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.
The prediction of lymph node metastasis using clinic-pathological data and molecular information from endometrial cancers lacks accuracy and is therefore currently not routinely used in patient management. Consequently, although only a small percentage of patients with endometrial cancers suffer from metastasis, the majority undergo radical surgery including removal of pelvic lymph nodes. Upon analysis of publically available data and published research, we compiled a list of 60 proteins having the potential to display differential abundance between primary endometrial cancers with versus those without lymph node metastasis. Using data dependent acquisition LC-ESI-MS/MS we were able to detect 23 of these proteins in endometrial cancers, and using data independent LC-ESI-MS/MS the differential abundance of five of those proteins was observed. The localization of the differentially expressed proteins, was visualized using peptide MALDI MSI in whole tissue sections as well as tissue microarrays of 43 patients. The proteins identified were further validated by immunohistochemistry. Our data indicate that annexin A2 protein level is upregulated, whereas annexin A1 and α actinin 4 expression are downregulated in tumours with lymph node metastasis compared to those without lymphatic spread. Moreover, our analysis confirmed the potential of these markers, to be included in a statistical model for prediction of lymph node metastasis. The predictive model using highly ranked m/z values identified by MALDI MSI showed significantly higher predictive accuracy than the model using immunohistochemistry data. In summary, using publicly available data and complementary proteomics approaches, we were able to improve the prediction model for lymph node metastasis in EC.