Crosstalk between glucocorticoid and mineralocorticoid receptors boosts glucocorticoid-induced killing of multiple myeloma cells.

The glucocorticoid receptor (GR) is a crucial drug target in multiple myeloma as its activation with glucocorticoids effectively triggers myeloma cell death. However, as high-dose glucocorticoids are also associated with deleterious side effects, novel approaches are urgently needed to improve GR action in myeloma. Here, we reveal a functional crosstalk between GR and the mineralocorticoid receptor (MR) that plays a role in improved myeloma cell killing. We show that the GR agonist dexamethasone (Dex) downregulates MR levels in a GR-dependent way in myeloma cells. Co-treatment of Dex with the MR antagonist spironolactone (Spi) enhances Dex-induced cell killing in primary, newly diagnosed GC-sensitive myeloma cells. In a relapsed GC-resistant setting, Spi alone induces distinct myeloma cell killing. On a mechanistic level, we find that a GR-MR crosstalk likely arises from an endogenous interaction between GR and MR in myeloma cells. Quantitative dimerization assays show that Spi reduces Dex-induced GR-MR heterodimerization and completely abolishes Dex-induced MR-MR homodimerization, while leaving GR-GR homodimerization intact. Unbiased transcriptomics analyses reveal that c-myc and many of its target genes are downregulated most by combined Dex-Spi treatment. Proteomics analyses further identify that several metabolic hallmarks are modulated most by this combination treatment. Finally, we identified a subset of Dex-Spi downregulated genes and proteins that may predict prognosis in the CoMMpass myeloma patient cohort. Our study demonstrates that GR-MR crosstalk is therapeutically relevant in myeloma as it provides novel strategies for glucocorticoid-based dose-reduction.

Reduced NCOR2 expression accelerates androgen deprivation therapy failure in prostate cancer.

This study addresses the roles of nuclear receptor corepressor 2 (NCOR2) in prostate cancer (PC) progression in response to androgen deprivation therapy (ADT). Reduced NCOR2 expression significantly associates with shorter disease-free survival in patients with PC receiving adjuvant ADT. Utilizing the CWR22 xenograft model, we demonstrate that stably reduced NCOR2 expression accelerates disease recurrence following ADT, associates with gene expression patterns that include neuroendocrine features, and induces DNA hypermethylation. Stably reduced NCOR2 expression in isogenic LNCaP (androgen-sensitive) and LNCaP-C4-2 (androgen-independent) cells revealed that NCOR2 reduction phenocopies the impact of androgen treatment and induces global DNA hypermethylation patterns. NCOR2 genomic binding is greatest in LNCaP-C4-2 cells and most clearly associates with forkhead box (FOX) transcription factor FOXA1 binding. NCOR2 binding significantly associates with transcriptional regulation most when in active enhancer regions. These studies reveal robust roles for NCOR2 in regulating the PC transcriptome and epigenome and underscore recent mutational studies linking NCOR2 loss of function to PC disease progression.

Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo.

Integrative analyses that summarize and link molecular data to treatment sensitivity are crucial to capture the biological complexity which is essential to further precision medicine. We introduce Weighted Orthogonal Nonnegative parallel factor analysis (WON-PARAFAC), a data integration method that identifies sparse and interpretable factors. WON-PARAFAC summarizes the GDSC1000 cell line compendium in 130 factors. We interpret the factors based on their association with recurrent molecular alterations, pathway enrichment, cancer type, and drug-response. Crucially, the cell line derived factors capture the majority of the relevant biological variation in Patient-Derived Xenograft (PDX) models, strongly suggesting our factors capture invariant and generalizable aspects of cancer biology. Furthermore, drug response in cell lines is better and more consistently translated to PDXs using factor-based predictors as compared to raw feature-based predictors. WON-PARAFAC efficiently summarizes and integrates multiway high-dimensional genomic data and enhances translatability of drug response prediction from cell lines to patient-derived xenografts.