The arrival of RNA-seq as a high-throughput method competitive to the established microarray technologies has necessarily driven a need for comparative evaluation. To date, cross-platform comparisons of these technologies have been relatively few in number of platforms analyzed and were typically gene name annotation oriented. Here, we present a more extensive and yet precise assessment to elucidate differences and similarities in performance of numerous aspects including dynamic range, fidelity of raw signal and fold-change with sample titration, and concordance with qRT-PCR (TaqMan). To ensure that these results were not confounded by incompatible comparisons, we introduce the concept of probe mapping directed “transcript pattern”. A transcript pattern identifies probe(set)s across platforms that target a common set of transcripts for a specific gene. Thus, three levels of data were examined: entire data sets, data derived from a subset of 15,442 RefSeq genes common across platforms, and data derived from the transcript pattern defined subset of 7,034 RefSeq genes.In general, there were substantial core similarities between all 6 platforms evaluated; but, to varying degrees, the two RNA-seq protocols outperformed three of the four microarray platforms in most categories. Notably, a fourth microarray platform, Agilent with a modified protocol, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments, especially in regards to fold-change evaluation. Furthermore, these 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80 % fold-change concordance with the gold standard qRT-PCR (TaqMan).This study suggests that microarrays can perform on nearly equal footing with RNA-seq, in certain key features, specifically when the dynamic range is comparable. Furthermore, the concept of a transcript pattern has been introduced that may minimize potential confounding factors of multi-platform comparison and may be useful for similar evaluations.The online version of this article (doi:10.1186/s12864-015-1913-6) contains supplementary material, which is available to authorized users. Gene expression microarrays have provided an efficient and cost effective means for estimating RNA levels and differences on a transcriptome scale for nearly two decades. Since their arrival, millions of biological samples have been processed and analyzed utilizing this workhorse technology providing a window to the inner workings of cells, tissues, and organisms. For many years microarrays remained the primary source of knowledge pertaining to large scale gene expression. However, within the last few years RNA-seq has arisen as a competitive technology , 2 that While microarrays have performed admirably through their tenure, they do suffer from some inherit limitations , 4. PrimA number of publications have produced fundamental comparisons between RNA-seq and microarrays in an effort to illuminate the differences and similarities –11. WhilUtilizing the approach described within the materials and methods, three different levels of gene/transcript data sets were defined and employed for the comparisons of differential expression across platforms Table . The firth percentile signal value of all 5 RNA samples along with the mean background value to calculate the signal-to-background ratio for all platforms , a normal pooled bone marrow RNA (labeled as BMO), and 3 of AGO/BMO mixed samples at a ratio of 1:4, 1:16, and 1:64  Table . These 5rms Fig. . For RNAA linear regression analysis was performed between the sample titration and the signal or read count for each feature  within each platform. The overall distribution of the coefficient of correlation can be seen in Fig. An assessment of overall fold-change magnitude was completed at sample titrations and at platforms as a whole, utilizing both the entire data sets and the transcript pattern restricted subsets. The average absolute fold-change was calculated at each of the four sample titrations with all of the above data sets for each platform Fig. , and the2) were also calculated. While the R2 provides a basic estimate for the overall correlation, the slope presents a rough indication as to the degree of compression observed within each platform as compared to AGLN. Based on the measured slope , were detected when the pure bone marrow was compared to pure Agilent Human Universal Reference RNA (i.e. BMO vs. AGO), the AGLN platform appeared to produce the highest degree of fidelity overall across the titration series. The overall fold-change concordance, as defined in the methods section, among the 34 genes ranged from 24  to 88 % between TaqMan and microarray/RNA-seq assays . In terms of array design and scanner attributes, Illumina arrays appear to have smaller spots and a smaller dynamic signal range than other microarray platforms. If the error in signal measurement does not compress with the overall signal, it may lead to reduced array sensitivity and the ability to capture subtle differences of transcript abundance in titrated samples . SomewhaLastly, we have noted that Affymetrix whole-transcript microarray platforms (Gene1.0 and HTA2.0) yielded the lowest magnitude of fold-change estimates while 3′ biased Agilent chips produced the highest fold-change magnitude, although the same 3′ biased Illumina chips failed to generate comparably high fold-change estimates. This may lead to a confusing concept that 3′ biased arrays generally have greater signal dynamic range than whole-transcript chips. Actually it is not a 3′ versus whole-transcript design issue, but a far more complex issue that involves varying RNA amplification and/or probe hybridization efficiencies, different sets of alternatively spliced transcripts in measurement, and different infrastructures of microarrays  , 21. As Prior studies have made fundamental comparisons between RNA-seq and microarrays with focus on dynamic range of raw signal/read values and overlap in observed differentially expressed genes. The present study has expanded the foundation laid in the prior studies by looking at the correlations of multiple platforms with regard in signal-to-background ratios, signal/read fidelity to sample titration, and platform performance for providing accurate fold-change estimates. Our results confirmed many of the general findings of the prior studies. RNA-seq methods tend to outperform microarray platforms in regards to certain features such as dynamic range . However, we did observe that the custom protocol Agilent platform performed quite comparably to RNA-Seq, at the given read depth, in virtually all categories. This is presumably due to the increased dynamic range of the platform, suggesting that small improvements in this key feature may put microarrays on a more even footing with RNA-Seq overall. Furthermore, even in the presence of compressed signal range and fold change estimates, the different Affymetrix platforms performed well in the categories of titration and TaqMan fidelity, as compared to Agilent and the RNA-Seq protocols. In addition, the concept of a “transcript pattern” was employed to better enable an “apples-to-apples” comparison of all platforms. As may have been anticipated, the most significant differences observed in the use of common transcript pattern probes, when compared to the larger RefSeq defined transcript set, was noted in the assessment of fold changes and correlation. This was particularly true for the Agilent microarrays and CloneTech RNA-Seq. These data are suggestive that utilization of transcript patterns may be useful for minimizing potential confounding factors in multi-platform comparisons of this nature.Two original total RNA samples were utilized in this study: one was a pool of RNA isolations from 4 human bone marrow specimens (labeled as BMO) and the other was a Universal Human Reference RNA from Agilent (labeled as AGO). The human bone marrow samples were collected from normal donors with written consent form for use in this study, and these individual RNA samples showed very similar expression profiles in a prior unrelated project. The AGO was made from multiple human cell lines. Both samples had a RNA integrity number (RIN) over 9 assessed by Agilent Bioanalyzer 2100 . In order to evaluate the ability of a specific platform to detect subtle changes in gene signals and differentials, we generated 3 new samples from the original AGO and BMO RNA samples by mixing the original pure BMO into AGO at a ratio of 1:4, 1:16, and 1:64, and the 3 new RNA samples were labeled as AG1BM4, AG1BM16, and AG1BM64, respectively. These 5 samples were processed in 1–3 replicates (duplicate on average) for microarrays and RNA-seq analyses  Agilent Whole Human Genome Microarray 4x44K v1 (symbolized as AGLN hereinafter), (2) Illumina HumanHT-12 v4 Expression BeadChip (ILMN), (3) Affymetrix Human Gene 1.0 ST Array (Gene1.0), and (4) Affymetrix Human Transcript Array 2.0 (HTA2.0). We followed vendors’ recommended standard protocols to process the samples for HTA2.0 and ILMN arrays but substituted RNA amplification system with NuGEN kit for Gene1.0 and cDNA labeling system with Kreatech kit for AGLN arrays (see following). For AGLN slides, 200 ng of starting RNAs were amplified with MessageAmp™ II aRNA Amplification Kit , 3,000 ng of amplified aRNA were labeled with Kreatech ULS™ Fluorescent Labeling Kit (with Cy5) , and 2,055 ng of cy5-labeled aRNA were hybridized. For ILMN BeadChips, 100 ng of starting RNAs were amplified with the MessageAmp™ aRNA TotalPrep kit (Ambion/Life Technologies) and 750 ng of biotin-labeled cRNA were hybridized. For Gene1.0 chips, 50 ng of starting RNAs were amplified with NuGEN Ovation PicoSL WTA V2 kit  and 2,500 ng of biotin-labeled cDNAs were hybridized. For HTA2.0 chips, 100 ng of starting RNAs were amplified with Ambion WT-plus amplification kit (Ambion/Life Technologies) and 5,000 ng of biotin-labeled cDNAs were hybridized.p < 0.05 was set to make “detected” calls. The AGLN and ILMN probe- and gene-level raw data were normalized by most commonly utilized “quantile” method. For Gene1.0 and HTA2.0, Affymetrix Expression Console (v1.3) was used to generate default raw data at both probeset and transcript cluster levels, and the raw data were RMA background corrected, median polish summarized and quantile normalized. Detection p-values were calculated for all probesets on Gene1.0 and HTA2.0 chips, a p < 0.01 was set to make “detected” calls at the probeset level.We process the raw data with vendors’ default settings. For AGLN, the Agilent Feature Extraction tool (v11.5.4) was used to generate raw data at probe level, rProcessedSignal was used in normalization and data analysis processes, and rIsWellAboveBG parameter was used to mark probes as “detected” or “un-detected”. For ILMN, Illumina GenomeStudio software (v2011) was used to export background-subtracted raw data and detection p-values at both probe and gene levels, and a In order to enable an exact comparison of transcript abundance across platforms, we first re-annotated vendors’ probes (probesets for Affymetrix chips) to correct any outdated gene information and to minimize probe(set)s with non-specific hybridization. Ensembl release version 72 (R72) of human reference transcriptome and genome databases were used to make new probe(set) annotations. For AGLN, ILMN and Gene1.0, probe(set) sequences provided by vendors were mapped onto Ensembl R72 genome using novoalign tool  to define probe(set) coordinates. For HTA2.0, probeset coordinates provided by the vendor were used to retrieve probeset sequences from Human Genome hg19 database. We then used the retrieved probeset sequences to map the probesets onto Ensembl R72 reference genome. Any probe(set)s with multiple alignments or partial alignments to the genome were omitted in downstream analysis. Finally, the uniquely aligned probe(set)s derived from all platforms were intersected with Ensembl R72 transcriptome using BEDtools  to obtaiTwo different library preparation protocols were tested: oligo-d(T)-priming based Clontech SMARTer Ultra Low RNA Kit (hereafter labeled as “ClonTech”) for Illumina® Sequencing from Clontech Laboratories , and random priming based Ribo-Zero™ rRNA Removal Kits (labeled as “RiboZero”) from EpiCentre . A starting amount of 10 ng RNAs was processed in ClonTech assays, and 1 ug were input with RiboZero protocols. For ClonTech libraries, amplified cDNA was sheared using a Covaris E210 instrument. RiboZero depleted RNA was chemically fragmented, then made into cDNA with Superscript III (Life Technologies) and random hexamers followed by a second strand reaction. cDNA from both methods was then end repaired, A tailed, and standard Illumina adapters were ligated on. Libraries were then amplified with primers to incorporate a unique index to each sample. In pooling of multiple libraries for running on a single lane, equal amount of library mass was determined by Qubit reading and Bioanalyzer for the 5 RNA libraries. Lastly, pooled libraries were amplified with Illumina® TruSeq™ Cluster kits and sequenced with Illumina® sequencing primers on Illumina® HiSeq2500™ next-generation sequencing system as a high output single read 50 cycle run .An average of 36.7 (range 18.1–53.0) and 27.9 (range 22.9–43.9) million 1x50 bps sequence reads were obtained from the 5 RNA samples with ClonTech and RiboZero protocols, respectively. Read sequences in fastq files were aligned to Ensembl R72 whole-genome with TopHat version 2.0.8 using Bowtie2 version 2.1.0 for quantification. Both exon- and gene-level count data were generated using scripts “dexseq_count” and “htseq-count”, respectively, in a Python package “HTSeq”  for diffOne primary goal of our study is to evaluate the platform performance in magnitude of differential expression, i.e. fold-change. Therefore, ratios of AG1BM4 vs. AGO, AG1BM16 vs. AGO, AG1BM64 vs. AGO, and BMO vs. AGO were first calculated utilizing signal and read count data. Furthermore, these were completed at the level of the entire data set as well as the transcript pattern derived subset data for each platform. Ratios were transformed to absolute fold-changes, i.e. ratios for up-regulated genes remained the ratio per se, and any ratios less than 1 for down-regulated genes were converted to its reciprocal values (i.e. switching the nominator and denominator to make the ratio greater than 1). Next, the absolute fold-change values in a data set were averaged to reflect the platform capability for providing differential expression estimates.TaqMan assays were run for 48 genes on each of the 5 RNA samples that were used in microarray and RNA-seq assays. These 48 genes were profiled as part of an unrelated project. The assays were carried out using microfluidic-based digital PCR system with 48.48 Dynamic Arrays . In brief, 500 ng total RNAs were subjected to specific target amplification (STA) using High Capacity cDNA Reverse Transcription Kit and TaqMan PreAmp Mastermix , and then each cDNA sample was diluted 1part sample into 4 parts low ethylenediaminetetraacetic acid (EDTA) DNA suspension buffer. The qPCR reaction was prepared with diluted cDNA samples, 20X Gene Expression Sample Loading Reagent (Fluidigm Corp.), TaqMan Universal PCR Master Mix (Applied Biosystems), and Dynamic Array Assay Loading Reagent that contained 9 μM of each primer and 2 μM of the probe (Fluidigm Corp). The qPCR program was as following: 50 °C for 2 min, 95 °C for 2 min, 40 cycles of 95 °C for 15 s and 60 °C for 1 min. Post-PCR analysis was performed to mark failed samples with a Ct value greater than 40 in over 50 % of replicates and to remove unreliable samples with coefficient of variance greater than 30 % in Ct values across replicates. Relative gene expression level was calculated by delta-delta Ct method .In determination of fold-change concordance between TaqMan (X) and microarrays/RNA-seq protocols (Y), 4 qualitative evaluations were assigned to each comparison: compressed, opposite, overestimate, and concordant. When two compared fold-changes are in the same direction but the ratio of X/Y greater than or equal to 2, a value of “compressed” is assigned. Similarly, if the fold-change ratio of X/Y is less than or equal to 0.5 the comparison is deemed “overestimate”. Fold-change ratios between these values are deemed “concordant”. When two fold-changes are not in the same direction and either of them is greater than 2 or less than 0.5, the comparison is determined to be “opposite”. Concordance rates were calculated by number of genes with “concordant” and “overestimate” calls divided by the total genes analyzed.All microarray and RNA-seq data are accessible in the GEO database, with accession number GSE66590 for RNA-seq_RiboZero, GSE66592 for RNA-seq_ClonTech, GSE66614 for Illumina_HT12, GSE66626 for Agilent_4x44K, GSE66628 for Affymetrix_Gene1.0 and GSE66648 for Affymetrix_HTA2.0. A SuperSeries accession number GSE66649 is created for access to all these data in the whole project.


Reference datasets are often used to compare, interpret or validate experimental data and analytical methods. In the field of gene expression, several reference datasets have been published. Typically, they consist of individual baseline or spike-in experiments carried out in a single laboratory and representing a particular set of conditions.http://www.expressiondata.org.Here, we describe a new type of standardized datasets representative for the spatial and temporal dimensions of gene expression. They result from integrating expression data from a large number of globally normalized and quality controlled public experiments. Expression data is aggregated by anatomical part or stage of development to yield a representative transcriptome for each category. For example, we created a genome-wide expression dataset representing the FDA tissue panel across 35 tissue types. The proposed datasets were created for human and several model organisms and are publicly available at  Baseline or reference data are important for the analysis and interpretation of experiments and for testing methods and algorithms. Two main types of baseline data exist: a) measurements that serve as negative controls within a given experiment, and b) experiments that were carried out for the purpose of creating a collection of default states. In the first type, data from control samples  are necessary to test the effect of individual factors in an experiment or to calibrate a technology or treatment. In the second type, baseline datasets typically aim at compiling a large collection of data points representative for development, anatomy, cancer or response to a particular condition. The main objective of the second type is to provide comparative value for improved interpretation or for verification. In the present work, we consider the second type of reference data.The nature of what are considered to be appropriate baseline conditions depends on community standards and on experimental design, but frequently they target unperturbed, healthy samples from a “wild-type” genetic background, or they represent a collection of perturbed states as reference for one’s own data. Several distinct types of baseline gene expression data exist: tissue, cancer or development profiles , perturbations and diseases  and time courses and dose responses , or a combination of these spatial, temporal and response profiles. Examples of baseline data sets that have been published include the GNF human tissue panel , The CanMost publicly available datasets originate from a single experiment with few independent biological replicates and performed in a particular experimental setting. The expression profiles of these samples therefore represent gene expression in that particular context, but it is not a priori clear whether these results are generally reproducible in other contexts. Furthermore, it is not immediately visible whether similar results have already been found previously or if they are novel. To verify these two questions requires the availability of comparable experiments. Comparing one’s own expression profiles with reference datasets composed of a variety of different experimental conditions allows interpreting similarities globally or at the level of individual gene signatures. It is clear that the composition and robustness of the reference datasets used will have a major impact on the outcome of such comparisons. It is therefore essential to create robust reference datasets containing representative expression values for individual biological contexts. Robustness and improved statistical power can be achieved through intensity-level integration of microarray data . The quahttp://www.expressiondata.org, provides reference datasets for human and several model organisms , for different technological platforms, and for a variety of biological dimensions . The resource primarily contains subsets of data that were generated from the GENEVESTIGATOR expression compendium [http://www.expressiondata.org.The new resource, called ExpressionData and available at mpendium . In briempendium  and Arrampendium . All samThe datasets chosen for the ExpressionData resource were typically generated from hundreds of experiments. For each anatomy or development category we calculated a representative vector of expression (meta-profile) for all probe sets from a given microarray platform. For example, all 616 human samples hybridized on the Affymetrix Human133 Plus 2.0 array and which were annotated as “liver” were combined into a single mean expression vector representing the tissue “liver”. We assume that this summarization into meta-profiles generates biologically representative expression vectors. As a proof of concept, we performed a Principle Component Analysis of the anatomy meta-profiles of 31 different mouse tissue types. The projections show a biologically meaningful clustering of related tissue types  is a series of Arabidopsis samples collected at 2 hours interval over a period of 48 hours and across two different schemes of day/night entrainment. As an example, Figure Many biological processes are repetitive or timed, leading to oscillations in their regulation. Two typical examples of oscillatory behaviour are circadian rythms and the cell cycle. In ExpressionData, several public experiments having at least one complete oscillation were curated and are made available. For example, the Arabidopsis circadian clock experiment available at NASC under experiment ID NASCARRAYS-196  plants from the relatively cold-intolerant Flue-Cured variety K326. Leaves were quickly chilled for 10 minutes in a blast chiller at a temperature between 0-5°C and monitored to avoid frost. Then, the samples were incubated at the same temperature for 5 hours or 24 hours before being frozen in liquid nitrogen. Two harvesting times, one in the morning (7:30am) and one in the afternoon (1:00pm) were chosen in order to avoid circadian effect. Two sets of control leaves were harvested together with the cold-treated samples and frozen immediately in liquid nitrogen (Time 0). Three biological replicates were prepared for each time point. Unlike in the previously described datasets focused on biological oscillations, the objective of this type of studies is to find genes that are continuously induced by cold treatment over the time period of 24 hours independently of harvesting time of the seedlings . The raw data from this experiment are available in Gene Expression Omnibus (GEO) under accession Nr. GSE44938.The dataset considered in this section was generated by harvesting leaves of 6 week old greenhouse-grown tobacco (Figure Microarray data experiments such as this may lead to the discovery of regulative elements which serve as powerful tools for expression of commercially valuable recombinant proteins in plants, and which are not circadian-sensitive. They may also lead to the identification of new unknown genes which could be targeted for selection of cold tolerant varieties.The datasets presented here are examples of curated and aggregated datasets of gene expression covering a broad range of biological conditions. We have created reference data sets that can be used for • Interpreting results from other studies. The created expression compendia, such as for anatomical parts, are representative for individual biological states and allow comparisons with expression data obtained in other experiments.• Finding novel gene regulatory modules and networks from aggregated datasets containing a high diversity of tissues and developmental stages.• Testing bioinformatics methods or algorithms with manually curated and biologically relevant data.http://www.expressiondata.org website demonstrate the biological relevance of such datasets. In fact, the results in Figure http://www.expressiondata.org website demonstrate that the compiled data contain robust and representative estimates for individual biological states such as tissue types or stages of development. In particular, a high concordance was found in the tissue transcriptomes between human and mouse by comparing aggregated expression data for each organism across the same set of tissues. While the ExpressionData resource is expected to grow as new datasets are processed and made available, it does not aim at having complete sets of data for every organism.The results shown here and on the http://www.expressiondata.org.ExpressionData is a new source of tested, high quality, manually curated, globally normalized and aggregated expression data ready for use in a variety of data interpretation and verification tasks. It is expected to simplify the search for robust and high quality datasets and to provide a set of reference data for comparison. Its content is open access and freely available for download at The authors declare that they have no competing interests.All authors were involved in data collection, data processing and creating the resource. PZ, SB, OL, NVI, FM and WG wrote the manuscript. All authors read and approved the final manuscript.


Endobronchial ultrasound (EBUS)-guided biopsy is the mainstay for investigation of mediastinal lymphadenopathy for laboratory diagnosis of malignancy, sarcoidosis, or TB. However, improved methods for discriminating between TB and sarcoidosis and excluding malignancy are still needed. We sought to evaluate the role of genomewide transcriptional profiling to aid diagnostic processes in this setting.Mediastinal lymph node samples from 88 individuals were obtained by EBUS-guided aspiration for investigation of mediastinal lymphadenopathy and subjected to transcriptional profiling in addition to conventional laboratory assessments. Computational strategies were used to evaluate the potential for using the transcriptome to distinguish between diagnostic categories.Molecular signatures associated with granulomas or neoplastic and metastatic processes were clearly discernible in granulomatous and malignant lymph node samples, respectively. Support vector machine (SVM) learning using differentially expressed genes showed excellent sensitivity and specificity profiles in receiver operating characteristic curve analysis with area under curve values > 0.9 for discriminating between granulomatous and nongranulomatous disease, TB and sarcoidosis, and between cancer and reactive lymphadenopathy. A two-step decision tree using SVM to distinguish granulomatous and nongranulomatous disease, then between TB and sarcoidosis in granulomatous cases, and between cancer and reactive lymphadenopathy in nongranulomatous cases, achieved > 90% specificity for each diagnosis and afforded greater sensitivity than existing tests to detect TB and cancer. In some diagnostically ambiguous cases, computational classification predicted granulomatous disease or cancer before pathologic abnormalities were evident.Machine learning analysis of transcriptional profiling in mediastinal lymphadenopathy may significantly improve the clinical utility of EBUS-guided biopsies. Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has transformed diagnostic evaluation of mediastinal lymphadenopathy in respiratory medicine. This has revolutionized lung cancer staging, providing a median sensitivity of 89% for detection of malignant cells, and also affords high sensitivity for detection of granulomatous lymphadenitis in sarcoidosis.5Genomewide transcriptional profiling can identify molecular signatures in peripheral blood or tumor specimens that could be used to improve diagnosis and risk stratification of patients with infectious and inflammatory diseases or to guide targeted therapeutic strategies for individuals with cancer.The study was approved by the North London Research Ethics Committee (10/H0724/72). Written informed consent was obtained from all participants.Lymph node samples were obtained from adult patients undergoing EBUS-TBNA for investigation of mediastinal lymphadenopathy (see t tests (P < .05) using MultiExperiment Viewer (version 4.6.0) and restricted to those with greater than twofold differences. Pathway overrepresentation analysis of differentially expressed genes was conducted using InnateDB, and transcriptional regulation of specific gene expression profiles was assessed by oPossum single transcription factor binding site enrichment analysis as previously described.www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-2547.Lymph node cores harvested into RNALater (Qiagen) and homogenized in Qiazol (Qiagen) were used to obtain total RNA using the RNEasy Micro kit (Qiagen). Samples were processed for Agilent microarrays, and data were normalized as previously described .18 PrincSupport vector machines (SVMs) for binary computational classification of high-dimensional data were trained to classify samples using selected gene signatures.We performed transcriptional profiling of EBUS-TBNA samples from 88 patients . DetaileDespite the overlap in the clustering analysis described above, direct comparison of gene expression data identified differentially expressed genes between granulomatous and nongranulomatous lymph node samples (488 genes), between TB and sarcoidosis samples (58 genes), and between malignant and reactive samples  see . In keepIn the nongranulomatous samples, malignant lymph node samples were significantly enriched for genes involved in cell cycle control and extracellular matrix interactions, consistent with processes related to cancer development and metastasis , and undSVM are data-driven computational algorithms that can “learn” to discriminate between high-dimensional data such as genomewide transcriptomes. Therefore, we assessed the performance of SVM discrimination between diagnostic categories using the differentially expressed transcriptional signatures described above. In this analysis, we used repeated bootstrap subsampling cross-validationNext we assessed the performance of SVM classification in a two-step decision tree sequence, using leave-one-out cross-validation as an alternative cross-validation strategy that optimizes the size of the training cohort . In stepWe also sought to identify the minimum number of genes needed to accurately classify cases using each SVM. The most discriminating genes were identified by their weighting in the training data sets using repeated bootstrap subsampling cross-validation see A. To achPeripheral blood gene signatures that distinguish TB and sarcoidosis have been reported previously.Finally, we tested the two-step SVM decision-tree sequence described above on cases in which a “definite” diagnosis could not be made at the time of EBUS-TBNA. We compared SVM classification of these samples with the final diagnosis based on follow-up data . SVM anaThe development of EBUS has revolutionized investigation of mediastinal lymphadenopathy by facilitating minimally invasive sampling of lymph nodes at the site of disease. Transcriptional profiling of EBUS-TBNA in granulomatous and malignant lymphadenopathy revealed clear evidence of the key biologic processes relevant to each disease. Samples from granulomatous diseases were enriched for immune cell recruitment and activation as well as antigen presentation and interferon-γ signaling, which characterize granulomatous inflammation.The similarity between TB and sarcoidosis profiles suggests that by the time of clinical presentation, the molecular pathology in these diseases is comparable. Sarcoidosis samples showed higher expression of genes with putative roles in granuloma formation, including cathepsin K, transmembrane 7 superfamily member 4, and chemokine (C-C motif) ligand 21.The performance of SVM using differential gene expression signatures to distinguish between granulomatous and nongranulomatous disease, cancer and reactive lymphadenopathy, and TB and sarcoidosis showed excellent promise to improve diagnostic classification, providing AUC statistics of > 0.9 in each case. Importantly, the SVM models identified the most influential genes and opportunity to test the feasibility of using targeted transcriptional analysis rather than genomewide technologies in future studies. Our analysis suggested that this might be possible for discriminating granulomatous from nongranulomatous disease and TB from sarcoidosis using multiplex quantitative polymerase chain reaction analysis of 20 to 30 genes. However, expression data from more than 150 genes were required to discriminate cancer and reactive lymphadenopathy.The application of SVM in a two-step decision tree model was significantly more sensitive than mycobacterial culture for identification of TB and equivalent to histologic detection of noncaseating granulomas for sarcoidosis.SVM also classified specimens from two individuals undergoing EBUS-guided lymph node sampling for lung cancer staging as “cancer,” despite no histologic evidence of malignancy. In keeping with this, SVM classification was slightly more sensitive than the reported median sensitivity of histologic identification of malignancy.We propose that transcriptional profiling of lymph node samples from the site of disease combined with machine learning data analysis offers a novel strategy with molecular-level resolution that could be applied to augment conventional investigation of clinically ambiguous cases of mediastinal lymphadenopathy. This merits further evaluation in future large-scale clinical trials.


Pooled samples are frequently used in experiments measuring gene expression. In this method, RNA from different individuals sharing the same experimental conditions and explanatory variables is blended and their concentrations are jointly measured. As a matter of principle, individuals are represented in equal shares in each pool. However, some degree of disproportionality may arise from the limits of technical precision. As a consequence a special kind of technical error occurs, which can be modelled by a respective variance component. Previously published theory - allowing for variable pool sizes - has been applied to four microarray gene expression data sets from different species in order to assess the practical relevance of this type of technical error in terms of significance and size of this variance component.The number of transcripts with a significant variance component due to imperfect blending was found to be 4329 (23 %) in mouse data and 7093 (49 %) in honey bees, but only 6 in rats and none whatsoever in human data. These results correspond to a false discovery rate of 5 % in each data set. The number of transcripts found to be differentially expressed between treatments was always higher when the blending error variance was neglected. Simulations clearly indicated overly-optimistic (anti-conservative) test results in terms of false discovery rates whenever this source of variability was not represented in the model.Imperfect equality of shares when blending RNA from different individuals into joint pools of variable size is a source of technical variation with relevance for experimental design, practice at the laboratory bench and data analysis. Its potentially adverse effects, incorrect identification of differentially expressed transcripts and overly-optimistic significance tests, can be fully avoided, however, by the sound application of recently established theory and models for data analysis.The online version of this article (doi:10.1186/s12864-015-2055-6) contains supplementary material, which is available to authorized users. In gene expression profiling pooling is a method to reduce hybridization costs and compensate for insufficient amounts of mRNA. In the subsequent statistical analyses of gene expression data, where a log-transformation during preprocessing is standard, it is important to consider how the expectation and variance of the gene expression of pools relate to individual samples. The impact of pooling on the identification of differential gene expression has been studied in Kendziorski et al. , separatConsequently a study was performed, in which gene expression data from experiments with four different species were analyzed to investigate the relevance of the aforementioned new kind of technical error in terms of size and significance of the corresponding variance component. Furthermore, we investigated potential consequences on the number of transcripts identified as differentially expressed between different treatments when analyses neglect this kind of error.This section offers a short recap of the underlying statistical models. The four experimental data sets are then introduced. In all of them - whether from single- or two-color arrays - there are more observations than pools  of a certain transcript, the model in matrix notation is: When aliquots of mRNA from different individuals are blended into common pools, the inaccuracies of this procedure may induce a special kind of technical error. Respective random effects, together with a corresponding variance component, were proposed  as a meaX and Z are the design matrices of the fixed ⊤) and random  effects. The distribution of uj is assumed to be γi pooled individuals have a biological variance u1 may comprise biological effects of single individuals as well as average biological effects of groups of individuals constituting common pools, according to the experimental design.where u2 consists of one effect per mixture, which had been prepared in the lab. The associated variance component is The random effect of blending  only applies to observations from pools and not to observations from single individuals. Therefore, The model of this variance structure is based on the closed form approximation of the variance of pools on the scale of log-intensities, proposed in  (3)\docwhere entclass1pt{minimaIn the following, the relevance of accounting for the blending error variance component Mouse data consisted of observations from 44 one-color microarrays. RNA for this experiment was extracted from the ovaries of 60 female mice, 30 of which came from a long-term selection line with an extraordinary litter size. All others came from a control line. Pooled samples were built by blending RNA from five mice per sample. Each mouse was only represented in a single pool. For the sake of technical replication, all 12 pooled samples were measured twice by preparing two microarrays per sample. Additionally, animals from two pools per line  were measured individually. These individual measurements were not included in the previously published analysis of this data [γi. The 22 observations from the first line are represented in the upper part of the design matrix Twenty-eight (14 per line) different biological effects were defined per transcript. The dimensions of the design matrix The technical variability due to blending individual samples only comes into play when observing pooled samples, not for measurements of individuals. Since blending was done only once per pool, there are 12 different effects due to imperfect blending. Therefore, the 44×12 design matrix g to Eq. .This study did not involve in vivo experiments. Animals were housed according to the German law for animal protection (TierSchG) and in compliance with the European legislation on the care and use of animals.This data set was analyzed by Kendziorki et al.  and contmentclass2pt{minimVarroa-parasitized section of a brood comb. Pooling was applied in a preliminary experiment with a limited number of bees and microarrays. For seven hygienic bees and seven control bees, mRNA was extracted from nerve tissues of the mushroom body (MB), antennal lobe (AL) and Antennae (ANT). The number of individuals blended into a pool was either two or four. Out of the 14 bees, six different sample compositions were built and analyzed for all three tissues with two-color arrays  and green (G) channel This data set stems from a honeybee project dealing with differences in the pathogen resistance of so-called hygienic and non-hygienic worker bees as far as they are reflected in gene expression differences. Bees designated as ’hygienic’ were observed to open brood cells and assisting the removal of diseased brood. The bees’ activities were recorded on a M is the vector of log-ratios , the differences Δ between the behaviors (hygienic minus control) and two differences between tissues . The latter effect has been included since data from all tissues were jointly analyzed due to the limited number of arrays. The random effect u1 for each sample composition has a variance structure determined by u2 for the blending of individuals is generated by Z1 and Z2 contains two non-zero elements (as opposed to a single one) in order to model the differences between effects with entries of 1 for the red and −1 for the green channel. The residual errors Here +, TST− and TB were compared, where TST stands for the tuberculosis skin test  and TB for acute tuberculosis. Overall, the data set consists of samples from 55 humans in 16 pools of three and in ten single samples, which were labeled on 30 two-color arrays. In the sample composition, one also sees correlations between pools in three cases, where individuals were used more than once, i.e. in different pools  project. For the project data, please see Maertzdorf et al.  and JacoThis study was approved by ethical committees in both Stellenbosch (South Africa) and Berlin (Germany) and written informed consent has been obtained from all study participants (details in ).The relationship between the variance of a random effect of a pool and deviations from the homogene aliquots of individuals in a pool sample, given in Eq. , is basew the vector of weights , we calculated a value for true gene expression on the log-scale as By setting Xβ=μg, the distortion due to biological variation i=1,…,γ was used. Then, w from [ai can be chosen in order to obtain weights with a given variance The technical errors, distributed as mentclass2pt{minimVarious proportions of transcripts  were simulated as affected by imperfect blending. In order to investigate the distribution of the RLRT-statistic under the null hypothesis . Transcp-values from all transcripts in one experiment under the null hypothesis deviates from the uniform distribution  of 5 %, the transcripts with a significant RLRT were determined. Beyond that, we evaluated the proportions of the estimated variance component For each transcript, a residual likelihood ratio test (RLRT) was used to test the null hypothesis see e.g. ). Accordentclass1pt{minimay=Xβ+Z1u1+e without a random effect of imperfect blending. Degrees of freedom for the applied F-Tests of fixed effects in mixed models were adjusted according to Kenward and Roger [% was applied to the p-values of the latter F-tests.The practical relevance of the variance component for imperfect blending of samples was further investigated by comparing the number of transcripts identified as differentially expressed in different treatment levels by means of the full model m2, Eq.  and the nd Roger . In ordeFor the mouse data set, the normalization was done with the gcrma method . Loess- The open-source statistical programming package R  was usedε=10−8 and n-th iteration. False discovery rates were computed with the help of the R-package qvalue [p-values from RLRT test statistics, the ’bootstrap’ option was used to estimate π0, as suggested by Storey [was fulfilled , where εe qvalue . In the y Storey .p-values can be observed on the interval [0,0.5) as expected  in comparison with m2 (102).Differences in both models’ abilities to find differential expression in the simulated data sets were also observed Table . The nulFurthermore, in a series of simulations, the pooling technical variance entclass1pt{minimaTherefore, it can be concluded at the very least that tests for differential expression with the m1 model tend to be too optimistic, depending on the given experimental conditions. To summarize, should the model contain the additional random effect of imperfect blending, the statistical analysis yields results which agree very well with the simulated characteristics.−6) to less than one hundred (102). A clear bimodal distribution can be observed in all cases, where the left part of each distribution  represents very small values close to zero while the other part represents more substantial values. In the mouse and the bee data, the proportion of transcripts with substantially large values clearly exceeds the proportion of small values. For the human data, the proportion of small estimates also prevails somewhat, while a balance between minor and substantial values can be observed for the rat data. This is also reflected in the average  of all three variance components obtained with the reduced (m1) and the full (m2) models, as shown in Table Histograms of log-transformed estimations of the variance components due to imperfect blending are shown in Fig. p-values from RLRT-tests for the hypothesis of a non-existing  includiwww.ncbi.nlm.nih.gov/geo), under the joint accession no. GSE72944. The human data is part of the data set GSE6112 and the rat data has the GEO accession no. GSE2331.The mouse and honey bee data sets have been deposited at the Gene Expression Omnibus (GEO) website (


Our results suggest the potential benefit of collecting and profiling matched normal tissues to gain more insights on disease etiology and patient progression.Although normal tissue samples adjacent to tumors are sometimes collected from patients in cancer studies, they are often used as normal controls to identify genes differentially expressed between tumor and normal samples. However, it is in general more difficult to obtain and clearly define paired normal samples, and whether these samples should be treated as “normal” due to their close proximity to tumors. In this article, by analyzing the accrued data in The Cancer Genome Atlas (TCGA), we show the surprising results that the paired normal samples are in general more informative on patient survival than tumors. Different lines of evidence suggest that this is likely due to tumor micro-environment instead of tumor cell contamination or field cancerization effect. Pathway analyses suggest that tumor micro-environment may play an important role in cancer patient survival either by boosting the adjacent metabolism or the  Although it is common practice to collect germ line information from cancer patients to identify somatic mutations in whole exome sequencing or whole genome sequencing (WGS) studies, it is much less common to have matched normal tissues from cancer patients for comparative gene expression studies. This is due to several reasons. Firstly, normal controls are not always available since the legitimate normal control for solid tumors should be collected from tissues residing near the tumor tissue site, which is harder to obtain than blood samples that can be used for germ line DNA sequence analysis. As a result, most solid tumor studies include no or only limited paired normal samples. Secondly, there is the concern of whether histologically-normal samples are truly ‘normal’ or they are actually in an abnormal state bearing genomic and translational aberrations corresponding to tumor, leading to potential biases in contrasting tumor to paired normal samples1Although the value of paired normal samples in solid tumors has not been carefully examined, recent reports suggest that expression level changes between tumor and paired normal samples may be more correlated with cancer relapse and survival than expression levels in tumor samples alone46http://cancergenome.nih.gov) with paired tumor and normal samples, we performed a comprehensive evaluation on whether and how much the paired normal samples may contribute to survival prediction. In particular, we aim to answer the following three questions: 1) in terms of prognosis signal, do paired normal samples offer additional information on patient prognosis beyond what can be explained by tumor cell contamination? 2) Across cancer types, how much benefit can we get from incorporating expression profiles from paired normal samples into patients’ survival prediction, and how much does the benefit vary across different cancer types? and 3) Do we have enough evidence to support or refute the three competing theories discussed above and what biological insights can be learned from analyzing paired normal samples?In this study, using the large datasets from multiple cancer cohorts collected by The Cancer Genome Atlas (TCGA)To answer the first question, we first considered the TCGA breast cancer data where 60 pairs of matched tumor and normal tissue samples were gene expression profiled using the UNC Agilent G4502A_07 microarrays. We investigated whether normal tissue samples carry additional information on tumor prognosis in addition to that from the tumor tissues. We performed hierarchical clustering on the paired tumor-normal samples with 12,000 most varying genes. There is clear clustering of two distinct groups based on tumor and normal tissues see . We thenk values see We then investigated the biological relevance of the clusters derived from tumor samples, normal samples, and fold changes between the tumor and normal samples by correlating these clusters with clinical information from the patients. For these 60 patients, we extracted 17 different types of clinical features from the TCGA database. These clinical features include molecular features originally defined in tumor tissues, such as PAM50 and TripleNegative; they also include survival and patient status related features, such as histology and tumor weight. A detailed list of the clinical features is shown in 4 = 16 different categories. The number of patients belonging to each category is listed in Besides associations with clinical features, we also explored how patient clustering is related to tumorigenesis and cancer progression. Cancer onset and progression result from the accumulation of somatic mutations on genes in the cancer-driving pathways. Mutual exclusivity refers to the observation that somatic mutations are often only observed in one of multiple genes in the cancer driving pathwaysTo directly assess the usefulness of gene expression data from paired normal tissues in predicting patients’ survival and to further generalize to other platforms and cancer cohorts, we performed survival analysis on the TCGA pan-cancer (PANCAN) data where IlluminaHiSeq_RNASeqV2 was used to measure gene expression levels. There are a total of 12 cancer cohorts with matched tumor and normal sample pairs. We removed samples with incomplete survival information and cancer cohorts with insufficient number of matched normal samples, resulting in six cancer cohorts with sufficient numbers of samples and events (death). C is then defined as the difference between the probability matrix of the relative risk comparison between each pair of samples and the correspondent probability matrix derived from the observed survival data. This matrix is informative as to suggest how the data perform in terms of survival prediction for every pair of samples. Each entry of the consistency matrix C is the consistency score of the predicted relative risk status for the corresponding two samples compared with the relative risk status from the real survival data. The heatmap plots of the matrix C for tumor data and normal data are shown in α parameter for the Elastic Net Regularized Cox Regression is set at 0.01, and the β parameter is chosen by five fold cross validation. It can be seen from the plot that the consistency level with the observed survival information from normal data is generally higher than that from tumor data. In the left panel, the blue arrow indicates an outlying sample with generally very low consistency in terms of survival prediction using tumor data. While the same patient is also highlighted with blue arrow in the right panel, with normal data, this sample’s performance in terms of survival prediction consistency is generally higher, and not very outlying compared with other samples. This result suggests that with the availability of paired normal samples, we can potentially elevate the overall survival prediction accuracy, especially for those patients with very poor and outlying survival prediction performance using only tumor tissue data.We also applied the Elastic Net Regularized Cox Regression model directly in the survival prediction scheme to assess survival related information contained in adjacent tumor samples in the microarray platform on the 60 patients breast cancer data set. For each pair of samples, we calculated the relative risk predictions from the model built using the rest of samples. The consistency matrix We used the gene set enrichment analysis (GSEA) method to identify biological pathways that are enriched for genes informative for cancer patient survival using different data sets , and looked for shared pathways across cancer cohorts. We hypothesize that if there is a general mechanism driving the normal tissues to offer additional information on tumor prognosis in all the cancer cohorts, pathway analysis may capture consistent changes across cancer cohorts and shed lights on this mechanism. To further examine the involvement of immune related functions and oncogenic related functions driving the survival related information in tumor and normal samples using an independent data set, we conducted individualized GSEA using the 60-sample breast cancer data set. For each individual sample, the signals from all the genes are summarized at the gene set level using individualized GSEA method. For each gene set, we used the gene set level perturbation values across patients to correlate with patient survival information using Univariate Cox Regression model. This analysis directly shows how the gene set perturbation levels for each pre-defined immune related and oncogenic related gene set signature are related to survival. To identify the top differentially performing pathways between tumor and normal tissues, we calculated the ratios of p values from the Cox model between normal and tumor data for each gene set, and identified the top 100 gene sets that are mostly differentiatedly related to survival between tumor and normal samples. The distributions of the log ratios is shown in In this report, we have systematically evaluated whether transcriptional profiles of tumor adjacent normal samples are predictive of patient survival across multiple cancer cohorts and different platforms using the TCGA data. The results show that adjacent normal samples’ transcriptional level signals likely provide more information than tumor samples on patient survival across cancer types and data collection platforms. This result implies that the tumor surrounding tissues may harbor meaningful signals for cancer biogenesis and prognosis. This phenomenon could possible be explained by the idea of “etiologic field effect” proposed in a recent paperThis finding could further our understanding of cancer etiology and better identify biomarkers for more accurate cancer diagnosis and prognosis, as well as personalized medicine and treatment design. The collection and molecular characterization of adjacent normal tissues, in addition to tumor tissues, may allow researchers to gain more insights on disease etiology and patient progression. The contribution of paired normal samples in prognosis prediction can be interpreted as the contribution of “tumorigenesis code” carried by the normal tissues. This code may represent the patient’s overall immunity or metabolic level, which may lead to more effective personalized medicine. These results also suggest that we may better understand tumorigenesis mechanisms and identify potential therapeutic targets through a more thorough exploration of surrounding microenvironment.There are several limitations in our study. Firstly, there are no clear definitions for adjacent normal tissues. For the TCGA data set, the normal tissues were collected with an approximate 2 cm or 3 cm distance from the tumor margin. However, how the survival related information in normal tissues varied accordingly to the distance of normal towards tumor tissues needs to be assessed. The survival related information is of various relevance across cancer types, likely due to different tissue features and how the adjacent normal tissues were obtained. More needs to be done to adjust for the effects from these factors. Secondly, adjacent normal tissues bear the neighboring microenvironment signals. It is of potential interest to consider the distal environment signals’ contribution towards tumor progression. There are publications already discussing the possibilities of finding marker genes in the circulating blood tissuesWe applied unsupervised clustering on the tumor and normal samples, as well as gene expression fold changes to match with groupings using clinical variables to assess whether normal samples offer clinically relevant information. We used Cox based and Random Forestshttps://tcga-data.nci.nih.gov/tcga/tcgaHome2.jsp). We downloaded data in the category of UNC Agilent G4502A 07 microarrays for breast cancer with 60 pairs of paired tumor and solid normal samples. We also downloaded data in the category of UNC IlluminaHiSeq RNASeqV2 for six cancer types with their paired tumor and normal samples. The numbers of pairs of tumor and normal samples for each cancer types are listed in Transcriptome data and clinical data were obtained from the TCGA Data Portal (Genes were ranked based on the Median Absolute Deviance (MAD) of expression levels and those genes with the largest MAD values were selected for clustering. We used Euclidean distance to measure the distance between genes and complete linkage for hierarchical clustering. Backer’s Gamma indexk was varied from 2–15. We performed k-means clustering on tumor samples, normal samples, and fold changes, respectively. We used the Rand indexFor each data set, we used MAD values to select the top 6000 most varying genes for K-means clustering, where Because of the high dimensionality of the data, we adopted the Elastic Net Penalized Cox Regression method2016β, which is a linear combination of L1 penalty and L2 penalty:The elastic net regularizationα and β were selected by grid search from five fold cross validation. The goodness of fit is measured by mean cross validation errorThe tuning parameters K being the total number of folds, and with the kth fold, D is defined as the residual sum of square deviance of real survival outcome versus predicted survival outcome using kth fold of data. The cross validation error of the kth fold is defined as the difference between deviances evaluated on full data with that on data with the kth fold excluded. We used the mean of each fold’s cross validation error to assess the goodness of fit.with M is defined as Ri and Rj are the risk predictions of sample i and sample j from the Cox model derived using the rest of all the samples s: C is then defined as Mr is the probability matrix derived from the real survival data. The entries of Mr are either 0 or 1 where the risk comparisons are identifiable for the specific sample pairs from the real survival data, and 0.5 anywhere else where the risk comparisons are uncertain because of the presence of censored data. The consistency matrix C is informative as to suggest how the data perform in terms of survival prediction.We also applied this method in the context of survival prediction to assess the prediction accuracy directly. For each pair of samples, we calculated the relative risk predictions from the Cox model built using the rest of samples. The data features were dimension-reduced and orthogonalized using PCA beforehand. The probability matrix In addition to penalized Cox regression, we used Random ForestsTo investigate the biological relevance of the signals in normal and tumor tissues, we performed pathway analysis to identify pathways predictive of survival outcome in various cancers. As input for pathway analysis, for each gene, we calculated the association between its gene expression levels and patient survival using Univariate Cox Regression modelTo further examine the relevance of immune signatures and oncogenic signatures with survival, we conducted individualized GSEA, which is to perform GSEA on each individual and get a summarization of gene level signal to pathway level signal. For gene expression data set with P genes and N samples, for each sample n, we will get the enrichment level of m predefined gene set signatures. For these m gene sets, we will then perform Univariate Cox Regression to identify gene sets that are mostly predictive of survival. The immune signature and oncogenic signatures are downloaded from MSigDBAll analyses were performed using the R programming platformHow to cite this article: Huang, X. et al. Transcriptional Profiles from Paired Normal Samples Offer Complementary Information on Cancer Patient Survival – Evidence from TCGA Pan-Cancer Data. Sci. Rep.6, 20567; doi: 10.1038/srep20567 (2016).


Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The “COXEN” method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm.p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn’t , and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis .The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs  contains supplementary material, which is available to authorized users. The discovery of biomarkers such as the breakpoint cluster region/Abelson (BCR/ABL) gene in patients with chronic myeloid leukemia, the Estrogen Receptor (ER) status of breast cancer patients, and the v-Raf murine sarcoma viral oncogene homologue B1 (BRAF) mutational status of melanoma patients has resulted in the development of effective targeted agents and successful patient stratification leading to better clinical outcomes –5. Altho1228942) , 17.In the recent past, clinical trials in human lung and breast cancer with similar strategies of selecting drug treatment based on gene signatures were suspended due to faulty preclinical data and improper validation of predictors, a strong reminder that proper pre-clinical validation is essential for the continued development of such methods in the clinic , 19. AdvCanine models of cancer have many advantages for translational research including a high incidence rate of spontaneous tumors that are comparable to humans both biologically and genetically , 21. AftCanine osteosarcoma (OS) is the most common primary bone tumor in dogs and has been shown to share similar molecular alterations with the human disease . AlthougThe purpose of this study was to investigate the utility of the COXEN method in an intra- and interspecies manner between human and canine datasets, specifically in canine osteosarcoma. The reasons behind this chosen application of COXEN are three-fold: first, a successful application of COXEN across species would show the robustness of this method. Secondly, success of this study would add strength to the growing evidence that the canine cancer model can greatly impact human research, specifically in the new genomics era. Lastly, considering the wealth of human genomic and pharmacologic data available to the public, the potential to extrapolate this information into the veterinary setting through molecular comparisons of tumors would be extremely advantageous for the field of canine oncology.A panel of 29 canine cancer cell lines used for drug screening at the Flint Animal Cancer Center (FACC) were maintained as previously described , carboplatin (CARBO), doxorubicin (DOX), and vinblastine (VBL) in the FACC panel as previously described , the onlRNA from the FACC was extracted using the Qiagen RNeasy Kit  according to manufacturer’s protocols. A DNase treatment step using the RNase-Free DNase Set  was included to ensure RNA purity.RNA from 33 frozen tumor samples was extracted as described previously . Yield aRNA samples for the FACC were hybridized onto Affymetrix GeneChip Canine Genome 2.0 arrays  and microarray analysis was performed at the Genomics and Microarray Core at the University of Colorado Denver. Raw microarray data was preprocessed with the Robust Multi-Array Average (RMA) algorithm. Information on the availability of the gene expression data for the different datasets can be found in the “In addition to the normalization across samples in a dataset that occurs as part of RMA preprocessing, the gene expression data underwent within gene standardization by subtracting the mean of a probe across samples and dividing by the standard deviation. This allows genes of different mean expression intensity to be an more equal footing for the differential gene expression analysis and makes the expression data across microarray platforms more comparable to each other, especially in the case of working between human and canine datasets.t-test. SAM analysis with a q-value cutoff of 0.1 was used comparing the top and bottom 20 % of samples in the reference set based on drug sensitivity. T-tests were run between sensitive and resistant samples with a q-value cutoff of 0.05 to identify DEGs. If too few DEGs made the q-value cutoff, a p-value cutoff < 0.001 was then used.Differentially expressed genes (DEGs) in our reference data sets were identified through either Significance Analysis of Microarrays (SAM) or We employed four different strategies for matching probesets selected from either the Human Genome U133A or HT Human Genome U133A array to the GeneChip Canine Genome 2.0 array.We used the Basic Local Alignment Search Tool (BLAST) to compare the target sequences of the human probesets with their canine orthologs and selected the match with the highest sequence homology. The blastn algorithm was selected for somewhat similar sequences, with word size = 7 and expected threshold = 100. In instances where no canine ortholog was annotated, an attempt at manual annotation was performed through sequence alignment using Affymetrix, BLAST, and USC genome browser tools. If manual annotation proved unsuccessful then the human probeset was removed from the list of candidates for COXEN analysis.rd correlation matrix created from each row of the first two matrices. Because the 3rd matrix is created from the two original matrices, the original matrices had to be identical in dimensions. To do this, duplicates were added to the human correlation matrix to equal the number of probesets mapping to each gene on the canine matrix. The best match was selected that had the highest concordant expression with other genes in the signature across species.This strategy was performed by creating correlation matrices for both human and canine probesets separately, and then a 3We took our list of candidate human PSIDs and their canine orthologs and in cases where multiples existed for a given gene on either the human or canine side, the expression values were averaged. This resulted in collapsing the probeset expression data down to the gene level, reducing the data for each gene to one human and matching canine expression value. Manual annotation was attempted when no annotated canine orthologs were available.collapseRows function from the R package “WGCNA” [Using the  “WGCNA”  we collard correlation matrix is created comparing each row of the first two matrices. Genes with correlation values higher than a cutoff  were selected as being strongly co-expressed between the two data sets. In other words, the genes that are retained have similar patterns of correlation with each other in both the reference and co-expression datasets. This subset of the gene signature was used for prediction model generation.The crucial step in the COXEN method is filtering the list of DEGs down to those that are most in “tune” with your target or test dataset, whether it is human derived DEGs that are most in tune with canine tumors, or a specific tumor type like osteosarcoma. A dataset with similar properties as your target or test set is used in this process and has been termed the “co-expression set” Fig. . The selPrediction models were generated in Bioconductor using the Missclassification-Penalized Posteriors (MiPP) algorithm developed by Soukup, Cho and Lee . Differe50 data by subtracting the mean and dividing by the standard deviation. A Cox proportional hazards regression correlation was then performed on the two data sets, as well as a binomial test based on random coin tossing for the testing of the models ability to successfully call a cell line “sensitive” or “resistant”. For in vivo models we plotted survival curves based on actual disease free interval data and the class predictions of “responder” or “non-responder”. Significant differences in survival curves were determined by Log Rank test.To see how well the prediction models correlated with actual sensitivity data, we standardized both the posterior probability scores for each sample and the actual GI50 values between the human and canine panels were compared and found to be strikingly similar .The two main cell line panels that were used for reference sets in our prediction model development Fig.  were thelar Fig. , althougbest sequence homology, data collapsing by averaging, and best correlative match are explained in full in the Supplemental Methods. Each strategy was used resulting in a human gene signature with matching canine data for both a DOX and VBL analysis. To test which strategy would produce the most predictive models, each were carried through the remaining steps of the COXEN analysis  involved collapsing both the human and canine array information to the gene level prior to differential gene expression analysis by selecting one probe per gene based on maximum variance between samples. It was performed using the collapseRows function in the “WGNCA” R package. We consider both of these methods as viable options for matching genes across platforms and used both in our attempts to optimize our prediction models.We next wanted to compare the genomic similarity of human and canine cancer cell lines in the context of drug sensitivity. RNA was extracted from untreated FACC cell lines and microarray gene expression data was generated. Thirty-nine differentially-expressed genes (DEGs) were identified in the NCI60 panel by using the 12 most and least sensitive cell lines to DOX in a SAM analysis. Since there are roughly twice as many probesets on the canine microarray chip as the human, and also on both chips there are often multiple probesets mapping to the same gene, it became apparent that a strategy for selecting the best match for the identified DEGs between species was needed. Three strategies were initially implemented and tested for matching up the probesets. The strategies named An unsupervised hierarchical cluster analysis was performed using expression data from the 39 NCI60-derived DEGs for DOX in both sensitive and resistant NCI60 and FACC cell lines, and we observed that all the NCI60 samples separated according to DOX sensitivity Fig. . Also, 6max variance method. The 12 most sensitive and resistant NCI60 lines were used as the training set and the 6 most and least sensitive cell lines in the FACC panel were used as the test set. Models with NCI60-derived genes that were co-expressed with the FACC and trained solely on the NCI60 panel were 83 % accurate in predicting DOX sensitivity in the FACC    Fig. . In contst) Fig. . Althougp = 0.0669 and 0.0466, binomial) . Sample information for the COS33 tumor panel is provide in Additional file p = 0.3388), whereas the FACCosteo model for DOX was equally accurate to the FACC model with an error rate of 0.3043  , however in the survival curve analysis the FACC model was superior to the FACCosteo model   and a very significant separation of survival curves   , and a separation of survival curves that approached significance   . However, none of the other tests for model performance were significant , Transducin-like enhancer of split 1 (E(sp1)homolog,Drosophila) (TLE1), Eukaryotic translation initiation factor 6 (EIF6), and Testis derived transcript (3 LIM domains) (TES). CHKA is involved in phospholipid biosynthesis and tumor cell growth. TLE1 is a transcriptional co-repressor known to inhibit NF-kappa-B expression and WNT signaling. EIF6 and TES both are involved with the cytoskeleton, EIF6 helps link ITGBB4 to the cytoskeleton, and TES is a scaffold protein that has roles in cell adhesion, cell spreading, reorganization of the actin cytoskeleton, regulates cell proliferation, and may act as a tumor suppressor , Hairy and enhancer of split 3 (Drosophila) (HES3), uncharacterized cytochrome C oxidase (LOC100688725), Proteasome  26 s subunit, non-ATPase, 3 (PSMD3), cOR4F25 olfactory receptor family 4 subfamily F-like (cOR4F25), and transmembrane protein 131-like (KIAA0922). OCA2 is a transporter of tyrosine within melanocytes. HES3 is a transcriptional repressor of gene requiring bHLH protein for transcription. LOC100688725 has cytochrome C oxidase activity. PSMD3 is involved in ATP-dependent degradation of ubiquinated proteins. cOR4F25 is involved in canine olfactory processes. KIAA0922 is an integral transmembrane protein with possible involvement in immune responses . These data show that dogs predicted to be sensitive to only DOX or both DOX and CARBO lived longer disease free than dogs predicted to be sensitive to CARBO or neither drug.Fifteen dogs from the COS33 panel received combination therapy of DOX and CARBO. Survival curves from dogs who received combination treatment were split into 4 groups and compared: dogs predicted to be sensitive to neither drug, dogs predicted to be sensitive to only CARBO, dogs predicted to be sensitive to only DOX, and dogs predicted to be sensitive to DOX and CARBO. In Fig. In order to more fully evaluate the benefit of this genomic approach of gene expression models to determine treatment in canine osteosarcoma, we classified our COS33 patients into two categories: “COXEN matched” and “COXEN mismatched”. We evaluated patient outcome if they actually received the specific treatment our COXEN models indicated. Our criteria for classification are detailed as follows: For patients treated only with DOX, if the DOX model predicted them to be sensitive (model score > 0.5), they were considered “COXEN matched”. For patients treated only with CARBO, if the CARBO model score > 0.5 they were considered “COXEN matched”. For patients receiving a combination of DOX and CARBO, both the DOX model and CARBO model score needed to be > 0.5 for them to be considered “COXEN matched”.p = 0.0006, Log Rank)    Fig. . When thp = 0.0353) . The other significant factor associated with DFI in our COS33 patients was body weight . The last factor in the model was proximal humeral tumor location . Age at diagnosis fell out of the model after stepwise regression , monocyte count, lymphocyte count, body weight, and age at diagnosis. COXEN model treatment match was the only factor significantly associated with DFI in the univariate analysis p = 0.053 .Other options for model building that might be explored in the future would be to adopt some machine learning methods with the ability to incorporate multiple types of genomic data into the modeling process. In 2014 an article reporting the results of a community-based contest of 44 competing drug sensitivity prediction algorithms concluded that although gene expression microarrays was consistently the top source for predictive power, performance could be improved with the addition of data such as methylation status of genes, copy number variation, and exome sequencing . These aThis study has shown that human and canine cancer cell lines are similarly sensitive to chemotherapy and that gene expression-based modeling using canine datasets or a combination of human and canine datasets can accurately predict clinical outcome in canine osteosarcoma patients treated with adjuvant DOX and/or CARBO therapy. These results are important for human and canine cancer research for two main reasons: First, it shows the potential of an advanced animal translational model for testing genomic methods of personalized cancer treatment in a clinical setting; second, it shows the potential for canine cancer research to expand in this genomic era through the incorporation of human genomic data into their model development design, which is currently much more prevalent and available than canine datasets.Gene expression data from Affymetrix Human Genome U133A arrays and U133 Plus 2.0 arrays for the human NCI-60  and a pa


The goal of metabolomics data pre-processing is to eliminate systematic variation, such that biologically-related metabolite signatures are detected by statistical pattern recognition. Although several methods have been developed to tackle the issue of batch-to-batch variation, each method has its advantages and disadvantages. In this study, we used a reference sample as a normalization standard for test samples within the same batch, and each metabolite value is expressed as a ratio relative to its counterpart in the reference sample. We then applied this approach to a large multi-batch data set to facilitate intra- and inter-batch data integration. Our results demonstrate that normalization to a single reference standard has the potential to minimize batch-to-batch data variation across a large, multi-batch data set.The online version of this article (doi:10.1186/2193-1801-3-439) contains supplementary material, which is available to authorized users. Success of metabolomics as a phenotyping or diagnostic platform depends on its ability to detect biologically-related global metabolite changes in complex biological samples. The variability in samples can arise from multiple sources including physiological differences and variability from the analytical method itself. Removing platform-specific sources of variability such as systematic error is one of the top priorities in metabolomics data preprocessing. However, metabolite diversity leads to different responses to variations at given experimental conditions, making normalization a very demanding task  steps and has been documented in large-scale NMR and LC-MS metabolomic studies  approach was used to monitor the analytical error. The IS is a known metabolite with a defined quantity. Expressing analyte abundances relative to the IS can suppress technical errors. The assumption underlying this method is that variation in IS can only result from systematic errors, and different analytes behave similar to IS. When isotope labeled metabolites do not co-elute with the unlabeled version, changes in the concentration of one compound can cause variance in the measurements of a different compound due to insufficient HPLC chromatographic separation or ion suppression in LC-MS  was developed  samples that are generally used to assess the performance of the system are now used for calibration purposes. Mean and median correction was used to account for the batch to batch variation , a maximum of 20 entries were grown in each site. The block design, planting, and sample harvesting were detailed previously  was added. Samples were incubated at 55°C with rotation for 30 min, then 0.75 mL of deionized water 18 MΩ) containing 5 μg/mL ribitol  was added and incubated for an additional 30 min. Samples were then centrifuged at 1500 g for 15 min to allow phase separation. Six hundred and sixty microliters of the upper aqueous phase were carefully transferred into a 2-mL glass GC vial and subsequently evaporated to dryness in a speed vac. Before GC-MS analysis, test samples from the same site were arranged into batches  in pyridine and incubated at 37°C for 90 min to form methoxyamine derivatives. Subsequently, 120 μL of N-methyl-N-(trimethylsiyl) trifluoroacetamide  plus 1%TMCS were added and the extracts were incubated at 37°C for 90 min to form trimethylsilyl derivatives.-1 to 310°C at which it was held for 6 min before dropping back to 80°C for the next cycle. Electron impact (70 eV) mass spectra were recorded from m/z 50 to 600 at 2.69 scans sec-1. The instrument was autotuned for mass calibration using perfluorotributylamine (PFTBA).Derivatized metabolite mixtures were analyzed by a Hewlett Packard 6890 gas chromatograph, 5973 mass selective detector, and 7683 series injector . Helium flow was 1 mL/min. One μL samples was injected with a split ratio of 1:30 and resolved on a 30 m × 0.25 mm × 0.25 μm ZB-5MSi column . The temperatures for the inlet, interface, and ion source were 230°C, 250°, and 200°C, respectively. After a 5-min solvent delay at 80°C, the oven temperature was increased at 5°C minhttp://csbdb.mpimp-golm.mpg.de/csbdb/gmd/msri/gmd_msri.html). A list of ion-retention time pairs (IRt) was generated. The IRt data were exported into METabolomics Ion-based Data Extraction Algorithm  were converted into network common data form (.netCDF) and exported into the Automatic Mass spectral Deconvolution and Identification System (AMDIS_32) for spectral deconvolution Stein  and dataThe dataset was then interrogated manually to remove system contaminants, correct annotations if necessary, and reduce uninformative data. Compounds that were identified from library matching with low confidence were eliminated. Compounds identified with high confidence but whose annotation was questionable were labeled with a “?” marker. We also included “known unknown” metabolites if they were found with high confidence. A total of 98 metabolites were identified in forage samples  for each metabolite was calculated in Microsoft Excel as a measure of the data variability  was performed on the correlation matrix by R statistical package (version 3.0), principal components (PC) 1 and 2 were used to plot the scores.Hierarchical clustering and heat map was conducted on mean centered and standardized data in R (Version 3.0). Replicate values were averaged where appropriate and Ward’s method on Euclidean distance matrix was used for clustering . We observed that the forage reference samples showed clustering based on different batches  and the sequence of analysis (progression: first to last) Figure B. The laTo determine if batch-to-batch variation affects analyte variability in a concentration-dependent manner, we plotted the RSD of all the metabolites against their relative abundance. Approximately 90% of metabolites have RSD values lower than 60%. Only 5% of metabolites had RSD values above 100% Figure .As batch-to-batch variation clearly exists in the forage reference samples, it is logical to infer that the test forage samples also had batch-to-batch variation. Since the test samples had similar matrix as the reference sample, it is also reasonable to infer that test samples could behave similarly to the reference sample analyzed within the same batch. Since reference samples were prepared from the same source, theoretically all of them should contain similar amounts of each analyte regardless of batch run. Thus, normalization of the test sample data to the reference sample analyzed within the same batch could potentially correct batch-to-batch variation. To test this possibility, we calculated the relative standard deviation (RSD) for each individual analyte from entry 1 to entry 5 before and after normalization to reference samples. Entries 1 through 5 were selected because they were analyzed in six different batches spanning an 8-week period, and thus potentially have greater batch associated variability. Compared to RSD value before normalization, about two thirds (60-69%) of the metabolites showed a reduced RSD after normalization to reference samples Figure A. HoweveA PCA was performed on entries 1-5 before and after normalization to reference samples. Since the alteration in PCA score plots is visually not too obvious, we calculated the percentage of variance. We observed that the percentages of variance of principal component 1 and 2 (PC1 and PC2) were reduced after normalization to reference samples  sample analysis. (DOCX 39 KB)Additional file 2: Table S2: The ID, retention time (min), m/z, mean concentration , and relative standard deviation RSD%) for 98 metabolites identified from corn forage reference samples. The “?” denotes those metabolites that were detected from the samples with high confidence but their identification were not independently verified. *IDs are based on the order of retention time of metabolites. (DOCX 51 KB)Additional file 3: Table S3: The RSD value of entry 1 to 5 before and after normalization to reference samples. (XLSX 33 KB)