Delineating the evolutionary dynamics of cancer from theory to reality.
Ivana BozicCatherine J WuPublished in: Nature cancer (2020)
Uncovering and quantifying the laws of the evolutionary dynamics of cancer, in particular in the context of specific genetic lesions and in individual patients, has the potential to revolutionize precision oncology. Recent technological advances in the study of human cancer have increased access to in vivo human data and have thereby facilitated the confirmation or refutation of existing theoretical models. In this Perspective, we discuss recent work at the intersection of quantitative mathematical models of cancer evolution and patient data that provides insights into different stages of tumor evolution, including premalignant and malignant progression and response to therapy.
Keyphrases
- papillary thyroid
- squamous cell
- endothelial cells
- genome wide
- lymph node metastasis
- end stage renal disease
- ejection fraction
- squamous cell carcinoma
- dna methylation
- childhood cancer
- chronic kidney disease
- gene expression
- stem cells
- induced pluripotent stem cells
- young adults
- mesenchymal stem cells
- deep learning
- climate change
- machine learning
- patient reported outcomes
- peritoneal dialysis
- human health
- virtual reality