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

June 13, 2025 updated by: Dr. Lucy Gilbert, McGill University
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.

Study Overview

Status

Recruiting

Conditions

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 Type

Observational

Enrollment (Estimated)

1200

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • Canada
    • Quebec
      • Montreal, Quebec, Canada, H4A 3J1
        • Recruiting
        • Royal Victoria Hospital (Glen Site)
        • Contact:
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

The participating hospitals run multiple weekly routine gynecology and gynecologic oncology clinics. The cases include women scheduled to undergo surgery for tumor removal, for either proven or suspected upper genital tract cancer. The controls include women scheduled to have a hysterectomy, bilateral salpingectomy (removal of the fallopian tubes) with/without bilateral oophorectomy (removal of the ovaries) to treat benign conditions.

Description

Case Inclusion:

  • Subjects should have suspected or confirmed cancer of the upper genital tract.
  • Participant will undergo surgery for tumour removal.

Control inclusion:

• Subjects should be scheduled to have a hysterectomy, bilateral salpingectomy, with or without bilateral oophorectomy, for presumed benign disease.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Case Group
Participants must have suspected or confirmed upper genital tract cancer (uterine, tubal and ovarian) and must be scheduled to undergo surgery for tumor removal.
Control Group
Participants must not be under investigation for any pre-cancerous or cancerous lesions of the genital tract, and must be scheduled for a hysterectomy, bilateral salpingectomy with/without bilateral oopherectomy for presumed benign condition.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Detection of cancer-related mutations
Time Frame: 3 years
Diagnosis ovarian and endometrial cancers by detection of cancer-related mutation taken by brush sample of uterus with high sensitivity and specificity.
3 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Patient related outcomes including pain and acceptability
Time Frame: 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.
3 years
Risks associated with the DOvEEgene test
Time Frame: 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.
3 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

McGill University

Collaborators

McGill University Health Centre/Research Institute of the McGill University Health Centre

Investigators

  • Study Director: Dr Ioannis Ragoussis, PhD, McGill Genome Center

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2014

Primary Completion (Estimated)

October 1, 2025

Study Completion (Estimated)

October 1, 2026

Study Registration Dates

First Submitted

October 28, 2014

First Submitted That Met QC Criteria

November 7, 2014

First Posted (Estimated)

November 11, 2014

Study Record Updates

Last Update Posted (Actual)

June 18, 2025

Last Update Submitted That Met QC Criteria

June 13, 2025

Last Verified

June 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • A08-M79-13B

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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