (AUC 0.92 (0.86–0.9)).

Conclusion
A patient-friendly, urine-based test could offer a non-invasive endometrial cancer detection tool in symptomatic women. Validation in a larger independent cohort is warranted.

Original language	English
Journal	British Journal of Cancer
Early online date	17 Feb 2023
DOIs	https://doi.org/10.1038/s41416-022-02139-0
Publication status	E-pub ahead of print - 17 Feb 2023

Research Beacons, Institutes and Platforms

Manchester Cancer Research Centre

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1038/s41416-022-02139-0Licence: CC BY

NIHR Manchester Biomedical Research Centre
Bruce, I. (PI), Lord, G. (CoI), Lennon, R. (CoI), Black, G. (CoI), Wedge, D. (CoI), Morris, A. (CoI), Hussell, T. (CoI), Sharrocks, A. (CoI), Stivaros, S. (CoI), Buch, M. (CoI), Gough, J. (CoI), Kostarelos, K. (CoI), Thistlethwaite, F. (CoI), Kadler, K. (CoI), Barton, A. (CoI), Hyrich, K. (CoI), Mcbeth, J. (CoI), O'Neill, T. (CoI), Vestbo, J. (CoI), Simpson, A. (CoI), Singh, S. (CoI), Smith, J. (CoI), Felton, T. (CoI), Murray, C. (CoI), Griffiths, C. (CoI), Cullum, N. (CoI), Rhodes, L. (CoI), Warren, R. (CoI), Paus, R. (CoI), Dumville, J. (CoI), Viros Usandizaga, A. (CoI), Keavney, B. (CoI), Tomaszewski, M. (CoI), Allan, S. (CoI), Body, R. (CoI), Cartwright, E. (CoI), Heagerty, A. (CoI), Kalra, P. (CoI), Miller, C. (CoI), Rutter, M. (CoI), Smith, C. (CoI), Trafford, A. (CoI), Evans, D. (CoI), Crosbie, E. (CoI), Crosbie, P. (CoI), Harvie, M. (CoI), Howell, S. (CoI), Renehan, A. (CoI), Dive, C. (CoI), Blackhall, F. (CoI), Landers, D. (CoI), Krebs, M. (CoI), Cook, N. (CoI), Clarke, R. (CoI), Taylor, S. (CoI), Jorgensen, C. (CoI), Lorigan, P. (CoI), Jayson, G. (CoI), Valle, J. (CoI), Mccabe, M. (CoI), Armstrong, A. (CoI), Freitas, A. (CoI), Illidge, T. (CoI), Choudhury, A. (CoI), Hoskin, P. (CoI), West, C. (CoI), Van Herk, M. (CoI), Faivre-Finn, C. (CoI), Bristow, R. (CoI), Kirkby, K. (CoI), Birtle, A. (CoI), Mackay, R. (CoI), Radford, J. (CoI), Linton, K. (CoI), Higham, C. (CoI), Munro, K. (CoI), Plack, C. (CoI), Arden Armitage, C. (CoI), Bruce, I. (CoI), Moore, D. (CoI), Saunders, G. (CoI), Stone, M. (CoI), Haddock, G. (CoI), Lewis, S. (CoI), Elliott, R. (CoI), Green, J. (CoI), Lovell, K. (CoI), Morrison, A. (CoI), Shaw, J. (CoI), Bucci, S. (CoI), Ainsworth, J. (CoI), Webb, R. (CoI), Newman, W. (CoI), Banka, S. (CoI), Clayton-Smith, J. (CoI), Payne, K. (CoI), Moldovan, R. (CoI), Wynn, R. (CoI) & Jones, S. (CoI)
1/12/22 → 30/11/27
Project: Research

Quantitative SWATH-Based Proteomic Profiling of Urine for the Identification of Endometrial Cancer Biomarkers in Symptomatic Women
Crosbie, E. (Creator) & Njoku, K. (Creator), ProteomeXchange, 14 Nov 2014
https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD038860
Dataset

Biological Mass Spectrometry (BioMS) Facility
Knight, D. (Core Facility Lead), Warwood, S. (Senior Technical Specialist), Selley, J. (Technical Specialist), Taylor, G. (Technical Specialist), Fullwood, P. (Technical Specialist), Keevill, E.-J. (Senior Technician) & Allsey, J. (Technician)
FBMH Platform Sciences, Enabling Technologies & Infrastructure
Facility/equipment: Facility

Cite this

Njoku, K., Pierce, A., Geary, B., Campbell, A. E., Kelsall, J., Reed, R., Armit, A., Da sylva, R., Zhang, L., Agnew, H., Baricevic-Jones, I., Chiasserini, D., Whetton, A. D., & Crosbie, E. J. (2023). Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women. British Journal of Cancer. Advance online publication. https://doi.org/10.1038/s41416-022-02139-0

@article{0e76bb789d68491cb4c3f3d04193cc1f,

title = "Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women",

abstract = "Background A non-invasive endometrial cancer detection tool that can accurately triage symptomatic women for definitive testing would improve patient care. Urine is an attractive biofluid for cancer detection due to its simplicity and ease of collection. The aim of this study was to identify urine-based proteomic signatures that can discriminate endometrial cancer patients from symptomatic controls. Methods This was a prospective case–control study of symptomatic post-menopausal women (50 cancers, 54 controls). Voided self-collected urine samples were processed for mass spectrometry and run using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning techniques were used to identify important discriminatory proteins, which were subsequently combined in multi-marker panels using logistic regression. Results The top discriminatory proteins individually showed moderate accuracy (AUC > 0.70) for endometrial cancer detection. However, algorithms combining the most discriminatory proteins performed well with AUCs > 0.90. The best performing diagnostic model was a 10-marker panel combining SPRR1B, CRNN, CALML3, TXN, FABP5, C1RL, MMP9, ECM1, S100A7 and CFI and predicted endometrial cancer with an AUC of 0.92 (0.96–0.97). Urine-based protein signatures showed good accuracy for the detection of early-stage cancers (AUC 0.92 (0.86–0.9)). Conclusion A patient-friendly, urine-based test could offer a non-invasive endometrial cancer detection tool in symptomatic women. Validation in a larger independent cohort is warranted.",

author = "Kelechi Njoku and Andrew Pierce and Bethany Geary and Campbell, \{Amy E.\} and Janet Kelsall and Rachel Reed and Alexander Armit and \{Da sylva\}, Rachel and Liqun Zhang and Heather Agnew and Ivona Baricevic-Jones and Davide Chiasserini and Whetton, \{Anthony D.\} and Crosbie, \{Emma J.\}",

year = "2023",

month = feb,

day = "17",

doi = "10.1038/s41416-022-02139-0",

language = "English",

journal = "British Journal of Cancer",

issn = "0007-0920",

publisher = "Springer Nature",

}

Njoku, K , Pierce, A, Geary, B, Campbell, AE, Kelsall, J, Reed, R, Armit, A, Da sylva, R, Zhang, L, Agnew, H, Baricevic-Jones, I, Chiasserini, D, Whetton, AD & Crosbie, EJ 2023, 'Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women', British Journal of Cancer. https://doi.org/10.1038/s41416-022-02139-0

TY - JOUR

T1 - Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women

AU - Njoku, Kelechi

AU - Pierce, Andrew

AU - Geary, Bethany

AU - Campbell, Amy E.

AU - Kelsall, Janet

AU - Reed, Rachel

AU - Armit, Alexander

AU - Da sylva, Rachel

AU - Zhang, Liqun

AU - Agnew, Heather

AU - Baricevic-Jones, Ivona

AU - Chiasserini, Davide

AU - Whetton, Anthony D.

AU - Crosbie, Emma J.

PY - 2023/2/17

Y1 - 2023/2/17

N2 - Background A non-invasive endometrial cancer detection tool that can accurately triage symptomatic women for definitive testing would improve patient care. Urine is an attractive biofluid for cancer detection due to its simplicity and ease of collection. The aim of this study was to identify urine-based proteomic signatures that can discriminate endometrial cancer patients from symptomatic controls. Methods This was a prospective case–control study of symptomatic post-menopausal women (50 cancers, 54 controls). Voided self-collected urine samples were processed for mass spectrometry and run using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning techniques were used to identify important discriminatory proteins, which were subsequently combined in multi-marker panels using logistic regression. Results The top discriminatory proteins individually showed moderate accuracy (AUC > 0.70) for endometrial cancer detection. However, algorithms combining the most discriminatory proteins performed well with AUCs > 0.90. The best performing diagnostic model was a 10-marker panel combining SPRR1B, CRNN, CALML3, TXN, FABP5, C1RL, MMP9, ECM1, S100A7 and CFI and predicted endometrial cancer with an AUC of 0.92 (0.96–0.97). Urine-based protein signatures showed good accuracy for the detection of early-stage cancers (AUC 0.92 (0.86–0.9)). Conclusion A patient-friendly, urine-based test could offer a non-invasive endometrial cancer detection tool in symptomatic women. Validation in a larger independent cohort is warranted.

AB - Background A non-invasive endometrial cancer detection tool that can accurately triage symptomatic women for definitive testing would improve patient care. Urine is an attractive biofluid for cancer detection due to its simplicity and ease of collection. The aim of this study was to identify urine-based proteomic signatures that can discriminate endometrial cancer patients from symptomatic controls. Methods This was a prospective case–control study of symptomatic post-menopausal women (50 cancers, 54 controls). Voided self-collected urine samples were processed for mass spectrometry and run using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning techniques were used to identify important discriminatory proteins, which were subsequently combined in multi-marker panels using logistic regression. Results The top discriminatory proteins individually showed moderate accuracy (AUC > 0.70) for endometrial cancer detection. However, algorithms combining the most discriminatory proteins performed well with AUCs > 0.90. The best performing diagnostic model was a 10-marker panel combining SPRR1B, CRNN, CALML3, TXN, FABP5, C1RL, MMP9, ECM1, S100A7 and CFI and predicted endometrial cancer with an AUC of 0.92 (0.96–0.97). Urine-based protein signatures showed good accuracy for the detection of early-stage cancers (AUC 0.92 (0.86–0.9)). Conclusion A patient-friendly, urine-based test could offer a non-invasive endometrial cancer detection tool in symptomatic women. Validation in a larger independent cohort is warranted.

U2 - 10.1038/s41416-022-02139-0

DO - 10.1038/s41416-022-02139-0

M3 - Article

SN - 0007-0920

JO - British Journal of Cancer

JF - British Journal of Cancer

ER -

Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women

Abstract

Research Beacons, Institutes and Platforms

UN SDGs

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Projects

NIHR Manchester Biomedical Research Centre

Datasets

Quantitative SWATH-Based Proteomic Profiling of Urine for the Identification of Endometrial Cancer Biomarkers in Symptomatic Women

Equipment

Biological Mass Spectrometry (BioMS) Facility

Cite this