The Effectiveness of Liquid Biopsy in Differential Diagnosis and Early Screening of Epithelial Ovarian Cancer

The Effectiveness of Liquid Biopsy in Differential Diagnosis of Benign and Malignant Ovarian Tumors and Early Screening of Epithelial Ovarian Cancer, and the Exploration of Invasive Mechanisms of Epithelial Ovarian Cancer

At present, there is a lack of effective screening methods. It is urgent to explore new non-invasive detection methods for early diagnosis of epithelial ovarian cancer and non-invasive differentiation methods for benign and malignant ovarian tumors.

Liquid biopsy technology has great potential for early screening of tumors. The fragmentation patterns of cfDNA fragments in plasma and the uneven coverage of the genome can indirectly reflect the state of gene expression regulation in vivo. Its characteristics mainly include copy number variation (CNV), Nucleosome footprint, fragment length and motif.

The number of proteins in a proteome can sometimes exceed the number of genomes. It includes "structural Proteomics" and "functional Proteomics". At present, research has explored the use of urinary protein biomarkers for early diagnosis of gastric cancer. "Deep Visual Proteomics (DVP)" reveals the mechanism driving tumor evolution and new therapeutic targets for tumors.

Using the currently mature low depth WGS sequencing technology, this study aims to explore its clinical application in the differentiation and early screening of epithelial ovarian cancer, as well as monitoring the course of epithelial ovarian cancer, including the detection of minimal residual lesions (MRD) and monitoring of recurrence (MOR). This study also explores the role of urine proteomics in the differentiation of benign and malignant ovarian tumors, early screening and invasiveness of epithelial ovarian cancer, and monitoring the course of epithelial ovarian cancer.

Study Overview

• Status: Not yet recruiting
• Conditions: Ovarian Cancer; Diagnosis; Differentiating
• Intervention / Treatment: Diagnostic test: Fragmentomics

Detailed Description

At present, there is a lack of effective screening methods. Numerous studies and practices have shown that tumor screening and early diagnosis and treatment can effectively prolong the overall survival period of cancer patients and reduce the economic burden of the disease. The traditional early screening methods for tumors in clinical practice, including imaging screening, endoscopic screening, and tumor marker screening, generally have defects such as strong invasiveness, significant discomfort during the screening process, low accuracy (false negative, false positive), and poor compliance. Therefore, it is urgent to explore new non-invasive detection methods for early diagnosis of epithelial ovarian cancer and non-invasive differentiation methods for benign and malignant ovarian tumors.

Liquid biopsy technology, as a non-invasive new detection technology, has great potential for early screening of tumors. CfDNA is an important marker for liquid biopsy and has been widely used in non-invasive prenatal examinations and cancer liquid biopsy research. The fragmentation patterns of cfDNA fragments in plasma and the uneven coverage of the genome can indirectly reflect the state of gene expression regulation in vivo. Its characteristics mainly include copy number variation (CNV), Nucleosome footprint, fragment length and motif.

Proteome changes with different tissue and even environmental states. During transcription, a gene can be spliced in multiple mRNA forms, and a proteome is not a direct product of a genome. The number of proteins in a proteome can sometimes exceed the number of genomes. It includes "structural Proteomics" and "functional Proteomics". The former is mainly the study of protein expression models, including protein amino acid sequence, analysis and spatial structure analysis, type analysis and quantity determination; The latter mainly focuses on the study of protein functional patterns, including protein function and protein-protein interactions. At present, research has explored the use of urinary protein biomarkers for early diagnosis of gastric cancer. "Deep Visual Proteomics (DVP)" reveals the mechanism driving tumor evolution and new therapeutic targets for tumors.

There is ample evidence to support the diagnostic value of fragment omics research in tumors. Using the currently mature low depth WGS sequencing technology, this study aims to explore its clinical application in the differentiation and early screening of epithelial ovarian cancer, as well as monitoring the course of epithelial ovarian cancer, including the detection of minimal residual lesions (MRD) and monitoring of recurrence (MOR). In addition, there is currently no research on the use of urine proteomics in the differentiation and early screening of ovarian cancer. This study also explores the role of urine proteomics in the differentiation of benign and malignant ovarian tumors, early screening and invasiveness of epithelial ovarian cancer, and monitoring the course of epithelial ovarian cancer.

• Study Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

• Study Contact: Name: Lei Li, doctor; Phone Number: +86 13911988831; Email: lileigh@163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Probability Sample

Study Population

female aged over 18 years old, having mass in accessory area, receiving surgery

Description

Inclusion Criteria:

1. Age ≥ 18 years old, female;
2. Suspicious or considering mass in the accessory area, requiring surgical treatment and obtaining pathology of ovarian tissue;
3. Having pelvic imaging evaluation results, including ultrasound, MRI, CT, or PET/CT;
4. Serum CA125 and HE4 tests are tested before surgery, and ROMA evaluation results is obtained;
5. Volunteer to participate in this research and sign an informed consent form; (6) Good compliance and regular follow-up.

Exclusion Criteria:

1. Patients with confirmed ovarian cancer who have undergone surgery and obtained histopathology;
2. Patients who have received chemotherapy or pelvic radiation therapy within 6 months prior to sample collection;
3. Researchers have confirmed patients with recurrent ovarian cancer, or ovarian cancer patients who have received chemotherapy and/or surgical treatment;
4. Contraindications for surgical evaluation and inability to obtain ovarian pathology or surgical pathological information;
5. Samples that do not meet the requirements for collecting and storing assessment reagent samples; Or contaminated or suspected contaminated samples;
6. Samples with missing, incomplete, and untraceable clinical information of corresponding patients;
7. Pregnant and lactating women;
8. Patients who cannot cooperate with examinations, have poor compliance, and cannot follow up regularly.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
ovarian cancer group; patients confirmed with ovarian cancer	Diagnostic test: Fragmentomics; The fragmentation patterns of cfDNA fragments in plasma and the uneven coverage of the genome can indirectly reflect the state of gene expression regulation in vivo. Its characteristics mainly include copy number variation (CNV), Nucleosome footprint, fragment length and motif. Fragomics relies on WGS (Whole Genome Sequencing), and the target covers the whole Genomics level. Thus, the cfDNA Fragmentomics testing can help differentiation and early diagnosis.
ovarian cyst group; patients confirmed with benign ovarian csyt	Diagnostic test: Fragmentomics; The fragmentation patterns of cfDNA fragments in plasma and the uneven coverage of the genome can indirectly reflect the state of gene expression regulation in vivo. Its characteristics mainly include copy number variation (CNV), Nucleosome footprint, fragment length and motif. Fragomics relies on WGS (Whole Genome Sequencing), and the target covers the whole Genomics level. Thus, the cfDNA Fragmentomics testing can help differentiation and early diagnosis.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Copy number variation	Exploring the characteristics of cfDNA copy number variation in patients with epithelial ovarian cancer	3 years follow-up after enrollment or till the end of research
Nucleosome Footprint	Exploring the characteristics of cfDNA nucleosome Footprint in patients with epithelial ovarian cancer	3 years follow-up after enrollment or till the end of research
Fragment length	Exploring the characteristics of cfDNA fragment length in patients with epithelial ovarian cancer	3 years follow-up after enrollment or till the end of research
Motif	Exploring the characteristics of cfDNA motif in patients with epithelial ovarian cancer	3 years follow-up after enrollment or till the end of research

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Construction and validation a risk prediction model	constructing a risk prediction model based on fragmentomics characteristics and clinical data, for early screening and differential diagnosis of epithelial ovarian cancer, and for monitoring the course of epithelial ovarian cancer patients, including detection of minimal residual lesions (MRD) and monitoring of recurrence (MOR). The fragmentomics characteristics include copy number variation, nucleosome Footprint, fragment length and motif.	3 years follow-up after enrollment or till the end of research
urinary proteomics	Exploring the characteristics of urinary proteomics in patients with epithelial ovarian cancer, combined with clinical and pathological data of patients, for early screening and differential diagnosis of epithelial ovarian cancer, and for monitoring the course of epithelial ovarian cancer patients, including detection of minimal residual lesions (MRD) and monitoring of recurrence (MOR). Further exploration of the invasive mechanism of epithelial ovarian cancer using urinary proteomics.	3 years follow-up after enrollment or till the end of research

Collaborators and Investigators

• Sponsor: Peking Union Medical College Hospital
• Investigators: Principal Investigator: Lei Li, doctor, Peking Union Medical College Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): January 1, 2024
• Primary Completion (Estimated): January 1, 2029
• Study Completion (Estimated): January 1, 2032

Study Registration Dates

• First Submitted: June 12, 2023
• First Submitted That Met QC Criteria: June 26, 2023
• First Posted (Actual): July 5, 2023

Study Record Updates

• Last Update Posted (Actual): July 5, 2023
• Last Update Submitted That Met QC Criteria: June 26, 2023
• Last Verified: June 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Neoplasms by Histologic Type; Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Carcinoma; Neoplasms, Glandular and Epithelial; Genital Neoplasms, Female; Endocrine System Diseases; Ovarian Diseases; Adnexal Diseases; Gonadal Disorders; Endocrine Gland Neoplasms; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Diseases; Genital Diseases, Female; Disease; Ovarian Neoplasms; Carcinoma, Ovarian Epithelial
• Other Study ID Numbers: K4059

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No