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Objectives: Inhibition of pregnancy-associated plasma protein-A (PAPP-A), an upstream activator of the insulin-like growth factor (IGF) pathway, is known to augment sensitivity to platinum-based chemotherapy. This study further tests the efficacy of PAPP-A inhibition with a monoclonal antibody inhibitor (mAb-PA) in ovarian cancer (OC) platinum-resistant patient-derived xenograft (PDX) models., Methods: PAPP-A expression was quantitated in platinum-resistant PDX models by ELISA. A subset with High (n = 5) and Low (n = 2) expression were revived in female SCID/beige mice for studies with either saline, carboplatin/paclitaxel (CP) + mAb-PA, or CP + IgG2a. The primary endpoint was tumor area by ultrasound on day 28 relative to baseline. Conversion to platinum-sensitive was defined by average tumor regression below baseline. Statistical analyses included linear mixed effects modeling and Kaplan Meier curves. Response to therapy was correlated with changes in the ratio of phosphorylated/total AKT and ERK 1/2 using Wes analysis., Results: The addition of mAb-PA to CP induced tumor regression below baseline in one High PAPP-A PDX model; another three models exhibited notable growth inhibition relative to CP + IgG2a. None of the Low PAPP-A PDX models regressed below baseline. The PDX model with the greatest magnitude of tumor regression from baseline after combination therapy was maintained on single agent mAb-PA or IgG2a, but no benefit was observed. Decreased phosphorylation of ERK1/2 correlated with conversion to platinum-sensitive., Conclusions: The addition of mAb-PA to CP overcame platinum-resistance in one of five High PAPP-A PDX models; three other models demonstrated improved platinum-response. This supports further clinical development of this novel therapeutic., Competing Interests: Dr. Cheryl A. Conover holds a patent(US 8,653,020) on the monoclonal antibody which can selective exosite inhibition of PAPP-A activity against IGFBP-4). This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: There are insufficient system-wide transcriptomic (or other) data that help explain the observed inter-individual variability in antibody titers after measles vaccination in otherwise healthy individuals., Methods: We performed a transcriptome(mRNA-Seq)-profiling study after in vitro viral stimulation of PBMCs from 30 measles vaccine recipients, selected from a cohort of 764 schoolchildren, based on the highest and lowest antibody titers. We used regression and network biology modeling to define markers associated with neutralizing antibody response., Results: We identified 39 differentially expressed genes that demonstrate significant differences between the high and low antibody responder groups (p-value≤0.0002, q-value≤0.092), including the top gene CD93 (p<1.0E-13, q<1.0E-09), encoding a receptor required for antigen-driven B-cell differentiation, maintenance of immunoglobulin production and preservation of plasma cells in the bone marrow. Network biology modeling highlighted plasma cell survival (CD93, IL6, CXCL12), chemokine/cytokine activity and cell-cell communication/adhesion/migration as biological processes associated with the observed differential response in the two responder groups., Conclusion: We identified genes and pathways that explain in part, and are associated with, neutralizing antibody titers after measles vaccination. This new knowledge could assist in the identification of biomarkers and predictive signatures of protective immunity that may be useful in the design of new vaccine candidates and in clinical studies.

Although many diseases and traits show large heritability, few genetic variants have been found to strongly separate phenotype groups by genotype. Complex regulatory networks of variants and expression of multiple genes lead to small individual-variant effects and difficulty replicating the effect of any single variant in an affected pathway. Interaction network modeling of GWAS identifies effects ignored by univariate models, but population differences may still cause specific genes to not replicate. Integrative network models may help detect indirect effects of variants in the underlying biological pathway. In this study, we used gene-level functional interaction information from the Integrative Multi-species Prediction (IMP) tool to reveal important genes associated with a complex phenotype through evidence from epistasis networks and pathway enrichment. We test this method for augmenting variant-based network analyses with functional interactions by applying it to a smallpox vaccine immune response GWAS. The integrative analysis spotlights the role of genes related to retinoid X receptor alpha (RXRA), which has been implicated in a previous epistasis network analysis of smallpox vaccine.

Failure to achieve a protected state after influenza vaccination is poorly understood but occurs commonly among aged populations experiencing greater immunosenescence. In order to better understand immune response in the elderly, we studied epigenetic and transcriptomic profiles and humoral immune response outcomes in 50-74 year old healthy participants. Associations between DNA methylation and gene expression reveal a system-wide regulation of immune-relevant functions, likely playing a role in regulating a participant's propensity to respond to vaccination. Our findings show that sites of methylation regulation associated with humoral response to vaccination impact known cellular differentiation signaling and antigen presentation pathways. We performed our analysis using per-site and regionally average methylation levels, in addition to continuous or dichotomized outcome measures. The genes and molecular functions implicated by each analysis were compared, highlighting different aspects of the biologic mechanisms of immune response affected by differential methylation. Both cis-acting (within the gene or promoter) and trans-acting (enhancers and transcription factor binding sites) sites show significant associations with measures of humoral immunity. Specifically, we identified a group of CpGs that, when coordinately hypo-methylated, are associated with lower humoral immune response, and methylated with higher response. Additionally, CpGs that individually predict humoral immune responses are enriched for polycomb-group and FOXP2 transcription factor binding sites. The most robust associations implicate differential methylation affecting gene expression levels of genes with known roles in immunity (e.g. HLA-B and HLA-DQB2) and immunosenescence. We believe our data and analysis strategy highlight new and interesting epigenetic trends affecting humoral response to vaccination against influenza; one of the most common and impactful viral pathogens.

Background: Although influenza causes significant morbidity and mortality in the elderly, the factors underlying the reduced vaccine immunogenicity and efficacy in this age group are not completely understood. Age and immunosenescence factors, and their impact on humoral immunity after influenza vaccination, are of growing interest for the development of better vaccines for the elderly., Methods: We assessed associations between age and immunosenescence markers (T cell receptor rearrangement excision circles - TREC content, peripheral white blood cell telomerase - TERT expression and CD28 expression on T cells) and influenza A/H1N1 vaccine-induced measures of humoral immunity in 106 older subjects at baseline and three timepoints post-vaccination., Results: TERT activity (TERT mRNA expression) was significantly positively correlated with the observed increase in the influenza-specific memory B cell ELISPOT response at Day 28 compared to baseline (p-value=0.025). TREC levels were positively correlated with the baseline and early (Day 3) influenza A/H1N1-specific memory B cell ELISPOT response (p-value=0.042 and p-value=0.035, respectively). The expression and/or expression change of CD28 on CD4+ and/or CD8+ T cells at baseline and Day 3 was positively correlated with the influenza A/H1N1-specific memory B cell ELISPOT response at baseline, Day 28 and Day 75 post-vaccination. In a multivariable analysis, the peak antibody response (HAI and/or VNA at Day 28) was negatively associated with age, the percentage of CD8+CD28 low T cells, IgD+CD27- naïve B cells, and percentage overall CD20- B cells and plasmablasts, measured at Day 3 post-vaccination. The early change in influenza-specific memory B cell ELISPOT response was positively correlated with the observed increase in influenza A/H1N1-specific HAI antibodies at Day 28 and Day 75 relative to baseline (p-value=0.007 and p-value=0.005, respectively)., Conclusion: Our data suggest that influenza-specific humoral immunity is significantly influenced by age, and that specific markers of immunosenescence (e.g., the baseline/early expression of CD28 on CD4+ and/or CD8+ T cells and T cell immune abnormalities) are correlated with different humoral immune response outcomes observed after vaccination in older individuals, and thus can be potentially used to predict vaccine immunogenicity.

Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k) for each gene to optimize the Relief-F test statistics (importance scores) for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak) Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to detect both main effects and interaction effects. Software Availability: http://insilico.utulsa.edu/ReliefSeq.php.

Ovarian cancer is an immune reactive malignancy with a complex immune suppressive network that blunts successful immune eradication. This suppressive microenvironment may be mediated by recruitment or induction of CD4(+) regulatory T cells (Tregs). Our study sought to investigate the association of tumor-infiltrating CD4(+)CD25(+)FOXP3(+) Tregs, and other immune factors, with clinical outcome in serous ovarian cancer patients. We performed immunofluorescence and quantification of intraepithelial tumor-infiltrating triple positive Tregs (CD4(+)CD25(+)FOXP3(+)), as well as CD4(+)CD25(+)FOXP3(-), CD3(+) and CD8(+) T cells in tumor specimens from 52 patients with high stage serous ovarian carcinoma. Thirty-one of the patients had good survival (i.e. > 60 months) and 21 had poor survival of < 18 months. Total cell counts as well as cell ratios were compared among these two outcome groups. The total numbers of CD4(+)CD25(+)FOXP3(+) Tregs, CD4(+)CD25(+)FOXP3(-), CD3(+) and CD8(+) cells were not significantly different between the groups. However, higher ratios of CD8(+)/CD4(+)CD25(+)FOXP3(+) Treg, CD8(+)/CD4(+) and CD8/CD4(+)CD25(+)FOXP3(-) cells were seen in the good outcome group when compared to the patients with poor outcome. These data show for the first time that the ratios of CD8(+) to both CD4(+)CD25(+)FOXP3(+) Tregs and CD4(+)CD25(+)FOXP3(-) T cells are associated with disease outcome in ovarian cancer. The association being apparent in ratios rather than absolute count of T cells suggests that the effector/suppressor ratio may be a more important indicator of outcome than individual cell count. Thus, immunotherapy strategies that modify the ratio of CD4(+)CD25(+)FOXP3(+) Tregs or CD4(+)CD25(+)FOXP3(-) T cells to CD8(+) effector cells may be useful in improving outcomes in ovarian cancer.

Immune responses to current rubella vaccines demonstrate significant inter-individual variability. We performed mRNA-Seq profiling on PBMCs from high and low antibody responders to rubella vaccination to delineate transcriptional differences upon viral stimulation. Generalized linear models were used to assess the per gene fold change (FC) for stimulated versus unstimulated samples or the interaction between outcome and stimulation. Model results were evaluated by both FC and p-value. Pathway analysis and self-contained gene set tests were performed for assessment of gene group effects. Of 17,566 detected genes, we identified 1,080 highly significant differentially expressed genes upon viral stimulation (p<1.00E(-15), FDR<1.00E(-14)), including various immune function and inflammation-related genes, genes involved in cell signaling, cell regulation and transcription, and genes with unknown function. Analysis by immune outcome and stimulation status identified 27 genes (p≤0.0006 and FDR≤0.30) that responded differently to viral stimulation in high vs. low antibody responders, including major histocompatibility complex (MHC) class I genes (HLA-A, HLA-B and B2M with p = 0.0001, p = 0.0005 and p = 0.0002, respectively), and two genes related to innate immunity and inflammation (EMR3 and MEFV with p = 1.46E(-08) and p = 0.0004, respectively). Pathway and gene set analysis also revealed transcriptional differences in antigen presentation and innate/inflammatory gene sets and pathways between high and low responders. Using mRNA-Seq genome-wide transcriptional profiling, we identified antigen presentation and innate/inflammatory genes that may assist in explaining rubella vaccine-induced immune response variations. Such information may provide new scientific insights into vaccine-induced immunity useful in rational vaccine development and immune response monitoring.

MicroRNAs play a role in regulating diverse biological processes and have considerable utility as molecular markers for diagnosis and monitoring of human disease. Several technologies are available commercially for measuring microRNA expression. However, cross-platform comparisons do not necessarily correlate well, making it difficult to determine which platform most closely represents the true microRNA expression level in a tissue. To address this issue, we have analyzed RNA derived from cell lines, as well as fresh frozen and formalin-fixed paraffin embedded tissues, using Affymetrix, Agilent, and Illumina microRNA arrays, NanoString counting, and Illumina Next Generation Sequencing. We compared the performance within- and between the different platforms, and then verified these results with those of quantitative PCR data. Our results demonstrate that the within-platform reproducibility for each method is consistently high and although the gene expression profiles from each platform show unique traits, comparison of genes that were commonly detectable showed that detection of microRNA transcripts was similar across multiple platforms.

Although ovarian cancer is the most lethal of gynecologic malignancies, wide variation in outcome following conventional therapy continues to exist. The presence of tumor-infiltrating regulatory T cells (Tregs) has a role in outcome of this disease, and a growing body of data supports the existence of inherited prognostic factors. However, the role of inherited variants in genes encoding Treg-related immune molecules has not been fully explored. We analyzed expression quantitative trait loci (eQTL) and sequence-based tagging single nucleotide polymorphisms (tagSNPs) for 54 genes associated with Tregs in 3,662 invasive ovarian cancer cases. With adjustment for known prognostic factors, suggestive results were observed among rarer histological subtypes; poorer survival was associated with minor alleles at SNPs in RGS1 (clear cell, rs10921202, p=2.7×10(-5)), LRRC32 and TNFRSF18/TNFRSF4 (mucinous, rs3781699, p=4.5×10(-4), and rs3753348, p=9.0×10(-4), respectively), and CD80 (endometrioid, rs13071247, p=8.0×10(-4)). Fo0r the latter, correlative data support a CD80 rs13071247 genotype association with CD80 tumor RNA expression (p=0.006). An additional eQTL SNP in CD80 was associated with shorter survival (rs7804190, p=8.1×10(-4)) among all cases combined. As the products of these genes are known to affect induction, trafficking, or immunosuppressive function of Tregs, these results suggest the need for follow-up phenotypic studies.

Pancreatic adenocarcinoma (PaC) is one of most difficult tumors to treat. Much of this is attributed to the late diagnosis. To identify biomarkers for early detection, we examined DNA methylation differences in leukocyte DNA between PaC cases and controls in a two-phase study. In phase I, we measured methylation levels at 1,505 CpG sites in treatment-naïve leukocyte DNA from 132 never-smoker PaC patients and 60 never-smoker healthy controls. We found significant differences in 110 CpG sites (false discovery rate <0.05). In phase II, we tested and validated 88 of 96 phase I selected CpG sites in 240 PaC cases and 240 matched controls (p≤0.05). Using penalized logistic regression, we built a prediction model consisting of five CpG sites (IL10&#95;P348, LCN2&#95;P86, ZAP70&#95;P220, AIM2&#95;P624, TAL1&#95;P817) that discriminated PaC patients from controls (C-statistic = 0.85 in phase I; 0.76 in phase II). Interestingly, one CpG site (LCN2&#95;P86) alone could discriminate resectable patients from controls (C-statistic= 0.78 in phase I; 0.74 in phase II). We also performed methylation quantitative trait loci (methQTL) analysis and identified three CpG sites (AGXT&#95;P180&#95;F, ALOX12&#95;E85&#95;R, JAK3&#95;P1075&#95;R) where the methylation levels were significantly associated with single nucleotide polymorphisms (SNPs) (false discovery rate <0.05). Our results demonstrate that epigenetic variation in easily obtainable leukocyte DNA, manifested by reproducible methylation differences, may be used to detect PaC patients. The methylation differences at certain CpG sites are partially attributable to genetic variation. This study strongly supports future epigenome-wide association study using leukocyte DNA for biomarker discovery in human diseases.

Changes in miRNA expression are a common feature in colon cancer. Those changes occurring in the transition from normal to adenoma and from adenoma to carcinoma, however, have not been well defined. Additionally, miRNA changes among tumor subgroups of colon cancer have also not been adequately evaluated. In this study, we examined the global miRNA expression in 315 samples that included 52 normal colonic mucosa, 41 tubulovillous adenomas, 158 adenocarcinomas with proficient DNA mismatch repair (pMMR) selected for stage and age of onset, and 64 adenocarcinomas with defective DNA mismatch repair (dMMR) selected for sporadic (n = 53) and inherited colon cancer (n = 11). Sporadic dMMR tumors all had MLH1 inactivation due to promoter hypermethylation. Unsupervised PCA and cluster analysis demonstrated that normal colon tissue, adenomas, pMMR carcinomas and dMMR carcinomas were all clearly discernable. The majority of miRNAs that were differentially expressed between normal and polyp were also differentially expressed with a similar magnitude in the comparison of normal to both the pMMR and dMMR tumor groups, suggesting a stepwise progression for transformation from normal colon to carcinoma. Among the miRNAs demonstrating the largest fold up- or down-regulated changes (≥4), four novel (miR-31, miR-1, miR-9 and miR-99a) and two previously reported (miR-137 and miR-135b) miRNAs were identified in the normal/adenoma comparison. All but one of these (miR-99a) demonstrated similar expression differences in the two normal/carcinoma comparisons, suggesting that these early tumor changes are important in both the pMMR- and dMMR-derived cancers. The comparison between pMMR and dMMR tumors identified four miRNAs (miR-31, miR-552, miR-592 and miR-224) with statistically significant expression differences (≥2-fold change).

Background: Expression level of many genes shows abundant natural variation in human populations. The variations in gene expression are believed to contribute to phenotypic differences. Emerging evidence has shown that microRNAs (miRNAs) are one of the key regulators of gene expression. However, past studies have focused on the miRNA target genes and used loss- or gain-of-function approach that may not reflect natural association between miRNA and mRNAs., Methodology/principal Findings: To examine miRNA regulatory effect on global gene expression under endogenous condition, we performed pair-wise correlation coefficient analysis on expression levels of 366 miRNAs and 14,174 messenger RNAs (mRNAs) in 90 immortalized lymphoblastoid cell lines, and observed significant correlations between the two species of RNA transcripts. We identified a total of 7,207 significantly correlated miRNA-mRNA pairs (false discovery rate q<0.01). Of those, 4,085 pairs showed positive correlations while 3,122 pairs showed negative correlations. Gene ontology analyses on the miRNA-correlated genes revealed significant enrichments in several biological processes related to cell cycle, cell communication and signal transduction. Individually, each of three miRNAs (miR-331, -98 and -33b) demonstrated significant correlation with the genes in cell cycle-related biological processes, which is consistent with important role of miRNAs in cell cycle regulation., Conclusions/significance: This study demonstrates feasibility of using naturally expressed transcript profiles to identify endogenous correlation between miRNA and miRNA. By applying this genome-wide approach, we have identified thousands of miRNA-correlated genes and revealed potential role of miRNAs in several important cellular functions. The study results along with accompanying data sets will provide a wealth of high-throughput data to further evaluate the miRNA-regulated genes and eventually in phenotypic variations of human populations.