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Recruiting NCT06249308

Liquid Biopsy-based Early Detection of Ovarian Cancer: a Proof-of-concept Study ( PROFOUND-OC )

Observational Ovarian Cancer

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Blood collection.
Who it may be relevant to
Registry conditions: Ovarian Cancer. Basic parameters: 40 years — 75 years · Female.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Official title

A Multi-center, Perspective, Observational Case-control Study to Develop and Validate an Ovarian Cancer Early Detection Model Based on Peripheral Blood Multi-omic Analysis and Machine Learning

Overview

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

  • Other Blood collection
    Blood sample will be collected

Primary outcome measures

  • The performance of cfDNA methylation-based model for discriminating ovarian cancer versus non-cancer. [Time frame: 12 months]
Secondary outcome measures (2)
  • The performance of model using multi-omics data for discriminating ovarian cancer versus non-cancer [Time frame: 12 months]
  • The performance of pre-defined model in clinical sub-groups of interest [Time frame: 12 months]

Eligibility criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Case-control

Study locations

China · 3 centers
  • Sun Yat-sen Memorial Hospital — Guangzhou
  • Liaoning Cancer Hospital & Institute — Shenyang
  • Fudan University Shanghai Cancer Center — Shanghai

Identifiers

NCT: NCT06249308 · PROFOUND -OC

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗