Minimally and Non-invasive Methods for Early Detection and Progression of Endometrial Cancer
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Endometrial Cancer
- Sponsor
- Andrea Romano
- Enrollment
- 400
- Locations
- 5
- Primary Endpoint
- Creation of a diagnostic algorithm
- Last Updated
- 7 years ago
Overview
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.
Investigators
Andrea Romano
PhD. Assistant Professor
Academisch Ziekenhuis Maastricht
Eligibility Criteria
Inclusion Criteria
- Not provided
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
Creation of a diagnostic algorithm
Time Frame: 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 Outcomes
- Creation of a prognostic algorithm(2021)