An AI-enabled single-cell approach to analyze the cancer immune microenvironment

Recent advances in single cell research enable analysis of specific immune cell populations within the cancer-specific tumor microenvironment (TME). Artificial intelligence (AI) has emerged as a key technology, utilizing machine learning and deep learning methodologies, and is capable to analyze single-cell RNA-Seq and domain level data to provide unprecedented insights into immune responses and tumor-immune interactions. The present work utilizes AI-based methods to explore two paths in cancer immunology through single cell transcriptomics and spatial multi-omics to study tumor evolution, immune escape, and resistance to immune-based therapy. AI methods can classify tumor infiltrating lymphocytes (TILs), establish functional immune cell states and assess patient-specific ICI responses. AI methods are also effective at developing novel immune biomarker discovery and therapeutic predictive response with high-dimension single cell data datasets. We further explore both AI models and single cell analysis integration with scRNA-seq in conjunction with spatial transcriptomics to help assess dynamics of immune and TME interactions and where to focus and evaluate immunotherapy strategies. This paper offers insight as AI leads to a rebirth in cancer immunology through biomarker discovery, patient stratification, and world's precision immunotherapy based on subsets of immune recognition. AI-enabled analysis of single-cell data demonstrates a considerable development toward agnostic personalized cancer therapy and clinical impacts for defined cancer pathways.