Next generation predictive biomarkers for immune checkpoint inhibition

Area of Science:

• Oncology
• Immunology
• Molecular Biology

Background:

• Targeted therapies, including immune checkpoint blockade (ICB), have transformed cancer treatment across various histologies.
• ICB leverages receptor-ligand interactions between immune and tumor cells but can cause toxicity in healthy tissues.
• Current FDA-approved indications for ICB often rely on programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1) protein assays, which have limitations.

Purpose of the Study:

• To review and synthesize biomarker detection methods for predicting response to immune checkpoint blockade therapies.
• To explore predictive tools beyond current protein-based assays for identifying responders versus non-responders.

Main Methods:

• Review of literature synthesizing biomarker detection at the DNA, RNA, and protein levels.
• Analysis of established and emerging predictive biomarkers for ICB efficacy.
• Discussion of limitations of current diagnostic assays and the need for complementary tools.

Main Results:

• Current PD-1/PD-L1 assays have caveats, necessitating additional predictive tools.
• Emerging biomarkers include microsatellite instability, PD-L1 amplification, CD8 infiltrate density, and tumor mutational burden.
• Biomarker detection across DNA, RNA, and protein levels offers a multi-faceted approach to forecasting treatment benefit.

Conclusions:

• Accurate prediction of response to immune checkpoint blockade is crucial for optimizing patient outcomes and minimizing toxicity.
• Integrating diverse biomarker data (DNA, RNA, protein) can enhance the precision of patient selection for ICB.
• Further research and validation of these predictive tools are essential for clinical implementation.