Targeting ASCT2-mediated glutamine uptake blocks prostate cancer growth and tumour development

Glutamine is conditionally essential in cancer cells, being utilized as a carbon and nitrogen source for macromolecule production, as well as for anaplerotic reactions fuelling the tricarboxylic acid (TCA) cycle. In this study, we demonstrated that the glutamine transporter ASCT2 (SLC1A5) is highly expressed in prostate cancer patient samples. Using LNCaP and PC-3 prostate cancer cell lines, we showed that chemical or shRNA-mediated inhibition of ASCT2 function in vitro decreases glutamine uptake, cell cycle progression through E2F transcription factors, mTORC1 pathway activation and cell growth. Chemical inhibition also reduces basal oxygen consumption and fatty acid synthesis, showing that downstream metabolic function is reliant on ASCT2-mediated glutamine uptake. Furthermore, shRNA knockdown of ASCT2 in PC-3 cell xenografts significantly inhibits tumour growth and metastasis in vivo, associated with the down-regulation of E2F cell cycle pathway proteins. In conclusion, ASCT2-mediated glutamine uptake is essential for multiple pathways regulating the cell cycle and cell growth, and is therefore a putative therapeutic target in prostate cancer.

Circulating tumour cells in metastatic head and neck cancers

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer with 650,000 new cases p/a worldwide. HNSCC causes high morbidity with a 5-year survival rate of less than 60%, which has not improved due to the lack of early detection (Bozec et al. Eur Arch Otorhinolaryngol. 2013;270: 2745–9). Metastatic disease remains one of the leading causes of death in HNSCC patients. This review article provides a comprehensive overview of literature over the past 5 years on the detection of circulating tumour cells (CTCs) in HNSCC; CTC biology and future perspectives. CTCs are a hallmark of invasive cancer cells and key to metastasis. CTCs can be used as surrogate markers of overall survival and progression-free survival. CTCs are currently used as prognostic factors for breast, prostate and colorectal cancers using the CellSearch® system. CTCs have been detected in HNSCC, however, these numbers depend on the technique applied, time of blood collection and the clinical stage of the patient. The impact of CTCs in HNSCC is not well understood, and thus, not in routine clinical practice. Validated detection technologies that are able to capture CTCs undergoing epithelial–mesenchymal transition are needed. This will aid in the capture of heterogeneous CTCs, which can be compiled as new targets for the current food and drug administration-cleared CellSearch® system. Recent studies on CTCs in HNSCC with the CellSearch® have shown variable data. Therefore, there is an immediate need for large clinical trials encompassing a suite of biomarkers capturing CTCs in HNSCC, before CTCs can be used as prognostic markers in HNSCC patient management.

Improving deep convultional neural networks with unsupervised feature learning

The latest generation of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks. However, there is very limited research on jointly exploiting the strength of these two approaches. In this paper, we investigate the learning capability of both methods. We compare the output of individual layers and show that many learnt filters and outputs of the corresponding level layer are almost similar for both approaches. Stacking the DCNN on top of unsupervised layers or replacing layers in the DCNN with the corresponding learnt layers in the CDBN can improve the recognition/classification accuracy and training computational expense. We demonstrate the validity of the proposal on ImageNet dataset.

The chromosome 16q region associated with ankylosing spondylitis includes the candidate gene tumour necrosis factor receptor type 1-associated death domain (TRADD)

Objective: To replicate and refine the reported association of ankylosing spondylitis (AS) with two nonsynonymous single nucleotide polymorphisms (nsSNPs) on chromosome 16q22.1. Methods: Firstly, 730 independent UK patients with AS were genotyped for rs9939768 and rs6979 and allele frequencies were compared with 2879 previously typed historic disease controls. Secondly, the two data sets were combined in meta-analyses. Finally, 5 tagging SNPs, located between rs9939768 and rs6979, were analysed in 1604 cases and 1020 controls. Results: The association of rs6979 with AS was replicated, p=0.03, OR=1.14 (95% CI 1.01 to 1.28), and a trend for association with rs9939768 detected, p=0.06, OR=1.25 (95% CI 0.99 to 1.57). Meta-analyses revealed association of both SNPs with AS, p=0.0008, OR=1.31 (95% CI 1.12 to 1.54) and p=0.0009, OR=1.15 (95% CI 1.06 to 1.23) for rs9939768 and rs6979, respectively. New associations with rs9033 and rs868213 (p=0.00002, OR=1.23 (95% CI 1.12 to 1.36) and p=0.00002 OR=1.45 (95% CI 1.22 to 1.72), respectively, were identified. Conclusions: The region on chromosome 16 that has been replicated in the present work is interesting as the highly plausible candidate gene, tumour necrosis factor receptor type 1 (TNFR1)-associated death domain (TRADD), is located between rs9033 and rs868213. It will require additional work to identify the primary genetic association(s) with AS.

Evidence of genetic association between TNFRSF1A encoding the p55 tumour necrosis factor receptor, and ankylosing spondylitis in UK Caucasians

Objectives: To replicate the possible genetic association between ankylosing spondylitis (AS) and TNFRSF1A. Methods: TNFRSF1A was re-sequenced in 48 individuals with AS to identify novel polymorphisms. Nine single nucleotide polymorphisms (SNPs) in TNFRSF1A and 5 SNPs in the neighbouring gene SCNN1A were genotyped in 1604 UK Caucasian individuals with AS and 1019 matched controls. An extended study was implemented using additional genotype data on 8 of these SNPs from 1400 historical controls from the 1958 British Birth Cohort. A meta-analysis of previously published results was also undertaken. Results: One novel variant in intron 6 was identified but no new coding variants. No definite associations were seen in the initial study but in the extended study there were weak associations with rs4149576 (p=0.04) and rs4149577 (p=0.007). In the metaanalysis consistent, somewhat stronger associations were seen with rs4149577 (p=0.002) and rs4149578 (p=0.006). Conclusions: These studies confirm the weak genetic associations between AS and TNFRSF1A. In view of the previously reported associations of TNFRSF1A with AS, in Caucasians and Chinese, and the biological plausibility of this candidate gene, replication of this finding in well powered studies is clearly indicated.

Imaging tumour distribution of a polymeric drug delivery platformin vivoby PET-MRI

Background Over the past decade, molecular imaging has played a key role in the progression of drug delivery platforms from concept to commercialisation. Of the molecular imaging techniques commonly utilised, positron emission tomography (PET) can yield a breadth of information not easily accessible by other methodologies and when combined with other complementary imaging modalities, is a powerful tool for pre- and clinical development of therapeutics. However, very little research has focussed on the information available from complimentary imaging modalities. This paper reports on the data-rich methodologies of contrast enhanced PET/CT and PET/MRI for probing efficacy of polymer drug delivery platforms. Results The information available from an ExiTron nano 6000 contrast enhanced PET/CT and a gadolinium (Gd) enhanced PET/MRI image of a 64Cu labeled HBP in the same mouse was qualitatively compared. Conclusions Gd contrast enhanced PET/MRI offers a powerful methodology for investigating the distribution of polymer drug delivery platforms in vivo and throughout a tumour volume. Furthermore, information about depth of penetration away from primary blood vessels can be gleaned, potentially leading to development of more efficacious delivery vehicles for clinical use.

Australian data do not support current pharmaceutical benefits scheme criteria for use of tumour necrosis factor-α inhibitors in ankylosing spondylitis

On the basis of local data, we write in support of the conclusions of Smith and Ahern that current Pharmaceu- tical Benefits Scheme (PBS) criteria for tumour necrosis factor (TNF)-a inhibitors in ankylosing spondylitis (AS) are not evidence-based. 1 As a prerequisite to the appropriate use of biological therapy in AS, three aspects of the disease need to be defined: (i) diagnosis, (ii) activity and (iii) therapeutic failure (Table 1)....

Implementation of Art1 and Art2 Artificial Neural Networks on Ring and Mesh Architectures

The Artificial Neural Networks (ANNs) are being used to solve a variety of problems in pattern recognition, robotic control, VLSI CAD and other areas. In most of these applications, a speedy response from the ANNs is imperative. However, ANNs comprise a large number of artificial neurons, and a massive interconnection network among them. Hence, implementation of these ANNs involves execution of computer-intensive operations. The usage of multiprocessor systems therefore becomes necessary. In this article, we have presented the implementation of ART1 and ART2 ANNs on ring and mesh architectures. The overall system design and implementation aspects are presented. The performance of the algorithm on ring, 2-dimensional mesh and n-dimensional mesh topologies is presented. The parallel algorithm presented for implementation of ART1 is not specific to any particular architecture. The parallel algorithm for ARTE is more suitable for a ring architecture.

A direct adaptive neural command controller design for an unstable helicopter

This paper presents an off-line (finite time interval) and on-line learning direct adaptive neural controller for an unstable helicopter. The neural controller is designed to track pitch rate command signal generated using the reference model. A helicopter having a soft inplane four-bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is used for the simulation studies. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using backpropagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval) network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller (DANC) is compared with feedback error learning neural controller (FENC).

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Denoising neural data with state-space smoothing: Method and application

Neural data are inevitably contaminated by noise. When such noisy data are subjected to statistical analysis, misleading conclusions can be reached. Here we attempt to address this problem by applying a state-space smoothing method, based on the combined use of the Kalman filter theory and the Expectation–Maximization algorithm, to denoise two datasets of local field potentials recorded from monkeys performing a visuomotor task. For the first dataset, it was found that the analysis of the high gamma band (60–90 Hz) neural activity in the prefrontal cortex is highly susceptible to the effect of noise, and denoising leads to markedly improved results that were physiologically interpretable. For the second dataset, Granger causality between primary motor and primary somatosensory cortices was not consistent across two monkeys and the effect of noise was suspected. After denoising, the discrepancy between the two subjects was significantly reduced.

Oxygen transport in a three-dimensional microvascular network incorporated with early tumour growth and preexisting vessel cooption: Numerical simulation study

We propose a dynamic mathematical model of tissue oxygen transport by a preexisting three-dimensional microvascular network which provides nutrients for an in situ cancer at the very early stage of primary microtumour growth. The expanding tumour consumes oxygen during its invasion to the surrounding tissues and cooption of host vessels. The preexisting vessel cooption, remodelling and collapse are modelled by the changes of haemodynamic conditions due to the growing tumour. A detailed computational model of oxygen transport in tumour tissue is developed by considering (a) the time-varying oxygen advection diffusion equation within the microvessel segments, (b) the oxygen flux across the vessel walls, and (c) the oxygen diffusion and consumption with in the tumour and surrounding healthy tissue. The results show the oxygen concentration distribution at different time points of early tumour growth. In addition, the influence of preexisting vessel density on the oxygen transport has been discussed. The proposed model not only provides a quantitative approach for investigating the interactions between tumour growth and oxygen delivery, but also is extendable to model other molecules or chemotherapeutic drug transport in the future study.

A hybrid cellular automata model of multicellular tumour spheroid growth in hypoxic microenvironment

A three-dimensional hybrid cellular automata (CA) model is developed to study the dynamic process of multicellular tumour spheroid (MTS) growth by introducing hypoxia as an important microenvironment factor which influences cell migration and cell phenotype expression. The model enables us to examine the effects of different hypoxic environments on the growth history of the MTS and to study the dynamic interactions between MTS growth and chemical environments. The results include the spatial distribution of different phenotypes of tumour cells and associated oxygen concentration distributions under hypoxic conditions. The discussion of the model system responses to the varied hypoxic conditions reveals that the improvement of the resistance of tumour cells to a hypoxic environment may be important in the tumour normalization therapy.

3D numerical simulation of avascular tumour growth: effect of hypoxic micro-environment in host tissue

A three-dimensional (3D) mathematical model of tumour growth at the avascular phase and vessel remodelling in host tissues is proposed with emphasis on the study of the interactions of tumour growth and hypoxic micro-environment in host tissues. The hybrid based model includes the continuum part, such as the distributions of oxygen and vascular endothelial growth factors (VEGFs), and the discrete part of tumour cells (TCs) and blood vessel networks. The simulation shows the dynamic process of avascular tumour growth from a few initial cells to an equilibrium state with varied vessel networks. After a phase of rapidly increasing numbers of the TCs, more and more host vessels collapse due to the stress caused by the growing tumour. In addition, the consumption of oxygen expands with the enlarged tumour region. The study also discusses the effects of certain factors on tumour growth, including the density and configuration of preexisting vessel networks and the blood oxygen content. The model enables us to examine the relationship between early tumour growth and hypoxic micro-environment in host tissues, which can be useful for further applications, such as tumour metastasis and the initialization of tumour angiogenesis.

miR-514a regulates the tumour suppressor NF1 and modulates BRAFi sensitivity in melanoma

To identify ‘melanoma-specific’ microRNAs (miRNAs) we used an unbiased microRNA profiling approach to comprehensively study cutaneous melanoma in relation to other solid malignancies, which revealed 233 differentially expressed (≥ 2 fold, p < 0.05) miRNAs. Among the top 20 most significantly different miRNAs was hsa-miR-514a-3p. miR-514a is a member of a cluster of miRNAs (miR-506-514) involved in initiating melanocyte transformation and promotion of melanoma growth. We found miR-514a was expressed in 38/55 (69%) melanoma cell lines but in only 1/34 (3%) other solid cancers. To identify miR-514a regulated targets we conducted a miR-514a-mRNA ‘pull-down’ experiment, which revealed hundreds of genes, including: CTNNB1, CDK2, MC1R, and NF1, previously associated with melanoma. NF1 was selected for functional validation because of its recent implication inacquired resistance to BRAFV600E-targeted therapy. Luciferase-reporter assays confirmed NF1 as a direct target of miR-514a and over-expression of miR-514a in melanoma cell lines inhibited NF1 expression, which correlated with increased survival of BRAFV600E cells treated with PLX4032. These data provide another mechanism for the dysregulation of the MAPK pathway which may contribute to the profound resistance associated with current RAF-targeted therapies.