Predictive Biomarkers for Immunotherapy Response Beyond PD-1/PD-L1

Area of Science:

• Oncology
• Immunology
• Biomarker Discovery

Background:

• Recent advances in immuno-oncology have led to novel FDA-approved agents and improved treatments for various cancers.
• Early biomarkers for predicting immunotherapy response included PD-1 receptor and PD-L1 expression via immunohistochemistry.
• Current predictive biomarkers encompass microsatellite instability, DNA mismatch repair, and tumor mutational burden.

Purpose of the Study:

• To highlight the evolving landscape of biomarkers in predicting cancer immunotherapy response.
• To emphasize the need for improved understanding of immune responses and evaluation of assessment methods.
• To explore how advanced assays and bioinformatics can enhance prediction of treatment efficacy in immuno-oncology.

Main Methods:

• Review of current and emerging biomarkers for immunotherapy response prediction.
• Discussion of the limitations of existing predictive markers.
• Exploration of novel approaches including comprehensive assays and bioinformatics tools.

Main Results:

• While PD-1/PD-L1, MSI, MMR, and TMB are clinically relevant, cancer immunotherapy still fails some patients.
• Existing biomarkers have limitations in accurately predicting response across all patients.
• Comprehensive assays and bioinformatics offer potential for more accurate prediction.

Conclusions:

• Further understanding of the human immune system is essential for advancing immuno-oncology.
• Careful evaluation of methods to predict and assess treatment response is necessary.
• Advanced assays and bioinformatics tools are key to navigating the complexity of immuno-oncology treatments and improving patient outcomes.