Graphical AbstractHighlights d CREB5 promotes resistance to AR inhibitors and androgen therapies in prostate cancer d CREB5 selectively enhances interaction of AR with target genes critical for survival d CREB5 is amplified or overexpressed in therapy-resistant metastatic prostate cancers d Targeting CREB5 is effective in patient-derived models that are therapy resistant SUMMARY Androgen-receptor (AR) inhibitors, including enzalutamide, are used for treatment of all metastatic castration-resistant prostate cancers (mCRPCs). However, some patients develop resistance or never respond. We find that the transcription factor CREB5 confers enzalutamide resistance in an open reading frame (ORF) expression screen and in tumor xenografts. CREB5 overexpression is essential for an enzalutamide-resistant patient-derived organoid. In AR-expressing prostate cancer cells, CREB5 interactions enhance AR activity at a subset of promoters and enhancers upon enzalutamide treatment, including MYC and genes involved in the cell cycle.In mCRPC, we found recurrent amplification and overexpression of CREB5. Our observations identify CREB5 as one mechanism that drives resistance to AR antagonists in prostate cancers.

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We report the development of novel reagents and approaches for generating recyclable biosensors. The use of aqueous media for the formation of protein binding alkylthiolate monolayers on Au surfaces results in accelerated alkylthiolate monolayer formation and improvement in monolayer integrity as visualized by fluorescence microscopy and CV techniques. We have also developed an electrocleaning protocol that is compatible with microfluidics devices, and this technique serves as an on-chip method for cleaning Au substrates both before and after monolayer formation. The techniques for the formation and dissociation of biotinylated SAMs from aqueous solvents reported here may be applied towards the development of Au-based sensor devices and microfluidics chips in the future. A potential use of these devices includes the specific capture and triggered release of target cells, proteins, or small molecules from liquid samples.

Buschke-Ollendorff syndrome (BOS) is a rare, often benign, autosomal skin disorder. BOS commonly presents with nontender connective tissue naevi and sclerotic bony lesions (osteopoikilosis [OPK]). Herein, we summarize the presenting features of BOS and potential associations by conducting a systematic review of the literature and summarizing a cohort seen at the Hospital for Sick Children (HSC), Toronto, Canada. PubMed was searched using the following terms: 'BOS'; 'dermatofibrosis lenticularis'; 'OPK'; 'LEMD3'; 'elastoma'; 'collagenoma'. Only case reports were included, without date or language restrictions. Cases were further narrowed to those where patients or their families had a combination of skin and bony lesions, or a positive genetic test. Data were summarized using frequencies. In total, 594 reports were discovered, of which 546 (92%) were excluded. The remaining 48 accounted for 164 cases. Skin lesions were noted in 24% of cases and bony lesions in 20%, while 54% of patients had both. In 1% of cases the diagnosis was made on genetic testing alone. A family history was noted in 92% of cases. All patients with spinal stenosis (2%) or shortened status (7%) had OPK. Six per cent of patients had neurological problems. However, 50% of the cohort from HSC had cognitive delays, and only cases from 2007 onwards reported cognitive delays (the prevalence was 17% among those cases). This review confirms the classical diagnostic features of BOS. In addition, it highlights a previously unreported association between a shortened stature and OPK, as well as a possible association with cognitive delays.

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Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is an endogenous secreted peptide and, in preclinical studies, preferentially induces apoptosis in tumor cells rather than in normal cells. The acquisition of resistance in cells exposed to TRAIL or its mimics limits their clinical efficacy. Because kinases are intimately involved in the regulation of apoptosis, we systematically characterized kinases involved in TRAIL signaling. Using RNA interference (RNAi) loss-of-function and cDNA overexpression screens, we identified 169 protein kinases that influenced the dynamics of TRAIL-induced apoptosis in the colon adenocarcinoma cell line DLD-1. We classified the kinases as sensitizers or resistors or modulators, depending on the effect that knockdown and overexpression had on TRAIL-induced apoptosis. Two of these kinases that were classified as resistors were PX domain-containing serine/threonine kinase (PXK) and AP2-associated kinase 1 (AAK1), which promote receptor endocytosis and may enable cells to resist TRAIL-induced apoptosis by enhancing endocytosis of the TRAIL receptors. We assembled protein interaction maps using mass spectrometry-based protein interaction analysis and quantitative phosphoproteomics. With these protein interaction maps, we modeled information flow through the networks and identified apoptosis-modifying kinases that are highly connected to regulated substrates downstream of TRAIL. The results of this analysis provide a resource of potential targets for the development of TRAIL combination therapies to selectively kill cancer cells.

Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generative models (GP-DGMs), which employ GP priors over the latent variables of DGMs. Existing approaches for performing inference in GP-DGMs do not support sparse GP approximations based on inducing points, which are essential for the computational efficiency of GPs, nor do they handle missing data -a natural occurrence in many spatio-temporal datasets -in a principled manner. We address these shortcomings with the development of the sparse Gaussian process variational autoencoder (SGP-VAE), characterised by the use of partial inference networks for parameterising sparse GP approximations. Leveraging the benefits of amortised variational inference, the SGP-VAE enables inference in multi-output sparse GPs on previously unobserved data with no additional training. The SGP-VAE is evaluated in a variety of experiments where it outperforms alternative approaches including multi-output GPs and structured VAEs.