Gene expression microarray analysis in cancer biology, pharmacology, and drug development: progress and potential

Machine learning approach yields epigenetic biomarkers of food allergy: A novel 13-gene signature to diagnose clinical reactivity

BACKGROUND

Current laboratory tests are less than 50% accurate in distinguishing between people who have food allergies (FA) and those who are merely sensitized to foods, resulting in the use of expensive and potentially dangerous Oral Food Challenges. This study presents a purely-computational machine learning approach, conducted using DNA Methylation (DNAm) data, to accurately diagnose food allergies and potentially find epigenetic targets for the disease.

METHODS AND RESULTS

An unbiased feature-selection pipeline was created that narrowed down 405,000+ potential CpG biomarkers to 18. Machine-learning models that utilized subsets of this 18-feature aggregate achieved perfect classification accuracy on completely hidden test cohorts (on an 8-fold hidden dataset). Ensemble classification was also shown to be effective for this High Dimension Low Sample Size (HDLSS) DNA methylation dataset. The efficacy of these machine learning classifiers and the 18 CpGs was further validated by their high accuracy on a large number of hidden data permutations, where the samples in the training, cross-validation, and hidden sets were repeatedly randomly allocated. The 18-CpG signature mapped to 13 genes, on which biological insights were collected. Notably, many of the FA-discriminating genes found in this study were strongly associated with the immune system, and seven of the 13 genes were previously associated with FA.

CONCLUSIONS

Previous studies have also created highly-accurate classifiers for this dataset, using both data-driven and a priori biological insights to construct a 96-CpG signature. This research builds on previous work because it uses a completely computational approach to obtain a perfect classification accuracy while using only 18 highly discriminating CpGs (0.005% of the total available features). In machine learning, simpler models, as used in this study, are generally preferred over more complex ones (other things being equal). Lastly, the completely data-driven methodology presented in this research eliminates the need for a priori biological information and allows for generalizability to other DNAm classification problems.

Differentially expressed alternatively spliced genes in skeletal muscle from cancer patients with cachexia

BACKGROUND

Alternative splicing (AS) is a post-transcriptional gene regulatory mechanism that contributes to proteome diversity. Aberrant splicing mechanisms contribute to various cancers and muscle-related conditions such as Duchenne muscular dystrophy. However, dysregulation of AS in cancer cachexia (CC) remains unexplored. Our objectives were (i) to profile alternatively spliced genes (ASGs) on a genome-wide scale and (ii) to identify differentially expressed alternatively spliced genes (DASGs) associated with CC.

METHODS

Rectus abdominis muscle biopsies obtained from cancer patients were stratified into cachectic cases (n = 21, classified based on International consensus diagnostic framework for CC) and non-cachectic controls (n = 19, weight stable cancer patients). Human transcriptome array 2.0 was used for profiling ASGs using the total RNA isolated from muscle biopsies. Representative DASG signatures were validated using semi-quantitative RT-PCR.

RESULTS

We identified 8960 ASGs, of which 922 DASGs (772 up-regulated and 150 down-regulated) were identified at ≥1.4 fold-change and P < 0.05. Representative DASGs validated by semi-quantitative RT-PCR confirmed the primary findings from the human transcriptome arrays. Identified DASGs were associated with myogenesis, adipogenesis, protein ubiquitination, and inflammation. Up to 10% of the DASGs exhibited cassette exon (exon included or skipped) as a predominant form of AS event. We also observed other forms of AS events such as intron retention, alternate promoters.

CONCLUSIONS

Overall, we have, for the first time, conducted global profiling of muscle tissue to identify DASGs associated with CC. The mechanistic roles of the identified DASGs in CC pathophysiology using model systems is warranted, as well as replication of findings in independent cohorts.

System Biology Approach to Identify Potential Receptor for Targeting Cancer and Biomolecular Interaction Studies of Indole[2,1-a]Isoquinoline Derivative as Anticancerous Drug Candidate Against it

Cancer is a public health concern which is spreading throughout the world. Different approaches have been employed to combat this disease. System biology approach has been used to understand the molecular mechanisms of drugs targeting cancer cell's receptor which have opened-up a window to develop effective drugs for it. We have demonstrated biomolecular interaction studies using the rational drug design of indole[2,1-a]isoquinoline derivative as a potent inhibitor against identified cancerous protein PIK3CA -a catalytic sub-unit of PI3K family protein-and compared its affinity with FDA approved drugs for receptors such as dactolisib, idelalisib, and several others such afatinib, avastin, ceritinib and crizotinib, etc.; by docking against potential receptor to set a cutoff value for our screening. Isoquinolines are small alkaloids with a vast variety of substitution depending upon their biogenetic pattern. Isoquinoline derivatives have been reported for their antimalarial, antibacterial, antifungal and anticancerous activities. The results obtained from the present studies conclude that membrane protein is an efficient drug that can be used to target cancer. Moreover, comparative study with ADMET prediction concludes that isoquinoline can be a potent drug for cancer treatment.

Inherent variability of cancer-specific aneuploidy generates metastases

Background

The genetic basis of metastasis is still unclear because metastases carry individual karyotypes and phenotypes, rather than consistent mutations, and are rare compared to conventional mutation. There is however correlative evidence that metastasis depends on cancer-specific aneuploidy, and that metastases are karyotypically related to parental cancers. Accordingly we propose that metastasis is a speciation event. This theory holds that cancer-specific aneuploidy varies the clonal karyotypes of cancers automatically by unbalancing thousands of genes, and that rare variants form new autonomous subspecies with metastatic or other non-parental phenotypes like drug-resistance – similar to conventional subspeciation.

Results

To test this theory, we analyzed the karyotypic and morphological relationships between seven cancers and corresponding metastases. We found (1) that the cellular phenotypes of metastases were closely related to those of parental cancers, (2) that metastases shared 29 to 96% of their clonal karyotypic elements or aneusomies with the clonal karyotypes of parental cancers and (3) that, unexpectedly, the karyotypic complexity of metastases was very similar to that of the parental cancer. This suggests that metastases derive cancer-specific autonomy by conserving the overall complexity of the parental karyotype. We deduced from these results that cancers cause metastases by karyotypic variations and selection for rare metastatic subspecies. Further we asked whether metastases with multiple metastasis-specific aneusomies are assembled in one or multiple, sequential steps. Since (1) no stable karyotypic intermediates of metastases were observed in cancers here and previously by others, and (2) the karyotypic complexities of cancers are conserved in metastases, we concluded that metastases are generated from cancers in one step – like subspecies in conventional speciation.

Conclusions

We conclude that the risk of cancers to metastasize is proportional to the degree of cancer-specific aneuploidy, because aneuploidy catalyzes the generation of subspecies, including metastases, at aneuploidy-dependent rates. Since speciation by random chromosomal rearrangements and selection is unpredictable, the theory that metastases are karyotypic subspecies of cancers also explains Foulds’ rules, which hold that the origins of metastases are “abrupt” and that their phenotypes are “unpredictable.”

Influence of 5-amino-3-methyl-4-isoxazolecarbohydrazide on selective gene expression in Caco-2 cultured cells

The 5-amino-3-methyl-4-isoxazolecarboxylic acid hydrazide (HIX) is a synthetic isoxazole derivative with a potential for development as an anti-inflammatory drug candidate. The goal of this study was to explore in vitro autoimmune and inflammatory gene modulation by HIX in human Caco-2 cultured cells. The effect of low dose of HIX was tested on the expression level of RNA in 24 h Caco-2 cultures using the QIAGEN Th17 for Autoimmunity & Inflammation RT² Profiler PCR Array. We choose the PCR technology as the most reliable and sensitive gene expression profiling method for analyzing specific gene regulatory networks. In all experiments, Leflunomide (5-methyl-N-[4-(trifluoromethyl)phenyl]-4-isoxazolecarboxamide), an immuno-suppressive disease-modifying antirheumatic drug was used, as a reference to clinical utility of the isoxazole derivatives. Changes in RNA levels were analyzed and differentially expressed genes with at least 2-fold change were identified. For the majority of genes tested, the effects of HIX and Leflunomide were similar, including up-regulation of CX3CL1 and IL-17F, and down-regulation of IL-10 and TLR4. However twelve genes were were differently regulated by the two compounds: interleukins (IL) IL-1B, IL-6 and a chemokine CCL22 were upregulated by HIX and significantly supressed by Leflunomide. In contrary, IL-2 and IL-27 were upregulated by Leflunomide and suppressed by HIX. The network search by Ingenuity Pathway Analysis showed, that majority of differentially expressed genes were involved in cellular inflammatory responses. These results suggest that 5-amino-3-methyl-4-isoxazolecarbohydrazide has a potential for future clinical developments with structure modification as a disease modifying agent in different than Leflunomide applications.

High expression of ID family and IGJ genes signature as predictor of low induction treatment response and worst survival in adult Hispanic patients with B-acute lymphoblastic leukemia

Background

B-Acute lymphoblastic leukemia (B-ALL) represents a hematologic malignancy with poor clinical outcome and low survival rates in adult patients. Remission rates in Hispanic population are almost 30 % lower and Overall Survival (OS) nearly two years inferior than those reported in other ethnic groups. Only 61 % of Colombian adult patients with ALL achieve complete remission (CR), median overall survival is 11.3 months and event-free survival (EFS) is 7.34 months. Identification of prognostic factors is crucial for the application of proper treatment strategies and subsequently for successful outcome. Our goal was to identify a gene expression signature that might correlate with response to therapy and evaluate the utility of these as prognostic tool in hispanic patients.

Methods

We included 43 adult patients newly diagnosed with B-ALL. We used microarray analysis in order to identify genes that distinguish poor from good response to treatment using differential gene expression analysis. The expression profile was validated by real-time PCR (RT-PCT).

Results

We identified 442 differentially expressed genes between responders and non-responders to induction treatment. Hierarchical analysis according to the expression of a 7-gene signature revealed 2 subsets of patients that differed in their clinical characteristics and outcome.

Conclusions

Our study suggests that response to induction treatment and clinical outcome of Hispanic patients can be predicted from the onset of the disease and that gene expression profiles can be used to stratify patient risk adequately and accurately. The present study represents the first that shows the gene expression profiling of B-ALL Colombian adults and its relevance for stratification in the early course of disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s13046-016-0333-z) contains supplementary material, which is available to authorized users.

Comprehensive evaluation of composite gene features in cancer outcome prediction

Owing to the heterogeneous and continuously evolving nature of cancers, classifiers based on the expression of individual genes usually do not result in robust prediction of cancer outcome. As an alternative, composite gene features that combine functionally related genes have been proposed. It is expected that such features can be more robust and reproducible since they can capture the alterations in relevant biological processes as a whole and may be less sensitive to fluctuations in the expression of individual genes. Various algorithms have been developed for the identification of composite features and inference of composite gene feature activity, which all claim to improve the prediction accuracy. However, because of the limitations of test datasets incorporated by each individual study and inconsistent test procedures, the results of these studies are sometimes conflicting and unproducible. For this reason, it is difficult to have a comprehensive understanding of the prediction performance of composite gene features, particularly across different cancers, cancer subtypes, and cohorts. In this study, we implement various algorithms for the identification of composite gene features and their utilization in cancer outcome prediction, and perform extensive comparison and evaluation using seven microarray datasets covering two cancer types and three different phenotypes. Our results show that, while some algorithms outperform others for certain classification tasks, no single algorithm consistently outperforms other algorithms and individual gene features.

biDCG: a new method for discovering global features of DNA microarray data via an iterative re-clustering procedure

Biclustering techniques have become very popular in cancer genetics studies, as they are tools that are expected to connect phenotypes to genotypes, i.e. to identify subgroups of cancer patients based on the fact that they share similar gene expression patterns as well as to identify subgroups of genes that are specific to these subtypes of cancer and therefore could serve as biomarkers. In this paper we propose a new approach for identifying such relationships or biclusters between patients and gene expression profiles. This method, named biDCG, rests on two key concepts. First, it uses a new clustering technique, DCG-tree [Fushing et al, PLos One, 8, e56259 (2013)] that generates ultrametric topological spaces that capture the geometries of both the patient data set and the gene data set. Second, it optimizes the definitions of bicluster membership through an iterative two-way reclustering procedure in which patients and genes are reclustered in turn, based respectively on subsets of genes and patients defined in the previous round. We have validated biDCG on simulated and real data. Based on the simulated data we have shown that biDCG compares favorably to other biclustering techniques applied to cancer genomics data. The results on the real data sets have shown that biDCG is able to retrieve relevant biological information.

Molecular neuro-oncology and the development of targeted therapeutic strategies for brain tumors Part 4: p53 signaling pathway

Brain tumors are a diverse group of malignancies that remain refractory to conventional treatment approaches. Molecular neuro-oncology has now begun to clarify the transformed phenotype of brain tumors and identify oncogenic pathways that might be amenable to targeted therapy. Loss of the tumor suppressor gene p53 and its encoded protein are the most common genetic events in human cancer and are a frequent occurrence in brain tumors. p53 functions as a transcription factor and is responsible for the transactivation and repression of key genes involved in cell growth, apoptosis and the cell cycle. Mutation of the p53 gene or dysfunction of its signaling pathway are early events in the transformation process of astrocytic gliomas. The majority of mutations are missense and occur in the conserved regions of the gene, within exons 5 through 8. Molecular therapeutic strategies to normalize p53 signaling in cells with mutant p53 include pharmacologic rescue of mutant protein, gene therapy approaches, small-molecule agonists of downstream inhibitory genes, antisense approaches and oncolytic viruses. Other strategies include activation of normal p53 activity, inhibition of mdm2-mediated degradation of p53 and blockade of p53 nuclear export. Further development of targeted therapies designed to restore or enhance p53 function, and evaluation of these new agents in clinical trials, will be needed to improve survival and quality of life for patients with brain tumors.

Molecular neuro-oncology and development of targeted therapeutic strategies for brain tumors. Part 2: PI3K/Akt/PTEN, mTOR, SHH/PTCH and angiogenesis

Brain tumors are a diverse group of malignancies that remain refractory to conventional treatment approaches. Molecular neuro-oncology has now begun to clarify the transformed phenotype of brain tumors and identify oncogenic pathways that might be amenable to targeted therapy. Activity of the phosphoinositide 3; kinase (PI3K)/Akt pathway is often upregulated in brain tumors due to excessive stimulation by growth factor receptors and Ras. Loss of function of the tumor suppressor gene PTEN also frequently contributes to upregulation of PI3K/Akt. Several compounds, such as wortmannin and LY-294002, can target PI3K and inhibit activity of this pathway. The mammalian target of rapamycin (mTOR) is an important regulator of cell growth and metabolism and is often upregulated by Akt. Clinical trials of CCI-779, an inhibitor of mTOR, are ongoing in recurrent malignant glioma patients. The sonic hedgehog/PTCH pathway is involved in the tumorigenesis of some familial and sporadic medulloblastomas. This pathway can be targeted by cyclopamine, which is under evaluation in preclinical studies. Angiogenesis is a critical process for development and progression of brain tumors. Targeted approaches to inhibit angiogenesis include monoclonal antibodies, receptor tyrosine kinase inhibitors, antisense oligonucleotides and gene therapy. Clinical trials are ongoing for numerous angiogenesis inhibitors, including thalidomide, CC-5103 and PTK 787/ZK 222584. Further development of targeted therapies and evaluation of these new agents in clinical trials will be needed to improve survival and quality of life of patients with brain tumors.

Pharmacogenetics and Pharmacogenomics as New Tools to Optimise Cancer Chemotherapy

Pharmacogenetics takes into account the individual variability of the genome and its relationships with drug activity. Numerous gene polymorphisms occurring at the level of drug metabolising enzymes, drug transporters or drug targets have been identified; they may be responsible for an alteration of the phenotype, and of individual changes in drug efficacy and/or toxicity. Pharmacogenomics takes into account the genetic alterations present in tumours and their role in drug activity. It may be possible to establish correlations between the activity of a drug and the level of expression of tumour genes, and thus select the most appropriate drugs to prescribe for the treatment of individual tumours. Pharmacogenetics and pharmacogenomics are acting synergistically for the personalisation of treatments, on the basis of the constitutive singularity of patients and of the molecular characteristics of tumours.

MicroRNAs as a selective channel of communication between competing RNAs: a steady-state theory

It has recently been suggested that the competition for a finite pool of microRNAs (miRNA) gives rise to effective interactions among their common targets (competing endogenous RNAs or ceRNAs) that could prove to be crucial for posttranscriptional regulation. We have studied a minimal model of posttranscriptional regulation where the emergence and the nature of such interactions can be characterized in detail at steady state. Sensitivity analysis shows that binding free energies and repression mechanisms are the key ingredients for the cross-talk between ceRNAs to arise. Interactions emerge in specific ranges of repression values, can be symmetrical (one ceRNA influences another and vice versa) or asymmetrical (one ceRNA influences another but not the reverse), and may be highly selective, while possibly limited by noise. In addition, we show that nontrivial correlations among ceRNAs can emerge in experimental readouts due to transcriptional fluctuations even in the absence of miRNA-mediated cross-talk.

Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset

Background

Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset.

Results

As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes.

Conclusions

The two-way AIC performs high specificity for operon gene detection on the microarray meta-dataset. This method can also be applied to estimation of mutual gene interactions.

Complexity in cancer biology: is systems biology the answer?

Complex phenotypes emerge from the interactions of thousands of macromolecules that are organized in multimolecular complexes and interacting functional modules. In turn, modules form functional networks in health and disease. Omics approaches collect data on changes for all genes and proteins and statistical analysis attempts to uncover the functional modules that perform the functions that characterize higher levels of biological organization. Systems biology attempts to transcend the study of individual genes/proteins and to integrate them into higher order information. Cancer cells exhibit defective genetic and epigenetic networks formed by altered complexes and network modules arising in different parts of tumor tissues that sustain autonomous cell behavior which ultimately lead tumor growth. We suggest that an understanding of tumor behavior must address not only molecular but also, and more importantly, tumor cell heterogeneity, by considering cancer tissue genetic and epigenetic networks, by characterizing changes in the types, composition, and interactions of complexes and networks in the different parts of tumor tissues, and by identifying critical hubs that connect them in time and space.

Comparative genomic hybridization (CGH) in genotoxicology

In the past two decades comparative genomic hybridization (CGH) and array CGH have become crucial and indispensable tools in clinical diagnostics. Initially developed for the genome-wide screening of chromosomal imbalances in tumor cells, CGH as well as array CGH have also been employed in genotoxicology and most recently in toxicogenomics. The latter methodology allows a multi-endpoint analysis of how genes and proteins react to toxic agents revealing molecular mechanisms of toxicology. This chapter provides a background on the use of CGH and array CGH in the context of genotoxicology as well as a protocol for conventional CGH to understand the basic principles of CGH. Array CGH is still cost intensive and requires suitable analytical algorithms but might become the dominating assay in the future when more companies provide a large variety of different commercial DNA arrays/chips leading to lower costs for array CGH equipment as well as consumables such as DNA chips. As the amount of data generated with microarrays exponentially grows, the demand for powerful adaptive algorithms for analysis, competent databases, as well as a sound regulatory framework will also increase. Nevertheless, chromosomal and array CGH are being demonstrated to be effective tools for investigating copy number changes/variations in the whole genome, DNA expression patterns, as well as loss of heterozygosity after a genotoxic impact. This will lead to new insights into affected genes and the underlying structures of regulatory and signaling pathways in genotoxicology and could conclusively identify yet unknown harmful toxicants.

Use of pharmacokinetic/pharmacodynamic biomarkers to support rational cancer drug development

The process of drug development in oncology has struggled to alter at a pace in keeping with the rapid discovery and testing of agents that act on a wide variety of molecular targets. The rational development of such agents requires an understanding of drug effect(s) on their purported target. It is likely that testing these drugs in a framework designed to examine cytotoxic agents will fail to establish their full potential. We discuss how data gained from biomarker investigation might impact on drug development and provide examples that highlight the development, validation and use of pharmacokinetic, and especially pharmacodynamic biomarkers as drug development moves from the laboratory into clinical testing. The challenges of performing assays to satisfy regulatory requirements have been the subject of much debate. We recommend the implementation of appropriate, fit-for-purpose biomarkers in clinical trials of all new cancer drugs.

Lysophosphatidic acid pretreatment prevents micromolar atorvastatin-induced endothelial cell death and ensures the beneficial effects of high-concentration statin therapy on endothelial gene expression

Because of the pleiotropic effects of statins, it may potentially be used as a locoregional adjuvant in vascular revascularization, tissue engineering, and regenerative procedures. Electron probe X-ray microanalyses and oligonucleotide microarrays were used to identify the global effects of micromolar concentrations of atorvastatin on the gene expression and cell viability of endothelial cells in different states of lysophosphatidic acid (LPA)-induced activation. Treatment with 1-μM atorvastatin for 24 hours significantly reduced the viability of human vascular endothelial cells (HUVECs). However, the same treatment of LPA-preactivated HUVECs produced elevated cell viability levels and an optimal vascular gene expression profile, including endothelial nitric oxide synthase overexpression, endothelin-1 repression, an anti-inflammatory genetic pattern, and upregulation of molecules involved in maintaining the endothelial barrier (vascular endothelial cadherin, claudin 5, tight junction protein 1, integrin β4). The atorvastatin treatment also produced a repression of microRNA 21 and genes involved in cell proliferation and neointimal formation (vascular endothelial growth factor [VEGF] A, VEGF receptor 1, VEGFC). Results obtained suggest that micromolar atorvastatin therapy can enhance global endothelial function, but its effects on cell viability vary according to the baseline state of cell activation (preactivated, postactivated, or not activated). Preactivation with LPA protects HUVECs against atorvastatin-induced apoptosis and delivers optimal levels of cell viability and functionality.

[Comparison between Real-Time PCR and Agarose Gel Electrophoresis for DNA Quantification.]

BACKGROUND

Real-time polymerase chain reaction (PCR) is generally regarded as a very accurate and time-saving method, but it is expensive to run. We evaluated the reliability of an inexpensive and a researcher-friendly gel electrophoresis-based PCR method for the quantification of mRNA, and the results were compared with those obtained by real-time PCR.

METHODS

We compared the results of relative quantification for MMP-1 measured by real-time PCR and by ethidium bromide stained-agarose gel electrophoresis after end-point PCR.

RESULTS

There was significant but very weak correlation between real-time PCR and end-point PCR for relative quantification of MMP-1 (r=0.16, P<0.01).

CONCLUSIONS

Our results suggest that the use of the gel electrophoresis-based end-point PCR is inappropriate for quantifying mRNA. Therefore, in order to confirm the result of relative quantification by end-point PCR, the newly established real-time PCR method or northern hybridization should be applied.

Combinatorial drug therapy for cancer in the post-genomic era

Over the past decade, whole genome sequencing and other 'omics' technologies have defined pathogenic driver mutations to which tumor cells are addicted. Such addictions, synthetic lethalities and other tumor vulnerabilities have yielded novel targets for a new generation of cancer drugs to treat discrete, genetically defined patient subgroups. This personalized cancer medicine strategy could eventually replace the conventional one-size-fits-all cytotoxic chemotherapy approach. However, the extraordinary intratumor genetic heterogeneity in cancers revealed by deep sequencing explains why de novo and acquired resistance arise with molecularly targeted drugs and cytotoxic chemotherapy, limiting their utility. One solution to the enduring challenge of polygenic cancer drug resistance is rational combinatorial targeted therapy.

Molecular diagnosis and prognosis with DNA microarrays

Microarray analysis makes it possible to determine thousands of gene expression values simultaneously. Changes in gene expression, as a response to diseases, can be detected allowing a better understanding and differentiation of diseases at a molecular level. By comparing different kinds of tissue, for example healthy tissue and cancer tissue, the microarray analysis indicates induced gene activity, repressed gene activity or when there is no change in the gene activity level. Fundamental patterns in gene expression are extracted by several clustering and machine learning algorithms. Certain kinds of cancer can be divided into subtypes, with different clinical outcomes, by their specific gene expression patterns. This enables a better diagnosis and tailoring of individual patient treatments.

Nanomedicine: application areas and development prospects

Nanotechnology, along with related concepts such as nanomaterials, nanostructures and nanoparticles, has become a priority area for scientific research and technological development. Nanotechnology, i.e., the creation and utilization of materials and devices at nanometer scale, already has multiple applications in electronics and other fields. However, the greatest expectations are for its application in biotechnology and health, with the direct impact these could have on the quality of health in future societies. The emerging discipline of nanomedicine brings nanotechnology and medicine together in order to develop novel therapies and improve existing treatments. In nanomedicine, atoms and molecules are manipulated to produce nanostructures of the same size as biomolecules for interaction with human cells. This procedure offers a range of new solutions for diagnoses and “smart” treatments by stimulating the body’s own repair mechanisms. It will enhance the early diagnosis and treatment of diseases such as cancer, diabetes, Alzheimer’s, Parkinson’s and cardiovascular diseases. Preventive medicine may then become a reality.

Magnetic scanometric DNA microarray detection of methyl tertiary butyl ether degrading bacteria for environmental monitoring

A magnetoresistive biosensing platform based on a single magnetic tunnel junction (MTJ) scanning probe and DNA microarrays labeled with magnetic particles has been developed to provide an inexpensive, sensitive and reliable detection of DNA. The biosensing platform was demonstrated on a DNA microarray assay for quantifying bacteria capable of degrading methyl tertiary butyl ether (MTBE), where concentrations as low as 10 pM were detectable. Synthetic probe bacterial DNA was immobilized on a microarray glass slide surface, hybridized with the 48 base pair long biotinylated target DNA and subsequently incubated with streptavidin-coated 2.8 μm diameter magnetic particles. The biosensing platform then makes use of a micron-sized MTJ sensor that was raster scanned across a 3 mm by 5 mm glass slide area to capture the stray magnetic field from the tagged DNA and extract two dimensional magnetic field images of the microarray. The magnetic field output is then averaged over each 100 μm diameter DNA array spot to extract the magnetic spot intensity, analogous to the fluorescence spot intensity used in conventional optical scanners. The magnetic scanning result is compared with results from a commercial laser scanner and particle coverage optical counting to demonstrate the dynamic range and linear sensitivity of the biosensing platform as a potentially inexpensive, sensitive and portable alternative for DNA microarray detection for field applications.

Probing the probes: fitness factors for small molecule tools

Chemical probes for interrogating biological processes are of considerable current interest. Cell permeable small molecule tools have a major role in facilitating the functional annotation of the human genome, understanding both physiological and pathological processes, and validating new molecular targets. To be valuable, chemical tools must satisfy necessary criteria and recent publications have suggested objective guidelines for what makes a useful chemical probe. Although recognizing that such guidelines may be valuable, we caution against overly restrictive rules that may stifle innovation in favor of a "fit-for-purpose" approach. Reviewing the literature and providing examples from the cancer field, we recommend a series of "fitness factors" to be considered when assessing chemical probes. We hope this will encourage innovative chemical biology research while minimizing the generation of poor quality and misleading biological data, thus increasing understanding of the particular biological area, to the benefit of basic research and drug discovery.

Gene expression signature and the prediction of lymph node metastasis in colorectal cancer by DNA microarray

PURPOSE

Lymph node metastasis is an important factor in determining the outcome of colorectal cancer. If we can predict the presence of lymph node metastasis before surgery, it may help in deciding the need for surgical lymph node dissection or additional preoperative treatment modalities that might improve survival. Our objective here was to identify a set of discriminating genes that can be used for characterization and prediction of lymph node metastasis.

METHODS

Eighty-nine colorectal cancer patients were studied. Gene expression profiles of cancer were determined by human U133 Plus 2.0 GeneChip, and patients with and without lymph node metastasis were compared.

RESULTS

We identified 73 novel discriminating genes in which expression was significantly different between patients with and without lymph node metastasis. Using this gene set, we were able to establish a new model to predict the presence of lymph node metastasis with an accuracy of 88.4%. Discriminating genes were associated with various functions, including receptor activity and transcription regulatory activity. The list of genes included transmembrane glycoprotein, which has been reported to have a close relationship with lymph node metastasis in prostate cancer. Transmembrane glycoprotein showed significantly higher expression in patients with lymph node metastasis.

CONCLUSIONS

The present study suggests the possibility that gene expression profiling may be useful in predicting the presence of lymph node metastasis. Thus, gene expression profiling could help to establish individualized tailored therapy for colorectal cancer and provide insights into the development of novel therapeutic targets.

Pharmacogenomics of brain cancer and personalized medicine in malignant gliomas

The pharmacogenetics of cancer treatment has been aimed at identifying genetic components of interindividual variability in patients' response to cancer chemotherapy and toxicity. This, in turn, will establish an individually based treatment, and also elucidate the molecular basis of the treatment regimen for further improvements. Brain cancer is an instructive example for the potential contributions of pharmacogenomics to improved treatment in the 21st century. Patients with oligodendrogliomas have benefited from phamacogenomics, as there is a clear relationship between response to chemotherapy and chromosomal profile. Drug efficacy, safety and response could be improved by using pharmacogenomics to identify genetic markers that differentiate responder from nonresponder patient groups, as well as identifying patients likely to develop adverse drug reactions. This review will focus on how pharmacogenomics by microarray studies may lead to much more accurate tumor classification, drug and biomarker discovery, and drug efficacy testing. We will discuss relevant scientific advances in pharmacogenetics for more personalized chemotherapy.

Development of a compact optical system for microarray scanning using a DVD pickup head

We present a compact optical system using a commercially available DVD pickup head for microarray scanning. Our instrument successfully provides a low-cost, compact, and simple microarray scanning optical system in comparison to conventional ones due to the use of small-sized optical components and the implementation of a simple autofocusing system using an embedded voice coil motor. The performance of this system was validated by using a microarray slide with spots of fluorescent dyes. It was confirmed that our optical head performed satisfactorily and was suitable for practical use in microarray scanners. This result provides evidence of the superiority of our microarray scanning optical system over conventional ones because of its space-saving properties and cost effectiveness.

Low prevalence of p53, p16(INK4a) and Ha-ras tumour-specific mutations in low-graded actinic keratosis

BACKGROUND

Ultraviolet radiation induces DNA damage and is the major risk factor for the development of non-melanoma skin cancer (NMSC). Different mutation rates of p53, p16(INK4a) and Ha-ras in cutaneous squamous cell carcinoma (SCC) and the earlier stage actinic keratosis (AK) have been reported.

OBJECTIVES

To assess the presence of missense mutations in hotspot exons of p53, p16(INK4a) and Ha-ras in low-graded AK.

PATIENTS/METHODS

Cryo-biopsies of 75 sun-exposed AK lesions and 75 sun-shielded areas of normal skin from 75 AK patients were analysed to identify mutations in p53 (exons 7 and 8), p16(INK4a) (exon 2) and Ha-ras (exon 1) using polymerase chain reaction (PCR) followed by direct sequencing. As a representative subset of the specimens, ten mutation-negative AK were also micro-dissected in order to exclude the possibility that additional mutations were undetected.

RESULTS

Eight missense and one nonsense point mutations were found in the 75 AK lesions examined (12%), of which seven (9%) were tumour-specific (i.e. present in AK lesions only) and two (3%) were p16(INK4a) mutations (i.e. also detected in normal skin). Three of the tumour-specific mutations (42%) were cytosine (C) to thymine (T) transitions at pyrimidine-rich sequences. Tumour-specific mutations were identified in 1% of p16(INK4a) (exon 2), 1% of Ha-ras (exon 1) and at a higher rate of 7% in p53 (exons 7 and 8), including one nonsense mutation.

CONCLUSIONS

The evaluation of a large number of AK specimens in this study have found a low gene mutation rate in low-graded AK lesions. p53 mutations rather than p16(INK4a) and/or Ha-ras mutations may be an early event in the development of AK to cutaneous SCC.

DNA microarray analysis: principles and clinical impact

In recent years, a new technology, allowing the measurements of the expression of thousands of genes simultaneously, has emerged in medicine. This method, called DNA microarray analysis, is today one of the most promising method in functional genomics. Fundamental patterns in gene expression are extracted by several clustering methods like: hierarchical clustering, self organizing maps and support vector machines. Changes in gene expression, as a response to changing environment conditions, diseases, drug treatment or chemotherapy medications, can be detected allowing insights into the dynamic of the genome. Microarrays seem to be an important tool for diagnosis of diseases at a molecular level. Applications are for example the improvement of diagnosis and treatment of cancer and the improvement of the effectiveness of drug treatment. In this introductory paper, we present the principles of DNA microarray experiments, selected clustering methods for gene expression analysis and the impact to clinical research.

Differential expression of genes associated with cell proliferation and apoptosis induced by okadaic acid during the transformation process of BALB/c 3T3 cells

Okadaic acid (OA) is a tumor promoter in two-stage carcinogenesis experiments. Nevertheless, the effects of OA on cell transformation, cell proliferation and apoptosis vary widely, and the molecular events underlying these effects of OA are not well understood. In the present study, we examined the promoting activity and the associated effects on cell growth and apoptosis mediated by OA in BALB/c 3T3 cells, and evaluated alterations of gene transcriptional expression by microarray analysis. The promoting activity of OA was estimated by a two-stage transformation assay, in which cells were treated first with a low dose of the initiator N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) and then with OA for 14 days. It showed that OA, at concentrations of 7.8-31.3 ng/ml, enhanced the transformation of MNNG-treated cells. In the promotion phase, cells exposed to OA (7.8 ng/ml) grew slowly for the first 2 days and subsequently died. As determined by Hoechst 33342 fluorescent dye and Annexin-V/PI dual-colored flow cytometry, OA induced morphologically apoptotic cells and increased the percentage of early apoptotic cells. The gene expression profile induced by OA at five time points in the promotion phase was determined by use of a specific mouse toxicological microarray containing 1796 clones, and a total of 177 differentially expressed genes were identified. By gene ontology analysis, 31 of these were determined to be functionally involved with cell growth and/or maintenance. In this group, numerous genes associated with the cell proliferation and cell cycle progression were down-regulated at early and/or middle time points. Among these was a subset of genes associated with apoptosis, in which Bnip3, Cycs, Casp3 and Bag1 genes are involved in the mitochondrial pathway of apoptosis. Ier3, Mdm2 and Bnip3 genes may be p53 targets. Furthermore, real-time PCR confirmed the expression changes of five genes selected at random from the differentially expressed genes. We conclude that OA induces cell growth inhibition and apoptosis in the two-stage, MNNG-initiated transformation of BALB/c 3T3 cells. The results of gene expression profile analysis imply that multiple molecular pathways are involved in OA-induced proliferation inhibition and apoptosis. Mitochondrial and p53-associated apoptotic pathways also may contribute to OA-induced apoptosis.

Mechanisms of acquired resistance to 2-(4-Amino-3-methylphenyl)benzothiazole in breast cancer cell lines

Compounds within the 2-(4-aminophenyl)benzothiazole class represent extremely potent and selective experimental antitumour agents. The lysylamide prodrug of 2-(4-amino-3-methylphenyl)-5-fluorobenzothiazole is undergoing phase I clinical evaluation. Extensive studies to elucidate mechanisms underlying the stark selectivity demonstrated potent cytosolic AhR ligand binding and cytochrome P450 1A1-catalysed bioactivation. Two human derived breast cell lines, initially exquisitely sensitive to this class of agent (GI50 < 5 nM) have been derived displaying acquired resistance to 2-(4-amino-3-methylphenyl)benzothiazole (DF 203; GI50 > 50 microM). Cross resistance to 2-(4-amino-3-iodophenyl)benzothiazole and 2-(4-amino-3-cyanophenyl)benzothiazole is observed (GI50 > 30 microM) as is > 100-fold reduced sensitivity of the two variant lines to 2-(4-amino-3-methylphenyl)-5-fluorobenzothiazole (5F 203). In contrast, cell lines possessing acquired resistance to DF 203 (203R) retain sensitivity to benzo[a]pyrene and doxorubicin. Examination of DF 203-treated cells by confocal microscopy and HPLC analyses of nutrient media concur revealing diminished depletion of DF 203 from medium and impaired intracellular DF 203 retention. In contrast to cytosolic arylhydrocarbon (AhR) receptors of wild type cells, AhR appears constitutively localised within nuclei of 203R cells; consequently, DF 203 fails to drive transcription of cyp1a1. DF 203- and 5F 203-derived DNA adducts fall significantly in 203R cells. Reduced number and intensity of gamma H2AX foci report protection against DF 203-evoked DNA double strand breaks. In conclusion, aberrant AhR signalling underlies at least in part acquired resistance to DF 203. Intriguingly, comparisons of gene transcription profiles between sensitive and resistant paired lines reveal > 5-fold up-regulation of cyp1b1 expression, a protein implicated in resistance to therapeutic agents.

Molecular cloning and functional analysis of a novel oncogene, cancer-upregulated gene 2 (CUG2)

We examined genome-wide differences in gene expression between tumor biopsies and normal tissues in order to identify differentially regulated genes in tumors. Cancer-upregulated gene 2 (CUG2) was identified as an expressed sequence tag (EST) that exhibits significant differential expression in multiple human cancer types. CUG2 showed weak sequence homology with the down-regulator of transcription 1 (DR1) gene, a human transcription repressor. We found that EGFP-CUG2 fusion proteins were predominantly localized in the nucleus, suggesting their putative role in gene regulation. In addition, CUG2-overexpressing mouse fibroblast cells exhibited distinct cancer-specific phenotypes in vitro and developed into tumors in nude mice. Taken together, these findings suggest that CUG2 is a novel tumor-associated gene that is commonly activated in various human cancers and exhibits high transforming activities; it possibly belongs to a transcription regulator family that is involved in tumor biogenesis.

Gene expression signature and the prediction of ulcerative colitis-associated colorectal cancer by DNA microarray

PURPOSE

Ulcerative colitis (UC) is associated with a high risk of colorectal cancer. To identify genes that could predict the development of cancer in UC, we conducted a DNA microarray analysis using nonneoplastic rectal mucosa of UC patients.

EXPERIMENTAL DESIGN

Gene expression in nonneoplastic mucosa of 53 UC patients were examined. Gene expression profiles were examined using human Genome U133 Plus 2.0 gene chip array (Affymetrix). Among 53 UC patients, 10 had UC-associated cancer (UC-Ca group) whereas 43 did not (UC-NonCa group).

RESULTS

By comparing gene expression profiles of nonneoplastic rectal mucosae between the UC-Ca and UC-NonCa groups, we could identify 40 genes that were differentially expressed between two groups. The list of discriminating genes included low-density lipoprotein receptor-related protein (LRP5 and LRP6). Previous studies suggested that LRP5 and LRP6 expression promotes cancer cell proliferation and tumorigenesis and are considered as candidate oncogenes. In the present study, both LRP5 and LRP6 showed significantly higher expression in the UC-Ca group, which suggests the importance of these genes in the development of UC-associated colorectal cancers. With the 40 selected discriminating genes, we did class prediction of the development of colorectal neoplasms in UC patients. Using the k-nearest neighbor method and the support vector machine, we could predict the development of UC-associated neoplasms with an accuracy of 86.8% and 98.1%, respectively.

CONCLUSIONS

These findings have important implications for the early detection of malignant lesions in UC and may provide directions for future research into the molecular mechanisms of UC-associated cancer.

Progress and new standards of care in the management of HER-2 positive breast cancer

The aim of this review article is to examine the available evidence regarding diagnosis and treatment of HER-2 positive breast cancer. This group of breast tumours (up to 30% of the total number of breast cancers) is known for having a more aggressive behaviour. The current recommendations for HER-2 positive tumour diagnosis are discussed since accurate identification of HER-2 amplification or overexpression is key for allowing a correct risk assessment and treatment. HER-2 positive tumours can be treated with trastuzumab (Herceptin, Hoffmann-La Roche, Basel, Switzerland), a monoclonal antibody targeted against the HER-2 receptor. The role of this drug in the metastatic, adjuvant and neoadjuvant setting is reviewed. The results of the recently reported adjuvant trials are commented, as the positive results of these trials changed the standard of care for patients with this particular type of breast cancer.

Pharmacodynamic biomarkers for molecular cancer therapeutics

Rational and efficient development of new molecular cancer therapeutics requires discovery, validation, and implementation of informative biomarkers. Measurement of molecular target status, pharmacokinetic (PK) parameters of drug exposure, and pharmacodynamic (PD) endpoints of drug effects on target, pathway, and downstream biological processes are extremely important. These can be linked to therapeutic effects in what we term a "pharmacological audit trail." Using biomarkers in preclinical drug discovery and development facilitates optimization of PK, PD, and therapeutic properties so that the best agent is selected for clinical evaluation. Applying biomarkers in early clinical trials helps identify the most appropriate patients; provides proof of concept for target modulation; helps test the underlying hypothesis; informs the rational selection of dose and schedule; aids decision making, including key go/no go questions; and may explain or predict clinical outcomes. Despite many successes such as trastuzumab and imatinib, exemplifying the value of targeting specific cancer defects, only 5% of oncology drugs that enter the clinic make it to marketing approval. Use of biomarkers should reduce this high level of attrition and bring forward key decisions (e.g., "fail fast"), thereby reducing the spiraling costs of drug development and increasing the likelihood of getting innovative and active drugs to cancer patients. In this chapter, we focus primarily on PD endpoints that demonstrate target modulation, including both invasive molecular assays and functional imaging technology. We also discuss related clinical trial design issues. Implementation of biomarkers in trials remains disappointingly low and we emphasize the need for greater cooperation between various stakeholders to improve this.

The application of DNA micro-arrays (gene arrays) to the study of herbal medicines

DNA micro-arrays (gene arrays) have become a popular and useful tool with which to study the effects of various agents and treatments on gene expression in cells and tissues. In theory one can simultaneously evaluate, in a single experiment, changes in gene expression (at the level of transcription) of the entire genome of the organism under study. Consequently these techniques have been used by many investigators interested in cancer research, differentiation and development, toxicology, and the effects of pharmaceuticals on cells and animals. In addition, recent studies have shown the capacity of the technique for revealing the importance of genes not previously implicated in a given response. However, relatively few attempts have been made so far to evaluate herbal medicines, although the potential to answer a number of relevant questions is there. In this review we first discuss the fundamental principles of the gene array technology, focusing on the individual steps in the process and their problems and pitfalls, and we discuss the analysis and interpretation of the data, the discipline of bio-informatics, without which meaningful evaluation of gene expression changes would be impossible. We next analyze specific studies, which utilized gene array technology, aimed at evaluating the effects of certain herbal medicine formulas and bioactive ingredients in animal tissues and in cell cultures. We also include a brief description of our own evaluation of Echinacea, which we have been studying for several years, to indicate possible mechanisms of action of this herbal, and also to illustrate how the techniques, especially the bio-informatics, continue to evolve. We believe, on the basis of experience acquired by us and other investigators to date, that the technology of gene array analysis can make significant contributions to understanding how herbal medicines work, and therefore can validate their applications in medicine.

DNA microarrays in herbal drug research

Natural products are gaining increased applications in drug discovery and development. Being chemically diverse they are able to modulate several targets simultaneously in a complex system. Analysis of gene expression becomes necessary for better understanding of molecular mechanisms. Conventional strategies for expression profiling are optimized for single gene analysis. DNA microarrays serve as suitable high throughput tool for simultaneous analysis of multiple genes. Major practical applicability of DNA microarrays remains in DNA mutation and polymorphism analysis. This review highlights applications of DNA microarrays in pharmacodynamics, pharmacogenomics, toxicogenomics and quality control of herbal drugs and extracts.

Advances in nasal drug delivery through tight junction technology

New approaches for enhancing intranasal drug delivery based on recent discoveries on the molecular biology of tight junctions (TJ) are significantly improving the bioavailability of 'non-Lipinsky' small molecules, and peptide, protein and oligonucleotide drugs. As knowledge of the structure and function of the TJ has developed, so has the ability to identify mechanism-based TJ modulators using high-throughput molecular biology-based screening methods. The present review focuses on recent developments on the TJ protein complex as a lipid raft-like membrane microdomain, the emerging role of unique endocytic pathways in regulating TJ dynamics, and the utility of techniques such as RNA interference and phage display to study TJ components and identify novel peptides and related molecules that can modulate their function. Experimental and statistical methodologies used for the identification of new classes of TJ modulators are described, which are capable of reversibly opening TJ barriers with broad potential to significantly improve intranasal and, eventually, oral drug delivery. The development of an advanced intranasal formulation for the obesity therapeutic PYY(3-36), the endogenous Y2 receptor agonist is also reviewed.

Biological data warehousing system for identifying transcriptional regulatory sites from gene expressions of microarray data

Identification of transcriptional regulatory sites plays an important role in the investigation of gene regulation. For this propose, we designed and implemented a data warehouse to integrate multiple heterogeneous biological data sources with data types such as text-file, XML, image, MySQL database model, and Oracle database model. The utility of the biological data warehouse in predicting transcriptional regulatory sites of coregulated genes was explored using a synexpression group derived from a microarray study. Both of the binding sites of known transcription factors and predicted over-represented (OR) oligonucleotides were demonstrated for the gene group. The potential biological roles of both known nucleotides and one OR nucleotide were demonstrated using bioassays. Therefore, the results from the wet-lab experiments reinforce the power and utility of the data warehouse as an approach to the genome-wide search for important transcription regulatory elements that are the key to many complex biological systems.

Molecular basis of cell-biomaterial interaction: insights gained from transcriptomic and proteomic studies

With the growing interest in clinical interventions that involve medical devices, the role for new biomaterials in modern medicine is currently expanding at a phenomenal rate. Failure of most implant materials stems from an inability to predict and control biological phenomena, such as protein adsorption and cell interaction, resulting in an inappropriate host response to the materials. Contemporary advances in biological investigation are starting to shift focus in the biomaterials field, in particular with the advent of high-throughput methodologies for gene and protein expression profiling. Here, we examine the role that emerging transcriptomic and proteomic technologies could play in relation to biomaterial development and usage. Moreover, a number of studies are highlighted which have utilized such approaches in order to try to create a deeper understanding of cell-biomaterial interactions and, hence, improve our ability to predict and control the biocompatibility of new materials.