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This multi-center study aims to develop a precise predictive model for colorectal cancer (CRC) recurrence and metastasis based on multi-omics analysis of portal vein blood. Despite advances in surgical treatments, approximately 40% of CRC patients experience tumor recurrence or metastasis post-surgery, with 80-90% of metastases being unresectable. The study will include stage I-IV CRC patients and will be conducted in two phases: a nested case-control study and a bidirectional cohort study. Comprehensive multi-omics sequencing will be performed on samples from primary tumors, adjacent tissues, normal intestinal tissues, portal vein blood, and peripheral blood. The goal is to identify specific biomarkers in the portal vein and peripheral blood associated with CRC recurrence and metastasis, and to compare the predictive accuracy of models based on these biomarkers. The ultimate objective is to provide a more effective method for early prediction and intervention of CRC recurrence and metastasis, thereby improving patient outcomes.
Project Information:
Project Title: A predictive model for recurrence of colorectal cancer based on multi-omics of portal vein blood: a multi-center study Project Duration: January 2020 to December 2026 Lead Institution: Peking University Shougang Hospital Principal Investigator: Gu Jin Contact: Hong Haopeng, 18059211195@163.com
Full description
Study Title A predictive model for recurrence of colorectal cancer based on multi-omics of portal vein blood: a multi-center study
Study Overview This multicenter study, led by Peking University Shougang Hospital, aims to develop a predictive model for the recurrence and metastasis of colorectal cancer (CRC) through multi-omics analysis of portal vein blood. The study collaborates with several prominent institutions including Peking University First Hospital, Chinese PLA General Hospital First Medical Center, Peking University Third Hospital, and Beijing Tiantan Hospital. The objective is to identify specific biomarkers indicative of CRC recurrence and metastasis, integrating these with clinical and pathological data to create highly accurate predictive models.
Background and Rationale Colorectal cancer poses a significant health challenge in China, with high incidence and mortality rates. Despite advancements in surgical and adjuvant therapies, approximately 40% of patients undergoing radical surgery experience tumor recurrence or metachronous metastasis, with most metastatic lesions being unresectable. Predicting the risk of metachronous metastasis accurately is crucial for CRC management. This study employs advanced multi-omics techniques to analyze portal vein blood, hypothesizing that it provides comprehensive and early detection of tumor-specific genetic and epigenetic alterations compared to peripheral blood.
Study Objectives
Primary Objective:
Secondary Objectives:
• Compare the predictive efficacy of portal vein blood versus peripheral venous blood.
• Validate the predictive models in a large, multicenter cohort.
• Explore the biological mechanisms underlying CRC recurrence and metastasis through in-depth multi-omics analysis.
Study Design
The study comprises two phases:
Phase 1: Nested Case-Control Study
Phase 2: Bidirectional Cohort Study
o Participants: CRC patients (stages I-IV).
o Sample Collection: Baseline data collection pre-surgery, including age, gender, tumor characteristics, and plasma samples.
Follow-up: Monitoring for recurrence and metastasis within 2 years post-surgery.
Methods: Integrating multi-omics data with clinical factors using machine learning to build predictive models.
Validation: Comparing the models' predictive efficacy in portal vein blood and peripheral blood.
(6) Sample Collection and Handling
Blood Sample Collection:
Veins for Blood Collection:
Handling and Storage:
• Peripheral Blood Sample Handling:
Processing Steps:
Blood Component Separation:
o Centrifuge at 4°C, 3000 rpm for 10 minutes to separate plasma.
o Plasma Preparation: Centrifuge at 3500 rpm for 10 minutes at 4°C, collect supernatant into 1.5 ml EP tubes, centrifuge again at 15000 × g for 10 minutes at 4°C, collect plasma into cryovials, label, and store at -80°C.
o Cell Preparation: Collect the buffy coat (white cells) into cryovials labeled as blood cells.
o Storage: Store all samples in labeled cryovials in liquid nitrogen or -80°C freezers.
(7) Methods and Techniques
Multi-Omics Analysis:
o Genomics: Using high-throughput sequencing platforms to detect genetic mutations.
o Epigenomics: Analyzing DNA methylation patterns specific to CRC.
o Transcriptomics: Profiling RNA to identify differentially expressed genes.
Data Integration and Predictive Modeling:
Machine Learning Algorithms: Employing SVM, Random Forest, XGBoost, and CNN to integrate multi-omics data with clinical factors.
Model Validation: Validating models internally and externally through large-scale cohorts.
(8) Expected Outcomes
Predictive Models:
Clinical Impact:
Scientific Contributions:
• High-impact publications detailing the study's findings and methodologies.
• Patents for novel predictive models and biomarkers.
Contributions to the global understanding of CRC biology and recurrence mechanisms.
(9) Challenges and Innovations
Technical Challenges:
Innovative Approaches:
o Combining genomics, epigenomics, and transcriptomics to provide a comprehensive biomarker profile.
Clinical and Research Implications:
• Establishing a new standard for CRC recurrence and metastasis prediction.
Providing a foundation for future research into other cancers and metastatic mechanisms.
Training and development of clinical researchers and practitioners in advanced multi-omics and predictive modeling techniques.
(10) Conclusion This study aims to revolutionize the prediction and management of CRC recurrence and metastasis by developing and validating highly accurate predictive models based on portal vein blood multi-omics analysis. Through extensive collaboration and innovative methodologies, the study seeks to enhance clinical outcomes for CRC patients and contribute significantly to the field of oncology.
By integrating advanced multi-omics technologies and machine learning, this study represents a significant step forward in the early detection and intervention of CRC recurrence and metastasis, ultimately aiming to improve patient survival and quality of life.
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350 participants in 1 patient group
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Central trial contact
Haopeng Hong, Ph.D.; Dandan Huang, M.D
Data sourced from clinicaltrials.gov
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