Even targeted therapy, immunotherapy, and combination therapy have revolutionized the clinical management of advanced urological tumors, various resistance mechanisms make only a subset of patients experience enduring clinical benefits. Genomics perturbation and complex tumor immune microenvironment components might induce poor response rates to those therapies. Thus, there is an urgent need to investigate the heterogenous therapy response rate of urologic tumors. Single omics could help scientists to find novel biomarkers and biological mechanisms of therapy resistance while ignoring the serial crosstalk among different omics levels. Previous studies also suggested that therapy resistance could be explained as the outcomes of perturbation of multi-omics including genomics, transcriptomics, proteomics, and metabolomics. Thus, comprehensive integration of multi-omics datasets could further reduce the biases and better investigate the landscape involved in therapy resistance.
This Research Topic aims to develop a novel risk evaluation system related to target and immunotherapy resistance at the multi-omics level, which might improve the design and outcome of clinical studies. We also encourage exploring the heterogeneity of urological cancers at the multi-omics level, single-cell sequence, patient-derived tumor xenograft model, and retrospective or prospective cohorts with the application of machine learning algorithms to better understand the perturbation of tumor microenvironment components. We welcome submissions covering but not limited to the following sub-topics:
1) Novel molecular subtypes of tumors benefiting precision medicine for urologic cancers.
2) Novel molecular mechanisms to predict sensitivity to targeted therapy, immunotherapy, and/or combination therapy.
3) Metabolism mechanisms in targeted therapy, immunotherapy, and/or combination therapy for urologic cancers.
4) Discovery of novel cell subcluster involved in therapy resistance by combining bulk sequencing and single cell sequencing.
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.
All other Guest Editors declare no competing interests with regards to the Research Topic subject.
Keywords:
targeted therapy, immunotherapy, resistance, combination therapy, urologic tumors, tumor heterogeneity
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.
Even targeted therapy, immunotherapy, and combination therapy have revolutionized the clinical management of advanced urological tumors, various resistance mechanisms make only a subset of patients experience enduring clinical benefits. Genomics perturbation and complex tumor immune microenvironment components might induce poor response rates to those therapies. Thus, there is an urgent need to investigate the heterogenous therapy response rate of urologic tumors. Single omics could help scientists to find novel biomarkers and biological mechanisms of therapy resistance while ignoring the serial crosstalk among different omics levels. Previous studies also suggested that therapy resistance could be explained as the outcomes of perturbation of multi-omics including genomics, transcriptomics, proteomics, and metabolomics. Thus, comprehensive integration of multi-omics datasets could further reduce the biases and better investigate the landscape involved in therapy resistance.
This Research Topic aims to develop a novel risk evaluation system related to target and immunotherapy resistance at the multi-omics level, which might improve the design and outcome of clinical studies. We also encourage exploring the heterogeneity of urological cancers at the multi-omics level, single-cell sequence, patient-derived tumor xenograft model, and retrospective or prospective cohorts with the application of machine learning algorithms to better understand the perturbation of tumor microenvironment components. We welcome submissions covering but not limited to the following sub-topics:
1) Novel molecular subtypes of tumors benefiting precision medicine for urologic cancers.
2) Novel molecular mechanisms to predict sensitivity to targeted therapy, immunotherapy, and/or combination therapy.
3) Metabolism mechanisms in targeted therapy, immunotherapy, and/or combination therapy for urologic cancers.
4) Discovery of novel cell subcluster involved in therapy resistance by combining bulk sequencing and single cell sequencing.
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.
All other Guest Editors declare no competing interests with regards to the Research Topic subject.
Keywords:
targeted therapy, immunotherapy, resistance, combination therapy, urologic tumors, tumor heterogeneity
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.