Cancer immunotherapy is a hot area of current oncology research, with its core focus on activating or enhancing the body's immune system's ability to recognize and kill cancer cells. However, cancer cells possess complex heterogeneity and dynamics, which affect the efficacy of immunotherapy in many ways. Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in recent years, providing us with an unprecedented insight into the cellular heterogeneity and dynamics within tumors. This technology has revolutionized our understanding of cancer biology, especially in the context of cancer immunotherapy. By enabling researchers to analyze individual cells, scRNA-seq allows them to identify distinct cell populations, track cellular responses to treatments, and discover new therapeutic targets. This collection aims to compile cutting-edge research in this field and explore the various applications of single-cell RNA sequencing in cancer immunotherapy.

This collection will cover the following topics: 1. The latest advances in single-cell RNA sequencing technology in cancer immunotherapy, including research on technology optimization and data interpretation; 2. Using single-cell RNA sequencing to reveal the characteristics of immune cell subgroups in the tumor microenvironment and their interaction mechanisms with cancer cells; 3. Analyzing the molecular basis of immune therapy response and resistance through single-cell RNA sequencing, exploring new biomarkers and therapeutic targets; 4. Combining single-cell RNA sequencing with clinical studies of immunotherapy to assess treatment outcomes, predict patient prognosis, and optimize treatment plans.

Keywords: cancer immunotherapy, single-cell RNA sequencing, therapeutic targets, tumor microenvironment, treatment response

Exploring the molecular mechanisms, clinical outcomes, and their interplay is crucial for cancer research. Several recent research works have highlighted the importance of understanding inter- and intra- relationship among molecular mechanisms assayed at several layers of biological process, e.g. genomic, transcriptomic, proteomic, metabolomic, and microbiomics. Advanced analytical methods are needed to better understand the crosstalk signals among the biological processes and to discover molecular markers for future therapeutic targeting. Understanding of such molecular processes and clinical outcomes will provide a more holistic approach to investigating cancer biology and therapeutics. Overall purpose of this collection is to provide a platform for discussion on emerging analytical methodologies for studying tumor heterogeneity, diversity, progression, and treatment response with a focus on advancing our knowledge of the complex disease better.

Considering the recent advances in statistical, bioinformatics, and machine learning approaches in cancer research, we propose a collection of the topics aiming to facilitate interdisciplinary dialogue and collaboration among researchers working in quantitative and clinical aspects of cancer biology and therapeutics. Authors are encouraged to submit articles that investigate the molecular mechanisms underlying the disease heterogeneity and progression, as well as studies that explore the potential therapeutic strategies targeting these interactions. We welcome submission of original research articles addressing the following themes but not limited to: (1) new analytic methods covering both traditional statistical, bioinformatics, and advanced machine learning methods, (2) review of the literature and perspectives, (3) tutorial on any recently developed analytical methods, (4) imaging and artificial intelligence analysis methods, etc. Submission of the articles from all types of cancer research using any type of quantitative analysis methods is welcome.

Keywords: tumor heterogeneity, statistical methods, bioinformatics, machine learning, cancer biology