Detection of endometrial cancer in cervico-vaginal fluid and blood plasma: leveraging proteomics and machine learning for biomarker discovery

Kelechi Njoku (Lead / Corresponding author), Andrew Pierce, Davide Chiasserini, Bethany Geary, Amy E. Campbell, Janet Kelsall, Rachel Reed, Nophar Geifman, Anthony D. Whetton, Emma J. Crosbie

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Abstract

BACKGROUND: The anatomical continuity between the uterine cavity and the lower genital tract allows for the exploitation of uterine-derived biomaterial in cervico-vaginal fluid for endometrial cancer detection based on non-invasive sampling methodologies. Plasma is an attractive biofluid for cancer detection due to its simplicity and ease of collection. In this biomarker discovery study, we aimed to identify proteomic signatures that accurately discriminate endometrial cancer from controls in cervico-vaginal fluid and blood plasma.

METHODS: Blood plasma and Delphi Screener-collected cervico-vaginal fluid samples were acquired from symptomatic post-menopausal women with (n = 53) and without (n = 65) endometrial cancer. Digitised proteomic maps were derived for each sample using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning was employed to identify the most discriminatory proteins. The best diagnostic model was determined based on accuracy and model parsimony.

FINDINGS: A protein signature derived from cervico-vaginal fluid more accurately discriminated cancer from control samples than one derived from plasma. A 5-biomarker panel of cervico-vaginal fluid derived proteins (HPT, LG3BP, FGA, LY6D and IGHM) predicted endometrial cancer with an AUC of 0.95 (0.91-0.98), sensitivity of 91% (83%-98%), and specificity of 86% (78%-95%). By contrast, a 3-marker panel of plasma proteins (APOD, PSMA7 and HPT) predicted endometrial cancer with an AUC of 0.87 (0.81-0.93), sensitivity of 75% (64%-86%), and specificity of 84% (75%-93%). The parsimonious model AUC values for detection of stage I endometrial cancer in cervico-vaginal fluid and blood plasma were 0.92 (0.87-0.97) and 0.88 (0.82-0.95) respectively.

INTERPRETATION: Here, we leveraged the natural shed of endometrial tumours to potentially develop an innovative approach to endometrial cancer detection. We show proof of principle that endometrial cancers secrete unique protein signatures that can enable cancer detection via cervico-vaginal fluid assays. Confirmation in a larger independent cohort is warranted.

FUNDING: Cancer Research UK, Blood Cancer UK, National Institute for Health Research.

Original languageEnglish
Article number105064
Pages (from-to)105064
Number of pages13
JournalEBioMedicine
Volume102
Early online date19 Mar 2024
DOIs
Publication statusPublished - Apr 2024

Keywords

  • Humans
  • Female
  • Proteomics
  • Endometrial Neoplasms/diagnosis
  • Biomarkers
  • Plasma
  • Machine Learning
  • Proteins
  • Biomarker
  • Endometrial cancer
  • Cervico-vaginal fluid

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology

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