Three FDA-approved biomarkers—PD-L1, microsatellite instability/deficient mismatch repair (MSI/dMMR), and tumor mutational burden (TMB)—currently guide immune checkpoint inhibitor treatment decisions, though each has distinct limitations and variable predictive accuracy across cancer types.
Emerging gene signature biomarkers, including T cell-inflamed gene expression profiles and tumor immune dysfunction signatures, demonstrate superior predictive performance compared to single biomarkers and may better identify responders among immunologically cold tumors.
Combinational biomarker approaches, such as pairing gene expression profiles with TMB, show enhanced predictive value and could address the complex, multifactorial nature of immune checkpoint inhibitor response.
An integrated nucleic acid biomarker signature incorporating DNA and RNA markers from tumor microenvironment, neoantigenicity, and multipotency factors represents the future direction for more comprehensive and accurate patient selection.