Artificial Intelligence in Cancer Immunotherapy: Navigating Challenges and Unlocking Opportunities

Abstract

Cancer continues to be a major cause of global mortality rates, with conventional treatments such as chemotherapy and radiotherapy exhibiting inconsistent efficacy, high costs, and considerable side effects. Over the past decade, a promising alternative has emerged: cancer immunotherapy, which leverages the body’s immune system to identify and eradicate cancer cells [1]. Significant progress has been made in several novel immunotherapies, including immune checkpoint inhibitors (ICIs), chimeric antigen receptor T-cell therapies, and personalized therapeutic cancer vaccines. However, the heterogeneity and complexity of cancer types still pose enormous challenges. The efficient use of large numbers of medical datasets is essential for overcoming these obstacles. Without artificial intelligence (AI), linking translational and clinical data to derive meaningful insights would remain an insurmountable task. Recently, single-cell RNA sequencing cell typing (scGPT), a pretrained generative model utilizing a database of more than 33 million cells, is developed to accurately extract important biological insights concerning cellular biology [2]. A personalized learning workflow is also proposed as a simple and effective pipeline to discover neoantigens for cancer immunotherapy [3].