SPARC regulates transforming growth factor beta induced (TGFBI) extracellular matrix deposition and paclitaxel response in ovarian cancer cells

TGFBI has been shown to sensitize ovarian cancer cells to the cytotoxic effects of paclitaxel via an integrin receptor-mediated mechanism that modulates microtubule stability. Herein, we determine that TGFBI localizes within organized fibrillar structures in mesothelial-derived ECM. We determined that suppression of SPARC expression by shRNA decreased the deposition of TGFBI in mesothelial-derived ECM, without affecting its overall protein expression or secretion. Conversely, overexpression of SPARC increased TGFBI deposition. A SPARC-YFP fusion construct expressed by the Met5a cell line co-localized with TGFBI in the cell-derived ECM. Interestingly, in vitro produced SPARC was capable of precipitating TGFBI from cell lysates dependent on an intact SPARC carboxy-terminus with in vitro binding assays verifying a direct interaction. The last 37 amino acids of SPARC were shown to be required for the TGFBI interaction while expression of a SPARC-YFP construct lacking this region (aa 1–256) did not interact and co-localize with TGFBI in the ECM. Furthermore, ovarian cancer cells have a reduced motility and decreased response to the chemotherapeutic agent paclitaxel when plated on ECM derived from mesothelial cells lacking SPARC compared to control mesothelial-derived ECM. In conclusion, SPARC regulates the fibrillar ECM deposition of TGFBI through a novel interaction, subsequently influencing cancer cell behavior

Models of endometriosis and their utility in studying progression to ovarian clear cell carcinoma.

Endometriosis is a common benign gynaecological condition affecting at least 10% of women of childbearing age and is characterized by pain--frequently debilitating. Although the exact prevalence is unknown, the economic burden is substantial (∼$50 billion a year in the USA alone) and it is associated with considerable morbidity. The development of endometriosis is inextricably linked to the process of menstruation and thus the models that best recapitulate the human disease are in menstruating non-human primates. However, the use of these animals is ethically challenging and very expensive. A variety of models in laboratory animals have been developed and the most recent are based on generating menstrual-like endometrial tissue that can be transferred to a recipient animal. These models are genetically manipulable and facilitate precise mechanistic studies. In addition, these models can be used to study malignant transformation in epithelial ovarian carcinoma. Epidemiological and molecular evidence indicates that endometriosis is the most plausible precursor of both clear cell and endometrioid ovarian cancer (OCCA and OEA, respectively). While this progression is rare, understanding the underlying mechanisms of transformation may offer new strategies for prevention and therapy. Our ability to pursue this is highly dependent on improved animal models but the current transgenic models, which genetically modify the ovarian surface epithelium and oviduct, are poor models of ectopic endometrial tissue. In this review we describe the various models of endometriosis and discuss how they may be applicable to developing our mechanistic understanding of OCCA and OEA.CMK was funded by a grant from CRUK (A13095). Part of the research work disclosed in this publication is funded by the Strategic Educational Pathways Scholarship (Malta) to CB. The scholarship is part-financed by the European Union-European Social Fund (ESF) under Operational Programme II-Cohesion Policy 2007-2013, "Empowering People for More Jobs and a Better Quality of Life”. JDB is supported by CRUK (A15601).This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/path.465

Phylogenetic quantification of intra-tumour heterogeneity.

Intra-tumour genetic heterogeneity is the result of ongoing evolutionary change within each cancer. The expansion of genetically distinct sub-clonal populations may explain the emergence of drug resistance, and if so, would have prognostic and predictive utility. However, methods for objectively quantifying tumour heterogeneity have been missing and are particularly difficult to establish in cancers where predominant copy number variation prevents accurate phylogenetic reconstruction owing to horizontal dependencies caused by long and cascading genomic rearrangements. To address these challenges, we present MEDICC, a method for phylogenetic reconstruction and heterogeneity quantification based on a Minimum Event Distance for Intra-tumour Copy-number Comparisons. Using a transducer-based pairwise comparison function, we determine optimal phasing of major and minor alleles, as well as evolutionary distances between samples, and are able to reconstruct ancestral genomes. Rigorous simulations and an extensive clinical study show the power of our method, which outperforms state-of-the-art competitors in reconstruction accuracy, and additionally allows unbiased numerical quantification of tumour heterogeneity. Accurate quantification and evolutionary inference are essential to understand the functional consequences of tumour heterogeneity. The MEDICC algorithms are independent of the experimental techniques used and are applicable to both next-generation sequencing and array CGH data.This is the final published version. It was originally published by PLoS in PLoS Computational Biology here: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003535

Assimilation of Wind Profiles from Multiple Doppler Radar Wind Profilers for Space Launch Vehicle Applications

Space launch vehicles utilize atmospheric winds in design of the vehicle and during day-of-launch (DOL) operations to assess affects of wind loading on the vehicle and to optimize vehicle performance during ascent. The launch ranges at NASA's Kennedy Space Center co-located with the United States Air Force's (USAF) Eastern Range (ER) at Cape Canaveral Air Force Station and USAF's Western Range (WR) at Vandenberg Air Force Base have extensive networks of in-situ and remote sensing instrumentation to measure atmospheric winds. Each instrument's technique to measure winds has advantages and disadvantages in regards to use for vehicle engineering assessments. Balloons measure wind at all altitudes necessary for vehicle assessments, but two primary disadvantages exist when applying balloon output on DOL. First, balloons need approximately one hour to reach required altitude. For vehicle assessments this occurs at 60 kft (18.3 km). Second, balloons are steered by atmospheric winds down range of the launch site that could significantly differ from those winds along the vehicle ascent trajectory. Figure 1 illustrates the spatial separation of balloon measurements from the surface up to approximately 55 kft (16.8 km) during the Space Shuttle launch on 10 December 2006. The balloon issues are mitigated by use of vertically pointing Doppler Radar Wind Profilers (DRWPs). However, multiple DRWP instruments are required to provide wind data up to 60 kft (18.3 km) for vehicle trajectory assessments. The various DRWP systems have different operating configurations resulting in different temporal and spatial sampling intervals. Therefore, software was developed to combine data from both DRWP-generated profiles into a single profile for use in vehicle trajectory analyses. Details on how data from various wind measurement systems are combined and sample output will be presented in the following sections

Expression microarray reproducibility is improved by optimising purification steps in RNA amplification and labelling.

BACKGROUND: Expression microarrays have evolved into a powerful tool with great potential for clinical application and therefore reliability of data is essential. RNA amplification is used when the amount of starting material is scarce, as is frequently the case with clinical samples. Purification steps are critical in RNA amplification and labelling protocols, and there is a lack of sufficient data to validate and optimise the process. RESULTS: Here the purification steps involved in the protocol for indirect labelling of amplified RNA are evaluated and the experimentally determined best method for each step with respect to yield, purity, size distribution of the transcripts, and dye coupling is used to generate targets tested in replicate hybridisations. DNase treatment of diluted total RNA samples followed by phenol extraction is the optimal way to remove genomic DNA contamination. Purification of double-stranded cDNA is best achieved by phenol extraction followed by isopropanol precipitation at room temperature. Extraction with guanidinium-phenol and Lithium Chloride precipitation are the optimal methods for purification of amplified RNA and labelled aRNA respectively. CONCLUSION: This protocol provides targets that generate highly reproducible microarray data with good representation of transcripts across the size spectrum and a coefficient of repeatability significantly better than that reported previously.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Light-Triggered Myosin Activation for Probing Dynamic Cellular Processes

Shining light on myosin: The incorporation of a caging group onto the essential phosphoserine residue of myosin by protein semisynthesis enables light-triggered activation of the protein (see picture). Caging eliminates the myosin activity, but exposure to 365 nm light restores its function to native levels. The caged protein can also be introduced into cells to facilitate studies of myosin with precise spatial and temporal resolution.American Heart Association (Fellowship)National Institutes of Health (U.S.) (NIH Cell Migration Consortium (GM064346))National Institute of General Medical Sciences (U.S.) (Biotechnology Training Grant

A metadata-aware application for remote scoring and exchange of tissue microarray images.

BACKGROUND: The use of tissue microarrays (TMA) and advances in digital scanning microscopy has enabled the collection of thousands of tissue images. There is a need for software tools to annotate, query and share this data amongst researchers in different physical locations. RESULTS: We have developed an open source web-based application for remote scoring of TMA images, which exploits the value of Microsoft Silverlight Deep Zoom to provide a intuitive interface for zooming and panning around digital images. We use and extend existing XML-based standards to ensure that the data collected can be archived and that our system is interoperable with other standards-compliant systems. CONCLUSION: The application has been used for multi-centre scoring of TMA slides composed of tissues from several Phase III breast cancer trials and ten different studies participating in the International Breast Cancer Association Consortium (BCAC). The system has enabled researchers to simultaneously score large collections of TMA and export the standardised data to integrate with pathological and clinical outcome data, thereby facilitating biomarker discovery.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Genomic landscape of platinum resistant and sensitive testicular cancers

While most testicular germ cell tumours (TGCTs) exhibit exquisite sensitivity to platinum chemotherapy, ~10% are platinum resistant. To gain insight into the underlying mechanisms, we undertook undertake whole exome sequencing and copy number analysis in 40 tumours from 26 cases with platinum-resistant TGCT, and combined this with published genomic data on an additional 624 TGCTs. We integrated analyses for driver mutations, mutational burden, global, arm-level and focal copy number (CN) events, and SNV and CN signatures. Albeit preliminary and observational in nature, these analyses provide the first support for a possible mechanistic link between early driver mutations in RAS and KIT and the widespread copy number events by which TGCT is characterised

Combined image and genomic analysis of high-grade serous ovarian cancer reveals PTEN loss as a common driver event and prognostic classifier.

BACKGROUND: TP53 and BRCA1/2 mutations are the main drivers in high-grade serous ovarian carcinoma (HGSOC). We hypothesise that combining tissue phenotypes from image analysis of tumour sections with genomic profiles could reveal other significant driver events. RESULTS: Automatic estimates of stromal content combined with genomic analysis of TCGA HGSOC tumours show that stroma strongly biases estimates of PTEN expression. Tumour-specific PTEN expression was tested in two independent cohorts using tissue microarrays containing 521 cases of HGSOC. PTEN loss or downregulation occurred in 77% of the first cohort by immunofluorescence and 52% of the validation group by immunohistochemistry, and is associated with worse survival in a multivariate Cox-regression model adjusted for study site, age, stage and grade. Reanalysis of TCGA data shows that hemizygous loss of PTEN is common (36%) and expression of PTEN and expression of androgen receptor are positively associated. Low androgen receptor expression was associated with reduced survival in data from TCGA and immunohistochemical analysis of the first cohort. CONCLUSION: PTEN loss is a common event in HGSOC and defines a subgroup with significantly worse prognosis, suggesting the rational use of drugs to target PI3K and androgen receptor pathways for HGSOC. This work shows that integrative approaches combining tissue phenotypes from images with genomic analysis can resolve confounding effects of tissue heterogeneity and should be used to identify new drivers in other cancers.This work was supported by Cancer Research UK [grant numbers A15601, A17197,A16561, A10124]; the University of Cambridge; National Institute for Health Research Cambridge Biomedical Research Centre and Academic Clinical Fellowship scheme (FCM); Cambridge Experimental Cancer Medicine Centre and Hutchison Whampoa Limited. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final published version. It first appeared at http://genomebiology.com/2014/15/12/526

Sequencing Structural Variants in Cancer for Precision Therapeutics.

The identification of mutations that guide therapy selection for patients with cancer is now routine in many clinical centres. The majority of assays used for solid tumour profiling use DNA sequencing to interrogate somatic point mutations because they are relatively easy to identify and interpret. Many cancers, however, including high-grade serous ovarian, oesophageal, and small-cell lung cancer, are driven by somatic structural variants that are not measured by these assays. Therefore, there is currently an unmet need for clinical assays that can cheaply and rapidly profile structural variants in solid tumours. In this review we survey the landscape of 'actionable' structural variants in cancer and identify promising detection strategies based on massively-parallel sequencing.This work was supported by Cancer Research UK [grant numbers A15973, A15601: 454 G.M, J.D.B], VUmc Cancer Center Amsterdam [VUmc-CCA: BY] and the Dutch 455 Cancer Society [VU 2015-7882: BY].This is the author accepted manuscript. The final version is available from Cell/Elsevier via http://dx.doi.org/10.1016/j.tig.2016.07.00