Multi-omics approaches have emerged as powerful tools to investigate the complex nature of cancer and its response to immunotherapy. These approaches integrate data from various sources, such as genomics, transcriptomics, proteomics, and metabolomics, to provide a comprehensive view of the molecular landscape of tumors. By using multi-omics approaches, researchers can identify new biomarkers, pathways, and targets that can guide the development of personalized cancer immunotherapies.
One of the major challenges in cancer immunotherapy is heterogeneity, which refers to the diverse characteristics of cancer cells within a single tumor or among different tumors. Heterogeneity can lead to differential responses to immunotherapy and the development of resistance. Multi-omics approaches can help to decode the heterogeneity of tumors by revealing the molecular mechanisms underlying the different cell types and states present in tumors. This can aid in the design of more effective immunotherapeutic strategies that target the specific features of individual tumors. Overall, multi-omics approaches hold great promise for advancing our understanding of cancer immunotherapy and improving patient outcomes.
The specific goal of this topic is to highlight the importance and potential of multi-omics approaches in understanding the heterogeneity of cancer and its response to immunotherapy. We aim to provide an overview of the current state-of-the-art in multi-omics technologies, as well as their applications in cancer immunotherapy research.
The topic also seeks to address the challenges associated with heterogeneity in cancer and the limitations of traditional single-omics approaches in capturing the complexity of the disease. By integrating multiple omics data sets, researchers can obtain a more comprehensive understanding of the molecular mechanisms underlying cancer heterogeneity, which can inform the development of more effective personalized cancer immunotherapies.
In this research topic, we welcome Reviews, Original Research Articles as well as Perspective, Clinical Trial and Systematic Review articles, which provide a comprehensive overview of the role of multi-omics approaches in decoding the heterogeneity of cancer and improving the efficacy of immunotherapy.
Areas to be covered in this research topic may include, but are not limited to:
1. Multi-omics technologies and their applications in immunotherapy in cancers.
2. Investigating the molecular mechanisms underlying cancer heterogeneity using multi-omics approaches.
3. Exploring new biomarkers, pathways, and targets for personalized cancer immunotherapy based on the use of multi-omics data.
4. Integration of multi-omics data with clinical data to improve patient outcomes.
5. Challenges of multi-omics approaches and potential solutions to overcome them.
6. Identifying the molecular subtypes by integrating multi-omics in cancers.
7. Artificial intelligence or machine learning to predict therapy response to immunotherapy.
8. Case studies and clinical trials evaluating the efficacy of personalized cancer immunotherapies guided by multi-omics data.
Please note that:
- If patient data are analyzed, a comprehensive description of the patients including sex, age, diagnostic criteria, inclusion and exclusion criteria, disease stage, therapy received, comorbidities as well as additional clinical information and assessment of clinical response/effects should be included.
- If genetic, proteomics, metabolomics, or other omics data are analyzed, a comprehensive description of the methods and the rationale for the selection of the specific data studied should be provided.
- Studies related to natural compounds, herbal extracts, or traditional medicine products, will not be included in this Research Topic.
Multi-omics approaches have emerged as powerful tools to investigate the complex nature of cancer and its response to immunotherapy. These approaches integrate data from various sources, such as genomics, transcriptomics, proteomics, and metabolomics, to provide a comprehensive view of the molecular landscape of tumors. By using multi-omics approaches, researchers can identify new biomarkers, pathways, and targets that can guide the development of personalized cancer immunotherapies.
One of the major challenges in cancer immunotherapy is heterogeneity, which refers to the diverse characteristics of cancer cells within a single tumor or among different tumors. Heterogeneity can lead to differential responses to immunotherapy and the development of resistance. Multi-omics approaches can help to decode the heterogeneity of tumors by revealing the molecular mechanisms underlying the different cell types and states present in tumors. This can aid in the design of more effective immunotherapeutic strategies that target the specific features of individual tumors. Overall, multi-omics approaches hold great promise for advancing our understanding of cancer immunotherapy and improving patient outcomes.
The specific goal of this topic is to highlight the importance and potential of multi-omics approaches in understanding the heterogeneity of cancer and its response to immunotherapy. We aim to provide an overview of the current state-of-the-art in multi-omics technologies, as well as their applications in cancer immunotherapy research.
The topic also seeks to address the challenges associated with heterogeneity in cancer and the limitations of traditional single-omics approaches in capturing the complexity of the disease. By integrating multiple omics data sets, researchers can obtain a more comprehensive understanding of the molecular mechanisms underlying cancer heterogeneity, which can inform the development of more effective personalized cancer immunotherapies.
In this research topic, we welcome Reviews, Original Research Articles as well as Perspective, Clinical Trial and Systematic Review articles, which provide a comprehensive overview of the role of multi-omics approaches in decoding the heterogeneity of cancer and improving the efficacy of immunotherapy.
Areas to be covered in this research topic may include, but are not limited to:
1. Multi-omics technologies and their applications in immunotherapy in cancers.
2. Investigating the molecular mechanisms underlying cancer heterogeneity using multi-omics approaches.
3. Exploring new biomarkers, pathways, and targets for personalized cancer immunotherapy based on the use of multi-omics data.
4. Integration of multi-omics data with clinical data to improve patient outcomes.
5. Challenges of multi-omics approaches and potential solutions to overcome them.
6. Identifying the molecular subtypes by integrating multi-omics in cancers.
7. Artificial intelligence or machine learning to predict therapy response to immunotherapy.
8. Case studies and clinical trials evaluating the efficacy of personalized cancer immunotherapies guided by multi-omics data.
Please note that:
- If patient data are analyzed, a comprehensive description of the patients including sex, age, diagnostic criteria, inclusion and exclusion criteria, disease stage, therapy received, comorbidities as well as additional clinical information and assessment of clinical response/effects should be included.
- If genetic, proteomics, metabolomics, or other omics data are analyzed, a comprehensive description of the methods and the rationale for the selection of the specific data studied should be provided.
- Studies related to natural compounds, herbal extracts, or traditional medicine products, will not be included in this Research Topic.
