Advances in genetics: widening our understanding of prostate cancer

The role of the p90 ribosomal S6 kinase family in prostate cancer progression and therapy resistance

Prostate cancer (PCa) is the second most commonly occurring cancer in men, with over a million new cases every year worldwide. Tumor growth and disease progression is mainly dependent on the Androgen Receptor (AR), a ligand dependent transcription factor. Standard PCa therapeutic treatments include androgen-deprivation therapy and AR signaling inhibitors. Despite being successful in controlling the disease in the majority of men, the high frequency of disease progression to aggressive and therapy resistant stages (termed castrate resistant prostate cancer) has led to the search for new therapeutic targets. The p90 ribosomal S6 kinase (RSK1-4) family is a group of highly conserved Ser/Thr kinases that holds promise as a novel target. RSKs are effector kinases that lay downstream of the Ras/Raf/MEK/ERK signaling pathway, and aberrant activation or expression of RSKs has been reported in several malignancies, including PCa. Despite their structural similarities, RSK isoforms have been shown to perform nonredundant functions and target a wide range of substrates involved in regulation of transcription and translation. In this article we review the roles of the RSKs in proliferation and motility, cell cycle control and therapy resistance in PCa, highlighting the possible interplay between RSKs and AR in mediating disease progression. In addition, we summarize the current advances in RSK inhibitor development and discuss their potential clinical benefits.

The Development of a Personalised Training Framework: Implementation of Emerging Technologies for Performance

Over the last decade, there has been considerable interest in the individualisation of athlete training, including the use of genetic information, alongside more advanced data capture and analysis techniques. Here, we explore the evidence for, and practical use of, a number of these emerging technologies, including the measurement and quantification of epigenetic changes, microbiome analysis and the use of cell-free DNA, along with data mining and machine learning. In doing so, we develop a theoretical model for the use of these technologies in an elite sport setting, allowing the coach to better answer six key questions: (1) To what training will my athlete best respond? (2) How well is my athlete adapting to training? (3) When should I change the training stimulus (i.e., has the athlete reached their adaptive ceiling for this training modality)? (4) How long will it take for a certain adaptation to occur? (5) How well is my athlete tolerating the current training load? (6) What load can my athlete handle today? Special consideration is given to whether such an individualised training framework will outperform current methods as well as the challenges in implementing this approach.