About this Research Topic
This Research Topic is the second volume of the “Novel Reliable Approaches for Prediction and Clinical Decision-making in Cancer” Community Series. Please see Volume I here
Cancer is one of the leading causes of death worldwide. Significant advances in the understanding of cancer biology have been reported in the last decade. Many important driving changes, especially immunological deregulations are implicated in cancer development and progression. These events lead to cancer complexity and heterogeneity in cancer and play a substantial role in pharmacokinetic variability in anticancer therapies. Furthermore, it remains difficult to quantify the prevalence of recurrence and metastasis. Interestingly, omics have shown promising results and are being used to better describe cancer susceptibility, prognosis, and response to treatment. Additionally, researchers are still searching for immunological and other cancer signatures particularly relevant for effective cancer prediction. Emerging data obtained independently are still insufficient to explain the complexity of cancer. Hopefully, new comprehensive systemic and combinatorial approaches will yield benefit in the future and will lead to the development of personalized treatment regimens and improved immunotherapies.
In this research topic, we welcome submissions that propose new panels including, and not limited to:
--Immunological profiles, such as cytokines, immune receptor signaling pathways, and immune tolerance mechanisms
--Genetic factors in immune function deregulation (e.g., multi-SNP variations, Copy Number Variations)
--Broad analyses involving patient medical histories and cutting-edge genetic testing approaches
--Comprehensive reviews on extracellular vesicles and their roles in cancer
New findings on immune cell behavior within the cancer microenvironment
Authors should present integrative and systemic approaches using clinical data to make reliable predictions of cancer diagnosis, risk and prognosis and to support cancer treatment management and clinical decision-making. They could use, but are not limited to, systemic analysis and machine learning-based prediction. Innovative analysis approaches are encouraged. Outstanding original articles and meta-analyses based on clinical data are eligible for publication.
Note that manuscripts must include immunological information. Further, those manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation (clinical cohort or biological validation in vitro or in vivo), are out of scope for this topic.
Cancer is one of the leading causes of death worldwide. Significant advances in the understanding of cancer biology have been reported in the last decade. Many important driving changes, especially immunological deregulations are implicated in cancer development and progression. These events lead to cancer complexity and heterogeneity in cancer and play a substantial role in pharmacokinetic variability in anticancer therapies. Furthermore, it remains difficult to quantify the prevalence of recurrence and metastasis. Interestingly, omics have shown promising results and are being used to better describe cancer susceptibility, prognosis, and response to treatment. Additionally, researchers are still searching for immunological and other cancer signatures particularly relevant for effective cancer prediction. Emerging data obtained independently are still insufficient to explain the complexity of cancer. Hopefully, new comprehensive systemic and combinatorial approaches will yield benefit in the future and will lead to the development of personalized treatment regimens and improved immunotherapies.
In this research topic, we welcome submissions that propose new panels including, and not limited to:
--Immunological profiles, such as cytokines, immune receptor signaling pathways, and immune tolerance mechanisms
--Genetic factors in immune function deregulation (e.g., multi-SNP variations, Copy Number Variations)
--Broad analyses involving patient medical histories and cutting-edge genetic testing approaches
--Comprehensive reviews on extracellular vesicles and their roles in cancer
New findings on immune cell behavior within the cancer microenvironment
Authors should present integrative and systemic approaches using clinical data to make reliable predictions of cancer diagnosis, risk and prognosis and to support cancer treatment management and clinical decision-making. They could use, but are not limited to, systemic analysis and machine learning-based prediction. Innovative analysis approaches are encouraged. Outstanding original articles and meta-analyses based on clinical data are eligible for publication.
Note that manuscripts must include immunological information. Further, those manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation (clinical cohort or biological validation in vitro or in vivo), are out of scope for this topic.
Keywords: cancer, omics, systemic analysis, Innovative methods, Artificial intelligence, prediction, decision-making, data science, cancer immunology, immunotherapy, clinical data, machine learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
