Mutagenic Consequences of Sublethal Cell Death Signaling.
Christine J HawkinsMark A MilesPublished in: International journal of molecular sciences (2021)
Many human cancers exhibit defects in key DNA damage response elements that can render tumors insensitive to the cell death-promoting properties of DNA-damaging therapies. Using agents that directly induce apoptosis by targeting apoptotic components, rather than relying on DNA damage to indirectly stimulate apoptosis of cancer cells, may overcome classical blocks exploited by cancer cells to evade apoptotic cell death. However, there is increasing evidence that cells surviving sublethal exposure to classical apoptotic signaling may recover with newly acquired genomic changes which may have oncogenic potential, and so could theoretically spur the development of subsequent cancers in cured patients. Encouragingly, cells surviving sublethal necroptotic signaling did not acquire mutations, suggesting that necroptosis-inducing anti-cancer drugs may be less likely to trigger therapy-related cancers. We are yet to develop effective direct inducers of other cell death pathways, and as such, data regarding the consequences of cells surviving sublethal stimulation of those pathways are still emerging. This review details the currently known mutagenic consequences of cells surviving different cell death signaling pathways, with implications for potential oncogenic transformation. Understanding the mechanisms of mutagenesis associated (or not) with various cell death pathways will guide us in the development of future therapeutics to minimize therapy-related side effects associated with DNA damage.
Keyphrases
- cell death
- cell cycle arrest
- dna damage
- induced apoptosis
- pi k akt
- signaling pathway
- dna damage response
- oxidative stress
- dna repair
- end stage renal disease
- endoplasmic reticulum stress
- gene expression
- chronic kidney disease
- stem cells
- endothelial cells
- ejection fraction
- climate change
- peritoneal dialysis
- small molecule
- mesenchymal stem cells
- machine learning
- transcription factor
- big data
- prognostic factors
- human health
- patient reported outcomes
- drug induced
- replacement therapy