Towards the Prediction of Responses to Cancer Immunotherapy: A Multi-Omics Review

Abstract

Tumor treatment has undergone revolutionary changes with the development of immunotherapy, especially immune checkpoint inhibitors. Because not all patients respond positively to immune therapeutic agents, and severe immune-related adverse events (irAEs) are frequently observed, the development of the biomarkers evaluating the response of a patient is key for the application of immunotherapy in a wider range. Recently, various multi-omics features measured by high-throughput technologies, such as tumor mutation burden (TMB), gene expression profiles, and DNA methylation profiles, have been proved to be sensitive and accurate predictors of the response to immunotherapy. A large number of predictive models based on these features, utilizing traditional machine learning or deep learning frameworks, have also been proposed. In this review, we aim to cover recent advances in predicting tumor immunotherapy response using multi-omics features. These include new measurements, research cohorts, data sources, and predictive models. Key findings emphasize the importance of TMB, neoantigens, MSI, and mutational signatures in predicting ICI responses. The integration of bulk and single-cell RNA sequencing has enhanced our understanding of the tumor immune microenvironment and enabled the identification of predictive biomarkers like PD-L1 and IFN-γ signatures. Public datasets and machine learning models have also improved predictive tools. However, challenges remain, such as the need for large and diverse clinical datasets, standardization of multi-omics data, and model interpretability. Future research will require collaboration among researchers, clinicians, and data scientists to address these issues and enhance cancer immunotherapy precision.