In the last decades, immunology and oncology made giant steps thanks to the great improvements in emerging complex biotechniques employed by researchers. These become always more sophisticated and have improved our knowledge of the important concepts and scenarios of the tumor microenvironment. The emerging field of immuno-oncology has been set up by the discovery that immunity can bridge cancer through the intimate interaction of the immune cells with tumor cells within the tumor microenvironment.
3D systems deserve particular attention, due to their decisive role to mimic the cell-cell environments. Spheroids represent the first and successful attempt to culture cells (of primary and tumor nature) in a 3D architecture. The direct evolution of these systems is represented by organoids, which can be defined as primary cells closely resembling their derivative organ. Among this category, there are the tumor organoids, which can be defined as organoids where the 3D architecture of the cancer cells also contains the infiltrating immune cells and can be evaluated as a single complex multicellular system, such as mouse or patient-derived tumor organoids.
3D bioprinting is another recent and innovative biotool allowing the recapitulation of the 3D moiety of a tumor with their matrix proteins and cells. These methods are emerging as valid tools to evaluate the effect of drugs in cancer to study drug resistance to tumors. If properly optimized, they can represent valid alternatives to in vivo models and can deeply impact personalized medicine in the context of immuno-oncology.
Other platforms to study the interactions between tumor cells and immune cells are represented by Organs-on-Chip systems. Created to study organ behavior in ad hoc closed compartments, these devices are now largely exploited in the field of tumor immunology to investigate the multifaceted tumor microenvironment cues. These systems are usually represented by ad hoc fabricated microfluidic chips with defined compartmental units and are coupled to complex microscope systems to follow the cell-cell interactions in real-time.
The fast growth of computer technology led machine learning systems and advanced mathematical algorithms to become valid tools for the automation of experimental processes and faster data collection coming from cell-cell interaction studies. Machine learning is also central to the development of in silico experiments, which will allow the collection of precious data for the optimization of in vivo or in vitro experimental settings. Therefore, machine learning systems used for organoid and organs-on-chip investigations represent another important argument fitting with this Research Topic.
The intensive use of these models boosted the findings revealed in the area of cancer research. This Research Topic will collect impact articles on how the development of advanced in vitro models, such as tumor spheroids, mouse or patient-derived organoids, and microfluidic devices has led to advanced knowledge of the tumor microenvironment. In parallel, review and research articles on recent advances in in silico models to be used in onco-immunology and to evaluate cancer therapy will also be considered.
This Research Topic will welcome the submission of research articles and reviews on the following themes:
• Mouse or human spheroid models (tumor spheroids);
• Mouse organoids for tumor studies and to investigate drug mechanisms/resistance
• Human or patient-derived organoids for drug resistance investigations or studies on particular aspects of the tumor microenvironment
• Exosomes and other extracellular vesicles released by tumor spheroids and organoids
• 3D bioprinting platforms for tumor microenvironment and drug resistance evaluations
• Other novel 3D models for drug resistance investigations or to recapitulate the tumor microenvironment.
• Nanoparticle-based approaches in drug resistance studies with spheroids and organoids.
• 3D Organs-on-chip systems for tumor studies: microfluidic devices and advanced organs-on-chip platforms for drug resistance
• Transcriptome and proteome analysis in spheroid, organoid, and organ-on-chip models of the tumor microenvironment
• Machine learning systems and algorithms for high-throughput studies with 3D systems: ad hoc scripts for automation of processes, decision-making systems for automated evaluation of results, advanced imaging, and time-lapse analysis, and automated evaluation of cell-cell interactions
• In silico experimental modeling of the tumor microenvironment and drug resistance studies upon cancer therapy
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
In the last decades, immunology and oncology made giant steps thanks to the great improvements in emerging complex biotechniques employed by researchers. These become always more sophisticated and have improved our knowledge of the important concepts and scenarios of the tumor microenvironment. The emerging field of immuno-oncology has been set up by the discovery that immunity can bridge cancer through the intimate interaction of the immune cells with tumor cells within the tumor microenvironment.
3D systems deserve particular attention, due to their decisive role to mimic the cell-cell environments. Spheroids represent the first and successful attempt to culture cells (of primary and tumor nature) in a 3D architecture. The direct evolution of these systems is represented by organoids, which can be defined as primary cells closely resembling their derivative organ. Among this category, there are the tumor organoids, which can be defined as organoids where the 3D architecture of the cancer cells also contains the infiltrating immune cells and can be evaluated as a single complex multicellular system, such as mouse or patient-derived tumor organoids.
3D bioprinting is another recent and innovative biotool allowing the recapitulation of the 3D moiety of a tumor with their matrix proteins and cells. These methods are emerging as valid tools to evaluate the effect of drugs in cancer to study drug resistance to tumors. If properly optimized, they can represent valid alternatives to in vivo models and can deeply impact personalized medicine in the context of immuno-oncology.
Other platforms to study the interactions between tumor cells and immune cells are represented by Organs-on-Chip systems. Created to study organ behavior in ad hoc closed compartments, these devices are now largely exploited in the field of tumor immunology to investigate the multifaceted tumor microenvironment cues. These systems are usually represented by ad hoc fabricated microfluidic chips with defined compartmental units and are coupled to complex microscope systems to follow the cell-cell interactions in real-time.
The fast growth of computer technology led machine learning systems and advanced mathematical algorithms to become valid tools for the automation of experimental processes and faster data collection coming from cell-cell interaction studies. Machine learning is also central to the development of in silico experiments, which will allow the collection of precious data for the optimization of in vivo or in vitro experimental settings. Therefore, machine learning systems used for organoid and organs-on-chip investigations represent another important argument fitting with this Research Topic.
The intensive use of these models boosted the findings revealed in the area of cancer research. This Research Topic will collect impact articles on how the development of advanced in vitro models, such as tumor spheroids, mouse or patient-derived organoids, and microfluidic devices has led to advanced knowledge of the tumor microenvironment. In parallel, review and research articles on recent advances in in silico models to be used in onco-immunology and to evaluate cancer therapy will also be considered.
This Research Topic will welcome the submission of research articles and reviews on the following themes:
• Mouse or human spheroid models (tumor spheroids);
• Mouse organoids for tumor studies and to investigate drug mechanisms/resistance
• Human or patient-derived organoids for drug resistance investigations or studies on particular aspects of the tumor microenvironment
• Exosomes and other extracellular vesicles released by tumor spheroids and organoids
• 3D bioprinting platforms for tumor microenvironment and drug resistance evaluations
• Other novel 3D models for drug resistance investigations or to recapitulate the tumor microenvironment.
• Nanoparticle-based approaches in drug resistance studies with spheroids and organoids.
• 3D Organs-on-chip systems for tumor studies: microfluidic devices and advanced organs-on-chip platforms for drug resistance
• Transcriptome and proteome analysis in spheroid, organoid, and organ-on-chip models of the tumor microenvironment
• Machine learning systems and algorithms for high-throughput studies with 3D systems: ad hoc scripts for automation of processes, decision-making systems for automated evaluation of results, advanced imaging, and time-lapse analysis, and automated evaluation of cell-cell interactions
• In silico experimental modeling of the tumor microenvironment and drug resistance studies upon cancer therapy
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
