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Updated: Jun 20, 2026

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
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.
Background:
Immune checkpoint inhibitors (ICIs) have transformed oncology practice. However, treatment response remains heterogeneous, rendering predictive biomarkers critical for optimal patient care. The 3 established biomarkers, programmed death-ligand 1, tumor mutational burden (TMB), and microsatellite instability-high/deficient mismatch repair, are approved and clinically validated but are modest predictors of benefit. As a result, multiple novel predictive biomarkers remain under investigation.
Content:
This review highlights established and investigational predictive ICI efficacy biomarkers. For established biomarkers, we describe biology, assay modalities, approved companion diagnostics, landmark studies, and notable limitations. Due to the multisystem nature of antitumor immune effects, investigational biomarkers span multiple domains, including tumor genomic biomarkers (e.g., mutational signatures, TMB, neoantigen clonality), tumor microenvironment (e.g., tumor-infiltrating lymphocytes [TILs], tertiary lymphoid structures), systemic immune biomarkers (e.g., cytokines, autoantibodies, glycoproteins, peripheral blood mononuclear cells), and the microbiome (e.g., gastrointestinal microbial diversity, responder-enriched taxa).
Summary:
The established biomarkers PD-L1, TMB, and microsatellite instability-high/deficient mismatch repair inform ICI use in clinical practice but have important limitations. Multiple investigational biomarkers show promise in refining patient selection and optimizing therapy. Moving forward, increased assay harmonization, prospective validation, and standardized parameters may improve performance. Composite models integrating complementary signals across domains may further individualize treatment and lead to an era of personalized cancer immunotherapy.
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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