Ovarian Cancer
Conditions
Brief summary
This study is a multi-center, observational study aiming at developing a machine learning-based early detection model using prospectively collected liquid biopsy samples from newly diagnosed ovarian cancer.
Detailed description
Peripheral blood samples from ovarian cancer (OC) patients will be prospectively collected to identify cancer-specific circulating signals by analyzing cell free DNA. Based on the comprehensive molecular profiling, a machine learning-driven noninvasive test will be trained and validated through a two-stage approach in clinically annotated individuals. Approximately 168 stage I-II OC patients will be enrolled in this study. Age-matched female controls included in model development were recruited in another study, which are volunteers without a cancer diagnosis after routine medical screening.
Interventions
OTHERBlood collection
Blood sample will be collected
Sponsors
Shanghai Weihe Medical Laboratory Co., Ltd.
Fudan University
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
40 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
* 40-75 years old * Clinically and/or pathologically diagnosed ovarian cancer * No prior or undergoing any systemic or local antitumor therapy, including but not limited to surgical resection, radiochemotherapy, endocrinotherapy, targeted therapy, immunotherapy, interventional therapy, etc. * Able to provide a written informed consent and willing to comply with all part of the protocol procedures
Exclusion criteria
* Pregnancy or lactating women * Known prior or current diagnosis of other types of malignancies comorbidities * Severe acute infection (e.g. severe or critical COVID-19, sepsis, etc.) or febrile illness (body temperature of ≥ 38.0 °C) within 14 days prior to blood draw * Recipients of organ transplant or prior bone marrow transplant or stem cell transplant * Recipients of blood transfusion within 30 days prior to study blood draw * Recipients of therapy in past 14 days prior to blood draw, including oral or IV antibiotics, glucocorticoid, azacitidine, decitabine, procainamine, hydrazine, arsenic trioxide * Other conditions that the investigators considered are not suitable for the enrollment
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The performance of cfDNA methylation-based model for discriminating ovarian cancer versus non-cancer. | 12 months | Sensitivities of cfDNA methylation-based model in detecting OC at specificity of 99% and 95%, respectively. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The performance of model using multi-omics data for discriminating ovarian cancer versus non-cancer | 12 months | Sensitivities of multi-omics model which combines methylation signature and fragmentomic features in detecting OC at specificity of 99% and 95%, respectively. |
| The performance of pre-defined model in clinical sub-groups of interest | 12 months | Sensitivity of pre-defined model in different pathological subtypes or different age groups or tumor marker-negative cases. |
Countries
China
Contacts
Primary ContactHao Wen, M.D., Ph.D.
Outcome results
None listed