Are androgen receptor agonists a treatment option in bladder cancer?

Sex-related differences in bladder cancer incidence and progression infer a role for sex hormones and their cognate receptors in this disease. In part due to the oncogenic role of androgen receptor signaling in prostate cancer, the focus of most preclinical and clinical research to-date has been on the potential pro-tumorigenic action of androgens in urothelial cancers. However, clinical studies of androgen receptor antagonism have yielded minimal success.

CDK9 inhibition inhibits multiple oncogenic transcriptional and epigenetic pathways in prostate cancer.

Background: Cyclin-dependent kinase 9 (CDK9) stimulates oncogenic transcriptional pathways in cancer and CDK9 inhibitors have emerged as promising therapeutic candidates.

Methods: The activity of an orally bioavailable CDK9 inhibitor, CDKI-73, was evaluated in prostate cancer cell lines, a xenograft mouse model, and patient-derived tumor explants and organoids. Expression of CDK9 was evaluated in clinical specimens by mining public datasets and immunohistochemistry.

ACSM1 and ACSM3 Regulate Fatty Acid Metabolism to Support Prostate Cancer Growth and Constrain Ferroptosis.

Solid tumors are highly reliant on lipids for energy, growth, and survival. In prostate cancer, the activity of the androgen receptor (AR) is associated with reprogramming of lipid metabolic processes. Here, we identified acyl-CoA synthetase medium chain family members 1 and 3 (ACSM1 and ACSM3) as AR-regulated mediators of prostate cancer metabolism and growth.

Library size confounds biology in spatial transcriptomics data.

Spatial molecular data has transformed the study of disease microenvironments, though, larger datasets pose an analytics challenge prompting the direct adoption of single-cell RNA-sequencing tools including normalization methods. Here, we demonstrate that library size is associated with tissue structure and that normalizing these effects out using commonly applied scRNA-seq normalization methods will negatively affect spatial domain identification. Spatial data should not be specifically corrected for library size prior to analysis, and algorithms designed for scRNA-seq data should be adopted with caution.