DOvEEgene/WISE Genomics: Diagnosing Ovarian and Endometrial Cancer Early Using Genomics

Recruiting

• Conditions: Ovarian Neoplasms; Endometrial Neoplasms; Endometrial Cancer; Reduced Mortality; Safety; Reduced Morbidity; Early Diagnosis; Ovarian Cancer; Screening

• Registration Number: NCT02288676
• Lead Sponsor: McGill University

Brief Summary

This study aims to develop and validate a test for detecting ovarian and endometrial cancers early. It relies on detecting somatic mutations that are associated with these cancers from a uterine pap test. A saliva sample is also collected that acts as an internal control and has the ability to detect deleterious germline mutations associated with common hereditary cancers (such as breast, ovarian, endometrial, colon, and pancreatic cancers). A machine learning classifier is then used to discriminate between cancer and benign disease.

Detailed Description

For women in high-income countries, ovarian/fallopian tube and endometrial cancers are within the top four cancers in terms of incidence, death and healthcare expenditure. The deaths associated with these cancers are largely caused by Stage III/IV disease, for which cure rates have not changed in three decades, despite escalating costs of treatment. Attempts at early detection have been ineffective in reducing mortality, because the high-grade subtypes, which account for the majority of deaths, metastasize while the primary cancer is still small, has not caused symptoms, and is undetectable by imaging or blood tumour markers.

In recent years, the recognition that somatic mutations are early steps in carcinogenesis has led to a shift from tests such as imaging and non-specific blood tumour markers to technology that detects cancer-associated mutations in cervical, uterine, or blood samples. Several DNA-tagging technologies have been shown to be capable of identifying small amount of cancer DNA among thousands of normal cells, the proverbial needle in a haystack.

This investigation aims to develop and validate a high-sensitivity capture using a panel of genes involved in ovarian and endometrial carcinogenesis, low-pass whole genome sequencing, coupled with a machine-learning derived classifier for discriminating cancer from benign gynecologic disease prevalent in peri/post-menopausal women.

Study Timeline

• Study Start: 2014-01
• Study First Submitted: 2014-10-28
• Study First Submitted QC: 2014-11-07
• Study First Posted: 2014-11-11
• Status Verified: 2025-06
• Last Update Submitted: 2025-06-13
• Last Update Posted: 2025-06-18
• Primary Completion: 2025-10
• Study Completion: 2026-10

Recruitment & Eligibility

• Status: RECRUITING
• Sex: Female
• Target Recruitment: 1200

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Detection of cancer-related mutations	3 years	Diagnosis ovarian and endometrial cancers by detection of cancer-related mutation taken by brush sample of uterus with high sensitivity and specificity.

Secondary Outcome Measures

Name	Time	Method
Risks associated with the DOvEEgene test	3 years	Evaluate all risks associated with the DOvEEgene test including complications from the sampling technique as well as unnecessarily interventions resulting from false positive tests.
Patient related outcomes including pain and acceptability	3 years	Pain scores reported by participants on numeric pain and discomfort scale (NPS). Patients' attitude towards the test including willingness to have it done on an annual basis will be evaluated.

Trial Locations

Locations (1)

Royal Victoria Hospital (Glen Site); 🇨🇦 Montreal, Quebec, Canada