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.
Subjects should have suspected or confirmed cancer of the upper genital tract.
Participant will undergo surgery for tumour removal.
None
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.
Case-control
Cross-sectional
Not listed
Detection of cancer-related mutations
Diagnosis ovarian and endometrial cancers by detection of cancer-related mutation taken by brush sample of uterus with high sensitivity and specificity.
Patient related outcomes including pain and acceptability
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.
Risks associated with the DOvEEgene test
Evaluate all risks associated with the DOvEEgene test including complications from the sampling technique as well as unnecessarily interventions resulting from false positive tests.
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McGill University
Lead sponsor
McGill University Health Centre/Research Institute of the McGill University Health Centre
Collaborator
