Immune checkpoint inhibitors produce durable remissions in some patients but not others; clinicians use a combination of tumor-intrinsic and host-derived biomarkers to anticipate benefit and guide treatment selection. Evidence from clinical research highlights several reproducible predictors, though none alone is perfect and context matters by cancer type and testing platform.
Biomarkers linked to tumor features
Expression of PD-L1 on tumor or immune cells correlates with response to PD-1/PD-L1 blockade in multiple studies. Suzanne L. Topalian, Johns Hopkins University, described early clinical correlations between PD-L1 immunohistochemistry and activity of anti–PD-1 therapy, establishing PD-L1 as a clinically actionable marker. High tumor mutational burden (TMB) predicts greater likelihood of neoantigen formation and response to PD-1 inhibitors; Naiyer A. Rizvi, Memorial Sloan Kettering Cancer Center, demonstrated that higher nonsynonymous mutation load associates with benefit in non–small cell lung cancer. Tumors with microsatellite instability–high or defective mismatch repair (MSI-H/dMMR) produce abundant frameshift neoantigens and show pronounced sensitivity to PD-1 blockade, a relationship characterized by Dung T. Le, Johns Hopkins University. These tumor-genomic features help explain biological causes of response: more foreign-looking antigens make immune recognition and checkpoint reactivation more effective.
Circulating and microbiome factors
Beyond tissue genomics, circulating biomarkers and the microbiome influence outcomes. Clearance dynamics of circulating tumor DNA (ctDNA) during treatment are emerging as early indicators of response and resistance in several institutional series, while systemic inflammatory markers such as neutrophil-to-lymphocyte ratio often reflect prognosis but are less specific as predictors. The gut microbiome also modulates immunotherapy; V. Gopalakrishnan, MD Anderson Cancer Center, reported microbiome signatures associated with response in melanoma, suggesting dietary, geographic and antibiotic-exposure factors can alter efficacy.
Clinical relevance and consequences include selection of patients for monotherapy versus combination regimens, sparing nonresponders from toxicity, and prioritizing biomarker testing in health systems. However, access to comprehensive assays and standardized thresholds varies worldwide, producing territorial and socioeconomic disparities in who benefits from precision immunotherapy. Combining multiple biomarkers—tumor genomics, immune contexture, circulating signals and host factors—offers the most reliable prediction, but continued prospective validation and equitable implementation remain essential for translating these tools into better outcomes.