Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors

• Published in: Computational and structural biotechnology journal Vol. 21; pp. 3912 - 3919
• Main Authors: Limeta, Angelo, Gatto, Francesco, Herrgård, Markus J., Ji, Boyang, Nielsen, Jens
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2023; Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: Biomarkers; Immune related adverse events; Immune-checkpoint inhibitors; Medicin och hälsovetenskap; Omics; Predictive models; Review; Biomarkers; Predictive models; Immune related adverse events; Omics; Immune-checkpoint inhibitors
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2023.07.032

A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use. [Display omitted]