Longitudinal peripheral blood markers as dynamic predictors to identify efficacy and safety of patients treated with immune checkpoint inhibitors (ICIs)

The immune system protects the body from illness by recognizing and killing invaders such as bacteria and viruses. It is tightly controlled by the immune checkpoint response to avoid fighting its own body (autoimmunity) while retaining the potential to fight off invaders. Immune checkpoint involves the initiation, boosting and dampening immune responses in a well-regulated spatio-temporal manner. However, cancer cells evade immune attack by harnessing the immune checkpoint, e.g. by expressing the programmed cell death ligand 1 (PD-L1) protein, which binds to the PD-1 receptor on T cells, to delude T cells into recognizing cancer as “self”. Hence, immune checkpoint inhibitors (ICIs) have been developed to prevent cancers from immune evasion. Due to its high specificity and low toxicity, ICI therapy has become popular in recent years. However, even though 46.3% of US cancer patients were considered eligible for ICI treatment as reported by a cross-sectional study in 2018, treatment response remained below 20%. Therefore, identifying biomarkers from blood will enable real-time disease monitoring to facilitate the evaluation of ICI therapeutic efficacy. Hence, this study aims to use blood biomarkers as dynamic predictors to monitor treatment outcomes and decide treatment termination. To identify the optimal dynamic predictors, this study analyzes the correlation between blood biomarkers over the course of treatment and treatment outcomes of cancer patients undergoing ICI therapy from shared clinical datasets. The identified predictors will be subject to validation in future prospective studies. The goal is to apply these biomarkers in future clinical practice to improve ICI treatment outcomes.