Predictive Biomarkers for Immune Checkpoint Inhibitor Efficacy: Challenges, Innovations, and a Pathway to Precision

Matthew Lee1, Jeffrey A SoRelle2,3, Arun Everest-Dass4

  • 1UT Southwestern Medical School, University of Texas Southwestern Medical Center, Dallas, TX, United States.

Clinical Chemistry
|June 18, 2026
PubMed
Abstract

Insights

Predicting response to immune checkpoint inhibitors (ICIs) is crucial. Established biomarkers like PD-L1 and tumor mutational burden (TMB) have limitations, prompting research into novel predictive biomarkers for personalized cancer immunotherapy.

Area of Science:

  • Oncology
  • Immunology
  • Biomarker Discovery

Background:

  • Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment but exhibit variable patient responses.
  • Established predictive biomarkers (PD-L1, TMB, MSI-H/dMMR) are clinically validated but offer modest predictive accuracy.
  • The need for improved biomarkers is critical for optimizing patient selection and treatment outcomes.

Purpose of the Study:

  • To review established and investigational biomarkers for predicting ICI efficacy.
  • To discuss the biology, assays, limitations, and clinical validation of current biomarkers.
  • To explore emerging biomarkers across genomic, microenvironment, systemic, and microbiome domains.

Main Methods:

  • Literature review of established and investigational ICI efficacy biomarkers.
  • Analysis of biomarker domains including tumor genomics, microenvironment, systemic immunity, and microbiome.
  • Discussion of assay modalities, companion diagnostics, and clinical validation studies.

Main Results:

  • Established biomarkers (PD-L1, TMB, MSI-H/dMMR) have limitations in predicting ICI response.
  • Investigational biomarkers encompass tumor mutational signatures, neoantigen clonality, TILs, TSLs, cytokines, autoantibodies, and microbial diversity.
  • These novel biomarkers show promise for refining patient selection and optimizing immunotherapy.

Conclusions:

  • Established biomarkers are informative but limited; novel biomarkers offer potential for improved patient stratification.
  • Future directions include assay harmonization, prospective validation, and standardized parameters for biomarker performance.
  • Composite biomarker models integrating diverse signals may enable personalized cancer immunotherapy.

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