Non-specific feature selection is a dimension reduction procedure performed prior to cluster analysis of high dimensional molecular data. Not all measured features are expected to show biological variation, so only the most varying are selected for analysis. In DNA methylation studies, DNA methylation is measured as a proportion, bounded between 0 and 1, with variance a function of the mean. Filtering on standard deviation biases the selection of probes to those with mean values near 0.5. We explore the effect this has on clustering, and develop alternate filter methods that utilize a variance stabilizing transformation for Beta distributed data and do not share this bias.We compared results for 11 different non-specific filters on eight Infinium HumanMethylation data sets, selected to span a variety of biological conditions. We found that for data sets having a small fraction of samples showing abnormal methylation of a subset of normally unmethylated CpGs, a characteristic of the CpG island methylator phenotype in cancer, a novel filter statistic that utilized a variance-stabilizing transformation for Beta distributed data outperformed the common filter of using standard deviation of the DNA methylation proportion, or its log-transformed M-value, in its ability to detect the cancer subtype in a cluster analysis. However, the standard deviation filter always performed among the best for distinguishing subgroups of normal tissue. The novel filter and standard deviation filter tended to favour features in different genome contexts; for the same data set, the novel filter always selected more features from CpG island promoters and the standard deviation filter always selected more features from non-CpG island intergenic regions. Interestingly, despite selecting largely non-overlapping sets of features, the two filters did find sample subsets that overlapped for some real data sets.We found two different filter statistics that tended to prioritize features with different characteristics, each performed well for identifying clusters of cancer and non-cancer tissue, and identifying a cancer CpG island hypermethylation phenotype. Since cluster analysis is for discovery, we would suggest trying both filters on any new data sets, evaluating the overlap of features selected and clusters discovered. Non-specific filtering of variables, the selection of a subset of variables based on a characteristic unrelated to the outcome of interest, is often applied for dimension reduction in high-dimensional data sets. The approach can be used for both supervised and unsupervised analysis. For differential expression, non-specific filtering of features prior to hypothesis testing can increase power to detect differentially expressed genes . For cluDNA methylation is an epigenetic mark that varies between different cell types, correlating with DNA packaging within the cell and facilitating cell-type specific function. Today, Illumina’s DNA methylation BeadArrays allow for high-throughput analysis, with their most recent platform measuring hundreds of thousands of targeted loci for large numbers of samples. On Illumina’s platform, DNA methylation is measured by the percentage of total fluorescence due to methylation. This value is bounded between 0 and 1, and can be modelled using a Beta distribution. To perform cluster analysis on such data, Houseman et al. 2008)  develope  developWe compare different filtering methods in a collection of real data sets generated on either Illumina’s HumanMethylation27 or HumanMethylation450 platform. The variety of examples considered will allow us to evaluate filter methods across different data distributions and structures. We find that the properties of the data set, specifically the fraction of samples in a subtype, or the variation of features within groups, can lead to very different behaviour of some filtering methods.Illumina’s BeadArray technology analyzes more than 27,000 targeted CpGs on the HumanMethylation27 (HM27) platform, and over 480,000 on the HumanMethylation450 (HM450) . WhereasWe refer to each targeted CpG site on the array as a feature, and evaluate a number of methods for ranking features and filtering for dimension reduction. A number of methods explicitly use parameters from, or variance-stabilizing transformations of, Beta distributions.Beta, with ϕ = α + β a precision parameter independent of the mean.For a single feature, we model the distribution of DNA methylation across independent samples using a Beta distribution. Let X ~ Beta, Transformations of the data can also lead to independence of the mean and variance. Du et al. (2010)  proposed11−tβ−1dt. If the We evaluate a total of eleven filters, eight based on ranking single statistics, and three based on a weighted score for combining ranks Table . FiltersFilters 5 through 8 are statistics that assume the data derive from a single Beta distribution. Filter 5, inverse precision  , ranks ts2 are the mean and variance for a given feature. For this method the features with lower precision have higher variation. Filter 6, Beta-quantile Goodness-of-Fit (BQ-GOF), is a comparison of the observed to theoretical quantiles from a Beta distribution. It sums for each feature, the absolute difference between corresponding quantiles from the observed cumulative distribution function and the theoretical one obtained using the estimated parameters where y, from their expected values. The complete procedure is summarized as follows: 1. For each feature, estimate y the mean and standard deviation of the transformed data; 4. Calculate sy and y across all features; 5. Rank features by their standardized Euclidean distance Filters 7 and 8 measure goodness-of-fit on the CDF-transformed data, Y = CDF(X). Filter 7, Beta-Transformed Moments Goodness-of-Fit (TM-GOF). The CDF-transformed data are ranked using the distance of the mean, Filters 9 through 11 are summaries of the ranks from the individual statistics used above. Filter 9, Best Rank (BR) selects as the statistic the top rank across the eight statistics, Filter 10, Average Rank (AR), averages the top 2 ranks, and Filter 11, Weighted Average Rank (WAR), averages the top four ranks using weights 4:3:2:1, respectively.We perform a simulation study to evaluate the ability of the eleven non-specific filters to enrich a ranked list of features with those informative of subgroups. The data are simulated from distributions observed in our colon cancer data set . In this data set, 6 out of 26 subjects (23%) contain a hypermethylation profile known as the CpG island methylator phenotype (CIMP), determined using a separate technology .ij,ϕij), where i = 1 or 2, for the CIMP and non-CIMP subsets, and j = 1,…,2000 indicates the feature. A random 10% of features are selected to be informative, with  and  estimated from the CIMP and non-CIMP subgroups, respectively. For the non-informative features, a single set of parameters  are estimated from the non-CIMP subgroup only, and used for both subgroups . We simulate 200 samples, considering sample size ratios of 1:9, 1:1, and 9:1. As the feature characteristics vary between the two groups  . RPMM waFinally, we apply our filtering methods to eight Illumina data sets. The data sets were selected to span a variety of biological conditions and include data generated on the HM450 and HM27 platform. We selected three cancer-only, four tumor-normal tissue, and one normal blood data set. The three cancer-only data sets include one colon and two glioblastoma, cancer types known to have a distinct subtype defined by the CpG island methylator phenotype (CIMP). The four tumor-normal tissue data sets include two kidney and two breast data sets. The normal blood data set is selected to evaluate the filter methods in the presence of the strong quantitative risk factor, age. Further details are provided in Table We perform RPMM cluster analysis on different lists of filtered features to assess the ability of different filtering approaches to identify (1) cancer subtypes, (2) cancer/normal tissue types, or (3) young from old individuals. For each data set we applied the 11 filtering methods, each time selecting the top 1000 features for RPMM clustering. We also considered a hybrid variable selection approach, in which we pool the top 500 features selected from two filters methods above (SD-b and TM-GOF). These filters are chosen because they each perform well for different biological conditions studied (see Results below). For data sets 1–7 the misclassification rate was computed by comparing the top two clusters to the known tissue types. We also evaluated the cluster agreement between the clusters identified by the RPMM routine  with our two known tissue classes using the adjusted rand index. For data set 8, we evaluated the differences in mean age between the two major cluster groups. To evaluate the general effect of filtering the data, we analyzed the HM27 data sets without any filtering, and the HM450 data sets after selecting a random subset of 1000 features. Feature reduction for the HM450 data was necessary, as the cluster analysis software required a lower dimensional data set in order to run. Also, since the HM450 clustering results varied with random selection of features, we repeated the analysis ten times and report the average misclassification error rate over the ten replicates (data sets 1–7).For two filter methods that show good performance , we report the frequency of the top selected features by genomic context. On the HM27 array the selected probes are characterized using the UCSC definition of CpG island and the gene-based definitions provided by Illumina: “promoter”, “transcribed region”, “exonic region”, and “intronic region”. For the HM450 array we stratified four gene-based categories (Promoter/Exon/Intron/Intergenic)  by theirhttp://www.r-project.org). Infinium data were processed using the methylumi package in Bioconductor, using a combination of Normal-Exponential background correction, dye bias equalization, and beta-mixture quantile normalization (BMIQ) to remove technical artifacts [All analyses are performed using the R programming language 2.15.2 , SD-b and SD-m performed best. None of the summary-based filter methods ever performed better than the best single-filter method. They also performed inconsistently across the different data sets. The hybrid approach that combined an equal number of top SD-b probes with TM-GOF probes showed some merit. Sometimes the hybrid selection scheme behaved as well as the best single filter method , and at other times it resulted in error rates that were intermediate between the other two . Overall, the TM-GOF and TQ-GOF methods consistently performed best for identifying the CIMP subgroup in cancer data, while the SD-b filter performed best at distinguishing cancer from non-cancer tissue.Table To visualize the different performance of our top filters, TM-GOF and SD-b, we created heatmaps of the top 1000 filtered features following RPMM clustering. Figures This same removal of features with outliers, followed by filtering the top 1000 features and cluster analysis, was performed on data sets #5-#7. Each time we found TM-GOF was able to find disease state clusters at the first division just as well as SD-b (results not shown). However, for the CIMP cancer data sets (data sets #1-3), a pre-filtering of features with outliers (using median+/−3*IQR criteria) resulted in equally bad clustering for all filtering methods (data not shown). These results suggest that broad pre-filtering using this definition removed features informative for a CIMP classification.Next we asked if we could find the same tumor substructure in the kidney data set if we had started with the 50 kidney tumors only. In general, this appeared to be the case. Using TM-GOF as our filter, we identified a distinct subgroup of five tumors, four of which were those previously identified when clustering the larger set of tumor and non-tumor tissue , selected to compare the different filter methods when there is structure due to age, a continuous variable. Table We report the genomic context of the features selected by TM-GOF and SD-b, our two best performing filter methods . The new filters that use a Beta variance stabilizing transformation are very sensitive to outlier measurements. This may benefit the search for low frequency cancer subtypes that have extreme values occurring across a large number of features (e.g. CpG island methylation phenotype), but may not translate to an ability to directly cluster cancer versus normal tissues well. For clustering cancer versus normal tissue, an outlier removal step was required before the tissue clusters could be properly recovered. In general, the cancer-based simulation study results were not generalizable to the clustering of normal tissue, or tumor versus non-tumor tissue, suggesting that the filter methods are sensitive to the variation in observed DNA methylation distributions due to the underlying biology.Overall, SD-b performed very well in the real data examples including normal tissues. One explanation could be that the SD-b filter enriches for features in regions having cell-type specific DNA methylation differences. It tended to find groups of approximately equal size, finding a separation of groups by mean age for the normal blood samples that was statistically significant ; in the cancer and non-cancer studies it identified clusters based on disease state in the first partitioning of samples. Although at first glance it appeared to perform poorly in detecting the CIMP subtype in the colon cancer data, upon further partitioning of the data the sub-cluster of interest appeared. It is unknown if this is a coincidence from the data selected in this study. Although the SD-b filter did not show a high AUC in the simulation study, it did cluster the samples perfectly using RPMM when we did not set boundaries on the largest effect size, suggesting that the cluster analysis can be strongly influenced by a small number of highly informative features.We found the SD-b and TM-GOF filters tended to prioritize features in different areas of the genome. For each HM450 data set, TM-GOF selected a higher number of features in CGI promoters compared to SD-b, while SD-b selected a larger number of features in non-CGI intergenic regions. The better performance of TM-GOF for detecting a CIMP subtype in cancer, and SD-b for clustering normal tissues, suggests that features in different regions of the genome are not equally informative for all biological conditions. A recent study reported a novel classification of breast cancer using markers of normal-breast epithelial cell subtypes , and migThe SD-m filter was a close competitor to SD-b, but Precision showed an unexplained sensitivity to tissue type. Both Precision and SD-m performed slightly better than SD-b in the analysis of CIMP cancers and nearly as well in clustering cancer and non-cancer kidney and breast. In the analysis of whole blood, SD-m found clusters that correlated with age, but Precision did not. Because of this unexplained sensitivity of Precision to non-cancer tissue we do not recommend its general use.One filter method not included in our comparison is arcsine-square-root transformation, also a good variance stabilization method suitable for data bounded between 0 and 1. Similar to the logit transformation, the arcsine-square-root transformation can be written as an incomplete beta function having only one parameter . Thus weAlthough the summary-based filtering methods take advantage of using the top ranked filter methods, they are not always more robust than the single-filter methods. This is because sometimes the best rank of a feature can be affected by a single non-informative filtering method. Thus, due to the different (and somewhat complimentary) characteristics of the features enriched by SD-b and TM-GOF methods, we prefer to use both SD-b and TM-GOF methods for any data when our main purpose of cluster analysis is to identify novel subgroups. Our results suggest that SD-b is very robust in enriching for features that identify big subgroups, while TM-GOF and TQ-GOF are very sensitive in enriching features to identify low frequency cancer subtypes that have outlying values occurring across a large number of features (e.g. CpG island methylation phenotype).We noticed that in the data sets comparing DNA methylation in cancer to non-cancer tissue, the differences in standard deviation (SD) between sample groups are not symmetrically distributed. The majority of features have a much higher SD in cancer samples than in normal samples. However, in data sets with non-CIMP vs. CIMP cancer subtypes, the differences in SD are symmetrically distributed with mean around zero (data not shown). This suggests that SD on beta values may be more informative in data sets with huge SD differences between subgroups than in data sets with balanced SD differences around zero.One limitation of our simulation design is that for each tissue subgroup our measures are simulated to follow a beta distribution and the best performing filter methods make proper use of this knowledge. In reality, a mixture of betas might yield a more realistic measure from a population of mixed cell types. However, instead of simulating this added complexity which would require additional model assumptions, we chose to look for patterns of behaviour from the analysis of a variety of real data sets that spanned different biological conditions . This evaluation shows: (1) the top two filter methods, TM-GOF and SD-b, prioritize features from different parts of the genome, (2) TM-GOF is much more susceptible to outlier measures, and (3) that the underlying biology can drive their performance.One question not addressed in this study is the number of features to carry forward to the cluster analysis. One might plot the filter statistics to see if they show a bimodal distribution, suggesting subgroups of features with different behaviour. In our experience, the statistics are unimodal so we tend to use a number of thresholds for features selection , and evaluate the robustness of our groups across the different feature lists.We found two filter statistics, SD-b and TM-GOF, outperform the rest in different data sets with different nature. We would suggest using each one, as cluster analysis is for the purpose of class discovery and the two methods tend to prioritize different sets of features, thus serving as complimentary feature enrichment methods for DNA methylation data.https://tcga-data.nci.nih.gov/tcga/). The blood samples (data set 8) are the subset of samples from plate 2 of GEO data set GSE40279 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40279). The exact samples included in our analysis are provided in Additional file The Cancer Genome Atlas data (data sets 2–7) are publicly available from the TCGA data portal .Click here for fileReal data sets.Click here for fileSample ids included in data sets 2 through 8.Click here for fileArea under the curve  for simulation results in Figure Click here for fileAdjusted Rand Index of RPMM cluster analysis result using a variety of filtering methods for multiple data sets.Click here for filea. For HM27 platform (N/%). b. For HM450 platform (N/%).Genomic context of the features selected by the top two filter methods. Click here for file


Pouchitis is common after ileal pouch-anal anastomosis (IPAA) surgery for ulcerative colitis (UC). Similar to inflammatory bowel disease (IBD), both host genetics and the microbiota are implicated in its pathogenesis. We use the IPAA model of IBD to associate mucosal host gene expression with mucosal microbiomes and clinical outcomes. We analyze host transcriptomic data and 16S rRNA gene sequencing data from paired biopsies from IPAA patients with UC and familial adenomatous polyposis. To achieve power for a genome-wide microbiome-transcriptome association study, we use principal component analysis for transcript and clade reduction, and identify significant co-variation between clades and transcripts.Sutterella, Akkermansia, Bifidobacteria, and Roseburia abundance, and positively correlated with Escherichia abundance.Host transcripts co-vary primarily with biopsy location and inflammation, while microbes co-vary primarily with antibiotic use. Transcript-microbe associations are surprisingly modest, but the most strongly microbially-associated host transcript pattern is enriched for complement cascade genes and for the interleukin-12 pathway. Activation of these host processes is inversely correlated with This study quantifies the effects of inflammation, antibiotic use, and biopsy location upon the microbiome and host transcriptome during pouchitis. Understanding these effects is essential for basic biological insights as well as for well-designed and adequately-powered studies. Additionally, our study provides a method for profiling host-microbe interactions with appropriate statistical power using high-throughput sequencing, and suggests that cross-sectional changes in gut epithelial transcription are not a major component of the host-microbiome regulatory interface during pouchitis.The online version of this article (doi:10.1186/s13059-015-0637-x) contains supplementary material, which is available to authorized users. Between 10% and 35% of ulcerative colitis (UC) patients ultimately undergo colectomy with subsequent ileal pouch-anal anastomosis (IPAA) or ‘J-pouch’ construction . IPAA isWhile it is known that both host genetics and the microbiome influence the development of pouchitis, precisely how they interact is less well-understood. Following IPAA surgery, the mucosal structure of the J-pouch becomes more colon-like; villous structures become more shallow, mucin expression changes , and theTo gain insight into these host-microbe interactions in the epithelial mucosa, we have collected paired host transcriptome and microbial metagenome data from a large J-pouch cohort, allowing us to measure whether elevated or depleted host epithelial transcripts are associated with specific microbial clades. While other studies have applied sequencing to the IPAA microbiome, these had small numbers of patients ,15 or diThe gut microbiome in this cohort was most affected by inter-individual differences in antibiotic usage, while epithelial transcription was more strongly influenced by tissue location (pouch vs. pre-pouch ileum). A very small proportion of microbial or transcriptional variation was explained by host-microbe correspondences, in that associations of the host transcriptome with the microbiome were relatively modest in comparison to other effects. We developed a dimensionality reduction process to ensure appropriate statistical power for testing these associations, due to the large number of transcripts and operational taxonomic units (OTUs) observed relative to number of samples, comparable to the analysis methods necessary for eQTL or similar studies -21. AfteIn order to better understand the relationships between the host and microbiome after IPAA surgery, we measured host gene expression by microarray  and the Previous studies in a subset of this cohort demonstrated that there were few differences in the microbiome between pouch and PPI samples , yet a g4 pairwise tests and retain 90% power and an alpha equal to 0.05 using Bonferroni correction  that explained 50% of observed variance Figure . The loaSupervised transcript reduction aimed to focus upon host genes of particular prior interest, specifically those that had been previously implicated in IBD, pouchitis, or host-microbe interactions. Thus, we curated a set of 174 IBD-associated genes , and 12 Through these data reduction methods, we transformed 19,908 host transcripts and 6,999 observed OTUs into a total of 138 features. There were nine transcript principal components and nine clade principal components, which had been chosen in an unsupervised manner. In addition, there were 75 gene medoids and 45 clades, which had been selected in a more supervised manner. These 138 features were used for subsequent analysis.Enterococcus, which is frequently resistant to both metronidazole and ciprofloxacin [Sutterella and beneficial Clostridia, including Ruminococcus and Blautia. The transcript patterns gPC1, gPC9, and gPC6 were most closely associated with FAP or no pouchitis , clinical outcome, antibiotic use, and inflammation, using univariate association tests of each transcript and each clade with the metadata. The extent of shift is summarized as the percentage of transcriptome or microbiome features differentially expressed at FDR <0.05 Table D to I. Aet al. . In contEnterococcus and Pasteurellaceae. Although Enterococcus was strongly associated (P <0.05) with the chronic pouchitis phenotype in univariate analysis, it was not significant when stratified by antibiotic use because it was not elevated in antibiotic-free samples . The top loadings of gPC9 reflected reduced expression of the complement cascade , interferon regulatory factor 1, interferon-induced guanylate binding protein, and the leukocyte chemotaxis factor CCL2, indicating that high expression of gPC9 may correspond to a lower overall state of inflammation. Indeed, when samples were stratified by clinical outcome, gPC9 was lowest-expressed in patients with Crohn’s disease-like inflammation, and highest-expressed in patients with FAP  to identify which combinations of genes and microbes were most able to cross-sectionally segregate clinical outcome in a training set, then assessed accuracy in cross-validation see . BecauseCDL and CP were best discriminated by this model, particularly with respect to FAP Figure . HoweverAlthough this study and many others have observed that the mucosal microbiome is highly variable between any two individuals ,34, the Gardnerella is highly susceptible [Eikenella is highly resistant [Propionibacterium appears to correlate with the presence of nim genes [Enterococcus and Streptoccus) and gammaproteobacteria . Enterococcus genomes are highly recombinant and remarkable as a reservoir of antibiotic resistance, and thus a public health concern [Pasteurella have been much less widely described, a study of swine-associated Pasteurella strains found that they were highly resistant to metronidazole (but not quinolones) [As expected, the largest effect on the microbiome is antibiotic use. Metronidazole, the antibiotic most commonly used to treat pouchitis, kills anaerobic bacteria by damaging their DNA , thus prceptible , while Eesistant , and resim genes . In our  concern . Their m concern -47, and  concern -50, whicnolones) , which iEscherichia, while there were non-specific inflammation-associated decreases in the class Actinobacteria and in the phylum Bacteroidetes    were functionally associated with inflammation, but expression of gPC9 itself was not correlated with the clinical inflammatory score , and the latter by tissue location. Thus, while pouchitis clinical outcomes were well-differentiated by naive linear discriminant analysis, this was due almost exclusively to differences in antibiotic usage among outcomes and may be a problem better-suited to longitudinal data. Although we are able to observe significant host-transcript associations, the effect sizes are modest, indicating that other factors, such as initial host colonization and diet, are also significant influencers of microbial composition. To distinguish these effects, we will need additional data from well-powered studies.In conclusion, the primary influences upon host gene expression and the microbiome appeared to be distinct by several measures in this cohort. We observed modest associations between groups of host transcripts involved in inflammation and clades such as et al. [Patients having undergone proctocolectomy with ileal pouch-anal anastomosis (IPAA) for treatment of UC or FAP at least 1 year prior to enrollment, were recruited at Mount Sinai Hospital . Individuals with a diagnosis of CD were excluded. Patients underwent pouch endoscopy with biopsy, and completed a questionnaire encompassing demographic and clinical elements. Physicians documented the appearance of the pouch using specific evaluation criteria outlined in the pouchitis activity score (PAS). Specifically, to numerically score inflammation, the severity of objective traits was graded  according to the numeric scale described by Tyler et al. , and theet al. : FamiliaFor this cohort, antibiotic use was reported as ‘true’ if patients had taken antibiotics in the 30 days prior to biopsy collections. The vast majority of antibiotic use was for pouchitis, and was either metronidazole, ciprofloxacin, or a combination of both. A very small number of pouch patients (two to three) were on vancomycin instead of more standard antibiotics. Antibiotic use was also reported as ‘true’ if the patient had taken antibiotic for a non-IBD purpose in the past 30 days .Tissue biopsies were obtained from the mid-portion of the pouch and the PPI during pouchoscopy. One biopsy from each site was immediately placed into a sterile, empty freezer vial and snap frozen in liquid nitrogen for subsequent microbial analysis. Two additional biopsies from each site were placed into RNAlater (Qiagen) for host transcriptomic analysis. Study samples were stored long-term at −80°C. Two biopsies were also taken for histological analysis as per standard clinical practice at our institution. Inflammation was measured according to the objective and location-specific components from the pouchitis activity score (PAS)  as previThe biopsy samples were immediately suspended in RNAlater (QIAGEN) stabilizing reagent upon collection to deter RNA degradation and were stored at −80°C. Total RNA was extracted with the miRNeasy Mini Kit (Qiagen) in two batches. A NanoDrop 1000 (Thermo Fisher Scientific) and Bioanalyzer 2100 (Agilent) were used to determine RNA concentration, quality and purity. Only samples with a RNA integrity number (RIN) greater than or equal to 5.0 were considered for further analysis .From samples that passed quality control, 400 ng of RNA was amplified with the Ambion WT Expression Kit (Ambion). A total of 5.5 μg of cDNA per sample were then labeled and hybridized to Human Gene 1.0 ST arrays (Affymetrix) in a Fluidics Station 450 (Affymetrix), utilizing standard protocol FS450_0007 with the GeneChip WT Terminal Labeling and Controls Kit (Affymetrix) and GeneChip Hybridization, Wash, and Stain Kit (Affymetrix). The GeneChip Scanner 3000 (Affymetrix) was used to scan the completed arrays. Summarized probe cell intensity data were generated with an Affymetrix GeneChip Command Console. Finally, probe-level summarization files were produced, and the data were background-adjusted, normalized, and log-transformed with the robust multiarray average (RMA) algorithm in Affymetrix Expression Console .et al. [The empirical Bayes (EB) method described by Johnson et al.  was applTotal microbial DNA was extracted from biopsies in two batches using the DNeasy blood and tissue kit (Qiagen), with an additional bead beating step to ensure adequate cell lysis. Bead beating was performed using both 5 mm stainless steel beads to disrupt tissue (Qiagen 69989) and glass beads  to disrupt bacterial cells, in conjunction with the FastPrep tissue homogenizer  set to speed 6 for 30 s. Additional enzymatic lysis was conducted through the addition of proteinase K (as per the Qiagen protocol) and incubation of samples at 95°C.The 16S gene dataset consists of Illumina MiSeq sequences targeting the V4 variable region. Detailed protocols used for 16S amplification and sequencing are as previously described . In brieThe overlapping paired-end reads were stitched together (approximately 97 bp overlap), size selected to reduce non-specific amplification products from host DNA (225–275 bp), and further processed in a data curation pipeline implemented in QIIME 1.5.0 as pick_reference_otus.py . In brieP values of the Spearman correlation test was calculated as a function of true covariance of the variables. The number of allowable tests for 90% power and 5% type I error rate was estimated by Bonferroni correction, 0.05 divided by the 90th percentile calculated as above. The number of allowable tests increases with the assumed true covariance of the variable pair, but is approximately 100 for a true covariance of 0.35, and 105 for a true covariance of 0.45  and supervised  reduction. For PCA, a variance-stabilizing arcsine square-root transformation was applied. Next, standard Principal Component Analysis of scaled features was used to capture major axes of variation, keeping enough components to account for 50% of variance. The previously documented ‘horseshoe effect’ in Principal Component Analysis of compositional data  was presSupervised feature reduction: Targeted gene selection was applied to the transcriptomic data in order to reduce its dimensionality. In a first wave of filtering, 174 genes prioritized as IBD-associated in the most recent and largest genome wide association study of the disease [Bacillus anthracis, Francisella tularensis, and Yersinia pestis based on yeast two-hybrid experiments [NOD2, IL23R, PTPN22, FUT2, NFKB1, MMEL1, IFNG, IL10, IL1RN, CD14, IL8, TLR1, TNF, and NOX3.eriments  were alseriments . A repreeriments . FinallyUnsupervised transcript reduction: Principal component analysis of host transcriptome data was performed on all PPI and pouch samples, keeping a sufficient number of components to account for 50% of variance. The only filter applied to whole-transcriptome data for PCA was to remove transcripts with variance below the median variance of all transcripts . Interpretation of the principal component axes was assisted by inspection of the top 25 genes by magnitude of loadings, and by Enrichment Analysis using the wilcoxGST function of the limma package with ‘C2.CP.biocarta’ v3.1 mSigDB pathways [PCA.Rnw script.pathways   samples were used; for antibiotics, inflammation, outcome, and the PPI samples from each of the 196 individuals were used. The latter tests were repeated using all samples, with a random intercept for individual, using the glmmPQL function of the MASS R package. This analysis was performed for whole transcriptome data, and for all microbial clades passing the ‘3 counts in 3 samples’ filter described above, by the associated sourcesOfVariation.Rnw script.A linear model was fit for each microbial clade and for each transcript separately, with respect to antibiotics (yes/no), outcome , inflammation (0–13), and tissue location (pouch/PPI), using the We used the scriptBiplotTSV.R script from the Breadcrumbs software package  to generEnterococcus) would be obviously due to either antibiotic use or inflammation, and thus of less interest than associations which were not directly attributable to either, we used a multivariate linear model to correct for antibiotic use, FAP/nonFAP outcome, and inflammation score. The model used was gene ~ clade + antibiotic + ISCORE + OutcomeFAP/nonFAP, with arcsin-square root variance stabilizing transformation of clade. Bonferroni false discovery correction was used with a threshold of q <0.25. Input files used for MaAsLin are available from [MaAsLin  ,28 was uble from .Linear discriminant analysis (LDA) was used to discriminate clinical outcome  based on expression patterns of 75 gene medoids and 45 clades. As there were many more PPI samples (196) than pouch samples (59), to ensure all samples were equally represented, only PPI samples were used. Because antibiotic use was not uniformly distributed across outcomes, we removed all samples with recent antibiotic use for discrimination of clinical outcome. This left 55 AP samples, 18 CDL samples, five CP samples, 20 FAP samples, and 46 NP samples for LDA analysis. Discrimination models were fit with three different sets of covariates: transcripts only, clades only, and transcripts plus clades together. Model fitting and assessment of discrimination by 10-fold cross validation were performed using the R package ‘caret,’ within the script ldaprediction.Rnw from .Ten-fold cross-validation was used to calculate accuracy of discrimination. For each clinical outcome and each model , a ROC plot was constructed using the roc function from the pROC library, using the 10-fold cross-validated posterior probabilities from the lda function of the MASS library. Ninety-five percent confidence intervals were estimated using the ci function from the pROC package (Additional file 16S sequence data for this project have been filtered to remove human sequences and are publicly available as Bioproject PRJNA269954; dbGaP accession number: phs000659.v1.p1 contains a subset of these data. Microarray data are available from GEO as GSE65270; GSE40292 contains a subset of these data. Metadata are available at .


While immunotherapies are rapidly becoming mainstays of cancer treatment, significant gaps remain in our understanding of how to optimally target them, alone or in combination. Here we describe a novel method to monitor levels of immune cells and pathways in expression data from solid tumors using pre-defined groups or modules of co-regulated immune genes. We show that expression of an interconnected sub-network of type I interferon-stimulated genes (ISGs) in melanomas at the time of diagnosis significantly predicted patient survival, as did, to a lesser extent, sub-networks of T helper/T regulatory and NK/T Cytotoxic cell genes. As a group, poor prognosis tumors with reduced ISG and immune gene levels exhibited significant copy number loss of the interferon gene cluster located at chromosome 9p21.3. Our studies demonstrate a link between type I interferon action and immune cell levels in melanomas, and suggest that therapeutic approaches augmenting both activities may be most beneficial. Immunotherapy of cancer is assuming a growing importance https://confluence.broadinstitute.org/display/GDAC/Home). For the studies reported here, we used stddata and analyses Runs, 09/23/13. The sample annotation data were downloaded as: SKCM.clin.merged.picked.txt. The expression profiles were downloaded as: SKCM.rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.data. We obtained a curated set of 291 annotated RNAseq profiles, each comprising approximately 20 million reads normalized by the RSEM procedure TCGA data designated as available without restrictions were obtained from public repositories. TCGA data and sample annotation for SKCM set were obtained from the Broad Institute GDAC Firehose (Immune molecule modules were constructed de novo as simple co-expression matrices between levels of marker genes (cell surface and transcription factor genes) and all other genes across a dataset of RNAseq profiles. The dataset comprised profiles from 134 samples of whole blood and/or purified cells  from healthy controls and individuals with autoimmune or infectious diseases. Notably, this dataset contained samples from multiple sclerosis patients before and 24 hr after treatment with AVONEX. Experiments to be described elsewhere show a strong type I interferon signature in patient samples taken after treatment when compared with pre- treatment samples . Here, we calculated Pearson correlation coefficients between marker gene transcript levels and levels of all 50,484 ENSEMBL gene models, across all samples. The top 100 most positively correlated transcripts  were designated as immune molecule modules and are presented in http://genepattern.broadinstitute.org/gp/pages/index.jsf ) were used to process data set GSE39088 from GEO (http://www.ncbi.nlm.nih.gov/geo/). Data were downloaded using GEOImporter and probe-level data collapsed into gene-level data using CollapseDataset. Normalized data set GSE22153 was downloaded from GEO and probe-level data were collapsed into gene-level data using the WGCNA package in R. For network viewing, gene lists were projected on to the STRING 9.1 http://string-db.org/). Nodes having ≥2 edges were then exported into Cytoscape http://www.cytoscape.org/) for manipulation and visualization.Tools from GenePattern  that gave survdiff p-values <0.01. We therefore used 1% of the total numbers of module tested as the expected value for the null distribution of survdiff having p-values <0.01. For all other tests used, we considered p-values <0.05 as significant.For On publication, RNAseq profiles used for creating immune molecular modules will be deposited into GEO, Accession GSE60424.SKin Cutaneous Melanoma) dataset (Methods) from The Cancer Genome Atlas (TCGA), a large-scale collaborative effort to characterize genomic changes that occur in cancer. Clinical characteristics of this data set are summarized in We hypothesized that transcript modules In preliminary analyses (not shown), we found that transcript modules We used the strategy shown in http://interferome.its.monash.edu.au/interferome/home.jspx). Since genes in these, but not other top modules, were up-regulated after in vitro treatment of whole blood with type I interferon , but others that were less significant . The best-scoring module in this group (MAF.mod) contained a sub-network of T cell activation and costimulatory genes . Many moThe third group of overlapping modules gave only weakly significant p-values . The best-scoring module in this group (KLRD1.mod) yielded a sub-network of genes characteristic of cytotoxic cells (‘Cytotoxic cell’ genes), including CD160, NCAM1, GZMA, GZMB, NKG7 and TBX21 . Taken tTo examine interconnections between the three sub-networks, we combined them into a single, larger scale network graph . On thisKM plots confirmed the effectiveness of the best-scoring module in each group at predicting patient survival . HoweverTo better judge the clinical significance of ISG levels, we examined how they associated with other clinical covariates. We created contingency tables comparing six clinical covariates against samples split on ISG levels . MeasureWe also compared the predictive ability of ISG module expression signatures with other clinical parameters, alone and in combination using a Cox proportional hazards model. Several variables gave significant p-values in univariate predictive analysis , but only ISG set and stage reached significance in the multivariable analysis . The comIt was also important to determine whether ISG hi and ISG lo tumor biopsies had distinctive genetic characteristics. We therefore searched for copy number variations that distinguish tumors with different ISG levels. We excluded ISG med samples from this analysis as the ISG hi and ISG lo samples were likely to show the greatest differences. We compared copy numbers for each gene in ISG hi and ISG lo samples using Wilcoxon p-values . The topBecause of the significance and magnitude of copy number differences at 9p21.3, we extended our analysis to determine how many genes from this locus were affected. As shown in It was important to validate our findings with an independent cohort. Jönsson et al Using a transcript module approach, we have demonstrated that expression levels of three distinct networks of immune genes in melanomas at time of diagnosis can predict patient survival. Two of these networks (Th/Treg and Cytotoxic cell genes) are likely derived from infiltrating immune cells. The third network (ISGs) is reduced in tumors having deletions at 9p21.3 and is likely derived, at least in part, from the tumors themselves. Expression of the ISG network is elevated in other tumors, but at present we do not know whether this elevation is due to the tumor itself, immune cell infiltrate or both.Although themes of Cytotoxic and T cells have been noted in other signatures Chromosome 9p21.23 contains genes important in melanoma susceptibility Interferon alpha, an approved therapy for late stage melanoma patients, is ineffective in some patients, but very effective in others, especially when treatment is accompanied by autoimmune symptoms Figure S1Induction of ISG module transcripts following in vitro treatment of whole blood with interferon alpha. Shown are density plots of gene expression of genes in the indicated molecular modules in healthy control whole blood samples without (N = 46) or with (N = 18) in vitro treatment with alpha-interferon for 4 hrs. Genome scale microarray data were as described (TIF)Click here for additional data file.Figure S2Immune molecular modules effective at stratifying patients contain distinctive sub-networks of genes. Genes in molecular modules ( modules  were pro(TIF)Click here for additional data file.Figure S3Conditional inference tree from Cox proportional hazard model. We constructed a Cox proportional hazards model ctree function in R (TIF)Click here for additional data file.Figure S4Validation in an independent cohort. We analyzed the data set published by Jönsson et al (TIF)Click here for additional data file.Table S1Genes in immune molecular modules. Shown are the top 100 genes best correlated in expression levels with marker genes across row 1 (Methods).(XLSX)Click here for additional data file.Table S2Groups of immune molecular modules best at predicting melanoma prognosis. Immune molecular modules were scored for their ability to predict survival of melanoma patients as described in survdiff p-value <0.01. Permutation testing predicted ∼1/111 of immune molecular modules to give survdiff p-values <0.01 by chance, as compared with the 16/111 we observed and show in this table (FDR∼6%). Survdiff p-value, p-value calculated using survdiff function; group, identity of genes in the module (e module .(DOCX)Click here for additional data file.Table S3Designation of melanoma samples according to immune molecular module expression. Shown are results of melanoma sample stratification by immune molecular modules, IRF7.mod, MAF.mod and KLRD1.mod. For columns labeled as two variable mods, melanoma samples were identified as IRF7.mod, MAF.mod or KLRD1.mod hi or lo as described in (XLSX)Click here for additional data file.Table S4Clinical characteristics of ISG hi and ISG lo sets. Shown are the numbers of ISG hi and ISG lo samples with six clinical characteristic variables, and chi-square p-values for differences between ISG hi and ISG lo sets. Asterisks indicate degree of significance: *, p<0.05; **, p<0.01, ***, p<0.001.(DOCX)Click here for additional data file.Table S5Univariate and multivariable models of survival. ISG expression and other clinical parameters were used to construct univariate and multivariable survival models. While we obtained similar results using patient age and Breslow thickness as discrete and continuous values, we show here only results obtained with the former. For univariate models, we show numbers of records and events for each variable, together with median survival, 95% confidence intervals and survdiff p-values. For the multivariable model, we show the Cox proportional hazard p-value. Asterisks indicate degree of significance: *, p<0.05; **, p<0.01, ***, p<0.001.(DOCX)Click here for additional data file.Table S6Copy number differences between ISG hi and ISG lo samples. Shown are median copy numbers for each gene in ISG hi and ISG lo samples, Wilcoxon p-values and absolute differences between the groups. Since adjacent genes subject to copy number variation at the chromosomal scale were unlikely to vary independently, we did not use multiple testing corrections for this analysis.(XLSX)Click here for additional data file.


Fathead minnow and zebrafish are among the most intensively studied fish species in environmental toxicogenomics. To aid the assessment and interpretation of subtle transcriptomic effects from treatment conditions of interest, better characterization and understanding are needed for natural variation in gene expression among fish individuals from lab cultures. Leveraging the transcriptomics data from a number of our toxicogenomics studies conducted over the years, we conducted a meta-analysis of nearly 600 microarrays generated from the ovary tissue of untreated, reproductively mature fathead minnow and zebrafish samples. As expected, there was considerable batch-to-batch transcriptomic variation; this “batch-effect” appeared to differentially impact subsets of fish transcriptomes in a nonsystematic way. Temporally more closely spaced batches tended to share a greater transcriptomic similarity among one another. The overall level of within-batch variation was quite low in fish ovary tissue, making it a suitable system for studying chemical stressors with subtle biological effects. The observed differences in the within-batch variability of gene expression, at the levels of both individual genes and pathways, were probably both technical and biological. This suggests that biological interpretation and prioritization of genes and pathways targeted by experimental conditions should take into account both their intrinsic variability and the size of induced transcriptional changes. There was significant conservation of both the genomes and transcriptomes between fathead minnow and zebrafish. The high degree of conservation offers promising opportunities in not only studying fish molecular responses to environmental stressors by a comparative biology approach, but also effective sharing of a large amount of existing public transcriptomics data for developing toxicogenomics applications. Among an organism’s responses to environmental perturbations, gene transcription could be regarded as one of the earliest molecular events along the genotype-phenotype continuum. Dynamic and transient in nature for most genes, variation in this transcriptional response and its complex regulatory mechanisms is believed to contribute to much of the phenotypic complexity across biota An organism’s transcriptome represents its full range of expressed gene transcripts regulated by a wide variety of molecular control mechanisms. At the molecular level, gene regulatory complexity is determined in part by the combinatorial nature of multiple cis-regulatory elements and trans-acting transcription factors It is, of course, also possible to introduce technical variation into transcriptomic data due to methodological differences across studies, for example, in RNA sample preparation, choice of profiling platforms, data processing approaches, and so on. Over the years, there have been extensive investigations into these technical issues and their impact on gene expression profiling by microarrays Among a wide range of applications of transcriptomics in various disciplines are human disease diagnostics/prognostics Danio rerio) arguably the most studied species in this diverse group. As a common biological model with extensive genome-level knowledge, the zebrafish is a logical species of choice for toxicogenomics work. The fathead minnow also is an attractive model species because it has been the dominant aquatic vertebrate test organism in regulatory toxicity testing for decades Pimephales promelas; PPR), based on samples across different gender-tissue-treatment conditions, variability among individuals was found to be very high, with a wide-ranging distribution among genes as well as molecular networks Fish in general are among the most commonly studied non-mammalian organisms in environmental toxicogenomics, with the fathead minnow and zebrafish  were exposed to a continuous flow of sand filtered, UV-sterilized, Lake Superior water  for 24, 48, or 96 h at the US Environmental Protection Agency (USEPA) laboratory in Duluth, Minnesota. At the end of each exposure period, fish were anesthetized in a buffered solution of tricaine methanesulfonate  and ovaries were collected and shipped overnight on dry ice to the USEPA laboratory in Cincinnati, Ohio. Total RNA isolated from selected tissue samples was then sent to Cogenics Corporation, an Agilent certified contract laboratory . Hybridization was conducted using a two-color protocol on Agilent zebrafish microarrays with 21K probes , followed by high-resolution scanning and image processing by Agilent Feature Extraction software. Eighty ovary controls for nine treatment conditions, all based on the design 015064, were included in this study.Reproductively mature fathead minnows (5–7 months old) reared at the US EPA laboratory in Duluth were exposed to LSW. All exposures were continuous flow-through exposures. Representative experimental designs for the experiments included as part of the current analysis have been detailed elsewhere www.r-project.org) primarily by using the Limma package Various analyses were conducted in the R environment  and variance partitioning http://www.is.titech.ac.jp/~shimo/prog/pvclust/).Between-batch variation was further evaluated by several approaches. To provide an empirical critical value to assess the statistical significance of DEG counts, a non-parametric distribution of the number of DEGs between batches was generated. Microarray sample labels were permuted according to the original number of replicates in each batch under the factor Experiment to generate 1000 simulated datasets, followed by identifying DEGs in paired comparisons between a selected common reference batch and each of the remaining batches in every dataset. Note that a different batch was selected as a common reference in the analysis of each simulated dataset. To determine if between-batch variation is biologically significant, the 100 DEGs with the highest F statistic p-values for between-batch variation under each factor were also combined and analyzed for possible enrichment in biological pathways by DAVID  http://regressit.com/index.html).Individuals within a batch are subjected to the same degree of technical variation, thus their differences should better reflect biological variability. We used two different approaches to evaluate within-batch variation. First, for a given factor, half the samples in each batch randomly assigned as “case” were compared to the remaining half assigned as “control” for DEGs during each of 250 permutations. Between-batch variation was controlled in these analyses by including the factor under consideration in the GLM to ensure the comparisons were made within a batch. This method essentially searched through samples’ possible membership assignments between the two classes to uncover the configuration where the number of DEGs was maximized. This DEG count served as an indirect measure of the variation among individuals. To be included in these permutations, each batch needed to contain a minimum of ten (zebrafish) or 12 (fathead minnow) samples, respectively, to ensure that there were at least 250 unique permutations and the maximum amount of data was utilized. Second, the coefficient of variation (CV) and intensity of individual probes were also calculated from multiple biological replicates in individual batches under the Experiment factor and averaged over all its batches. An assessment of these two simple metrics both within and between the two species, at the levels of entire transcriptome, KEGG (Kyoto Encyclopedia of Genes and Genomes) molecular pathways, and individual genes, would be informative of not only gene expression variability among individuals and relative biological contribution to this variability, but also the interspecific transcriptomic conservation as well. The simple linear regressions and validations of their underlying statistical assumptions were conducted for selected inter- and intraspecific comparisons of these two metrics using “RegressIt”, an Excel macro developed jointly at Duke University and University of Texas (ndenslow@ufl.edu) by TBLASTX so the latter with a greater sequence length could be used as queries for more successful mapping across species. The EST sequences were then mapped to the NCBI  nucleotide  and protein  databases by TBLASTX and BLASTX respectively, effectively associating fathead minnow probe IDs to their corresponding NCBI accession IDs. All three rounds of mapping had a minimum E-value cutoff of E−06. These fathead minnow IDs were then joined to a variety of zebrafish accession IDs prepared by the NCBI , and finally to Agilent probe annotations (https://earray.chem.agilent.com/earray/). In the end, a total of 9311 probes from probes from Agilent 019597 through 6617 common Entrez GeneIDs in NCBI (http://rest.kegg.jp/link/dre/pathway) by these GeneIDs. Before the pathway level the CV and intensity of those duplicated probes were consolidated first by Entrez GeneIDs.To allow interspecific comparison of within-batch variation, probes from Agilent design 019597 (fathead minnow) and 015064 (zebrafish) representing orthologous genes were identified through several successive steps of identification (ID) mapping. Probe sequences from Agilent 019597 were first mapped to their corresponding EST (Expressed Sequence Tag) target sequences  scripts developed by the authors based primarily on the R. In contrast to DEG determination by a regular t-test (or F-test), this software fits expression data of each gene into a linear model and generates a modified t-statistic (or F-statistic for multiple contrasts) using an empirical Bayesian and hierarchical modeling approach to adjust for unreliable variance estimates caused by small sample size The analytical procedures described above for both between- and within-batch variation were incorporated into several R  under the Experiment factor, dividing the samples into two classes with each containing four to 11 samples will result in 2.4×EThere have been a limited number of studies reporting transcriptomic variation among individuals within a population, which are similar in nature to the within-batch variation under consideration here. These studies were limited to several organisms sampled from wild or lab populations with different levels of heterozygosity. Most of them were based on utilizing technical replicates to various degrees to estimate variability. The range of variation by percentage of genes identified as DEGs is quite wide: 2 to 9% in nematode (whole organism) 2 = 0.46 and 0.49, The expression intensities of the orthologs and their variation within-batch differed considerably among the molecular pathways in the two species. For example, in both species, some of the pathways involved in basic cellular functions such as Ribosome, RNA Transport, Citrate Cycle, Cell Cycle, and RNA Polymerase tended to be less variable. Some of the metabolic pathways, on the other hand, were more variable in both species but involved in different functions. Many pathways involved in signal transduction were expressed on average at a fairly low level, while those participating in basic cellular functions like DNA replication and protein synthesis were highly expressed. Although this difference in the average variability of pathways could be explained to a considerable extent by an inverse correlation with average intensity , a large portion of the variance in the average CV remains unaccounted for by the average intensity at pathway level  and 019597 (fathead minnow) via their orthologs. A total of 9311 probes from Agilent 015064 were linked to 6950 probes from Agilent 019597 through 6617 common Entrez GeneIDs in NCBI.(XLSX)Click here for additional data file.S2 TableThe DAVID analysis of fathead minnow DEGs. The top 100 DEGs were combined from the between-batch variation under each factor for possible enrichment in biological pathways.(PDF)Click here for additional data file.S3 TableThe DAVID analysis of zebrafish DEGs. The top 100 DEGs were combined from the between-batch variation under each factor for possible enrichment in biological pathways.(PDF)Click here for additional data file.S4 TableInterspecific comparison of within-batch variation at the molecular pathway level. A total of 6617 orthologous genes were grouped into 144 out of the 162 KEGG pathways as of April, 2014. The intensity and CV were calculated by individual batches each containing multiple biological replicates under the Experiment factor, and then averaged over all batches. Only pathways with at least five orthologs were included. KEGG, Kyoto Encyclopedia of Genes and Genomes; DRE, zebrafish; PPR, fathead minnow.(XLSX)Click here for additional data file.S5 TableThe top 50 most variable fathead minnow (PPR) genes based on average within-batch coefficient of variation (CV) under the Experiment factor.(DOCX)Click here for additional data file.S6 TableThe top 50 most variable zebrafish (DRE) genes based on average within-batch coefficient of variation (CV) under the Experiment factor.(DOCX)Click here for additional data file.S1 FileAverage number of DEGs per pair of between-batch comparison in 1000 permutations. Fathead minnow  critical cutoffs were: 5%, 15; 1%, 27; 0.1%, 104. Zebrafish  critical cutoffs were: 5%, 418; 1%, 479; 0.1%, 594. Samples were grouped by the factor Experiment.(PDF)Click here for additional data file.S2 FileThe PCA plots of fathead minnow samples based on all the DEGs identified as between-batch variation. Samples were grouped by Experiment , RNA Date , RNA Person , Sampling Date , and Scan Date . Each figure was based on either the average gene intensity by individual batches (A) or the gene intensity of individual samples (B). DEGs were based on the simulated reference method.(PDF)Click here for additional data file.S3 FileThe PCA plots of zebrafish samples based on all the DEGs identified as between-batch variation. Samples were grouped by Experiment , RNA Date , RNA Person , Sampling Date , and Scan Date . Each figure was based on either the average gene intensity by individual batches (A) or the gene intensity of individual samples (B). DEGs were based on the simulated reference method.(PDF)Click here for additional data file.S4 FileThe dendrograms from resampling clustering of fathead minnow samples based on all the DEGs identified as between-batch variation. Samples were grouped by Experiment , RNA Date , RNA Person , Sampling Date , and Scan Date . Each figure was based on either the average gene intensity by individual batches (A) or the gene intensity of individual samples (B). DEGs were based on the simulated reference method.(PDF)Click here for additional data file.S5 FileThe dendrogram from resampling clustering of zebrafish samples based on all the DEGs identified as between-batch variation. Samples were grouped by Experiment , RNA Date , RNA Person , Sampling Date , and Scan Date . Each figure was based on either the average gene intensity by individual batches (A) or the gene intensity of individual samples (B). DEGs were based on the simulated reference method.(PDF)Click here for additional data file.S6 FileThe distribution of within-batch coefficients of variation (CV) of probe intensities. Fathead minnow  CVs were based on 15208 probes, and zebrafish  CVs were based on 21495 probes.(PDF)Click here for additional data file.


High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action.in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine (SVM) methods, LibSVM and sequential minimal optimization, had better classification performance than other methods. SVM recursive feature selection (SVM-RFE) had the highest overfitting rate when an independent dataset was used for a prediction. Therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested. Analysis of biomarkers that distinguished the 14 classes of compounds identified a group of genes principally involved in cell cycle function that were significantly downregulated by metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators.In this study, Our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. Choosing the right feature and classification algorithms for this multiple category classification and prediction is critical. Efficient and precise evaluation of the potential hazards that drugs, environmental and industrial chemicals pose to humans and other organisms remains a challenge -4. TradiBased on the concept that the similar gene expression patterns for similarly classified chemicals may indicate similar toxicity and underlying molecular mechanisms , scientiSeveral strategies have been employed to classify expression profiles based on exposure to chemicals of known mechanisms of action ,20. Diffin vitro system that can easily be manipulated and used to screen chemicals for toxicity using different molecular and biochemical methods. Primary cell cultures can also reduce concerns regarding animal availability, cost, and welfare that affect in vivo studies [in vitro cultured primary rat hepatocytes. Primary rat hepatocytes were exposed in triplicate to one of 105 compounds or controls for 24 h followed by microarray analysis of the chemical effects. A total of 105 compounds were divided into 14 classes based on their known functional properties, modes of action, and health and safety concern lists , chisquare, gainratio, inforgain, relief, and SVM recursive feature election (SVM-RFE). Classification methods used included decision tree J48, random forest (RF), Naive Bayes (NB), simple logistic (SL), and two support vector machine methods, LibSVM and SMO. We also proposed a new feature selection algorithm called gradient method, which had a high training classification rate as well as prediction accuracy with the lowest overfitting rate. Biomarkers that can distinguish compounds into the 14 classes were identified that can be used to predict molecular and toxic actions of chemicals based on gene expression profiles.Microarray experiments were performed using Agilent rat whole genome array (4X44k) in order to identify biomarkers that would distinguish and predict which of 14 classes of compounds, including control classes, a chemical exposure belonged. Cultured primary hepatocytes were treated with 105 distinct compounds, as well as their respective vehicle controls, for 24 h after which total RNA was isolated for array hybridization  filtering methods including One-Way ANOVA, Kruscal Wallis and One-way ANOVA unequal variance, four feature selection algorithms including ChiSquare, GainRatio, Inforgain, and Relief, and 5 classification methods including J48, RF, NB, SL and SMO. As depicted in Figure p-value ranked features, we chose different number of probe sets to compare the mean prediction accuracies for each method by averaging the prediction accuracy of the five classification algorithms decision Tree j48 (J48), RF, NB, SL and SMO. We observed that when the feature size was increased, the prediction accuracies for the three feature filtering methods increased as well  to separate microarrays into the 14 classes of compounds, we compared three initial feature filtering methods including One-Way ANOVA, Kruscal Wallis and One-Way ANOVA unequal variance. Using l Figure . The aveSubsequently, we employed the median normalization and Kruscal Wallis feature filtering methods, we then focused on comparing a variety of classification algorithms. Six classification algorithms were used for the comparison. The prediction accuracy shown in Figure We compared prediction results for six feature selection methods: PCA, chisquare, gainratio, inforgain, relief, and SVM-RFE Figure . The preAfter comparing a series of methods, we found that using the SVM-RFE feature selection method and the LibSVM classification algorithm would achieve a high prediction accuracy for the 14 classes of compounds. Nearly 100% accuracy was observed for all feature sizes from 100 to 500 genes  prediction accuracy was achieved suggesting that the method may be overfitting the data. To overcome these problems, we developed a new feature selection method called gradient method . We identified the best model for each feature selection method by comparing the different feature selection methods and classification algorithms in training on dataset 2. The best training accuracy by the feature selection methods was achieved using SVM-RFE  that could yield good classification accuracy. We found that the gradient feature selection method had the lowest overfitting rate for both SMO and SL classification algorithms Figure . When LiWe chose 300 transcripts  and the IPA function analysis tool, we found that cell cycle was the most significantly enriched functional term detected by both tools Table . SurprisSeveral gene families had multiple gene numbers participating in cell cycle and other functional processes Table . Two sucPathway analyses also showed that cell cycle associated pathways were the most significant pathways in the gene list, which included mitotic roles of polo-like kinase, and cell cycle: G2/M DNA damage checkpoint regulation pathways Table . Cancer-In this study, we compared the efficacy of a variety of methods including different normalization, feature selection, classification algorithms for the classification of gene expression profiles of 105 chemicals into 14 classes of compounds. Few reports have studied the impact of normalization methods on the classification accuracy based on microarray data. Since microarray experiments were analyzed over different time periods in our study, a batch effect could significantly affect our analyses as has been observed in other studies -32. Remoin vitro study, time is an issue, we do not want to be too long to increase the cost and also not too short and some effects may not occur. We have added the discussion and the reference in the discussion part. Note also that primary hepatocytes rapidly adapt to cell culture changing from the in vivo liver cells they once were. Perkins et al. 2006 [For a quick evaluation, we chose 24 h for our microarray test. Our previous experiments did find that there were more genes were changed at 24 h than 48 h . For an al. 2006  found thTo examine if the microarray data could be used to predict what chemical class the rat hepatocytes were exposed to, we compared different classification algorithms for their performance in predicting which of the 14 classes a chemical exposure belongs. We found that decision tree analysis always performed the poorest, which was consistent with other comparative reports . NB methWe also conducted a comprehensive comparison of various feature selection methods on the classification of the 14 classes of compounds. For training purposes, we found that SVM-RFE usually outperformed other methods as has been confirmed elsewhere ,39,40. HGenes selected as biomarkers generally tend to have similar expression patterns relative to one or more classes of chemicals they are exposed to. When this common effect is seen between multiple classes a similar function may be impacted across the classes thereby providing some insight into mechanisms by which a chemical causes effects. We identified a cluster of 104 transcripts that were significantly down-regulated by metals and inflammatory compounds, but were up-regulated by anti-microbials, cancer-related drugs, pesticides, and PXR mediators. Over half of the genes in this cluster are involved in cell cycling. This result indicates that metals and inflammatory mediators may share similar activity. The inflammatory mediator class contained compounds such as aspirin, ibuprofen, IL-3, LPS, TNFα, have inflammation regulation function. A series of studies has also revealed that metal complexes could affect inflammation -45. The We have manually divided the chemicals into 14 classes according to their known mode actions and toxicities. While imperfect, it is a simple and usable classification scheme. Based on the classification system and gene expression profiles, we have successfully developed a gene expression classifier to predict different compound classes. This gene list should have applicability in predicting what class a new compound belongs to when a gene expression profile available with the 300 genes.Our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. Choosing the right feature and classification algorithms for this multiple category classification and prediction is critical.Chemicals were purchased from Chem Service  and SRI International , Sigma-Aldrich , and Fisher Scientific . The purity of all these compounds was equal or greater than 98%. Prior to testing, the compounds were made up and serially diluted in dimethyl sulfoxide, DMSO . Metal compounds were prepared and serially diluted in 0.1 μm-filtered, ultra-pure water before testing. The 24-h toxicity of each compound was first determined in the human hepatocyte cell line HepG2 using the Neutral Red cytotoxicity kit (In Vitro Toxicology Kit Tox-4) obtained from Sigma-Aldrich.The primary rat hepatocytes, rtNHeps (AC-2630), isolated from male Sprague Dawley and its hepatocyte culture medium (HCM), and supplements and growth factors (CC-3198) were purchased from Cambrex BioScience .6 cells per (Type 1 collagen-coated) T-75 flask. The flasks were left in a 37°C, 5% CO2 incubator overnight to allow for cell attachment.The primary rtNHeps cells were reconstituted in HCM supplemented with ascorbic acid, fatty acid-free bovine serum albumin, transferrin, insulin, recombinant human epidermal growth factor, hydrocortisone 21 hemisuccinate, Gentamicin sulfate, and Amphotericin B immediately upon receipt. An aliquot of the cell suspension was stained in a 0.05% Trypan Blue solution and counted under an inverted microscope. The cells were then seeded at 3 × 10The cells were replenished with fresh HCM and dosed in triplicate flasks with the non-toxic concentration of each compound  at 1% DMSO (v/v) or with solution at 1% water (v/v). Another set of triplicate flasks were dosed with the appropriate solvent control. Hence, for every 3 chemicals and a solvent control, a total of 12 flasks were used. A total of 105 chemicals were used, and each chemical has 3 treatments plus controls . Total RNA concentrations were measured using NanoDrop® ND-1000 Spectrophotometer . The integrity and quality of total RNA was checked on an Agilent 2100 Bioanalyzer . Nuclease-free water (Ambion) was used to elute total RNA.Rat whole genome oligo arrays in the format of 4X44K were purchased from Agilent . Sample cRNA synthesis, labeling, hybridization and microarray processing were performed according to manufacturer’s protocol "One-Color Microarray-Based Gene Expression Analysis" (version 1.0). The Agilent One-Color Spike-Mix (part number 5188–5282) was diluted 5000-fold and 5 μL of the diluted spike-in mix was added to 1 ug each of the total RNA samples prior to labeling reactions. The labeling reactions were performed using the Agilent Low RNA Input Linear Amplification Kit in the presence of cyanine 3-CTP. The labeled cRNA from each labeling reaction was hybridized to individual arrays at 65°C for 17 hours using Agilent’s Gene Expression Hybridization Kit. After washing, the arrays were scanned at PMT levels 350 setting using GenePix 4200AL scanner (Molecular Device Inc.), the Feature extraction software (V. 9.5.1) from Agilent was used to automatically find and place microarray grids, reject outlier pixels, accurately determine feature intensities and ratios, flag outlier pixels, and calculate statistical confidences.Feature Extraction 9.5.1 software. Only a probe that had present flags in at least 50% samples of all the arrays was kept for further analyses. Data were subsequently log (base 2) transformed for statistical analyses. Initial feature filtering was conducted by One-Way ANOVA, Kruscal Wallis and One-Way ANOVA unequal variance with a cut off p-value less than 0.05.Microarray data analyses were processed with GeneSpring version 7.0 and 10.0 (Agilent). The sample quality control was based on the Pearson correlation of a sample with other samples in the whole experiment. If the average Pearson correlation for a given sample with other samples was less than 80%, the sample was excluded for further analysis. If the scanned intensity was less than 5.0 for a probe, it was transformed to 5. A per chip (within) array normalization was performed using 50 percentile values of all the probe values in the array. Per gene (between) array normalization was also applied using either the median value of a gene across all samples  or relative control samples  in the experiment. Probe features were first filtered using flags. A "present" or "absent" flag was defined using the Agilent i = (wi)2, (4) eliminate the lower-ranked 50% of the features, (5) return to step 2. At each RFE step 4, a number of features are discarded from the active variables of an SVM classification model. The features are eliminated according to a criterion related to their support for the discrimination function, and the SVM is re-trained at each step. All other methods were performed using Weka program [Several feature selection algorithms were used in the project including SVM-RFE, PCA, chisquare, gainratio, inforgain, and relief. SVM-RFE is an algorithm for selecting a subset of features for a particular learning task. The basic algorithm is the following: (1) initialize the dataset to contain features, (2) train an SVM on the dataset, (3) Rank features according to cThe classification algorithms used in the project included decision tree J48, random forest (RF), Naive Bayes (NB), simple logistic (SL), two support vector machine (SVM) methods LibSVM and SMO. In decision tree structures, leaves represent classifications and branches represent conjunctions of features that lead to classifications. Iterative Dichotomiser 3 (ID3) is an algorithm used to generate a decision tree  which isAfter running classification algorithms, we tested the cross-validation error estimation using 10-fold cross-validation with 10 iterations. The classification and prediction accuracy was calculated by the correctly predicted cases for each class or all the samples .This is a feature selection method of multiple classes—at least 3 classes. The goal of feature selection is to choose those features whose expression is different in every class. We consider power of sample size as another important factor to conduct feature selection. Here is the whole procedure of Gradient Feature Selection Method.First, we do statistical testing across all classes, then filter out the features whose p-value is bigger than 0.05.Second, observation by observation, we perform the following steps:1. Calculate the observation’s median value across samples in every class.2. Sort these median values from smallest to largest.3. Calculate the differences between each two sorted neighbor classes.4. Calculate power of sample size between each two sorted neighbor classes. Calculate standard deviation of step 3.5. Calculate geometric mean of step 4.Rank each observation equal to the multiplication product of the results from step 3 and step 4. Last, we use the highest ranked ones to be candidate eigenvectors.To have a clear understanding for this idea, let’s see an example, a matrix whose structure is like that showing in the following table.m genes and n classes, and there are x1 samples in class1, x2 samples in class2,… and xn samples in classn. Every gene has a median value across each class. Here is our rule of naming, for gene y, its median across samples 1 through xk in class k, then it will be named gy_median_Ck, so g2_median_C2 means median value of gene 2 across samples in class 2.In the matrix, there are For each gene, after sorting the median values from smallest to largest, the fold-change of this gene between each pair of neighbor classes forms a stair, which can be diagrammed as below.i stands for the power of sample size between classi and classi+1; while fold change i stands for the fold change between classi and classi+1. For the gene shown above, its rank R as a bio-marker is:Fold change between two sorted neighbor classes decides the height of stair, and the power of sample size of the two classes decides if the marker is reliable enough. Here P stands for power, Pp-value less than 0.05 was considered significant. Pathway analysis was performed using the IPA canonical pathways analysis tool. Similar to functional term analysis, a pathway with an enrichment p-value less than 0.05 was considered to be a significantly regulated pathway. Gene networks were constructed based on the IPA knowledge base. A score was assigned to a network according to the fit of the original set of significant genes. This score reflects the negative logarithm of the p-value that indicates the likelihood of the focus genes in a network being found together due to random chance [Significantly regulated probes were employed for two-way hierarchical clustering of both genes and samples using GeneSpring 10.0. A euclidean distance with average linkage was applied for the clustering. Gene functional categories were classified according to gene ontology  as well m chance .The authors declare that they have no competing interests.in vitro experiments. XG performed the microarray hybridization. CZ participated in designing the algorithms in the manuscript. EJP conceived the study, coordinated the whole project. All authors read and approved the final manuscript.WX participated in writing the manuscript and data analysis and paper submission. JA processed microarray data, conducted statistical data analysis, and developed new algorithm and software. YD conceived and directed the study, analyzed the results, and drafted the manuscript. DRJ and CYA conducted the Chemical classes and their contained compounds.Click here for fileCompounds and their existing mechanism and toxic effects.Click here for file300 markers to distinguish 14 classes.Click here for fileIn vitro experimental design.Click here for file


IntClust is a classification of breast cancer comprising 10 subtypes based on molecular drivers identified through the integration of genomic and transcriptomic data from 1,000 breast tumors and validated in a further 1,000. We present a reliable method for subtyping breast tumors into the IntClust subtypes based on gene expression and demonstrate the clinical and biological validity of the IntClust classification.We developed a gene expression-based approach for classifying breast tumors into the ten IntClust subtypes by using the ensemble profile of the index discovery dataset. We evaluate this approach in 983 independent samples for which the combined copy-number and gene expression IntClust classification was available. Only 24 samples are discordantly classified. Next, we compile a consolidated external dataset composed of a further 7,544 breast tumors. We use our approach to classify all samples into the IntClust subtypes. All ten subtypes are observable in most studies at comparable frequencies. The IntClust subtypes are significantly associated with relapse-free survival and recapitulate patterns of survival observed previously. In studies of neo-adjuvant chemotherapy, IntClust reveals distinct patterns of chemosensitivity. Finally, patterns of expression of genomic drivers reported by TCGA (The Cancer Genome Atlas) are better explained by IntClust as compared to the PAM50 classifier.IntClust subtypes are reproducible in a large meta-analysis, show clinical validity and best capture variation in genomic drivers. IntClust is a driver-based breast cancer classification and is likely to become increasingly relevant as more targeted biological therapies become available.The online version of this article (doi:10.1186/s13059-014-0431-1) contains supplementary material, which is available to authorized users. This classification was named IntClust owing to the clustering of tumors based on the integration of genomic and transcriptomic data [The classification of breast tumors based on morphology  and two key markers, estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2), remains the mainstay of current clinical practice. Early attempts to improve this situation by using genomic technology focused on data-driven methods including unsupervised transcriptome-based classification -3 and gemic data  to find mic data . The schmic data . Furthercis eQTLs which were in the original clustering algorithm [We used the dataset in which the IntClust subtypes were originally discovered (N = 997) to train a gene expression-based classifier. The selected genes corresponded to particular lgorithm . A panellgorithm  in the olgorithm . Based olgorithm . This melgorithm  classifiESR1 and ERBB2 expression was not bimodal for this study and, in general, there appeared to be a low signal-to-noise ratio. The Pearson’s correlation coefficient between goodness of fit and number of samples per study was 0.53 and between goodness of fit and number of features per study was 0.38. As a comparator, we also classified samples into the ‘intrinsic subtypes’ using the PAM50 classifier [ESR1, ERBB2 and AURKA) using the SCMGENE classifier [In order to quantify the efficacy of our method by study, we used a correlation statistic to estimate the goodness of fit of the classification model where a score of 1.0 indicates perfect correlation between the gene expression profiles of new samples and those contained within the index dataset. Figure assifier  and intoassifier . We evalWe also applied our classifier to a large panel of cell lines from two data repositories (Sanger COSMIC database and the Cancer Cell Lines Encyclopedia (CCLE)). We applied three versions of our classifier to these data: copy number data alone, gene expression alone and the combined copy number/gene expression feature set. The goodness of fit statistics for these classifiers are depicted in a scatter plot in Additional file The platform and feature flexibility of our classifier enabled the classification of a large collection of independent samples. For comparison we also classified tumors into the ‘intrinsic’ subtypes using the PAM50 and SCMGENE classifiers ,24. The A subset of patients in some of the studies received neo-adjuvant (before definitive surgery) chemotherapy, and tissue would have been derived from biopsies or fine needle aspirates. Here, we note that based even on these samples, IntClust subtype could be reliably assigned and resulted in proportions comparable to those from studies in patients who did not receive neo-adjuvant chemotherapy Figure A. This iOne important measure of a novel method for disease classification is the degree to which subtypes show an association with clinical outcome. Here, we have undertaken an extensive comparative analysis of the PAM50, SCMGENE and IntClust classifiers. Figure P = 0.005) and SCMGENE (P = 0.03) significantly outperformed PAM50 in the prediction of late events (8 to 15 years) in ER-positive breast cancer  subtypes as categorical variables and were adjusted for tumor size , node status (negative versus positive) and histological grade . The coefficients for these models were derived using Cox-regression in the METABRIC dataset and then applied to external studies with available data in order to avoid over-optimistic estimates. Harrell’s C-index was used to estimate the relative discrimination of models where an index of 1 reflects perfect discrimination between high and low risk patients while an index of less than 0.5 reflects discrimination which is no better than chance. We conducted analyses separately by ER status and within three brackets of follow-up time  in order to account for violations of Cox-proportional hazards assumption  and to eA second determinant of the relative utility of a disease classification scheme is whether differences in chemosensitivity are reflected in different subtypes. In order to investigate this, we used a collection of breast cancer studies where patients had received neo-adjuvant cytotoxic chemotherapy -29 and fWe next investigated the extent to which copy number-driven breast cancer genes are captured by the IntClust classification compared with PAM50 or SCMGENE classification. We used an independent list of copy number aberrations which were reported by TCGA as occurring recurrently in breast cancer . We deteThe landscape of somatic alterations in breast cancer is complex and heterogeneous. This variety is reflected in the diverse clinical behavior of breast tumors and provides critical insight for the development of rational therapies. Therefore, a method for capturing this complexity which can be readily implemented in a clinical setting is urgently required. We have extensively investigated the potential of the IntClust classification to meet this need, in terms of its reproducibility, association with clinical outcome and representation of copy number-driven cancer genes. We find that IntClust subtypes are observable across studies, are significantly associated with clinical outcome and best capture the repertoire of breast cancer genomic drivers. These data provide a compelling rationale for IntClust as a driver-based molecular taxonomy with considerable potential for clinical application. Indeed, a recent clinical trial (SAFIR01) shows that CNAs are the drivers for which targeted therapies are most frequently identified in breast cancer .TP53, one of the two most frequently mutated genes in breast cancer, is mutated at comparable frequencies across IntClust subtypes in both METABRIC and TCGA [IntClust subtypes were observed across studies at comparable frequencies. This important observation demonstrates that these entities are reproducible and represent true breast tumor subtypes. The discovery study used for identifying the IntClust groups comprised 997 tumors from five centers spanning two continents . This apand TCGA .CCND1, EMSY [PAK1, whereas IntClust 1 tumors harbor 17q23 amplification encompassing RPS6KB1, PPM1D, PTRH2 and APPBP2 [The clinical validity of the IntClust subtypes has here been demonstrated by their association with relapse-free survival and propensity to undergo pCR in studies of neo-adjuvant chemotherapy. An important observation was the recapitulation of survival patterns originally observed in the METABRIC study . This shD1, EMSY  and PAK1cis eQTLs were included for classification based on the strength of association between alteration in copy number and levels of gene expression. Third, clustering retained only those features which contributed to the separation of tumors into distinct subgroups (754 features) [Based on an independent list of recurrent CNAs in breast cancer and using samples compiled from external studies , we founeatures) . This apWe have developed an expression-based method for classification of breast tumors into the IntClust subtypes. We used this method and public datasets of breast tumor transcriptomes to investigate the validity of IntClust. We confirmed that the IntClust subtypes are reproducible entities, demonstrated their association with clinical outcome and found that IntClust best captures expression patterns of breast cancer drivers. Our method is a powerful tool for independent researchers to investigate the significance of IntClusters. Moreover, our findings highlight the potential of IntClust in the era of targeted therapies. Our classifier lays the foundation for the generation of a clinical test to assign tumors to IntClust subtypes.We modified the method for IntClust classification which was originally reported for subtype validation . Probes Several quality statistics were included as part of our method for inspection of results. A goodness of fit, which was a Pearson correlation coefficient, was computed. It represented the correlation between the average  gene expression profile for each cohort and the centroids from the training data set, within each IntClust subtype for those genes where data were available in the external study. In short, the statistic represents a measure of the similarity, in terms of gene expression, of IntClust subtypes from external studies compared with the training data set. We plotted centroids in order to inspect their representation within each subtype in the test dataset - several functions are included in the iC10 package to achieve this. We have made our method freely available for download as an R package under the name 'iC10' at CRAN .et al. [et al. as replicates were removed; and 3) cases identified by the doppelgangR package [et al. with only an additional three cases being removed. For each dataset, the iC10 package was run with expression data only (using probe names for the METABRIC study and gene names for the rest) and normalizing each probe to a z-score . PAM50 classification was conducted accounting for imbalances in ER status, as defined in [We applied this method to breast cancer gene-expression datasets available in public repositories. A large proportion of these studies had previously been compiled and curated by Haibe-Kains et al.  and we det al. . Additio package  as replifined in . SCMGENEERBB2 (IntClust 5) and 8q24 (IntClust 9), which contains the MYC oncogene. In these cases the classifier mostly assigned an IntClust 9 subtype . As a comparison, 10% (28/268) of primary tumors with amplification of ERBB2 also showed co-amplification of MYC in 1,980 samples from the METABRIC study. Cell lines were also classified in IntClust subtypes based on gene expression alone and combined copy number/gene expression and into PAM50 and SCMGENE subtypes based on gene expression alone.In order to classify breast cancer cell lines, we used copy number and gene expression data from two collections of cell lines: Sanger COSMIC database and CCLE. Copy number data from the COSMIC database consisted of segmented copy number calls. The CCLE database provided copy number data on 579 genes  as the summarized log ratio for each gene. Nevertheless, the fit of the IntClust classifier based on copy number was similar for both datasets (0.74 for COSMIC and 0.75 for CCLE). We noted that some cell lines are characterized by copy number amplification of both Associations between subtype and survival were estimated using Cox regression. Of the studies with available time-to-event data, relapse-free survival was available for some and distant metastasis-free survival for others. Our survival time variable comprised relapse-free survival but where this was unavailable distant metastasis-free survival was used.Comparison of univariable hazard ratios associated with IntClust subtype between the METABRIC  and external studies  Figure B was conPerformance of predictive models was assessed as follows: Cox regression models which contained either PAM50 or IntClust as a categorical variable and were adjusted for tumor size , node status (negative versus positive) and histological grade  as continuous variables were fit within the METABRIC study (the largest study) against available time-to-event data . These models were stratified by each of the five centers of the METABRIC consortium. Separate models were fit for ER-positive and ER-negative breast cancer within three time brackets  in order to investigate differences in model performance in short- versus long-term survival and to account for violations of the proportional hazards assumption. The coefficients derived from these models were then applied to external studies with available data. Comparison of model discrimination in this test population was conducted using the method suggested by Newson  using HaAssociations between subtype and pCR were estimated using logistic regression. Logistic regression models comprising either PAM50 or IntClust as categorical variables and adjusted for tumor size (T-stage), positive lymph nodes (N-stage) and histological grade were fit in the largest study of neo-adjuvant chemotherapy . CoefficFor each gene in each list of amplified and deleted genes, we fitted an ANOVA linear model relating the expression of that gene to IntClust groups or the PAM50 groups. We measured the goodness of fit of these two models using the adjusted R-squared - a measure that accounts for differences in degrees of freedom of the two models when the models have been completely pre-specified . We compAnnotated R and Stata code used to generate the reported analyses is provided as Additional file Data from the METABRIC study is deposited in the European Genome-phenome Archive and can be downloaded from . The IDs