Harnessing Omics Approaches on Advanced Preclinical Models to Discovery Novel Therapeutic Targets for the Treatment of Metastatic Colorectal Cancer.
Manuela PorruPasquale ZizzaNadia PaneraAnna AlisiAnnamaria BiroccioCarlo LeonettiPublished in: Cancers (2020)
Metastatic colorectal cancer (mCRC) remains challenging because of the emergence of resistance mechanisms to anti-epidermal growth factor receptor (EGFR) therapeutics, so more effective strategies to improve the patients' outcome are needed. During the last decade, the application of a multi-omics approach has contributed to a deeper understanding of the complex molecular landscape of human CRC, identifying a plethora of drug targets for precision medicine. Target validation relies on the use of experimental models that would retain the molecular and clinical features of human colorectal cancer, thus mirroring the clinical characteristics of patients. In particular, organoids and patient-derived-xenografts (PDXs), as well as genetically engineered mouse models (GEMMs) and patient-derived orthotopic xenografts (PDOXs), should be considered for translational purposes. Overall, omics and advanced mouse models of cancer represent a portfolio of sophisticated biological tools that, if optimized for use in concert with accurate data analysis, could accelerate the anticancer discovery process and provide new weapons against cancer. In this review, we highlight success reached following the integration of omics and experimental models; moreover, results produced by our group in the field of mCRC are also presented.
Keyphrases
- epidermal growth factor receptor
- metastatic colorectal cancer
- end stage renal disease
- single cell
- ejection fraction
- small molecule
- endothelial cells
- data analysis
- mouse model
- chronic kidney disease
- newly diagnosed
- tyrosine kinase
- small cell lung cancer
- peritoneal dialysis
- papillary thyroid
- prognostic factors
- emergency department
- machine learning
- stem cells
- single molecule
- artificial intelligence
- cell therapy
- deep learning
- bone marrow
- adverse drug
- replacement therapy