Early Diagnosis, Endometrial Cancer, Endometrial Neoplasms, Ovarian Cancer, Ovarian Neoplasms, Reduced Morbidity, Reduced Mortality, Safety, Screening
Conditions
Keywords
genomics, somatic mutations, genetic mutations, high-grade serous cancer, ovarian cancer, endometrial cancer, DNA tagging
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.
Interventions
None listed
Sponsors
McGill University Health Centre/Research Institute of the McGill University Health Centre
McGill University
Study design
Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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. |
| 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. |
Countries
Canada
Contacts
Primary ContactDr. Lucy Gilbert, MD,MSc,FRCOG
Backup ContactDr. Claudia Martins, PhD
Outcome results
None listed