Neoplasms

Digestive System Diseases

All Conditions

Rare Diseases

Stomach Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Stomach Diseases

Stomach Cancer

Cell-free DNA collected from plasma samples of 200 patients with stage I-II gastric cancer will undergo whole-genome sequencing

Cell-free DNA collected from plasma samples of 100 non-cancer individuals will serve as controls

Pengfei Yu, MD, PhD

Pengfei Yu, MD，PhD

Xiangdong Cheng, MD，PhD

Yu Pengfei, MD，PhD

Xiangdong Cheng, MD, PhD

The purpose of this study is to enable non-invasive early detection of gastric cancer in high-risk populations through the establishment of a multimodal machine learning model using plasma cell-free DNA fragmentomics. Plasma cell-free DNA from early stage gastric cancer patients and healthy individuals will be subjected to whole-genome sequencing. Features, such as cell-free DNA fragmentation, copy number variations and microbiome, will be assessed to generate this model.

Improvement in the specificity of early cancer detection reduces financial and mental burdens from unnecessary screenings. Advances in liquid biopsy approaches have expanded the clinical scope of cell-free DNA analysis in cancer early detection, by moving away from cell-free DNA methylome toward an integrative approach that enables the simultaneous assessment of multimodal cell-free DNA features. Integration of liquid biopsy-based cancer early detection into the clinic requires optimization of detection techniques, large-scale studies and prospective clinical validation. In the early detection of gastric cancer, the top research priorities are to identify relevant target features and to improve the sensitivity and specificity of detection. This large-scale early detection study will randomly enroll 200 stage I/II pathologically diagnosed gastric patients and 100 age- and sex-matched healthy individuals upon providing written informed consent. Plasma samples will be collected and extracted cell-free DNA will be subjected to whole genome sequencing. We aimed to incorporate genome-wide copy number variations, cell-free DNA fragmentomics, and microbiome features into the development of a multimodal biomarker-based prediction model.

Early Detection of Gastric Cancer Using Plasma Cell-free DNA Fragmentomics

Early Detection of Stage I-II Gastric Cancer Using Plasma Cell-free DNA Fragmentomics

The area under curve of the model for the ultrasensitive early detection of stage I/II gastric cancer would be evaluate

The sensitivity of the model for the ultrasensitive early detection of stage I/II gastric cancer would be evaluate

The specificity of the model for the ultrasensitive early detection of stage I/II gastric cancer would be evaluate

The area under curve of the model at sequencing depth downsampling for the ultrasensitive early detection of stage I/II gastric cancer would be evaluate

Nanjing Geneseeq Technology Inc.

Zhejiang Cancer Hospital

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Gland and Hormone Related Diseases

Ovarian Neoplasms

Carcinoma, Ovarian Epithelial

Carcinoma

Endocrine Gland Neoplasms

Ovarian Diseases

Adnexal Diseases

Genital Diseases, Female

Genital Diseases

Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

Genital Neoplasms, Female

Urogenital Neoplasms

Endocrine System Diseases

Gonadal Disorders

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Ovarian Cancer

Ovarian Epithelial Cancer

54 patients with stage I-II ovarian cancer

54 patients with stage III-IV ovarian cancer

Bingzhong Zhang, MD

Rao Qunxian

The purpose of this study is to enable non-invasive early detection of ovarian cancer in high-risk populations through the establishment of a multimodal machine learning model using plasma cell-free DNA fragmentomics. Plasma cell-free DNA from early stage ovarian cancer patients and healthy individuals will be subjected to whole-genome sequencing. Five diferent feature types, including Fragment Size Coverage (FSC), Fragment Size Distribution (FSD), EnD Motif (EDM), BreakPoint Motif (BPM), and Copy Number Variation (CNV) will be assessed to generate this model.

At present, there are many problems in the detection of ovarian cancer in China, such as a large number of high-risk population, lack of effective screening and management methods, and the value of vaginal ultrasound and CA125 in early screening of ovarian cancer is limited. There is an urgent need for a more sensitive screening method for ovarian cancer in clinical practice. In a more advanced window period, a group with higher risk of disease will be screened to enter clinical diagnosis, so as to achieve early prevention and treatment of early patients and win valuable opportunities for effective prevention and treatment of ovarian cancer. Although there are some studies on early screening data of ovarian cancer at home and abroad, most of them use single detection dimension or somatic mutation combined with methylation analysis. At present, the optimization of detection technology, sample accumulation or validation of prospective clinical trials are still under way. In short, the space for early screening of ovarian cancer is vast, and liquid biopsy is non-invasive, convenient and easy to accept. It is an important technical means for early screening research of ovarian cancer, and has great potential to improve the performance of early screening of ovarian cancer. In order to further verify the application value of cfDNA-based fragmentomics in early screening of ovarian cancer and better screen the high-risk population of ovarian cancer in China, this study intends to analyze the characteristics of five cfDNA fragments based on low-depth whole-genome sequencing technology (WGS), and integrate artificial intelligence machine learning technology to establish a prediction model for early screening of ovarian cancer based on cfDNA.

Early Detection of Ovarian Cancer Using Plasma Cell-free DNA Fragmentomics (Prospective Study)

A Prospective Study of Early Detection of Ovarian Cancer Using Plasma Cell-free DNA Fragmentomics

The area under curve of the model for the ultrasensitive early detection of ovarian cancer would be evaluate

The sensitivity of the model for the ultrasensitive early detection of ovarian cancer would be evaluate

The specificity of the model for the ultrasensitive early detection of ovarian cancer would be evaluate

Study title	NCT ID	Conditions	Eligibility	Organization
Early Detection of Gastric Cancer Using Plasma Cell-free DNA Fragmentomics	NCT05269056	Early Gastric C...	18 Years - 75 Years	Zhejiang Cancer Hospital
Early Detection of Ovarian Cancer Using Plasma Cell-free DNA Fragmentomics (Prospective Study)	NCT05693987	Ovarian Cancer	18 Years -	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Early Detection of Ovarian Cancer Using Plasma Cell-free DNA Fragmentomics (Prospective Study)	NCT05693987	Ovarian Cancer	18 Years -	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Spots Global Cancer Trial Database for fragmentomic assay