Cancer
Conditions
Keywords
Muti-omics, Pan-cancer screening, Early detection
Brief summary
The integrative study by Fudan and Singlera for cancer early detection(The FuSion Program ) will evaluate sensitivity,specificity and positive/negative predictive value of the screening model jointly developed by FuDan University and Singlera in a 2-year follow-up corhort including 10,000 persons in routine annual physicals from dozens of hospitals. The multi-omics model for pan-cancer screening will be developed in a 3-year follow-up corhort including 50,000 natural persons in community containing genetic information of tumor families, assessment of epidemiological risk factors, tumor markers, proteomics, genomics and DNA methylation. After optimizing, the ability of this model will be validated in the Taizhou corhort in reality.
Interventions
None listed
Sponsors
Fudan University
Singlera Genomics Inc.
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
40 Years to 75 Years
Healthy volunteers
Yes
Inclusion criteria
1. Take physical examinations in our research centers and have no cancer history; 2. Population Health tracking Survey - simplified version of the questionnaire must be filled according to the research program and an annual physical examination can be received as follow-up ; 3. Timely feed back the information related to tumor diagnosis in other hospitals to the investigator during the program; 4. Have no birth plan for the last 3 years; 5. Fully understand the study and voluntarily sign the informed consent.
Exclusion criteria
1. Have been diagnosed with esophageal cancer, gastric cancer, colorectal cancer, liver cancer, lung cancer, pancreatic cancer, breast cancer (including non-primary, such as recurrence, metastasis or other complications) and other malignant tumors; 2. Received blood transfusion, transplantation and other major operations within 3 months; 3. Participated in other interventional clinical researchs within 3 months; 4. Pregnant or lactating women; 5. Patients with autoimmune diseases, genetic diseases, mental diseases/disabilities and other diseases considered unsuitable for the study by the investigator; 6. Due to poor compliance, the researcher judged that the study could not be completed.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| To develop a multi-omics model for pan-cancer screening integrating the markers of ctDNA mutation, DNA fragmentation and methylation et al. | assessed up to 36 months | To construct a multi-dimensional ensembled stacked machine learning approach, employing several different base models on ctDNA mutation, DNA fragmentation and mehylation, to provide an effective model for cancer early detection. |
| To evaluate sensitivity,specificity,positive/negative predictive value of the screening model in participants taking routine annual physicals | assessed up to 24 months | Sensitivity: the ability of a test to correctly identify patients with a disease. Specificity: the ability of a test to correctly identify people without the disease. Positive predictive value refers to the probability of the person having the disease when the test is positive. Negative predictive value refers to the probability of the person not having the disease when the test is negative. |
| To validate model's efficacy and clinical value in the diagnosis of cancers in Taizhou cohort. | assessed up to 12 months | Cancer early detection could increase detection of cancer at early stages, when survival outcomes are better and treatment costs are lower. we will explore whether this model with high specificity could potentially improve long-term health outcomes and reduce cancer treatment costs. |
Countries
China
Contacts
Primary ContactWen Zou, Ph.D
Backup ContactRui Liu, Ph.D
Outcome results
None listed