Analysis of global gene expression by DNA microarrays is widely used in experimental molecular biology. However, the complexity of such high-dimensional data sets makes it difficult to fully understand the underlying biological features present in the data.Pseudomonas aeruginosa isolated from the airways of cystic fibrosis patients.The aim of this study is to introduce a method for DNA microarray analysis that provides an intuitive interpretation of data through dimension reduction and pattern recognition. We present the first “Archetypal Analysis” of global gene expression. The analysis is based on microarray data from five integrated studies of Our analysis clustered samples into distinct groups with comprehensible characteristics since the archetypes representing the individual groups are closely related to samples present in the data set. Significant changes in gene expression between different groups identified adaptive changes of the bacteria residing in the cystic fibrosis lung. The analysis suggests a similar gene expression pattern between isolates with a high mutation rate (hypermutators) despite accumulation of different mutations for these isolates. This suggests positive selection in the cystic fibrosis lung environment, and changes in gene expression for these isolates are therefore most likely related to adaptation of the bacteria.P. aeruginosa. The combination of clustering and matrix factorization made it possible to reveal minor similarities among different groups of data, which other analytical methods failed to identify. We suggest that this analysis could be used to supplement current methods used to analyze DNA microarray data.Archetypal analysis succeeded in identifying adaptive changes of  DNA microarrays simultaneously monitor expression levels of thousands of genes, and this technology is widely used in experimental molecular biology. However, the complexity of such high-dimensional data sets makes it difficult to fully comprehend the underlying biological features present in the data. Different dimension reduction techniques aim to find patterns in high complexity data sets. The choice of analytical method can influence the interpretation of the data, and it can be useful to combine different methods.K-means clustering and principal component analysis (PCA) are techniques for unsupervised pattern recognition commonly used in microarray data analysis. K-means clustering aims to group samples (or genes) with similar behavior . Each saClustering approaches give easy interpretable features but pay a price in terms of modeling flexibility, because each sample must be grouped in only one cluster and no intermediate between clusters is allowed. PCA on the other hand can lead to complex representations from which we learn relatively little about the data. Archetypal analysis (AA) combines the virtues of both clustering and PCA in that AA results in easy interpretable components that have added flexibility over clustering by allowing intermediates . Cutler AA estimates the principle convex hull of a data set. The convex hull can be described as a minimal set of points that can wrap a given data set. The idea of AA is to find a few representative points (archetypes) in a data set such that all data can be described as a convex combination of these archetypes. The archetypes are related to experimental data but they are not necessarily observed points in the data set. Each archetype represents distinct characteristic features. Explaining data as a combination of these features can make the data set easier to interpret . Unlike AA has been shown to be useful in extracting features from different high-dimensional data sets. So far, the method has not been applied to gene expression data despite clear advantages such as the intuitive and straightforward interpretation of the AA components. AA can be considered an unmixing approach that decomposes each observation into a weighted average of features defining distinct aspects in the data. In the related unmixing framework for gene expression data proposed in  the dataVariation of phenotypes found in nature has recently been described as weighted averages of archetypes, where archetypes represent phenotypes that are optimized for a single adaptive task . The phePseudomonas aeruginosa isolated from the lungs of cystic fibrosis patients. The five data sets were based on different experimental conditions including growth medium and growth state during cell harvesting. A method like PCA most likely captures this experimental variance in the first few components. The first components will make restrictions for the additional components due to the orthogonality constraint, and information that is linked to the real biological difference between the samples may be difficult to extract. Since AA is not restricted by orthogonality like PCA, we propose that AA will be able to extract biological information despite the different experimental conditions of the five studies. We show how AA succeeds in identifying genes that undergo changes in gene expression during evolutionary adaptation of the bacteria to the cystic fibrosis lung.In this study, we apply AA to five gene expression data sets for cel-files in R by use of the package affy[robust multiarray average expression measure [The diagram in Figure kage affy. Then, dkage affy and gene measure . The nexin vitro studies (study 1–4) of P. aeruginosa sampled from CF lung infections. Three of the data sets were obtained from the online NCBI Gene Expression Omnibus (GEO) Database with the accession numbers GSE21966 [We analyzed cDNA microarray data from four previously published GSE21966 , GSE3122GSE21966  and GSE1GSE21966 . The fou. This gene expression data set consists of 17 samples (in duplicates) representing clonal isolates sampled from three CF patients on timescales ranging from 3 months to 8 years. Two of the patients each harbored a unique clone (A and B), whereas a strain replacement occurred in the third patient, and two individual clones Ca and Cb were therefore isolated from this patient. For each isolate, information about colony morphology is available, and for the present analysis, we grouped these morphotypes into two categories: Mucoid ('mucoid’ morphotypes) and non-mucoid ('dwarf’ and 'classic’ morphotypes).Study 1 . This ge600) of 0.4-0.5 prior to Affymetrix P. aeruginosa GeneChip microarray analysis. The strains P. aeruginosa PAO1 and P. aeruginosa PA14 (referred to as PAO1 and PA14 respectively) were included as controls in their study. PAO1 was originally isolated from a burn wound [P. aeruginosa. PA14 is a highly virulent laboratory strain that most likely represents an environmental strain of P. aeruginosa, although it has also been isolated from CF lungs in Europe [.The experimental procedures are fully described by Huse et al. . In briern wound  and has n Europe ,22.. This data set consists of different clonal lineages isolated from the lungs of CF patients  between 1973 and 2008 spanning early stage infection to chronic stage infection [in vitro by allelic replacement of its mucA allele [600 of 0.5 (OD600 = 1 for samples #129-140) prior to Affymetrix P. aeruginosa GeneChip microarray analysis. PAO1 was included as control in this study.Study 2 . This danfection . Many of. This data set consists of twelve clonally related, sequential mucoid and non-mucoid paired P. aeruginosa isolates. The isolates were obtained from three CF patients. All isolates from study 3 share the same clonal type called “DK1”. Cells were grown in beef broth (BB) to an OD600 of 1 prior to Affymetrix P. aeruginosa GeneChip microarray analysis. Each experiment was done in duplicate. Isolates with high mutation rates  were identified within the data set.Study 3 . This da. This data set consists of eight sequential isogenic isolates recovered over a period of three to five years from a single CF patient (patient M). The isolates included both hypermutators and non-hypermutators and one isolate was mucoid. Cells were grown in LB medium and harvested during late-logarithmic growth phase at optical density above 3. Each sample was triplicated.Study 4 . This da600 of 1 prior to Affymetrix P. aeruginosa GeneChip microarray analysis. Microarray data were generated using Affymetrix protocols as previously described [Study 5 (this study). This data set consists of four isolates from the same patient (CF211). The isolates are two mucoid/non-mucoid pairs isolated together in 1997 and 2006 respectively. Cells were grown in BB to an ODescribed . Each exThe fundamental principle of AA is briefly introduced below. AA is fully described by Cutler and Breiman . AA is dS ≥ 0 to denote that the entries of a matrix S are constrained non-negative. The archetypes (components) are given as the columns of the matrix A defined by A = XC such that the columns of A are formed by convex combinations of the samples.Where we use the notation K-component AA finds a matrix with elements Amk defining K M-dimensional archetypes and each data point can be represented by a convex combination of these archetypes. Each archetype thereby has a specific gene profile that is saved in the k’th column of A, i.e. ak. The coefficients  for a given data point xn are saved in the nth column of the matrix S, i.e. sn, with elements Skn. The values of these coefficients range from 0 to 1 and the sum of the coefficients equals 1.A The AA algorithm as for PCA and k-means attempts to minimize the residual sum of squares (RSS).Where M is the number of attributes and N the number of observations.Determining the characteristics of each of the archetypes can clarify the features of the data set.Principal component analysis and k-means clustering were applied to the same data set.Principal component analysis is given by the decompositionI is the identity matrix and D is a diagonal matrix with the elements in the diagonal are sorted according to their magnitude.where S is constrained to be a binary assignment matrix such that A = XST (SST)-1 represents the Euclidean centers of each cluster.In k-means clustering For AA, it is necessary to set the number of components prior to analysis similar to k-means clustering. Our choice of archetype component was guided by plotting the explained variance as a function of number of components Figure . For theth original data point xn and the derived data point XCsn based on the seven archetypes was calculated. The measure is given as the Explained Sample Variance ESV values, it is possible to state which data points are well described by the model. No conclusions should be made for data points where ESV is low, because these data points are poorly described by the model.As a quality measure the deviation between the nP. aeruginosa PAO1 from the Pseudomonas Genome Database [Each archetype was characterized based on its specific gene profile. This was done by identifying genes with statistically significant transcriptional changes. Genes with more than a two-fold change in expression value, compared to the mean expression of the respective gene for all samples, were indicated as up-regulated whereas genes with less than 0.5-fold change were indicated as down-regulated. Genes were assigned to 26 different gene ontology (GO) classes based on the gene annotation for Database . If a gehttp://www.mortenmorup.dk. This code estimates C and S using a projected gradient descent iterative approach initialized by the FurthestSum procedure, for details see also [The methods mentioned above were implemented in Matlab unless otherwise stated. The Matlab Code for AA is available online at see also . A briefP. aeruginosa sampled from CF lung infections [P. aeruginosa isolates under diverse in vitro growth conditions. The studied bacterial isolates exhibited different clinically relevant phenotypes such as mucoidy and hypermutability, were different clone types, and were isolated from patients at different time points in relation to disease progression  using the code provided in  and addiP. aeruginosa strains [rpoN and mucA and these mutations are common to the adapted isolates and they are associated with an adapted phenotype [Archetype 1, 2 and 5 represent samples from study 2. This appears from Figure  strains . Archetyhenotype . This isSome of the samples from both groups of study 2 are also, to a greater or lesser degree, based on archetype 5. These samples all have a mucoid phenotype characterized by an over-production of alginate. The transition from a non-mucoid to a mucoid phenotype is often observed during adaptation of the bacteria to the CF lung and this shift is important for establishment of chronic infections .We next studied the up-and down-regulated genes within each archetype to find patterns that would suggest specific biological properties associated with archetype 1, 2 and 5. Figure A full list of up-and down-regulated genes for all archetypes can be found in Additional file AA succeeds in clustering study 2 into biologically meaningful groups. At the same time, it is easy to extract biological features important for all groups. So far, the AA analysis is verified since the characteristics of the archetypes 1, 2 and 5 are consistent with results from genotypic and phenotypic studies of study 2 .The identification of these genes thereby validates this model and we are able to find biological characteristics of the different samples by analyzing the archetypes. For each of the archetypes the lists of up-and down-regulated genes also include genes encoding hypothetical proteins and it is possible that such genes are also involved in the adaptation process. For archetype 5 there are a large proportion of up-regulated genes belonging to the GO class “hypothetical proteins”. Further experimental studies are required to understand the function of these genes and their relation to the adaptation process.Archetype 3 is defined close to a subset of samples #1-10) from study 1. These samples all have the same genotype (clone A) and they are considered as non-adapted since they were isolated early during the infection history of the patient  from study 1 and they are also closely related to archetype 3. Samples #25-30 represent early isolates of two other clones (clone Ca and Cb) from study 1 and they are best described by archetype 3. However, they also show recognizable coefficients  and #33-34.Archetype 2 and 5 best describe study 5. The samples that have a coefficient close to one for archetype 5 are mucoid and this is consistent to the results for study 2. The non-mucoid isolates are close to archetype 2, which represents non-mucoid isolates from study 2 sharing the same clonal type as the isolates in study 5. This shows a strong consistency between study 2 and 5, although study 5 was performed under experimental conditions similar to those in study 3.The five analyzed studies were performed under diverse experimental conditions including different media types. We compare the characteristics of the seven described archetypes and some of the differences are most likely due to the effect of the different media. This study does not account for how the different media alone affect the transcriptome. However, when we compare the different archetypes we have seen that the samples cluster more into groups of clonally related bacteria than into clusters of samples exposed to the same experimental procedure e.g. PAO1 in study 1 and study 2 and the samples from study 2 and study 5. The effect of the diverse media types does therefore not override the real biological relation between the bacteria and we justify comparing the samples from the five studies despite different experimental procedures. Future investigations of clonally related bacteria may further examine the effect from the media alone on the transcriptome.Archetype 4 mainly represents study 3. Study 3 is also composed of samples derived from the Danish CF clinic representing adapted isolates as for study 2. However, the samples share another clonal type (DK1) and the experiments are performed under different conditions than those used for study 2. The differences between archetype 2 and 4 are most likely due to clonal differences more than experimental differences since the same differences in experimental conditions did not separate study 2 and study 5 in this analysis. A plot of enriched gene ontology classes for archetype 4 similar to plots in Figure The samples from study 3 with recognizable coefficients for archetype 6 are also hypermutators. One of the hypermutator samples in study 3 is not identified as having a recognizable coefficient for archetype 6. However, this sample stands out from the rest of the hypermutators by also being mucoid. The analysis thereby suggests an archetype that is able to characterize hypermutators in general. The similarity of the hypermutators could be due to similar selective pressures present in the lung environments of CF patients. This analysis could suggest that the hypermutators follow the same path of evolution despite many changes arising as a consequence of mutations.mutS or mutL genes that are part of the mismatch repair system [P. aeruginosa[mutS gene or the mutL gene possesses a regulatory function that is altered due to the mutation in the respective gene. There is some evidence that bacteria can sense the missing mismatch repair function and this will influence transcriptional regulation [Hypermutation is often due to mutations in the r system . The hyperuginosa,19,31,32eruginosa,30. A regulation . This wogulation . This anThe characteristics of archetype 6 might be used to better understand the features shared by the hypermutators. However, the experimental procedure used for study 4 is markedly changed since the samples are harvested in late-logarithmic growth phase  compared with exponential growth conditions for study 1, 2, 3 and 5. The observed up-and down-regulated genes can therefore be ascribed to changes during the transition from exponential to stationary growth phase more than to changes due to accumulated mutations. In order to exclude effects due to the growth phase, archetype 6 is compared to archetype 7. Both archetypes represent samples from study 4. Archetype 7 is mainly represented by non-hypermutators that constitute isogenic pairs to the samples represented by archetype 6.For archetype 7 many GO classes are overrepresented by either up-or downregulated genes Figure C. This iP. aeruginosa to the CF lung for the hypermutators.Archetype 6 is also characterized by up-regulation of genes involved in the GO-class 2 ('Amino acid biosynthesis and metabolism’) and down-regulation of genes from GO-class 4  suggesting that these are important during adaptation of These findings are to a certain extent similar to what Hoboth et al.  found whResults of k-means clustering and PCA of the data set are illustrated together with the results of AA in Figure PCA captures most of the explained variance in the first three components (50.3%). However, the components do not give an apparent grouping of samples in Figure This is the first time Archetypal Analysis has been applied to analysis of gene expression data. Seven archetypes were able to extract the main characteristics of the dataset. The results show that Archetypal Analysis is successful in clustering of data into biologically meaningful groups. At the same time, the analysis is strengthened by matrix factorization making it possible to describe data points as a combination of archetypes.Archetype 1 and 2 represent non-adapted and adapted isolates respectively, and characterization of the two archetypes identifies the main changes during adaptation of the bacteria to the CF lung. In this study, it is shown that one archetype represents a group of hypermutators (result of clustering) and other data points share characteristics with this group (result of factorization) enabling identification of hypermutators from another group. The analysis provides results that are easy to interpret and we suggest that this analysis could be used to supplement current methods of gene expression analysis.http://www.mortenmorup.dk.The Matlab code for our method is freely available online on the website The authors declared that they have no competing interests.JCT participated in the design of the study, performed the statistical analysis and wrote the manuscript. MM participated in the statistical analysis and helped to draft the manuscript. SD designed and performed DNA microarray experiments. LJ and SM participated in the design of the study and helped to draft the manuscript. All authors read and approved the final manuscript.List of up-and down-regulated genes for each archetype.Click here for fileShort description of Matlab scripts.Click here for fileEnriched gene ontology classes for archetype 4.Click here for file


Molecularly-guided trials (i.e. PMed) now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting.A proof-of-concept study was conducted by the Comparative Oncology Trials Consortium (COTC) to determine if tumor collection, prospective molecular profiling, and PMed report generation within 1 week was feasible in dogs. Thirty-one dogs with cancers of varying histologies were enrolled. Twenty-four of 31 samples (77%) successfully met all predefined QA/QC criteria and were analyzed via Affymetrix gene expression profiling. A subsequent bioinformatics workflow transformed genomic data into a personalized drug report. Average turnaround from biopsy to report generation was 116 hours (4.8 days). Unsupervised clustering of canine tumor expression data clustered by cancer type, but supervised clustering of tumors based on the personalized drug report clustered by drug class rather than cancer type.Collection and turnaround of high quality canine tumor samples, centralized pathology, analyte generation, array hybridization, and bioinformatic analyses matching gene expression to therapeutic options is achievable in a practical clinical window (<1 week). Clustering data show robust signatures by cancer type but also showed patient-to-patient heterogeneity in drug predictions. This lends further support to the inclusion of a heterogeneous population of dogs with cancer into the preclinical modeling of personalized medicine. Future comparative oncology studies optimizing the delivery of PMed strategies may aid cancer drug development. Novel approaches are needed to improve outcomes for cancer patients. In the last decade, advances in biological platforms and investigative tools have permitted the molecular characterization of cancer in a clinically relevant setting. Indeed, the field of personalized medicine (PMed) represents the integration of genomic, proteomic and epigenetic data in the characterization of a patient's cancer BCR-ABL gene translocations, HER2Neu positive breast cancer treatment with trastuzumab (Herceptin), and, prior to this, tamoxifen in ER/PR positive breast cancer patients Molecular features of cancers have been the basis for selecting specific treatments of patients for over a decade. Initial approaches were candidate-based, such as the use of imatinib (Gleevec) for acute myeloid leukemias harboring Comparative oncology is most often used to describe the study of cancer biology and therapy in pet animals that naturally develop cancer To begin to realize these opportunities to model PMed strategies, a proof of concept study was conducted through the Comparative Oncology Trials Consortium (COTC) to determine if the collection and analysis of tumor samples from dogs with cancer, within a PMed framework, could be completed in a time period (<1 week) considered feasible for implementation in a future therapeutic trial. Tumor biopsies across multiple histologies and in cohorts of canine bladder transitional cell carcinoma (TCC), lymphoma, and melanoma were collected and quality assurance/control measures applied to each step in the process of generating molecular data to support a PMed derived therapeutic report. The results revealed that high-quality, prospective tumor collections, and large-scale target/drug identification studies in canine cancers are feasible. As observed in human PMed trials, tumor gene expression signatures in dogs cluster by cancer type, whereas the personalized drug reports were uniquely patient defined. Data from this study serves as rationale to now include dogs with spontaneous cancers in the advancement and optimization of PMed for human patients. Study Schedule) provided for prospective tumor collection and real-time molecular profiling in dogs with cancer. A total of 31 dogs were enrolled and assigned to one of four cohorts. The first cohort was open to all cancer types , while the remaining three cohorts were breed and/or cancer type specific. The cancer type specific cohorts included Scottish terriers with bladder transitional cell carcinoma , golden retrievers with lymphoma , and American cocker spaniels with melanoma. The melanoma cohort was opened to all breeds after three months to enhance accrual . Age , sex  and breed (5 mixed-breed and 26 purebred) were recorded variables for all dogs enrolled ( Study Cohorts). The trial opened on May 11, 2011 and closed on October 19, 2011 upon achieving its accrual goals. There were no significant adverse events reported (according to VCOG-CTCAE convention) The study design ( Study Cohorts) Reasons for histopathology QA/QC failures included samples with too little viable tumor, high degree of necrosis, small sample size, or non-cancer diagnosis ( Reasons samples failed QA/QC).Histopathology quality assurance and control (QA/QC) assessment of all biopsies were performed by one pathologist (EJE). Twenty-four of 31 cases enrolled (77%) passed QA/QC with an average tumor surface area of 75–100%, tumor nuclei of 75–100%, and necrosis < or equal to 10%. (http://wwwn.cdc.gov/clia) 260/A280≥1.8, RIN≥8.0) measured by Nanodrop and Agilent Bioanalyzer respectively. Thirty of the 31 cases (96.78%) passed RNA QA/QC. One sample (0507) failed QA/QC due to poor RNA quality (low RIN score = 2.60), likely due to its abundant connective tissue component (andReasons samples failed QA/QC). Finally, cDNA was then amplified for all remaining samples. Quality control for amplified cDNA included isolation quantity  and integrity (260/280≥1.8); all 30 samples passed cDNA assessment.RNA isolation and QA/QC assessment was performed for all enrolled cases (n = 31) at a Clinical Laboratory Improvement Amendments (CLIA) accredited facility  to ensure quality laboratory testing. QA/QC standards defined here have been previously used for the conduct of human tissue processing and clinical trials ( Reasons samples failed QA/QC). These results are consistent with those of tissues collected for human PMed trials.Each case underwent the above described histopathologic and RNA/cDNA evaluations. Samples (n = 24/31) that passed all stages of QA/QC were analyzed for gene expression on an Affymetrix platform (Canine Genome v 2.0). Common reasons for QA/QC failures were small specimens or specimens with an inadequate amount of viable tumor present (p)) and reported individually for each specific algorithm then summated (sum of (−log(p) across algorithms) to provide an overall prediction of drug selection. A summary table and drug method comparison defined the top selected agents (Table S1). The summary gives more weight to drugs suggested by more than one algorithm. PMed reports were not intended to be used therapeutically in this pilot study, although their timely generation demonstrated the bioinformatics feasibility to use the dog as a model for future PMed clinical trials.Gene expression data from each tumor was compared to that of a reference gene set to define a relative gene expression profile. The reference set consisting of forty normal canine tissues was used to estimate variance in gene expression across normal physiology Clinical Turn Around Time). Clinical turnaround time for case 0508 (TCC) was an outlier (completed in 212 business hours). The expression data was generated in 91 business hours (3.79 days), but there was a delay in sending the PMed report to investigators. Its inclusion in the analysis did not impact the study conclusions. Overall the turnaround for sample analyses fit a relevant clinical window for future comparative oncology trials to model human PMed advancements.The minimum feasible clinical time line (time from sample ascertainment and shipment from COTC site to completed PMed report returned to attending clinician) was defined prior to study initiation as 168 business hours  Figure 3Figure S1). Both pure bred and mixed breed dog samples were grouped by histologic description.To characterize the utility of the resultant canine tumor expression data for future therapeutic consideration, clustering analysis was performed. Multidimensional scaling (MDS) coordinates were generated using individual tumor gene (mRNA) expression and drug prediction scores. Consistent with others efforts using MDS and principal component analysis (PCA) of human tumors, gene expression in the dog tumors clustered by cancer type Figure 4The second phase of MDS analysis used total nested PMed drug score, a summation of individual method scores, to cluster individual samples by drug susceptibility Figure 5In this study, our objective was to determine the feasibility of real time transcriptome analysis of canine tumors as part of a PMed strategy to allow selection of potentially active drugs for personalized patient therapy. The timeline from tumor biopsy to PMed report generation was <5 business days, confirming the practicality of prospective tumor collection, molecular profiling, and generation of an actionable PMed report in dogs with cancer. Tumor samples collected were of high quality measured by both histopathologic and molecular standards of nucleic acid integrity and yield.MDS analysis revealed that canine tumor gene expression was strongly tied to cancer type. Although the number of histologic subgroups analyzed was small, the data was consistent. Additionally, clustergram analysis of personalized drug reports across samples demonstrated heterogeneity in predictions even within a single cancer type. This lends support to the inclusion of dogs with naturally occurring cancers in PMed preclinical studies, where patient-to-patient variability within a given cancer type (histology) exists. Indeed, review of the drug predictions derived from canine expression studies includes several therapeutic agents that are reasonably predicted to have efficacy in a given cancer (i.e. mitoxantrone in lymphoma) as well as agents not commonly used in that cancer but used in other cancer types (i.e. the knase inhibitor sunitinib in bladder cancer), and also drugs that are not commonly used in cancer patients (i.e. theophylline). This selection of PMed derived agents, supports the over-riding premise of this approach, since conventionally used drugs are included as options (proof of concept), but is extended by agents that may not be considered without this approach.Breed and type-specific collections for Golden Retrievers with lymphoma, Scottish terriers with TCC, and an open histology/open breed cohort also allowed for comparisons across tumor type and breed. Cancer type-defined clusters trumped breed associations. However it is possible that, if sequenced, tumor mutational status might be more specifically descriptive of breed. Also of note, accrual was slower for some breed and cancer type-specific cohorts (notably American Cocker Spaniels with melanoma), and, therefore subsequent breed based efforts require additional large-scale incentivized accrual to be sustainable.Companion animals with cancer have been increasingly used to provide insight into tumor biology and in clinical studies of drug development PMed comparative oncology studies are however limited by challenges in the translation of genomic signatures across species. For example, although there are data demonstrating similarities in gene expression between canine and human cancer types, the use of human expression signatures to query canine expression data is not well-established Complex models are needed to effectively evaluate PMed study designs and this proof of concept trial validates the dog with cancer as a model for clinical evaluation of novel PMed approaches. It is now reasonable that dogs with cancer can begin to fill the gap in optimizing the delivery of these approaches for translation to human patients. Our study used operational, analytical and clinical aspects of a comparative approach to identify potentially active agents in spontaneously derived cancers. This study sets the foundation for trials that will become more integrative and comprehensive in nature though the generation and analysis of multiple dimensions of genomic data in conjunction with prospective clinical outcomes. Comparative oncology models have the potential to expedite this evaluation and lead advancements in personalized medicine.The goals and infrastructure of the COTC have been previously described Client-owned pet dogs with histologically confirmed cancer, favorable performance status (grade 0 or 1 modified Eastern Cooperative Oncology Group (ECOG) performance status), and informed owner consent were eligible for enrollment. Specific subsets including Scottish terriers with transitional cell carcinoma of the bladder, golden retrievers with multi-centric lymphoma and all breeds with oral melanoma were eligible for enrollment. Eligibility criteria required a tumor amenable to a peripheral biopsy . Only dogs with naïve disease were eligible for enrollment. Physical examination and laboratory  evaluations were performed to evaluate eligibility prior to enrollment. Exclusion criteria removed dogs with significant co-morbidities , history of chemotherapy (including corticosteroids in lymphoma cases and NSAIDs in TCC), radiation therapy or immunotherapy. All dogs were evaluated uniformly and treated within a defined clinical protocol with IACUC approval at each COTC enrollment site . The NCI-Comparative Oncology Program (COP) reviewed the eligibility screening and approved trial entry of each patient. Potential adverse events related to the research protocol were monitored according to accepted VCOG-CTCAE criteria Defining the clinical turnaround time for prospective gene expression analysis and personalized medicine report generation was a main objective of this study. The Confluence Enterprise Wiki online database tracked the location of the specimens and time spent at each step of the QA/QC process. Confluence Enterprise Wiki was created by Atlassian and was utilized for this study. Researchers involved in the study were given usernames and passwords to access the common study site. At the start of the study 4 tables, one for each cohort, were constructed on the wiki space. When a patient enrolled, their patient ID, sex, date of birth, breed, and tumor type were entered into the table by the study monitor (CM). The specimens and results were tracked in real time with each investigator entering the date and time a sample arrived in their laboratory, when analysis began, and was completed, as well as the sample results. The wiki space allowed us to track the location of the specimens and derived data at each step of the QA/QC process.Tumor biopsies on Day 1 of the study were required from all dogs. Tumors must have been at least 3 cm in longest diameter to be eligible for biopsy. Biopsy techniques were prospectively defined by standard operating procedures (SOPs) and used uniformly at all participating COTC sites. Biopsies were collected by either a 10–14 gauge Tru-cut instrument, 6 mm punch biopsy, or an open biopsy technique. Biopsies for dogs with transitional cell carcinoma were collected by cystoscope or ultrasound guided. Two samples of at least 1 cm in length were obtained at various planes within the tumor to capture natural disease heterogeneity. The two planes were labeled as Tumor A and Tumor B. Each of the sections were bisected equally. Tumor A was divided and equal specimens placed in either RNAlater or formalin. Tumor B was divided and equal specimens placed in formalin or flash frozen. Tumor A samples (RNAlater) were shipped overnight on a −20 C ice pack to the Clinical Reference Laboratory (CRL) for RNA isolation. Tumor A and B samples  were shipped overnight on a −20 C ice pack to Colorado State University.Plasma and serum samples were collected for all patients by standardized procedures on Day 1. These were flash frozen in liquid nitrogen and stored at −80 C. At the end of the study samples were shipped overnight on dry ice to the NCI-COP for permanent storage. These were stored for potential post hoc secondary analyses.Formalin-fixed, paraffin-embedded tumor biopsy samples were routinely sectioned, paraffin embedded, and stained with H&E for light microscopic evaluation. Histopathology quality assurance and control (QA/AC) assessment for all biopsies were performed by one pathologist (EJE). Data was subjectively classified into ordinal catagories: % tumor surface area was defined as the percentage surface area of each examined tissue that was determined to be tumor; % tumor nuclei was defined as the percentage of each examined tissue's nuclei that were determined to be tumor nuclei; % tumor necrosis was defined as the percentage of each examined tumor that was determined to be necrotic. Samples were evaluated for all 3 parameters  at both the top and bottom of the tumor specimen and then averaged. Categories for percent tumor surface area were 0%–24%, 25%–49%, 50%–74%, and 75–100%. Categories for percent tumor nuclei were 0%–24%, 25%–49%, 50%–74%, and 75–100%. Categories for percent tumor necrosis were <10%, 10–20%, and >20%. Passing parameters were considered > or equal to 75% tumor and < or equal to 20% necrosis. Biopsies that failed to pass assessment were excluded from the remainder of the study. Representative microscopic images were captured for all examined samples and banked for future use.http://wwwn.cdc.gov/clia). RNA was extracted from canine tumor biopsy tissues (Tumor A) taken on Day 1 of the study and stored in RNAlater stabilization solution . The quality measures for RNA isolation were quantity  and integrity  measured by Nanodrop and Agilent Bioanalyzer respectively.RNA isolation and QA/QC assessment from samples collected in the study was performed at a Clinical Laboratory Improvement Amendments (CLIA) accredited facility  to ensure quality laboratory testing. Standards defined here have been previously used for the conduct of human tissue processing and clinical trials (cDNA synthesis and amplification were accomplished using the NuGen Ovation Pico WTA System (Cat # 3300-12). Fifty nanograms of total RNA was used for cDNA synthesis using the following steps as per the Ovation Pico WTA System protocol: First Strand cDNA synthesis, second strand cDNA synthesis, cDNA purification, SPIA cDNA amplification, and amplified SPIA cDNA purification. The amplified DNA was checked for quality and quantity using the Nanodrop spectrophotometer. cDNA samples with a 260/280 ratio of ≥1.8 and a concentration above 5 µg in 30 ul were considered acceptable for further processing using the Affymetrix Canine genome 2.0 array.Amplified cDNA samples generated with the NuGen Ovation Pico WTA system were used for fragmentation and labelling process using the NuGen Encore Biotin Module (Cat # 4200-12). The resulting fragmented and labeled cDNA was used for Affymetrix Canine 2.0 array hybridization. The hybridized arrays were washed and stained using GeneChip Hybridization, Wash and Stain Kit .Initial QC analysis of the scanned array was accomplished using the Affymetrix Expression Console Software. Background noise <100, % present call ≥30%, scale factor 100 and appropriate spike in control signals are necessary for adequate sample quality. Upon passing all criteria, MAS5.0 processed .CEL and normalized pivot .TXT files were extracted and deposited on a secure FTP site at Van Andel Research Institute (VARI) for subsequent analysis. This data has also been uploaded to GEO, accession #GSE51131.ftp://ftp.ncbi.nih.gov/pub/HomoloGene/current/homologene.data dated 11/15/2010). Any canine ID's that had ambiguity in the mapping to human genes were removed and only values whose canine ID's exhibited clear and concise (one-to-one mapping from canine to human genes) conversion to human ID's were retained. The final step in the conversion process was to convert the human Entrez gene identifiers to the appropriate Affymetrix U133 Plus 2.0 probesets (U133 Plus 2.0 annotation version 31). Only concisely mapped Entrez gene IDs to Affymetrix probesets were retained. Use of the U133 Plus 2.0 probeset data facilitates the use of the standard workflow and application of the previously-detailed predictive methods developed for human subjects The general analytical workflow undertaken upon receipt of a tumor-derived gene expression profile is shown diagrammatically in Table S2).This first and most rudimentary method utilizes a human drug-target (mechanism of action) knowledge base and rules-based method to identify over-expressed genes (z-score ≥+3) in a patient's tumor that represent known molecular targets of antagonists, then match the appropriate drug from the knowledge base (example rule: IF EGFR Expression z-score ≥+3 THEN INDICATE Cetuximab). Multiple sources of public domain knowledge have been used to establish the internally-curated drug-target knowledge base including DrugBank, MetaCore (GeneGo-Thomson Reuters), MedTrack, PharmGKB, UpToDate and DrugDex (Thomson Reuters) Table S3).Much like target expression, this method employs predefined and published rules maintained in a drug-biomarker database mined from public knowledge in which the efficacy of a specific drug has been associated with the expression of a specific molecular marker Table S4) and drug match scores are calculated using Kolmogorov-Smirnov statistics with p-values estimated using permutation testing with 50,000 permutations. Only those patterns that match with a p-value less than 0.05 are reported.This method reproduces the Connectivity Map (CMAP) concept initially developed by the Broad Institute in which the genomic consequence of drug exposure is used to connect drug effect to disease signatures in vitro drug screen reported in the COMPARE database are compared to the canine tumor-derived gene expression signature  through the PGSEA analysis tool which in this iteration utilizes a one sample t-test to determine sensitivity to a subset of 11 drugs selected from the NCI-60 list (Table S5). Each drug is assigned a p-value for predictive efficacy and only those with a p-value less than 0.05 are reported. This approach is consistent with well-published methods for inferring drug sensitivity utilizing the NCI-60 cell line dataset This method reproduces a previously published implementation of the Parametric Gene Set Enrichment Analysis (PGSEA) method using the NCI-60 cell line sensitivity data provided in the COMPARE database K in formula below). K thus represents a subset of a global interaction network of size N. Construction of a directed shortest path network connecting nodes from K to each other is performed utilizing the MetaCore GeneGO database of over 200,000 protein-protein and protein-small molecule interactions. The shortest path network, S, is constructed by building directed paths from each node in K to other nodes in K, traversing via other nodes in the global network as necessary. The number of node pairs in the shortest path network which are connected through each pair of nodes i and j is determined (ijK). This process is repeated in the global network to calculate the number of node pairs connected through nodes i and j (ijN). The probability that the number of nodes would be ijK or larger given the size of the input network and the distribution of shortest paths in the global network is given by:Table S2).This method predicts the activity (rather than expression) level of drug targets on the basis of a specific type of molecular network analysis referred to as topological analysis described previously Upon execution of the different analytical methodologies, a compiled report was generated. The personalized drug report conveys the predicted efficacy (or resistance in the case of biomarker resistance rules) of the drugs identified by each of the methods described above. These reports contain a summary section combining results of each method with scores that represent stronger indications for drugs that were predicted by multiple methods. These scores are adjusted for multiple methods and are based on the sum of scores from individual methods. In addition, each method contains scores reflecting the contribution of multiple genes to the suggested therapy. Further, personalized drug reports also associate public clinical and contextual knowledge to show any evidence that may support the use of the predicted drug in the context of the patient's disease state. The supporting evidence comes from a variety of sources including PubMed, clinicaltrials.gov and DrugDex (Thomson Reuters).All sample processing time points were recorded on the COTC016 wiki page. This included the time of biopsy, time shipped from COTC site and time tumor specimens arrived at the Clinical Reference Laboratory (CRL) for expression analysis and Colorado State University (CSU) for histopathology analysis. Also included were the QA/QC analysis start and completion times at the CRL and CSU. Each lab entered the results in the wiki and uploaded data and/or representative images. All recorded times were listed in their respective time zone. When calculating the total elapsed time from biopsy shipment to PMed report generation, all times were converted to EST then military time for calculation purposes. Holiday and weekend hours were subtracted from total times to calculate business hours necessary for sample evaluation.http://www.r-project.org; v2.14.1) based on sample to sample distances calculated using Pearson's correlation distance (one minus Pearson's correlation coefficient).mRNA expression values for each gene in the canine tumor samples were normalized relative to the average expression of that gene in 40 canine normal tissue samples from a reference set Figure S1Canine tumor gene expression signatures cluster independently of breed. In a cursory evaluation of the potential effect of breed on tumor classification in this limited sample set, breed did not influence MDS analysis of gene expression z-scores. Both pure bred and mixed breed dog samples clustered by cancer type.(TIF)Click here for additional data file.Table S1Summary of drug predictions: Top drug prediction by algorithm [−log(p) score] (Inferring gene(s) where applicable). Drug recommendations based on the six drug prediction methodologies are shown for each tumor and includes the highest ranked recommended according to a summary score. This summary score gives more weight to drugs suggested by more than one algorithm.(DOCX)Click here for additional data file.Table S2Expression and network-based drugs and targets. Two hundred and sixty unique drug targets for 123 FDA approved human are shown alongside supporting evidence for the drug-target interactions that guideds4 inclusion in the network-based prediction algorithm.(DOCX)Click here for additional data file.Table S3Biomarker rules. Thirty-four biomarker rules are shown that match 20 FDA approved drugs to 22 targets according to sensitivity or resistance alongside supporting evidence for these interactions.(DOCX)Click here for additional data file.Table S4Response signature drugs. The subset of 107 CMAP drugs refined based on literature support and used to match drugs based on disease genotypes.(DOCX)Click here for additional data file.Table S5Sensitivity profile drugs. The subset of 11 drugs selected from the NCI-60 list COMPARE database and matched to gene expression signatures.(DOCX)Click here for additional data file.


The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate.We compare several widely used normalization approaches  applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures.T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially. For over a decade, two-channel transcriptomics microarrays have provided a powerful approach to study genome-wide gene expression events. Now, continued development of two-channel microarray technology has enabled extending our experimentation to the next level: regulation of gene transcription. One of the most popular techniques in this field combines chromatin immunoprecipitation (ChIP) assays with two-channel microarray technology (ChIP-on-chip ). ChIP-oEven though newer technologies such as ChIP-sequencing (ChIP-seq) are on the rise, two-channel microarrays still present a valuable approach to understanding gene transcription regulation events, and during the last decade have opened opportunities to identify novel targets and markers in complex diseases such as cancer -5, heartThe design and the experimental approach for regulation microarrays are very different from the more extensively studied transcriptomics microarrays, which has implications for data pre-processing procedures. The key difference is that in transcriptomics microarrays both channels contain amplified transcript samples, usually corresponding to two different experimental conditions, while in regulation microarrays the channels comprise an experimental sample and a reference sample. The cyanine 3 (Cy3), or green, channel of regulation microarrays generally contains the total DNA sample that gives the reference baseline signal, and the cyanine 5 (Cy5), or red, channel contains an experimentally enriched DNA sample, extracted using a specific antibody binding to a DNA-interacting protein (ChIP) or directly to methylated CpGs on the DNA (MeDIP). Hence, while the log-ratio between the channel signals represents the differential expression between two conditions in transcriptomics studies, for regulation microarrays it is used as a measure of enrichment: the higher the log-ratio of a probe or set of tiling probes, the higher the likelihood that the corresponding region in the genome has a high level of methylation or is targeted by a DNA-interacting protein.Another important assumption in regulation microarrays is that a DNA-interacting protein is either bound or not bound (for ChIP) and that a target sequence is either methylated or not (for MeDIP). Regardless, depending on binding affinity, mean time of residence and other factors, the fraction of cells with bound protein or a particular methylation status is not an all-or-nothing condition, especially in heterogeneous tissues. Combined with the characteristics of the data distribution surrounding a site of interest Figure  and probNormalization is a process that is applied at multiple levels connected to spatial , probe -15 and dBased on these observations, the main challenge of the normalization of regulation microarrays is (i) to make the signals of individual microarrays quantitatively comparable and (ii) to retain the separation between the enriched and un-enriched components present in the data. Programs like CoCAS  offer a To determine the efficacy in correcting for technical biases and improving comparability between microarrays, quality control and bias assessment was performed on all datasets before and after normalization for each of the six normalization approaches. Complete results are available in additional file Methods section for dataset details and numbering) shows largely overlapping control probe and gene promoter probe distributions  show equal performance of all approaches for dataset #1 Figure , resultiDataset #2, the second ChIP-on-chip dataset gives different results Figure  and 4b. Tukey's biweight scaling and T-quantile normalization appear to perform comparably with respect to conserving the component separation. Tukey's biweight scaling adjusts the log-ratio data with a scaling factor for each microarray in the dataset individually, which means that the ROC curves will be identical to those of the raw data, and that the distributions will be the same as those before normalization save for a shift. This may explain the variability observed in the individual ROC curves and AUC values of the Tukey's biweight scaling normalized data. T-quantile normalization reduces the variability between the data distributions of individual microarrays, resulting in ROC curves that are more comparable in both shape and AUC Figure .The results of the MeDIP-on-chip datasets support the conclusions reached for the ChIP-on-chip datasets: separation of the components present before normalization Figure  and 3e aAny appropriate normalization method should preserve the biological information present in the raw data. Assessing the distributions of the negative control probes and the gene promoter probes is a global indicator of this conservation of biological information. In addition, three datasets with suitable positive controls were used to assess the impact of the normalization approaches on the power to identify significant enrichment for specific genomic regions. ACME  was used10log-transformed enrichment p-values plotted along the HOXA region are shown in Figure top). Using Tukey's biweight scaling or T-quantile normalization results in identification of several enriched loci, most of which are moderately methylated. Less loci are found when using VSN, quantile or LOWESS normalization. Peng's method results in identification of only a few loci with moderate enrichment.For dataset #3 enrichment of the HOXA group of developmental genes was calculated. HOXA genes are located in a cluster on chromosome 7 and are known to be switched off and moderately to highly methylated in most tissues . The negbottom).For dataset #4 enrichment was determined for the Dlk1-Gtl2 cluster on chromosome 12, a region reported in the original results  to be hi8Two-channel transcriptomics and regulation microarrays should not be pre-processed in the same manner. Appropriate normalization strategies for regulation microarrays are characterized by their ability to retain the separation between the enriched and un-enriched components present in the data whilst enhancing comparability between microarrays. Six normalization methods were tested by (i) assessing the separation between the control probe and gene promoter probe distributions before and after normalization using ROC curves and (ii) by verifying whether known enriched genes and regions could be identified as such after normalization. We have shown that the result of each approach depends heavily on the situation before normalization, specifically the amount of enriched and un-enriched probes and the separation between the corresponding components in the raw data. These two characteristics are different for each experiment, depending largely on the biological system studied and the applied assay.In the ChIP-on-chip datasets used here, the distributions of the control probes and gene promoter probes overlap to a large extent before and after normalization. This may be explained by the small proportion of the genome generally covered by the potential binding sites of a DNA-interacting protein and the resulting small contribution of the enriched component. Hence in general, the lower the amount of binding sites, the more similar the control probe and gene promoter distributions, and the more comparable the performance of the normalization approaches, based on ROC curves of both distributions before and after normalization. However, in some cases VSN can cause a sizeable rescaling of the distributions, and to a spurious control probe distribution with a higher mean and spread than the gene promoter probe distribution. This renders gene promoter probes in the upper quantile of the data indistinguishable from random data, strongly impacting the biological interpretation.In DNA methylation microarrays the amount of enriched probes and un-enriched probes is of the same order, since in general the proportion of methylated CpG di-nucleotides in a genome is substantial. We have shown that for such microarrays, the choice for a normalization procedure will be crucial for the downstream analysis. All three MeDIP-on-chip datasets show a large degree of separation between the gene promoter and control probe distributions. The separation is lost when using normalization methods that normalize channels together, such as VSN, LOWESS, Peng's method and quantile normalization. Using LOWESS approaches on MeDIP-on-chip data has been reported elsewhere to result in increased bias, because the underlying assumption that the log-ratio should be independent of the average individual channel signals does not hold for this type of data. DNA methylation levels are related to CpG and GC density, while signal intensity is also known to be influenced by GC content. Forcing the log-ratio to be independent of the average signal intensity using LOWESS normalization thus introduces bias instead of removing it .T-quantile normalization, applied separately on the channels, and Tukey's biweight scaling are the only approaches that are able to preserve the component separation in all example datasets. In dataset #1, individual microarrays already showed comparable distributions before normalization; hence for this dataset, Tukey's biweight scaling would be sufficient. In contrast, dataset #4 for example showed a large heterogeneity between individual microarrays, in which case between-microarray normalization is better suited to improve the overall comparability and enable quantitative data comparison. This can be achieved either by doing an additional normalization step after scaling, but ideally by using a between-microarray normalization approach from the beginning, such as applying T-quantile normalization as demonstrated here.In regulation microarrays the sequence content of the input DNA sample and the experimental DNA sample always differs to a large extent. There are also instances for transcriptomics microarrays, such as dedicated microarrays designed for a specific biological context, where the assumption that the majority of genes are not differentially expressed does not hold, hence requiring adapted normalization strategies. Most of these strategies involve the use of invariant genes, either present on the slide ,30 or deThe research described herein is limited to the normalization of replicate microarrays. In many cases however, a study will consist of multiple conditions, such as different tissues, or treatment and control samples as demonstrated in dataset #1. In these cases, the experimental DNA samples may differ to a large extent between treatment and control groups, warranting application of normalization to each condition separately. However, when only a relatively small amount of loci is expected to be differentially enriched and the total amount of enrichment can be assumed constant between conditions, normalization approaches applied to the dataset as a whole are more appropriate. This holds for experiments such as DNA methylation studies on the same tissue treated with a micronutrient , where oThe results of known targets and enriched regions show consistent differences between the various normalization approaches. When looking at the Dlk1-Gtl2 cluster for the DNA methylation data of dataset #4, a region reported to be highly enriched in the original findings, it is clear that such highly enriched regions will be identified as such regardless of the chosen normalization approach. This is not the case when studying moderately enriched regions, as illustrated by the results of the HOXA cluster in dataset #3, where the degree to which this region is identified as being enriched depends strongly on the applied normalization approach. Overall, T-quantile normalization and Tukey's biweight scaling again give the best results. A potential cause of the observed difference between the tested normalization approaches is observed in the results on global level: the ranking of probes changes when using some normalization approaches, increasing the likelihood of un-enriched probes being spread over the whole dynamic range of the enriched probe distribution. Ultimately, such changes in the ranking can be destructive on the power to call differences in methylation or protein binding. Also, enrichment finding algorithms  as used The main issue of ChIP-on-chip and DNA methylation microarray normalization is to enhance comparability between microarrays, while keeping the separation between the enriched and un-enriched components present in the data. Within-channel approaches give the best performance, with enhanced comparability between individual microarrays for approaches that also normalize between microarrays. More specifically, quantile, LOWESS, Peng's and VSN normalization alter the signal distributions to such an extent that it will impact the reliability of the downstream analysis substantially. Better results are obtained with T-quantile normalization applied separately on the channels or Tukey's biweight scaling. For all datasets tested, these two methods consistently outperform the other tested methods in conservation of separation between the enriched and un-enriched distributions, as well as in identification of genomic regions known to be enriched. The T-quantile approach is preferable because it additionally yields enhanced comparability between microarrays.Five published datasets were selected from ArrayExpress. Selection criteria were set to select several assay types (MeDIP and ChIP), several species (human and mouse) and cover several research fields. Due to the selection criteria, all datasets were chosen from the same microarray manufacturer, NimbleGen table .Sub-selections of microarrays and experimental groups were made to keep only the microarrays of sufficient quality and homogeneous replicate groups of sufficiently large size. In dataset #1, one microarray of the 17beta-estradiol stimulated group was removed because the red channel was saturated, as reported previously . Insteadhttp://www.bigcat.unimaas.nl/userfiles/adriaens/arrayQualityMetrics/.Quality control and bias assessment of the raw and normalized data was performed using the arrayQualityMetrics package . IndividMicroarray data is subject to multiple sources of variation. The goal of normalization is to remove all technical biases from the microarray data, while retaining the biological variation. There are many normalization procedures available for two-channel microarray data, but the choice for a specific procedure has to be fuelled by the characteristics of the dataset: (i) the procedure should correct all the systematic biases in the dataset diagnosed during the quality control process and (ii) the underlying assumptions of the particular method must be met. In regulation studies, there is the additional goal to retain the separation between the enriched and un-enriched components of the log-ratio distribution.To illustrate this, data from five human and mouse ChIP-on-chip and MeDIP-on-chip datasets were normalized using six different methods: (i) LOWESS normalization ,16 appliNimbleGen uses Tukey's biweight scaling in-house. It consists of two steps: calculating the log-ratio between channels and subsequently correcting these by subtracting the robust Tukey's biweight estimate of the median. For this estimate, each data point is given a weight using a bi-square function. The weights assigned by this function are inversely correlated to the distance from the median, so outliers have a minimal effect on the estimate. The method developed by Peng et al.  makes stThe separation between the enriched and un-enriched components present in the data of two-channel regulation microarrays should be conserved after applying normalization. To determine this conservation, the log-ratio distribution of negative control probes  and the log-ratio distribution of gene promoter probes were assessed before and after normalization using ROC curves. For creating the ROC curves, the negative control probes represent the negative class of outcomes, while the gene promoter probes represent the positive class of outcomes. If there are any enriched probes, the gene promoter probe distribution should extend beyond the control probe distribution in the upper quantile and is expected to have a higher mean than the control probe distribution. If this separation is retained, the ROC curves are expected to have comparable AUC values before and after normalization, while if the separation is not retained, the ROC curves will have lower AUC values after normalization.a priori to be enriched were used as positive controls, verifying to what extent these regions are identified after using each of the six normalization approaches. To this end, 33 well established ER-a targets [Genomic regions known  targets  were cho targets . Enrichm targets . For dat targets . Enrichm targets . The othData was imported and analyzed using Bioconductor  in the sMA and MJ carried out the analysis and interpretation of the microarray data and wrote the manuscript. CM, LE and CE helped drafting the manuscript and advised on the technical aspects and interpretation of the results. CE coordinated the project. All authors have read and approved the final manuscript.ArrayQualityMetrics quality control and bias assessment results (part 1). A ZIP file containing a folder with the results of the quality control and bias assessment generated with the arrayQualityMetrics package for datasets #1 and #2. The results are formatted as webpages. Individual results can be accessed by opening the 'index.html' file in any subfolder. An overview of all results can be accessed by combining the contents of the main folder in additional file 1 with the contents of the main folder of additional file 2, and subsequently opening the 'index.html' file in the main folder.Click here for fileArrayQualityMetrics quality control and bias assessment results (part 2). A ZIP file containing a folder with the results of the quality control and bias assessment generated with the arrayQualityMetrics package for datasets #3, #4 and #5. The results are formatted as webpages. Individual results can be accessed by opening the 'index.html' file in any subfolder. An overview of all results can be accessed by combining the contents of the main folder in additional file 1 with the contents of the main folder of additional file 2, and subsequently opening the 'index.html' file in the main folder.Click here for file


Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research.We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome.Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect.Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that “small” departures from normality in the expression data distributions are analytically-insignificant and that “robust” gene-calling algorithms can fully compensate for these effects. Microarray-based assays of gene expression have become a mainstay of basic and translational cancer research. A significant number of modern investigations rely on these tools to inform hypothesis generation Against this backdrop, the importance of accurate interpretation of microarray results and the significant consequences of systematic analytic errors becomes apparent. In the early days of microarray analysis, high experimental costs and significant technical variability limited the available information with which comprehensive analyses of the practical effects of subtle biases in microarray data or in its interpretation could be studied One common assumption is that data from microarray-based genome expression analyses conform to a standard Gaussian  distribution. This assumption is rarely explicit but rather is most commonly made implicitly when investigators apply analytic algorithms predicated upon the Gaussian assumption. Distribution-related assumptions are relevant to at least two, distinct sets of expression data generated in microarray analyses, and the normality assumption has been variably (often implicitly) applied to both The first dataset to which distribution is relevant comprises the complete set of individual expression values across all genes and all samples in a given experiment. For example, in a study examining the expression of 25,000 genes in 100 tumors, this is the set of all 2.5 million gene expression values. The distribution of this composite dataset may be particularly relevant to downstream clustering and class discrimination analyses, as many of these algorithms are typically applied to the entire dataset as a whole. When algorithms predicated upon a standard Gaussian distribution are used, the normal assumption is implicitly introduced.The second dataset to which distribution is relevant is the dataset comprising the individual expression values for a single gene across the entire range of experimental samples. Continuing the previous example, this experiment would generate 25,000 such datasets, each with 100 data points. The distribution of these 100 data points may be particularly relevant to studies that examine the consistency of behavior of a specific gene in a specific tumor type or analyze the pattern of its change across a range of “classes” or “grades” of a specific tumor. Here the distribution may provide a useful description of the behavior of this single gene across multiple independent samples, but the normal assumption may be implicitly introduced if algorithms used to analyze the behavior of this gene are predicated upon a standard Gaussian distribution.The assumption of normality has been explicitly investigated in gene expression analysis, although to a limited degree. While it initially appeared to have both theoretical The possibility that gene expression data violate the normality assumption may be of considerable significance to clinical and translational investigators. Most current and proposed medical applications of microarray expression data are derived from analyses predicated upon this assumption, many of which have relied upon parametric statistics for gene calling and class discovery The present investigation has two objectives and has been structured in two parts: the first is theoretical – to study the distributions of cancer gene expression data – both at the individual gene and at the complete dataset level – and to assess the extent to which these deviate from normality. This provides the foundation for the second, translational objective: to study the implications of non-Gaussian gene expression distributions on clinically-oriented genomic analyses. The experimental model has been deliberately designed to recapitulate faithfully the workflow of a typical, translational pipeline for gene expression analysis .F-test of the variance demonstrates statistically-significant departures from normality for all samples  distribution.We also examined the data distributions of individual genes across the 180 samples of each of the 5 cancer data sets. Many investigators examining data from an experiment containing microarrays of multiple, similar tumors may assume that an “overexpressed” gene would exhibit a Gaussian distribution centered around a positive mean value, an “underexpressed” gene will have a similar distribution around a negative value, and a gene whose expression is unchanged will have a Gaussian distribution centered around zero. Our analysis, however, demonstrates that variable degrees of skewness and kurtosis as well as marked deviations from unity among the standard deviations are characteristic of the expression distributions for individual genes. Empiric curve fitting was used to further investigate the actual morphology of the cancer gene expression distributions , S5, S6.While these distributions fit the data more accurately than the normal distribution, KS testing indicates that they are imperfect fits . MoreoveUp to this point the analysis has been focused on investigating the actual distributions of gene expression datasets and comparing these to a theoretical, normal distribution. This analysis has demonstrated that human cancer gene expression data is not normally-distributed, either on the experiment or on the single-gene level. An appropriate next question would be whether these deviations from normality affect commonly-performed gene expression analytics, including molecular classification, gene calling, and functional annotation.Class 1) and another subset of eight oligodendrogliomas with chromosome 1p/19q codeletions Class 2), was used to study the effects of the data distribution on identification genes that are differentially-expressed between known tumor subsets. This was accomplished by applying a uniform transform , including a unique subset of eleven tumors with intact chromosomes 1p and 19q (arbitrarily designated  Box-Cox  to the e Box-Cox . In thist-test with a standard Bonferroni correction (p<0.01), identified 50 differentially-expressed genes between Class 1 and Class 2 using the parent distribution and 55 using the transformed distribution (9.1% difference). Forty-nine (49) of 56 total differentially-expressed genes were common to both lists (87.5%), while 7 were uniquely identified in only one of the two lists (12.5%) (The two-sided student's  (12.5%) , S3.t-test is a parametric test that makes assumptions regarding the shape of the underlying distribution. To eliminate this effect, we applied two, nonparametric methods for gene calling. A two-class, unpaired significance analysis of microarrays (SAM) Even with the stringent Bonferroni correction, the t-test. While this method assumes normality of the underlying data, it is viewed by many to be superior to standard and corrected t-tests and is considered robust to a variety of confounding mathematical and statistical effects An alternate strategy for gene calling uses linear modeling for microarrays (LIMMA) The effects of the distribution on functional annotation were studied first by using DAVID a priori knowledge Class 1 and Class 2) to simulate the classification process and to study the relationship of the results to the shape of the underlying data distribution. Discriminant analysis (DA) and k-nearest neighbors (KNN) classifiers were trained on a subset of the tumors with representatives from each class and were then used to classify ten, novel tumors into one of the two classes. Identical analyses were performed on data from the parent and transformed distributions. The results of these analyses demonstrate a 20% difference in class assignment (2/10 samples) for the DA and 30% (3/10 samples) for the KNN classifier when used with the parent data but identical classifications for both models when used with the transformed dataset (t-test)  .The distribution of gene expression data is typically assumed to conform to a standard Gaussian  distribution 8 features per dataset to allow statistical significance assessments of seemingly-minor deviations from normality. In so doing, it reveals that these deviations achieve a high degree of statistical significance for all of the first four central moments. This provides convincing evidence that these cancer gene expression data do not conform to a standard Gaussian distribution  and have performed a comprehensive analysis of the higher-order central moments for these distributions. This analysis exploits the availability of nearly 10ribution , Table 1Empiric curve fitting identifies, in an unbiased fashion, distributions that most accurately model the observed distributions of the expression data. Analysis of the empirically-fit distributions provides additional information regarding data distribution and can be used to draw general conclusions regarding the types of downstream analyses that may be applicable to these datasets. This analysis demonstrates that the expression distributions are not well modeled by simplified, two- parameter distributions  but instead require distributions with multiple (3–4) shape parameters to model the data accurately. Several derivatives of the β-prime distribution   . The extibutions , S6. Conibutions , S5. AltNonparametric methods may not be sufficient, however, to completely offset the effect of non-Gaussian distributions on gene calling. SAM, a nonparametric algorithm This simulation illustrates the potential effects of non-Gaussian distributions on gene calling. More importantly, it provides direct evidence against two common beliefs regarding gene expression analysis: that “small” departures from normality are insignificant to practical gene expression analyses and that “robust” gene calling algorithms can compensate completely for these effects.a priori knowledge of the clinical phenotype of interest  to train molecular classifiers that are subsequently capable of making phenotypic predictions for novel tumors using gene expression data. This process is mathematically-complex, and we performed a series of simulations to analyze the extent to which these training and prospective classification strategies may be affected by non-Gaussian data distributions.An important objective of translational oncology research is to identify patterns of gene expression that distinguish phenotypically-significant subclasses of malignant disease. A common strategy is to use gene expression profiles in conjunction with We used a representative subset of 13 randomly-selected LGGs as the training set for two “robust” molecular classifiers, the parametric DA classifier and the nonparametric KNN classifier. Each classification strategy was used in conjunction with both the parent and the transformed data distributions, allowing the effects of the shape of the distribution on the classification algorithm to be examined. The results of this analysis  demonstrdifferences in the classifications attributable to the shape of the data distribution rather than to compare the classification accuracy. While both classifiers resulted in classifications that better fit our current disease models when used in conjunction with the transformed data, it is impossible to know if this truly represents an improvement in accuracy without knowing with certainty that our current disease models accurately reflect the true nature of the disease. At present the classification scheme applied in this analysis differences in classification (regardless of the ultimate accuracy), and we have illustrated that the potential for such differences exists for both parametric and nonparametric, prospective, molecular classifiers.It is important to note that the goal of this analysis was to perform a semi-quantitative analysis of the t-test strategies, including Bonferroni corrections or linear modeling (LIMMA). The effects may be mitigated in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis therefore provides direct evidence refuting two, common beliefs in translational cancer gene expression analysis: that, “small,” departures from normality in the expression data distributions are insignificant in practice and that “robust” gene calling algorithms can fully compensate for these effects.Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. They exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of these data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects are not fully corrected by multiple-testing and other “robust” modifications to normal distribution will refer in this manuscript to the special instance of the Gaussian distribution with mean (μ) equal to zero and standard deviation (σ) equal to unity . The term first central moment will be taken to mean the first moment, which describes the mean of a distribution. All other references to higher-order central moments will be used literally.The term We examined the distributions of cancer gene expression data and investigated the translational implications of deviations from normality. To increase the practical applicability of these results, the experimental model has been structured to simulate the flow of expression data through a prototypic analytic pipeline, similar to those used in modern basic and translational gene expression investigations. An overview of the pipeline, the statistical methods used at each step, and the corresponding analyses of the data distributions and their implications are presented in Five publicly-available gene expression datasets derived from five unique, primary human cancer types were analyzed, including brain (glioblastoma), breast (adenocarcinoma), colon (adenocarcinoma), gastric (adenocarcinoma), and ovarian (adenocarcinoma) cancers, each representing the largest available dataset meeting inclusion criteria . These fTo control for potential effects of cross-platform and cross-assay variability, all included expression data were assayed using the Affymetrix® Human Genome U133A-Plus 2.0 array. Because the five datasets contained a variable number of arrays, sample size-related bias was controlled by setting the experimental sample size to 180 for each group . A total of 180 samples for each of the five cancer types were therefore selected at random from the parent datasets. These five sets of 180 samples  comprised the experimental datasets. A sixth, smaller dataset containing expression data for 23 low-grade gliomas was also used for analyses of gene calling, functional annotation, and tumor classification. This dataset has been previously generated by our group and comprises at least two molecular tumor subclasses, thereby facilitating this analysis Similarly, gene expression data from normal corresponding tissues, assayed with the U133A-Plus 2.0 array, were analyzed and used as the normal controls in the Log-subtraction-based analyses.2 converted form.To simulate typical analytic workflow of a translational molecular oncology investigation, raw expression data were normalized using the RMA method 2-converted and then subtracted from the Log2-converted expression of the corresponding feature in each tumor sample. This is standard practice in many semi-quantitative gene expression analyses; see the Text S1 for further details.Remaining consistent with a prototypic translational analysis workflow, we next calculated expression ratios for each feature  using Log-subtraction. The gene expression profiles for normal tissue corresponding to each tumor type were calculated by averaging three, comparable gene expression profiles from the appropriate normal tissue, and the average value was Log7 elements . Means were compared using both parametric and nonparametric tests (student's t-test and KW), variances were compared using the F-test, and skew and kurtosis were compared using standard error and Fisher's coefficients of skewness and kurtosis p-value of the KS calculation  by first constructing expression density histograms for each of the datasets and then by empiric fitting and parameter estimation for each of 57 potential distributions  on this dataset, again suggesting that it was not normally-distributed (data not shown). A systematic, Box-Cox transformation ribution .a priori definition of the two, genotypically- and phenotypically-distinct tumor subsets contained within the LGG dataset to define two classes Class 1 for the intact gliomas and Class 2 for the codeleted oligodendrogliomas. We applied four different statistical tests to identify genes with statistically-significant differences between the two subsets: one nonparameric method, the two-class, unpaired student's t-test with a standard Bonferroni correction for multiple testing (p<0.01) t-test that assumes normality but is considered robust p<0.01) GO Next, a series of identical classification and gene calling procedures was performed on the pre- and post-transformation datasets and the results were compared to estimate, in a semi-quantitative fashion, the effects of the shape of the underlying distribution on identification of differentially-expressed genes, functional annotation, and molecular classification. We used the The effect of the distribution on the accuracy of prospective, molecular classification algorithms was tested using a DA classifier and a KNN classifier. The DA p<0.01) was first used to eliminate genes whose expression did not differ significantly from control. Next, a training set consisting of 8 samples randomly selected from Class 1 and 5 from Class 2  was used to train the classifier, which was then tested against a novel set known to contain 7 Class 1 and 3 Class 2 tumors . These classification strategies were applied to both the parent and to the transformed distributions, and the classification results were compared in a semi-quantitative fashion  data normalized using the Robust Multichip Average (RMA) method.Distribution analysis for Log The source data for these graphs are the Log2 (unsbtracted) datasets. Bin widths have been set between 190–250 to improve visualization. Red curves represent the best-fit normal distribution. The primary image gives the histogram with the superimposed theoretical normal curve. The inset presents the quantile-quantile (QQ) plot, where deviation from the line  illustrates deviation of the empiric from the theoretical normal distribution. A: Brain; B: Breast; C: Colon; D: Gastric; E: Ovarian.(TIF)Click here for additional data file.Figure S22 (unsubtracted) data normalized using the DChip method.Distribution analysis for Log The source data for these graphs are the Log2 (unsbtracted) datasets. Bin widths have been set between 190–250 to improve visualization. Red curves represent the best-fit normal distribution. The primary image gives the histogram with the superimposed theoretical normal curve. The inset presents the quantile-quantile (QQ) plot, where deviation from the line  illustrates deviation of the empiric from the theoretical normal distribution. A: Brain; B: Breast; C: Colon; D: Gastric; E: Ovarian.(TIF)Click here for additional data file.Figure S3Distribution Fitting. Distribution fitting for the breast cancer dataset for RMA (top) and DChip (bottom) normalized data. The three best-fit curves are superimposed on the histogram, and the normal distribution curve is included for comparison. The specific parameters for the best-fit distributions are given. The inset displays the quantile-quantile (QQ) plot for the best-fit and normal distributions. These charts demonstrate that multiparameter distributions capable of modeling skewness and kurtosis better characterize the data than the standard Gaussian  distribution.(TIF)Click here for additional data file.Figure S4Distribution Fitting. Distribution fitting for the colon cancer dataset for RMA (top) and DChip (bottom) normalized data. The three best-fit curves are superimposed on the histogram, and the normal distribution curve is included for comparison. The specific parameters for the best-fit distributions are given. The inset displays the quantile-quantile (QQ) plot for the best-fit and normal distributions. These charts demonstrate that multiparameter distributions capable of modeling skewness and kurtosis better characterize the data than the standard Gaussian  distribution.(TIF)Click here for additional data file.Figure S5Distribution Fitting. Distribution fitting for the gastric cancer dataset for RMA (top) and DChip (bottom) normalized data. The three best-fit curves are superimposed on the histogram, and the normal distribution curve is included for comparison. The specific parameters for the best-fit distributions are given. The inset displays the quantile-quantile (QQ) plot for the best-fit and normal distributions. These charts demonstrate that multiparameter distributions capable of modeling skewness and kurtosis better characterize the data than the standard Gaussian  distribution.(TIF)Click here for additional data file.Figure S6Distribution Fitting. Distribution fitting for the ovarian cancer dataset for RMA (top) and DChip (bottom) normalized data. The three best-fit curves are superimposed on the histogram, and the normal distribution curve is included for comparison. The specific parameters for the best-fit distributions are given. The inset displays the quantile-quantile (QQ) plot for the best-fit and normal distributions. These charts demonstrate that multiparameter distributions capable of modeling skewness and kurtosis better characterize the data than the standard Gaussian  distribution.(TIF)Click here for additional data file.Figure S7Distribution-Dependent Effects on Molecular Tumor Subclassification Based on Two Different Methods of Gene Calling. Two different gene calling methods, the student's t-test (top) and the Significance Analysis for Microarrays  were used to verify that the gene calling method used in conjunction with the K-Nearest Neighbors classifier (KNN). Both analyses use the parent (untransformed) low-grade glioma expression datasets. Class 1 represents low-grade astrocytomas, and Class 2 represents chromosome 1p/19q codeleted, low-grade oligodendrogliomas. The topmost color bars represent the known class of each sample . The area below the color bars is a portion of the gene expression profile . Both gene calling algorithms lead to identical results, indicating that this is not the primary reason for the observed differences in molecular classification (see tion see .(TIF)Click here for additional data file.Table S12 (unsubtracted) data.Central Moments Analysis using Logf-test # = Standard Error of Skewness (Fisher's Skewness Index) † = Standard Error of Kurtosis (Fisher's Kurtosis Index) ‡ = p-value [95% CI] φ = p-value.(XLS)Click here for additional data file.Table S2Empiric Distributions. Categorized list of the 57 empiric distributions tested in the curve fitting analysis.(XLS)Click here for additional data file.Table S3Differentially-Expressed Genes between Low-Grade Glioma Class 1 and Class 2, as identified by the student's t-test. Affymetrix ID is the unique probeset identifier of the gene. The second column indicates whether each gene is present in the lists from both the parent (untransformed) and transformed (Box-Cox) distributions.(XLS)Click here for additional data file.Table S4Differentially-Expressed Genes between Low-Grade Glioma Class 1 and Class 2, as identified by Significance Analysis for Microarrays (SAM). Affymetrix ID is the unique probeset identifier of the gene. The second column indicates whether each gene is present in the lists from both the parent (untransformed) and transformed (Box-Cox) distributions. FDR = False Discovery Rate.(XLS)Click here for additional data file.Table S5Differentially-Expressed Genes between Low-Grade Glioma Class 1 and Class 2, as identified by the Kruskal-Wallis test. Affymetrix ID is the unique probeset identifier of the gene. The second column indicates whether each gene is present in the lists from both the parent (untransformed) and transformed (Box-Cox) distributions. FDR = False Discovery Rate.(XLS)Click here for additional data file.Table S6Differentially-Expressed Genes between Low-Grade Glioma Class 1 and Class 2, as identified by linear modeling (LIMMA). Affymetrix ID is the unique probeset identifier of the gene. The second column indicates whether each gene is present in the lists from both the parent (untransformed) and transformed (Box-Cox) distributions.(XLS)Click here for additional data file.Table S7Functional Category Enrichment Analysis (SAM). Functional category (GO and KEGG) enrichment analysis for the list of genes differentially-expressed between low-grade glioma Class 1 and Class 2, as identified by the Significance Analysis for Microarrays (SAM) algorithm. Only categories with a Bonferroni-corrected p-value<0.01 are included. The GO or KEGG term is given in the left column, and the second column indicates whether each gene is present in the lists from both the parent (untransformed) and transformed (Box-Cox) distributions. Descriptive statistics are given at the bottom of the list.(XLS)Click here for additional data file.Table S8Functional Category Enrichment Analysis (KW). Functional category (GO and KEGG) enrichment analysis for the list of genes differentially-expressed between low-grade glioma Class 1 and Class 2, as identified by the Kruskal-Wallis test. Only categories with a Bonferroni-corrected p-value<0.01 are included. The GO or KEGG term is given in the left column, and the second column indicates whether each gene is present in the lists from both the parent (untransformed) and transformed (Box-Cox) distributions. Descriptive statistics are given at the bottom of the list.(XLS)Click here for additional data file.Table S9Datasets. These are the cancer gene expression datasets used in this analysis. GEO indicated data take from the Gene Expression Omnibus.(XLS)Click here for additional data file.Text S1(DOC)Click here for additional data file.