Big Data and Computational Strategies in Tumor Immunity: Targets, Drug Discovery and Resistance

The rapid accumulation of big biomedical data and the emergence of advanced computational methods have revolutionized our ability to dissect the complexity of cancer. Despite significant strides in immunotherapy and targeted therapies, clinical efficacy is often limited by the intricate interplay between tumor immunity and the evolving genomic landscape. This complexity frequently drives tumor evolution, leading to inevitable drug resistance and treatment failure in a substantial proportion of patients.To overcome these barriers, the integration of multidimensional data, including genomics, transcriptomics, proteomics, and notably single-cell and spatial omics has become essential. In parallel, by applying emerging methodologies such as machine learning and artificial intelligence, researchers can now systematically interrogate the tumor immune microenvironment (TIME). This approach enables the decoding of complex cellular interactions and the identification of hidden patterns of immune evasion that traditional methods often fail to capture.The integration of big data with multimodal analysis is crucial for advancing precision oncology. It enables the accurate identification of potential therapeutic targets. Building on this, such strategies can accelerate drug discovery by facilitating the screening of candidate compounds. Furthermore, they are vital for uncovering the molecular mechanisms of drug resistance. Ultimately, these advances will support the development of more precise and effective personalized cancer treatments.This Research Topic aims to integrate computational innovation with clinical medicine by leveraging big data and advanced methodologies. We invite investigations that apply artificial intelligence, machine learning, and multi-omics approaches to systematically decode the tumor immune microenvironment. The primary objective is to improve the identification of specific tumor antigens and therapeutic targets, thereby establishing a robust foundation for precision medicine. Furthermore, this collection seeks to accelerate drug discovery and the screening of novel compounds, while elucidating the molecular mechanisms underlying drug resistance. Ultimately, we aim to translate these data-driven insights into effective, personalized therapeutic strategies for cancer patients.We welcome submissions of Original Research and Review articles that leverage integrated big data and advanced computational methods to address challenges in precision oncology. Potential themes include, but are not limited to:• Application of machine learning and artificial intelligence to identify novel therapeutic targets• Integrated multi-omics analyses (genomics, transcriptomics, proteomics) to characterize the tumor immune microenvironment for translational applications• Data-driven strategies for drug discovery, including high-throughput screening and computational drug repurposing• Identification and validation of robust prognostic biomarkers and signatures to predict responses to immunotherapy• Elucidation of mechanisms by which immune heterogeneity drives drug resistance and immune evasion• Translational research bridging computational findings and precision medicine, using real-world clinical data to guide personalized treatment decisions Topic editors: Chao Qin, Hanxun Jin, Yize Li Source: https://www.frontiersin.org/research-topics/80525/big-data-and-computational-strategies-in-tumor-immunity-targets-drug-discovery-and-resistance