What this Trial Is About

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.

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