Aptamer-mediated cancer gene therapy

Cancer as a genetic disorder is one of the leading causes of death worldwide. Conventional anticancer options such as chemo- and/or radio-therapy have their own drawbacks and could not provide a cure in most cases at present. More effective therapeutic strategies with less side effects are urgently needed. Aptamers, also known as chemical antibodies, are single strand DNA or RNA molecules that can bind to their target molecules with high affinity and specificity. Such site-specific binding ability of aptamers facilitates the delivery and interaction of exogenous nucleic acids with diseased genes. Thus, aptamer-guided gene therapy has emerged as a promising anticancer strategy in addition to the classic treatment regimen. Aptamers can directly deliver anti-cancer nucleic acids, e.g. small interfering RNA, micro RNA, antimicroRNA and small hairpin RNA, to cancer cells or function as a targeting ligand to guide nanoparticles containing therapeutic nucleic acids. This review focuses on recent progress in aptamer-mediated gene therapy for the treatment of hepatocellular carcinoma and other types of cancers, shedding light on the potential of this novel approach of targeted cancer gene therapy.

Molecular insights and oligonucleotide based targeted gene therapy against cancer

The thesis helps to unravel the function of T lymphoma invasion and metastasis protein (TIAM1) and nucleolin, a nucleolar protein in retinoblastoma tumorigenesis. Aptamer based targeted imaging; drug and gene delivery to retinoblastoma and epithelial cancer cells was attained. The work work finally opened up avenues for cancer stem cell targeting using aptamers, imaging of cancer cells using novel bio-orthogonal agent and use of aptamer for blocking the miRNA-17-92 cluster maturation.

Can cancer therapy be achieved by bridging apoptosis and autophagy: A method based on microRNA-dependent gene therapy and phytochemical targets

A failure of a cell to self destruct has long been associated with cancer progression and development. The fact that tumour cells may not instigate cell arrest or activate cell death mechanisms upon cancer drug delivery is a major concern. Autophagy is a mechanism whereby cell material can be engulfed and digested while apoptosis is a self-killing mechanism, both capable of hindering multiplication after cell injury. In particular situations, autophagy and apoptosis seem to co-exist simultaneously or interdependently with the aid of mutual proteins. This review covers roles of microRNAs and chemopreventive agents and makes an attempt at outlining possible partnerships in maximizing cancer cell death with minimal normal cell damage.

Low-flow oxygen therapy: selecting the right device

The authors provide and overview of oxygen therapy principles, describing the indications and care requirements of three low flow oxygen therapy devices and providing an algorithm for managing refractory hypoxaemia.

Microbial-based therapy of cancer: a new twist to age old practice

The use of bacteria in the regression of tumors has long been known. Various approaches for using bacteria in cancer therapy include the use of bacteria as sensitizing agents for chemotherapy, as delivery agents for cancer drugs and as agents for gene therapy. The tumor regression stimulated by infecting microorganisms has been attributed to activation of the immune system of the host. However, recent studies indicate that when tumor-harboring mice with defective immune systems are infected with certain microorganisms, the regression of the tumor is still observed, suggesting that there are other host factors contributing to the microbial associated regression of tumors. Since the use of live or attenuated bacteria for tumor regression has associated toxic effects, studies are in progress to identify a pure microbial metabolite or any component of the microbial cell that might have anti-cancer activity. It has now been demonstrated that a redox protein from Pseudomonas aeruginosa, a cupredoxin, can enter into human cancer cells and trigger the apoptotic cell death. In vivo, this cupredoxin can lead to the regression of tumor growth in immunodeficient mice harboring xenografted melanomas and breast cancer tumors without inducing significant toxic effects, suggesting that it has potential anti-cancer activity. This bacterial protein interacts with p53 and modulates mammalian cellular activity. Hence, it could potentially be used as an anti-cancer agent for solid tumors and has translational value in tumor-targeted or in combinational-biochemotherapy strategies for cancer treatments. Here, we focus on diverse approaches to cancer biotherapy, including bacteriolytic and bacterially-derived anti-cancer agents with an emphasis on their mechanism of action and therapeutic potential.

A novel aggregate gene selection method for microarray data classification

This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice.

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

Timing of androgen-deprivation therapy in patients with prostate cancer with a rising PSA (TROG 03.06 and VCOG PR 01-03 [TOAD]): a randomised, multicentre, non-blinded, phase 3 trial

Background Androgen-deprivation therapy is offered to men with prostate cancer who have a rising prostate-specific antigen after curative therapy (PSA relapse) or who are considered not suitable for curative treatment; however, the optimal timing for its introduction is uncertain. We aimed to assess whether immediate androgen-deprivation therapy improves overall survival compared with delayed therapy. Methods In this randomised, multicentre, phase 3, non-blinded trial, we recruited men through 29 oncology centres in Australia, New Zealand, and Canada. Men with prostate cancer were eligible if they had a PSA relapse after previous attempted curative therapy (radiotherapy or surgery, with or without postoperative radiotherapy) or if they were not considered suitable for curative treatment (because of age, comorbidity, or locally advanced disease). We used a database-embedded, dynamically balanced, randomisation algorithm, coordinated by the Cancer Council Victoria, to randomly assign participants (1:1) to immediate androgen-deprivation therapy (immediate therapy arm) or to delayed androgen-deprivation therapy (delayed therapy arm) with a recommended interval of at least 2 years unless clinically contraindicated. Randomisation for participants with PSA relapse was stratified by type of previous therapy, relapse-free interval, and PSA doubling time; randomisation for those with non-curative disease was stratified by metastatic status; and randomisation in both groups was stratified by planned treatment schedule (continuous or intermittent) and treatment centre. Clinicians could prescribe any form and schedule of androgen-deprivation therapy and group assignment was not masked. The primary outcome was overall survival in the intention-to-treat population. The trial closed to accrual in 2012 after review by the independent data monitoring committee, but data collection continued for 18 months until Feb 26, 2014. It is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12606000301561) and ClinicalTrials.gov (NCT00110162). Findings Between Sept 3, 2004, and July 13, 2012, we recruited 293 men (261 with PSA relapse and 32 with non-curable disease). We randomly assigned 142 men to the immediate therapy arm and 151 to the delayed therapy arm. Median follow-up was 5 years (IQR 3·3–6·2) from the date of randomisation. 16 (11%) men died in the immediate therapy arm and 30 (20%) died in the delayed therapy arm. 5-year overall survival was 86·4% (95% CI 78·5–91·5) in the delayed therapy arm versus 91·2% (84·2–95·2) in the immediate therapy arm (log-rank p=0·047). After Cox regression, the unadjusted HR for overall survival for immediate versus delayed arm assignment was 0·55 (95% CI 0·30–1·00; p=0·050). 23 patients had grade 3 treatment-related adverse events. 105 (36%) men had adverse events requiring hospital admission; none of these events were attributable to treatment or differed between treatment-timing groups. The most common serious adverse events were cardiovascular, which occurred in nine (6%) patients in the delayed therapy arm and 13 (9%) in the immediate therapy arm. Interpretation Immediate receipt of androgen-deprivation therapy significantly improved overall survival compared with delayed intervention in men with PSA-relapsed or non-curable prostate cancer. The results provide benchmark evidence of survival rates and morbidity to discuss with men when considering their treatment options. Funding Australian National Health and Medical Research Council and Cancer Councils, The Royal Australian and New Zealand College of Radiologists, Mayne Pharma Australia.

Modified AHP for gene selection and cancer classification using type-2 fuzzy logic

This paper proposes a modification to the analytic hierarchy process (AHP) to select the most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) for cancer classification. Unlike the conventional AHP, the modified AHP allows us to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test, and signal-to-noise ratio. The IT2FLS is introduced for the classification task due to its great ability for handling nonlinear, noisy, and outlier data, which are common problems in cancer microarray gene expression profiles. An unsupervised learning strategy using the fuzzy c-means clustering is employed to initialize parameters of the IT2FLS. Other classifiers such as multilayer perceptron network, support vector machine, and fuzzy ARTMAP are also implemented for comparisons. Experiments are carried out on three well-known microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, and prostate. Rather than the traditional cross validation, leave-one-out cross-validation strategy is applied for the experiments. Results demonstrate the performance dominance of the IT2FLS against the competing classifiers. More noticeably, the modified AHP improves the classification performance not only of the IT2FLS but of all other classifiers as well. Accordingly, the proposed combination between the modified AHP and IT2FLS is a powerful tool for cancer classification and can be implemented as a real clinical decision support system that is useful for medical practitioners.

Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models. Prediction performance evaluations and comparisons between the authors' GEP models and three representative machine learning methods, support vector machine, multi-layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.

Novel therapeutics for complex diseases from genome-wide association data

The development of novel therapies is essential to lower the burden of complex diseases. The purpose of this study is to identify novel therapeutics for complex diseases using bioinformatic methods. Bioinformatic tools such as candidate gene prediction tools allow identification of disease genes by identifying the potential candidate genes linked to genetic markers of the disease. Candidate gene prediction tools can only identify candidates for further research, and do not identify disease genes directly. Integration of drug-target datasets with candidate gene data-sets can identify novel potential therapeutics suitable for repositioning in clinical trials. Drug repositioning can save valuable time and money spent in therapeutic development of complex diseases.

Multiple origins of the endemic Australian Macrobrachium (Descapoda: Palaemonidae) based on 16S rRNA mitochondrial sequences

The evolutionary relationships of the freshwater prawn genus Macrobrachium are obscure. Members of this genus are widely distributed across tropical and subtropical regions. The phylogenetic relationships among the seven endemic and six non-endemic Australian Macrobrachium, along with five non-Australian species, were inferred from the mitochondrial 16S rRNA gene sequences. Methods of analysis yielded phylogenetic trees of differing topologies; however, none supported a monophyletic origin for endemic Australian Macrobrachium. Enforced monophyly of a single origin of endemic Macrobrachium was statistically tested and rejected. These results support the view that the endemic Australian Macrobrachium arose from multiple origins. Previous biogeographical hypotheses related to the radiation of Macrobrachium into Australia are re-examined in the context of these results.

Insights into exazaphosphorine resistance and possible approaches to its circumvention

The oxazaphosphorines cyclophosphamide, ifosfamide and trofosfamide remain a clinically useful class of anticancer drugs with substantial antitumour activity against a variety of solid tumors and hematological malignancies. A major limitation to their use is tumour resistance, which is due to multiple mechanisms that include increased DNA repair, increased cellular thiol levels, glutathione S-transferase and aldehyde dehydrogenase activities, and altered cell-death response to DNA damage. These mechanisms have been recently re-examined with the aid of sensitive analytical techniques, high-throughput proteomic and genomic approaches, and powerful pharmacogenetic tools. Oxazaphosphorine resistance, together with dose-limiting toxicity (mainly neutropenia and neurotoxicity), significantly hinders chemotherapy in patients, and hence, there is compelling need to find ways to overcome it. Four major approaches are currently being explored in preclinical models, some also in patients: combination with agents that modulate cellular response and disposition of oxazaphosphorines; antisense oligonucleotides directed against specific target genes; introduction of an activating gene (CYP3A4) into tumor tissue; and modification of dosing regimens. Of these approaches, antisense oligonucleotides and gene therapy are perhaps more speculative, requiring detailed safety and efficacy studies in preclinical models and in patients. A fifth approach is the design of novel oxazaphosphorines that have favourable pharmacokinetic and pharmacodynamic properties and are less vulnerable to resistance. Oxazaphosphorines not requiring hepatic CYP-mediated activation (for example, NSC 613060 and mafosfamide) or having additional targets (for example, glufosfamide that also targets glucose transport) have been synthesized and are being evaluated for safety and efficacy. Characterization of the molecular targets associated with oxazaphosphorine resistance may lead to a deeper understanding of the factors critical to the optimal use of these agents in chemotherapy and may allow the development of strategies to overcome resistance.