Engineering an Escherichia coli based in vivo mRNA manufacturing platform.

Synthetic mRNA is currently produced in standardized in vitro transcription systems. However, this one-size-fits-all approach has associated drawbacks in supply chain shortages, high reagent costs, complex product-related impurity profiles, and limited design options for molecule-specific optimization of product yield and quality. Herein, we describe for the first time development of an in vivo mRNA manufacturing platform, utilizing an Escherichia coli cell chassis. Coordinated mRNA, DNA, cell and media engineering, primarily focussed on disrupting interactions between synthetic mRNA molecules and host cell RNA degradation machinery, increased product yields >40-fold compared to standard "unengineered" E. coli expression systems. Mechanistic dissection of cell factory performance showed that product mRNA accumulation levels approached theoretical limits, accounting for ~30% of intracellular total RNA mass, and that this was achieved via host-cell's reallocating biosynthetic capacity away from endogenous RNA and cell biomass generation activities. We demonstrate that varying sized functional mRNA molecules can be produced in this system and subsequently purified. Accordingly, this study introduces a new mRNA production technology, expanding the solution space available for mRNA manufacturing.

Jmjd2c facilitates the assembly of essential enhancer-protein complexes at the onset of embryonic stem cell differentiation.

Jmjd2 H3K9 demethylases cooperate in promoting mouse embryonic stem cell (ESC) identity. However, little is known about their importance at the exit of ESC pluripotency. Here, we reveal that Jmjd2c facilitates this process by stabilising the assembly of mediator-cohesin complexes at lineage-specific enhancers. Functionally, we show that Jmjd2c is required in ESCs to initiate appropriate gene expression programs upon somatic multi-lineage differentiation. In the absence of Jmjd2c, differentiation is stalled at an early post-implantation epiblast-like stage, while Jmjd2c-knockout ESCs remain capable of forming extra-embryonic endoderm derivatives. Dissection of the underlying molecular basis revealed that Jmjd2c is re-distributed to lineage-specific enhancers during ESC priming for differentiation. Interestingly, Jmjd2c-bound enhancers are co-occupied by the H3K9-methyltransferase G9a (also known as Ehmt2), independently of its H3K9-modifying activity. Loss of Jmjd2c abrogates G9a recruitment and further destabilises loading of the mediator and cohesin components Med1 and Smc1a at newly activated and poised enhancers in ESC-derived epiblast-like cells. These findings unveil Jmjd2c and G9a as novel enhancer-associated factors, and implicate Jmjd2c as a molecular scaffold for the assembly of essential enhancer-protein complexes with an impact on timely gene activation.

Somatic variants in epigenetic modifiers can predict failure of response to imatinib but not to second-generation tyrosine kinase inhibitors.

There are no validated molecular biomarkers to identify newly-diagnosed individuals with chronic-phase chronic myeloid leukemia likely to respond poorly to imatinib and who might benefit from first-line treatment with a more potent second-generation tyrosine kinase inhibitor. Our inability to predict these 'high-risk' individuals reflects the poorly understood heterogeneity of the disease. To investigate the potential of genetic variants in epigenetic modifiers as biomarkers at diagnosis, we used Ion Torrent next-generation sequencing of 71 candidate genes for predicting response to tyrosine kinase inhibitors and probability of disease progression. A total of 124 subjects with newly-diagnosed chronic-phase chronic myeloid leukemia began with imatinib (n=62) or second-generation tyrosine kinase inhibitors (n=62) and were classified as responders or non-responders based on the BCRABL1 transcript levels within the first year and the European LeukemiaNet criteria for failure. Somatic variants affecting 21 genes (e.g. ASXL1, IKZF1, DNMT3A, CREBBP) were detected in 30% of subjects, most of whom were non-responders (41% non-responders, 18% responders to imatinib, 38% non-responders, 25% responders to second-generation tyrosine kinase inhibitors). The presence of variants predicted the rate of achieving a major molecular response, event-free survival, progression-free survival and chronic myeloid leukemia-related survival in the imatinib but not the second-generation tyrosine kinase inhibitors cohort. Rare germline variants had no prognostic significance irrespective of treatment while some pre-leukemia variants suggest a multi-step development of chronic myeloid leukemia. Our data suggest that identification of somatic variants at diagnosis facilitates stratification into imatinib responders/non-responders, thereby allowing earlier use of second-generation tyrosine kinase inhibitors, which, in turn, may overcome the negative impact of such variants on disease progression.

Network-guided regression for detecting associations between DNA methylation and gene expression.

MOTIVATION: High-throughput profiling in biological research has resulted in the availability of a wealth of data cataloguing the genetic, epigenetic and transcriptional states of cells. These data could yield discoveries that may lead to breakthroughs in the diagnosis and treatment of human disease, but require statistical methods designed to find the most relevant patterns from millions of potential interactions. Aberrant DNA methylation is often a feature of cancer, and has been proposed as a therapeutic target. However, the relationship between DNA methylation and gene expression remains poorly understood. RESULTS: We propose Network-sparse Reduced-Rank Regression (NsRRR), a multivariate regression framework capable of using prior biological knowledge expressed as gene interaction networks to guide the search for associations between gene expression and DNA methylation signatures. We use simulations to show the advantage of our proposed model in terms of variable selection accuracy over alternative models that do not use prior network information. We discuss an application of NsRRR to The Cancer Genome Atlas datasets on primary ovarian tumours. AVAILABILITY AND IMPLEMENTATION: R code implementing the NsRRR model is available at http://www2.imperial.ac.uk/∼gmontana CONTACT: giovanni.montana@kcl.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association of maternal diabetes with autism in offspring.

IMPORTANCE: Information about the association of maternal diabetes and autism spectrum disorders (ASDs) in offspring is limited, with no report on the importance of timing of exposure during gestation. OBJECTIVE: To assess ASD risk associated with intrauterine exposure to preexisting type 2 diabetes and gestational diabetes mellitus (GDM) by gestational age at GDM diagnosis. DESIGN, SETTING, AND PATIENTS: Retrospective longitudinal cohort study including 322 323 singleton children born in 1995-2009 at Kaiser Permanente Southern California (KPSC) hospitals. Children were tracked from birth until the first of the following: date of clinical diagnosis of ASD, last date of continuous KPSC health plan membership, death due to any cause, or December 31, 2012. Relative risks of ASD were estimated by hazard ratios (HRs) using Cox regression models adjusted for birth year. EXPOSURES: Maternal preexisting type 2 diabetes (n = 6496), GDM diagnosed at 26 weeks' gestation or earlier (n = 7456) or after 26 weeks' gestation (n = 17 579), or no diabetes (n = 290 792) during the index pregnancy. MAIN OUTCOMES AND MEASURES: Clinical diagnosis of ASD in offspring. RESULTS: During follow-up, 3388 children were diagnosed as having ASD (115 exposed to preexisting type 2 diabetes, 130 exposed to GDM at ≤26 weeks, 180 exposed to GDM at >26 weeks, and 2963 unexposed). Unadjusted annual ASD incidences were 3.26, 3.02, 1.77, and 1.77 per 1000 among children of mothers with preexisting type 2 diabetes, GDM diagnosed at 26 weeks or earlier, GDM diagnosed after 26 weeks, and no diabetes, respectively. The birth year-adjusted HRs were 1.59 (95% CI, 1.29-1.95) for preexisting type 2 diabetes, 1.63 (95% CI, 1.35-1.97) for GDM diagnosed at 26 weeks or earlier, and 0.98 (95% CI, 0.84-1.15) for GDM diagnosed after 26 weeks relative to no exposure. After adjustment for maternal age, parity, education, household income, race/ethnicity, history of comorbidity, and sex of the child, maternal preexisting type 2 diabetes was not significantly associated with risk of ASD in offspring (HR, 1.21; 95% CI, 0.97-1.52), but GDM diagnosed at 26 weeks or earlier remained so (HR, 1.42; 95% CI, 1.15-1.74). Antidiabetic medication exposure was not independently associated with ASD risk. Adjustment for a mother or older sibling with ASD in the full cohort and for maternal smoking, prepregnancy body mass index, and gestational weight gain in the subset with available data (n = 68 512) did not affect the results. CONCLUSIONS AND RELEVANCE: In this large, multiethnic clinical cohort of singleton children born at 28 to 44 weeks' gestation, exposure to maternal GDM diagnosed by 26 weeks' gestation was associated with risk of ASD in offspring.

A framework for generalized subspace pattern mining in high-dimensional datasets.

BACKGROUND: A generalized notion of biclustering involves the identification of patterns across subspaces within a data matrix. This approach is particularly well-suited to analysis of heterogeneous molecular biology datasets, such as those collected from populations of cancer patients. Different definitions of biclusters will offer different opportunities to discover information from datasets, making it pertinent to tailor the desired patterns to the intended application. This paper introduces 'GABi', a customizable framework for subspace pattern mining suited to large heterogeneous datasets. Most existing biclustering algorithms discover biclusters of only a few distinct structures. However, by enabling definition of arbitrary bicluster models, the GABi framework enables the application of biclustering to tasks for which no existing algorithm could be used. RESULTS: First, a series of artificial datasets were constructed to represent three clearly distinct scenarios for applying biclustering. With a bicluster model created for each distinct scenario, GABi is shown to recover the correct solutions more effectively than a panel of alternative approaches, where the bicluster model may not reflect the structure of the desired solution. Secondly, the GABi framework is used to integrate clinical outcome data with an ovarian cancer DNA methylation dataset, leading to the discovery that widespread dysregulation of DNA methylation associates with poor patient prognosis, a result that has not previously been reported. This illustrates a further benefit of the flexible bicluster definition of GABi, which is that it enables incorporation of multiple sources of data, with each data source treated in a specific manner, leading to a means of intelligent integrated subspace pattern mining across multiple datasets. CONCLUSIONS: The GABi framework enables discovery of biologically relevant patterns of any specified structure from large collections of genomic data. An R implementation of the GABi framework is available through CRAN (http://cran.r-project.org/web/packages/GABi/index.html).

Molecular subtypes of serous borderline ovarian tumor show distinct expression patterns of benign tumor and malignant tumor-associated signatures.

Borderline ovarian tumors show heterogeneity in clinical behavior. Most have excellent prognosis, although a small percentage show recurrence or progressive disease, usually to low-grade serous carcinoma. The aim of this study was to understand the molecular relationship between these entities and identify potential markers of tumor progression and therapeutic targets. We studied gene expression using Affymetrix HGU133plus2 GeneChip microarrays in 3 low-grade serous carcinomas, 13 serous borderline tumors and 8 serous cystadenomas. An independent data set of 18 serous borderline tumors and 3 low-grade serous carcinomas was used for validation. Unsupervised clustering revealed clear separation of benign and malignant tumors, whereas borderline tumors showed two distinct groups, one clustering with benign and the other with malignant tumors. The segregation into benign- and malignant-like borderline molecular subtypes was reproducible on applying the same analysis to an independent publicly available data set. We identified 50 genes that separate borderline tumors into their subgroups. Functional enrichment analysis of genes that separate borderline tumors to the two subgroups highlights a cell adhesion signature for the malignant-like subset, with Claudins particularly prominent. This is the first report of molecular subtypes of borderline tumors based on gene expression profiling. Our results provide the basis for identification of biomarkers for the malignant potential of borderline ovarian tumor and potential therapeutic targets for low-grade serous carcinoma.

A distance-based test of association between paired heterogeneous genomic data.

MOTIVATION: Due to rapid technological advances, a wide range of different measurements can be obtained from a given biological sample including single nucleotide polymorphisms, copy number variation, gene expression levels, DNA methylation and proteomic profiles. Each of these distinct measurements provides the means to characterize a certain aspect of biological diversity, and a fundamental problem of broad interest concerns the discovery of shared patterns of variation across different data types. Such data types are heterogeneous in the sense that they represent measurements taken at different scales or represented by different data structures. RESULTS: We propose a distance-based statistical test, the generalized RV (GRV) test, to assess whether there is a common and non-random pattern of variability between paired biological measurements obtained from the same random sample. The measurements enter the test through the use of two distance measures, which can be chosen to capture a particular aspect of the data. An approximate null distribution is proposed to compute P-values in closed-form and without the need to perform costly Monte Carlo permutation procedures. Compared with the classical Mantel test for association between distance matrices, the GRV test has been found to be more powerful in a number of simulation settings. We also demonstrate how the GRV test can be used to detect biological pathways in which genetic variability is associated to variation in gene expression levels in an ovarian cancer sample, and present results obtained from two independent cohorts. AVAILABILITY: R code to compute the GRV test is freely available from http://www2.imperial.ac.uk/∼gmontana

The DNA methylomes of serous borderline tumors reveal subgroups with malignant- or benign-like profiles.

Serous borderline tumors (SBOTs) are a challenging group of ovarian tumors positioned between benign and malignant disease. We have profiled the DNA methylomes of 12 low-grade serous carcinomas (LGSCs), 19 SBOTs, and 16 benign serous tumors (BSTs) across 27,578 CpG sites to further characterize the epigenomic relationship between these subtypes of ovarian tumors. Unsupervised hierarchical clustering of DNA methylation levels showed that LGSCs differ distinctly from BSTs, but not from SBOTs. Gene ontology analysis of genes showing differential methylation at linked CpG sites between LGSCs and BSTs revealed significant enrichment of gene groups associated with cell adhesion, cell-cell signaling, and the extracellular region, consistent with a more invasive phenotype of LGSCs compared with BSTs. Consensus clustering highlighted differences between SBOT methylomes and returned subgroups with malignant- or benign-like methylation profiles. Furthermore, a two-loci DNA methylation signature can distinguish between these SBOT subgroups with benign- and malignant-like methylation characteristics. Our findings indicate striking similarities between SBOT and LGSC methylomes, supporting a common origin and the view that LGSC may arise from SBOT. A subgroup of SBOTs can be classified into tumors with a benign- or a malignant-like methylation profile that may help in identifying tumors more likely to progress into LGSCs.

Expanding the RNA polymerase biocatalyst solution space for mRNA manufacture.

All mRNA products are currently manufactured in in vitro transcription (IVT) reactions that utilize single-subunit RNA polymerase (RNAP) biocatalysts. Although it is known that discrete polymerases exhibit highly variable bioproduction phenotypes, including different relative processivity rates and impurity generation profiles, only a handful of enzymes are generally available for mRNA biosynthesis. This limited RNAP toolbox restricts strategies to design and troubleshoot new mRNA manufacturing processes, which is particularly undesirable given the continuing diversification of mRNA product lines toward larger and more complex molecules. Herein, we describe development of a high-throughput RNAP screening platform, comprising complementary in silico and in vitro testing modules, that enables functional characterization of large enzyme libraries. Utilizing this system, we identified eight novel sequence-diverse RNAPs, with associated active cognate promoters, and subsequently validated their performance as recombinant enzymes in IVT-based mRNA production processes. By increasing the number of available characterized functional RNAPs by more than 130% and providing a platform to rapidly identify further potentially useful enzymes, this work significantly expands the RNAP biocatalyst solution space for mRNA manufacture, thereby enhancing the capability for application-specific and molecule-specific optimization of both product yield and quality.

Synergistic effects of PARP inhibition and cholesterol biosynthesis pathway modulation.

An in-depth multi-omic molecular characterisation of poly(adenosine 5'-diphosphate [ADP]-ribose) polymerase (PARP) inhibitors revealed a distinct poly-pharmacology of niraparib (Zejula®) mediated by its interaction with lanosterol synthase (LSS), which is not observed with other PARP inhibitors. Niraparib, in a similar way to the LSS inhibitor Ro-48-8071, induced activation of the 24,25-epoxysterol shunt pathway, which is a regulatory signalling branch of the cholesterol biosynthesis pathway. Interestingly, the combination of a LSS inhibitor with a PARP inhibitor that does not bind to LSS, such as olaparib, had an additive effect on killing of cancer cells to levels comparable to Niraparib as single agent. In addition, the combination of PARP inhibitors and statins, inhibitors of HMGCR, an enzyme catalysing the rate-limiting step in the mevalonate pathway, had a synergistic effect on tumor cell killing in cell lines and patient-derived ovarian tumor organoids. These observations suggest that concomitant inhibition of cholesterol biosynthesis pathway and PARP activity might result in stronger efficacy of these inhibitors against tumor types highly dependent on cholesterol metabolism.

Spatial and temporal intra-tumoral heterogeneity in advanced HGSOC: Implications for surgical and clinical outcomes.

Limited evidence exists on the impact of spatial and temporal heterogeneity of high-grade serous ovarian cancer (HGSOC) on tumor evolution, clinical outcomes, and surgical operability. We perform systematic multi-site tumor mapping at presentation and matched relapse from 49 high-tumor-burden patients, operated up front. From SNP array-derived copy-number data, we categorize dendrograms representing tumor clonal evolution as sympodial or dichotomous, noting most chemo-resistant patients favor simpler sympodial evolution. Three distinct tumor evolutionary patterns from primary to relapse are identified, demonstrating recurrent disease may emerge from pre-existing or newly detected clones. Crucially, we identify spatial heterogeneity for clinically actionable homologous recombination deficiency scores and for poor prognosis biomarkers CCNE1 and MYC. Copy-number signature, phenotypic, proteomic, and proliferative-index heterogeneity further highlight HGSOC complexity. This study explores HGSOC evolution and dissemination across space and time, its impact on optimal surgical cytoreductive effort and clinical outcomes, and its consequences for clinical decision-making.

Investigating response time and accuracy in online classifier learning for multimedia publish-subscribe systems.

The enormous growth of multimedia content in the field of the Internet of Things (IoT) leads to the challenge of processing multimedia streams in real-time. Event-based systems are constructed to process event streams. They cannot natively consume multimedia event types produced by the Internet of Multimedia Things (IoMT) generated data to answer multimedia-based user subscriptions. Machine learning-based techniques have enabled rapid progress in solving real-world problems and need to be optimised for the low response time of the multimedia event processing paradigm. In this paper, we describe a classifier construction approach for the training of online classifiers, that can handle dynamic subscriptions with low response time and provide reasonable accuracy for the multimedia event processing. We find that the current object detection methods can be configured dynamically for the construction of classifiers in real-time, by tuning hyperparameters even when training from scratch. Our experiments demonstrate that deep neural network-based object detection models, with hyperparameter tuning, can improve the performance within less training time for the answering of previously unknown user subscriptions. The results from this study show that the proposed online classifier training based model can achieve accuracy of 79.00% with 15-min of training and 84.28% with 1-hour training from scratch on a single GPU for the processing of multimedia events.

Chromatin accessibility changes at intergenic regions are associated with ovarian cancer drug resistance.

BACKGROUND: Resistance to DNA damaging chemotherapies leads to cancer treatment failure and poor patient prognosis. We investigated how genomic distribution of accessible chromatin sites is altered during acquisition of cisplatin resistance using matched ovarian cell lines from high grade serous ovarian cancer (HGSOC) patients before and after becoming clinically resistant to platinum-based chemotherapy. RESULTS: Resistant lines show altered chromatin accessibility at intergenic regions, but less so at gene promoters. Clusters of cis-regulatory elements at these intergenic regions show chromatin changes that are associated with altered expression of linked genes, with enrichment for genes involved in the Fanconi anemia/BRCA DNA damage response pathway. Further, genome-wide distribution of platinum adducts associates with the chromatin changes observed and distinguish sensitive from resistant lines. In the resistant line, we observe fewer adducts around gene promoters and more adducts at intergenic regions. CONCLUSIONS: Chromatin changes at intergenic regulators of gene expression are associated with in vivo derived drug resistance and Pt-adduct distribution in patient-derived HGSOC drug resistance models.

Dynamic CpG methylation delineates subregions within super-enhancers selectively decommissioned at the exit from naive pluripotency.

Clusters of enhancers, referred as to super-enhancers (SEs), control the expression of cell identity genes. The organisation of these clusters, and how they are remodelled upon developmental transitions remain poorly understood. Here, we report the existence of two types of enhancer units within SEs typified by distinctive CpG methylation dynamics in embryonic stem cells (ESCs). We find that these units are either prone for decommissioning or remain constitutively active in epiblast stem cells (EpiSCs), as further established in the peri-implantation epiblast in vivo. Mechanistically, we show a pivotal role for ESRRB in regulating the activity of ESC-specific enhancer units and propose that the developmentally regulated silencing of ESRRB triggers the selective inactivation of these units within SEs. Our study provides insights into the molecular events that follow the loss of ESRRB binding, and offers a mechanism by which the naive pluripotency transcriptional programme can be partially reset upon embryo implantation.

HNF4A and GATA6 Loss Reveals Therapeutically Actionable Subtypes in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) can be divided into transcriptomic subtypes with two broad lineages referred to as classical (pancreatic) and squamous. We find that these two subtypes are driven by distinct metabolic phenotypes. Loss of genes that drive endodermal lineage specification, HNF4A and GATA6, switch metabolic profiles from classical (pancreatic) to predominantly squamous, with glycogen synthase kinase 3 beta (GSK3ß) a key regulator of glycolysis. Pharmacological inhibition of GSK3ß results in selective sensitivity in the squamous subtype; however, a subset of these squamous patient-derived cell lines (PDCLs) acquires rapid drug tolerance. Using chromatin accessibility maps, we demonstrate that the squamous subtype can be further classified using chromatin accessibility to predict responsiveness and tolerance to GSK3ß inhibitors. Our findings demonstrate that distinct patterns of chromatin accessibility can be used to identify patient subgroups that are indistinguishable by gene expression profiles, highlighting the utility of chromatin-based biomarkers for patient selection in the treatment of PDAC.

Genes Predisposed to DNA Hypermethylation during Acquired Resistance to Chemotherapy Are Identified in Ovarian Tumors by Bivalent Chromatin Domains at Initial Diagnosis.

Bivalent chromatin domains containing both active H3K4me3 and repressive H3K27me3 histone marks define gene sets poised for expression or silencing in differentiating embryonic stem (ES) cells. In cancer cells, aberrantly poised genes may facilitate changes in transcriptional states after exposure to anticancer drugs. In this study, we used ChIP-seq to characterize genome-wide positioning of H3K4me3- and H3K27me3-associated chromatin in primary high-grade serous ovarian carcinomas and in normal ovarian surface and fallopian tube tissue. Gene sets with proximal bivalent marks defined in this manner were evaluated subsequently as signatures of systematic change in DNA methylation and gene expression, comparing pairs of tissue samples taken from patients at primary presentation and relapse following chemotherapy. We found that gene sets harboring bivalent chromatin domains at their promoters in tumor tissue, but not normal epithelia, overlapped with Polycomb-repressive complex target genes as well as transcriptionally silenced genes in normal ovarian and tubal stem cells. The bivalently marked genes we identified in tumors before chemotherapy displayed increased promoter CpG methylation and reduced gene expression at relapse after chemotherapy of ovarian cancer. Overall, our results support the hypothesis that preexisting histone modifications at genes in a poised chromatin state may lead to epigenetic silencing during acquired drug resistance.Significance: These results suggest epigenetic targets for intervention to prevent the emergence of cancer drug resistance. Cancer Res; 78(6); 1383-91. ©2018 AACR.

DNA methylation-based chromatin compartments and ChIP-seq profiles reveal transcriptional drivers of prostate carcinogenesis.

BACKGROUND: Profiles of DNA methylation of many tissues relevant in human disease have been obtained from microarrays and are publicly available. These can be used to generate maps of chromatin compartmentalization, demarcating open and closed chromatin across the genome. Additionally, large sets of genome-wide transcription factor binding profiles have been made available thanks to ChIP-seq technology. METHODS: We have identified genomic regions with altered chromatin compartmentalization in prostate adenocarcinoma tissue relative to normal prostate tissue, using DNA methylation microarray data from The Cancer Genome Atlas. DNA binding profiles from the Encyclopedia of DNA Elements (ENCODE) ChIP-seq studies have been systematically screened to find transcription factors with inferred DNA binding sites located in discordantly open/closed chromatin in malignant tissue (compared with non-cancer control tissue). We have combined this with tests for corresponding up-/downregulation of the transcription factors' putative target genes to obtain an integrated measure of cancer-specific regulatory activity to identify likely transcriptional drivers of prostate cancer. RESULTS: Generally, we find that the degree to which transcription factors preferentially bind regions of chromatin that become more accessible during prostate carcinogenesis is significantly associated to the level of systematic upregulation of their targets, at the level of gene expression. Our approach has yielded 11 transcription factors that show strong cancer-specific transcriptional activation of targets, including the novel candidates KAT2A and TRIM28, alongside established drivers of prostate cancer MYC, ETS1, GABP and YY1. CONCLUSIONS: This approach to integrated epigenetic and transcriptional profiling using publicly available data represents a cheap and powerful technique for identifying potential drivers of human disease. In our application to prostate adenocarcinoma data, the fact that well-known drivers are amongst the top candidates suggests that the discovery of novel candidate drivers may unlock pathways to future medicines. Data download instructions and code to reproduce this work are available at GitHub under 'edcurry/PRAD-compartments'.

WWOX sensitises ovarian cancer cells to paclitaxel via modulation of the ER stress response.

There are clear gaps in our understanding of genes and pathways through which cancer cells facilitate survival strategies as they become chemoresistant. Paclitaxel is used in the treatment of many cancers, but development of drug resistance is common. Along with being an antimitotic agent paclitaxel also activates endoplasmic reticulum (ER) stress. Here, we examine the role of WWOX (WW domain containing oxidoreductase), a gene frequently lost in several cancers, in mediating paclitaxel response. We examine the ER stress-mediated apoptotic response to paclitaxel in WWOX-transfected epithelial ovarian cancer (EOC) cells and following siRNA knockdown of WWOX. We show that WWOX-induced apoptosis following exposure of EOC cells to paclitaxel is related to ER stress and independent of the antimitotic action of taxanes. The apoptotic response to ER stress induced by WWOX re-expression could be reversed by WWOX siRNA in EOC cells. We report that paclitaxel treatment activates both the IRE-1 and PERK kinases and that the increase in paclitaxel-mediated cell death through WWOX is dependent on active ER stress pathway. Log-rank analysis of overall survival (OS) and progression-free survival (PFS) in two prominent EOC microarray data sets (Tothill and The Cancer Genome Atlas), encompassing ~800 patients in total, confirmed clinical relevance to our findings. High WWOX mRNA expression predicted longer OS and PFS in patients treated with paclitaxel, but not in patients who were treated with only cisplatin. The association of WWOX and survival was dependent on the expression level of glucose-related protein 78 (GRP78), a key ER stress marker in paclitaxel-treated patients. We conclude that WWOX sensitises EOC to paclitaxel via ER stress-induced apoptosis, and predicts clinical outcome in patients. Thus, ER stress response mechanisms could be targeted to overcome chemoresistance in cancer.

High Incidence of Undiagnosed Cervical Myelopathy in Patients With Hip Fracture Compared With Controls.

OBJECTIVES: To identify the incidence of undiagnosed cervical myelopathy in patients who fall and develop hip fractures compared with age-matched controls. DESIGN: Prospective, case-control study. SETTING: University level 1 Trauma Center. PATIENTS/PARTICIPANTS: Consecutive patients who presented with hip fractures after a fall. A total of 159 patients were screened; 66 patients (38 arthroplasty, 28 fracture) were eligible for enrollment in the study. Exclusion criteria included cognitive impairment, known diagnosis of cervical myelopathy, previous cervical spine surgery, inability to comply with examination, or refusal to participate. The control group was age-matched elderly patients who underwent total hip arthroplasty (THA). INTERVENTION: Patient interview and physical examination for cervical myelopathy. MAIN OUTCOME MEASUREMENTS: Myelopathy was diagnosed by clinical history elements (Japanese Orthopaedic Association score ≤15) and pathologic reflexes. Comparison of the incidence of myelopathy in the study population with the control population was performed using Fisher exact test. RESULTS: There were no statistically significant differences between the fracture and THA groups in mean patient age or male/female ratio. There was a statistically significant increased incidence of myelopathy in hip fracture patients (18%) compared with the THA group (0%, P = 0.01). CONCLUSIONS: Hip fracture is a complex multifactorial process, and most patients (60%) were excluded due to known cognitive impairment. However, 18% of previously undiagnosed patients who were cognitively intact manifested clinical findings consistent with cervical spondylotic myelopathy. Consideration should be given to screening for undiagnosed myelopathy among patients with hip fracture to reduce the risk of subsequent fractures. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.