The Immunotherapy Revolution's Path To Precision

By Igor Radanović

Over the past decade, immunotherapy has redefined cancer treatment, transforming outcomes for patients once considered untreatable. The introduction of immune checkpoint inhibitors (ICIs) targeting CTLA-4 and PD-1/PD-L1 marked a turning point in oncology, establishing durable survival benefits and ushering in a new era of biomarker-guided therapy. Advances in molecular profiling, and more recently, artificial intelligence, have accelerated this progress by improving our ability to predict who will benefit from immunotherapy and to tailor treatment accordingly. Deep learning models now detect key biomarkers such as microsatellite instability directly from digital histology, while transcriptomic algorithms integrate complex immune signatures to refine response prediction.

Yet, as immune modulation grows more precise, so too does the need to understand and manage its risks. The same pathways that drive anti-tumor efficacy can provoke harmful immune activation, making early immune characterization a critical step in development. This article explores how comprehensive immune profiling and translational models can illuminate these mechanisms, inform dose selection, and guide the next generation of immunotherapies toward safer, more targeted innovation.