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 stillsearching 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 information (cytokines, signal transduction pathways involving immune receptors, immune tolerance molecules, extracellular vesicles, immune cells, etc.), genetic information related to genes involved in the deregulation of immune functions (eg,multi-SNP, CNV), medical history, imaging results. Authors should present integrative and systemic approaches using clinical data to make reliable predictions of cancer risk and its prognosis and to support cancer treatment management and clinical decision-making.Authors could use and 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. 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 stillsearching 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 information (cytokines, signal transduction pathways involving immune receptors, immune tolerance molecules, extracellular vesicles, immune cells, etc.), genetic information related to genes involved in the deregulation of immune functions (eg,multi-SNP, CNV), medical history, imaging results. Authors should present integrative and systemic approaches using clinical data to make reliable predictions of cancer risk and its prognosis and to support cancer treatment management and clinical decision-making.Authors could use and 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. 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.