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Expression of brain-derived neurotrophic factor (BDNF) and somatostatin (SST) mRNAs in the brain decreases progressively and robustly with age, and lower BDNF and SST expression in the brain has been observed in many brain disorders. BDNF is known to regulate SST expression; however, the mechanisms underlying decreased expression of both genes are not understood. DNA methylation (DNAm) is an attractive candidate mechanism. To investigate the contribution of DNAm to the age-related decline in BDNF and SST expression, the Illumina Infinium HumanMethylation450 Beadchip Array was used to quantify DNAm of BDNF (26 CpG loci) and SST (9 CpG loci) in the orbital frontal cortices of postmortem brains from 22 younger (age 60 years) with known age-dependent BDNF and SST expression differences. Relative to the younger individuals, 10 of the 26 CpG loci in BDNF and 8 of the 9 CpG loci in SST were significantly hypermethylated in the older individuals. DNAm in BDNF exons/promoters I, II, and IV negatively correlated with BDNF expression (r=−0.37, p

Accurate prediction of prostate cancer clinical courses remains elusive. In this study, we performed whole genome copy number analysis on leukocytes of 273 prostate cancer patients using Affymetrix SNP6.0 chip. Copy number variations (CNV) were found across all chromosomes of the human genome. An average of 152 CNV fragments per genome was identified in the leukocytes from prostate cancer patients. The size distributions of CNV in the genome of leukocytes were highly correlative with prostate cancer aggressiveness. A prostate cancer outcome prediction model was developed based on large size ratio of CNV from the leukocyte genomes. This prediction model generated an average prediction rate of 75.2%, with sensitivity of 77.3% and specificity of 69.0% for prostate cancer recurrence. When combined with Nomogram and the status of fusion transcripts, the average prediction rate was improved to 82.5% with sensitivity of 84.8% and specificity of 78.2%. In addition, the leukocyte prediction model was 62.6% accurate in predicting short prostate specific antigen doubling time. When combined with Gleason’s grade, Nomogram and the status of fusion transcripts, the prediction model generated a correct prediction rate of 77.5% with 73.7% sensitivity and 80.1% specificity. To our knowledge, this is the first study showing that CNVs in leukocyte genomes are predictive of clinical outcomes of a human malignancy.

© 2015 Wang et al.; licensee BioMed Central Ltd. Background: Identification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DEtotal) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DEtotal of each gene. MFSelector can successfully identify genes with monotonic characteristics.Results: We have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages.Conclusions: We have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method. The MFSelector R function and data sets can be downloaded from: http://microarray.ym.edu.tw/tools/MFSelector/.

Microarray analysis to monitor expression activities in thousands of genes simultaneously has become routine in biomedical research during the past decade. A tremendous amount of expression profiles are generated and stored in the public domain and information integration by meta-analysis to detect differentially expressed (DE) genes has become popular to obtain increased statistical power and validated findings. Methods that aggregate transformed $p$-value evidence have been widely used in genomic settings, among which Fisher's and Stouffer's methods are the most popular ones. In practice, raw data and $p$-values of DE evidence are often not available in genomic studies that are to be combined. Instead, only the detected DE gene lists under a certain $p$-value threshold (e.g., DE genes with $p$-value${}, Comment: Published in at http://dx.doi.org/10.1214/14-AOAS747 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Trauma and hemorrhagic shock (HS) elicit severe physiological disturbances that predispose the victims to subsequent organ dysfunction and death. The general lack of effective therapeutic options for these patients is mainly due to the complex interplay of interacting inflammatory and physiological elements working at multiple levels. Systems biology has emerged as a new paradigm that allows the study of large portions of physiological networks simultaneously. Seeking a better understanding of the interplay among known inflammatory pathways, we constructed a mathematical model encompassing the dynamics of the acute inflammatory response that incorporates the intertwined effects of inflammation and global tissue damage. The model was calibrated using data from C57Bl/6 mice subjected to endotoxemia, sham operation (i.e., surgical trauma induced by cannulation [ST]) or ST + HS+ resuscitation (ST-HS-R). An in silico simulation, made at whole-organism level, suggested that similar pathways of different magnitudes were operant as the degree of total body damage increased. We sought to validate this hypothesis by subjecting mice to HS and comparing the models predictions to circulating markers of inflammation and tissue injury as well as the global transcriptomic response of the liver. C57Bl/6 mice were subjected to ST or ST-HS (without resuscitation). Liver gene expression was assessed using an Affymetrix DNA microarray (GeneChip Mouse Expression Set 430A, Affymetrix, Santa Clara, CA), which contains 22,621 probe sets and effectively interrogates 12,341 mouse genes. The microarray data sets were subjected to hierarchical clustering and pathway analysis. In agreement with model predictions, circulating levels of inflammation/tissue injury markers and the microarray analysis both demonstrated that ST alone accounts for a substantial proportion of the observed phenotypic and genetic/molecular changes versus untreated animals. The addition of HS further increased the magnitude of gene expression, but relatively few additional genes were recruited. Mathematical simulations and DNA microarrays, both systems biology tools, may provide valuable insight into the complex global physiological interactions that occur in response to trauma and hemorrhagic shock.

Objectives Myopodin has recently been characterized as a tumor suppressor gene whose encoded product inhibits prostate cancer growth and metastasis both in vitro and in animal models. However, the clinical evidence of tumor suppression in humans is still lacking. In this study, we conducted a large-scale analysis of myopodin expression in prostate cancer samples to examine the relationship between myopodin expression and prostate cancer grade and stage and the probability of clinical relapse. Methods Immunostaining using anti-myopodin antibodies was performed on 746 formalin-fixed paraffin-embedded tissue samples to evaluate the level of myopodin expression in normal and malignant prostatic tissue. Results The expression of myopodin was semiquantitatively determined by immunostaining. The myopodin expression levels were examined in relation to prostate cancer grade and stage, preoperative prostate-specific antigen level, surgical margin involvement, tumor volume, and clinical relapse. The mean myopodin expression score (range 0 to 3) was 2.1 and 0.88 for benign prostatic tissue and prostate cancer, respectively. Minimal variation in myopodin expression was observed among the various grades, stages, tumor volumes, and preoperative prostate-specific antigen levels of prostate cancer. However, samples with positive surgical margins were associated with lower myopodin expression. Complete inactivation of myopodin expression correlated with a greater than 86% rate of clinical relapse. Conclusions Our results suggest that myopodin is an important predictor of prostate cancer metastasis, independent of Gleason score, preoperative prostate-specific antigen level, and tumor stage.

Prostate cancer is frequent among men over 45 years of age, but it generally only becomes lethal with metastasis. In this study, we identified a gene called cellular stress response 1 (CSR1) that was frequently down-regulated and methylated in prostate cancer samples. Survival analysis indicated that methylation of the CSR1 promoter, and to a lesser extent down-regulation of CSR1 protein expression, was associated with a high rate of prostate cancer metastasis. Forced expression of CSR1 in prostate cancer cell lines DU145 and PC3 resulted in a two- to threefold decrease in colony formation and a 10-fold reduction in anchorage-independent growth. PC3 cells stably expressing CSR1 had an average threefold decrease in their ability to invade in vitro. Expression of CSR1 in PC3 cell xenografts produced a dramatic reduction (>8-fold) in tumor size, rate of invasion (0 versus 31%), and mortality (13 versus 100%). The present findings suggest that CSR1 is a potent tumor sup-pressor gene.

Microarray analysis to monitor expression activities in thousands of genes simultaneously has become routine in biomedical research during the past decade. a tremendous amount of expression profiles are generated and stored in the public domain and information integration by meta-analysis to detect differentially expressed (DE) genes has become popular to obtain increased statistical power and validated findings. Methods that aggregate transformed p-value evidence have been widely used in genomic settings, among which Fisher's and Stouffer's methods are the most popular ones. In practice, raw data and p-values of DE evidence are often not available in genomic studies that are to be combined. Instead, only the detected DE gene lists under a certain p-value threshold (e.g., DE genes with p-value < 0.001) are reported in journal publications. The truncated p-value information makes the aforementioned meta-analysis methods inapplicable and researchers are forced to apply a less efficient vote counting method or naïvely drop the studies with incomplete information. The purpose of this paper is to develop effective meta-analysis methods for such situations with partially censored p-values. We developed and compared three imputation methods—mean imputation, single random imputation and multiple imputation—for a general class of evidence aggregation methods of which Fisher's and Stouffer's methods are special examples. The null distribution of each method was analytically derived and subsequent inference and genomic analysis frameworks were established. Simulations were performed to investigate the type Ierror, power and the control of false discovery rate (FDR) for (correlated) gene expression data. The proposed methods were applied to several genomic applications in colorectal cancer, pain and liquid association analysis of major depressive disorder (MDD). The results showed that imputation methods outperformed existing naïve approaches. Mean imputation and multiple imputation methods performed the best and are recommended for future applications.

Activation of oncogenic Kras in murine lung leads to the development of numerous small adenomas, only some of which progress over time to overt adenocarcinoma. Thus, although Kras is the initiating oncogene, it is likely that secondary genetic events are required for progression from adenoma to adenocarcinoma. Some of these secondary events may also be important in human lung adenocarcinoma. By comparing gene expression profiles with DNA copy number changes, we sought to identify genes that play key roles in tumor progression in this model. Gene expression profiling revealed significant heterogeneity among the tumor samples. In 27% of the tumors analyzed, whole- or subchromosome duplications or deletions in one or more chromosomes were seen. Recurrent duplications were seen on chromosomes 6, 8, 16, and 19, whereas chromosomes 4, 11, and 17 were frequently lost. Notably, focal amplifications or deletions were not seen. Despite the lack of focal amplication, we showed that chromosome duplication has a measurable effect on gene expression that is not uniform across the genome. We identified a group of genes whose gene expression was highly correlated with changes in DNA copy number. These highly correlated genes were enriched for gene ontology categories involved in the DNA damage response and telomere maintenance. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat. © 2005 Wiley-Liss, Inc.

In this article, we propose a method for clustering that produces tight and stable clusters without forcing all points into clusters. The methodology is general but was initially motivated from cluster analysis of microarray experiments. Most current algorithms aim to assign all genes into clusters. For many biological studies, however, we are mainly interested in identifying the most informative, tight, and stable clusters of sizes, say, 20-60 genes for further investigation. We want to avoid the contamination of tightly regulated expression patterns of biologically relevant genes due to other genes whose expressions are only loosely compatible with these patterns. "Tight clustering" has been developed specifically to address this problem. It applies K-means clustering as an intermediate clustering engine. Early truncation of a hierarchical clustering tree is used to overcome the local minimum problem in K-means clustering. The tightest and most stable clusters are identified in a sequential manner through an analysis of the tendency of genes to be grouped together under repeated resampling. We validated this method in a simulated example and applied it to analyze a set of expression profiles in the study of embryonic stem cells.

The concept of large margins have been recognized as an important principle in analyzing learning methodologies, including boosting, neural networks, and support vector machines (SVMs). However, this concept alone is not adequate for learning in nonseparable cases. We propose a learning methodology, called ψ-learning, that is derived from a direct consideration of generalization errors. We provide a theory for ψ-learning and show that it essentially attains the optimal rates of convergence in two learning examples. Finally, results from simulation studies and from breast cancer classification confirm the ability of ψ-learning to outperform SVM in generalization.

Summary: With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case–control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines. Availability: http://www.biostat.pitt.edu/bioinfo/software.htm Contact: ctseng@pitt.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

Invasive lobular carcinoma (ILC) is the second most common type of breast cancer following invasive ductal carcinoma (IDC) and accounts for 10-15% of all cases. Unlike the masses or lumps formed by IDCs, ILCs grow as small, dis-cohesive cells in a single-file pattern within dense layers of extracellular matrix. Paradoxically, while patients with ILC display favorable prognostic and predictive factors (ER+, PR+, HER2-, low Ki67), their long-term response to endocrine therapy is worse than patients with IDC. Despite such histological and clinical differences, ILC has remained a gravely understudied subtype of breast cancer. Thus, there is urgent need to investigate molecular drivers of ILC progression in order to develop more effective therapeutic strategies and improve patient outcome. To this end, we used the Nanostring platform to measure the expression of 577 copy number variation-associated genes in 131 primary ILC tumors and identified two candidate drivers that exhibit higher expression in tumors from patients with recurrent versus non-current disease: CTTN (cortical actin binding protein) and QSOX1 (quiescin sulfhydryl oxidase). In follow-up studies, we observed high CTTN and QSOX1 expression in human ILC cell lines, tumors and patient-derived xenografts. In addition, QSOX1 expression was further increased in ILC ovarian metastases compared to their matched primary tumors. In functional in vitro studies, RNAi-mediated inhibition of CTTN led to decreased adhesion and haptotaxis to Collagen I, while QSOX1 knockdown diminished the growth of human ILC cell lines. We are currently assessing the in vivo therapeutic utility of CTTN and QSOX1 inhibition in tumor growth and metastasis using human ILC cell line and patient-derived xenograft mouse models. This study greatly advances our understanding of ILC disease mechanisms and identifies novel therapeutic targets towards improving the clinical outcome of patients with this understudied subtype of breast cancer. Citation Format: Nilgun Tasdemir, Matthew Sikora, Zhu Li, Kevin Levine, Ahmed Basudan, Soumya Luthra, Esther Elishaev, Uma Chandran, George C. Tseng, Rachel Jankowitz, David J. Dabbs, Priscilla McAuliffe, Nancy E. Davidson, Steffi Oesterreich. Investigating drivers of disease progression in invasive lobular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2841. doi:10.1158/1538-7445.AM2017-2841

Background Invasive Lobular Carcinoma (ILC) is an understudied subtype of breast cancer. Distinctive properties of ILC include growth patterns, metastatic behavior, receptor status (almost universally estrogen receptor (ER) positive), and survival outcomes (Long-term survival is lower in patients with ER+ ILC compared to the other main histological subtype, invasive ductal carcinoma). Our lab recently generated six long-term estrogen deprivation (LTED) models of ILC cells. We performed RNA-Sequencing on these six LTED cell lines and identified differentially expressed genes compared to their parental cells cultured with estrogen. We overlapped these results with a previously published analysis of a tamoxifen-resistant ILC cell line and found that FGFR4 is the most consistently overexpressed gene in the setting of acquired resistance to endocrine therapy in ILC cells. Hypothesis FGFR4 is an important mediator of resistance to endocrine therapy in ILC. Methods To study the role of FGFR4 in acquired resistance to endocrine therapy, we used siRNA, multiple shRNAs, and specific small molecule inhibition for growth assays of ILC cells. To study the role of FGFR4 in de novo resistance to endocrine therapy, we collected 129 well curated ER+ ILC tumor specimens. We performed gene expression analysis on the pre-treatment samples using a custom NanoString panel. Results FGFR4 inhibition decreases parental and LTED ILC cell growth in classic 2D growth conditions, in the setting of ultra-low attachment, and in colony formation assays. Mechanistically, FGFR4 and estrogen signaling are antagonistic in parental ILC cells. In our clinical samples, increased expression of FGFR4 is predictive of shorter time to distant recurrence. Conclusion FGFR4 may play an important role in both acquired and de novo resistance to endocrine therapy in ILC. Future studies will assess the efficacy of combining FGFR4 inhibitors with ER-targeted therapy for patients with ILC. Citation Format: Kevin Levine, Jian Chen, Matthew Sikora, Nilgun Tasdemir, George Tseng, Shannon Puhalla, Rachel Jankowitz, David Dabbs, Priscilla McAuliffe, Adrian Lee, Steffi Oesterreich. Combination FGFR4 and ER-targeted therapy for invasive lobular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3612. doi:10.1158/1538-7445.AM2017-3612

The visinin-like 1 (VSNL1) gene encodes visinin-like protein 1, a peripheral biomarker for Alzheimer disease (AD). Little is known, however, about normal VSNL1 expression in brain and the biologic networks in which it participates. Frontal cortex gray matter obtained from 209 subjects without neurodegenerative or psychiatric illness, ranging in age from 16 to 91, was processed on Affymetrix GeneChip 1.1 ST and Human SNP Array 6.0. VSNL1 expression was unaffected by age and sex, and not significantly associated with SNPs in cis or trans. VSNL1 was significantly co-expressed with genes in pathways for calcium signaling, AD, long-term potentiation, long-term depression, and trafficking of AMPA receptors. The association with AD was driven, in part, by correlation with amyloid precursor protein (APP) expression. These findings provide an unbiased link between VSNL1 and molecular mechanisms of AD, including pathways implicated in synaptic pathology in AD. Whether APP may drive increased VSNL1 expression, VSNL1 drives increased APP expression, or both are downstream of common pathogenic regulators will need to be evaluated in model systems.

Background In modern biomedical research of complex diseases, a large number of demographic and clinical variables, herein called phenomic data, are often collected and missing values (MVs) are inevitable in the data collection process. Since many downstream statistical and bioinformatics methods require complete data matrix, imputation is a common and practical solution. In high-throughput experiments such as microarray experiments, continuous intensities are measured and many mature missing value imputation methods have been developed and widely applied. Numerous methods for missing data imputation of microarray data have been developed. Large phenomic data, however, contain continuous, nominal, binary and ordinal data types, which void application of most methods. Though several methods have been developed in the past few years, not a single complete guideline is proposed with respect to phenomic missing data imputation. Results In this paper, we investigated existing imputation methods for phenomic data, proposed a self-training selection (STS) scheme to select the best imputation method and provide a practical guideline for general applications. We introduced a novel concept of “imputability measure” (IM) to identify missing values that are fundamentally inadequate to impute. In addition, we also developed four variations of K-nearest-neighbor (KNN) methods and compared with two existing methods, multivariate imputation by chained equations (MICE) and missForest. The four variations are imputation by variables (KNN-V), by subjects (KNN-S), their weighted hybrid (KNN-H) and an adaptively weighted hybrid (KNN-A). We performed simulations and applied different imputation methods and the STS scheme to three lung disease phenomic datasets to evaluate the methods. An R package “phenomeImpute” is made publicly available. Conclusions Simulations and applications to real datasets showed that MICE often did not perform well; KNN-A, KNN-H and random forest were among the top performers although no method universally performed the best. Imputation of missing values with low imputability measures increased imputation errors greatly and could potentially deteriorate downstream analyses. The STS scheme was accurate in selecting the optimal method by evaluating methods in a second layer of missingness simulation. All source files for the simulation and the real data analyses are available on the author’s publication website. Electronic supplementary material The online version of this article (doi:10.1186/s12859-014-0346-6) contains supplementary material, which is available to authorized users.

Monoclonal antibodies that block inhibitory immune checkpoint molecules and enhance anti-tumor responses show clinical promise in advanced solid tumors. Most of the preliminary evidence on therapeutic efficacy of immune checkpoint blockers comes from studies in melanoma, lung and renal cancer. To test the in vivo potential of programmed death-ligand 1 (PD-L1) blockade in ovarian cancer, we recently generated a new transplantable tumor model using human mucin 1 (MUC1)-expressing 2F8 cells. The MUC1 transgenic (MUC1.Tg) mice develop large number of intraperitoneal (IP) tumors following IP injection of 8 × 10(5) syngeneic 2F8 cells. The tumors are aggressive and display little T cell infiltration. Anti-PD-L1 antibody was administered IP every 2 weeks (200 μg/dose) for a total of three doses. Treatment was started 21 days post-tumor challenge, a time point which corresponds to late tumor stage. The anti-PD-L1 treatment led to substantial T cell infiltration within the tumor and significantly increased survival (p = 0.001) compared to isotype control-treated mice. When the same therapy was administered to wild-type mice challenged with 2F8 tumors, no survival benefit was observed, despite the presence of high titer anti-MUC1 antibodies. However, earlier treatment (day 11) and higher frequency of IP injections restored the T cell responses and led to prolonged survival. Splenocyte profiling via Nanostring using probes for 511 immune genes revealed a treatment-induced immune gene signature consistent with increased T cell-mediated immunity. These findings strongly support further preclinical and clinical strategies exploring PD-L1 blockade in ovarian cancer.

Major depressive disorder (MDD) in general, and anxious-depression in particular, are characterized by poor rates of remission with first-line treatments, contributing to the chronic illness burden suffered by many patients. Prospective research is needed to identify the biomarkers predicting nonremission prior to treatment initiation. We collected blood samples from a discovery cohort of 34 adult MDD patients with co-occurring anxiety and 33 matched, nondepressed controls at baseline and after 12 weeks (of citalopram plus psychotherapy treatment for the depressed cohort). Samples were processed on gene arrays and group differences in gene expression were investigated. Exploratory analyses suggest that at pretreatment baseline, nonremitting patients differ from controls with gene function and transcription factor analyses potentially related to elevated inflammation and immune activation. In a second phase, we applied an unbiased machine learning prediction model and corrected for model-selection bias. Results show that baseline gene expression predicted nonremission with 79.4% corrected accuracy with a 13-gene model. The same gene-only model predicted nonremission after 8 weeks of citalopram treatment with 76% corrected accuracy in an independent validation cohort of 63 MDD patients treated with citalopram at another institution. Together, these results demonstrate the potential, but also the limitations, of baseline peripheral blood-based gene expression to predict nonremission after citalopram treatment. These results not only support their use in future prediction tools but also suggest that increased accuracy may be obtained with the inclusion of additional predictors (eg, genetics and clinical scales). Fil: Guilloux, Jean Philippe. Universite Paris Sud; Francia. University of Pittsburgh; Estados Unidos Fil: Bassi, Sabrina Cecilia. University of Pittsburgh; Estados Unidos. Hospital Italiano. Instituto de Ciencias Básicas y Medicina Experimental; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Ding, Ying. University of Pittsburgh; Estados Unidos Fil: Walsh, Chris. University of Pittsburgh; Estados Unidos Fil: Turecki, Gustavo. Douglas Mental Health Institute; Canadá Fil: Tseng, George. University of Pittsburgh; Estados Unidos Fil: Cyranowski, Jill M.. University of Pittsburgh; Estados Unidos Fil: Sibille, Etienne. University of Pittsburgh; Estados Unidos

DNA methylation is one of the most important epigenetic mechanisms in regulating gene expression. Genome hypermethylation has been speculated as a critical mechanism in human malignancies. However, whole genome quantification of DNA methylation of human malignancies has rarely been investigated, and the significance of the genome distribution of CpG methylation is unclear. In this report, we performed whole genome methylation sequencing to investigate the methylation profiles of 13 prostate samples, including 5 prostate cancers, 4 matched benign prostate tissues adjacent to cancer and 4 age-matched organ donor prostate tissues. Our results showed that alterations of methylation patterns occurred in prostate cancer and benign prostate tissues adjacent to cancer in comparison with age matched organ donor prostates. Over 95% alterations of genome methylation occurred in sequences outside CpG islands. Only a small fraction of the methylated CpG islands exerts an impact on RNA expression. Both intragene and pr...

Large scale gene expression (transcriptome) analysis and genome-wide association studies (GWAS) for single nucleotide polymorphisms have generated a considerable amount of gene- and disease-related information, but heterogeneity and various sources of noise have limited the discovery of disease mechanisms. As systematic dataset integration is becoming essential, we developed methods and performed meta-clustering of gene coexpression links in 11 transcriptome studies from postmortem brains of human subjects with major depressive disorder (MDD) and non-psychiatric control subjects. We next sought enrichment in the top 50 meta-analyzed coexpression modules for genes otherwise identified by GWAS for various sets of disorders. One coexpression module of 88 genes was consistently and significantly associated with GWAS for MDD, other neuropsychiatric disorders and brain functions, and for medical illnesses with elevated clinical risk of depression, but not for other diseases. In support of the superior discriminative power of this novel approach, we observed no significant enrichment for GWAS-related genes in coexpression modules extracted from single studies or in meta-modules using gene expression data from non-psychiatric control subjects. Genes in the identified module encode proteins implicated in neuronal signaling and structure, including glutamate metabotropic receptors (GRM1, GRM7), GABA receptors (GABRA2, GABRA4), and neurotrophic and development-related proteins [BDNF, reelin (RELN), Ephrin receptors (EPHA3, EPHA5)]. These results are consistent with the current understanding of molecular mechanisms of MDD and provide a set of putative interacting molecular partners, potentially reflecting components of a functional module across cells and biological pathways that are synchronously recruited in MDD, other brain disorders and MDD-related illnesses. Collectively, this study demonstrates the importance of integrating transcriptome data, gene coexpression modules and GWAS results for providing novel and complementary approaches to investigate the molecular pathology of MDD and other complex brain disorders.

We aimed to identify peripheral blood mononuclear cell (PBMC) gene expression profiles predictive of poor outcomes in idiopathic pulmonary fibrosis (IPF) by performing microarray experiments of PBMCs in discovery and replication cohorts of IPF patients. Microarray analyses identified 52 genes associated with transplant-free survival (TFS) in the discovery cohort. Clustering the microarray samples of the replication cohort using the 52-gene outcome-predictive signature distinguished two patient groups with significant differences in TFS. We studied the pathways associated with TFS in each independent microarray cohort and identified decreased expression of "The costimulatory signal during T cell activation" Biocarta pathway and, in particular, the genes CD28, ICOS, LCK, and ITK, results confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR). A proportional hazards model, including the qRT-PCR expression of CD28, ICOS, LCK, and ITK along with patient's age, gender, and percent predicted forced vital capacity (FVC%), demonstrated an area under the receiver operating characteristic curve of 78.5% at 2.4 months for death and lung transplant prediction in the replication cohort. To evaluate the potential cellular source of CD28, ICOS, LCK, and ITK expression, we analyzed and found significant correlation of these genes with the PBMC percentage of CD4(+)CD28(+) T cells in the replication cohort. Our results suggest that CD28, ICOS, LCK, and ITK are potential outcome biomarkers in IPF and should be further evaluated for patient prioritization for lung transplantation and stratification in drug studies.

Although circulating hormones and inhibitory gamma-aminobutyric acid (GABA)-related factors are known to affect mood, considerable knowledge gaps persist for biological mechanisms underlying the female bias in mood disorders. Here, we combine human and mouse studies to investigate sexual dimorphism in the GABA system in the context of major depressive disorder (MDD) and then use a genetic model to dissect the role of sex-related factors in GABA-related gene expression and anxiety-/depressive-like behaviors in mice. First, using meta-analysis of gene array data in human postmortem brain (N = 51 MDD subjects, 50 controls), we show that the previously reported down-regulation in MDD of somatostatin (SST), a marker of a GABA neuron subtype, is significantly greater in women with MDD. Second, using gene co-expression network analysis in control human subjects (N = 214; two frontal cortex regions) and expression quantitative trait loci mapping (N = 170 subjects), we show that expression of SST and the GABA-synthesizing enzymes glutamate decarboxylase 67 (GAD67) and GAD65 are tightly co-regulated and influenced by X-chromosome genetic polymorphisms. Third, using a rodent genetic model [Four Core Genotypes (FCG) mice], in which genetic and gonadal sex are artificially dissociated (N ≥ 12/group), we show that genetic sex (i.e., X/Y-chromosome) influences both gene expression (lower Sst, Gad67, Gad65 in XY mice) and anxiety-like behaviors (higher in XY mice). This suggests that in an intact male animal, the observed behavior represents the outcomes of male genetic sex increasing and male-like testosterone decreasing anxiety-like behaviors. Gonadal sex was the only factor influencing depressive-like behavior (gonadal males

Background Studies on major depressive and anxiety disorders suggest dysfunctions in brain corticolimbic circuits, including altered gamma-aminobutyric acid (GABA) and modulatory (serotonin and dopamine) neurotransmission. Interestingly, sexual dimorphisms in GABA, serotonin, and dopamine systems are also reported. Understanding the mechanisms behind these sexual dimorphisms may help unravel the biological bases of the heightened female vulnerability to mood disorders. Here, we investigate the contribution of sex-related factors (sex chromosome complement, developmental gonadal sex, or adult circulating hormones) to frontal cortex expression of selected GABA-, serotonin-, and dopamine-related genes. Methods As gonadal sex is determined by sex chromosome complement, the role of sex chromosomes cannot be investigated individually in humans. Therefore, we used the Four Core Genotypes (FCG) mouse model, in which sex chromosome complement and gonadal sex are artificially decoupled, to examine the expression of 13 GABA-related genes, 6 serotonin- and dopamine-related genes, and 8 associated signal transduction genes under chronic stress conditions. Results were analyzed by three-way ANOVA (sex chromosome complement × gonadal sex × circulating testosterone). A global perspective of gene expression changes was provided by heatmap representation and gene co-expression networks to identify patterns of transcriptional activities related to each main factor. Results We show that under chronic stress conditions, sex chromosome complement influenced GABA/serotonin/dopamine-related gene expression in the frontal cortex, with XY mice consistently having lower gene expression compared to XX mice. Gonadal sex and circulating testosterone exhibited less pronounced, more complex, and variable control over gene expression. Across factors, male conditions were associated with a tightly co-expressed set of signal transduction genes. Conclusions Under chronic stress conditions, sex-related factors differentially influence expression of genes linked to mood regulation in the frontal cortex. The main factor influencing expression of GABA-, serotonin-, and dopamine-related genes was sex chromosome complement, with an unexpected pro-disease effect in XY mice relative to XX mice. This effect was partially opposed by gonadal sex and circulating testosterone, although all three factors influenced signal transduction pathways in males. Since GABA, serotonin, and dopamine changes are also observed in other psychiatric and neurodegenerative disorders, these findings have broader implications for the understanding of sexual dimorphism in adult psychopathology.

As the clinical and research focus of chronic obstructive pulmonary disease (COPD) evolves from regarding obstructive lung disease as a single disease entity to recognizing the complexity of disease expression, the importance of COPD phenotyping rises to the forefront. The reclassification of COPD holds both prognostic and therapeutic implications but does not come without issues that may complicate classification efforts. In this review, we discuss the significance of refining the definition of the term phenotype , consider the impact of variations in cohort severity and attribute mix, account for the contrast of longitudinal vs cross-sectional cohort analysis, recognize the differing criteria used to define disease traits along with the nuances of combining cohorts, and identify the interaction of covariates as we advance in the field of COPD phenotyping.

Rationale: Ovarian cancer patients receive platinum/taxane-based chemotherapy as standard of care. Five year survival has remained unchanged for several decades, at less than 45%, pointing to the need for new and improved therapies. We recently reported in vivo anti-tumor efficacy of PD-L1 immune checkpoint blockade in a new transplantable ovarian cancer mouse model. However, chemo-induced effects on ovarian tumor PD-L1 expression have not been addressed. We studied here the effect of platinum/taxane on tumor PD-L1 expression in vitro and in vivo and tested the efficacy of PD-L1 blockade in combination with cisplatin, using several treatment regimens. Methods and Results: Our results demonstrate that in vitro exposure of several human ovarian cancer cell lines, with various baseline susceptibilities to cisplatin and taxol, triggers PD-L1 upregulation. Similar effects were observed when newly tumor-derived, platinum sensitive (2F8) and platinum resistant (2F8cis) murine ovarian cancer cell lines were exposed to chemo drugs in vitro. Based on these findings we postulated that PD-L1 immune checkpoint blockade in combination with cisplatin may provide therapeutic benefit in ovarian cancer. In vivo, we have challenged n = 37 mice IP with 0.8 million 2F8 cells. Tumor-bearing mice were treated with cisplatin, anti-PD-L1 antibody, both drugs or isotype control every two weeks for three doses starting at day 14 post-inoculation. Study endpoint was overall survival. Secondary endpoints were tumor CD8 infiltration, changes in Th1/cytotoxic immunity. Tumors and ascites-derived cancer cells were analyzed with flow cytometry. RNA was extracted from splenocytes and analyzed with Nanostring using probes for n = 511 immune genes. In line with in vitro results, tumor cells isolated ex vivo from cisplatin-treated mice expressed increased PD-L1. Compared to control treated mice, both Cisplatin alone and anti-PD-L1 alone increase overall survival (p = 0.002 and p = 0.02, respectively). No increase in survival was observed in anti-PD-L1/Cisplatin-treated mice, due to an over-responsive immune cascade overwhelming the animal. Addition of celecoxib, a cyclooxygenase-2 inhibitor, to the anti-PD-L1/cisplatin combination was well tolerated and led to improved overall survival. Conclusion: Ovarian cancer cells upregulate PD-L1 in response to chemotherapy exposure in vivo and in vitro. Though effective independently, combination cisplatin and anti-PD-L1 blockade did not improve survival, likely due to cytokine release syndrome. Celecoxib added to cisplatin and anti-PD-L1 improves overall survival. Anti-tumor immunity in responder mice revealed a cytotoxic T cell mediated gene signature. These findings reveal benefits and pitfalls of chemotherapy in combination with immune checkpoint blockade and have high translational potential for ovarian cancer treatment. Citation Format: Shannon Grabosch, Feitianzhi Zeng, Lixin Zhang, Tianzhou Ma, George Tseng, Robert P. Edwards, Anda M. Vlad. Chemo-induced biology of PD-L1 and in vivo combination immune therapy for ovarian cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3208.

Electron transfer dissociation (ETD) is an alternative peptide dissociation method developed in recent years. Compared with the traditional collision induced dissociation (CID) b and y ion formation, ETD generates c and z ions and the backbone cleavage is believed to be less selective. We have reported previously the application of a statistical data mining strategy, K-means clustering, to discover fragmentation patterns for CID, and here we report application of this approach to ETD spectra. We use ETD data sets from digestions with three different proteases. Data analysis shows that selective cleavages do exist for ETD, with the fragmentation patterns affected by protease, charge states, and amino acid residue compositions. It is also noticed that the c(n-1) ion, corresponding to loss of the C-terminal amino acid residue, is statistically strong regardless of the residue at the C-terminus of the peptide, which suggests that the peptide gas phase conformation plays an important role in the dissociation pathways. These patterns provide a basis for mechanism elucidation, spectral prediction, and improvement of ETD peptide identification algorithms.

Sparse k-means clustering (Sparse_kM) can exclude uninformative variables and yield reliable parsimonious clustering results, especially for p>>n. In this work, Sparse_kM and data resampling were combined to identify variables of greatest interest and define confidence levels for the clustering. The method was evaluated by statistical simulation and applied to PiB PET amyloid imaging data to identify normal control (NC) subjects with (+) or without (−) evidence of amyloid, i.e., PiB(+/−). Simulations. A dataset of n=60 observations (3 groups of 20) and p=500 variables was generated for each simulation run; only 50 variables were truly different across groups. The dataset was resampled 20 times, Sparse_kM was applied to each sample and average variable weights were calculated. Probabilities of cluster membership, also called confidence levels, were computed (n=60). Simulations were performed 250 times. The 50 truly different variables were identified by variable weights that were 13–32 times greater than those for the 450 uninformative variables. Human Data. For the PiB PET dataset, images (ECAT HR+, 10–15 mCi, 90 min) were acquired for 64 cognitively normal subjects (74.1±5.4 yrs). Parametric PiB distribution volume ratio images were generated (Logan method, cerebellum reference) and normalized to the MNI template (SPM8) to produce a dataset of n=64 subjects and p=343,099 voxels/image. The dataset was resampled 10 times and Sparse_kM was applied. An average voxel weight image was computed that indicated cortical areas of greatest interest that included precuneus and frontal cortex; these are key areas linked to early amyloid deposition. Seven of 64 subjects were identified as PiB(+) and 47 as PiB(−) with confidence = 90%, where another subject was PiB(+) at lower confidence (80%) and the other 9 subjects were PiB(−) at confidence in the range of 50–70%. In conclusion, Sparse_kM with resampling can help to establish confidence levels for clustering when p>>n and may be a promising method for revealing informative voxels/spatial patterns that distinguish levels of amyloid load, including that at the transitional amyloid +/− boundary.

BACKGROUND: Thyroid papillary microcarcinoma (TPMC) is an incidentally discovered papillary carcinoma that measures ≤1.0 cm in size. Most TPMCs are indolent, whereas some behave aggressively. The objective of the study was to evaluate whether the combination of v-raf murine sarcoma viral oncogene homolog B1 (BRAF) mutation and specific histopathologic features allows risk stratification of TPMC. METHODS: A group aggressive TPMCs was selected based on the presence of lymph node metastasis or tumor recurrence. Another group of nonaggressive tumors included TPMCs matched with the first group for age, sex, and tumor size, but with no extrathyroid spread. A molecular analysis was performed, and histologic slides were scored for multiple histopathologic criteria. A separate validation cohort of 40 TPMCs was evaluated. RESULTS: BRAF mutations were detected in 77% of aggressive TPMCs and in 32% of nonaggressive tumors (P = .001). Several histopathologic features differed significantly between the groups. By using multivariate regression analysis, a molecular-pathologic (MP) score was developed that included BRAF status and 3 histopathologic features: superficial tumor location, intraglandular tumor spread/multifocality, and tumor fibrosis. By adding the histologic criteria to BRAF status, sensitivity was increased from 77% to 96%, and specificity was increased from 68% to 80%. In the independent validation cohort, the MP score stratified tumors into low-risk, moderate-risk, and high-risk groups with the probability of lymph node metastases or tumor recurrence in 0%, 20%, and 60% of patients, respectively. CONCLUSIONS: BRAF status together with several histopathologic features allowed clinical risk stratification of TPMCs. The combined MP risk stratification model was a better predictor of extrathyroid tumor spread than either mutation or histopathologic findings alone. Cancer 2012;. © 2011 American Cancer Society.

Background: Idiopathic Pulmonary Fibrosis (IPF) is a chronic and fatal lung disease with lung transplant as the only available therapy. The course of IPF is variable and unpredictable. In this study we aimed to identify and validate a PBMC gene expression signature indicative of outcome in IPF. Methods: Microarray analysis of PBMC from patients with IPF was performed in two independent cohorts and two different microarray technologies: a derivation cohort of 75 patients at the University of Pittsburgh and a replication cohort of 45 patients at the University of Chicago. Mortality was the major outcome studied. After blood draw and PBMC isolation, RNA was extracted, labeled and hybridized to human gene expression microarrays (Agilent – Pittsburgh, Affymetrix – Chicago). Data was analyzed using BRB Array Tools. A Cox regression model was used to evaluate individual gene association with survival followed by Hierarchical clustering of these genes. Survival gene set analysis was performed to identify pathways associated with survival in both cohorts. For PCR validation of prognostic genes we designed a multi-sample high-throughput qRT-PCR SmarChip (Wafergen) that allowed us to measure their transcripts in the combined cohort of 139 patients. PCR results were adjusted to age, sex and FVC. Results: 2595 genes were associated with survival in the derivation cohort (p

Global expression analyses using microarray technologies are becoming more common in genomic research, therefore, new statistical challenges associated with combining information from multiple studies must be addressed. In this paper we will describe our proposal for an adaptively weighted (AW) statistic to combine multiple genomic studies for detecting differentially expressed genes. We will also present our results from comparisons of our proposed AW statistic to Fisher's equally weighted (EW), Tippett's minimum p-value (minP) and Pearson's (PR) statistics. Due to the absence of a uniformly powerful test, we used a simplified Gaussian scenario to compare the four methods. Our AW statistic consistently produced the best or near-best power for a range of alternative hypotheses. AW-obtained weights also have the additional advantage of filtering discordant biomarkers and providing natural detected gene categories for further biological investigation. Here we will demonstrate the superior performance of our proposed AW statistic based on a mix of power analyses, simulations and applications using data sets for multi-tissue energy metabolism mouse, multi-lab prostate cancer and lung cancer., Comment: Published in at http://dx.doi.org/10.1214/10-AOAS393 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Motivation: Traditional genomic prediction models based on individual genes suffer from low reproducibility across microarray studies due to the lack of robustness to expression measurement noise and gene missingness when they are matched across platforms. It is common that some of the genes in the prediction model established in a training study cannot be matched to another test study because a different platform is applied. The failure of inter-study predictions has severely hindered the clinical applications of microarray. To overcome the drawbacks of traditional gene-based prediction (GBP) models, we propose a module-based prediction (MBP) strategy via unsupervised gene clustering. Results: K-means clustering is used to group genes sharing similar expression profiles into gene modules, and small modules are merged into their nearest neighbors. Conventional univariate or multivariate feature selection procedure is applied and a representative gene from each selected module is identified to construct the final prediction model. As a result, the prediction model is portable to any test study as long as partial genes in each module exist in the test study. We demonstrate that K-means cluster sizes generally follow a multinomial distribution and the failure probability of inter-study prediction due to missing genes is diminished by merging small clusters into their nearest neighbors. By simulation and applications of real datasets in inter-study predictions, we show that the proposed MBP provides slightly improved accuracy while is considerably more robust than traditional GBP. Availability: http://www.biostat.pitt.edu/bioinfo/ Contact: ctseng@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Minichromosome complex maintenance component 7 (MCM7) is a critical component of DNA replication licensing. Amplification and overexpression of MCM7 leads to high rate of prostate cancer metastasis. Recent studies indicate that MCM7 genome encodes a putative 'super-oncogene' cluster including MCM7 oncogene and a miRNA cluster that knocks down the expression of several critical tumor-suppressor genes. In this study, we constructed a vector that constitutively expresses small interference RNA (siRNA) specific for MCM7. Introduction of this vector into prostate cancer cell lines PC3 or Du145 decreases the expression of MCM7 by 80%. The vector inhibits DNA synthesis and generates growth arrest of these cancer cells. Severe combined immunodeficient mice were xenografted PC3 or Du145 tumors, and subsequently treated with this vector through tail vein injection with polyethylenimine. The animals had dramatically smaller tumor volume, less metastasis and better survival rate in comparison with the controls. As a result, intervention of MCM7 expression using siRNA approach may hold the promise for treating androgen refractory prostate cancer.

Lobular carcinoma in situ (LCIS) is considered a risk factor for development of invasive carcinoma (IC). Many variants of LCIS have been described based on pathologic features such as nuclear grade, pleomorphism, and necrosis, but little is known about the biology of these variants. The proposed 3-tier grading system for LCIS has not been validated or endorsed across laboratories. We found significant upstaging of pure pleomorphic LCIS (LCIS with nuclear grade [NG] 3), up to 25% in core needle biopsy (CNB) specimens, in an earlier study. The aim of the current study was to address the importance of pure classical LCIS (NGs 1 and 2) in CNB specimens along with clinicopathologic follow-up. In follow-up resection specimens, IC or ductal carcinoma in situ was seen in 18% (7/39), a high incidence of residual LCIS was seen in 69% (27/39), and other high-risk lesions, such as atypical ductal hyperplasia, were seen in 36% (14/39) of LCIS NG 2 cases. Our study illustrates the importance of grading LCIS; we recommend follow-up excision in LCIS NG 2 cases owing to a high incidence of residual LCIS and the likelihood of identifying other high-risk lesions.

Liver regeneration after a two-thirds partial hepatectomy (PHx) is a complex process requiring interaction and cooperation of many growth factors and cytokines and cross talk between multiple pathways. Along with hepatocyte growth factor and its receptor MET (HGF-MET), the epidermal growth factor receptor (EGFR) signaling pathway is activated within 60 minutes after PHx. To investigate the role of EGFR in liver regeneration, we used two EGFR-specific short hairpin silencing RNAs to inhibit EGFR expression in regenerating normal rat liver. Suppression of EGFR mRNA and protein was evident in treated rats. There was also a demonstrable decrease but not complete elimination of bromo-deoxyuridine incorporation and mitoses at 24 hours after PHx. In addition, we observed up-regulation of MET and Src as well as activation of the ErbB-3-ErbB-2-PI3K-Akt pathway and down-regulation of STAT 3, cyclin D1, cyclin E1, p21, and C/EBP beta. The decrease in the ratio of C/EBP alpha to C/EBP beta known to occur after PHx was offset in shEGFR-treated rats. Despite suppression of hepatocyte proliferation lasting into day 3 after PHx, liver weight restoration occurred. Interestingly, hepatocytes in shEGFR-treated rats were considerably larger when compared with ScrRNA-treated controls. The data indicate that although the MET and EGFR pathways are similar, the contributions made by MET and EGFR are unique and are not compensated by each other or other cytokines.

High-throughput experiments have become more and more prevalent in biomedical research. The resulting high-dimensional data have brought new challenges. Effective data reduction, summarization and visualization are important keys to initial exploration in data mining. In this paper, we introduce a visualization tool, namely a quantile map, to present information contained in a probabilistic distribution. We demonstrate its use as an effective visual analysis tool through the application of a tandem mass spectrometry data set. Information of quantiles of a distribution is presented in gradient colors by concentric doughnuts. The width of the doughnuts is proportional to the Fisher information of the distribution to present unbiased visualization effect. A parametric empirical Bayes (PEB) approach is shown to improve the simple maximum likelihood estimate (MLE) approach when estimating the Fisher information. In the motivating example from tandem mass spectrometry data, multiple probabilistic distributions are to be displayed in two-dimensional grids. A hierarchical clustering to reorder rows and columns and a gradient color selection from a Hue-Chroma-Luminance model, similar to that commonly applied in heatmaps of microarray analysis, are adopted to improve the visualization. Both simulations and the motivating example show superior performance of the quantile map in summarization and visualization of such high-throughput data sets.

Breast cancer in premenopausal women (preM) is frequently associated with worse prognosis compared to that in postmenopausal women (postM) even when controlling for prognostic variables. In particular, preM ER+ tumors have a poor prognosis on endocrine therapy. There is, however, a paucity of studies characterizing molecular alterations in premenopausal tumors, a potential avenue for finding personalized therapies for this group of women. We analyzed gene expression, CNV, methylation, and somatic mutations in tumors from preM (≤45; ER+ n = 110, and ER- n = 39) and postM (≥55, ER+ n = 392, and ER- n = 102) women in The Cancer Genome Atlas (TCGA). Unbiased hierarchical clustering of 2,900 most variably expressed genes (using both RNA-seq and Agilent expression array data) in the whole dataset (n = 643) identified four major subtypes which correlated highly with the PAM50 defined subtypes LumA, LumB, Basal and HER2; however, there wasn't any separation between preM and postM samples. Similarly, principal component analysis using 10,000 genes with the highest inter-quartile range (IQR) demonstrated high similarity across preM and postM samples. Direct examination of gene expression differences between PreM and PostM ER+ tumors using unpaired t-test (5% FDR) identified 3,044 differentially expressed genes. The genes most upregulated in premenopausal tumors included AREG, TFPI2, MSMB, TCN1, and GLRA3. Ingenuity Pathway Analysis revealed a highly significant enrichment for TGFb (p Comparison of methylation (450K Illumina array) between preM and postM ER+ tumors showed a difference in 1% (n = 1,738) of the probes. Genes with the largest difference included ESR1, SIM2, and KLF6. Significant differences in DNA copy number variation (Affymetrix SNP 6.0 array) were also identified in ER+ preM tumors. A number of somatic mutations were significantly enriched in preM ER+ tumors including DSPP and GATA3. Integrated analysis also showed that approximately half of the observed differences in gene expression are driven by CNVs. Conclusion: Our in silico study has identified a number of genes and pathways which are significantly altered between preM and postM ER+ breast cancer. Distinct genetic and epigenetic differences suggest unique etiology for some preM tumors. Currently ongoing Paradigm analysis, and confirmatory studies using METABRIC data are expected to further identify pathways that could specifically be targeted in premenopausal breast cancer. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-05-03.

Objective: Major depressive disorder is a heterogeneous illness with a mostly uncharacterized pathology. Recent gene array attempts to identify the molecular underpinnings of the illness in human postmortem subjects have not yielded a consensus. The authors hypothesized that controlling several sources of clinical and technical variability and supporting their analysis with array results from a parallel study in the unpredictable chronic mild stress (UCMS) rodent model of depression would facilitate identification of the molecular pathology of major depression. Method: Large-scale gene expression was monitored in postmortem tissue from the anterior cingulate cortex and amygdala in paired male subjects with familial major depression and matched control subjects without major depression (N=14–16 pairs). Area dissections and analytical approaches were optimized. Results from the major depression group were compared with those from the UCMS study and confirmed by quantitative polymerase chain reaction and Western blot. Gene coexpression network analysis was performed on transcripts with conserved major depression-UCMS effects. Results: Significant and bidirectional predictions of altered gene expression were identified in amygdala between major depression and the UCMS model of depression. These effects were detected at the group level and also identified a subgroup of depressed subjects with a more homogeneous molecular pathology. This phylogenetically conserved “molecular signature” of major depression was reversed by antidepressants in mice, identified two distinct oligodendrocyte and neuronal phenotypes, and participated in highly cohesive and interactive gene coexpression networks. Conclusions: These studies demonstrate that the biological liability to major depression is reflected in a persistent molecular pathology that affects the amygdala, and support the hypothesis of maladaptive changes in this brain region as a putative primary pathology in major depression.

Flat epithelial atypia (FEA) is an emerging entity of uncertain clinical significance, and outcome data are sparse. The aim of this study was to evaluate the clinicopathologic significance of this entity for proper management. All core needle biopsy (CNB) specimens diagnosed as atypical ductal hyperplasia (ADH) from January 2006 to April 2008 were retrieved. H&E-stained slides of 5 levels on each case were reviewed. The differences in upstaging in subsequent excisions in the FEA and ADH group (31/189 [16.4%]) vs the pure FEA group (5/35 [14%]) and pure FEA (5/35 [14%]) vs pure ADH (5/45 [11%]) were not statistically significant. We observed that FEA evolved into ADH at the same site at an average of 3 to 4 levels. Our study concludes that there is an association of FEA with ADH on multiple levels of CNB specimens, and follow-up surgical excision findings for FEA are clinically significant.

Regressive changes (RC) have been described in malignant melanoma, carcinomas of the prostate and cervix. The presence of RC in these neoplasms may signify some degree of host response to tumor and seems to be a sign of poor prognosis for some neoplasms. RC in breast cancer is vaguely defined in the older literature. We have observed periodically similar RC in a subset of high-grade ductal carcinoma in situ (HGDCIS) in breast specimens. The aim of our study is to demonstrate how to recognize RC in the diagnostic setting and an attempt to understand the biologic behavior in this subset of HGDCIS cases. Fifty-nine cases of HG-DCIS (35 cases with RC and 24 cases without RC) were included. We defined RC in our study as demonstrating thick periductal fibrosis, dense lymphocytic infiltrate, and a thin rim of intact neoplastic cells. A short panel of immunomarkers to study this entity included myoepithelial markers. Reduced expression of myoepithelial markers (p63 and smooth muscle heavy chain myosin) were seen more frequently in the HGDCIS group with RC than without RC cases. Invasion as well as metastatic disease was seen in association with HGDCIS with RC nearly 4 times as often. It is also critically important to recognize HGDCIS-RC for diagnostic purposes, as the differential diagnosis of RC includes, benign associations such as papilloma, fibrocystic changes and periductal mastitis. HGDCIS-RC may also be a sign of an aggressive phenotype than other HGDCIS subtypes. Further outcome studies are necessary to determine if it has a clinical impact akin to other tumors with RC.

In recent years, emerging evidences indicate that epigenetic alteration in the promoter region or the 5’ untranslated region of genes is one of the most common molecular events associated with human malignancies1. To analyze methylation patterns in human malignancies, large numbers of samples are needed. Most of the human issues, however, are in the archived form of formalin-fixed and paraffin embedded (FFPE)blocks. Currently, methylation specific PCR or methylation sequencing requires the usage of DNA samples extracted only from fresh or frozen tissue. DNA extracted from FFPE tissues is of poor quality largely due to containing DNA-DNA and DNA-protein cross-links that survive the DNA extraction process. These cross-links interfere with bisulfite deamination of cytosine, and produce inconsistent, and sometimes, outright false positive results. As a result, there are only sporadic reports of epigenetic analysis of FFPE samples thus far. This greatly limits the usefulness of these samples for epigenetic analysis. A new method that eliminates these cross-links is needed for analyzing the methylation patterns of these samples. In this report, we generate a new method that has significant improvement on reducing the false positive or false negative rate of sodium bisulfite deamination. In this study, we retrieve modifiable genomic DNA by incorporating trypsin digestion, cross-linking reduction, protein striping and DNA denaturing into a four-step process (figure 1a). In the first step, the microdissected FFPE samples are treated with 0.25% trypsin at 37°C for 30 minutes to digest DNA associated proteins and to reduce the complexity of DNA-proteins complex. This process is followed by incubating the sample with lysis buffer (80 mM Tris/HCl pH 6.8, 10 % Mercaptoethanol, 200 mM NaCl, 2 % SDS) in boiling condition for 30 minutes. This step intends to denature protein, to reduce DNA-protein cross-linking, and to remove proteins from genome DNA. The samples are then treated with phenol/chloroform to remove the reduced/dissociated proteins. The DNA is subsequently purified through ammonia acetate/ethanol precipitation. Figure 1 Trypsin and reduction protocol for formalin-fixed and paraffin-embedded samples In order to validate this methodology, we fixed fresh PC-3 cells with formalin and subsequently created a paraffin block containing these PC-3 cells. Five microns thin sections of PC-3 cell block were sliced onto glass slides. The DNA from these sections was extracted using trypsin/reduction protocol. An aliquot of fresh PC-3 cells from the same batch of culture was used as controls to determine the status of gene methylation. The DNA was treated with sodium bisulfite. Methylation PCRs were performed on each DNA preparation using degenerative methylation primers specific for MT1e, MT1h and SOX4. The PCR products from each preparation were ligated into TA cloning vector, and transformed into E.coli. Five colonies from each preparation were analyzed for CpG island methylation status. To evaluate the accuracy of this method, we used the methylation sequencing results from fresh prepared PC-3 cell samples as the standard. When there is a complete concordance between FFPE and fresh samples, the accuracy of the method reaches 100%, and the error rate is 0%. Vice versa, when there is no concordance between FFPE cells and fresh samples, the accuracy will be 0%, and error rate will be 100%. As shown in Table 1, the overall accuracy for trypsin/reduction method is 99.3% for MT1e, 99.4% for MT1h and 100% for SOX4. Trypsin/reduction has only 4 errors in total. To investigate the utility of this method in analyzing methylation status of genes of archived tissues, we selected five samples of FFPE tissues with relatively long storage time (10 to 20 years) and their matched frozen counterpart. Subsequently, we performed methylation PCR and methylation sequencing on these samples as described above. The results from FFPE samples were then compared with those from matched frozen tissues to evaluate the accuracy of the methylation analysis. As shown in table 1, the accuracy rate of cytosine deamination ranges from 97% to 100% for the CpG islands of these 3 genes, appears not to be affected by long storage time. As a result, the trypsin/reduction method presented in the study appears reproducible and reliable for analyzing FFPE archived tissues. In order to compare our method with the existing Heat/alkaline2 and Heat/proteinase K3 methods, we extracted DNA from the FFPE PC3 cell blocks using these methods, and performed methylation analysis on MT1e and MT1h genes. As shown in table 2, the overall error rate of heat/alkaline is 3%, while heat/proteinase K is 9.5%, significantly higher than that of trypsin/reduction method (0.6%, p=0.003 versus heat/alkaline, p=1.7 × 10−12 versus heat/proteinase K). Furthermore, trypsin/reduction method appears to accurately analyze the methylation status of FFPE tissues with long storage time, making this method highly useful in analyzing archived clinical samples. Table 1 Utility of trypsin and reduction method in formalin-fixed and paraffin-embedded tissues Table 2 Detail error rate of methodology

Studies of age-related molecular profiles have separately focused on the human and rodent brains, but the extent to which each organism predicts molecular events across species for the global signature of aging and for specific biological functions has only begun to be characterized. We previously showed that the molecular correlates of aging in the mouse cortex moderately, but significantly, predicted transcript changes in human frontal cortex. Using orthologous gene links between large-scale gene expression datasets, we now report a similar reciprocal human-to-mouse prediction of molecular aging in frontal cortex, but a limited and variable conservation of age-effects across a wide spectrum of biological functions. Thus, the moderate transcriptome correlations and partial functional concordance between late-life human and rodent cohorts (13–77 years in humans and 3–24 months in mice) suggest limitations of the mouse to model normal aging of the human brain cortex.

BackgroundThe Signal-to-Noise-Ratio (SNR) is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs). These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems.Results and discussionTo evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer). For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC), Nearest Mean Classifier (NMC), Support Vector Machine (SVM) classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA) and one-vs-one (OVO) strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the same computational protocols. The dominant genes are usually easy to find while good dormant genes may not always be available as dormant genes require stronger constraints to be satisfied; but when they are available, they can be used for authentication of diagnosis.ConclusionSince GDI based schemes can find a small set of dominant/dormant biomarkers that is adequate to design diagnostic prediction systems, it opens up the possibility of using real-time qPCR assays or antibody based methods such as ELISA for an easy and low cost diagnosis of diseases. The dominant and dormant genes found by GDIs can be used in different ways to design more reliable diagnostic prediction systems.

Background Gene expression data frequently contain missing values, however, most down-stream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures × time series data. We then introduce two complementary selection schemes for determining the most appropriate imputation method for any given data set. Results We found that the optimal imputation algorithms (LSA, LLS, and BPCA) are all highly competitive with each other, and that no method is uniformly superior in all the data sets we examined. The success of each method can also depend on the underlying "complexity" of the expression data, where we take complexity to indicate the difficulty in mapping the gene expression matrix to a lower-dimensional subspace. We developed an entropy measure to quantify the complexity of expression matrixes and found that, by incorporating this information, the entropy-based selection (EBS) scheme is useful for selecting an appropriate imputation algorithm. We further propose a simulation-based self-training selection (STS) scheme. This technique has been used previously for microarray data imputation, but for different purposes. The scheme selects the optimal or near-optimal method with high accuracy but at an increased computational cost. Conclusion Our findings provide insight into the problem of which imputation method is optimal for a given data set. Three top-performing methods (LSA, LLS and BPCA) are competitive with each other. Global-based imputation methods (PLS, SVD, BPCA) performed better on mcroarray data with lower complexity, while neighbour-based methods (KNN, OLS, LSA, LLS) performed better in data with higher complexity. We also found that the EBS and STS schemes serve as complementary and effective tools for selecting the optimal imputation algorithm.

Although tandem mass spectrometry (MS/MS) has become an integral part of proteomics, intensity patterns in MS/MS spectra are rarely weighted heavily in most widely used algorithms because they are not yet fully understood. Here a knowledge mining approach is demonstrated to discover fragmentation intensity patterns and elucidate the chemical factors behind such patterns. Fragmentation intensity information from 28 330 ion trap peptide MS/MS spectra of different charge states and sequences went through unsupervised clustering using a penalized K-means algorithm. Without any prior chemistry assumptions, four clusters with distinctive fragmentation patterns were obtained. A decision tree was generated to investigate peptide sequence motif and charge state status that caused these fragmentation patterns. This data-mining scheme is generally applicable for any large data sets. It bypasses the common prior knowledge constraints and reports on the overall peptide fragmentation behavior. It improves the understanding of gas-phase peptide dissociation and provides a foundation for new or improved protein identification algorithms.

Glutathione peroxidase 3 is a selenium-dependent enzyme playing a critical role in detoxifying reactive oxidative species and maintaining the genetic integrity of mammalian cells. In this report, we found that the expression of glutathione peroxidase 3 (GPx3) was widely inactivated in prostate cancers. Complete inactivation of GPx3 correlates with a poor clinical outcome. Deletions (hemizygous and homozygous) of GPx3 gene are frequent in prostate cancer samples, occurring in 39% of the samples studied. The rate of methylation of the GPx3 exon 1 region in prostate cancer samples reaches 90%. Overexpression of GPx3 in prostate cancer cell lines induced the suppression of colony formation and anchorage-independent growth of PC3, LNCaP, and Du145 cells. PC3 cells overexpressing GPx3 reduced invasiveness in Matrigel transmigration analysis by an average of 2.7-fold. Xenografted PC3 cells expressing GPx3 showed reduction in tumor volume by 4.8-fold, elimination of metastasis (0/16 versus 7/16), and reduction of animal death (3/16 versus 16/16). The tumor suppressor activity of GPx3 seems to relate to its ability to suppress the expression of c-met. The present findings suggest that GPx3 is a novel tumor suppressor gene. [Cancer Res 2007;67(17):8043–50]