Untangling cell–cell communication networks and on-treatment response in immunotherapy

Abstract

Immunotherapies have shown efficacy in improving autoimmune conditions such as rheumatoid arthritis and are now widely established for various cancer entities. Nevertheless, predicting patient outcomes prior to therapy remains very challenging, likely attributable to the diversity and complex, interactive dynamics of immune cells. Recent advancements in statistical analysis as well as machine learning and mathematical modeling techniques have provided insights into immune-cell regulation and tumor-immune dynamics. Here, we discuss recent developments in this field, with the aim of deriving a path to improvements in treatment biomarker identification and adverse effect prediction. Deriving a quantitative understanding of the complex interactions among immune cell subpopulations holds promise for optimizing treatment strategies in numerous health conditions from chronic inflammation to cancer.