A Predictive Model for Recurrence of Colorectal Cancer Based on Multi-omics of Portal Vein Blood

• 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:

• 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.
• Study Design

The study comprises two phases:

• 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.
• 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:

• Timing: Blood samples are collected from the tumor region veins after ligation of the colorectal tumor's 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 -80°C.

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 4°C.
• Samples are delivered to the biobank within 2 hours, processed for plasma and cell separation, and frozen at -80°C or in liquid nitrogen for long-term storage.

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:

• 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 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:

• 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.

Inclusion criteria

• Age 18 years or older
• Histologically confirmed stage I-IV colorectal cancer (CRC)
• Availability of pre-operative and intra-operative blood samples (portal vein and peripheral blood) and primary tumor tissue samples
• No neoadjuvant therapy before surgery

Exclusion criteria

• Prior history of other malignancies
• Incomplete clinical information or pathology reports
• Incomplete follow-up records