About this Research Topic
Cancer cells can adapt their metabolic phenotype to meet their bioenergetic and biosynthetic needs, to adapt to the heterogeneous tumor microenvironment, and to survive the various therapeutic treatments. Only recently it became clear that cancer metabolism is not just a binary decision-making process between glycolysis and oxidative phosphorylation (OXPHOS). Instead, studies in the past decade show that cancer cells can mix and match various metabolic phenotypes and exhibit a hybrid metabolic phenotype where both glycolysis and OXPHOS are actively used or a metabolically “low-low” phenotype where cells exhibit low activity of glycolysis and OXPHOS simultaneously. More importantly, the hybrid metabolic phenotype can characterize highly metastatic breast cancer cells and the low-low phenotype can characterize drug-tolerant melanoma cells. Meanwhile, with the development of single-cell omics technologies, recent works start to deeply characterize and understand the tumor heterogeneity in gene expression, genetic mutations, and epigenomic levels. However, metabolic heterogeneity of cancer remains mostly invisible, largely due to the lack of effective technologies. Therefore, rigorous quantification of various metabolic activity in cancer cells, especially at the single-cell or sub-cellular level, would provide valuable insights into cancer metabolism and how different metabolic phenotypes contribute to other hallmarks of cancer, such as metastasis, dormancy, stemness, drug-tolerance/resistance, immune evasion, etc.
To decipher the sheer complexity and the multi-faceted nature of cancer, systems biology approaches that emphasize the interactions among genes, proteins, metabolites etc. have been developed and applied. Such approaches have successfully created a synergy between theoretical/computational and experimental biology and have led to new discoveries at a rapid pace. In this Research Topic, we will highlight studies that utilize interdisciplinary approaches to elucidate how cancer cells adapt their metabolic activity under selective pressure, how the reprogrammed cancer metabolic activity contributes to cancer emergent properties. We will also cover studies that utilized advanced technologies to investigating the metabolic heterogeneity of cancer at single-cell or sub-cellular resolutions. Studies that investigate immune metabolism and its association with cancer will also be highlighted.
We welcome Original Research, Perspectives, Reviews, Mini-Review articles. The contents covered in this Research Topic include but are not limited to the following subtopics:
1. Using integrated theoretical/computational-experimental approaches to identify new therapeutic strategies that target cancer metabolism.
2. Using integrative “omics” approaches to explore tumor metabolic heterogeneity, clonal evolution, interactions among tumor cells and with their microenvironment, etc.
3. Development of bottom-up (biochemical modeling) or top-down (machine learning) approaches in construction and analysis of cellular networks involved in cancer metabolism and its coupling with other hallmarks of cancer.
4. Single-cell or sub-cellular level analysis of cancer metabolism that reveals novel insights.
5. Elucidate the mutual interactions between cancer metabolism and the change of the tumor immune microenvironment (TIM). For example, effects of autophagy on the changes of TIM, reshaping of TIM by secondary metabolites produced by cancer cells, etc.
6. Analysis of immune cell metabolism and their association with cancer progression, tumor killing efficacy, etc.
7. Bridging the gap between the metabolism and stemness of cancer cells. There are studies showing different subpopulations of breast cancer stem cells (epithelial-like or mesenchymal-like) depend on different metabolism pathways. A rigorous characterization of the metabolic phenotypes of cancer stem cells, and the cause-and-effect between metabolism and stemness would be appreciated.
8. Revealing the mechanisms of how reprogrammed cancer metabolic activity contributes to drug resistance. For example, what is the metabolic characterization of drug-resistance cancer cells? Whether targeting metabolism can attenuate cancer drug-resistance?
Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases that are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in Frontiers in Oncology. For such manuscripts, please submit through the journal, Frontiers in Genetics - Cancer Genetics and Oncogenomics.
To decipher the sheer complexity and the multi-faceted nature of cancer, systems biology approaches that emphasize the interactions among genes, proteins, metabolites etc. have been developed and applied. Such approaches have successfully created a synergy between theoretical/computational and experimental biology and have led to new discoveries at a rapid pace. In this Research Topic, we will highlight studies that utilize interdisciplinary approaches to elucidate how cancer cells adapt their metabolic activity under selective pressure, how the reprogrammed cancer metabolic activity contributes to cancer emergent properties. We will also cover studies that utilized advanced technologies to investigating the metabolic heterogeneity of cancer at single-cell or sub-cellular resolutions. Studies that investigate immune metabolism and its association with cancer will also be highlighted.
We welcome Original Research, Perspectives, Reviews, Mini-Review articles. The contents covered in this Research Topic include but are not limited to the following subtopics:
1. Using integrated theoretical/computational-experimental approaches to identify new therapeutic strategies that target cancer metabolism.
2. Using integrative “omics” approaches to explore tumor metabolic heterogeneity, clonal evolution, interactions among tumor cells and with their microenvironment, etc.
3. Development of bottom-up (biochemical modeling) or top-down (machine learning) approaches in construction and analysis of cellular networks involved in cancer metabolism and its coupling with other hallmarks of cancer.
4. Single-cell or sub-cellular level analysis of cancer metabolism that reveals novel insights.
5. Elucidate the mutual interactions between cancer metabolism and the change of the tumor immune microenvironment (TIM). For example, effects of autophagy on the changes of TIM, reshaping of TIM by secondary metabolites produced by cancer cells, etc.
6. Analysis of immune cell metabolism and their association with cancer progression, tumor killing efficacy, etc.
7. Bridging the gap between the metabolism and stemness of cancer cells. There are studies showing different subpopulations of breast cancer stem cells (epithelial-like or mesenchymal-like) depend on different metabolism pathways. A rigorous characterization of the metabolic phenotypes of cancer stem cells, and the cause-and-effect between metabolism and stemness would be appreciated.
8. Revealing the mechanisms of how reprogrammed cancer metabolic activity contributes to drug resistance. For example, what is the metabolic characterization of drug-resistance cancer cells? Whether targeting metabolism can attenuate cancer drug-resistance?
Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases that are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in Frontiers in Oncology. For such manuscripts, please submit through the journal, Frontiers in Genetics - Cancer Genetics and Oncogenomics.
Keywords: Cancer metabolism, Systems biology, Omics, Tumor heterogeneity, Cancer emergent properties, Metastasis, Dormancy, Stemness, Drug-tolerance/resistance, Immune evasion
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.
