Ignite Creation Date:
2024-10-26 @ 3:36 PM
Last Modification Date:
2024-10-26 @ 3:36 PM
Study NCT ID:
NCT06524245
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-07-23
Brief Title:
A Predictive Model for Recurrence of Colorectal Cancer Based on Multi-omics of Portal Vein Blood
Sponsor:
None
Organization:
None
Study Overview
Official Title:
A Predictive Model for Recurrence of Colorectal Cancer Based on Multi-omics of Portal Vein Blood a Multi-center Study
Status:
RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
PVMRD
Brief Summary:
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 18059211195163com
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 18059211195163com
Detailed Description:
1 Study Title A predictive model for recurrence of colorectal cancer based on multi-omics of portal vein blood a multi-center study
2 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
3 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
4 Study Objectives
Primary Objective
Develop a predictive model for CRC recurrence and metastasis using biomarkers identified from multi-omics analysis of portal vein blood
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
5 Study Design
The study comprises two phases
1 Phase 1 Nested Case-Control Study
Participants CRC patients stages II-IV post-radical surgery
Sample Collection Plasma from primary tumors adjacent tissues normal tissues portal vein blood and peripheral blood
Methods High-throughput sequencing and multi-omics analysis to identify specific biomarkers associated with recurrence and metastasis
Analysis Comparing patients with and without recurrence or metastasis to identify significant biomarkers
2 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 models39 predictive efficacy in portal vein blood and peripheral blood
6 Sample Collection and Handling
Blood Sample Collection
Timing Blood samples are collected from the tumor region veins after ligation of the colorectal tumor39s arterial and venous supply but before tumor resection
Method A blood sampling needle is inserted into the tumor region veins for collection
Volume Each patient provides 10-20 ml of venous blood from the tumor region
Post-Collection Processing Blood samples are centrifuged and aliquoted within 30 minutes of collection and stored at -80C
Veins for Blood Collection
Right Hemicolon Tumors Ileocolic vein
Transverse Colon Tumors Middle colic vein
Left Hemicolon Tumors Inferior mesenteric vein
Sigmoid Colon Tumors Inferior mesenteric vein
Rectal Tumors Inferior mesenteric vein
Alternative Collection If difficulty arises or insufficient blood volume is collected puncturing the marginal colonic mesenteric arch vessels is permissible
Handling and Storage
Peripheral Blood Sample Handling
Samples are collected preoperatively intraoperatively portal vein blood and postoperatively 3-10 days 3 months 6 months 12 months 24 months
Each sample is 10 ml using EDTA anticoagulant or coagulation-promoting tubes stored at 4C
Samples are delivered to the biobank within 2 hours processed for plasma and cell separation and frozen at -80C or in liquid nitrogen for long-term storage
Processing Steps
Blood Component Separation
o Centrifuge at 4C 3000 rpm for 10 minutes to separate plasma
o Plasma Preparation Centrifuge at 3500 rpm for 10 minutes at 4C collect supernatant into 15 ml EP tubes centrifuge again at 15000 g for 10 minutes at 4C collect plasma into cryovials label and store at -80C
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 -80C 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
Development of robust models for predicting CRC recurrence and metastasis using portal vein blood biomarkers
Comparative analysis of predictive efficacy between portal vein and peripheral blood-based models
Clinical Impact
Improved early detection and intervention strategies for CRC recurrence and metastasis
Enhanced patient prognosis and survival rates through timely therapeutic interventions
Integration of advanced predictive models into routine clinical practice
Scientific Contributions
High-impact publications detailing the study39s 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
High ctDNA content in pre-surgery samples may complicate biomarker identification
Low DNA yields in early-stage CRC patients necessitate sensitive detection methods
Innovative Approaches
o Combining genomics epigenomics and transcriptomics to provide a comprehensive biomarker profile
Utilizing GutSeer technology for enhanced detection and localization of digestive system cancers
Implementing advanced machine learning techniques to improve model accuracy and reliability
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
2 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
3 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
4 Study Objectives
Primary Objective
Develop a predictive model for CRC recurrence and metastasis using biomarkers identified from multi-omics analysis of portal vein blood
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
5 Study Design
The study comprises two phases
1 Phase 1 Nested Case-Control Study
Participants CRC patients stages II-IV post-radical surgery
Sample Collection Plasma from primary tumors adjacent tissues normal tissues portal vein blood and peripheral blood
Methods High-throughput sequencing and multi-omics analysis to identify specific biomarkers associated with recurrence and metastasis
Analysis Comparing patients with and without recurrence or metastasis to identify significant biomarkers
2 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 models39 predictive efficacy in portal vein blood and peripheral blood
6 Sample Collection and Handling
Blood Sample Collection
Timing Blood samples are collected from the tumor region veins after ligation of the colorectal tumor39s arterial and venous supply but before tumor resection
Method A blood sampling needle is inserted into the tumor region veins for collection
Volume Each patient provides 10-20 ml of venous blood from the tumor region
Post-Collection Processing Blood samples are centrifuged and aliquoted within 30 minutes of collection and stored at -80C
Veins for Blood Collection
Right Hemicolon Tumors Ileocolic vein
Transverse Colon Tumors Middle colic vein
Left Hemicolon Tumors Inferior mesenteric vein
Sigmoid Colon Tumors Inferior mesenteric vein
Rectal Tumors Inferior mesenteric vein
Alternative Collection If difficulty arises or insufficient blood volume is collected puncturing the marginal colonic mesenteric arch vessels is permissible
Handling and Storage
Peripheral Blood Sample Handling
Samples are collected preoperatively intraoperatively portal vein blood and postoperatively 3-10 days 3 months 6 months 12 months 24 months
Each sample is 10 ml using EDTA anticoagulant or coagulation-promoting tubes stored at 4C
Samples are delivered to the biobank within 2 hours processed for plasma and cell separation and frozen at -80C or in liquid nitrogen for long-term storage
Processing Steps
Blood Component Separation
o Centrifuge at 4C 3000 rpm for 10 minutes to separate plasma
o Plasma Preparation Centrifuge at 3500 rpm for 10 minutes at 4C collect supernatant into 15 ml EP tubes centrifuge again at 15000 g for 10 minutes at 4C collect plasma into cryovials label and store at -80C
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 -80C 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
Development of robust models for predicting CRC recurrence and metastasis using portal vein blood biomarkers
Comparative analysis of predictive efficacy between portal vein and peripheral blood-based models
Clinical Impact
Improved early detection and intervention strategies for CRC recurrence and metastasis
Enhanced patient prognosis and survival rates through timely therapeutic interventions
Integration of advanced predictive models into routine clinical practice
Scientific Contributions
High-impact publications detailing the study39s 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
High ctDNA content in pre-surgery samples may complicate biomarker identification
Low DNA yields in early-stage CRC patients necessitate sensitive detection methods
Innovative Approaches
o Combining genomics epigenomics and transcriptomics to provide a comprehensive biomarker profile
Utilizing GutSeer technology for enhanced detection and localization of digestive system cancers
Implementing advanced machine learning techniques to improve model accuracy and reliability
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None