Abstract P4-04-16: Highly variably expressed exons (HVEE) of cancer genes and survival disparity in human breast cancer

Background: RNA-Seq is a powerful technology for accurately quantifying the transcriptome of human cancers. The human breast cancer section of the TCGA has generated RNA-Seq data for over 950 samples from primary tumors and adjacent normal tissues. Analysis of these data has provided insights into transcript isoforms and gene fusions related to the etiology of breast cancer. These rich data no doubt contain other insights into the expression of cancer genes and how it relates to cancer clinical outcomes.

Methods: TCGA RNA-Seq data were downloaded from TCGA data portal. A total of 712 samples were available when this project started, including data from 607 primary breast tumors of which 108 were Basal-like, 316 Luminal A, 139 Luminal B, and 54 HER2-enriched. Data from 65 adjacent normal tissues were also included to be used as controls. We examined the exon-level expression for a group of 137 strongly cancer related genes that can be grouped into 12 pathways (Vogelstein et al. 2013). We focused on exons that display a significant (p <0.05) difference in their expression variation, compared to the other exons of the same gene (> 2 fold changes in terms of standard deviation based on bootstrap resampling). We refer to these exons as HVEEs (Highly variably expressed exons). Using both unsupervised and supervised algorithms (e.g. non-negative matrix factorization), we classified the samples from various subtypes based on the expression patterns of the HVEEs and looked for patterns that associate with clinical outcomes.

Results: For many cancer genes, such as BRCA2 and PTEN, the distribution of within-gene exon expression is similar across all tumor samples and the control group. Subtype-specific amplification of oncogenes (e.g. ERBB2 in the HER2 subtype) and lower expression of cancer genes (e.g. AR in the basal subtype) have also been observed. We observed that exon expression across genes and across samples has in general small variations. Out of the 2,153 exons from the 137 cancer genes, we identified only 41 genes with HVEEs. Most of these genes have only one HVEE but a small number of them have multiple HVEEs (48 in total). Interestingly, HVEEs in 31 of these genes are consistent across the four breast cancer subtypes, as well as the normal group. However, the medians of such HVEE expression across the normal samples are in general significantly lower than those across the tumor samples in any tumor subtypes. Further, using the exon expression data of only the HVEEs, we identified distinct clusters for the basal-like subtype. The 108 basal-like subtype samples can be classified into two clusters with 41 and 67 samples, respectively, based on the expression level of the HVEEs from the above mentioned 31 genes. Overall survival for patients in these two clusters trends toward significance (log-rank test p = 0.058).

Discussion: The consistency of HVEEs across breast cancer and the normal samples and the ability of the HVEE expression level to stratify basal-like breast cancer in a clinically meaningful way is intriguing. We are extending the study to a larger dataset for validation.

The views expressed in this abstract are those of the authors and do not reflect the official policy of the Department of Defense, or US Government.

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-04-16.

Mice immunogenicity after vaccination by DNA vaccines containing individual genes of a new type of reovirus

In this study, we investigated humoral and cellular immune responses in mice to DNA vaccines containing individual S or M genes of a new type of reovirus (nRV) isolate from a severe acute respiratory syndrome (SARS) patient in Beijing, China. Mice were immunized intramuscularly (i.m.) with 100 μg of S1, S2, S3, S4, M1, M2, and M3 DNA vaccine each 4 times in 2-week intervals and assayed for humoral IgG, IgG1, IgG2, and IgG2b antibodies by ELISA and for cellular immune response, particularly IFN-γ induction by ELISpot assay. Moreover, CD4+ and CD8+ T cell levels in peripheral blood mononuclear cells were assayed by flow cytometry. We found that all DNA vaccines induced IgG antibodies, predominantly of the IgG2a class and S3 DNA vaccine was the strongest inducer. M2 and S3 DNA vaccines elicited Th1- and Th2-based immune responses, respectively, while S1 and M3 DNA vaccines induced a mixed Th1/Th2 response. M1, S2, and S4 DNA vaccines were poorly immunogenic. To our knowledge, this is the first report characterizing mammalian reovirus DNA vaccines applied to a mouse model.

LINE-1 hypomethylation in spermatozoa is associated with Bisphenol A exposure

Bisphenol A (BPA) is an endocrine disruptor with potentially harmful effects on humans. However, epigenetic mechanisms that modulate the effects of BPA remain unclear. Methylation of long interspersed nucleotide elements (LINE-1) is a marker of genome-wide methylation status. This study aims to examine whether BPA exposure was associated with LINE-1 methylation changes in men. Male factory workers in Hunan, China (N = 149) were studied, 77 with BPA exposure in workplace (BPA-exposed group) and 72 without BPA exposure in workplace (control group). Pre-shift and post-shift urine samples were collected from the BPA-exposed group and spot urine samples were collected from the control group. Urine samples were assessed for BPA. In addition, blood and semen samples were collected from both groups for LINE-1 methylation analysis. In multivariate analysis adjusted for age, education, smoking habits and alcohol consumption, sperm LINE-1 methylation level was significantly lower in BPA exposed workers (p < 0.001) compared to that in the unexposed workers. Linear regression analysis also showed that log-transformed urine BPA levels were inversely associated with sperm LINE-1 methylation (p < 0.0001), but not peripheral blood cell LINE-1 methylation. Moreover, the association between urine BPA level and semen quality was not attenuated after adjustments for LINE-1 level. In summary, the observed independent relationship between BPA exposure and LINE-1 methylation may have public health implications on reproductive health in men because of ubiquitous exposure to BPA.

Model predictions of toxaphene degradation in the atmosphere over North America

Technical toxaphene, a broad-spectrum pesticide mixture, degrades in the environment, resulting in potential changes in toxicity. The present study uses a multimedia model that the authors developed to estimate toxaphene degradation in the atmosphere over North America. The predicted degradation has strong spatial and temporal variability determined by processes such as emission and transport of technical toxaphene, as well as the complex interactions among many species (e.g., toxaphene, hydroxyl [OH] radicals, and ozone). More toxaphene is degraded in warmer months due to higher concentrations of technical toxaphene (primarily due to higher technical toxaphene emissions in the southeastern United States and transport to other regions) and OH radicals. In the model, OH radicals are created primarily through the reactions of water vapor with the excited oxygen atom, O(¹D), generated by the photolysis of ozone, which is produced primarily by reactions of volatile organic compounds and nitrogen oxides (NOx) in the presence of sunlight. The higher OH concentrations in warmer months are primarily the result of higher solar radiation and ozone concentrations. The spatial distribution of degradation depends on the distribution of technical toxaphene soil residues as well as atmospheric transport and chemistry; significant chemical degradation occurs in the southeastern United States where soils are most heavily contaminated by past applications of toxaphene.

In vitro study on cytotoxicity and intracellular formaldehyde concentration changes after exposure to formaldehyde and its derivatives

HeLa cells were exposed to formaldehyde and its metabolic derivatives, methanol, formic acid, and acetaldehyde, to investigate that the toxicity of formaldehyde is not caused by the chemical group. After 1 h of treatment with formaldehyde, mitochondrial assays showed that low concentrations (e.g. 10 μmol/L) of formaldehyde promoted growth of the HeLa cells, while higher concentrations (e.g. ≥62.5 μmol/L) inhibited cell growth; while all four chemicals at a concentration of 125 μmol/L affected cell growth, formaldehyde affected the largest. Reactive oxygen species concentration increased with the concentration of the exposure chemical. The endogenous formaldehyde content increased the most in the formaldehyde group, but in other three groups, it did not increase as the exposure concentration increased. Expression of dehydrogenase (formaldehyde dehydrogenase (FDH)) in the formaldehyde (10.40) and methanol (10.60) groups increased significantly compared with the control (1), while it was similar to the control in formic acid (0.90) and acetaldehyde (1.10) groups. Our results suggest that formaldehyde could affect cell activity and even enter cells. Exposure to formaldehyde changes the endogenous formaldehyde concentration in cells within 24 h, and this induces expression of FDH for formaldehyde degradation to maintain the formaldehyde balance. The toxicity of formaldehyde is not caused by the carbon atoms in the aldehyde, hydroxyl, or carboxyl groups. Formaldehyde is hypothesized to be an important signaling molecule in the regulation of cell growth and maintenance of the endogenous formaldehyde level.

Implementation of high-resolution SNP arrays in the investigation of fetuses with ultrasound malformations: 5 years of clinical experience

Chromosome microarray analysis (CMA) has proven to be a powerful tool in postnatal patients with intellectual disabilities, and it is increasingly used in prenatal diagnosis. However, its diagnostic capabilities in prenatal diagnosis vary, and clinical experiences have failed to establish a consensus regarding CMA indications, the design and resolution of microarrays, and the notification and interpretation of copy number variations (CNVs). We present our 5 years of clinical experience using whole-genome high-resolution single nucleotide polymorphism (SNP) arrays to investigate 446 fetuses that had structural malformations detected with ultrasound but for which standard karyotyping analysis showed normal karyotypes. CMA revealed genomic CNVs in 323 (72.4%) cases and clinically significant CNVs in 11.4% of the fetuses (51/446), including 2 cases of uniparental disomy (UPD) as well as 1 case of cryptic mosaic monosomy of chromosome X. Variants of unknown significance (VOUS) existed in 2.0% of the tested fetuses (9/446). Our results demonstrate the value of whole-genome high-resolution SNP arrays in fetuses with congenital malformations and give a higher detection rate of clinically significant genomic imbalance, especially for detecting UPD. Sufficient communication between technicians and genetic counselors, along with parental testing and comparison with data from in-house or international sources, can significantly reduce VOUS.

Laboratory studies on secondary organic aerosol formation from crude oil vapors

Airborne measurements of aerosol composition and gas phase compounds over the Deepwater Horizon (DWH) oil spill in the Gulf of Mexico in June 2010 indicated the presence of high concentrations of secondary organic aerosol (SOA) formed from organic compounds of intermediate volatility. In this work, we investigated SOA formation from South Louisiana crude oil vapors reacting with OH in a Potential Aerosol Mass flow reactor. We use the dependence of evaporation time on the saturation concentration (C*) of the SOA precursors to separate the contribution of species of different C* to total SOA formation. This study shows consistent results with those at the DWH oil spill: (1) organic compounds of intermediate volatility with C* = 10(5)-10(6) μg m(-3) contribute the large majority of SOA mass formed, and have much larger SOA yields (0.37 for C* = 10(5) and 0.21 for C* = 10(6) μg m(-3)) than more volatile compounds with C*≥10(7) μg m(-3), (2) the mass spectral signature of SOA formed from oxidation of the less volatile compounds in the reactor shows good agreement with that of SOA formed at DWH oil spill. These results also support the use of flow reactors simulating atmospheric SOA formation and aging.

Sequencing-based gene network analysis provides a core set of gene resource for understanding thermal adaptation in Zhikong scallop Chlamys farreri

Marine organisms are commonly exposed to variable environmental conditions, and many of them are under threat from increased sea temperatures caused by global climate change. Generating transcriptomic resources under different stress conditions are crucial for understanding molecular mechanisms underlying thermal adaptation. In this study, we conducted transcriptome-wide gene expression profiling of the scallop Chlamys farreri challenged by acute and chronic heat stress. Of the 13 953 unique tags, more than 850 were significantly differentially expressed at each time point after acute heat stress, which was more than the number of tags differentially expressed (320-350) under chronic heat stress. To obtain a systemic view of gene expression alterations during thermal stress, a weighted gene coexpression network was constructed. Six modules were identified as acute heat stress-responsive modules. Among them, four modules involved in apoptosis regulation, mRNA binding, mitochondrial envelope formation and oxidation reduction were downregulated. The remaining two modules were upregulated. One was enriched with chaperone and the other with microsatellite sequences, whose coexpression may originate from a transcription factor binding site. These results indicated that C. farreri triggered several cellular processes to acclimate to elevated temperature. No modules responded to chronic heat stress, suggesting that the scallops might have acclimated to elevated temperature within 3 days. This study represents the first sequencing-based gene network analysis in a nonmodel aquatic species and provides valuable gene resources for the study of thermal adaptation, which should assist in the development of heat-tolerant scallop lines for aquaculture.

The influence of food on the pharmacokinetics of amlodipine and losartan after single-dose of its compound tablets in healthy chinese subjects

We aim to identify the effects of food on the pharmacokinetics (PK) of amlodipine, losartan and losartan's active metabolite (EXP3174) after oral administration of the Compound Amlodipine Tablets with single dose in healthy Chinese subjects. 12 subjects took the compounds (10 mg/100 mg, amlodipine/losartan) at the conditions of a high-fat breakfast and an overnight fast with a washout period of 14 days. Plasma samples were obtained at scheduled time, and determined by HPLC-MS/MS for the concentrations of amlodipine and HPLC-MS for the concentrations of losartan and EXP3174, respectively. PK parameters were calculated using Software Drug and Statistics (Version 2.0). When tablets were co-administered with food, there was no significant difference of AUC for amlodipine and losartan, but the AUC of EXP3174 was reduced by 19.1%. Meanwhile, the Cmax of amlodipine, losartan and EXP3174 were reduced by 11.4%, 20.0% and 41.4%, and the Tmax of losartan and EXP3174 were 1.3 and 1.8 h longer, respectively. No significant difference was found at t1/2 following food intake. In conclusion, the Compound Amlodipine Tablets, are affected by food administration by reducing the AUC of EXP3174. It is thus suggested that the Compound Amlodipine Tablets should be administered 1 h before or 2 h after meal.

Microbial basis of oral malodor development in humans

To better understand the microbial basis of oral malodor development in humans, we used a cross-sectional and longitudinal study design and the pyrosequencing approach to track and compare the tongue microbiota associated with oral malodor in 29 Chinese adults who underwent a consecutive three-day evaluation for the amount of H2S excreted orally. Three levels of the oral malodor state (healthy, oral malodor, and severe oral malodor) were defined based on the H2S level. Community structure of the tongue plaques was more sensitive to changes of malodor state than to interpersonal variations or differences in sampling times. Within each individual, the structure of microbiota was relatively stable, while their variations were correlated with the change in the H2S level. Severe oral malodor microbiota were the most conserved in community structure, whereas the healthy ones were relatively varied. Oral-malodor-associated bacteria were identified. The relative abundance of Leptotrichia and Prevotella was positively correlated with oral malodor severity, whereas Hemophilus and Gemella exhibited a negative relationship with oral malodor severity. Our study provides one of the first landscapes of oral microbiota changes associated with oral malodor development and reveals microbes potentially useful to the evaluation and control of oral malodor.

Transcriptome profiling of the whitefly Bemisia tabaci reveals stage-specific gene expression signatures for thiamethoxam resistance

Bemisia tabaci has developed high levels of resistance to many insecticides including the neonicotinoids and there is strong evidence that for some compounds resistance is stage-specific. To investigate the molecular basis of B. tabaci resistance to the neonicotinoid thiamethoxam we used a custom whitefly microarray to compare gene expression in the egg, nymph and adult stages of a thiamethoxam-resistant strain (TH-R) with a susceptible strain (TH-S). Gene ontology and bioinformatic analyses revealed that in all life stages many of the differentially expressed transcripts encoded enzymes involved in metabolic processes and/or metabolism of xenobiotics. Several of these are candidate resistance genes and include the cytochrome P450 CYP6CM1, which has been shown to confer resistance to several neonicotinoids previously, a P450 belonging to the Cytochrome P450s 4 family and a glutathione S-transferase (GST) belonging to the sigma class. Finally several ATP-binding cassette transporters of the ABCG subfamily were highly over-expressed in the adult stage of the TH-R strain and may play a role in resistance by active efflux. Here, we evaluated both common and stage-specific gene expression signatures and identified several candidate resistance genes that may underlie B. tabaci resistance to thiamethoxam.

CALIBRATING NON-CONVEX PENALIZED REGRESSION IN ULTRA-HIGH DIMENSION

We investigate high-dimensional non-convex penalized regression, where the number of covariates may grow at an exponential rate. Although recent asymptotic theory established that there exists a local minimum possessing the oracle property under general conditions, it is still largely an open problem how to identify the oracle estimator among potentially multiple local minima. There are two main obstacles: (1) due to the presence of multiple minima, the solution path is nonunique and is not guaranteed to contain the oracle estimator; (2) even if a solution path is known to contain the oracle estimator, the optimal tuning parameter depends on many unknown factors and is hard to estimate. To address these two challenging issues, we first prove that an easy-to-calculate calibrated CCCP algorithm produces a consistent solution path which contains the oracle estimator with probability approaching one. Furthermore, we propose a high-dimensional BIC criterion and show that it can be applied to the solution path to select the optimal tuning parameter which asymptotically identifies the oracle estimator. The theory for a general class of non-convex penalties in the ultra-high dimensional setup is established when the random errors follow the sub-Gaussian distribution. Monte Carlo studies confirm that the calibrated CCCP algorithm combined with the proposed high-dimensional BIC has desirable performance in identifying the underlying sparsity pattern for high-dimensional data analysis.

Performance of imaging modalities in the diagnosis of hepatocellular carcinoma: a systematic review and meta-analysis

PURPOSE

The recent guidelines published in 2011 suggest the use of only one imaging method for the final imaging diagnosis of hepatocellular carcinoma. To evaluate the methods in the context of the available literature evidence, this systematic review aimed at assessing the relative performance of different imaging techniques currently used in clinical practice.

MATERIALS AND METHODS

MEDLINE and EMBASE were searched from January 1996 to June 2011, with no language limitation. Eligible trials had to be conducted in patients with suspicion or diagnosis of hepatocellular carcinoma; compare at least two of the following imaging modalities: magnetic resonance imaging, computed tomography, ultrasound; have pathological findings as a reference standard. An analysis also including non-comparative studies was performed as a validation of the main comparison results.

RESULTS

Of 5,144 screened papers, 16 studies fulfilled the eligibility criteria for the comparative analysis and 65 were eligible for the non-comparative analysis. The overall sensitivity and specificity derived by the pooled analysis were 0.78 and 0.77 for computed tomography, 0.84 and 0.84 for magnetic resonance imaging and 0.86 and 0.77 for ultrasound, respectively. In the pair-wise comparisons, ultrasound showed a statistically better specificity than magnetic resonance imaging (0.86 vs. 0.78; p = 0.014) and a statistically better sensitivity than computed tomography (0.88 vs. 0.78; p = 0.030).

CONCLUSION

The present systematic review did not show an obvious superiority of one imaging method. Since their accuracy is not completely overlapping, the possibility of reaching better performance by combining methods should be considered in future prospective trials.

Functional data analysis for dynamical system identification of behavioral processes

Efficient new technology has made it straightforward for behavioral scientists to collect anywhere from several dozen to several thousand dense, repeated measurements on one or more time-varying variables. These intensive longitudinal data (ILD) are ideal for examining complex change over time but present new challenges that illustrate the need for more advanced analytic methods. For example, in ILD the temporal spacing of observations may be irregular, and individuals may be sampled at different times. Also, it is important to assess both how the outcome changes over time and the variation between participants' time-varying processes to make inferences about a particular intervention's effectiveness within the population of interest. The methods presented in this article integrate 2 innovative ILD analytic techniques: functional data analysis and dynamical systems modeling. An empirical application is presented using data from a smoking cessation clinical trial. Study participants provided 42 daily assessments of pre-quit and post-quit withdrawal symptoms. Regression splines were used to approximate smooth functions of craving and negative affect and to estimate the variables' derivatives for each participant. We then modeled the dynamics of nicotine craving using standard input-output dynamical systems models. These models provide a more detailed characterization of the post-quit craving process than do traditional longitudinal models, including information regarding the type, magnitude, and speed of the response to an input. The results, in conjunction with standard engineering control theory techniques, could potentially be used by tobacco researchers to develop a more effective smoking intervention.

A Bayesian semiparametric model for bivariate sparse longitudinal data

Mixed-effects models have recently become popular for analyzing sparse longitudinal data that arise naturally in biological, agricultural and biomedical studies. Traditional approaches assume independent residuals over time and explain the longitudinal dependence by random effects. However, when bivariate or multivariate traits are measured longitudinally, this fundamental assumption is likely to be violated because of intertrait dependence over time. We provide a more general framework where the dependence of the observations from the same subject over time is not assumed to be explained completely by the random effects of the model. We propose a novel, mixed model-based approach and estimate the error-covariance structure nonparametrically under a generalized linear model framework. We use penalized splines to model the general effect of time, and we consider a Dirichlet process mixture of normal prior for the random-effects distribution. We analyze blood pressure data from the Framingham Heart Study where body mass index, gender and time are treated as covariates. We compare our method with traditional methods including parametric modeling of the random effects and independent residual errors over time. We conduct extensive simulation studies to investigate the practical usefulness of the proposed method. The current approach is very helpful in analyzing bivariate irregular longitudinal traits.

Nonparametric Mixture of Regression Models

Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.