Cancer
Conditions
Keywords
cell-free DNA, methylation, cancer early detection
Brief summary
This study is a multi-center, case-control study aiming at developing and blinded testing machine learning-based multiple cancers early detection model by prospectively collecting blood samples from newly diagnosed cancer patients and individuals without confirmed cancer diagnosis.
Detailed description
Blood samples from newly diagnosed cancer patients and individuals without confirmed cancer diagnosis will be prospectively collected to identify cancer-specific circulating signals through integrative multi-omic analysis. Based on the comprehensive molecular profiling, a machine learning-driven model will be trained and blinded validated independent through a two-stage approach in clinically annotated individuals. Approximately 10327 cancer patients will be enrolled in this study and early-stage cancer patients will be enriched to improve the model sensitivity on distinguishing cancers with favorable prognosis. Approximately 6339 age and sex matched controls will be included in model development, which are volunteers without a cancer diagnosis after routine cancer screening tests.
Interventions
None listed
Sponsors
Shanghai Weihe Medical Laboratory Co., Ltd.
Peking University People's Hospital
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
40 Years to 74 Years
Healthy volunteers
Yes
Inclusion criteria
for Case Arm Participants: * 40-74 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 * Severe acute infection (e.g. severe or critical COVID-19, sepsis, etc.) within 14 days prior to screen * 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 glucocorticoid, azacitidine, decitabine, procainamide, hydrazine, arsenic trioxide * Unsuitable for this trial determined by the researchers Inclusion Criteria for Control Arm Participants: * 40-74 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 | Description |
|---|---|---|
| The performance of cfDNA methylation-based multiple cancers early detection model in case-control study | 12 months | The sensitivity, specificity and tissue origin accuracy of cfDNA methylation-based multiple cancers early detection model in detecting cancer or non-cancer at 95% confidence interval. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The performance of cfDNA methylation-based multiple cancers early detection model in early stage cancer cases | 12 months | The sensitivity and tissue origin accuracy of cfDNA methylation-based multiple cancers early detection model in detecting stage I to II cancer at 95% confidence interval. |
| The performance of multi-omic-based multiple cancers early detection model in case-control study | 12 months | The sensitivity, specificity and tissue origin accuracy of multi-omic-based multiple cancers early detection model in detecting cancer or non-cancer at 95% confidence interval. |
| The performance of different multi-cancer early detection models in different subgroups | 12 months | The sensitivity and specificity of cfDNA methylation-based or multi-omic-based multiple cancers early detection model in different subgroups of the population (such as age, gender, cancer pathological classification, and clinical stage) at 95% confidence interval. |
Countries
China
Contacts
CONTACTYang Wang
PRINCIPAL_INVESTIGATORJun Wang
Peking University People's Hospital
STUDY_DIRECTORXiaohui Wu
Shanghai Weihe Medical Laboratory Co., Ltd.
Outcome results
None listed