Endometrial Cancer
Conditions
Keywords
metabolomics, proteomics, early diagnosis, prognosis
Brief summary
Endometrial cancer (EC) is the most frequent gynecological malignancy but there is currently lack of both non-invasive diagnostic tools and novel markers to stratify patients based on their risk of future recurrence. Patient care could be improved by advances in these two aspects. In the present study, the investigators aim to identify diagnostic serum 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.
Detailed description
Rationale: 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 this project the investigators are addressing this lack of non-invasive diagnostic and prognostic biomarkers of EC. Objective: the investigators aim to identify diagnostic serum metabolite and protein biomarker signatures for early detection of cancer in asymptomatic high-risk population and (secondary objective) prognostic biomarkers for selection of patients with poor prognosis.
Interventions
OTHERBlood sampling
Blood sampling (10 mL) prior to standard care (e.g. surgery, medical treatment)
Sponsors
University of Ljubljana, Faculty of Medicine
Medical University of Lublin
Andrea Romano
Study design
Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Cases: * endometrioid, serous, clear cell or mucinous endometrial cancer * dedifferentiated endometrial cancer * high grade or low grade endometrial cancer Inclusion Criteria Controls: * benign uterine diseases, e.g. myoma uteri, prolapsed uterus * prophylactic hysterectomy for Lynch syndrome
Exclusion criteria
Cases: * atypical hyperplasia * other types of cancer * sarcoma uteri * previous diagnosis of endometrial cancer
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Creation of a diagnostic algorithm | 2020-2021 | Blood metabolome and proteome will be analysed and bioinformatics/biostatistical analysis will be used to derive diagnostic algorithms based on blood metabolites, proteins and clinical data. Algorithms in the biomarker discovery study will be developed by comparing EC and patients with benign uterine pathologies. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Creation of a prognostic algorithm | 2021 | Blood metabolome and proteome will be analysed and bioinformatics/biostatistical analysis will be used to derive prognostic algorithms based on blood metabolites, proteins, clinical data at baseline and follow up information. Algorithms in the biomarker discovery study will be developed by comparing EC patients with low risk and high risk for cancer progression and recurrence. |
Countries
Netherlands, Poland, Slovenia
Contacts
Primary ContactAndrea Romano, Dr.
Backup ContactRoy Kruitwagen, Prof. Dr.
Outcome results
None listed