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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.

Background As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations. Results We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS A : DE genes with non-zero effect sizes in all studies, (2) HS B : DE genes with non-zero effect sizes in one or more studies and (3) HS r : DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively. Conclusions The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HS A , HS B , and HS r ). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author’s publication website.

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.

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.

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.

Normal aging of the brain differs from pathological conditions and is associated with increased risk for psychiatric and neurological disorders. In addition to its role in the etiology and treatment of mood disorders, altered serotonin (5-HT) signaling is considered a contributing factor to aging; however, no causative role has been identified in aging. We hypothesized that a deregulation of the 5-HT system would reveal its contribution to age-related processes and investigated behavioral and molecular changes throughout adult life in mice lacking the regulatory presynaptic 5-HT(1B) receptor (5-HT(1B)R), a candidate gene for 5-HT-mediated age-related functions. We show that the lack of 5-HT(1B)R (Htr1b(KO) mice) induced an early age-related motor decline and resulted in decreased longevity. Analysis of life-long transcriptome changes revealed an early and global shift of the gene expression signature of aging in the brain of Htr1b(KO) mice. Moreover, molecular changes reached an apparent maximum effect at 18-months in Htr1b(KO) mice, corresponding to the onset of early death in that group. A comparative analysis with our previous characterization of aging in the human brain revealed a phylogenetic conservation of age-effect from mice to humans, and confirmed the early onset of molecular aging in Htr1b(KO) mice. Potential mechanisms appear independent of known central mechanisms (Bdnf, inflammation), but may include interactions with previously identified age-related systems (IGF-1, sirtuins). In summary, our findings suggest that the onset of age-related events can be influenced by altered 5-HT function, thus identifying 5-HT as a modulator of brain aging, and suggesting age-related consequences to chronic manipulation of 5-HT.

Background: Fusion transcripts are formed by either fusion genes (DNA level) or trans-splicing events (RNA level). They have been recognized as a promising tool for diagnosing, subtyping and treating cancers. RNA-seq has become a precise and efficient standard for genome-wide screening of such aberration events. Many fusion transcript detection algorithms have been developed for paired-end RNA-seq data but their performance has not been comprehensively evaluated to guide practitioners. In this paper, we evaluated 15 popular algorithms by their precision and recall trade-off, accuracy of supporting reads and computational cost. We further combine top-performing methods for improved ensemble detection. Results: Fifteen fusion transcript detection tools were compared using three synthetic data sets under different coverage, read length, insert size and background noise, and three real data sets with selected experimental validations. No single method dominantly performed the best but SOAPfuse generally performed well, followed by FusionCatcher and JAFFA. We further demonstrated the potential of a meta-caller algorithm by combining top performing methods to re-prioritize candidate fusion transcripts with high confidence that can be followed by experimental validation. Conclusion: Our result provides insightful recommendations when applying individual tool or combining top performers to identify fusion transcript candidates.

Objectives: Programmed cell death ligand 1 (PD-L1) is an immune checkpoint cell surface molecule expressed on many types of cancers including ovarian cancer. Its interaction with the programmed death (PD-1) receptor on T cells essentially halts the immune response and allows tumor escape. Monoclonal antibodies to either PD-L1 or PD-1 block this interaction and show efficacy in a variety of cancers, though to a lesser degree in ovarian cancer. We have evaluated the efficacy of intraperitoneal anti-PD-L1 therapy in two ovarian cancer mouse models. Methods: Murine 2F8 tumor cells derived from an orthotopic tumor isolated from a triple transgenic MUC1+/−KrasG12D/+PtenloxP/loxP (MKP) mouse as we recently described. MUC1 transgenic (MUC1.Tg) mice received an intraperitoneal inoculation of 8×105 syngeneic 2F8 cells. Untreated mice develop extensive tumor burden and expire around 29 days. Mice were treated with 200 μg anti-PD-L1 antibody (or rat IgG as control) every two weeks for three doses starting at 21 days post-inoculation, thus corresponding with late tumor stage. C57BL/6 mice were inoculated via intraperitoneal (IP) injection with 1×106 IG10 (spontaneously transformed mouse ovarian surface epithelial) tumor cells. Treatment with anti-PD-L1 (or rat IgG as control) started 22 days following inoculation when the mice began showing ascites. Immunohistochemistry for perforin was performed on tumor tissue sections. Immune gene profiling of spleen cells collected at necropsy was performed via Nanostring, using 561 mouse immunology-related genes. Results: The 2F8 tumors are aggressive with little T cell infiltration. The anti-PD-L1 treated MUC1.Tg mice showed substantial infiltration of perforin positive cells within the tumor and significantly increased survival (p = 0.001) compared to isotype control- treated mice. Splenocyte profiling of 561 immune genes via Nanostring revealed a treatment-induced immune gene signature that points to T cell functions and cytotoxic anti-tumor immune responses. A similar gene signature favoring cytotoxic anti-tumor activity was also revealed in IG10 tumor bearing C57Bl/6 mice treated with anti-PD-L1. Conclusion: These preclinical results from two different ovarian cancer tumor models demonstrate that targeting PD-L1 is a viable therapeutic strategy in ovarian cancer and increases survival despite (1) non-immunogenic tumors, (2) late treatment initiation, (3) low dose and administration frequency. The tumors respond well to anti-PD-L1 blockade, due to increased systemic T cell responses and intratumoral T cell accumulation, without changing tumor-specific antibody levels. Our studies have high translational potential and support IP administration of PD-L1 blockers in ovarian cancer patients. Citation Format: Shannon Grabosch, Jyothi T. Mony, Lixin Zhang, Tianzhou Ma, Tejas Tirodkar, Joan Brozick, George Tseng, Esther Elishaev, Robert P. Edwards, Xin Huang, Anda M. Vlad. In vivo efficacy of intraperitoneal anti-PD-L1 therapy in ovarian cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 260. doi:10.1158/1538-7445.AM2015-260

Introduction: Patients with ovarian cancer are usually diagnosed late, when tumors have already spread to the surrounding tissues. Hematogenous dissemination of ovarian cancer cells can also occur and lung metastasis can be found in 12% of ovarian cancer patients. Human mucin1 (MUC1) is a large transmembrane glycoprotein with roles in cell adhesion and migration. More than 80% of ovarian cancer overexpress MUC1, regardless of histology. MUC1 is a widely used vaccine target for numerous adenocarcinomas, including ovarian tumors. Approach and results: We have recently described the triple transgenic (Tg) mice with conditional (cre-loxP) mutations in Kras and Pten pathways that progress to human MUC1-expressing endometrioid ovarian tumors. Using orthotopic tumors from these mice we generated here several new murine cell lines. The MKP-L cell line was derived from an ovarian tumor implant attached to the liver surface. Intraperitoneal (IP) injection of MKP-L cells into syngeneic mice triggers IP tumors and multiple lung metastatic masses. Cells isolated from lung metastases (MKP-Lung cell line) triggered peritoneal tumors as well as lung metastases, suggesting that they retain the original homing capacity. Compared to the parental, endometrioid-looking and low-MUC1 expressing MKP-L cells, the metastatic MKP-Lung cells showed morphological changes consistent to epithelial-to-mesenchymal transition (EMT) and are homogeneously high in MUC1. Mouse WG6 microarray analysis of MKP-L and MKP-Lung cells revealed expression changes of several EMT-associated transcription factors as well as their downstream proteins. Western blot confirmed upregulation of ZEB1, vimentin and downregulation of cytokeratins. To dissect the mechanistic roles of Kras and Pten pathways in EMT and MUC1 regulation, we also generated a series of ovarian surface epithelial (OSE)-derived cell lines with conditional (Cre-loxP) oncogenic mutations in Kras (MKOSE), deletion in Pten (MPOSE) or both (MKPOSE). Using these cell lines we recapitulated in vitro the EMT phenotype and MUC1 upregulation acquired by MKP-Lung cells in vivo. Furthermore, small molecule inhibitors of oncogenic Kras (AZD6244) and Pi3K/Pten pathway (BEZ235) demonstrated that MUC1 expression is significantly upregulated in response to Pten deletion and that Pi3K inhibitor (BEZ235) downregulated MUC1. In contrast, Kras activation slightly lowered MUC1 expression while cell exposure to AZD6244 upregulated MUC1. Conclusions: We report here the first transgenic mouse metastatic ovarian cancer model that expresses human MUC1.Usig this model we identified a gene signature consistent with lung metastasis and EMT and identified mechanisms of MUC1 upregulation during these processes. These findings have important consequences for MUC1 immunobiology in metastatic ovarian tumors with mutations in Kras, Pten/Pi3K or both. Citation Format: Lixin Zhang, Tianzhou Ma, Joan Brozick, Kathlene Babalola, Raluca Budiu, George Tseng, Anda Vlad. Lung metastasis of ovarian cancer in a transplantable MUC1.Tg mouse model is accompanied by upregulation of MUC1 expression and epithelial to mesenchymal transition. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4081. doi:10.1158/1538-7445.AM2015-4081

Objectives: The concept of personalized approaches for recurrence in ovarian cancer has progressed at a modest pace, compared to other cancers. This is partly due to our limited understanding of the local tumor microenvironment during and immediately following chemotherapy-induced tumor involution, which requires sampling of the peritoneal cavity. Most studies to date have focused on chemo-naive tumors and on the more easily accessible peripheral blood samples, yet these are often not reflective of local changes. We hypothesize that sequential sampling and analysis of blood and intraperitoneal (IP) fluid during IP chemotherapy is feasible and will provide molecular insight into chemotherapy-induced systemic and local changes in the tumor environment. Methods: Peritoneal fluid aspiration or wash and blood samples were obtained from patients (n = 13) at three time points (baseline and following the first and second rounds of chemotherapy, respectively) Nanostring was performed to profile miRNA from peritoneal samples and plasma. RNA extraction from peripheral blood mononuclear cells (PBMC) followed by Nanostring was performed to evaluate changes in 511 immune genes during chemotherapy. Flow cytometry for surface and intracellular immune markers was performed on PBMCs. Results: From 13 patients, 30 blood, 12 intraperitoneal fluid (PF), and 20 intraperitoneal wash (PW) samples were obtained. Despite small volumes, cell counts were measurable in 7 of 12 (58%) PF and 11 of 20 (55%) PW samples. Nanostring results revealed 12 immune genes and 29 miRNAs differentially expressed during chemotherapy (p Conclusions: Sampling of the peritoneal cavity through IP catheters during chemotherapy is feasible and can be implemented through standardized protocols. Despite a small sample size, we were able to identify chemotherapy-induced molecular changes in immune genes and miRNAs systemically and loco-regionally. The findings suggest that miRNA alterations may be more suggestive of early tumor microenvironment alterations compared to immune genes in which changes are seen after subsequent cycles of chemotherapy. Larger studies are needed for validation to assess these changes as therapeutic targets. Citation Format: Shannon Grabosch, Anda M. Vlad, Tianzhou Ma, Jyothi T. Mony, Mary Strange, Joan Brozick, Julia Thaller, George Tseng, Xin Huang, Kathleen Moore, Kunle Odunsi, Robert P. Edwards. Prospective profiling of systemic and loco-regional alterations during intraperitoneal chemotherapy for ovarian cancer, results from GOG 271. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 414. doi:10.1158/1538-7445.AM2015-414

This study analyzed gene expression patterns and global genomic alterations in hepatocellular carcinomas (HCC), hepatoblastomas (HPBL), tissue adjacent to HCC and normal liver tissue derived from normal livers and hepatic resections. We found that HCC and adjacent nonneoplastic cirrhotic tissue have considerable overlap in gene expression patterns compared to normal liver. Several genes including Glypican 3, spondin-2, PEG10, EDIL3 and Osteopontin are over-expressed in HCC vs. adjacent tissue whereas Ficolin 3 is the most consistently under-expressed gene. HCC can be subdivided into three clusters based on gene expression patterns. HCC and HPBL have clearly different patterns of gene expression, with genes IGF2, Fibronectin, DLK1, TGFb1, MALAT1 and MIG6 being over-expressed in HPBL versus HCC. In addition, specific areas of the genome appear unstable in HCC, with the same regions undergoing either deletion or increased gene dosage in all HCC.

Motivation: Microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for discovering groups of correlated genes potentially co-regulated or associated to the disease or conditions under investigation. Many clustering methods including hierarchical clustering, K-means, PAM, SOM, mixture model-based clustering and tight clustering have been widely used in the literature. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of these methods. Results: In this paper, six gene clustering methods are evaluated by simulated data from a hierarchical log-normal model with various degrees of perturbation as well as four real datasets. A weighted Rand index is proposed for measuring similarity of two clustering results with possible scattered genes (i.e. a set of noise genes not being clustered). Performance of the methods in the real data is assessed by a predictive accuracy analysis through verified gene annotations. Our results show that tight clustering and model-based clustering consistently outperform other clustering methods both in simulated and real data while hierarchical clustering and SOM perform among the worst. Our analysis provides deep insight to the complicated gene clustering problem of expression profile and serves as a practical guideline for routine microarray cluster analysis. Contact: ctseng@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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 sub-chromosome 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 amplification, 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.

The genomic DNA profiles of prostate cancers with aggressive features were compared to the profiles of matched normal DNA to identify genes that are selectively amplified in the cancer cells. One of the identified genes, MCM7, which is a component of the DNA replication licensing complex, has been studied extensively both at the DNA and protein levels in human prostate tissues. Approximately half of the prostate cancer specimens studied showed MCM7 gene amplification, and 60% of the aggressive prostate cancer specimens had increased MCM7 protein expression. Amplification or overexpression of MCM7 was significantly associated with relapse, local invasion and a worse tumor grade. Constitutive expression of MCM7 in a human prostate cancer cell line, DU145, resulted in markedly increased DNA synthesis and cell proliferation compared to vector-only controls, and an increased cell invasion in vitro. Indeed, MCM7 overexpression produced primary tumors 12 times larger than vector-only controls and resulted in a rapid demise of mice bearing those tumors. These studies implicate MCM7, and the DNA replication licensing gene family, in prostate cancer progression, growth and invasion.

Data mining was performed on 28 330 unique peptide tandem mass spectra for which sequences were assigned with high confidence. By dividing the spectra into different sets based on structural features and charge states of the corresponding peptides, chemical interactions involved in promoting specific cleavage patterns in gas-phase peptides were characterized. Pairwise fragmentation maps describing cleavages at all Xxx-Zzz residue combinations for b and y ions reveal that the difference in basicity between Arg and Lys results in different dissociation patterns for singly charged Arg- and Lys-ending tryptic peptides. While one dominant protonation form (proton localized) exists for Arg-ending peptides, a heterogeneous population of different protonated forms or more facile interconversion of protonated forms (proton partially mobile) exists for Lys-ending peptides. Cleavage C-terminal to acidic residues dominates spectra from singly charged peptides that have a localized proton and cleavage N-terminal to Pro dominates those that have a mobile or partially mobile proton. When Pro is absent from peptides that have a mobile or partially mobile proton, cleavage at each peptide bond becomes much more prominent. Whether the above patterns can be found in b ions, y ions, or both depends on the location of the proton holder(s) in multiply protonated peptides. Enhanced cleavages C-terminal to branched aliphatic residues (Ile, Val, Leu) are observed in both b and y ions from peptides that have a mobile proton, as well as in y ions from peptides that have a partially mobile proton; enhanced cleavages N-terminal to these residues are observed in b ions from peptides that have a partially mobile proton. Statistical tools have been designed to visualize the fragmentation maps and measure the similarity between them. The pairwise cleavage patterns observed expand our knowledge of peptide gas-phase fragmentation behaviors and may be useful in algorithm development that employs improved models to predict fragment ion intensities.

Microarray experiments have been widely used to screen biological activities and cellular changes under different conditions at molecular level. Its ability to simultaneously monitor expression changes of thousands of genes has acquired its popularity but, at the same time, posed many challenging statistical and computational problems. Gene clustering problem is one of them. The purpose of gene clustering is to search for groups of genes with similar expression patterns, which likely have related biological functions or interactions. In this paper, popular gene clustering methods, including hierarchical clustering, AT-means, self-organizing maps, CLICK, model-based clustering and tight clustering, are introduced and compared with simulated data and a real data. The result shows the superior ability of tight clustering to deal with complex structure of microarray data. All the other methods encounter difficult problems of either scattered genes, local optimization or estimation of number of clusters. Tight clustering, on the other hand, provides an alternative to avoid these difficulties and systematically produce a sequence of tight clusters.

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.

In this paper we propose a method for clustering that produces tight and stable clusters without forcing all points into clusters. Many existing clustering algorithms have been applied in microarray data to search for gene clusters with similar expression patterns. However, none has provided a way to deal with an essential feature of array data: many genes are expressed sporadically and do not belong to any of the significant biological functions (clusters) of interest. In fact, most current algorithms aim to assign all genes into clusters. For many biological studies, however, we are mainly interested in the most informative, tight and stable clusters with sizes of, say, 20-60 genes for farther investigation. Tight Clustering has been developed specifically to address this problem. 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 the expression profiles of the Drosophila life cycle. The result is shown to better serve biological needs in microarray analysis.

Despite similar clinical endpoints, heart failure resulting from dilated cardiomyopathy (DCM) or hypertrophic cardiomyopathy (HCM) appears to develop through different remodeling and molecular pathways. Current understanding of heart failure has been facilitated by microarray technology. We constructed an in-house spotted cDNA microarray using 10,272 unique clones from various cardiovascular cDNA libraries sequenced and annotated in our laboratory. RNA samples were obtained from left ventricular tissues of precardiac transplantation DCM and HCM patients and were hybridized against normal adult heart reference RNA. After filtering, differentially expressed genes were determined using novel analyzing software. We demonstrated that normalization for cDNA microarray data is slide-dependent and nonlinear. The feasibility of this model was validated by quantitative real-time reverse transcription-PCR, and the accuracy rate depended on the fold change and statistical significance level. Our results showed that 192 genes were highly expressed in both DCM and HCM (e.g., atrial natriuretic peptide, CD59, decorin, elongation factor 2, and heat shock protein 90), and 51 genes were downregulated in both conditions (e.g., elastin, sarcoplasmic/endoplasmic reticulum Ca2+-ATPase). We also identified several genes differentially expressed between DCM and HCM (e.g., αB-crystallin, antagonizer of myc transcriptional activity, β-dystrobrevin, calsequestrin, lipocortin, and lumican). Microarray technology provides us with a genomic approach to explore the genetic markers and molecular mechanisms leading to heart failure.

Introduction: Endometriosis is a largely benign, chronic inflammatory disease defined by the presence of endometrial-like glands surrounded by stroma. Epidemiologic studies suggest that endometriosis is an independent risk factor for endometrioid and clear cell epithelial ovarian tumors, collectively called endometriosis-associated ovarian cancers (EAOC). Histopathology findings demonstrate that EAOC occur in the presence of atypical endometriosis (AE), often found in direct continuity with the tumor, suggesting AE as the transitioning entity from benign lesions to malignant variants. Although it is widely accepted that chronic inflammation drives cancer, immune deregulation in chronic precursor lesions to ovarian cancer have not been studied in a systematic way. Experimental procedure: We extracted RNA from 135 paraffin tissue blocks comprising of normal endometrium (n=32), benign endometriosis consisting of ovarian and extra ovarian endometriosis cases (n=30,) atypical endometriosis (n=15) and EAOC, (n=43). Serous tumors (n=15) were included as non-endometriosis associated controls. Using Nanostring and the nCounter® GX Human Immunology Kit, we profiled a total of 511 immune genes. Cluster analyses and differential expressions (DE) were calculated using EdgeR. Protein expression of candidate proteins was validated with immunohistochemistry (IHC). To identify mechanisms of complement activation during early stages of genomic instability, we used murine cell lines derived from mice with conditional mutations in Kras and Pten pathways. Results: Nanostring immune gene expression profile eveal the predominant role of adaptive immunity and of the adaptive-innate immune cross-talk. The complement pathway was most prominently expressed, suggesting its roles as one of the major immune pathways involved in the transition from chronic endometriosis to atypical (premalignant) endometriosis and to EAOC. Complement pathway genes were upregulated in human endometriosis and atypical endometriosis. Tissue deposition of several complement components and changes in peripheral blood were confirmed by IHC and ELISA, respectively. Genomic instability in murine ovarian cancer cell lines with conditional mutations resulted in upregulation of complement genes expression in epithelial cells, but without an advantage on cell death. These findings further support a paradigm shift on complement roles in cancer, suggesting its pro-tumorigenic roles. Conclusions: We performed the first comprehensive gene profile of the tissue immune microenvironment in benign endometriosis, EAOC and precursor lesions and identified the previously unrecognized roles for the complement pathway. Complement activation may be an early trigger of inflammation and an early link between epithelium and immune environment. These findings have high translational potential for immune therapy and prevention in EAOC. Citation Format: Swati Maruti Suryawanshi, Xin Huang, Raluca Budiu, SungHwan Kim, George Tseng, Esther Elishaev, Marcia Klein-Patel, Ted Lee, Suketu Mansuria, Robert Edwards, Anda Vlad. Complement roles in endometriosis and endometriosis-associated ovarian cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1653. doi:10.1158/1538-7445.AM2014-1653

Detection of human genome copy number variation (CNV) is one of the most important analyses in diagnosing human malignancies. Genome CNV detection in formalin-fixed and paraffin-embedded (FFPE) tissues remains challenging due to suboptimal DNA quality and failure to use appropriate baseline controls for such tissues. Here, we report a modified method in analyzing CNV in FFPE tissues using microarray with Affymetrix Cytoscan HD chips. Gel purification was applied to select DNA with good quality and data of fresh frozen and FFPE tissues from healthy individuals were included as baseline controls in our data analysis. Our analysis showed a 91% overlap between CNV detection by microarray with FFPE tissues and chromosomal abnormality detection by karyotyping with fresh tissues on 8 cases of lymphoma samples. The CNV overlap between matched frozen and FFPE tissues reached 93.8%. When the analyses were restricted to regions containing genes, 87.1% concordance between FFPE and fresh frozen tissues was found. The analysis was further validated by Fluorescence In Situ Hybridization on these samples using probes specific for BRAF and CITED2. The results suggested that the modified method using Affymetrix Cytoscan HD chip gave rise to a significant improvement over most of the previous methods in terms of accuracy in detecting CNV in FFPE tissues. This FFPE microarray methodology may hold promise for broad application of CNV analysis on clinical samples.

We consider the problem of comparing the gene expression levels of cells grown under two different conditions using cDNA microarray data. We use a quality index, computed from duplicate spots on the same slide, to filter out outlying spots, poor quality genes and problematical slides. We also perform calibration experiments to show that normalization between fluorescent labels is needed and that the normalization is slide dependent and non-linear. A rank invariant method is suggested to select non-differentially expressed genes and to construct normalization curves in comparative experiments. After normalization the residuals from the calibration data are used to provide prior information on variance components in the analysis of comparative experiments. Based on a hierarchical model that incorporates several levels of variations, a method for assessing the significance of gene effects in comparative experiments is presented. The analysis is demonstrated via two groups of experiments with 125 and 4129 genes, respectively, in Escherichia coli grown in glucose and acetate.