Prostate cancer development and progression is largely dependent on androgen receptor (AR) signaling. AR is a hormone-dependent transcription factor, which binds to thousands of sites throughout the human genome to regulate expression of directly responsive genes, including pro-survival genes that enable tumor cells to cope with increased cellular stress. ERN1 and XBP1 - two key players of the unfolded protein response (UPR) - are among such stress-associated genes. Here, we show that XBP1 levels in primary prostate cancer are associated with biochemical recurrence in five independent cohorts. Patients who received AR-targeted therapies had significantly lower XBP1 expression, whereas expression of the active form of XBP1 (XBP1s) was elevated. In vitro results show that AR-induced ERN1 expression led to increased XBP1s mRNA and protein levels. Furthermore, ChIP-seq analysis revealed that XBP1s binds enhancers upon stress stimuli regulating genes involved in UPR processes, eIF2 signaling and protein ubiquitination. We further demonstrate genomic overlap of AR- and XBP1s-binding sites, suggesting genomic conversion of the two signaling cascades. Transcriptomic effects of XBP1 were further studied by knockdown experiments, which lead to decreased expression of androgen-responsive genes and UPR genes. These results suggest a two-step mechanism of gene regulation, which involves androgen-induced expression of ERN1, thereby enhancing XBP1 splicing and transcriptional activity. This signaling cascade may prepare the cells for the increased protein folding, mRNA decay and translation that accompanies AR-regulated tumor cell proliferation.

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.

Chromatin immunoprecipitation (ChIP)-seq analyses of transcription factors in clinical specimens are challenging due to the technical limitations and low quantities of starting material, often resulting in low enrichments and poor signal-to-noise ratio. Here, we present an optimized protocol for transcription factor ChIP-seq analyses in human tissue, yielding an ~100% success rate for all transcription factors analyzed. As proof of concept and to illustrate general applicability of the approach, human tissue from the breast, prostate, and endometrial cancers were analyzed. In addition to standard formaldehyde fixation, disuccinimidyl glutarate was included in the procedure, greatly increasing data quality. To illustrate the sensitivity of the optimized protocol, we provide high-quality ChIP-seq data for three independent factors (AR, FOXA1, and H3K27ac) from a single core needle prostate cancer biopsy specimen. In summary, double-cross-linking strongly improved transcription factor ChIP-seq quality on human tumor samples, further facilitating and enhancing translational research on limited amounts of tissue.