Predictive biomarkers for immune checkpoint inhibitors therapy in lung cancer.

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Abstract

Immune checkpoint inhibitors (ICIs) have changed the treatment mode of lung cancer, extending the survival time of patients unprecedentedly. Once patients respond to ICIs, the median duration of response is usually longer than that achieved with cytotoxic or targeted drugs. Unfortunately, there is still a large proportion of lung cancer patients do not respond to ICI. Effective biomarkers are crucial for identifying lung cancer patients who can benefit from them. The first predictive biomarker is programmed death-ligand 1 (PD-L1), but its predictive value is limited to specific populations. With the development of single-cell sequencing and spatial imaging technologies, as well as the use of deep learning and artificial intelligence, the identification of predictive biomarkers has been greatly expanded. In this review, we will dissect the biomarkers used to predict ICIs efficacy in lung cancer from the tumor-immune microenvironment and host perspectives, and describe cutting-edge technologies to further identify biomarkers.