Affiliations 

  • 1 Adelaide Proteomics Centre, School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Austraila
  • 2 Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
  • 3 Faculty of Health and Medicine, University of New South Wales, Callaghan, New South Wales, Australia
  • 4 School of Women's and Infants' Health, University of Western Australia, Crawley, Western Australia, Austraila
  • 5 Department of Gynaecological Oncology, Chris O'Brien Lifehouse, Camperdown, New South Wales, Australia
  • 6 Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Pulau Pinang, Malaysia
  • 7 Discipline of Obstetrics and Gynaecology, Research Centre for Reproductive Health, School of Medicine, Robinson Research Institute, University of Adelaide, Adelaide, South Australia, Austraila
Proteomics, 2016 06;16(11-12):1793-801.
PMID: 27061135 DOI: 10.1002/pmic.201500455

Abstract

Metastasis is a crucial step of malignant progression and is the primary cause of death from endometrial cancer. However, clinicians presently face the challenge that conventional surgical-pathological variables, such as tumour size, depth of myometrial invasion, histological grade, lymphovascular space invasion or radiological imaging are unable to predict with accuracy if the primary tumour has metastasized. In the current retrospective study, we have used primary tumour samples of endometrial cancer patients diagnosed with (n = 16) and without (n = 27) lymph node metastasis to identify potential discriminators. Using peptide matrix assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI), we have identified m/z values which can classify 88% of all tumours correctly. The top discriminative m/z values were identified using a combination of in situ sequencing and LC-MS/MS from digested tumour samples. Two of the proteins identified, plectin and α-Actin-2, were used for validation studies using LC-MS/MS data independent analysis (DIA) and immunohistochemistry. In summary, MALDI-MSI has the potential to identify discriminators of metastasis using primary tumour samples.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.