Spatial Modeling of Prostate Cancer Metabolic Gene Expression Reveals Extensive Heterogeneity and Selective Vulnerabilities
This week we profile a recent publication in Scientific Reports from Dr.
Yuliang Wang at the Institute for Stem Cell and Regenerative Medicine.
Can you provide a brief overview of your lab’s current research focus?
My lab uses high resolution omics data, such as spatial transcriptomics and single cell RNA-sequencing, to understand how the metabolic network and signaling network operates in developmental processes and how such biological networks are impacted by diseases like cancer. Spatial transcriptomics is a novel set of technologies that enable researchers to measure the expression levels of thousands of genes across hundreds of locations within a tissue biopsy. This is a significant improvement over previous technologies that measure the average expression levels across the whole biopsy.
What is the significance of the findings in this publication?
Targeting metabolic aberrations has been a major focus in cancer research, and genome-scale metabolic network models have been used to guide the identification of selective targets. However, such models are often based on gene expression data of the whole tumor biopsy, which contains both malignant and non-malignant cells, and lacks specificity. Our study showed that within a tumor biopsy, there is extensive spatial heterogeneity of metabolic activities – with adjacent regions engaged in fatty acid desaturation vs. oxidation due to hypoxia, and distinct spatial distributions of immunomodulatory metabolites such as prostaglandins and leukotrienes within the tumor microenvironment. Our analysis exploited such heterogeneity and pinpointed a few selective vulnerabilities of prostate cancer cells, including their additional reliance on unsaturated fatty acids and the amino acid cysteine. This publication provides a computational framework to leverage the emerging spatial transcriptomics datasets to identify aspects of cancer metabolism that can be targeted by small molecule drugs, some of which are already FDA-approved for other diseases.
What are the next steps for this research?
We will apply our computational framework to other spatial transcriptomics datasets that are becoming publicly available for diverse organs and diseases almost every week (e.g., heart, brain, pancreatic cancer, amyotrophic lateral sclerosis). We have two focuses in the future. First, we will develop a computational approach that enable us to model more complex spatial patterns of metabolic activities such as gradients, stripes with higher resolution. Second, we will investigate how ligand-receptor mediated intercellular crosstalk affects the distribution of metabolic activity in a tissue structure. Ultimately, this will provide computational roadmaps that enable clinicians to target metabolic malfunctions in multiple diseases with high precision.
This work was funded by:
This project is supported by the University of Washington Royalty Research Fund and the University of Washington Institute for Stem Cell & Regenerative Medicine.