The field of oncology has seen significant advancements with the discovery and study of Circulating Tumor Cells (CTCs). These cells, released into the bloodstream from primary and metastatic tumors, have the potential to form new tumors in distant organs. CTCs serve as a real-time liquid biopsy, reflecting tumors spatial and temporal heterogeneity. They have shown promise as a prognostic indicator in various types of cancer, with higher levels in the bloodstream associated with poorer outcomes. However, despite the clinical validity and utility of CTCs, their use in clinical practice is primarily for prognostic purposes.
The primary objective of this research topic is to explore the clinical relevance of CTCs beyond their prognostic value. This includes emerging methods application for CTCs characterization that have not been incorporated into clinical practices outside of clinical trials. The goal is to leverage the unique opportunity provided by CTCs to study genetic mutations, gene expression patterns, and biomarkers expressed by tumor cells in real time. This information can be used to identify specific molecular targets for understanding drug resistance mechanisms and drug development, leading to personalized treatment options that can prevent metastasis and improve patient outcomes.
The scope of this research topic is focused on the recognition methods of CTCs based on deep learning and emerging tools to investigate the genome, transcriptome, proteome, and secretome of CTCs as biomarkers. We welcome articles addressing, but not limited to, the following themes:
- Biomarker discovery
- Treatment guidance
- Drug resistance mechanisms
- Personalized medicine
- New drug targets
- Drug efficacy assessment
Please note that 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.
The field of oncology has seen significant advancements with the discovery and study of Circulating Tumor Cells (CTCs). These cells, released into the bloodstream from primary and metastatic tumors, have the potential to form new tumors in distant organs. CTCs serve as a real-time liquid biopsy, reflecting tumors spatial and temporal heterogeneity. They have shown promise as a prognostic indicator in various types of cancer, with higher levels in the bloodstream associated with poorer outcomes. However, despite the clinical validity and utility of CTCs, their use in clinical practice is primarily for prognostic purposes.
The primary objective of this research topic is to explore the clinical relevance of CTCs beyond their prognostic value. This includes emerging methods application for CTCs characterization that have not been incorporated into clinical practices outside of clinical trials. The goal is to leverage the unique opportunity provided by CTCs to study genetic mutations, gene expression patterns, and biomarkers expressed by tumor cells in real time. This information can be used to identify specific molecular targets for understanding drug resistance mechanisms and drug development, leading to personalized treatment options that can prevent metastasis and improve patient outcomes.
The scope of this research topic is focused on the recognition methods of CTCs based on deep learning and emerging tools to investigate the genome, transcriptome, proteome, and secretome of CTCs as biomarkers. We welcome articles addressing, but not limited to, the following themes:
- Biomarker discovery
- Treatment guidance
- Drug resistance mechanisms
- Personalized medicine
- New drug targets
- Drug efficacy assessment
Please note that 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.