Profiling of immune features to predict immunotherapy efficacy

Immune checkpoint blockade (ICB) therapies exhibit substantial clinical benefit in different cancers, but relatively low response rates in the majority of patients highlight the need to understand mutual relationships among immune features. Here, we reveal overall positive correlations among immune checkpoints and immune cell populations. Clinically, patients benefiting from ICB exhibited increases for both immune stimulatory and inhibitory features after initiation of therapy, suggesting that the activation of the immune microenvironment might serve as the biomarker to predict immune response. As proof-of-concept, we demonstrated that the immune activation score (IS(Δ)) based on dynamic alteration of interleukins in patient plasma as early as two cycles (4–6 weeks) after starting immunotherapy can accurately predict immunotherapy efficacy. Our results reveal a systematic landscape of associations among immune features and provide a noninvasive, cost-effective, and time-efficient approach based on dynamic profiling of pre- and on-treatment plasma to predict immunotherapy efficacy.