About this Research Topic
The tumor microenvironment (TME), composed of multiple cell types, is complex, heterogeneous, and continuously evolving. In addition to stromal cells, fibroblasts, and endothelial cells, immune cells are also important constituents of the TME. They play a critical role in tumor survival and progression by secreting many factors that drive chronic inflammation, pro-angiogenesis, and immunosuppression. Technology has advanced, so has the understanding that the complex and diverse tumor immune microenvironments (TIME) play a determinative role in immunotherapy response. However, given the differences in responsiveness to immunotherapy across patients, more novel technologies are needed to comprehensively study TIME and decipher its complexity.
To more precisely characterize the immune cellular and molecular landscape of the TME, machine learning and artificial intelligence (AI)-driven high-throughput sequencing are key. It is urgent to leverage cutting-edge technologies to uncover new tumor-specific cell subtypes, reveal the cross-talk between cells in tumor tissues, and identify novel targets that can help improve the effectiveness of immunotherapy. Recently, single-cell RNA sequencing and spatial multiomics have ushered in a new era of tumor immunity at the transcriptome, proteome, and metabolome levels, enabling a comprehensive exploration of cancer, understanding of tumor heterogeneity, and prediction of response to immunotherapy. These advancements are expected to advance the precision treatment of cancer.
The scopes of the research topic may include, but are not limited to:
• The application of machine learning and AI-driven high-throughput sequencing in the study of TIME
• Identifying the cellular and molecular landscape of TIME
• Identifying cell interactions in tumor tissue
• Prediction of new targets for immunotherapy
• Immunotyping of tumors
• Advanced multi-omics analysis
To more precisely characterize the immune cellular and molecular landscape of the TME, machine learning and artificial intelligence (AI)-driven high-throughput sequencing are key. It is urgent to leverage cutting-edge technologies to uncover new tumor-specific cell subtypes, reveal the cross-talk between cells in tumor tissues, and identify novel targets that can help improve the effectiveness of immunotherapy. Recently, single-cell RNA sequencing and spatial multiomics have ushered in a new era of tumor immunity at the transcriptome, proteome, and metabolome levels, enabling a comprehensive exploration of cancer, understanding of tumor heterogeneity, and prediction of response to immunotherapy. These advancements are expected to advance the precision treatment of cancer.
The scopes of the research topic may include, but are not limited to:
• The application of machine learning and AI-driven high-throughput sequencing in the study of TIME
• Identifying the cellular and molecular landscape of TIME
• Identifying cell interactions in tumor tissue
• Prediction of new targets for immunotherapy
• Immunotyping of tumors
• Advanced multi-omics analysis
Keywords: machine learning; artificial Intelligence; high-throughput sequencing; tumor microenvironment; tumor immunology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
