Power and sample size for DNA microarray studies

Extensive plasma proteomic profiling revealed receptor activator of nuclear factor kappa-Β ligand (RANKL) as emerging biomarker of nivolumab clinical benefit in patients with metastatic renal cell carcinoma

Background

The advent of immune checkpoint inhibitors (ICIs) have led to a paradigm change in the management of metastatic renal cell carcinoma (mRCC), nevertheless, the benefit of treatment is confined to a limited proportion of patients. Therefore, the identification of predictive biomarkers for response to ICIs represents an unmet clinical need. Here, we performed a large-scale plasma proteomic profile of patients with mRCC, treated with nivolumab, to identify soluble molecules potentially associated with clinical benefit.

Methods

We analyzed the levels of 507 soluble molecules in the pretreatment plasma of 16 patients with mRCC (discovery set) who received nivolumab therapy as a single agent. The ELISA assay was performed to confirm the protein level of candidate biomarkers associated to clinical benefit in 15 patients with mRCC (validation set). Survival curves of complete cohort were estimated by the Kaplan-Meier method and compared with the log-rank test.

Results

Out of 507 screened molecules, 135 factors were selected as expressed above background and 12 of them were significantly overexpressed in patients who did not benefit from treatment (non-responders (NR)) compared with responders (R) group. After multiplicity adjustment, receptor activator of nuclear factor kappa-Β ligand (RANKL) was the only molecule that retained the statistical significance (false discovery rate: 0.023). RANKL overexpression in NR patients was confirmed both in discovery (median NR: 528 pg/mL vs median R: 288 pg/mL, p=0.011) and validation set (median NR: 440 pg/mL vs median R: 253 pg/mL, p<0.001). Considering the complete cohort of patients (discovery+validation set), significantly higher RANKL levels were found in patients who primarily progressed from treatment compared with those who had a partial response (p=0.003) or stable disease (p=0.006). Moreover, patients with low RANKL levels had significant improvements in progression-free survival (median 14.0 months vs 3.4 months, p=0.004) and overall survival (median not reached vs 30.1 months, p=0.003).

Conclusions

Our exploratory study suggests RANKL as a novel independent biomarker of response and survival in patients with mRCC treated with nivolumab.

The Endometrial Transcriptome of Metabolic and Inflammatory Pathways During the Window of Implantation Is Deranged in Infertile Obese Polycystic Ovarian Syndrome Women

Introduction and Aim: Obese women with polycystic ovarian syndrome (PCOS) have a reduced rate of spontaneous conception even when their cycles are ovulatory. Endometrial receptivity is an important factor for poor implantation and increased miscarriage rates. Mechanisms in which both pathologies modify the endometrium are not fully clarified. The aim of our study was to compare the endometrial transcriptomic profiles between infertile obese PCOS (O-PCOS) women and infertile normal weight subjects during the window of implantation in ovulatory menstrual cycles. Methods: We conducted a prospective transcriptomic analysis of the endometrium using RNA sequencing. In this way, potential endometrial mechanisms leading to the poor reproductive outcome in O-PCOS patients could be characterized. Endometrial samples during days 21-23 of the menstrual cycle were collected from infertile O-PCOS women (n = 11) and normal weight controls (n = 10). Subgroups were defined according to the ovulatory/anovulatory status in the natural cycles, and O-PCOS women were grouped into the O-PCOS ovulatory (O-PCOS-ovul) subgroup. RNA isolation, sequencing with library reparation, and subsequent RNAseq data analysis were performed. Results: Infertile O-PCOS patients had 610 differentially expressed genes (DEGs), after adjustment for multiple comparisons with normal weight infertile controls, related to obesity (MXRA5 and ECM1), PCOS (ADAMTS19 and SLC18A2), and metabolism (VNN1 and PC). In the ovulatory subgroup, no DEGs were found, but significant differences in canonical pathways and the upstream regulator were revealed. According to functional and upstream analyses of ovulatory subgroup comparisons, the most important biological processes were related to inflammation (TNFR1 signaling), insulin signaling (insulin receptor signaling and PI3/AKT), fatty acid metabolism (stearate biosynthesis I and palmitate biosynthesis I), and lipotoxicity (unfolded protein response pathway). Conclusions: We demonstrated that endometrial transcription in ovulatory O-PCOS patients is deranged in comparison with the control ovulatory endometrium. The most important pathways of differentiation include metabolism and inflammation. These processes could also represent potential mechanisms for poor embryo implantation, which prevent the development of a successful pregnancy. ClinicalTrials.gov ID: NCT03353948.

Large−Scale Profiling of Extracellular Vesicles Identified miR−625−5p as a Novel Biomarker of Immunotherapy Response in Advanced Non−Small−Cell Lung Cancer Patients

Simple Summary

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of advanced non-small-cell lung cancer (NSCLC) leading to substantial improvement in survival time and quality of life. Nevertheless, the clinical benefit of treatment is still limited to a minority of patients, reflecting the need to identify novel noninvasive biomarkers to improve patient selection. Currently available markers such as PD-L1 expression have important limitations. In this study, we focused on extracellular vesicles (EV)-associated miRNAs produced by cancer cells and their microenvironment that can be easily detected in blood. In particular, after a large-scale screening of 799 EV-miRNAs, we identified EV-miR-625-5p as a novel independent biomarker of response and survival in ICI-treated NSCLC patients, in particular in patients with PD-L1 expression ≥ 50%. EV-miR-625-5p integrated with PDL-1 test could allow the clinician to identify in advance patients that would benefit from ICIs.

Abstract

Immune checkpoint inhibitors (ICIs) are largely used in the treatment of patients with advanced non-small-cell lung cancer (NSCLC). Novel biomarkers that provide biological information that could be useful for clinical management are needed. In this respect, extracellular vesicles (EV)-associated microRNAs (miRNAs) that are the principal vehicle of intercellular communication may be important sources of biomarkers. We analyzed the levels of 799 EV-miRNAs in the pretreatment plasma of 88 advanced NSCLC patients who received anti-PD-1 therapy as single agent. After data normalization, we used a two-step approach to identify candidate biomarkers associated to both objective response (OR) by RECIST and longer overall survival (OS). Univariate and multivariate analyses including known clinicopathologic variables and new findings were performed. In our cohort, 24/88 (27.3%) patients showed OR by RECIST. Median OS in the whole cohort was 11.5 months. In total, 196 EV-miRNAs out 799 were selected as expressed above background. After multiplicity adjustment, abundance of EV-miR-625-5p was found to be correlated with PD-L1 expression and significantly associated to OR by RECIST (p = 0.0366) and OS (p = 0.0031). In multivariate analysis, PD-L1 staining and EV-miR-625-5p levels were constantly associated to OR and OS. Finally, we showed that EV-miR-625-5p levels could discriminate patients with longer survival, in particular in the class expressing PD-L1 ≥50%. EV-miRNAs represent a source of relevant biomarkers. EV-miR-625-5p is an independent biomarker of response and survival in ICI-treated NSCLC patients, in particular in patients with PD-L1 expression ≥50%.

Blood Transcriptomics Predicts Progression of Pulmonary Fibrosis and Associated Natural Killer Cells

Rationale: Disease activity in idiopathic pulmonary fibrosis (IPF) remains highly variable, poorly understood, and difficult to predict. Objectives: To identify a predictor using short-term longitudinal changes in gene expression that forecasts future FVC decline and to characterize involved pathways and cell types. Methods: Seventy-four patients from COMET (Correlating Outcomes with Biochemical Markers to Estimate Time-Progression in IPF) cohort were dichotomized as progressors (≥10% FVC decline) or stable. Blood gene-expression changes within individuals were calculated between baseline and 4 months and regressed with future FVC status, allowing determination of expression variations, sample size, and statistical power. Pathway analyses were conducted to predict downstream effects and identify new targets. An FVC predictor for progression was constructed in COMET and validated using independent cohorts. Peripheral blood mononuclear single-cell RNA-sequencing data from healthy control subjects were used as references to characterize cell type compositions from bulk peripheral blood mononuclear RNA-sequencing data that were associated with FVC decline. Measurements and Main Results: The longitudinal model reduced gene-expression variations within stable and progressor groups, resulting in increased statistical power when compared with a cross-sectional model. The FVC predictor for progression anticipated patients with future FVC decline with 78% sensitivity and 86% specificity across independent IPF cohorts. Pattern recognition receptor pathways and mTOR pathways were downregulated and upregulated, respectively. Cellular deconvolution using single-cell RNA-sequencing data identified natural killer cells as significantly correlated with progression. Conclusions: Serial transcriptomic change predicts future FVC decline. An analysis of cell types involved in the progressor signature supports the novel involvement of natural killer cells in IPF progression.

The Molecular Floodgates of Stress-Induced Senescence Reveal Translation, Signalling and Protein Activity Central to the Post-Mortem Proteome

The transitioning of cells during the systemic demise of an organism is poorly understood. Here, we present evidence that organismal death is accompanied by a common and sequential molecular flood of stress-induced events that propagate the senescence phenotype, and this phenotype is preserved in the proteome after death. We demonstrate activation of “death” pathways involvement in diseases of ageing, with biochemical mechanisms mapping onto neurological damage, embryonic development, the inflammatory response, cardiac disease and ultimately cancer with increased significance. There is sufficient bioavailability of the building blocks required to support the continued translation, energy, and functional catalytic activity of proteins. Significant abundance changes occur in 1258 proteins across 1 to 720 h post-mortem of the 12-week-old mouse mandible. Protein abundance increases concord with enzyme activity, while mitochondrial dysfunction is evident with metabolic reprogramming. This study reveals differences in protein abundances which are akin to states of stress-induced premature senescence (SIPS). The control of these pathways is significant for a large number of biological scenarios. Understanding how these pathways function during the process of cellular death holds promise in generating novel solutions capable of overcoming disease complications, maintaining organ transplant viability and could influence the findings of proteomics through “deep-time” of individuals with no historically recorded cause of death.

Long Noncoding RNA Analyses for Osteoporosis Risk in Caucasian Women

INTRODUCTION

Osteoporosis is a prevalent bone metabolic disease characterized by bone fragility. As a key pathophysiological mechanism, the disease is caused by excessive bone resorption (by osteoclasts) over bone formation (by osteoblasts). Peripheral blood monocytes (PBMs) is a major systemic cell model for bone metabolism by serving as progenitors of osteoclasts and producing cytokines important for osteoclastogenesis. Protein-coding genes for osteoporosis have been widely studied by mRNA analyses of PBMs in high versus low hip bone mineral density (BMD) subjects. However, long noncoding RNAs (lncRNAs), which account for a large proportion of human transcriptome, have seldom been studied.

METHODS

In this study, microarray analyses of monocytes were performed using Affymetrix exon 1.0 ST arrays in 73 Caucasian females (age: 47-56). LncRNA profile was generated by re-annotating exon array for lncRNAs detection, which yielded 12,007 lncRNAs mapped to the human genome.

RESULTS

575 lncRNAs were differentially expressed between the two groups. In the high BMD subjects, 309 lncRNAs were upregulated and 266 lncRNAs were downregulated (nominally significant, raw p-value < 0.05). To investigate the relationship between mRNAs and lncRNAs, we used two approaches to predict the target genes of lncRNAs and found that 26 candidate lncRNAs might regulate mRNA expression. The majority of these lncRNAs were further validated to be potentially correlated with BMD by GWAS analysis.

CONCLUSION

Overall, our findings for the first time reported the lncRNAs profiles for osteoporosis and suggested the potential regulatory mechanism of lncRNAs on protein-coding genes in bone metabolism.

Effect of different anaesthetic techniques on gene expression profiles in patients who underwent hip arthroplasty

Objectives

To investigate the modulation of genes whose expression level is indicative of stress and toxicity following exposure to three anaesthesia techniques, general anaesthesia (GA), regional anaesthesia (RA), or integrated anaesthesia (IA).

Methods

Patients scheduled for hip arthroplasty receiving GA, RA and IA were enrolled at Rizzoli Orthopaedic Institute of Bologna, Italy and the expression of genes involved in toxicology were evaluated in peripheral blood mononuclear cells (PBMCs) collected before (T0), immediately after surgery (T1), and on the third day (T2) after surgery in association with biochemical parameters.

Results

All three anaesthesia methods proved safe and reliable in terms of pain relief and patient recovery. Gene ontology analysis revealed that GA and mainly IA were associated with deregulation of DNA repair system and stress-responsive genes, which was observed even after 3-days from anaesthesia. Conversely, RA was not associated with substantial changes in gene expression.

Conclusions

Based on the gene expression analysis, RA technique showed the smallest toxicological effect in hip arthroplasty.

Trial registration

ClinicalTrials.gov number NCT03585647.

Impact of inflammatory signaling on radiation biodosimetry: mouse model of inflammatory bowel disease

Background

Ionizing Radiation (IR) is a known pro-inflammatory agent and in the process of development of biomarkers for radiation biodosimetry, a chronic inflammatory disease condition could act as a confounding factor. Hence, it is important to develop radiation signatures that can distinguish between IR-induced inflammatory responses and pre-existing disease. In this study, we compared the gene expression response of a genetically modified mouse model of inflammatory bowel disease (Il10^−/−) with that of a normal wild-type mouse to potentially develop transcriptomics-based biodosimetry markers that can predict radiation exposure in individuals regardless of pre-existing inflammatory condition.

Results

Wild-type (WT) and Il10^−/− mice were exposed to whole body irradiation of 7 Gy X-rays. Gene expression responses were studied using high throughput whole genome microarrays in peripheral blood 24 h post-irradiation. Analysis resulted in identification of 1962 and 1844 genes differentially expressed (p < 0.001, FDR < 10%) after radiation exposure in Il10^−/− and WT mice respectively. A set of 155 genes was also identified as differentially expressed between WT and Il10^−/− mice at the baseline pre-irradiation level. Gene ontology analysis revealed that the 155 baseline differentially expressed genes were mainly involved in inflammatory response, glutathione metabolism and collagen deposition. Analysis of radiation responsive genes revealed that innate immune response and p53 signaling processes were strongly associated with up-regulated genes, whereas B-cell development process was found to be significant amongst downregulated genes in the two genotypes. However, specific immune response pathways like MHC based antigen presentation, interferon signaling and hepatic fibrosis were associated with radiation responsive genes in Il10^−/− mice but not WT mice. Further analysis using the IPA prediction tool revealed significant differences in the predicted activation status of T-cell mediated signaling as well as regulators of inflammation between WT and Il10^−/− after irradiation.

Conclusions

Using a mouse model we established that an inflammatory disease condition could affect the expression of many radiation responsive genes. Nevertheless, we identified a panel of genes that, regardless of disease condition, could predict radiation exposure. Our results highlight the need for consideration of pre-existing conditions in the population in the process of development of reliable biodosimetry markers.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5689-y) contains supplementary material, which is available to authorized users.

Sex Differences in Plasma MicroRNA Biomarkers of Early and Complicated Diabetes Mellitus in Israeli Arab and Jewish Patients

MicroRNAs play functional roles in the etiology of type 2 diabetes mellitus (T2DM) and complications, and extracellular microRNAs have attracted interest as potential biomarkers of these conditions. We aimed to identify a set of plasma microRNAs, which could serve as biomarkers of T2DM and complications in a mixed Israeli Arab/Jewish patient sample. Subjects included 30 healthy volunteers, 29 early-stage T2DM patients, and 29 late-stage T2DM patients with renal and/or vascular complications. RNA was isolated from plasma, and the levels of 12 candidate microRNAs were measured by quantitative reverse transcription and polymerase chain reaction (qRT-PCR). MicroRNA levels were compared between the groups and correlated to clinical measurements, followed by stepwise regression analysis and discriminant analysis. Plasma miR-486-3p and miR-423 were respectively up- and down-regulated in T2DM patients compared to healthy controls. MiR-28-3p and miR-423 were up-regulated in patients with complicated T2DM compared to early T2DM, while miR-486-3p was down-regulated. Combined, four microRNAs (miR-146a-5p, miR-16-2-3p, miR-126-5p, and miR-30d) could distinguish early from complicated T2DM with 77% accuracy and 79% sensitivity. In male patients only, the same microRNAs, with the addition of miR-423, could distinguish early from complicated T2DM with 83.3% accuracy. Furthermore, plasma microRNA levels showed significant correlations with clinical measurements, and these differed between men and women. Additionally, miR-183-5p levels differed significantly between the ethnic groups. Our study identified a panel of specific plasma microRNAs which can serve as biomarkers of T2DM and its complications and emphasizes the importance of sex differences in their clinical application.

Optimism Bias Correction in Omics Studies with Big Data: Assessment of Penalized Methods on Simulated Data

Big Data generated by omics technologies require simultaneous analyses of large numbers of variables. This leads to complex model selection and parameter estimates that show optimism bias. This study on simulated data sets examined optimism-bias correction by penalty regression methods in case-control studies that involve clinical and omics variables. Least absolute shrinkage and selection operator (LASSO)-based methods (LASSO-penalized logistic regression, adaptive LASSO, and regularized LASSO for selection + ridge regression) were evaluated using power, the false positive rate (FPR), false discovery rate (FDR), and by estimated versus theoretical parameter comparisons. The "ordinary" LASSO overcorrects the optimism bias. The adaptive LASSO with LASSO estimation of the weights was unable to provide a sufficient correction. Importantly, the adaptive LASSO with ridge estimation of the weights showed the best parameter estimation. The regularized LASSO selection showed a slight optimism bias that decreased with the increase in the training set size. The optimism bias decreased with the increase of the number of variables selected among truly differentially expressed variables; however, power, FPR, and FDR were correlated. A compromise between model selection and estimation accuracy should be found. These results might prove useful because Big Data analyses are becoming commonplace in omics/multiomics studies in integrative biology, precision medicine, and planetary health.

The Feasibility of Examining the Effects of Gastric Bypass Surgery on Intestinal Metabolism: Prospective, Longitudinal Mechanistic Clinical Trial

Background

Bariatric surgery, especially Roux-en-Y gastric bypass (RYGB), is the best treatment for severe obesity and its complications including type 2 diabetes mellitus (T2DM). Understanding the mechanisms responsible for the beneficial metabolic effects will help to engineer ways to improve the procedure or produce these effects without surgery.

Objective

The aim is to present data on recruitment and feasibility of a translational study designed to collect intestinal samples before and after bariatric surgery. The goal of biobanking is to allow future studies to test the hypothesis that the mechanism of action of RYGB involves specific changes in the postsurgical short- and long-term metabolism and morphology of the jejunum (Roux limb). Specifically, to test whether the intestine enhances its metabolism and activity after RYGB and increases its fuel utilization, we designed a prospective, longitudinal study, which involved the recruitment of candidates for RYGB with and without T2DM. We describe the tissue bank that we have generated, and our experience, hoping to further facilitate the performance of longitudinal mechanistic studies in human patients undergoing bariatric surgery and especially those involving post-RYGB intestinal biology.

Methods

We conducted a trial to characterize the effects of RYGB on intestinal metabolism. Intestinal tissue samples were collected from the jejunum at surgery, 1, 6, and 12 months postoperatively for the analysis of intestinal gene expression and metabolomic and morphologic changes. The target number of patients who completed at least the 6-month follow-up was 26, and we included a 20% attrition rate, increasing the total number to 32.

Results

To enroll 26 patients, we had to approach 79 potential participants. A total of 37 agreed to participate and started the study; 33, 30, and 26 active participants completed their 1-month, 6-month, and 12-month studies, respectively. Three participants withdrew, and 30 participants are still active. Altruism and interest in research were the most common reasons for participation. Important factors for feasibility and successful retention included (1) large volume case flow, (2) inclusion and exclusion criteria broad enough to capture a large segment of the patient population but narrow enough to ensure the completion of study aims and protection of safety concerns, (3) accurate assessment of willingness and motivation to participate in a study, (4) seamless integration of the recruitment process into normal clinical flow, (5) financial reimbursement and nonfinancial rewards and gestures of appreciation, and (6) nonburdensome follow-up visits and measures and reasonable time allotted.

Conclusions

Human translational studies of the intestinal mechanisms of metabolic and weight changes after bariatric surgery are important and feasible. A tissue bank with unique samples has been established that could be used by investigators in many research fields, further enabling mechanistic studies on the effects of bariatric surgery.

Trial Registration

ClinicalTrials.gov NCT02710370; https://clinicaltrials.gov/ct2/show/NCT02710370 (Archived by WebCite at http://www.webcitation.org/75HrQT8Dl)

RNASeqDesign: A framework for RNA-Seq genome-wide power calculation and study design issues

Massively parallel sequencing (a.k.a. next-generation sequencing, NGS) technology has emerged as a powerful tool in characterizing genomic profiles. Among many NGS applications, RNA sequencing (RNA-Seq) has gradually become a standard tool for global transcriptomic monitoring. Although the cost of NGS experiments has dropped constantly, the high sequencing cost and bioinformatic complexity are still obstacles for many biomedical projects. Unlike earlier fluorescence-based technologies such as microarray, modelling of NGS data should consider discrete count data. In addition to sample size, sequencing depth also directly relates to the experimental cost. Consequently, given total budget and pre-specified unit experimental cost, the study design issue in RNA-Seq is conceptually a more complex multi-dimensional constrained optimization problem rather than one-dimensional sample size calculation in traditional hypothesis setting. In this paper, we propose a statistical framework, namely "RNASeqDesign", to utilize pilot data for power calculation and study design of RNA-Seq experiments. The approach is based on mixture model fitting of p-value distribution from pilot data and a parametric bootstrap procedure based on approximated Wald test statistics to infer genome-wide power for optimal sample size and sequencing depth. We further illustrate five practical study design tasks for practitioners. We perform simulations and three real applications to evaluate the performance and compare to existing methods.

Transcriptome Profiling Uncovers Potential Common Mechanisms in Fetal Trisomies 18 and 21

Human trisomies have recently been investigated using transcriptomics approaches to identify the gene expression (GE) signatures characteristic of each of these specific aneuploidy conditions. We hypothesized that the viability of cells with gross genomic imbalances might be associated with the activation of resilience mechanisms that are common to different trisomies and that are reflected by specific shared GE patterns. We report in this article our microarray GE analyses of amniocytes from fetuses with viable trisomy conditions, trisomy 21 or trisomy 18, to detect such common expression signatures. Comparative analysis of significantly differentially expressed genes in trisomies 18 and 21 revealed six dysregulated genes common to both: OTUD5, ADAMTSL1, TADA2A, PPID, PIAS2, and MAPRE2. These genes are involved in ubiquitination, protein folding, cell proliferation, and apoptosis. Pathway-based enrichment analyses demonstrated that both trisomies showed dysregulation of the PI3K/AKT pathway, cell cycle G2/M DNA damage checkpoint regulation, and cell death and survival, as well as inhibition of the upstream regulator TP53. Our data collectively suggest that trisomies 18 and 21 share common functional GE signatures, implying that common mechanisms of resilience might be activated in aneuploid cells to resist large genomic imbalances. To the best of our knowledge, this is the first study to use global GE profiling data to identify potential common mechanisms in fetal trisomies. Studies of other trisomies using transcriptomics and multiomics approaches might further clarify mechanisms activated in trisomy syndromes.

Thinking BIG rheumatology: how to make functional genomics data work for you

High-throughput sequencing assays have become an increasingly common part of biological research across multiple fields. Even as the resulting sequences pile up in public databases, it is not always obvious how to make use of these data sets. Functional genomics offers approaches to integrate these "big" data into our understanding of rheumatic diseases. This review aims to provide a primer on thinking about big data from functional genomics in the context of rheumatology, using examples from the field's literature as well as the author's own work to illustrate the execution of functional genomics research. Study design is crucial to ensure the right samples are used to address the question of interest. In addition, sequencing assays produce a variety of data types, from gene expression to 3D chromatin structure and single-cell technologies, that can be integrated into a model of the underlying gene regulatory networks. The best approach for this analysis uses the scientific process: bioinformatic methods should be used in an iterative, hypothesis-driven manner to uncover the disease mechanism. Finally, the future of functional genomics will see big data fully integrated into rheumatology, leading to computationally trained researchers and interactive databases. The goal of this review is not to provide a manual, but to enhance the familiarity of readers with functional genomic approaches and provide a better sense of the challenges and possibilities.

Global Gene Expression Response in Mouse Models of DNA Repair Deficiency after Gamma Irradiation

In the event of an improvised nuclear device or "dirty bomb" in a highly populated area, potentially hundreds of thousands of people will require screening to ensure that exposed individuals receive appropriate treatment. For this reason, there is a need to develop tools for high-throughput radiation biodosimetry. Gene expression represents an emerging approach to biodosimetry and could potentially provide an estimate of both absorbed dose and individual radiation-induced injury. Since approximately 2-4% of humans are thought to be radiosensitive, and would suffer greater radiological injury at a given dose than members of the general population, it is of interest to explore the potential impact of such sensitivity on the biodosimetric gene expression signatures being developed. In this study, we used wild-type mice and genetically engineered mouse models deficient in two DNA repair pathways that can contribute to radiation sensitivity to estimate the maximum effect of differences in radiosensitivity. We compared gene expression in response to a roughly equitoxic (LD_50/30) dose of gamma rays in wild-type C57BL/6 (8 Gy) and DNA double-strand break repair-deficient Atm^-/- (4 Gy) and Prkdc^scid (3 Gy) mutants of C57BL/6. Overall, 780 genes were significantly differentially expressed in wild-type mice one day postirradiation, 232 in Atm^-/- and 269 in Prkdc^scid. Upstream regulators including TP53 and NFκB were predicted to be activated by radiation exposure in the wild-type mice, but not in either of the DNA repair-deficient mutant strains. There was also a significant muting of the apparent inflammatory response triggered by radiation in both mutant strains. These differences impacted the ability of gene expression signatures developed in wild-type mice to detect potentially fatal radiation exposure in the DNA repair-deficient mice, with the greatest impact on Atm^-/- mice. However, the inclusion of mutant mice in gene selection vastly improved performance of the classifiers.

Oral mucosa tissue gene expression profiling before, during, and after radiation therapy for tonsil squamous cell carcinoma

Background

Radiation-therapy (RT) induces mucositis, a clinically challenging condition with limited prophylactic interventions and no predictive tests. In this pilot study, we applied global gene-expression analysis on serial human oral mucosa tissue and blood cells from patients with tonsil squamous cell cancer (TSCC) to identify genes involved in mucositis pathogenesis.

Methods and findings

Eight patients with TSCC each provided consecutive buccal biopsies and blood cells before, after 7 days of RT treatment, and 20 days following RT. We monitored clinical mucositis and performed gene-expression analysis on tissue samples. We obtained control tissue from nine healthy individuals. After RT, expression was upregulated in apoptosis inducer and inhibitor genes, EDA2R and MDM2, and in POLH, a DNA-repair polymerase. Expression was downregulated in six members of the histone cluster family, e.g., HIST1H3B. Gene expression related to proliferation and differentiation was altered, including MKI67 (downregulated), which encodes the Ki-67-proliferation marker, and KRT16 (upregulated), which encodes keratin16. These alterations were not associated with the clinical mucositis grade. However, the expression of LY6G6C, which encodes a surface immunoregulatory protein, was upregulated before treatment in three cases of clinical none/mild mucositis, but not in four cases of ulcerative mucositis.

Conclusion

RT caused molecular changes related to apoptosis, DNA-damage, DNA-repair, and proliferation without a correlation to the severity of clinical mucositis. LY6G6C may be a potential protective biomarker for ulcerative mucositis. Based on these results, our study model of consecutive human biopsies will be useful in designing a prospective clinical validation trial to characterize molecular mucositis and identify predictive biomarkers.

Molecular Characteristics of High-Dose Melphalan Associated Oral Mucositis in Patients with Multiple Myeloma: A Gene Expression Study on Human Mucosa

Background

Toxicity of the oral and gastrointestinal mucosa induced by high-dose melphalan is a clinical challenge with no documented prophylactic interventions or predictive tests. The aim of this study was to describe molecular changes in human oral mucosa and to identify biomarkers correlated with the grade of clinical mucositis.

Methods and Findings

Ten patients with multiple myeloma (MM) were included. For each patient, we acquired three buccal biopsies, one before, one at 2 days, and one at 20 days after high-dose melphalan administration. We also acquired buccal biopsies from 10 healthy individuals that served as controls. We analyzed the biopsies for global gene expression and performed an immunohistochemical analysis to determine HLA-DRB5 expression. We evaluated associations between clinical mucositis and gene expression profiles. Compared to gene expression levels before and 20 days after therapy, at two days after melphalan treatment, we found gene regulation in the p53 and TNF pathways (MDM2, INPPD5, TIGAR), which favored anti-apoptotic defense, and upregulation of immunoregulatory genes (TREM2, LAMP3) in mucosal dendritic cells. This upregulation was independent of clinical mucositis. HLA-DRB1 and HLA-DRB5 (surface receptors on dendritic cells) were expressed at low levels in all patients with MM, in the subgroup of patients with ulcerative mucositis (UM), and in controls; in contrast, the subgroup with low-grade mucositis (NM) displayed 5–6 fold increases in HLA-DRB1 and HLA-DRB5 expression in the first two biopsies, independent of melphalan treatment. Moreover, different splice variants of HLA-DRB1 were expressed in the UM and NM subgroups.

Conclusions

Our results revealed that, among patients with MM, immunoregulatory genes and genes involved in defense against apoptosis were affected immediately after melphalan administration, independent of the presence of clinical mucositis. Furthermore, our results suggested that the expression levels of HLA-DRB1 and HLA-DRB5 may serve as potential predictive biomarkers for mucositis severity.

Understanding gene expression in coronary artery disease through global profiling, network analysis and independent validation of key candidate genes

Molecular mechanism underlying the patho-physiology of coronary artery disease (CAD) is complex. We used global expression profiling combined with analysis of biological network to dissect out potential genes and pathways associated with CAD in a representative case-control Asian Indian cohort. We initially performed blood transcriptomics profiling in 20 subjects, including 10 CAD patients and 10 healthy controls on the Agilent microarray platform. Data was analysed with Gene Spring Gx12.5, followed by network analysis using David v 6.7 and Reactome databases. The most significant differentially expressed genes from microarray were independently validated by real time PCR in 97 cases and 97 controls. A total of 190 gene transcripts showed significant differential expression (fold change>2,P<0.05) between the cases and the controls of which 142 genes were upregulated and 48 genes were downregulated. Genes associated with inflammation, immune response, cell regulation, proliferation and apoptotic pathways were enriched, while inflammatory and immune response genes were displayed as hubs in the network, having greater number of interactions with the neighbouring genes. Expression of EGR1/2/3, IL8, CXCL1, PTGS2, CD69, IFNG, FASLG, CCL4, CDC42, DDX58, NFKBID and NR4A2 genes were independently validated; EGR1/2/3 and IL8 showed >8-fold higher expression in cases relative to the controls implying their important role in CAD. In conclusion, global gene expression profiling combined with network analysis can help in identifying key genes and pathways for CAD.

Approaches to Sample Size Determination for Multivariate Data: Applications to PCA and PLS-DA of Omics Data

Sample size determination is a fundamental step in the design of experiments. Methods for sample size determination are abundant for univariate analysis methods, but scarce in the multivariate case. Omics data are multivariate in nature and are commonly investigated using multivariate statistical methods, such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). No simple approaches to sample size determination exist for PCA and PLS-DA. In this paper we will introduce important concepts and offer strategies for (minimally) required sample size estimation when planning experiments to be analyzed using PCA and/or PLS-DA.

Power and sample size estimation in high dimensional biology

Genomic scientists often test thousands of hypotheses in a single experiment. One example is a microarray experiment that seeks to determine differential gene expression among experimental groups. Planning such experiments involves a determination of sample size that will allow meaningful interpretations. Traditional power analysis methods may not be well suited to this task when thousands of hypotheses are tested in a discovery oriented basic research. We introduce the concept of expected discovery rate (EDR) and an approach that combines parametric mixture modelling with parametric bootstrapping to estimate the sample size needed for a desired accuracy of results. While the examples included are derived from microarray studies, the methods, herein, are ‘extraparadigmatic’ in the approach to study design and are applicable to most high dimensional biological situations. Pilot data from three different microarray experiments are used to extrapolate EDR as well as the related false discovery rate at different sample sizes and thresholds.

Effective gene expression data generation framework based on multi-model approach

OBJECTIVE

Overcome the lack of enough samples in gene expression data sets having thousands of genes but a small number of samples challenging the computational methods using them.

METHODS AND MATERIAL

This paper introduces a multi-model artificial gene expression data generation framework where different gene regulatory network (GRN) models contribute to the final set of samples based on the characteristics of their underlying paradigms. In the first stage, we build different GRN models, and sample data from each of them separately. Then, we pool the generated samples into a rich set of gene expression samples, and finally try to select the best of the generated samples based on a multi-objective selection method measuring the quality of the generated samples from three different aspects such as compatibility, diversity and coverage. We use four alternative GRN models, namely, ordinary differential equations, probabilistic Boolean networks, multi-objective genetic algorithm and hierarchical Markov model.

RESULTS

We conducted a comprehensive set of experiments based on both real-life biological and synthetic gene expression data sets. We show that our multi-objective sample selection mechanism effectively combines samples from different models having up to 95% compatibility, 10% diversity and 50% coverage. We show that the samples generated by our framework has up to 1.5x higher compatibility, 2x higher diversity and 2x higher coverage than the samples generated by the individual models that the multi-model framework uses. Moreover, the results show that the GRNs inferred from the samples generated by our framework can have 2.4x higher precision, 12x higher recall, and 5.4x higher f-measure values than the GRNs inferred from the original gene expression samples.

CONCLUSIONS

Therefore, we show that, we can significantly improve the quality of generated gene expression samples by integrating different computational models into one unified framework without dealing with complex internal details of each individual model. Moreover, the rich set of artificial gene expression samples is able to capture some biological relations that can even not be captured by the original gene expression data set.

Intuitive web-based experimental design for high-throughput biomedical data

Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approaches are needed to keep track of the experimental design, including the conditions that are studied as well as information that might be interesting for failure analysis or further experiments in the future. In addition to the management of this information, means for an integrated design and interfaces for structured data annotation are urgently needed by researchers. Here, we propose a factor-based experimental design approach that enables scientists to easily create large-scale experiments with the help of a web-based system. We present a novel implementation of a web-based interface allowing the collection of arbitrary metadata. To exchange and edit information we provide a spreadsheet-based, humanly readable format. Subsequently, sample sheets with identifiers and metainformation for data generation facilities can be created. Data files created after measurement of the samples can be uploaded to a datastore, where they are automatically linked to the previously created experimental design model.

RNAseqPS: A Web Tool for Estimating Sample Size and Power for RNAseq Experiment

Sample size and power determination is the first step in the experimental design of a successful study. Sample size and power calculation is required for applications for National Institutes of Health (NIH) funding. Sample size and power calculation is well established for traditional biological studies such as mouse model, genome wide association study (GWAS), and microarray studies. Recent developments in high-throughput sequencing technology have allowed RNAseq to replace microarray as the technology of choice for high-throughput gene expression profiling. However, the sample size and power analysis of RNAseq technology is an underdeveloped area. Here, we present RNAseqPS, an advanced online RNAseq power and sample size calculation tool based on the Poisson and negative binomial distributions. RNAseqPS was built using the Shiny package in R. It provides an interactive graphical user interface that allows the users to easily conduct sample size and power analysis for RNAseq experimental design. RNAseqPS can be accessed directly at http://cqs.mc.vanderbilt.edu/shiny/RNAseqPS/.

Perspective: a systems approach to diabetes research

We review here the status of human type 2 diabetes studies from a genetic, epidemiological, and clinical (intervention) perspective. Most studies limit analyses to one or a few omic technologies providing data of components of physiological processes. Since all chronic diseases are multifactorial and arise from complex interactions between genetic makeup and environment, type 2 diabetes mellitus (T2DM) is a collection of sub-phenotypes resulting in high fasting glucose. The underlying gene–environment interactions that produce these classes of T2DM are imperfectly characterized. Based on assessments of the complexity of T2DM, we propose a systems biology approach to advance the understanding of origin, onset, development, prevention, and treatment of this complex disease. This systems-based strategy is based on new study design principles and the integrated application of omics technologies: we pursue longitudinal studies in which each subject is analyzed at both homeostasis and after (healthy and safe) challenges. Each enrolled subject functions thereby as their own case and control and this design avoids assigning the subjects a priori to case and control groups based on limited phenotyping. Analyses at different time points along this longitudinal investigation are performed with a comprehensive set of omics platforms. These data sets are generated in a biological context, rather than biochemical compound class-driven manner, which we term “systems omics.”

Exact calculations of average power for the Benjamini-Hochberg procedure

Exact analytic expressions are developed for the average power of the Benjamini and Hochberg false discovery control procedure. The result is based on explicit computation of the joint probability distribution of the total number of rejections and the number of false rejections, and expressed in terms of the cumulative distribution functions of the p-values of the hypotheses. An example of analytic evaluation of the average power is given. The result is confirmed by numerical experiments and applied to a meta-analysis of three clinical studies in mammography.

Power and sample size calculation for microarray studies

Microarray is a technology to screen a large number of genes to discover those differentially expressed between clinical subtypes or different conditions of human diseases. Gene discovery using microarray data requires adjustment for the large-scale multiplicity of candidate genes. The family-wise error rate (FWER) has been widely chosen as a global type I error rate adjusting for the multiplicity. Typically in microarray data, the expression levels of different genes are correlated because of coexpressing genes and the common experimental conditions shared by the genes on each array. To accurately control the FWER, the statistical testing procedure should appropriately reflect the dependency among the genes. Permutation methods have been used for accurate control of the FWER in analyzing microarray data. It is important to calculate the required sample size at the design stage of a new (confirmatory) microarray study. Because of the high dimensionality and complexity of the correlation structure in microarray data, however, there have been no sample size calculation methods accurately reflecting the true correlation structure of real microarray data. We propose sample size and power calculation methods that are useful when pilot data are available to design a confirmatory experiment. If no pilot data are available, we recommend a two-stage sample size recalculation based on our proposed method using the first stage data as pilot data. The calculated sample sizes are shown to accurately maintain the power through simulations. A real data example is taken to illustrate the proposed method.

Sequential stopping for high-throughput experiments

In high-throughput experiments, the sample size is typically chosen informally. Most formal sample-size calculations depend critically on prior knowledge. We propose a sequential strategy that, by updating knowledge when new data are available, depends less critically on prior assumptions. Experiments are stopped or continued based on the potential benefits in obtaining additional data. The underlying decision-theoretic framework guarantees the design to proceed in a coherent fashion. We propose intuitively appealing, easy-to-implement utility functions. As in most sequential design problems, an exact solution is prohibitive. We propose a simulation-based approximation that uses decision boundaries. We apply the method to RNA-seq, microarray, and reverse-phase protein array studies and show its potential advantages. The approach has been added to the Bioconductor package gaga.

Proton radiation-induced miRNA signatures in mouse blood: characterization and comparison with 56Fe-ion and gamma radiation

PURPOSE

Previously, we showed that microRNA (miRNA) signatures derived from the peripheral blood of mice are highly specific for both radiation energy (γ-rays or high linear energy transfer [LET] (56)Fe ions) and radiation dose. Here, we investigate to what extent miRNA expression signatures derived from mouse blood can be used as biomarkers for exposure to 600 MeV proton radiation.

MATERIALS AND METHODS

We exposed mice to 600 MeV protons, using doses of 0.5 or 1.0 Gy, isolated total RNA at 6 h or 24 h after irradiation, and used quantitative real-time polymerase chain reaction (PCR) to determine the changes in miRNA expression.

RESULTS

A total of 26 miRNA were differentially expressed after proton irradiation, in either one (77%) or multiple conditions (23%). Statistical classifiers based on proton, γ, and (56)Fe-ion miRNA expression signatures predicted radiation type and proton dose with accuracies of 81% and 88%, respectively. Importantly, gene ontology analysis for proton-irradiated cells shows that genes targeted by radiation-induced miRNA are involved in biological processes and molecular functions similar to those controlled by miRNA in γ ray- and (56)Fe-irradiated cells.

CONCLUSIONS

Mouse blood miRNA signatures induced by proton, γ, or (56)Fe irradiation are radiation type- and dose-specific. These findings underline the complexity of the miRNA-mediated radiation response.

Reproducibility and concordance of differential DNA methylation and gene expression in cancer

Background

Hundreds of genes with differential DNA methylation of promoters have been identified for various cancers. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed.

Methodology/Principal Findings

Using array data for seven types of cancers, we first evaluated the effects of experimental batches on differential DNA methylation detection. Second, we compared the directions of DNA methylation changes detected from different datasets for the same cancer. Third, we evaluated the concordance between methylation and gene expression changes. Finally, we compared DNA methylation changes in different cancers. For a given cancer, the directions of methylation and expression changes detected from different datasets, excluding potential batch effects, were highly consistent. In different cancers, DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression, whereas hypomethylation was only weakly correlated with the up-regulation of genes. Finally, we found that genes commonly hypomethylated in different cancers primarily performed functions associated with chronic inflammation, such as ‘keratinization’, ‘chemotaxis’ and ‘immune response’.

Conclusions

Batch effects could greatly affect the discovery of DNA methylation biomarkers. For a particular cancer, both differential DNA methylation and gene expression can be reproducibly detected from different studies with no batch effects. While DNA hypermethylation is significantly linked to gene down-regulation, hypomethylation is only weakly correlated with gene up-regulation and is likely to be linked to chronic inflammation.

Sample Size Calculation for Controlling False Discovery Proportion

The false discovery proportion (FDP), the proportion of incorrect rejections among all rejections, is a direct measure of abundance of false positive findings in multiple testing. Many methods have been proposed to control FDP, but they are too conservative to be useful for power analysis. Study designs for controlling the mean of FDP, which is false discovery rate, have been commonly used. However, there has been little attempt to design study with direct FDP control to achieve certain level of efficiency. We provide a sample size calculation method using the variance formula of the FDP under weak-dependence assumptions to achieve the desired overall power. The relationship between design parameters and sample size is explored. The adequacy of the procedure is assessed by simulation. We illustrate the method using estimated correlations from a prostate cancer dataset.

Statistical considerations for analysis of microarray experiments

Microarray technologies enable the simultaneous interrogation of expressions from thousands of genes from a biospecimen sample taken from a patient. This large set of expressions generates a genetic profile of the patient that may be used to identify potential prognostic or predictive genes or genetic models for clinical outcomes. The aim of this article is to provide a broad overview of some of the major statistical considerations for the design and analysis of microarrays experiments conducted as correlative science studies to clinical trials. An emphasis will be placed on how the lack of understanding and improper use of statistical concepts and methods will lead to noise discovery and misinterpretation of experimental results.

Low dose irradiation of thyroid cells reveals a unique transcriptomic and epigenetic signature in RET/PTC-positive cells

The high doses of radiation received in the wake of the Chernobyl incident and the atomic bombing of Hiroshima and Nagasaki have been linked to the increased appearance of thyroid cancer in the children living in the vicinity of the site. However, the data gathered on the effect of low doses of radiation on the thyroid remain limited. We have examined the genome wide transcriptional response of a culture of TPC-1 human cell line of papillary thyroid carcinoma origin with a RET/PTC1 translocation to various doses (0.0625, 0.5, and 4Gy) of X-rays and compared it to response of thyroids with a RET/PTC3 translocation and against wild-type mouse thyroids irradiated with the same doses using Affymetrix microarrays. We have found considerable overlap at a high dose of 4Gy in both RET/PTC-positive systems but no common genes at 62.5mGy. In addition, the response of RET/PTC-positive system at all doses was distinct from the response of wild-type thyroids with both systems signaling down different pathways. Analysis of the response of microRNAs in TPC-1 cells revealed a radiation-responsive signature of microRNAs in addition to dose-responsive microRNAs. Our results point to the fact that a low dose of X-rays seems to have a significant proliferative effect on normal thyroids. This observation should be studied further as opposed to its effect on RET/PTC-positive thyroids which was subtle, anti-proliferative and system-dependent.

Reproducible cancer biomarker discovery in SELDI-TOF MS using different pre-processing algorithms

Background

There has been much interest in differentiating diseased and normal samples using biomarkers derived from mass spectrometry (MS) studies. However, biomarker identification for specific diseases has been hindered by irreproducibility. Specifically, a peak profile extracted from a dataset for biomarker identification depends on a data pre-processing algorithm. Until now, no widely accepted agreement has been reached.

Results

In this paper, we investigated the consistency of biomarker identification using differentially expressed (DE) peaks from peak profiles produced by three widely used average spectrum-dependent pre-processing algorithms based on SELDI-TOF MS data for prostate and breast cancers. Our results revealed two important factors that affect the consistency of DE peak identification using different algorithms. One factor is that some DE peaks selected from one peak profile were not detected as peaks in other profiles, and the second factor is that the statistical power of identifying DE peaks in large peak profiles with many peaks may be low due to the large scale of the tests and small number of samples. Furthermore, we demonstrated that the DE peak detection power in large profiles could be improved by the stratified false discovery rate (FDR) control approach and that the reproducibility of DE peak detection could thereby be increased.

Conclusions

Comparing and evaluating pre-processing algorithms in terms of reproducibility can elucidate the relationship among different algorithms and also help in selecting a pre-processing algorithm. The DE peaks selected from small peak profiles with few peaks for a dataset tend to be reproducibly detected in large peak profiles, which suggests that a suitable pre-processing algorithm should be able to produce peaks sufficient for identifying useful and reproducible biomarkers.

Using DNA microarrays to assay part function

In recent years, the capability of synthetic biology to design large genetic circuits has dramatically increased due to rapid advances in DNA synthesis technology and development of tools for large-scale assembly of DNA fragments. Large genetic circuits require more components (parts), especially regulators such as transcription factors, sigma factors, and viral RNA polymerases to provide increased regulatory capability, and also devices such as sensors, receivers, and signaling molecules. All these parts may have a potential impact upon the host that needs to be considered when designing and fabricating circuits. DNA microarrays are a well-established technique for global monitoring of gene expression and therefore are an ideal tool for systematically assessing the impact of expressing parts of genetic circuits in host cells. Knowledge of part impact on the host enables the user to design circuits from libraries of parts taking into account their potential impact and also to possibly modify the host to better tolerate stresses induced by the engineered circuit. In this chapter, we present the complete methodology of performing microarrays from choice of array platform, experimental design, preparing samples for array hybridization, and associated data analysis including preprocessing, normalization, clustering, identifying significantly differentially expressed genes, and interpreting the data based on known biology. With these methodologies, we also include lists of bioinformatic resources and tools for performing data analysis. The aim of this chapter is to provide the reader with the information necessary to be able to systematically catalog the impact of genetic parts on the host and also to optimize the operation of fully engineered genetic circuits.

Gefitinib, cisplatin, and concurrent radiotherapy for locally advanced head and neck cancer: EGFR FISH, protein expression, and mutational status are not predictive biomarkers

BACKGROUND

Gefitinib was demonstrated to be synergistic with cisplatin and radiotherapy (RT) in in vitro studies. Biomarkers predictive of response to gefitinib in squamous cell head and neck cancer is still lacking.

METHODS

Thirty-one patients with locally advanced and easily accessible primary tumor sites for biopsies were recruited. Gefitinib was started 3 weeks before the start of cisplatin/concurrent radiotherapy (CTRT) and continued during the CTRT phase and thereafter for 4 months as consolidation phase. Two baselines and a repeat tumor sample were taken after 2 weeks of gefitinib alone to study its impact on tumor gene expression. Epidermal growth factor receptor (EGFR) protein expression, FISH and mutational status, and matrix metallopeptidase 11 (MMP11) protein expression were correlated with response and survival outcome.

RESULTS

The overall response rate to gefitinib alone was 9.7%. The survival outcome is as follows: median disease free 1.3 years, median survival time 2.4 years, 3-year disease free 42.9%, and 3-year overall survival 48.4%. EGFR FISH, protein expression, and mutational status did not predict for response nor survival outcome of patients. Although MMP11 overexpression did not predict for response, it predicted significantly for a poorer survival outcome.

CONCLUSIONS

Gefitinib can be combined safely with cisplatin/RT. More studies are needed to uncover predictive biomarkers of benefit to gefitinib.

Design and validation issues in RNA-seq experiments

The next-generation sequencing technologies are being rapidly applied in biological research. Tens of millions of short sequences generated in a single experiment provide us enormous information on genome composition, genetic variants, gene expression levels and protein binding sites depending on the applications. Various methods are being developed for analyzing the data generated by these technologies. However, the relevant experimental design issues have rarely been discussed. In this review, we use RNA-seq as an example to bring this topic into focus and to discuss experimental design and validation issues pertaining to next-generation sequencing in the quantification of transcripts.

Whole mouse blood microRNA as biomarkers for exposure to γ-rays and (56)Fe ion

PURPOSE

Biomarkers of ionising radiation exposure are useful in a variety of scenarios, such as medical diagnostic imaging, occupational exposures, and spaceflight. This study investigates to what extent microRNA (miRNA) expression signatures in mouse peripheral blood can be used as biomarkers for exposures to radiation with low and high linear energy transfers.

MATERIALS AND METHODS

Mice were irradiated with doses of 0.5, 1.5, or 5.0 Gy γ-rays (dose rate of 0.0136 Gy/s) or with doses of 0.1 or 0.5 Gy (56)Fe ions (dose rate of 0.00208 Gy/s). Total RNA was isolated from whole blood at 6 h or 24 h after irradiation. Three animals per irradiation condition were used. Differentially expressed miRNA were determined by means of quantitative real-time polymerase chain reaction.

RESULTS

miRNA expression signatures were radiation type-specific and dose- and time-dependent. The differentially expressed miRNA were expressed in either one condition (71%) or multiple conditions (29%). Classifiers based on the differentially expressed miRNA predicted radiation type or dose with accuracies between 75% and 100%. Gene-ontology analyses show that miRNA induced by irradiation are involved in the control of several biological processes, such as mRNA transcription regulation, nucleic-acid metabolism, and development.

CONCLUSION

miRNA signatures induced by ionising radiation in mouse blood are radiation type- and radiation dose-specific. These findings underline the complexity of the radiation response and the importance of miRNA in it.

Designing, performing, and interpreting a microarray-based gene expression study

Microarray-based assays have significantly expanded their scope and range of applications over the last 10 years, and--at least for gene expression--can be considered mainstream applications. High-throughput, microarray-based gene expression studies have proven particularly useful in the study of neurodegenerative diseases, for which they have provided key insights in understanding disease pathogenesis, regional and cellular specificity, and identification of therapeutic targets. Even though many experimental steps are currently performed in specialized core facilities, the key steps of a microarray study--experimental design, and data analysis and interpretation--are performed by the primary investigator. Knowledge of the issues related to these key steps is essential to properly perform and interpret a microarray experiment and constitutes the main focus of the present chapter. The basic analytical steps are covered, and annotated R code for the analysis of a published dataset is provided.

Clinical variations modulate patterns of gene expression and define blood biomarkers in major depression

The aim of the study is to compare the expression level of candidate genes between patients suffering from a severe major depressive episode (MDE) and controls, and also among patients during MDE evolution. After a comprehensive review of the biological data related to mood disorders, we initiated a hypothesis-driven exploration of candidate mRNAs. Using RT-qPCR, we analyzed peripheral blood mononuclear cells (PBMCs) mRNA obtained from a homogeneous population of 11 patients who suffered from severe melancholic MDE. To assess the evolution of MDE, we analyzed PBMC mRNAs that were collected on Day 1 and 8 weeks later. Data from these patient samples were analyzed in comparison to age- and sex-matched healthy controls. Among 40 candidate genes consistently transcribed in PBMCs, 10 were differentially expressed in at least one comparison. We found that variations of mRNA levels for NRG1, SORT1 and TPH1 were interesting state-dependent biological markers of the disease. We also observed that variations in other mRNA expression were associated with treatment efficacy or clinical improvement (CREB1, HDAC5, HSPA2, HTR1B, HTR2A, and SLC6A4/5HTT). Significantly, 5HTT exhibited a strong correlation with clinical score evolution. We also found a state-independent marker, IL10. Moreover, the analysis of 2 separate MDEs concerning a same patient revealed comparable results for the expression of CREB1, HSPA2, HTR1B, NRG1 and TPH1. Overall, our results indicate that PBMCs obtained at different time points during MDE progression represent a promising avenue to discover biological markers for depression.

Intertwining threshold settings, biological data and database knowledge to optimize the selection of differentially expressed genes from microarray

Background

Many tools used to analyze microarrays in different conditions have been described. However, the integration of deregulated genes within coherent metabolic pathways is lacking. Currently no objective selection criterion based on biological functions exists to determine a threshold demonstrating that a gene is indeed differentially expressed.

Methodology/Principal Findings

To improve transcriptomic analysis of microarrays, we propose a new statistical approach that takes into account biological parameters. We present an iterative method to optimise the selection of differentially expressed genes in two experimental conditions. The stringency level of gene selection was associated simultaneously with the p-value of expression variation and the occurrence rate parameter associated with the percentage of donors whose transcriptomic profile is similar. Our method intertwines stringency level settings, biological data and a knowledge database to highlight molecular interactions using networks and pathways. Analysis performed during iterations helped us to select the optimal threshold required for the most pertinent selection of differentially expressed genes.

Conclusions/Significance

We have applied this approach to the well documented mechanism of human macrophage response to lipopolysaccharide stimulation. We thus verified that our method was able to determine with the highest degree of accuracy the best threshold for selecting genes that are truly differentially expressed.

Assessment of LD matrix measures for the analysis of biological pathway association

Complex diseases will have multiple functional sites, and it will be invaluable to understand the cross-locus interaction in terms of linkage disequilibrium (LD) between those sites (epistasis) in addition to the haplotype-LD effects. We investigated the statistical properties of a class of matrix-based statistics to assess this epistasis. These statistical methods include two LD contrast tests (Zaykin et al., 2006) and partial least squares regression (Wang et al., 2008). To estimate Type 1 error rates and power, we simulated multiple two-variant disease models using the SIMLA software package. SIMLA allows for the joint action of up to two disease genes in the simulated data with all possible multiplicative interaction effects between them. Our goal was to detect an interaction between multiple disease-causing variants by means of their linkage disequilibrium (LD) patterns with other markers. We measured the effects of marginal disease effect size, haplotype LD, disease prevalence and minor allele frequency have on cross-locus interaction (epistasis). In the setting of strong allele effects and strong interaction, the correlation between the two disease genes was weak (r=0.2). In a complex system with multiple correlations (both marginal and interaction), it was difficult to determine the source of a significant result. Despite these complications, the partial least squares and modified LD contrast methods maintained adequate power to detect the epistatic effects; however, for many of the analyses we often could not separate interaction from a strong marginal effect. While we did not exhaust the entire parameter space of possible models, we do provide guidance on the effects that population parameters have on cross-locus interaction.

Parallel multiplicity and error discovery rate (EDR) in microarray experiments

BACKGROUND

In microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statistical tests. It is important to control the number of false positives or errors that are present in the resultant DEG list. To date, more than 20 different multiple test methods have been reported that compute overall Type I error rates in microarray experiments. However, these methods share the following dilemma: they have low power in cases where only a small number of DEGs exist among a large number of total genes on the array.

RESULTS

This study contrasts parallel multiplicity of objectively related tests against the traditional simultaneousness of subjectively related tests and proposes a new assessment called the Error Discovery Rate (EDR) for evaluating multiple test comparisons in microarray experiments. Parallel multiple tests use only the negative genes that parallel the positive genes to control the error rate; while simultaneous multiple tests use the total unchanged gene number for error estimates. Here, we demonstrate that the EDR method exhibits improved performance over other methods in specificity and sensitivity in testing expression data sets with sequence digital expression confirmation, in examining simulation data, as well as for three experimental data sets that vary in the proportion of DEGs. The EDR method overcomes a common problem of previous multiple test procedures, namely that the Type I error rate detection power is low when the total gene number used is large but the DEG number is small.

CONCLUSIONS

Microarrays are extensively used to address many research questions. However, there is potential to improve the sensitivity and specificity of microarray data analysis by developing improved multiple test comparisons. This study proposes a new view of multiplicity in microarray experiments and the EDR provides an alternative multiple test method for Type I error control in microarray experiments.

Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms

BACKGROUND

data generated using 'omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of subjects in the study. In this paper, we consider issues relevant in the design of biomedical studies in which the goal is the discovery of a subset of features and an associated algorithm that can predict a binary outcome, such as disease status. We compare the performance of four commonly used classifiers (K-Nearest Neighbors, Prediction Analysis for Microarrays, Random Forests and Support Vector Machines) in high-dimensionality data settings. We evaluate the effects of varying levels of signal-to-noise ratio in the dataset, imbalance in class distribution and choice of metric for quantifying performance of the classifier. To guide study design, we present a summary of the key characteristics of 'omics' data profiled in several human or animal model experiments utilizing high-content mass spectrometry and multiplexed immunoassay based techniques.

RESULTS

the analysis of data from seven 'omics' studies revealed that the average magnitude of effect size observed in human studies was markedly lower when compared to that in animal studies. The data measured in human studies were characterized by higher biological variation and the presence of outliers. The results from simulation studies indicated that the classifier Prediction Analysis for Microarrays (PAM) had the highest power when the class conditional feature distributions were Gaussian and outcome distributions were balanced. Random Forests was optimal when feature distributions were skewed and when class distributions were unbalanced. We provide a free open-source R statistical software library (MVpower) that implements the simulation strategy proposed in this paper.

CONCLUSION

no single classifier had optimal performance under all settings. Simulation studies provide useful guidance for the design of biomedical studies involving high-dimensionality data.

Reassessing design and analysis of two-colour microarray experiments using mixed effects models

Gene expression microarray studies have led to interesting experimental design and statistical analysis challenges. The comparison of expression profiles across populations is one of the most common objectives of microarray experiments. In this manuscript we review some issues regarding design and statistical analysis for two-colour microarray platforms using mixed linear models, with special attention directed towards the different hierarchical levels of replication and the consequent effect on the use of appropriate error terms for comparing experimental groups. We examine the traditional analysis of variance (ANOVA) models proposed for microarray data and their extensions to hierarchically replicated experiments. In addition, we discuss a mixed model methodology for power and efficiency calculations of different microarray experimental designs.

CGHpower: exploring sample size calculations for chromosomal copy number experiments

BACKGROUND

Determining a suitable sample size is an important step in the planning of microarray experiments. Increasing the number of arrays gives more statistical power, but adds to the total cost of the experiment. Several approaches for sample size determination have been developed for expression array studies, but so far none has been proposed for array comparative genomic hybridization (aCGH).

RESULTS

Here we explore power calculations for aCGH experiments comparing two groups. In a pilot experiment CGHpower estimates the biological diversity between groups and provides a statistical framework for estimating average power as a function of sample size. As the method requires pilot data, it can be used either in the planning stage of larger studies or in estimating the power achieved in past experiments.

CONCLUSIONS

The proposed method relies on certain assumptions. According to our evaluation with public and simulated data sets, they do not always hold true. Violation of the assumptions typically leads to unreliable sample size estimates. Despite its limitations, this method is, at least to our knowledge, the only one currently available for performing sample size calculations in the context of aCGH. Moreover, the implementation of the method provides diagnostic plots that allow critical assessment of the assumptions on which it is based and hence on the feasibility and reliability of the sample size calculations in each case.The CGHpower web application and the program outputs from evaluation data sets can be freely accessed at http://www.cangem.org/cghpower/

Protein mass spectra data analysis for clinical biomarker discovery: a global review

The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years there has been a growing interest in using high throughput technologies for the detection of such biomarkers. In particular, mass spectrometry appears as an exciting tool with great potential. However, to extract any benefit from the massive potential of clinical proteomic studies, appropriate methods, improvement and validation are required. To better understand the key statistical points involved with such studies, this review presents the main data analysis steps of protein mass spectra data analysis, from the pre-processing of the data to the identification and validation of biomarkers.