Ignite Creation Date:
2024-05-06 @ 3:30 PM
Last Modification Date:
2025-12-17 @ 12:18 AM
Study NCT ID:
NCT04656119
Status:
None
Last Update Posted:
2020-12-07 00:00:00
First Post:
2020-11-18 00:00:00
Brief Title:
Discovery of Biomarkers Related to the Efficacy and Adverse Reactions of Immune Checkpoint Inhibitors Based on Metabolism-genomics
Sponsor:
Beijing Chao Yang Hospital
Organization:
Beijing Chao Yang Hospital
Study Overview
Official Title:
Discovery of Biomarkers Related to the Efficacy and Adverse Reactions of Immune Checkpoint Inhibitors Based on Metabolism-genomics
Status:
None
Status Verified Date:
2020-11
Last Known Status:
RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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.
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.
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None