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 journalArticlepeer-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 languageEnglish
JournalBritish Journal of Cancer
Early online date17 Feb 2023
DOIs
Publication statusE-pub ahead of print - 17 Feb 2023

Research Beacons, Institutes and Platforms

  • Manchester Cancer Research Centre

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