Discovery of Biomarkers Related to the Efficacy and Adverse Reactions of Immune Checkpoint Inhibitors Based on Metabolism-genomics

Immune checkpoint inhibitors (ICIs) are an important breakthrough in cancer therapy and have been increasingly used.However, ICIs can cause a unique spectrum of side effects termed immune-related adverse events (irAEs),which can affect any organ systems and in some cases are fulminant or even fatal.In clinical practice, irAEs and clinical efficacy maybe various for patients with same standard treatment, and some studies have shown that gene and metabolic differences in cancer patients may be an important factor. In this project, peripheral blood samples from cancer patients will be collected prospectively at baseline, and at regular intervals (2 cycles, about 6 weeks) for about 30 weeks, then these blood samples will be analyzed using the technique of genomics, metabonomics. The investigators aim to find biomarkers associated with irAE or clinical efficacy. When the sample size and data is big enough, the investigators plan to establish a prediction model using machine learning to access the safety and efficacy of ICIs for cancer patients. Our study have important clinical implications in the prediction and early management of severe, potentially life-threatening immune-related toxicity.