Obesity and type 2 diabetes are recognised risk factors for the development of some cancers and, increasingly, predict more aggressive disease, treatment failure, and cancer-specific mortality. Many factors may contribute to this clinical observation. Hyperinsulinaemia, dyslipidaemia, hypoxia, ER stress, and inflammation associated with expanded adipose tissue are thought to be among the main culprits driving malignant growth and cancer advancement. This observation has led to the proposal of the potential utility of “old players” for the treatment of type 2 diabetes and metabolic syndrome as new cancer adjuvant therapeutics. Androgen-regulated pathways drive proliferation, differentiation, and survival of benign and malignant prostate tissue. Androgen deprivation therapy (ADT) exploits this dependence to systemically treat advanced prostate cancer resulting in anticancer response and improvement of cancer symptoms. However, the initial therapeutic response from ADT eventually progresses to castrate resistant prostate cancer (CRPC) which is currently incurable. ADT rapidly induces hyperinsulinaemia which is associated with more rapid treatment failure. We discuss current observations of cancer in the context of obesity, diabetes, and insulin-lowering medication. We provide an update on current treatments for advanced prostate cancer and discuss whether metabolic dysfunction, developed during ADT, provides a unique therapeutic window for rapid translation of insulin-sensitising medication as combination therapy with antiandrogen targeting agents for the management of advanced prostate cancer.
Cancer outlier profile analysis (COPA) has proven to be an effective approach to analyzing cancer expression data, leading to the discovery of the TMPRSS2 and ETS family gene fusion events in prostate cancer. However, the original COPA algorithm did not identify down-regulated outliers, and the currently available R package implementing the method is similarly restricted to the analysis of over-expressed outliers. Here we present a modified outlier detection method, mCOPA, which contains refinements to the outlier-detection algorithm, identifies both over- and under-expressed outliers, is freely available, and can be applied to any expression dataset.
We compare our method to other feature-selection approaches, and demonstrate that mCOPA frequently selects more-informative features than do differential expression or variance-based feature selection approaches, and is able to recover observed clinical subtypes more consistently. We demonstrate the application of mCOPA to prostate cancer expression data, and explore the use of outliers in clustering, pathway analysis, and the identification of tumour suppressors. We analyse the under-expressed outliers to identify known and novel prostate cancer tumour suppressor genes, validating these against data in Oncomine and the Cancer Gene Index. We also demonstrate how a combination of outlier analysis and pathway analysis can identify molecular mechanisms disrupted in individual tumours.
We demonstrate that mCOPA offers advantages, compared to differential expression or variance, in selecting outlier features, and that the features so selected are better able to assign samples to clinically annotated subtypes. Further, we show that the biology explored by outlier analysis differs from that uncovered in differential expression or variance analysis. mCOPA is an important new tool for the exploration of cancer datasets and the discovery of new cancer subtypes, and can be combined with pathway and functional analysis approaches to discover mechanisms underpinning heterogeneity in cancers.
Cancer; Outliers; Expression data; Expression profile; Cluster; Subtype; Heterogeneous; Bioinformatics; Percentile; Feature selection
miRDeep and its varieties are widely used to quantify known and novel micro RNA (miRNA) from small RNA sequencing (RNAseq). This article describes miRDeep*, our integrated miRNA identification tool, which is modeled off miRDeep, but the precision of detecting novel miRNAs is improved by introducing new strategies to identify precursor miRNAs. miRDeep* has a user-friendly graphic interface and accepts raw data in FastQ and Sequence Alignment Map (SAM) or the binary equivalent (BAM) format. Known and novel miRNA expression levels, as measured by the number of reads, are displayed in an interface, which shows each RNAseq read relative to the pre-miRNA hairpin. The secondary pre-miRNA structure and read locations for each predicted miRNA are shown and kept in a separate figure file. Moreover, the target genes of known and novel miRNAs are predicted using the TargetScan algorithm, and the targets are ranked according to the confidence score. miRDeep* is an integrated standalone application where sequence alignment, pre-miRNA secondary structure calculation and graphical display are purely Java coded. This application tool can be executed using a normal personal computer with 1.5 GB of memory. Further, we show that miRDeep* outperformed existing miRNA prediction tools using our LNCaP and other small RNAseq datasets. miRDeep* is freely available online at http://www.australianprostatecentre.org/research/software/mirdeep-star.
Effective management of chronic diseases such as prostate cancer is important. Research suggests a tendency to use self-care treatment options such as over-the-counter (OTC) complementary medications among prostate cancer patients. The current trend in patient-driven recording of health data in an online Personal Health Record (PHR) presents an opportunity to develop new data-driven approaches for improving prostate cancer patient care. However, the ability of current online solutions to share patients’ data for better decision support is limited. An informatics approach may improve online sharing of self-care interventions among these patients. It can also provide better evidence to support decisions made during their self-managed care.
To identify requirements for an online system and describe a new case-based reasoning (CBR) method for improving self-care of advanced prostate cancer patients in an online PHR environment.
A non-identifying online survey was conducted to understand self-care patterns among prostate cancer patients and to identify requirements for an online information system. The pilot study was carried out between August 2010 and December 2010. A case-base of 52 patients was developed.
The data analysis showed self-care patterns among the prostate cancer patients. Selenium (55%) was the common complementary supplement used by the patients. Paracetamol (about 45%) was the commonly used OTC by the patients.
The results of this study specified requirements for an online case-based reasoning information system. The outcomes of this study are being incorporated in design of the proposed Artificial Intelligence (Al) driven patient journey browser system. A basic version of the proposed system is currently being considered for implementation.
Advanced Prostate Cancer; Self-care; Patient Journey; PHR; Case-based reasoning
Biophysical and biochemical properties of the microenvironment regulate cellular responses such as growth, differentiation, morphogenesis and migration in normal and cancer cells. Since two-dimensional (2D) cultures lack the essential characteristics of the native cellular microenvironment, three-dimensional (3D) cultures have been developed to better mimic the natural extracellular matrix. To date, 3D culture systems have relied mostly on collagen and Matrigel™ hydrogels, allowing only limited control over matrix stiffness, proteolytic degradability, and ligand density. In contrast, bioengineered hydrogels allow us to independently tune and systematically investigate the influence of these parameters on cell growth and differentiation. In this study, polyethylene glycol (PEG) hydrogels, functionalized with the Arginine-glycine-aspartic acid (RGD) motifs, common cell-binding motifs in extracellular matrix proteins, and matrix metalloproteinase (MMP) cleavage sites, were characterized regarding their stiffness, diffusive properties, and ability to support growth of androgen-dependent LNCaP prostate cancer cells. We found that the mechanical properties modulated the growth kinetics of LNCaP cells in the PEG hydrogel. At culture periods of 28 days, LNCaP cells underwent morphogenic changes, forming tumor-like structures in 3D culture, with hypoxic and apoptotic cores. We further compared protein and gene expression levels between 3D and 2D cultures upon stimulation with the synthetic androgen R1881. Interestingly, the kinetics of R1881 stimulated androgen receptor (AR) nuclear translocation differed between 2D and 3D cultures when observed by immunofluorescent staining. Furthermore, microarray studies revealed that changes in expression levels of androgen responsive genes upon R1881 treatment differed greatly between 2D and 3D cultures. Taken together, culturing LNCaP cells in the tunable PEG hydrogels reveals differences in the cellular responses to androgen stimulation between the 2D and 3D environments. Therefore, we suggest that the presented 3D culture system represents a powerful tool for high throughput prostate cancer drug testing that recapitulates tumor microenvironment.
Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental conditions. However, functionally related genes generally do not show coherent expression across all conditions since any given cellular process is active only under a subset of conditions. Biclustering finds gene clusters that have similar expression levels across a subset of conditions. This paper proposes a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner.
In order to find the biclusters in a gene expression dataset, we exhaustively select combinations of genes and conditions as seeds to create candidate bicluster tables. The tables have two columns (a) a gene set, and (b) the conditions on which the gene set have dissimilar expression levels to the seed. First, the genes with less than the maximum number of dissimilar conditions are identified and a table of these genes is created. Second, the rows that have the same dissimilar conditions are grouped together. Third, the table is sorted in ascending order based on the number of dissimilar conditions. Finally, beginning with the first row of the table, a test is run repeatedly to determine whether the cardinality of the gene set in the row is greater than the minimum threshold number of genes in a bicluster. If so, a bicluster is outputted and the corresponding row is removed from the table. Repeating this process, all biclusters in the table are systematically identified until the table becomes empty.
This paper presents a novel biclustering algorithm for the identification of additive biclusters. Since it involves exhaustively testing combinations of genes and conditions, the additive biclusters can be found more readily.
An association between the metabolic syndrome and reduced testosterone levels has been identified, and a specific inverse relationship between insulin and testosterone levels suggests that an important metabolic crosstalk exists between these two hormonal axes; however, the mechanisms by which insulin and androgens may be reciprocally regulated are not well described. Androgen-dependant gene pathways regulate the growth and maintenance of both normal and malignant prostate tissue, and androgen-deprivation therapy (ADT) in patients exploits this dependence when used to treat recurrent and metastatic prostate cancer resulting in tumour regression. A major systemic side effect of ADT includes induction of key features of the metabolic syndrome and the consistent feature of hyperinsulinaemia. Recent studies have specifically identified a correlation between elevated insulin and high-grade PCa and more rapid progression to castrate resistant disease. This paper examines the relationship between insulin and androgens in the context of prostate cancer progression. Prostate cancer patients present a promising cohort for the exploration of insulin stabilising agents as adjunct treatments for hormone deprivation or enhancers of chemosensitivity for treatment of advanced prostate cancer.
Secretory clusterin (sCLU) is a stress-activated, cytoprotective chaperone that confers broad-spectrum cancer treatment resistance and its targeted inhibitor (OGX-011) is currently in Phase II trials for prostate, lung, and breast cancer. However, molecular mechanisms by which sCLU inhibits treatment-induced apoptosis in prostate cancer remain incompletely defined. We report that sCLU increases NF-κB nuclear translocation and transcriptional activity by serving as a ubiquitin binding protein that enhances COMMD1 and I-κB proteasomal degradation by interacting with members of the SCF-βTrCP E3 ligase family. Knockdown of sCLU in prostate cancer cells stabilizes COMMD1 and I-κB, thereby sequestrating NF-κB in the cytoplasm and decreasing NF-κB transcriptional activity. Comparative microarray profiling of sCLU over-expressing and knockdown prostate cancer cells confirmed that the expression of many NF-κB regulated genes positively correlate with sCLU levels. We propose that elevated levels of sCLU promote prostate cancer cell survival by facilitating degradation of COMMD1 and I-κB, thereby activating the canonical NF-κB pathway.
prostate cancer; clusterin; COMMD1; I-κB; NF-κB
Cell-cell and cell-matrix interactions play a major role in tumor morphogenesis and cancer metastasis. Therefore, it is crucial to create a model with a biomimetic microenvironment that allows such interactions to fully represent the pathophysiology of a disease for an in vitro study. This is achievable by using three-dimensional (3D) models instead of conventional two-dimensional (2D) cultures with the aid of tissue engineering technology. We are now able to better address the complex intercellular interactions underlying prostate cancer (CaP) bone metastasis through such models. In this study, we assessed the interaction of CaP cells and human osteoblasts (hOBs) within a tissue engineered bone (TEB) construct. Consistent with other in vivo studies, our findings show that intercellular and CaP cell-bone matrix interactions lead to elevated levels of matrix metalloproteinases, steroidogenic enzymes and the CaP biomarker, prostate specific antigen (PSA); all associated with CaP metastasis. Hence, it highlights the physiological relevance of this model. We believe that this model will provide new insights for understanding of the previously poorly understood molecular mechanisms of bone metastasis, which will foster further translational studies, and ultimately offer a potential tool for drug screening.
3D in vitro model; bone metastasis; co-culture; tissue engineering; prostate cancer; osteoblasts
Time–course microarray data consist of mRNA expression from a common set of genes collected at different time points. Such data are thought to reflect underlying biological processes developing over time. In this article we propose a model that allows us to examine differential expression and gene network relationships using time course microarray data. We model each gene expression profile as a random functional transformation of the scale, amplitude and phase of a common curve. Inferences about the gene–specific amplitude parameters allow us to examine differential gene expression. Inferences about measures of functional similarity based on estimated time transformation functions allow us to examine gene networks while accounting for features of the gene expression profiles. We discuss applications to simulated data as well as to microarray data on prostate cancer progression.
Bayesian hierarchical model; differential expression; functional data; functional similarity; gene networks; time–course microarray data; time transformation
The purpose of this study was to identify candidate metastasis suppressor genes from a mouse allograft model of prostate cancer (NE-10). This allograft model originally developed metastases by twelve weeks after implantation in male athymic nude mice, but lost the ability to metastasize after a number of in vivo passages. We performed high resolution array comparative genomic hybridization on the metastasizing and non-metastasizing allografts to identify chromosome imbalances that differed between the two groups of tumors.
This analysis uncovered a deletion on chromosome 2 that differed between the metastasizing and non-metastasizing tumors. Bioinformatics filters were employed to mine this region of the genome for candidate metastasis suppressor genes. Of the 146 known genes that reside within the region of interest on mouse chromosome 2, four candidate metastasis suppressor genes (Slc27a2, Mall, Snrpb, and Rassf2) were identified. Quantitative expression analysis confirmed decreased expression of these genes in the metastasizing compared to non-metastasizing tumors.
This study presents combined genomics and bioinformatics approaches for identifying potential metastasis suppressor genes. The genes identified here are candidates for further studies to determine their functional role in inhibiting metastases in the NE-10 allograft model and human prostate cancer.
The androgen receptor is a ligand-induced transcriptional factor, which plays an important role in normal development of the prostate as well as in the progression of prostate cancer to a hormone refractory state. We previously reported the identification of a novel AR coactivator protein, L-dopa decarboxylase (DDC), which can act at the cytoplasmic level to enhance AR activity. We have also shown that DDC is a neuroendocrine (NE) marker of prostate cancer and that its expression is increased after hormone-ablation therapy and progression to androgen independence. In the present study, we generated tetracycline-inducible LNCaP-DDC prostate cancer stable cells to identify DDC downstream target genes by oligonucleotide microarray analysis.
Comparison of induced DDC overexpressing cells versus non-induced control cell lines revealed a number of changes in the expression of androgen-regulated transcripts encoding proteins with a variety of molecular functions, including signal transduction, binding and catalytic activities. There were a total of 35 differentially expressed genes, 25 up-regulated and 10 down-regulated, in the DDC overexpressing cell line. In particular, we found a well-known androgen induced gene, TMEPAI, which wasup-regulated in DDC overexpressing cells, supporting its known co-activation function. In addition, DDC also further augmented the transcriptional repression function of AR for a subset of androgen-repressed genes. Changes in cellular gene transcription detected by microarray analysis were confirmed for selected genes by quantitative real-time RT-PCR.
Taken together, our results provide evidence for linking DDC action with AR signaling, which may be important for orchestrating molecular changes responsible for prostate cancer progression.
We have developed and fabricated a salmonid microarray containing cDNAs representing 16,006 genes. The genes spotted on the array have been stringently selected from Atlantic salmon and rainbow trout expressed sequence tag (EST) databases. The EST databases presently contain over 300,000 sequences from over 175 salmonid cDNA libraries derived from a wide variety of tissues and different developmental stages. In order to evaluate the utility of the microarray, a number of hybridization techniques and screening methods have been developed and tested.
We have analyzed and evaluated the utility of a microarray containing 16,006 (16K) salmonid cDNAs in a variety of potential experimental settings. We quantified the amount of transcriptome binding that occurred in cross-species, organ complexity and intraspecific variation hybridization studies. We also developed a methodology to rapidly identify and confirm the contents of a bacterial artificial chromosome (BAC) library containing Atlantic salmon genomic DNA.
We validate and demonstrate the usefulness of the 16K microarray over a wide range of teleosts, even for transcriptome targets from species distantly related to salmonids. We show the potential of the use of the microarray in a variety of experimental settings through hybridization studies that examine the binding of targets derived from different organs and tissues. Intraspecific variation in transcriptome expression is evaluated and discussed. Finally, BAC hybridizations are demonstrated as a rapid and accurate means to identify gene content.
Hexachlorobenzene (HCB) is a persistent environmental contaminant that has the potential to interfere with steroid hormone regulation. The prostate requires precise control by androgens to regulate its growth and function. To determine if HCB impacts androgen action in the prostate, we used a number of methods. Our in vitro cell-culture-based assay used a firefly luciferase reporter gene driven by an androgen-responsive promoter. In the presence of dihydrotestosterone, low concentrations (0.5-5 nM) of HCB increased the androgen-responsive production of firefly luciferase and high concentrations of HCB (> 10 microM) suppressed this transcriptional activity. Results from a binding assay showed no evidence of affinity between HCB and the androgen receptor. We also tested HCB for in vivo effects using transgenic mice in which the transgene was a prostate-specific, androgen-responsive promoter upstream of a chloramphenicol acetyl transferase (CAT) reporter gene. In 4-week-old mice, the proportion of dilated prostate acini, a marker of sexual maturity, increased in the low HCB dose group and decreased in the high HCB dose mice. In the 8-week-old mice, there was a significant decrease in both CAT activity and prostate weight upon exposure to 20 mg/kg/day HCB. Therefore, in vitro and in vivo data suggest that HCB weakly agonizes androgen action, and consequently, low levels of HCB enhanced androgen action but high levels of HCB interfered. Environmental contaminants have been implicated in the rising incidence of prostate cancer, and insight into the mechanisms of endocrine disruption will help to clarify their role.
Genome sequencing revealed that all six chlamydiae genomes contain three groEL-like genes (groEL1, groEL2, and groEL3). Phylogenetic analysis of groEL1, groEL2, and groEL3 indicates that these genes are likely to have been present in chlamydiae since the beginning of the lineage. Comparison of deduced amino acid sequences of the three groEL genes with those of other organisms showed high homology only for groEL1, although comparison of critical amino acid residues that are required for polypeptide binding of the Escherichia coli chaperonin GroEL revealed substantial conservation in all three chlamydial GroELs. This was further supported by three-dimensional structural predictions. All three genes are expressed constitutively throughout the developmental cycle of Chlamydia trachomatis, although groEL1 is expressed at much higher levels than are groEL2 and groEL3. Transcription of groEL1, but not groEL2 and groEL3, was elevated when HeLa cells infected with C. trachomatis were subjected to heat shock. Western blot analysis with polyclonal antibodies raised against recombinant GroEL1, GroEL2, and GroEL3 demonstrated the presence of the three proteins in C. trachomatis elementary bodies, with GroEL1 being present in the largest amount. Only C. trachomatis groEL1 and groES together complemented a temperature-sensitive E. coli groEL mutant. Complementation did not occur with groEL2 or groEL3 alone or together with groES. The role for each of the three GroELs in the chlamydial developmental cycle and in disease pathogenesis requires further study.