Topic: Integrating Multi-Omics and Artificial Intelligence in Deciphering Cancer Drug Resistance

Cancer drug resistance remains one of the most formidable challenges in oncology, accounting for over 90% of treatment failures in metastatic disease. The emergence of resistance represents a complex, multifaceted evolutionary process driven by intricate interactions among genomic instability, epigenetic reprogramming, metabolic rewiring, tumor microenvironment remodeling, and immune evasion mechanisms. Traditional reductionist approaches, which examine individual molecular alterations in isolation, have proven insufficient to capture the systems-level complexity underlying resistance phenotypes.

The convergence of multi-omics technologies and artificial intelligence (AI) has ushered in a transformative era in cancer research, enabling unprecedented opportunities to dissect resistance mechanisms at unprecedented resolution and scale. Multi-omics integration—encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and spatial omics—provides a holistic view of cellular states and their dynamic transitions during therapy. Concurrently, AI-driven computational frameworks, including deep learning, graph neural networks, foundation models, and causal inference algorithms, have demonstrated remarkable capabilities in extracting meaningful patterns from high-dimensional biological data, predicting therapeutic responses, and identifying novel therapeutic vulnerabilities.

Despite these advances, significant gaps remain in our understanding of how to effectively harness multi-omics data and AI methodologies to predict, prevent, and overcome drug resistance. This Special Issue aims to catalyze interdisciplinary dialogue and showcase cutting-edge research at the intersection of multi-omics biology, computational medicine, and resistance biology.

Topics of interest include, but are not limited to：

● Multi-Omics Profiling of Cancer Drug Resistance Mechanisms;

● AI-Driven Approaches for Predicting Drug Resistance in Cancer;

● Spatial and Single-Cell Omics for Decoding Tumor Heterogeneity in Drug Resistance;

● Epigenomic and Epitranscriptomic Regulation in Cancer Drug Resistance;

● Tumor Microenvironment and Immune Evasion in Drug Resistance;

● Clinical Applications of Multi-Omics and AI for Overcoming Cancer Drug Resistance.

Cancer drug resistance, multi-omics integration, artificial intelligence (AI), immune evasion, epigenetic reprogramming