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

McGill University

Status: Enrolling

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

Study type: Observational

Funder types: Other

Identifiers

NCT02288676

A08-M79-13B

Details and patient eligibility

About

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.

Full 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.

Enrollment: 1,200 estimated patients

Sex: Female

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

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.

Trial design

1,200 participants in 2 patient groups

Case Group

Description:

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

Description:

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.