Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics.
Souzana LogothetiEugenia PapadakiVasiliki ZolotaChristopher LogothetisAristidis G VrahatisRama SoundararajanVasiliki TzelepiPublished in: Cancers (2023)
Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.
Keyphrases
- single cell
- prostate cancer
- rna seq
- machine learning
- end stage renal disease
- ejection fraction
- stem cells
- deep learning
- newly diagnosed
- prognostic factors
- radical prostatectomy
- dna methylation
- epithelial mesenchymal transition
- induced apoptosis
- cell fate
- gene expression
- squamous cell carcinoma
- patient reported outcomes
- case report
- signaling pathway
- cell proliferation
- oxidative stress
- single molecule
- cell cycle arrest
- big data