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Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging

  • Maria Luisa Dória
  • , James S. McKenzie
  • , Anna Mroz
  • , David L. Phelps
  • , Abigail Speller
  • , Francesca Rosini
  • , Nicole Strittmatter
  • , Ottmar Golf
  • , Kirill Veselkov
  • , Robert Brown
  • , Sadaf Ghaem-Maghami
  • , Zoltan Takats
  • Imperial College London

Research output: Contribution to journalArticlepeer-review

67 Scopus citations

Abstract

Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-MSI) generates spatially resolved metabolic profiles of tissues and supports an objective investigation of tumour biology. In this study, various ovarian tissue types were analysed by DESI-MSI and co-registered with their corresponding haematoxylin and eosin (H and E) stained images. The mass spectral data reveal tissue type-dependent lipid profiles which are consistent across the n = 110 samples (n = 107 patients) used in this study. Multivariate statistical methods were used to classify samples and identify molecular features discriminating between tissue types. Three main groups of samples (epithelial ovarian carcinoma, borderline ovarian tumours, normal ovarian stroma) were compared as were the carcinoma histotypes (serous, endometrioid, clear cell). Classification rates >84% were achieved for all analyses, and variables differing statistically between groups were determined and putatively identified. The changes noted in various lipid types help to provide a context in terms of tumour biochemistry. The classification of unseen samples demonstrates the capability of DESI-MSI to characterise ovarian samples and to overcome existing limitations in classical histopathology.

Original languageEnglish
Article number39219
JournalScientific Reports
Volume6
DOIs
StatePublished - 15 Dec 2016
Externally publishedYes

UN SDGs

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

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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