The article "Immunotherapy and Cancer: The Multi-Omics Perspective" by Clelia Donisi et al. explores the advancements and challenges in cancer immunotherapy, emphasizing the importance of multi-omics approaches in understanding tumor biology and improving patient outcomes. The authors highlight that while immunotherapies have revolutionized cancer treatment, not all patients respond positively, leading to the need for understanding resistance mechanisms. Multi-omics approaches, including genomics, transcriptomics, proteomics, metabolomics, radiomics, and immunomics, are crucial for decoding the tumor immune microenvironment (TIME) and identifying biomarkers for personalized treatment. The review discusses various algorithms and tools, such as CIBERSORT, ESTIMATE, xCell, and TIDE, which help in predicting response to immunotherapy and stratifying patients. It also covers the role of post-translational modifications, microbiome, and immune-related adverse events (irAEs) in immunotherapy outcomes. Additionally, the article explores the integration of artificial intelligence (AI) with multi-omics data to enhance predictive models and improve clinical decision-making. The authors conclude by emphasizing the need for further research to integrate these technologies and develop more effective immunotherapies.The article "Immunotherapy and Cancer: The Multi-Omics Perspective" by Clelia Donisi et al. explores the advancements and challenges in cancer immunotherapy, emphasizing the importance of multi-omics approaches in understanding tumor biology and improving patient outcomes. The authors highlight that while immunotherapies have revolutionized cancer treatment, not all patients respond positively, leading to the need for understanding resistance mechanisms. Multi-omics approaches, including genomics, transcriptomics, proteomics, metabolomics, radiomics, and immunomics, are crucial for decoding the tumor immune microenvironment (TIME) and identifying biomarkers for personalized treatment. The review discusses various algorithms and tools, such as CIBERSORT, ESTIMATE, xCell, and TIDE, which help in predicting response to immunotherapy and stratifying patients. It also covers the role of post-translational modifications, microbiome, and immune-related adverse events (irAEs) in immunotherapy outcomes. Additionally, the article explores the integration of artificial intelligence (AI) with multi-omics data to enhance predictive models and improve clinical decision-making. The authors conclude by emphasizing the need for further research to integrate these technologies and develop more effective immunotherapies.