Cancer immunotherapy and chemotherapy are at the forefront of modern cancer treatment, offering significant advancements over traditional methods. Despite their success, the intricate nature of tumor heterogeneity and immune interactions presents considerable challenges in achieving optimal patient outcomes. Recent advancements have identified single-cell technologies as a powerful tool for understanding cellular diversity within tumors. When combined with machine learning, these technologies allow researchers to dissect regulatory networks and therapeutic responses more effectively. However, despite progress, a gap remains in decoding critical regulators that could enhance the efficacy of cancer therapies.

This Research Topic aims to explore the integration of single-cell omics and advanced computational methods to reveal fundamental regulators that drive cancer immunity and chemoresistance. By leveraging these cutting-edge approaches, researchers can identify novel biomarkers, unravel underlying mechanisms of drug resistance, and discern potential therapeutic targets, thereby refining precision medicine strategies. Investigations within this domain should focus on developing innovative analytical frameworks, validating experimental findings, and providing translational insights into the dynamic interplay between tumors and the immune system under treatment pressures. The objective is to accelerate the development of more effective, personalized cancer therapies through the seamless integration of biological and computational research.

To gather further insights into cancer immunotherapy and chemotherapy, we welcome articles addressing, but not limited to, the following themes:

• Dynamics of tumor microenvironment (TME) through single-cell and spatial profiling during therapy.
• Identification and analysis of resistance mechanisms, such as immunotherapy and chemoresistance.
• Development of novel machine learning models for predicting key regulators and clinical outcomes.
• Exploration of cell-cell communication and therapy-modulated signaling hubs.
• Integration of multimodal data, linking single-cell findings with clinical and imaging features.
• Experimental validation of computational predictions using advanced methodologies (e.g., CRISPR and organoids).

Please Note: Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases, without robust validation such as clinical cohort studies or biological validation, are out of scope for this topic.

Are you interested in contributing a manuscript to this Research Topic?

Decoding Key Regulators in Cancer Immunotherapy and Chemotherapy: Integrating Single-Cell Technologies and Machine Learning