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The second-generation antiandrogen, enzalutamide, is approved for castrate-resistant prostate cancer (CRPC) and targets androgen receptor (AR) activity in CRPC. Despite initial clinical activity, acquired resistance to enzalutamide arises rapidly and most patients develop terminal disease. Previous work has established Stat5 as a potent inducer of prostate cancer growth. Here, we investigated the significance of Jak2-Stat5 signaling in resistance of prostate cancer to enzalutamide. The levels of Jak2 and Stat5 mRNA, proteins and activation were evaluated in prostate cancer cells, xenograft tumors, and clinical prostate cancers before and after enzalutamide therapy. Jak2 and Stat5 were suppressed by genetic knockdown using lentiviral shRNA or pharmacologic inhibitors. Responsiveness of primary and enzalutamide-resistant prostate cancer to pharmacologic inhibitors of Jak2-Stat5 signaling was assessed in vivo in mice bearing prostate cancer xenograft tumors. Patient-derived prostate cancers were tested for responsiveness to Stat5 blockade as second-line treatment after enzalutamide ex vivo in tumor explant cultures. Enzalutamide-liganded AR induces sustained Jak2-Stat5 phosphorylation in prostate cancer leading to the formation of a positive feed-forward loop, where activated Stat5, in turn, induces Jak2 mRNA and protein levels contributing to further Jak2 activation. Mechanistically, enzalutamide-liganded AR induced Jak2 phosphorylation through a process involving Jak2-specific phosphatases. Stat5 promoted prostate cancer growth during enzalutamide treatment. Jak2-Stat5 inhibition induced death of prostate cancer cells and patient-derived prostate cancers surviving enzalutamide treatment and blocked enzalutamide-resistant tumor growth in mice. This work introduces a novel concept of a pivotal role of hyperactivated Jak2-Stat5 signaling in enzalutamide-resistant prostate cancer, which is readily targetable by Jak2 inhibitors in clinical development.

Summary The limited resolution of spatial transcriptomics (ST) assays in the past has led to the development of cell type annotation methods that separate the convolved signal based on available external atlas data. In light of the rapidly increasing resolution of the ST assay technologies, we made available and investigated the performance of a deconvolution-free marker-based cell annotation method called scType. In contrast to existing methods, the spatial application of scType does not require computationally strenuous deconvolution, nor large single-cell reference atlases. We show that scType enables ultra-fast and accurate identification of abundant cell types from ST data, especially when a large enough panel of genes is detected. Examples of such assays are Visium and Slide-seq, which currently offer the best trade-off between high resolution and number of genes detected by the assay for cell type annotation. Availability and implementation scType source R and python codes for spatial data are openly available in GitHub (https://github.com/kris-nader/sp-type or https://github.com/kris-nader/sc-type-py). Step-by-step tutorials for R and python spatial data analysis can be found in https://github.com/kris-nader/sp-type and https://github.com/kris-nader/sc-type-py/blob/main/spatial%5ftutorial.md , respectively. [ABSTRACT FROM AUTHOR]