Predictive biomarkers for tumor immune checkpoint blockade

Area of Science:

• Oncology
• Immunology
• Biomarker Discovery

Background:

• Immune checkpoint inhibitors (ICIs), targeting PD-1 and PD-L1, have revolutionized advanced cancer treatment.
• Clinical application of ICIs is limited by the absence of reliable predictive biomarkers.
• Identifying effective biomarkers is crucial for optimizing ICI therapy and patient selection.

Purpose of the Study:

• To review the current status and progress of Programmed Death-Ligand 1 (PD-L1) expression as a predictive biomarker for tumor immunotherapy.
• To evaluate other potential predictive biomarkers, including tumor mutation burden, tumor-infiltrating lymphocytes, gene expression profiling, and peripheral blood markers, for novel immunotherapies.

Main Methods:

• Comprehensive literature review of recent domestic and international research.
• Analysis of ongoing and completed clinical trials related to tumor immunotherapy biomarkers.
• Synthesis of data on PD-L1 expression and emerging biomarkers.

Main Results:

• PD-L1 expression shows promise but faces challenges as a sole predictive biomarker for ICIs.
• Tumor mutation burden, tumor-infiltrating lymphocytes, gene expression, and peripheral markers are under investigation as complementary or alternative biomarkers.
• No single biomarker has emerged as universally effective, highlighting the need for multi-marker approaches.

Conclusions:

• Effective predictive biomarkers are essential for advancing tumor immunotherapy and improving patient outcomes.
• Further research is needed to validate novel biomarkers and develop robust predictive models for ICI therapy.
• A combination of biomarkers may be necessary to accurately predict response to various cancer immunotherapies.