BioEndoCar

Endometrial cancer (EC) is the most frequent gynaecological malignancy in the developed world. Optimal treatment of EC depends on early diagnostics and pre-operative stratification to appropriately select the extent of surgery and to plan further therapeutic approach. Currently, invasive endometrial histology is the gold standard for diagnosis, as there are no valid non-invasive methods available, and patient stratification is based on histopathology and surgical findings. There is a great need for efficient and reliable screening test for asymptomatic women with high risk of EC including Lynch syndrome patients and tamoxifen treated patients. In addition, a prognostic test is needed to stratify pre-operatively EC patients with high risk of progression in need of radical surgery together with adjuvant chemo/ratio therapy from EC patients with good prognosis. In our project we are addressing this lack of non-invasive diagnostic and prognostic biomarkers of EC. We hypothesize that discrete panels of metabolites and proteins are associated with early EC and aggressive EC, where bioinformatics combined with statistical modelling allows development of diagnostic and prognostic models with high sensitivity and specificity. We thus aim to identify diagnostic metabolite and protein biomarker signatures for early detection of cancer in asymptomatic high-risk population and prognostic biomarkers for selection of patients with poor prognosis. So far there has been no published report on using blood metabolomics and proteomics to search for diagnostic and prognostic biomarkers of EC. To accomplish our aims we will employ non-targeted and targeted metabolomics and semi-targeted proteomics approaches. Our biomarker discovery study will include patients with EC and patients with benign uterine pathologies. EC patients will be further divided into groups with low risk and high risk for cancer progression and recurrence. Blood metabolome comprising over 850 metabolites will be analysed by UHPLC/MS, ESI/MS/MS. Blood proteome including 900 different cancer-related proteins will be analysed in parallel using high content antibody microarrays. These analytical approaches will be combined with bioinformatics/ biostatistical analysis to derive diagnostic and prognostic algorithms based on blood metabolites, proteins and clinical data. Diagnostic biomarkers would be instrumental for early diagnosis and development of screening test, while prognostic biomarkers would allow pre-surgical selection of EC patients with poor prognosis and would thus lead to improved treatment outcomes for the high-risk patients. These biomarkers would reduce overtreatment of EC patients with low risk for progression and would also lower concurrent unnecessary burden to these patients as well as treatment associated health-care costs