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Complex diseases such as cancers usually result from accumulated disturbance of pathways instead of the disruptions of one or a few major genes. As opposed to single-platform analyses, it is likely that integrating diverse molecular regulatory elements and their interactions can lead to more insights on pathway-level disturbances of biological systems and their potential consequences in disease development and progression. To explore the benefit of pathway-based analysis, we focus on multi-platform genomics, epigenomics, and transcriptomics (-omics, for short) from 11 cancer types collected by The Cancer Genome Atlas project. Specifically, we use a well-studied oncogenic pathway, the BRAF pathway, to investigate the relevant copy number variants (CNVs), methylations, and gene expressions, and quantify their effects on discovering tumor-specific aberrations across multiple tumor lineages. We also perform simulation studies to further investigate the effects of network topology and multiple omics on dissecting pathway disturbances. Our analysis shows that adding molecular regulatory elements such as CNVs and/or methylations to the baseline mRNA molecules can improve our power of discovering tumorous aberrances. Also, incorporating CNVs with the baseline mRNA molecules can be more beneficial than incorporating methylations. Moreover, employing regulatory topologies can improve the discoveries of tumorous aberrances. Finally, our analysis reveals similarities and differences among diverse cancer types based on disturbance of the BRAF pathway. [ABSTRACT FROM AUTHOR]

Purpose: Abnormal fatty acid (FA) synthesis is one of the common features of cancer. Fatty acid synthase (FASN), a multifunctional enzyme playing a key role in biosynthesis of FA, is up-regulated in prostate, breast, and lung carcinomas. Orlistat is a FDA-approved anti-obesity drug that inhibits the thioesterase domain of FASN, interferes with cellular FA synthesis, can arrest tumor cell proliferation, and induces tumor cell apoptosis. The current study was aimed to investigate the metabolic changes associated with FASN inhibition by orlistat and to understand the molecular mechanisms behind the observed metabolic changes in non-small cell lung carcinoma (NSCLC) cell lines. Procedures: Changes in metabolite pools in four NSCLC cell lines (H441, H1975, H3255, and PC14) with different mutational profiles were studied using NMR spectroscopy before and after in vitro incubation with sub-toxic concentration of orlistat and [1-C] d-glucose or [1,2-C]choline. In vitro radiotracer accumulation assays in cells were performed with [H]acetate, [C]fluoroacetate, and 2-deoxy-2-[F]fluoro- d-glucose. In parallel, microarray profiling of genes involved in the regulation of carbohydrate and lipid metabolism was performed. Results: In orlistat-treated NSCLC cells, FASN inhibition results in characteristic changes in intermediary metabolites (FAs, choline, phospholipids, and TCA cycle metabolites) as observed by magnetic resonance spectroscopy. Further, FASN inhibition by orlistat induces multiple adaptive changes in FA synthetic pathway and associated metabolic pathways, including induction of ketone metabolism and glutaminolysis, as well as the up-regulation of 5' adenosine monophosphate-activated protein kinase. Conclusions: These observed changes in metabolic pools in orlistat-treated cells demonstrate the critical role of fatty acid de novo synthesis and metabolism for cellular energy production, especially in tumor cells with low glycolytic activity, which goes beyond the widely accepted concept that FA synthesis is important for cell membrane biosynthesis in rapidly proliferating tumor cells. [ABSTRACT FROM AUTHOR]

Myotonic dystrophy (DM) is the most common adult-onset muscular dystrophy with an estimated prevalence of 1/8000. There are two genetically distinct types, DM1 and DM2. DM2 is generally milder with more phenotypic variability than the classic DM1. Our previous data on co-segregation of heterozygous recessive CLCN1 mutations in DM2 patients indicated a higher than expected DM2 prevalence. The aim of this study was to determine the DM2 and DM1 frequency in the general population, and to explore whether the DM2 mutation functions as a modifier in other neuromuscular diseases (NMD) to account for unexplained phenotypic variability. We genotyped 5535 Finnish individuals: 4532 normal blood donors, 606 patients with various non-myotonic NMD, 221 tibial muscular dystrophy patients and their 176 healthy relatives for the DM2 and DM1 mutations. We also genotyped an Italian idiopathic non-myotonic proximal myopathy cohort (n=93) for the DM2 mutation. In 5496 samples analyzed for DM2, we found three DM2 mutations and two premutations. In 5511 samples analyzed for DM1, we found two DM1 mutations and two premutations. In the Italian cohort, we identified one patient with a DM2 mutation. We conclude that the DM2 mutation frequency is significantly higher in the general population (1/1830; P-value=0.0326) than previously estimated. The identification of DM2 mutations in NMD patients with clinical phenotypes not previously associated with DM2 is of particular interest and is in accord with the high overall prevalence. On the basis of our results, DM2 appears more frequent than DM1, with most DM2 patients currently undiagnosed with symptoms frequently occurring in the elderly population. [ABSTRACT FROM AUTHOR]

High-throughput technologies are widely used, for example to assay genetic variants, gene and protein expression, and epigenetic modifications. One often overlooked complication with such studies is batch effects, which occur because measurements are affected by laboratory conditions, reagent lots and personnel differences. This becomes a major problem when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. Using both published studies and our own analyses, we argue that batch effects (as well as other technical and biological artefacts) are widespread and critical to address. We review experimental and computational approaches for doing so. [ABSTRACT FROM AUTHOR]

The goal of this study was to develop pharmacogenomic predictors in response to standard chemotherapy drugs in breast cancer cell lines and test their predictive value in patients who received treatment with the same drugs. Nineteen human breast cancer cell lines were tested for sensitivity to paclitaxel (T), 5-fluorouracil (F), doxorubicin (A) and cyclophosphamide (C) in vitro. Baseline gene expression data were obtained for each cell line with Affymetrix U133A gene chips, and multigene predictors of sensitivity were derived for each drug separately. These predictors were applied individually and in combination to human gene expression data generated with the same Affymetrix platform from fine needle aspiration specimens of 133 stage I-III breast cancers. Tumor samples were obtained at baseline, and each patient received 6 months of preoperative TFAC chemotherapy followed by surgery. Cell line-derived prediction results were correlated with the observed pathologic response to chemotherapy. Statistically robust differentially expressed genes between sensitive and resistant cells could only be found for paclitaxel. False discovery rates associated with the informative genes were high for all other drugs. For each drug, the top 100 differentially expressed genes were combined into a drug-specific response predictor. When these cell line-based predictors were applied to patient data, there was no significant correlation between observed response and predicted response either for individual drug predictors or combined predictions. Cell line-derived predictors of response to four commonly used chemotherapy drugs did not predict response accurately in patients. [ABSTRACT FROM AUTHOR]

Aberrant transcription and mRNA processing of multiple genes due to RNA-mediated toxic gain-of-function has been suggested to cause the complex phenotype in myotonic dystrophies type 1 and 2 (DM1 and DM2). However, the molecular basis of muscle weakness and wasting and the different pattern of muscle involvement in DM1 and DM2 are not well understood. We have analyzed the mRNA expression of genes encoding muscle-specific proteins and transcription factors by microarray profiling and studied selected genes for abnormal splicing. A subset of the abnormally regulated genes was further analyzed at the protein level. TNNT3 and LDB3 showed abnormal splicing with significant differences in proportions between DM2 and DM1. The differential abnormal splicing patterns for TNNT3 and LDB3 appeared more pronounced in DM2 relative to DM1 and are among the first molecular differences reported between the two diseases. In addition to these specific differences, the majority of the analyzed genes showed an overall increased expression at the mRNA level. In particular, there was a more global abnormality of all different myosin isoforms in both DM1 and DM2 with increased transcript levels and a differential pattern of protein expression. Atrophic fibers in DM2 patients expressed only the fast myosin isoform, while in DM1 patients they co-expressed fast and slow isoforms. However, there was no increase of total myosin protein levels, suggesting that aberrant protein translation and/or turnover may also be involved. [ABSTRACT FROM AUTHOR]

We describe an approach to analyzing single- and multiunit (ensemble) discharge patterns based on information-theoretic distance measures and on empirical theories derived from work in universal signal processing. In this approach, we quantify the difference between response patterns, whether time-varying or not, using information-theoretic distance measures. We apply these techniques to single- and multiple-unit processing of sound amplitude and sound location. These examples illustrate that neurons can simultaneously represent at least two kinds of information with different levels of fidelity. The fidelity can persist through a transient and a subsequent steady-state response, indicating that it is possible for an evolving neural code to represent information with constant fidelity. [ABSTRACT FROM AUTHOR]

The effects of AURKA overexpression associated with poor clinical outcomes have been attributed to increased cell cycle progression and the development of genomic instability with aneuploidy. We used RNA interference to examine the effects of AURKA overexpression in human bladder cancer cells. Knockdown had minimal effects on cell proliferation but blocked tumor cell invasion. Whole genome mRNA expression profiling identified nicotinamide N-methyltransferase (NNMT) as a downstream target that was repressed by AURKA. Chromatin immunoprecipitation and NNMT promoter luciferase assays revealed that AURKA's effects on NNMT were caused by PAX3-mediated transcriptional repression and overexpression of NNMT blocked tumor cell invasion in vitro. Overexpression of AURKA and activation of its downstream pathway was enriched in the basal subtype in primary human tumors and was associated with poor clinical outcomes. We also show that the FISH test for the AURKA gene copy number in urine yielded a specificity of 79.7% (95% confidence interval [CI] = 74.2% to 84.1%), and a sensitivity of 79.6% (95% CI = 74.2% to 84.1%) with an AUC of 0.901 (95% CI = 0.872 to 0.928; P < 0.001). These results implicate AURKA as an effective biomarker for bladder cancer detection as well as therapeutic target especially for its basal type. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Microarrays: retracing steps," by Anil Potti and Joseph R. Nevins in the previous issue.

The article presents information on serum proteomics profiling a young technology that began to mature with time. During the three years since the U.S. National Cancer Institute-Food and Drug Administration (NCI-FDA) proteomics group published their seminal but flawed study using mass spectrometry to profile the serum proteome of ovarian cancer patients, more than 60 published studies have applied similar technology to a wide range of cancers and other diseases. It is important to recognize the provisional nature of the conclusions of most serum proteomic studies carried out to date. In most cases where the protein peaks have been identified, they have turned out to be well-known, acute-phase proteins.

The article offers information on the suspension of three clinical trials due to incomplete supporting data involving the confidentiality of cancer patients' data in 2009 at Duke University in Durham, North Carolina.

The immune system has many important regulatory roles in cancer development and progression. Given the emergence of effective immune therapies against many cancers, reliable predictors of response are needed. One method of determining response is by evaluating immune cell populations from treated and untreated tumor samples. The amount of material obtained from tumor biopsies can be limited; therefore, gene-based or protein-based analyses may be attractive because they require minimal tissue. Cell-specific signatures are being analyzed with use of the latest technologies, including NanoString's nCounter technology, intracellular staining flow cytometry, cytometry by time-of-flight, RNA-Seq, and barcoding antibody-based protein arrays. These signatures provide information about the contributions of specific types of immune cells to bulk tumor samples. To date, both tumor tissue and immune cells have been analyzed for molecular expression profiles that can assess genes and proteins that are specific to immune cells, yielding results of varying specificity. Here, we discuss the importance of profiling tumor tissue and immune cells to identify immune-cell-associated genes and proteins and specific gene profiles of immune cells. We also discuss the use of these signatures in cancer treatment and the challenges faced in molecular expression profiling of immune cell populations., Competing Interests: The authors declare that they have no competing financial interests.