Harnessing multi-omics and machine learning for predicting immune checkpoint blockade responses: Advances, challenges, and future directions

Abstract

Immune checkpoint blockade (ICB) therapies have revolutionized cancer treatment, showing success across various cancer types. However, there is variability in response rates among different cancers and individual patients. This highlights the critical need for precise patient stratification. Machine Learning and Deep Learning models are increasingly utilized to predict ICB responses by integrating multi-omics data, such as clinical, genomic, radiomic, and transcriptomic information. This review outlines the key methodologies of these predictive models. It underscores their role in enhancing response prediction. We delve into the advanced mechanisms of ICB response and discuss the biological foundations that inform these models. This demonstrates how basic research informs clinical application. We aim to offer comprehensive insights into how artificial intelligence can optimize patient stratification for ICB therapy.

Keywords: Artificial intelligence; Cancer-immune cycle; ICB response mechanisms; Immune checkpoint blockade therapy; Multi-omics analysis; Predictive modeling.