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Recently, preclinical microarray experiments have become increasingly common laboratory tools to investigate the activity of thousands of genes simultaneously and their response to a certain treatment (Amaratunga and Cabrera 2004). In some experiments, in addition to the gene expressions, other responses are also available. In such situations, the primary question of interest is to identify whether or not the gene expressions can serve as biomarkers for the responses. In addition to gene expressions, metabolites are potential biomarkers for some responses as well. In the present study, we focus on the identification of genomic biomarkers, based on gene and metabolite expression for depression. One measure of the level of depression is the Hamilton Depression Scale (HDS or HAMD) which is a test measuring the severity of depressive symptoms in individuals. The data for this study are a result of a clinical trial in which both HAMD and gene/metabolites expression were measured. We use three modeling approaches commonly used in the surrogate marker validation theory to select and evaluate a set of genes and metabolites as possible biomarkers for depression, as measured by the HAMD score. In addition to gene and metabolite specific biomarkers, we use supervised principal components analysis and supervised partial least squares regression technique to construct a joint biomarker that uses information from all genes/metabolites in the array. The authors gratefully acknowledge support from IAP research Network P6/03 of the Belgian Government (Belgian Science Policy).

Conditions experienced during early development may affect both adult phenotype and performance later during life. Phenotypic traits may hence be used to indicate past growing conditions and predict future survival probabilities. Relationships between phenotypic markers and future survival are, however, highly heterogeneous, possibly because poor- and high-quality individuals cannot be morphologically discriminated when developing under good environmental conditions. Sub-optimal breeding conditions, in contrast, may unmask poor-quality individuals in a measurable way at the morphological level. We thus predict stronger associations between phenotype and performance under stress. In this field study, we test this hypothesis, experimentally challenging the homeostasis of great tit (Parus major) nestlings by short-term deprivation of parental care, which had no immediate effect on nestling fitness. The experiment was replicated during two subsequent breeding seasons with contrasting ambient weather conditions. Experimental (short-term) stress affected tarsus growth but not residual mass at fledging, whereas ambient (continuous) stress affected residual mass but not tarsus growth. Short-term stress effects on tarsus length and tarsus fluctuating asymmetry were only apparent when ambient conditions were unfavourable. Residual mass and hatching date, but none of the other phenotypic traits, predicted local survival, whereby the strength of the relationship did not vary between both years. Because effects of stress on develop- mental homeostasis are likely to be trait-specific and condition-dependent, studies on the use of phenotypic markers for individual fitness should integrate multiple traits comprising different levels of developmental complexity. © 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 103-110.

Purpose: Histone deacetylase (HDAC) inhibitors have shown promising clinical activity in the treatment of hematologic malignancies, but their activity in solid tumor indications has been limited. Most HDAC inhibitors in clinical development only transiently induce histone acetylation in tumor tissue. Here, we sought to identify a second-generation class I HDAC inhibitor with prolonged pharmacodynamic response in vivo, to assess whether this results in superior antitumoral efficacy. Experimental Design: To identify novel HDAC inhibitors with superior pharmacodynamic properties, we developed a preclinical in vivo tumor model, in which tumor cells have been engineered to express fluorescent protein dependent on HDAC1 inhibition, thereby allowing noninvasive real-time evaluation of the tumor response to HDAC inhibitors. Results: In vivo pharmacodynamic analysis of 140 potent pyrimidyl-hydroxamic acid analogues resulted in the identification of JNJ-26481585. Once daily oral administration of JNJ-26481585 induced continuous histone H3 acetylation. The prolonged pharmacodynamic response translated into complete tumor growth inhibition in Ras mutant HCT116 colon carcinoma xenografts, whereas 5-fluorouracil was less active. JNJ-26481585 also fully inhibited the growth of C170HM2 colorectal liver metastases, whereas again 5-fluorouracil/Leucovorin showed modest activity. Further characterization revealed that JNJ-26481585 is a pan-HDAC inhibitor with marked potency toward HDAC1 (IC50, 0.16 nmol/L). Conclusions: The potent antitumor activity as a single agent in preclinical models combined with its favorable pharmacodynamic profile makes JNJ-26481585 a promising second-generation HDAC inhibitor. The compound is currently in clinical studies, to evaluate its potential applicability in a broad spectrum of both solid and hematologic malignancies. (Clin Cancer Res 2009;15(22):684151)

SNP arrays offer the opportunity to get a genome-wide view on copy number alterations and are increasingly used in oncology. DNA from formalin-fixed paraffin-embedded material (FFPE) is partially degraded which limits the application of those technologies for retrospective studies. We present the use of Affymetrix GeneChip SNP6.0 for identification of copy number alterations in fresh frozen (FF) and matched FFPE samples. Fifteen pairs of adenocarcinomas with both frozen and FFPE embedded material were analyzed. We present an optimization of the sample preparation and show the importance of correcting the measured intensities for fragment length and GC-content when using FFPE samples. The absence of GC content correction results in a chromosome specific "wave pattern" which may lead to the misclassification of genomic regions as being altered. The highest concordance between FFPE and matched FF were found in samples with the highest call rates. Nineteen of the 23 high level amplifications (83%) seen using FF samples were also detected in the corresponding FFPE material. For limiting the rate of "false positive" alterations, we have chosen a conservative False Discovery Rate (FDR). We observed better results using SNP probes than CNV probes for copy number analysis of FFPE material. This is the first report on the detection of copy number alterations in FFPE samples using Affymetrix GeneChip SNP6.0.

Capsule Regrowth rate of tail feathers is more strongly affected compared to feather length and symmetry. Aims To assess the value of avian feathers as bioindicators. Methods The origin and persistence of fluctuating asymmetry (FA) in homologous pairs of regrowing feathers was studied in captive birds under different levels of environmental stress, respresented by exposure to lead (Pb). Homologous feathers of individually housed birds were plucked synchronously or with a delay of seven days. We measured growth rate, regeneration time, final size and FA of regrown feathers and related them to Pb stress. Results Asymmetry decreased as feathers reached their final length. This was not due to compensatory growth but rather a consequence of the programmed growth trajectory of single feathers. Tail feathers grown under higher Pb pollution showed increased regeneration times, decreased growth rates and shorter lengths, but no changes in development times nor in FA. For differences between both (i) original and i...

Probe-level data from Affymetrix GeneChips can be summarized in many ways to produce probe-set level gene expression measures (GEMs). Disturbingly, the different approaches not only generate quite different measures but they could also yield very different analysis results. Here, we explore the question of how much the analysis results really do differ, first at the gene level, then at the biological process level. We demonstrate that, even though the gene level results may not necessarily match each other particularly well, as long as there is reasonably strong differentiation between the groups in the data, the various GEMs do in fact produce results that are similar to one another at the biological process level. Not only that the results are biologically relevant. As the extent of differentiation drops, the degree of concurrence weakens, although the biological relevance of findings at the biological process level may yet remain.Contact: damaratu@prdus.jnj.comSupplementary information: Supplementary data are available at Bioinformatics online.

Summary Background Fibrosis progression in hepatitis C virus (HCV)-infected patients varies greatly between individuals. Chemokines recruit immune cells to the infected liver and may thus play a role in the fibrosis process. Aim To investigate plasma levels of a diverse chemokine panel in relation to liver fibrosis. Methods African-American and Caucasian HCV genotype 1 infected patients were treated with peginterferon (pegIFN) and ribavirin (RBV) for 48 weeks (VIRAHEP-C cohort). Plasma levels of 13 cytokines were studied at baseline (n = 386). Subsequently, GROα levels were assessed in a sub cohort (n = 99) at baseline, and at 4 and 12 weeks after start of pegIFN/RBV treatment. Results Increased severity of fibrosis (Ishak fibrosis score 0–2 vs. 3–6) was associated with increased plasma IP-10 (CXCL10) and IL-8 (CXCL8) levels, and decreased plasma levels of the chemokine growth-related oncogene (GRO, CXCL1-3). Plasma GRO levels were also positively correlated with platelet counts, and were higher in African-American as compared to Caucasian patients. In response to pegIFN/RBV treatment, GROα levels increased in Caucasian but not African-American patients from week 4 onwards. Conclusions The association with severity of fibrosis and platelet count positions plasma GRO as a potential biomarker for liver fibrosis in HCV-infected patients. The secretion of GRO by platelets may explain the correlation between GRO plasma level and platelet count. The ethnic difference in GRO levels both pre-treatment and in response to pegIFN/RBV might be driven by a genetic polymorphism in GROα associated with higher plasma levels and more common in the African-American population.

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein-ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD. The targets and genes are linked via PPAR signalling pathway and induction of apoptosis, generating a hypothesis for the MoA of thiazolidinediones. Our analysis show one or more underlying MoA for compounds and were well-substantiated with literature.

Bayesian modeling of dose–response data offers the possibility to establish the relationship between a clinical or a genomic response and increasing doses of a therapeutic compound and to determine the nature of the relationship wherever it exists. In this article, we focus on an order-restricted one-way ANOVA model which can be used to test the null hypothesis of no dose effect against an ordered alternative. Within the framework of the dose–response modeling, a model uncertainty can be addressed using model averaging techniques. In this setting, the uncertainty is related to the number of all possible models that can be fitted to the data and should be taken into account for both inference and estimation. In this article, we propose an order-restricted Bayesian variable selection model that addresses the model uncertainty and can be used for both inference and estimation. The proposed method is applied to two case studies and is compared to the likelihood ratio test and the multiple contrast tests in both the analyses of the case studies and a simulation study. This article has online supplementary material. Martin Otava and Ziv Shkedy gratefully acknowledge the support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy).

Inhibition of conditioned avoidance behavior in rats is generally considered predictive for antipsychotic activity in man. The present study investigated the mGlu2-mediated modulation of conditioned avoidance and compared mGlu2 agonists with available antipsychotics for their relative effects on conditioned avoidance behavior and locomotion. The mGlu2/3 orthosteric agonist 4-amino-2-thiabicyclo[3.1.0]hexane-4,6-dicarboxylic acid 2,2-dioxide (LY-404039) and mGlu2 positive allosteric modulator (PAM) 3-(cyclopropylmethyl)-7-(4-phenylpiperidin-1-yl)-8-(trifluoromethyl)[1,2,4]triazolo[4,3-a]pyridine (JNJ-42153605) inhibited avoidance and blocked escape behavior. The mGlu2/3 negative allosteric modulators (NAMs) 7-(dimethylamino)-4-(3-pyridin-3-ylphenyl)-8-(trifluoromethyl)-1,3-dihydro-2 H-1,5-benzodiazepin-2-one (JNJ-42112265) and 4-[3-(2,6-dimethylpyridin-4-yl)phenyl]-7-methyl-8-(trifluoromethyl)-1,3-dihydro-2H-1,5-benzodiazepin-2-one (RO-4491533) reversed the LY-404039-induced impairment of avoidance and escape. JNJ-42112265 also reversed the impairment of avoidance and escape induced by the mGlu2-specific PAM JNJ-42153605, suggesting that the effects on conditioned avoidance are specifically mGlu2-mediated. The mGlu2/3 antagonist (2-(2-carboxycyclopropyl)-3-(9H-xanthen-9-yl)-d-alanine (LY-341495; s.c.) reversed the LY-404039-induced escape impairment but failed to restore avoidance, suggesting interfering side effects. Like the tested antipsychotics, mGlu2/3 orthosteric and allosteric agonists inhibited avoidance behavior and locomotion at similar doses. Hence no clear-cut differences between mGlu2 modulators and currently available antipsychotics in the way they interfere with avoidance behavior in relation to inhibition of locomotion could be established.

Major depressive disorder has a large impact on patients and society and is projected to be the second greatest global burden of disease by 2020. The brain-derived neurotrophic factor (BDNF) gene is considered to be one of the important factors in the etiology of major depressive disorder. In a recent study, alpha power was found to mediate between BDNF Met and subclinical depressed mood. The current study looked at a population of patients with major depressive disorder (N = 107) to examine the association between the BDNF Val66Met polymorphism, resting state EEG alpha power, and depression severity. For this purpose, repeated-measures analysis of variance, partial correlation, and multiple linear models were used. Results indicated a negative association between parietal-occipital alpha power in the eyes open resting state and depression severity. In addition, Met/Met patients showed lower global absolute alpha power in the eyes closed condition compared with Val-carriers. These findings are in accordance with the previously uncovered pathway between BDNF Val66Met, resting state EEG alpha power, and depression severity. Additional research is needed for the clarification of this tentative pathway and its implication in personalized treatment of major depressive disorder.

Benchmark datasets are important for the validation and optimization of the analysis routes. Lately, a new benchmark dataset, ‘Platinum Spike’, for the Affymetrix GeneChip experiments has been introduced. We performed a quality check of the Platinum Spike dataset by using probe-level linear mixed models. The results have shown that there are ‘empty’ probe sets detecting transcripts, spiked in at different concentrations, and, reversely, there are probe sets that do not detect transcripts, spiked in at different concentrations, even though they were designed to do so. We proposed a formal inference procedure for testing the assumption of independence of all technical replicates in the data and concluded that for almost 10% of probe sets arrays cannot be treated independently, which has strong implications for the normalization procedures and testing for the differential expression. The proposed diagnostics procedure is used to facilitate a thorough exploration of gene expression Affymetrix data beyond the preprocessing and differential expression analysis.

The modern process of discovering candidate molecules in early drug discovery phase includes a wide range of approaches to extract vital information from the intersection of biology and chemistry. A typical strategy in compound selection involves compound clustering based on chemical similarity to obtain representative chemically diverse compounds (not incorporating potency information). In this paper, we propose an integrative clustering approach that makes use of both biological (compound efficacy) and chemical (structural features) data sources for the purpose of discovering a subset of compounds with aligned structural and biological properties. The datasets are integrated at the similarity level by assigning complementary weights to produce a weighted similarity matrix, serving as a generic input in any clustering algorithm. This new analysis work flow is semi-supervised method since, after the determination of clusters, a secondary analysis is performed wherein it finds differentially expressed genes associated to the derived integrated cluster(s) to further explain the compound-induced biological effects inside the cell. In this paper, datasets from two drug development oncology projects are used to illustrate the usefulness of the weighted similarity-based clustering approach to integrate multi-source high-dimensional information to aid drug discovery. Compounds that are structurally and biologically similar to the reference compounds are discovered using this proposed integrative approach.

Illumina bead arrays are microarrays that contain a random number of technical replicates (beads) for every probe (bead type) within the same array. Typically around 30 beads are placed at random positions on the array surface, which opens unique opportunities for quality control. Most preprocessing methods for Illumina bead arrays are ported from the Affymetrix microarray platform and ignore the availability of the technical replicates. The large number of beads for a particular bead type on the same array, however, should be highly correlated, otherwise they just measure noise and can be removed from the downstream analysis. Hence, filtering bead types can be considered as an important step of the preprocessing procedure for Illumina platform. This paper proposes a filtering method for Illumina bead arrays, which builds upon the mixed model framework. Bead types are called informative/non-informative (I/NI) based on a trade-off between within and between array variabilities. The method is illustrated on a publicly available Illumina Spike-in data set (Dunning et al., 2008) and we also show that filtering results in a more powerful analysis of differentially expressed genes.

The significance analysis of microarrays (SAM) Tusher et al. (Proc Natl Acad Sci 98:5116–5121, 2001) is a widely used testing procedure that estimates the FDR by using permutations under the assumption that all null hypotheses are true. The procedure consists of three components: (1) the adjusted test statistics, (2) an approximation of the distribution of the test statistics based on permutations, and (3) the control of the FDR. In Chap. 8, we discuss the implementation SAM for the setting of dose-response microarray experiments and illustrate how to use the IsoGene package for SAM.

In Chaps. and 8, we discussed several testing procedures to detect differentially expressed genes with monotone relationship with respect to dose. The second question of primary interest in dose-response studies is the nature (or the shape of curve) of the dose-response relationship. In the context of dose-response microarray experiments, we wish to group (or classify) genes with similar dose-response relationship. Similar to the previous chapters, the subset of genes with monotone relationship is of interest.

The aim of the analysis presented in Chap. 14 is not to detect genes with a significant dose-response relationship but to use parametric dose-response models in order to compare between several compounds or between the characteristics of the dose-response curves for several genes in a single or multiple compound experiment. The basic methodology for parametric dose-response models presented in the chapter was introduced in Chap. 4. In contrast with Chap. 10 in which the classification post-selection procedure was applied to order-restricted ANOVA models, in this chapter, we focus on the case in which several parametric models are fitted to the gene expression data and we discuss model averaging techniques for the estimation of the ED50 parameter.

In the first part of the book, we discussed different aspects of the analysis of dose-response data such as estimation, inference, and modeling. In the second part of the book, we focus on dose-response microarray experiments. Within the microarray setting, a dose-response experiment has the same structure as described in Part I of the book. The response is the gene expression at a certain dose level. The role of functional genomics, particularly in this setting, is to find indications of both safety and efficacy before the drug is administrated to patients. In Chap. 5, we give an overview about dose-response microarray experiments and their data structure.

In this article, we discuss methods to select three different types of genes (treatment related, response related, or both) and investigate whether they can serve as biomarkers for a binary outcome variable. We consider an extension of the joint model introduced by Lin et al. (2010) and Tilahun et al. (2010) for a continuous response. As the model has certain drawbacks in a binary setting, we also present a way to use classical selection methods to identify subgroups of genes, which are treatment and/or response related. We evaluate their potential to serve as biomarkers by applying DLDA to predict the response level. We gratefully acknowledge support of the IAP research network P6/03 of the Belgian Government (Belgian Science Policy).

High-density oligonucleotide microarrays are commonly used for GWAS studies as well as for tumor genome alteration identifications. The recent Affymetrix Genome-Wide SNP 6.0 microarray generation has two major advantages: (1) showing high genome coverage and (2) starting with very small amount of DNA material. The hybridization protocol needs to be standardized and highly reproducible, as DNA is first digested by restriction enzymes and then PCR-amplified to reduce genome complexity. Especially the restriction digestion step is highly sensitive to degradation of the initial material. The stronger the sample is degraded, the lower the number of restriction sites still present in the genome, and hence the less-efficient amplification step.Paraffin-embedded material generally only allows to extract partially degraded DNA, and therefore is difficult to analyze using SNP array technology. We and others (Jacobs et al., Cancer Res 67:2544-2551, 2007; Tuefferd et al., Genes Chromosomes Cancer 47:957-964, 2008) have shown that target preparation protocol can be adjusted to improve hybridization performances. The final in silico data analysis procedure should be modified accordingly to extract most of the biological information from the signal measured. By optimizing these crucial steps, it is possible to use Affymetrix SNP array 6.0 -technology in the context of genome variation, even for FFPE partially degraded material. This opens a lot of potential for large retrospective series of samples.

Hepatitis C (HCV) is a single stranded RNA virus that usually establishes persistent infection in its host. Of patients exposed to HCV, about 80% will develop chronic viral infection characterized by liver infiltration of HCV-specific and nonspecific T cells accompanied by “proinflammatory” cytokines resulting in damage to virus-infected, as well as bystander, hepatocytes with resultant fibrosis formation. About 30–35% will develop cirrhosis and once a patient is cirrhotic, there is a 1–4% annual rate of hepatocellular carcinoma development (1). Combined treatment with peginterferon (pegIFN) and ribavirin achieves sustained virological response (SVR) in 42–52% of genotype 1 patients (2–4). Unfortunately, the remainder either fail to respond, or must discontinue treatment prematurely due to adverse events. Response rates to pegIFN and ribavirin are associated with both viral and host factors. Pretreatment predictors of non-response include genotype 1 infection, high viral load (>800,000 IU/mL), advanced fibrosis or cirrhosis, high body mass index (BMI), age older than 40, and African American race (2–4). Currently, on-treatment predictors of response to pegIFN and ribavirin include viral kinetics at weeks 4 and 12. Patients who do not attain an early virologic response have only a 1–3% chance of viral clearance and therapy is usually halted (2, 5). Conversely, 87% of those who achieve a rapid virological response (defined as HCV RNA undetectable at week 4 of therapy) achieve SVR (6). While viral kinetics have proven useful, better predictors of SVR and non-response would be helpful to identify patients with the best chance of response before the initiation of combination antiviral therapy. The US population has proven to be a more difficult group to treat than many others with lower SVR rates, perhaps due in part to higher BMI’s and a greater racial variation. African-Americans (AA) harbor predominantly genotype 1 virus and have notably lower overall response rates to pegIFN and ribavirin (approximately 26–28%) compared to Caucasian Americans (CA) (7–9). Determining why AA patients respond less well to antiviral therapy with pegIFN and ribavirin compared to CA was the focus of The Study of Viral Resistance to Antiviral Therapy of Chronic Hepatitis C (VIRAHEP-C). This NIH-sponsored study examined a variety of clinical, immunologic, virologic, and host genetic causes for the lack of response to treatment, although no single factor was identified that could account for the diminished rate of response (8, 10–12). Chemokines and cytokines are attractive as potential markers for treatment outcome as they are regulators of immunity and inflammation in HCV infection. Many are modulated by exogenous interferon and play critical roles in viral clearance. Responders tend to have a lower baseline activation of the immune system prior to treatment that is more markedly induced in response to IFN treatment (13–15). Several studies have shown that the non-ELR CXC chemokine interferon-γ (IFNγ) inducible protein-10 (IP-10 or CXCL10) may be a prognostic marker for HCV treatment outcome in genotype 1 infection (15–20). Elevated pretreatment IP-10 levels correlate with nonresponse to pegIFN and ribavirin therapy although this relationship has not been validated in AA patients. More recently, data has been published on a gene polymorphism (rs12979860) upstream of IL28B that is favorably associated with treatment response to pegIFN and ribavirin in both AA and Caucasian patients (21). Regardless of race, carriage of the C allele increases treatment response rates with those who have the CC genotype having the highest SVR rates, CT intermediate and TT the lowest (21). This favorable genotype is seen more frequently in Caucasian patients and likely explains about half of the difference in response between AA and CA of European ancestry. The IL28B gene encodes interferon-λ-3 which is a type III IFN induced by viral infections (22, 23). While the mechanism underlying the association of IL28B genotype and HCV clearance has not been elucidated, modulation of the innate immune response is likely playing a role in control of this viral infection. IL28B genotyping may provide useful pretreatment stratification of patients for HCV treatment in the future but it does not completely explain response discrepancies between AA and CA patients. In this study, we measured pre-treatment IP-10 levels in serum samples from 272 patients in the VIRAHEP-C cohort (115 non-responders and 157 SVR). This analysis demonstrated IP-10 to be equally predictive of SVR in both CA and AA patients. We then assessed the combination of pretreatment serum IP-10 levels with IL28B genotype as predictors of response to pegIFN and ribavirin in this cohort.

High-density oligonucleotide microarrays are commonly used for GWAS studies as well as for tumor genome alteration identifications. The recent Affymetrix Genome-Wide SNP 6.0 microarray generation has two major advantages: (1) showing high genome coverage and (2) starting with very small amount of DNA material. The hybridization protocol needs to be standardized and highly reproducible, as DNA is first digested by restriction enzymes and then PCR-amplified to reduce genome complexity. Especially the restriction digestion step is highly sensitive to degradation of the initial material. The stronger the sample is degraded, the lower the number of restriction sites still present in the genome, and hence the less-efficient amplification step.Paraffin-embedded material generally only allows to extract partially degraded DNA, and therefore is difficult to analyze using SNP array technology. We and others (Jacobs et al., Cancer Res 67:2544-2551, 2007; Tuefferd et al., Genes Chromosomes Cancer 47:957-964, 2008) have shown that target preparation protocol can be adjusted to improve hybridization performances. The final in silico data analysis procedure should be modified accordingly to extract most of the biological information from the signal measured. By optimizing these crucial steps, it is possible to use Affymetrix SNP array 6.0 -technology in the context of genome variation, even for FFPE partially degraded material. This opens a lot of potential for large retrospective series of samples.

of response (28.9%), personal reasons (29.6%), adverse events (AEs, 38.2%), decompensation (1.3%). 263/500 (52.6%) obtained a sustained virological response (SVR), 71 (14.2%) relapsed and 61 (12.2%) were non-responders. Treatment outcome information were not available in 105/500 (21%) since: lost on-treatment (33.3%), lost during follow-up (25.7%), withdrawn for AEs (19.1%), administrative reasons (21.9%). Based on HCV genotype, intention-to-treat (n = 500) and per-protocol (n = 395) analyses of SVR rates are shown in Table 1.

In the context of the plethora of data currently generated in molecular biology, the paper by Bourgon et al. in PNAS (1) is pivotal, because it shows that an initial data filter can appropriately increase the detection power of a high-throughput experiment. Bourgon et al. (1) showed that filtering on overall variance outperforms filtering on overall mean, but they do not address two weaknesses of the methodology. First, because filtering is done on the overall variance, it does not disentangle the biological variation (containing the potentially interesting signals) from the technical variation (i.e., the measurement noise). Second, the threshold choice when the overall variation should be considered too low is very arbitrary and makes the method … 1To whom correspondence should be addressed. E-mail: wtalloen{at}its.jnj.com.

Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called ‘FABIA: Factor Analysis for Bicluster Acquisition’. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques. Results: On 100 simulated datasets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. On these datasets, FABIA was able to separate spurious biclusters from true biclusters by ranking biclusters according to their information content. FABIA was tested on three microarray datasets with known subclusters, where it was two times the best and once the second best method among the compared biclustering approaches. Availability: FABIA is available as an R package on Bioconductor (http://www.bioconductor.org). All datasets, results and software are available at http://www.bioinf.jku.at/software/fabia/fabia.html Contact: hochreit@bioinf.jku.at Supplementary information: Supplementary data are available at Bioinformatics online.

The strength and weakness of microarray technology can be attributed to the enormous amount of information it is generating. To fully enhance the benefit of microarray technology for testing differentially expressed genes and classification, there is a need to minimize the amount of irrelevant genes present in microarray data. A major interest is to use probe-level data to call genes informative or noninformative based on the trade-off between the array-to-array variability and the measurement error. Existing works in this direction include filtering likely uninformative sets of hybridization (FLUSH; Calza et al., 2007) and I/NI calls for the exclusion of noninformative genes using FARMS (I/NI calls; Talloen et al., 2007; Hochreiter et al., 2006). In this paper, we propose a linear mixed model as a more flexible method that performs equally good as I/NI calls and outperforms FLUSH. We also introduce other criteria for gene filtering, such as, R2 and intra-cluster correlation. Additionally, we include some objective criteria based on likelihood ratio testing, the Akaike information criteria (AIC; Akaike, 1973) and the Bayesian information criterion (BIC; Schwarz, 1978 ). Based on the HGU-133A Spiked-in data set, it is shown that the linear mixed model approach outperforms FLUSH, a method that filters genes based on a quantile regression. The linear model is equivalent to a factor analysis model when either the factor loadings are set to a constant with the variance of the latent factor equal to one, or if the factor loadings are set to one together with unconstrained variance of the latent factor. Filtering based on conditional variance calls a probe set informative when the intensity of one or more probes is consistent across the arrays, while filtering using R2 or intra-cluster correlation calls a probe set informative only when average intensity of a probe set is consistent across the arrays. Filtering based on likelihood ratio test AIC and BIC are less stringent compared to the other criteria.

The Significance Analysis of Microarrays (SAM; Tusher et al., 2001) method is widely used in analyzing gene expression data while controlling the FDR by using resampling-based procedure in the microarray setting. One of the main components of the SAM procedure is the adjustment of the test statistic. The introduction of the fudge factor to the test statistic aims at deflating the large value of test statistics due to the small standard error of gene-expression. Lin et al. (2008) pointed out that the fudge factor does not effectively improve the power and the control of the FDR as compared to the SAM procedure without the fudge factor in the presence of small variance genes. Motivated by the simulation results presented in Lin et al. (2008), in this article, we extend our study to compare several methods for choosing the fudge factor in the modified t-type test statistics and use simulation studies to investigate the power and the control of the FDR of the considered methods.

Multitargeted kinase inhibitors have shown clinical efficacy in a range of cancer types. However, two major problems associated with these drugs are the low fraction of patients for which these treatments provide initial clinical benefit and the occurrence of resistance during prolonged therapy. Several types of predictive biomarkers have been suggested, such as expression level and phosphorylation status of the major targeted kinase(s), mutational status of the kinases involved and of key components of the downstream signaling cascades, and gene expression signatures. In this work, we describe the development of a response prediction platform that does not require prior knowledge of the relevant kinases targeted by the inhibitor; instead, a phosphotyrosine peptide profile using peptide arrays with a kinetic readout is derived in lysates in the presence and absence of a kinase inhibitor. We show in a range of cell lines and in xenograft tumors that this approach allows for the stratification of responders and nonresponders to a multitargeted kinase inhibitor. [Mol Cancer Ther 2009;8(7):1846–55]

Butterfly wing characteristics are extensively used as model system in studies of development, quantitative genetics and phenotypic plasticity. In spite of its evolutionary relevance, however, the effect of stress on the expression of genetic variation itself has only rarely been studied. In this paper, we explore genetic variation of wing characteristics of the Speckled wood Pararge aegeria along a host plant drought stress gradient. Forewing area, basal and distal degree of melanization and the area of five yellow wing spots were measured. We found an increase in (additive) genetic variation in degree of melanization at higher drought stress, and a similar, yet non-significant, relationship for forewing size. As a result, both the upper limits of the narrow-sense heritability and the coefficient of additive genetic variation of wing size and melanization increased with drought stress. Patterns for the different yellow wing spots were less consistent, suggesting trait-specificity in the relationships between genetic variation and environmental stress.

Specific aspects of analyzing microarray data are described, which include size and shape of gene expression profiles, taking logarithms, and the biplot graphic for visualizing associations between genes and cells. Three methods of factor analysis are presented that find application to microarray data: principal component analysis, correspondence analysis, and spectral map analysis. It is shown that these three methods differ only in the way the data are preprocessed and that spectral map analysis has advantages over the other two methods. Two graphical devices that are helpful in exploring and interpreting microarray data are also described.

Background & Aims: Irritable bowel syndrome (IBS) has been associated with mucosal dysfunction, mild inflammation, and altered colonic bacteria. We used microarray expression profiling of sigmoid colon mucosa to assess whether there are stably expressed sets of genes that suggest there are objective molecular biomarkers associated with IBS. Methods: Gene expression profiling was performed using Human Genome U133 Plus 2.0 (Affymetrix) GeneChips with RNA from sigmoid colon mucosal biopsy specimens from 36 IBS patients and 25 healthy control subjects. Real-time quantitative polymerase chain reaction was used to confirm the data in 12 genes of interest. Statistical methods for microarray data were applied to search for differentially expressed genes, and to assess the stability of molecular signatures in IBS patients. Results: Mucosal gene expression profiles were consistent across different sites within the sigmoid colon and were stable on repeat biopsy over approximately 3 months. Differentially expressed genes suggest functional alterations of several components of the host mucosal immune response to microbial pathogens. The most strikingly increased expression involved a yet uncharacterized gene, DKFZP564O0823. Identified specific genes suggest the hypothesis that molecular signatures may enable distinction of a subset of IBS patients from healthy controls. By using 75% of the biopsy specimens as a validation set to develop a gene profile, the test set (25%) was predicted correctly with approximately 70% accuracy. Conclusions: Mucosal gene expression analysis shows there are relatively stable alterations in colonic mucosal immunity in IBS. These molecular alterations provide the basis to test the hypothesis that objective biomarkers may be identified in IBS and enhance understanding of the disease.

Nonidentical development of bilateral traits due to disturbing genetic or developmental factors is called fluctuating asymmetry (FA) if such deviations are continuously distributed. Fluctuating asymmetry is believed to be a reliable indicator of the fitness and welfare of an animal. Despite an increasing body of research, the link between FA and animal performance or welfare is reported to be inconsistent, possibly, among other reasons, due to inaccurate measuring protocols or incorrect statistical analyses. This paper reviews problems of interpreting FA results in poultry and provides guidelines for the measurement and analysis of FA, applied to broilers. A wide range of morphological traits were measured by 7 different techniques (ranging from measurements on living broilers or intact carcasses to X-rays, bones, and digital images) and evaluated for their applicability to estimate FA. Following 4 selection criteria (significant FA, absence of directional asymmetry or antisymmetry, absence of between-trait correlation in signed FA values, and high signal-to-noise ratio), from 3 to 14 measurements per method were found suitable for estimating the degree of FA. The accuracy of FA estimates was positively related to the complexity and time investment of the measuring method. In addition, our study clearly shows the importance of securing adequate statistical power when designing FA studies. Repeatability analyses of FA estimates indicated the need for larger sample sizes, more repeated measurements, or both, than are commonly used in FA studies.

Motivation: DNA microarray technology typically generates many measurements of which only a relatively small subset is informative for the interpretation of the experiment. To avoid false positive results, it is therefore critical to select the informative genes from the large noisy data before the actual analysis. Most currently available filtering techniques are supervised and therefore suffer from a potential risk of overfitting. The unsupervised filtering techniques, on the other hand, are either not very efficient or too stringent as they may mix up signal with noise. We propose to use the multiple probes measuring the same target mRNA as repeated measures to quantify the signal-to-noise ratio of that specific probe set. A Bayesian factor analysis with specifically chosen prior settings, which models this probe level information, is providing an objective feature filtering technique, named informative/non-informative calls (I/NI calls).Results: Based on 30 real-life data sets (including various human, rat, mice and Arabidopsis studies) and a spiked-in data set, it is shown that I/NI calls is highly effective, with exclusion rates ranging from 70% to 99%. Consequently, it offers a critical solution to the curse of high-dimensionality in the analysis of microarray data.Availability: This filtering approach is publicly available as a function implemented in the R package FARMS (www.bioinf.jku.at/software/farms/farms.html).Contact: wtalloen@prdbe.jnj.comSupplementary information: Supplementary data are available at Bioinformatics online.

SummaryPhenotypic and genetic variation and covariation in developmental instability (DI) have been the subject of many debates. In this paper we develop and apply a statistical model in a Bayesian context to analyse different traits simultaneously in a multivariate model of DI. We apply the model to measurements of yellow spots on the front wing of the Speckled Wood butterfly (Pararge aegeriaL.) in a full-sib breeding experiment. We estimated the posterior distribution of the broad-sense heritability of DI averaged across the five yellow spots, which had a median of 0·19 and a 95% credibility interval ranging between 0·04 and 0·64. Phenotypic and genetic correlations in DI could not be estimated accurately with the present sample size. Yellow spots 4 and 5 appeared to show some degree of developmental integration. The importance of this model and its possible extensions are discussed.

It has recently been shown that disease associated gene signatures can be identified by profiling tissue other than the disease related tissue. In this paper, we investigate gene signatures for Irritable Bowel Syndrome (IBS) using gene expression profiling of both disease related tissue (colon) and surrogate tissue (rectum). Gene specific joint ANOVA models were used to investigate differentially expressed genes between the IBS patients and the healthy controls taken into account both intra and inter tissue dependencies among expression levels of the same gene. Classification algorithms in combination with feature selection methods were used to investigate the predictive power of gene expression levels from the surrogate and the target tissues. We conclude based on the analyses that expression profiles of the colon and the rectum tissue could result in better predictive accuracy if the disease associated genes are known.

Chemical and biological monitoring of pollution in the aquatic environment is essential to assess the quality of surface waters. Zebra mussels (Dreissena polymorpha) have been used extensively to monitor pollution in freshwater environments, especially in bioaccumulation studies, whereby pollutant levels in tissues have been used as a measure of exposure. However, there is a need for good biomarkers that reflect the impact of exposure to pollutants. Bilateral asymmetry, commonly used as a measure of developmental instability, has a high potential as a biomarker to monitor stress caused by pollution. Nevertheless, until recently, no studies have evaluated bilateral asymmetry as a biomarker in zebra mussels. Biomarkers related to the energy metabolism may give a good indication of the physiological cost of exposure to pollution.In this study, we investigated whether the physiological condition (energy reserves and condition indices) and bilateral asymmetry of shells of zebra mussels are potentially useful biomarkers to monitor the impact of micropollution, such as trace metals, polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and di(p-chlorophenyl) dichloroethylene (p,p'-DDE) in the freshwater environment. Bilateral asymmetry of the zebra mussel shells was examined with respect to levels of pollutants accumulated in the mussels and compared to the physiological condition of the mussels.Levels of PCBs and several trace metals (especially Cd, Cu and Zn) were very high in four of the six sampling locations and in some locations the physiological condition of the mussels was significantly depressed. Nevertheless we did not find any relation (on individual or population level) with bilateral asymmetry of zebra mussel shells. Therefore our results suggest that bilateral asymmetry of zebra mussel shells is not a good measure for the impact of pollution in freshwater ecosystems. The energy reserves and condition indices, on the other hand, gave a valuable indication of the physiological condition of zebra mussels and are useful to monitor the impact of pollution if physiological and environmental factors are taken into account.

The developmental mechanisms behind developmental instability (DI) are only poorly understood. Nevertheless, fluctuating asymmetry (FA) is often used a surrogate for DI. Based on statistical arguments it is often assumed that individual levels of FA are only weakly associated with the underlying DI. Patterns in FA therefore need to be interpreted with caution, and should ideally be transformed into patterns in DI. In order to be able to achieve that, assumptions about the distribution of developmental errors must be made. Current models assume that errors during development are additive and independent such that they yield a normal distribution. The observation that the distribution of FA is often leptokurtic has been interpreted as evidence for between-individual variation in DI. This approach has led to unrealistically high estimates of between-individual variation in DI, and potentially incorrect interpretations of patterns in FA, especially at the individual level. Recently, it has been suggested that the high estimates of variation in DI may be biased upward because either developmental errors are log-normal or gamma distributed and/or low measurement resolution of FA. A proper estimation of the amount (and shape) of heterogeneity in DI is crucial for the interpretation of patterns in FA and their transformation into patterns in DI. Yet, incorrect model assumptions may render misleading inferences. We therefore develop a statistical model to evaluate the sensitivity of results under the normal error model against the two alternative distributions as well as to investigate the importance of low measurement resolution. An analysis of simulated and empirical data sets indicated that bias due to misspecification of the developmental error distribution can be substantial, yet, did not appear to reduce estimates of variation in DI in empirical data sets to a large extent. Effects of low measurement resolution were neglectable. The importance of these results are discussed in the context of the interpretation of patterns in FA.

To organize and prioritise species-specific conservation efforts, we delineate ‘functional conservation units' for the threatened Alcon Blue butterfly Maculinea alcon in Belgium. We used detailed distribution data on the butterfly, its host plant and its habitat, present-day population sizes and its mobility and colonization capacity to determine functional conservation units (FCUs) on different spatial scales: FCU-1, i.e., the 12 presently occupied habitat patches plus the area within a range of 500 m surrounding them (the maximum local movement distance, based on mark-release-recapture data), FCU-2, i.e., the areas within a range of 2 km around the occupied habitat patches (the maximum observed colonization capacity) and FCU-3, i.e., potential re-introduction sites (sites where M. alcon went extinct recently). We suggest different management and planning measures for each type of functional conservation unit and discuss translocation and re-introduction as ‘intensive care' conservation measures for this threatened and sedentary species.

Evolutionary studies typically focus on adaptations to particular environmental conditions, thereby often ignoring the role of possible constraints. Here we focus on the case of variation in dorsal wing melanization in a satyrine butterfly Pararge aegeria. Because melanin is a complex polymer, its synthesis may be constrained if ambient conditions limit the resource budget. This hypothesis was tested by comparing melanization among butterflies that fed as larvae on host grasses experiencing different drought-stress treatments. Treatment differences were validated both at the level of the host plant (nitrogen, carbonate, and water content) and of the butterfly (life-history traits: survival, development time, and size at maturity). Melanization rate was measured as average gray value of the basal dorsal wing area. This area, close to the thorax, is known to be functionally significant for basking in order to thermoregulate. Individuals reared on drought-stressed host plants developed paler wings, and development of darker individuals was slower and less stable as estimated by their level of fluctuating asymmetry. These results provide evidence that melanin is indeed costly to synthesize, and that differences in environmental quality can induce phenotypic variation in wing melanization. Therefore, studies dealing with spatial and/or temporal patterns of variation in wing melanization should not focus on adaptive explanations alone, but rather on a cost-benefit balance under particular sets of environmental conditions.