The key concept of personalized medicine is to identify the best treatment possible for a selected patient, in order to maximize therapeutic efficacy, reduce side effects, and minimize the risk of drug resistance development. To achieve this, it is fundamental to define effective cancer classifiers, which would allow clinicians to stratify patients in appropriate risk groups, minimizing overtreatment of indolent disease and administering new therapies at the right time. With the advances in metabolomics, the evaluation of metabolites has emerged as a strategy to identify new biomarkers. Additionally, the refinement of in vitro 3D models, such as organoids and ex-vivo models recapitulating tumor heterogeneity, has opened new possibilities for the development of personalized therapies.
The prognosis and clinical outcome of urological malignancies, such as prostate and bladder cancer, is directly dependent on metastatic occurrence. The definition of the optimal therapy for urological metastatic cancers remains, however, a challenge.
There is increasing evidence that oncogenic metabolic signaling related to sensors of cellular energy status has deep implications in metastatic disease. Recent studies have demonstrated how the main sensor of cellular energy status, AMP-activated protein kinase (AMPK) acts upstream of the anabolic controls mediated by mTOR, opening new therapeutic possibilities to target cancer metabolism.
The last innovations in cancer metabolism are oriented towards understanding the involvement of oncogenic metabolic signaling, the metabolic control of the epigenome and the metabolic heterogeneity of tumor and stroma. However, these studies are mainly conducted on cellular or animal models.
The aim of this Research Topic is to collect the latest advances in targeting cancer metabolism by using near-patient models such as organoids and ex-vivo models recapitulating tumor heterogeneity.
Specific themes we would like to address are on the following non-exhaustive list:
- Metabolic heterogeneity in primary and metastatic urological cancers
- Targeting cancer metabolism in urological cancers
- Model systems for the study of cancer metabolism
- Model systems for personalized medicine in urological cancers
- Lipid metabolism in urological cancers
We welcome the submission of all article types, including but not limited to Original Research, Review, Mini-Review, Methods, Protocols and Hypothesis and Theory articles.
The key concept of personalized medicine is to identify the best treatment possible for a selected patient, in order to maximize therapeutic efficacy, reduce side effects, and minimize the risk of drug resistance development. To achieve this, it is fundamental to define effective cancer classifiers, which would allow clinicians to stratify patients in appropriate risk groups, minimizing overtreatment of indolent disease and administering new therapies at the right time. With the advances in metabolomics, the evaluation of metabolites has emerged as a strategy to identify new biomarkers. Additionally, the refinement of in vitro 3D models, such as organoids and ex-vivo models recapitulating tumor heterogeneity, has opened new possibilities for the development of personalized therapies.
The prognosis and clinical outcome of urological malignancies, such as prostate and bladder cancer, is directly dependent on metastatic occurrence. The definition of the optimal therapy for urological metastatic cancers remains, however, a challenge.
There is increasing evidence that oncogenic metabolic signaling related to sensors of cellular energy status has deep implications in metastatic disease. Recent studies have demonstrated how the main sensor of cellular energy status, AMP-activated protein kinase (AMPK) acts upstream of the anabolic controls mediated by mTOR, opening new therapeutic possibilities to target cancer metabolism.
The last innovations in cancer metabolism are oriented towards understanding the involvement of oncogenic metabolic signaling, the metabolic control of the epigenome and the metabolic heterogeneity of tumor and stroma. However, these studies are mainly conducted on cellular or animal models.
The aim of this Research Topic is to collect the latest advances in targeting cancer metabolism by using near-patient models such as organoids and ex-vivo models recapitulating tumor heterogeneity.
Specific themes we would like to address are on the following non-exhaustive list:
- Metabolic heterogeneity in primary and metastatic urological cancers
- Targeting cancer metabolism in urological cancers
- Model systems for the study of cancer metabolism
- Model systems for personalized medicine in urological cancers
- Lipid metabolism in urological cancers
We welcome the submission of all article types, including but not limited to Original Research, Review, Mini-Review, Methods, Protocols and Hypothesis and Theory articles.
