Integrated Multimodal Spatial Biomarker Analysis For Personalized Checkpoint Inhibitor Therapies In Solid Tumor Cancers

Checkpoint inhibitor therapy has emerged as a pivotal approach in cancer treatment, revolutionizing outcomes for many patients across various tumor types. However, responses to these therapies remain highly variable—while some individuals experience profound and lasting clinical benefits, others may endure immune-related adverse effects without significant therapeutic improvement.

This variability underscores the urgent need for reliable tools to predict patient response and guide treatment decisions. Accurately identifying which patients are most likely to benefit from checkpoint inhibitors is critical to improving efficacy, minimizing unnecessary exposure, and optimizing resource allocation.

In this context, the discovery and analysis of predictive biomarkers are crucial. Key among these are immune checkpoint protein expression levels, such as PD-L1 and TIGIT, as well as detailed cytokine profiling within the tumor microenvironment. Together, these biomarkers offer valuable insights into tumor-immune dynamics and hold the potential to transform immunotherapy into a more precise and personalized treatment strategy.