About this Research Topic
Personalized medicine is an emerging field of medicine that aims to provide tailor-made prevention and treatment strategies according to the individual genotypic and phenotypic characteristics of each patient. This approach has been mainly focused on the application of genomic profiling to select the best treatment for each patient minimizing the risk of treatment failure, relapse, and adverse drug effects. The importance of the integration of genomics with other omics technologies in the context of personalized cancer medicine has been recognized.
Cancer cells rewire their operating metabolic machinery to support proliferation, growth, invasion, and resistance to cancer treatments. Hence, the metabolomics approach may provide biomarkers to predict treatment response, predisposition to drug-related adverse effects, and potential relapse through the profiling of the by-products of metabolism (low molecular weight compounds or metabolites) present in biological samples. The steady improvement of the analytical methodologies and data analysis tools of the last few years may provide new opportunities for the integration of metabolic biomarkers in the current guidelines of personalized cancer treatment.
The purpose of this Research Topic is to collect the latest advances and challenges in the application of metabolomics for personalized cancer medicine.
Specific themes include, but are not limited to:
● Discovery and validation studies of biomarkers for monitoring treatment, or predicting the treatment response, adverse drug effects, and recurrence
● Studies of patient stratification based on the molecular phenotype
● Discovery of new metabolic therapeutic targets to improve treatment outcome
● Improved analytical protocols for the translation of metabolomics data into clinical practice
● Artificial intelligence strategies for metabolomics data analysis
● Challenges and emergent solutions for metabolomics in personalized cancer treatment
We warmly welcome the submission of all article types, including but not limited to Original Research, Review, Mini-Review, Methods, Protocols and Perspective articles.
Jacopo Troisi holds patents related to this Research Topic and is founder and CEO of Theoreo srl, a spinoff company of the department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana” at the University of Salerno
All other Topic Editors declare no conflict of interest
Cancer cells rewire their operating metabolic machinery to support proliferation, growth, invasion, and resistance to cancer treatments. Hence, the metabolomics approach may provide biomarkers to predict treatment response, predisposition to drug-related adverse effects, and potential relapse through the profiling of the by-products of metabolism (low molecular weight compounds or metabolites) present in biological samples. The steady improvement of the analytical methodologies and data analysis tools of the last few years may provide new opportunities for the integration of metabolic biomarkers in the current guidelines of personalized cancer treatment.
The purpose of this Research Topic is to collect the latest advances and challenges in the application of metabolomics for personalized cancer medicine.
Specific themes include, but are not limited to:
● Discovery and validation studies of biomarkers for monitoring treatment, or predicting the treatment response, adverse drug effects, and recurrence
● Studies of patient stratification based on the molecular phenotype
● Discovery of new metabolic therapeutic targets to improve treatment outcome
● Improved analytical protocols for the translation of metabolomics data into clinical practice
● Artificial intelligence strategies for metabolomics data analysis
● Challenges and emergent solutions for metabolomics in personalized cancer treatment
We warmly welcome the submission of all article types, including but not limited to Original Research, Review, Mini-Review, Methods, Protocols and Perspective articles.
Jacopo Troisi holds patents related to this Research Topic and is founder and CEO of Theoreo srl, a spinoff company of the department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana” at the University of Salerno
All other Topic Editors declare no conflict of interest
Keywords: metabolomics, lipidomics, metabolic profiling, artificial intelligence, personalized cancer treatments, biomarker discovery, prediction models, treatment outcome, patient stratification, therapeutic targets
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