Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated “Omics” Approaches to Explore Measurable Metrics

SIMPLE SUMMARY: Prostate cancer remains the most frequent cause of cancer morbidity, the second most frequent cause of cancer mortality in men in the developed world and is an exemplar of a heterogeneous disease. Stemness phenotypes and lineage plasticity have been highlighted as key drivers of heterogeneity observed both across patients and within the same patient. However, markers that indicate the presence or absence of these events remain to be identified. Next-generation sequencing has proven to be a beneficial approach to distinguish predictive and prognostic biomarkers in various diseases, including prostate cancer. This review explores measurable metrics that can reliably reflect lineage plasticity at the genomic, transcriptomic, and epigenomic levels, as well as bioinformatic tools that can be used to identify measures of lineage-plasticity in prostate cancer, in order to inform preclinical and clinical research. ABSTRACT: 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.