Probing Isoform Switching Events in Various Cancer Types: Lessons From Pan-Cancer Studies

Recreating metabolic interactions of the tumour microenvironment

Tumours are heterogeneous tissues containing diverse populations of cells and an abundant extracellular matrix (ECM). This tumour microenvironment prompts cancer cells to adapt their metabolism to survive and grow. Besides epigenetic factors, the metabolism of cancer cells is shaped by crosstalk with stromal cells and extracellular components. To date, most experimental models neglect the complexity of the tumour microenvironment and its relevance in regulating the dynamics of the metabolism in cancer. We discuss emerging strategies to model cellular and extracellular aspects of cancer metabolism. We highlight cancer models based on bioengineering, animal, and mathematical approaches to recreate cell-cell and cell-matrix interactions and patient-specific metabolism. Combining these approaches will improve our understanding of cancer metabolism and support the development of metabolism-targeting therapies.

Quantitative Analysis of Isoform Switching in Cancer

Over the past 8 years, multiple studies examined the phenomenon of isoform switching in human cancers and discovered that isoform switching is widespread, with hundreds to thousands of such events per cancer type. Although all of these studies used slightly different definitions of isoform switching, which in part led to a rather poor overlap of their results, they all leveraged transcript usage, a proportion of the transcript's expression in the total expression level of the parent gene, to detect isoform switching. However, how changes in transcript usage correlate with changes in transcript expression is not sufficiently explored. In this article, we adopt the most common definition of isoform switching and use a state-of-the-art tool for the analysis of differential transcript usage, SatuRn, to detect isoform switching events in 12 cancer types. We analyze the detected events in terms of changes in transcript usage and the relationship between transcript usage and transcript expression on a global scale. The results of our analysis suggest that the relationship between changes in transcript usage and changes in transcript expression is far from straightforward, and that such quantitative information can be effectively used for prioritizing isoform switching events for downstream analyses.