Bladder Cancer, Breast Cancer, Cervical Cancer, Cholangiocarcinoma, Colorectal Cancer, Endometrial Cancer, Esophageal Cancer, Gastric Cancer, Liver Cancer, Lung Cancer, Ovarian Cancer, Pancreatic Cancer, Prostate Cancer
Conditions
Brief summary
This research constitutes a multi-centric, case-control designed investigation aimed at developing and implementing a blinded validation of a machine learning-powered, multi-cancer early detection model. This is to be achieved through the prospective collection of blood specimens from newly diagnosed cancer patients and individuals devoid of a confirmed cancer diagnosis
Detailed description
Cancerous tissues, their adjacent non-cancerous tissues, along with white blood cells (WBCs) and normal tissue samples will be utilized to identify potential methylation candidate markers and investigate variations in methylation patterns among patients diagnosed with distinct cancer types. Building upon previous research and current study, a comprehensive methylation signature panel tailored specifically to cancer patients will be established. We will prospectively collect blood samples from newly diagnosed cancer patients and non-cancer individuals to analyze and identify specific cancer signals via the detection of cfDNA methylation patterns. Following a rigorous and comprehensive research framework, a machine learning-driven model will be developed and validated through blinded testing in an independent cohort. The study aims to enroll approximately 2,650 cancer patients, with a focus on including early-stage cases to enhance the model's sensitivity in detecting cancers with favorable prognoses. Furthermore, around 2,400 control subjects, matched with cancer patients by age and gender and screened negative for cancer through routine tests, will participate as healthy or benign-condition volunteers in model development. Lastly, samples from an additional 300 patients with other tumors will be gathered to conduct interference testing, ensuring the robustness of the model's performance.
Interventions
None listed
Sponsors
Fudan University
Hubei Cancer Hospital, Huazhong University of Science and Technology
China-Japan Friendship Hospital
Shanxi Provincial Cancer Hospital
Xuhui Central Hospital, Fudan University
Shanghai Electric Power Hospital
East Hospital, Tongji University
The First Affiliated Hospital of Xi' an Jiaotong University
GaoZhou People's Hosipital
Anhui Provincial Hospital
Beijing Friendship Hospital
Singlera Genomics Inc.
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
40 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
for Case Arm Participants: * 40-75 years old * Clinically and/or pathologically diagnosed 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
for Case Arm Participants: * Pregnancy or lactating women * Known prior or current diagnosis of other types of malignancies comorbidities * Have participated in an interventional clinical trial within the past 30 days and have taken the experimental drug; * Recipients of organ transplant or prior bone marrow transplant or stem cell transplant * Recipients of blood transfusion within 30 days prior to screen * Recipients of therapy in past 14 days prior to screen, including oral or IV antibiotics, glucocorticoid, azacitidine, decitabine, procainamide, hydrazine, arsenic trioxide * Failure to collect blood on time according to plan * The blood sample does not meet the requirements Inclusion Criteria for Control Arm Participants: * 40-75 years old * Without confirmed cancer diagnosis * Able to provide a written informed consent and willing to comply with all part of the protocol procedures
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The AUC, sensitivity, specificity and tissue origin accuracy of the multi-cancer early detection model in detecting cancer or non-cancer | 12 months |
Secondary
| Measure | Time frame |
|---|---|
| The performance of the multi-cancer early detection model in early stage cancer and precancerous lesion cases | 12 months |
| The performance of the multi-cancer early detection model in different subgroups (such as age, gender, cancer pathological classification, and clinical stage) | 12 months |
Countries
China
Contacts
Primary ContactZhixi Su, PHD
Backup ContactRui Liu
Outcome results
None listed