Systems biology approaches in hormone dependent cancer research: what a long strange trip it’s been

Systems biology approaches in hormone dependent cancer research collection banner

Endocrine-Related Cancer is pleased to announce a themed collection entitled “Systems biology approaches in hormone dependent cancer research”. The concept of systems approaches to biology was first discussed in the 1960s and was catalyzed by the application of omic approaches in the 1990s to catalog systems with ever greater clarity. These approaches are scalable and offer the opportunity to define, from multiple perspectives, the control structure of hormone signaling ranging from genomic architecture to the population level in a quantitative and predictive manner. The rationale for developing this understanding is that establishing the architecture of hormone signaling, coupled with revealing how genomic and epigenomic disruptions dismantle these networks, will pave the way to identifying novel cancer-driver mechanisms and therapeutic opportunities as well as revealing how therapy resistance arises.

This themed collection is timely as empirical discovery driven by reductionist biology has potentially reached a point of diminishing returns, and systems perspectives may pave the way to a more comprehensive insight into hormone dependent cancers. The collection will highlight where both bottom-up predicative modelling and top-down integrative approaches are revealing insights into well-known hormone signaling systems, such as those of steroid hormone signaling in breast and prostate cancers. The collection will complement these new insights with emerging areas, for example in defining systems through single cell and spatial approaches to generate understanding of hormonal interactions in the tumor microenvironment. Importantly, the collection will discuss where roadblocks also impede progress, which range from both biological understanding such as how phase separation impacts transcriptional control, to the training of biologists to provide high-level quantitative insight, and inevitably, to speculate on where artificial intelligence approaches may move the field forward.

If you are interested in contributing an original research article to this collection, please submit your article proposal to erc@bioscientifica.com.