Co-targeting BET, CBP, and p300 inhibits neuroendocrine signalling in androgen receptor-null prostate cancer.

There are diverse phenotypes of castration-resistant prostate cancer, including neuroendocrine disease, that vary in their sensitivity to drug treatment. The efficacy of BET and CBP/p300 inhibitors in prostate cancer is attributed, at least in part, to their ability to decrease androgen receptor (AR) signalling. However, the activity of BET and CBP/p300 inhibitors in prostate cancers that lack the AR is unclear. In this study, we showed that BRD4, CBP, and p300 were co-expressed in AR-positive and AR-null prostate cancer. A combined inhibitor of these three proteins, NEO2734, reduced the growth of both AR-positive and AR-null organoids, as measured by changes in viability, size, and composition. NEO2734 treatment caused consistent transcriptional downregulation of cell cycle pathways. In neuroendocrine models, NEO2734 treatment reduced ASCL1 levels and other neuroendocrine markers, and reduced tumour growth in vivo. Collectively, these results show that epigenome-targeted inhibitors cause decreased growth and phenotype-dependent disruption of lineage regulators in neuroendocrine prostate cancer, warranting further development of compounds with this activity in the clinic. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Defining the challenges and opportunities for using patient-derived models in prostate cancer research.

BACKGROUND: There are relatively few widely used models of prostate cancer compared to other common malignancies. This impedes translational prostate cancer research because the range of models does not reflect the diversity of disease seen in clinical practice. In response to this challenge, research laboratories around the world have been developing new patient-derived models of prostate cancer, including xenografts, organoids, and tumor explants. METHODS: In May 2023, we held a workshop at the Monash University Prato Campus for researchers with expertise in establishing and using a variety of patient-derived models of prostate cancer. This review summarizes our collective ideas on how patient-derived models are currently being used, the common challenges, and future opportunities for maximizing their usefulness in prostate cancer research. RESULTS: An increasing number of patient-derived models for prostate cancer are being developed. Despite their individual limitations and varying success rates, these models are valuable resources for exploring new concepts in prostate cancer biology and for preclinical testing of potential treatments. Here we focus on the need for larger collections of models that represent the changing treatment landscape of prostate cancer, robust readouts for preclinical testing, improved in vitro culture conditions, and integration of the tumor microenvironment. Additional priorities include ensuring model reproducibility, standardization, and replication, and streamlining the exchange of models and data sets among research groups. CONCLUSIONS: There are several opportunities to maximize the impact of patient-derived models on prostate cancer research. We must develop large, diverse and accessible cohorts of models and more sophisticated methods for emulating the intricacy of patient tumors. In this way, we can use the samples that are generously donated by patients to advance the outcomes of patients in the future.

Disruption of metazoan gene regulatory networks in cancer alters the balance of co-expression between genes of unicellular and multicellular origins.

BACKGROUND: Metazoans inherited genes from unicellular ancestors that perform essential biological processes such as cell division, metabolism, and protein translation. Multicellularity requires careful control and coordination of these unicellular genes to maintain tissue integrity and homeostasis. Gene regulatory networks (GRNs) that arose during metazoan evolution are frequently altered in cancer, resulting in over-expression of unicellular genes. We propose that an imbalance in co-expression of unicellular (UC) and multicellular (MC) genes is a driving force in cancer. RESULTS: We combine gene co-expression analysis to infer changes to GRNs in cancer with protein sequence conservation data to distinguish genes with UC and MC origins. Co-expression networks created using RNA sequencing data from 31 tumor types and normal tissue samples are divided into modules enriched for UC genes, MC genes, or mixed UC-MC modules. The greatest differences between tumor and normal tissue co-expression networks occur within mixed UC-MC modules. MC and UC genes not commonly co-expressed in normal tissues form distinct co-expression modules seen only in tumors. The degree of rewiring of genes within mixed UC-MC modules increases with tumor grade and stage. Mixed UC-MC modules are enriched for somatic mutations in cancer genes, particularly amplifications, suggesting an important driver of the rewiring observed in tumors is copy number changes. CONCLUSIONS: Our study shows the greatest changes to gene co-expression patterns during tumor progression occur between genes of MC and UC origins, implicating the breakdown and rewiring of metazoan gene regulatory networks in cancer development and progression.