Construction and Evaluation of Tumor Immunotherapy and Organ Damage Early Warning System Based on Multi-omics

NCT ID: NCT07131007

Last Updated: 2025-09-04

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

2000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-08-30

Study Completion Date

2029-01-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

This project is based on the in-depth analysis and integration of multi-omics data, including but not limited to genomics, transcriptomics, proteomics, and metabolomics. It aims to construct a comprehensive early-warning system for organ function damage in immune-related adverse events (irAEs) associated with immune checkpoint inhibitors (ICIs) during tumor immunotherapy. The core objective of this system is to enhance the overall safety and efficacy of tumor immunotherapy.

First, the project leverages a database to mine the differential omics data of tumor immunotherapy patients with combined organ dysfunction (including combined and non-combined severe infections) within the scope of this project. By integrating biochemical indicators and related hemodynamic data, it constructs a risk early-warning system for organ damage in patients undergoing tumor immunotherapy, while verifying its clinical value and guiding significance.

The specific contents mainly include: capturing specific molecules of organ damage in severe patients after tumor immunotherapy, screening genes, proteins, and metabolic products related to organ damage (including the heart, lungs, brain, liver, kidneys, gastrointestinal tract, etc.), and identifying new specific organ damage biomarkers under different pathogenic factors such as tumor immunotherapy, infections, and irAEs. It collects general clinical information, biochemical indicators, and hemodynamic indicators, and combines multi-omics data to establish an organ damage prediction model. Machine learning algorithms are used for optimization to construct an early-warning system.

Model optimization within the system will be carried out, along with prospective clinical research and multi-dimensional verification. By evaluating the accuracy and cost-effectiveness of the model, it provides decision-making support for clinicians and promotes the development of personalized treatment.

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Malignant Neoplasm Organ Damage

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Tumor Immunotherapy Cohort

Cancer patients receiving immune checkpoint inhibitors (ICIs). We observe their clinical course, collect organ function data, and perform multi - omics analysis to construct an organ damage early - warning system.

Immunotherapy Monitoring and Sample Collection

Intervention Type BEHAVIORAL

For cancer patients receiving immune checkpoint inhibitors (ICIs), we conduct behavioral monitoring: collect blood, urine, and feces samples before medication and 7 days after medication for multi - omics analysis. Monitor organ function indicators at 24 hours, 72 hours, and 1 week post - medication. No interference with standard ICI treatment; focus on observational data collection to construct an organ damage early - warning system.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Immunotherapy Monitoring and Sample Collection

For cancer patients receiving immune checkpoint inhibitors (ICIs), we conduct behavioral monitoring: collect blood, urine, and feces samples before medication and 7 days after medication for multi - omics analysis. Monitor organ function indicators at 24 hours, 72 hours, and 1 week post - medication. No interference with standard ICI treatment; focus on observational data collection to construct an organ damage early - warning system.

Intervention Type BEHAVIORAL

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

ยท Patients with cancer who are receiving immune checkpoint inhibitor treatment.

Exclusion Criteria

* Active phase of severe autoimmune disease.
* Severe organ dysfunction.
* Presence of active infection.
* Pregnancy or lactation.
* Allergy to drug components.
Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

The First Affiliated Hospital of Dalian Medical University

OTHER

Sponsor Role collaborator

NuoQing Biotechnology (Shanghai) Co., Ltd.

UNKNOWN

Sponsor Role collaborator

Shandong Sanhe Tongyuan Medical Equipment Co., Ltd.

UNKNOWN

Sponsor Role collaborator

Hebei Medical University Fourth Hospital

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Responsibility Role SPONSOR

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

2024ZD0526105

Identifier Type: -

Identifier Source: org_study_id

More Related Trials

Additional clinical trials that may be relevant based on similarity analysis.