NCT02288676

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

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,200 (estimated)
• Sponsor: McGill University · Academic / Other
• Sex: Female
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Conditions

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

Timeline

• Start date: 2014-01-01
• Primary completion: 2025-10-01
• Completion: 2026-10-01
• First posted: 2014-11-11
• Last updated: 2025-06-18

Locations

1 site across 1 country: Canada