Immunotherapy has revolutionized the treatment landscape for solid tumors, achieving remarkable success in some patients while others show variable responses. Traditional biomarkers like PD-L1 expression and tumor mutational burden (TMB) have not consistently predicted outcomes, highlighting the need for more sophisticated approaches. The integration of multi-omics—spanning genomics, transcriptomics, proteomics, and radiomics—provides a deeper understanding of tumor biology and the microenvironment, offering avenues to improve predictive accuracy and therapeutic strategies.

This Research Topic aims to examine how multi-omics technologies can synthesize data from various biological levels to unveil factors and mechanisms predictive of immunotherapy responses in solid tumors. By harnessing comprehensive datasets including genomics, proteomics, metabolomics, epigenomics, and radiomics, the topic seeks to discover new predictive biomarkers and therapeutic targets. Emphasis is also placed on correlating multi-omics data with tumor microenvironment characteristics and immune evasion tactics.

To gather further insights within the boundaries of precision immunotherapy, we welcome articles addressing, but not limited to, the following themes:
• Genomic and transcriptomic approaches for predicting immunotherapy response.
• Proteomic and metabolomic insights into immune responses within the tumor microenvironment.
• Radiomics integration with multi-omics data for immunotherapy efficacy prediction.
• Identification of novel immunotherapy biomarkers through multi-omics integration.
• Artificial intelligence and machine learning applications in multi-omics data analysis and response prediction.
• Personalized immunotherapy strategies based on multi-omics research.

We welcome Original Research, Review Articles, Case Studies, and Methodological Innovations that provide significant insights into the field of precision immunotherapy.

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Frontiers in Molecular Biosciences

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