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.

N1 - © 2023. The Author(s).

PY - 2023/5/18

Y1 - 2023/5/18

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.

KW - Humans

KW - Female

KW - Biomarkers, Tumor

KW - Case-Control Studies

KW - Proteomics/methods

KW - Biomarkers

KW - Mass Spectrometry/methods

KW - Endometrial Neoplasms/diagnosis

KW - Fatty Acid-Binding Proteins

KW - Extracellular Matrix Proteins

U2 - 10.1038/s41416-022-02139-0

DO - 10.1038/s41416-022-02139-0

M3 - Article

C2 - 36807337

VL - 128

SP - 1723

EP - 1732

JO - British Journal of Cancer

JF - British Journal of Cancer

SN - 0007-0920

IS - 9

ER -