Immunotherapy with immune checkpoint inhibitors has changed the treatment landscape in many solid tumors. Despite the unprecedent success, many patients will develop primary or secondary resistance to treatment or will hold up therapy due to the emerging immune-related toxicity. Traditionally, tissue-based immune biomarkers, such as PD-L1 expression, have been used to select patients who will benefit most from immunotherapy. However, these markers demonstrate major limitations, such as tumor heterogeneity and sample constraints. In addition, they do not reflect the dynamic interplay of tumor and hosts immune response during treatment. Peripheral blood immunomarkers offer a minimally invasive, real-time assessment of the immune system and its interaction with the tumor. Integration of traditional tissue-based and peripheral blood markers coupled with the recent developments in computational platforms, artificial intelligence, and machine learning models may provide more successful biomarkers for prognosis, prediction of immunotherapy-related outcomes, the early evaluation of forthcoming disease progression, and the prediction of the emerging immune-related adverse events. Despite the promising developments in the field of immune biomarkers, several issues including assay standardization, clinical validation, and biological variability should be addressed to improve personalized immunotherapy approaches. In this comprehensive review we provide an update on immune biomarker evolution, and we discuss the current limitations and future directions.
Trontzas et al. (Wed,) studied this question.