Biomarkers for Diagnosis and Prognosis of Endometrial Carcinoma

• Conditions: Endometrial Cancer

• Registration Number: NCT03553589
• Lead Sponsor: Andrea Romano

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.

Study Timeline

• Study First Submitted: 2018-05-30
• Study First Submitted QC: 2018-05-30
• Study First Posted: 2018-06-12
• Study Start: 2018-10-01
• Status Verified: 2018-12
• Last Update Submitted: 2018-12-03
• Last Update Posted: 2018-12-05
• Primary Completion: 2021-06-01
• Study Completion: 2021-06-01

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: Female
• Target Recruitment: 400

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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 Outcome Measures

Name	Time	Method
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.

Trial Locations

Locations (5)

Maastricht University Medical Centre; 🇳🇱 Maastricht, Netherlands

Maxima Medical Centre; 🇳🇱 Veldhoven, Netherlands

Lublin Medical University; 🇵🇱 Lublin, Poland

Faculty of Medicine, University of Ljubljana; 🇸🇮 Ljubljana, Slovenia

University Medical Centre, Ljubljana; 🇸🇮 Ljubljana, Slovenia