Human cancer is caused by the accumulation of genetic alterations in cells. Of special importance are changes that occur early during malignant transformation because they may result in oncogene addiction and thus represent promising targets for therapeutic intervention. We have previously described a computational approach, called Retracing the Evolutionary Steps in Cancer (RESIC), to determine the temporal sequence of genetic alterations during tumorigenesis from cross-sectional genomic data of tumors at their fully transformed stage. Since alterations within a set of genes belonging to a particular signaling pathway may have similar or equivalent effects, we applied a pathway-based systems biology approach to the RESIC methodology. This method was used to determine whether alterations of specific pathways develop early or late during malignant transformation. When applied to primary glioblastoma (GBM) copy number data from The Cancer Genome Atlas (TCGA) project, RESIC identified a temporal order of pathway alterations consistent with the order of events in secondary GBMs. We then further subdivided the samples into the four main GBM subtypes and determined the relative contributions of each subtype to the overall results: we found that the overall ordering applied for the proneural subtype but differed for mesenchymal samples. The temporal sequence of events could not be identified for neural and classical subtypes, possibly due to a limited number of samples. Moreover, for samples of the proneural subtype, we detected two distinct temporal sequences of events: (i) RAS pathway activation was followed by TP53 inactivation and finally PI3K2 activation, and (ii) RAS activation preceded only AKT activation. This extension of the RESIC methodology provides an evolutionary mathematical approach to identify the temporal sequence of pathway changes driving tumorigenesis and may be useful in guiding the understanding of signaling rearrangements in cancer development. Cancer is a deadly disease that develops through the accumulation of genetic changes over time. Many biological models do not incorporate this temporal aspect of tumor formation and progression, in part due to the difficulty of determining the sequence of events through biological experimentation for most cancer types. We previously developed a computational algorithm with which we can quickly and cost-effectively determine the order in which mutations arise in the tumor even when large numbers of mutations are considered. In this paper, we extended our method to incorporate biological knowledge of the common pathways by which cancer progresses. We applied these techniques to primary glioblastoma, the most common form of brain cancer. We found that when all samples are taken into account, a temporal sequence of pathway events emerges; however, different subtypes of glioblastoma vary in their temporal sequence of events. This algorithm can also be easily applied to other cancer types as clinical data becomes available, showing the benefit of computational and mathematical tools in cancer research. Using temporal information, cancer biologists will be able to develop more accurate animal models of tumor formation and learn more about how mutations interact in time, thus leading to better treatments for cancer. New high-throughput sequencing and microarray technologies provide researchers with access to increasingly large and complex datasets comprising genome-level alterations in cancer There is a growing literature of mathematical, statistical and computational approaches to determining the temporal sequence of events arising during tumorigenesis. Previously published methods include the linear model Retracing the Evolutionary Steps In Cancer (RESIC) We have previously described a computational approach, called In many cases, specific genetic alterations are not necessary for malignant transformation; instead, particular oncogenic phenotypes must be achieved through the emergence of any of a number of alternative mutations  [26], , [3Mμ, where M is the number of genes involved in the pathway and μ is the per gene mutation rate. In a similar manner, the TCGA copy number alteration data We considered a single alteration event occurring within any of the genes in the network to be sufficient to result in a change in signaling of that pathway. Since only focal copy number alterations were considered, and since in most cases, alterations within a signaling pathway reside on separate arms of different chromosomes, we considered the first mutational hit on each gene to be independent of all other mutations in the pathway. Thus, a particular pathway alteration had a mutation rate equivalent to We restricted the set of genes related to GBM development to those that were defined in the pathway diagram in N cells at risk of accumulating the genetic changes leading to cancer. These cells proliferate according to the Moran process We used the RESIC methodology Depending on the fitness values of the various combinations of potential mutations, each path through the network of mutations may have a different likelihood of occurring. The Markov process described above can then be used to calculate the transition rates between individual mutational states, and thus the likelihood of each path through the network. This model is then used to describe the dynamics of populations of patients, each harboring a population of cells at risk of accumulating mutations. The scaling from this micro-model (on the level of a population of cells within a single patient) to the macro-model  is obtained by multiplying the transition rates between mutational states within one patient by the number of patients in each mutational state. We considered the dynamics of patients in steady state: there is a constant influx into the unmutated state and an equal constant outflux from the fully mutated state, accounting for diagnosis of the disease (influx) and deaths of patients or their cure (outflux). These parameters are selected to ensure that fitness values are within biologically meaningful ranges, ie. that a single mutation does not confer too large a fitness effect to be biologically implausible. According to this model, patients enter the system in an unmutated state and then accumulate the mutations of interest. This starting point could be late during tumorigenesis, if the mutations of interest are late events, or pre-cancerous if the mutations of interest are initiating events; either way, since the predictions of the model are compared to data of patients after diagnosis of their disease, this starting point in the model is also after diagnosis of the disease. Note that small influx values into other mutational states, representing diagnosis with a different set of alterations, does not significantly alter the results.At steady state, the population is distributed across all possible states; this steady state distribution can then be compared to the numbers of clinical samples with the corresponding genotypes, where the total number of patients in a dataset is equal to the sum of patients in all states. Since all samples analyzed are collected from patients after diagnosis of their disease, the earliest time point identifiable using such datasets is the time of diagnosis. As outlined above, this definition does not imply that the first mutation arose exactly at the time of diagnosis: since the event resulting in patients entering the system is diagnosis, and all considered events are observable in the patient samples, many events arise prior to diagnosis. This mapping is used to optimize a subset of parameters in the Markov process model  by minimizing the difference between the prediction and the observed frequencies in the dataset. Other parameters, such as cellular population size, mutation rate and influx rate, are estimated from experimental results Similarly, Beerenwinkel et al. found that the selective advantage of mutations has the largest effect on the evolutionary dynamics of tumorigenesis −7 per gene in the pathway per cell division and the stem cell population size per niche was set as N = 100. This choice of value for N was made for computational speed; we had previously found that an order of magnitude variation in this parameter value results in robust results We applied the RESIC methodology We applied the RESIC methodology to the 145 TCGA patient samples for which CNA and point mutation data was available. We analyzed two sets of data: the dataset including CNA only, and the dataset containing CNA and point mutation date. We analyzed both datasets through our pathway-based approach as well as the gene-based approach using pairwise and three-way analyses. We compared the resulting temporal orderings, considering the order to be consistent between the two data types when the percent flux through the mutational diagram varied by no more than three and the dominant temporal sequence remained dominant. We considered an ordering to be weakened if either fewer alleles of a gene could be analyzed, the percent flux decreased by more than three, or a pair or triplet of events could no longer be analyzed when using CNA data only. We considered an ordering to be inconsistent if the dominant temporal order changed between the two analyses, or if an ordering could be determined when using CNA data only, but not when using CNA and point mutation data.In pair-wise analyses, the temporal order of all alterations can be determined simply by combining the pairs of genetic or pathway alterations in the predicted order. For example, if mutation A arises before mutation B, and mutation C arises before mutation A, the order of events is C→A→B. Figure S1Pairwise comparison of three genes results in an ordering of all three genes. We perform pairwise comparisons between alterations to determine the overall order of events. Here we depict all possible combinations of two pairwise orderings that can result in a single linear order of events. We denote the three events A (in red), B (in blue), and C (in green). Black arrows denote the determined orders and gray arrows denote the set of pairwise orderings that result in a three-event order.(EPS)Click here for additional data file.Text S1RESIC analysis results for all GBM samples. The results from the pairwise, three-way, and pathway-based RESIC analyses are displayed.(TXT)Click here for additional data file.Text S2RESIC analysis results for GBM samples belonging to the proneural subtype. The results from the pairwise, three-way, and pathway-based RESIC analyses are shown.(TXT)Click here for additional data file.Text S3RESIC analysis results for GBM samples belonging to the mesenchymal subtype. The results from the pairwise, three-way, and pathway-based RESIC analyses are displayed.(TXT)Click here for additional data file.Text S4RESIC analysis results for GBM samples belonging to the neural subtype. The results from the pairwise, three-way, and pathway-based RESIC analyses are shown.(TXT)Click here for additional data file.Text S5RESIC analysis results for GBM samples belonging to the classical subtype. The results from the pairwise, three-way, and pathway-based RESIC analyses are shown.(TXT)Click here for additional data file.


Glioblastoma Multiforme (GBM) is a tumor with high mortality and no known cure. The dramatic molecular and clinical heterogeneity seen in this tumor has led to attempts to define genetically similar subgroups of GBM with the hope of developing tumor specific therapies targeted to the unique biology within each of these subgroups. Recently, a subset of relatively favorable prognosis GBMs has been identified. These glioma CpG island methylator phenotype, or G-CIMP tumors, have distinct genomic copy number aberrations, DNA methylation patterns, and (mRNA) expression profiles compared to other GBMs. While the standard method for identifying G-CIMP tumors is based on genome-wide DNA methylation data, such data is often not available compared to the more widely available gene expression data. In this study, we have developed and evaluated a method to predict the G-CIMP status of GBM samples based solely on gene expression data. Glioblastoma Multiforme (GBM) is a deadly brain tumor with few effective therapies. Identification of the underlying pathogenic mechanisms involved in the initiation and progression of this tumor is critical for developing more effective treatments. Recent studies have demonstrated the profound genetic and molecular heterogeneity of GBMs. This molecular heterogeneity complicates the identification of the core elements within the cellular signaling network of any given GBM thereby limiting out ability to offer targeted therapies for a specific tumor.Recent developments in genomic technology  have enabled a large number of GBMs to be genetically characterized at unprecedented levels of detail. Although such studies have revealed the heterogeneity between GBMs, they have also allowed the identification of subgroups of tumors that are more closely related than others. A number of recent studies using supervised and unsupervised analyses have been published stratifying GBMs into similar subgroups based on mRNA expression profiles Our understanding of the epigenome has improved substantially in the last decade. In particular, epigenetic biomarkers such as DNA methylation and their effects on tumor biology have been analyzed in a number of GBM studies The ability to acquire whole genome-wide DNA methylation data has opened up the possibility for its use as an alternative classification methodology, as demonstrated recently in GBM Secondary to the fact that the G-CIMP status of GBMs identifies a group of patients with genetically distinct tumors with very different clinical outcomes, there is an increasing need to identify such tumors clinically. Unfortunately few clinical or even research laboratories generate whole genome methylation data on tumor specimens making the identification of G-CIMP tumors impossible. By contrast, an increasingly larger number of laboratories are generating mRNA expression data from their clinical tumor specimens. In this study, we demonstrate how a computational algorithm we have devised allows one to identify the G-CIMP phenotype of any given GBM at nearly 100% accuracy using only mRNA expression data.http://cancergenome.nih.gov/) and REMBRANDT (http://caintegrator-info.nci.nih.gov/rembrandt) data repositories as our primary source of samples. For methylation-based validation, we used twenty-one GBMs and one non-tumor brain sample. Twenty-one GBM samples were obtained from the prospective NCI-sponsored Glioma Molecular Diagnostic Initiative (GMDI), which were provided as snap frozen sections. Pathological diagnosis of these samples was determined by the local institutional neuropathologist and centrally reviewed by two NIH neuropathologists who were blinded to the original diagnosis. Only tumors that met the criteria of having a consensus pathological diagnosis from the NIH neuropathologists were utilized for our analyses. The one non-tumor sample was obtained from a medically indicated therapeutic temporal lobe resection from an NIH patient with refractory epilepsy.We used public TCGA (We isolated DNA from cell pellets and fresh frozen tumor tissue using the QIAmp DNA micro kit (Qiagen). One microgram of the DNA was bisulfite converted and processed on Human Methylation450 BeadChips (Illumina) using the Infinium HD Methylation Assay as described previously We used level-2 methylation data from Illumina Infinium 27 k and 450 k platforms. For the 27 k methylation data we filtered 23,487/27,578 sites with no missing values on non-sex chromosomes. Similarly there were 486,412 methylation sites with no missing values retained in 450 k methylation data. We found 22,270 of these methylation sites to be shared between 27 k and 450 k platforms.Methylation data sets from both platforms based on common methylation sites were then filtered to 1509 sites which had a standard deviation of 0.2 or greater as in We imported 413 Affymetrix U133A samples (raw data) using RMA from the TCGA portal . There were 403 GBM and 10 normal brain samples in this data set. We performed unsupervised clustering based on 1670 probe sets (std. dev.>1) to check if any GBM samples cluster with normal samples due to possible normal tissue contamination. Three samples clustered with normal brain samples and were excluded from data set .We used gene expression data for 201 GBM and 31 normal brain samples in the NOB database. Most of these samples have been publicized in REMBRANDT public repository previously Raw methylation data imported using GenomeStudio Software (include ref version). Methylation sites, which have detection p-value less than 0.05 for all samples, have been retained.We used a mixed model ANOVA based algorithm by Partek 6.6beta ) to remove the batch effects between TCGA and NOB gene expression data sets. To measure the efficiency of batch effect removal, we used gene expression data for normal brain samples. We assumed that normal brain samples in both data sets would cluster together in a combined data set if the batch effect removal were successful.There were two variables within the ANOVA model that were used for batch removal. These variables are sample origin (TCGA or NOB) and tissue origin . We first showed that normal brain samples from two different data sources cluster together after batch effect removal; from this we concluded that the batch effect removal was successful. We then excluded non-tumor samples from both data sets and applied batch effect removal on sample origin to obtain final data set.We used Partek Software  to perform HC and PCA. Average linkage and Euclidean distance were used for all HCs. Expression data was standardized by sample columns prior to performing the HC, although this was not done for the methylation data. PCAs were performed using correlation dispersion matrix and normalized eigenvector scaling.We generated five prediction models, which differ only by the use of the top number of differentially expressed probe sets . All models used nearest neighbor algorithm, which classifies unknown samples based on the closest known sample. Euclidean distance was used as the distance measure. We used the Partek implementation of this algorithm. Probe sets included in these five models are provided as supplementary files , S4, S5.We obtained TCGA methylation data for 368 GBM samples and performed unsupervised clustering to identify G-CIMP status of these samples as described in https://wiki.nci.nih.gov/display/TCGAM/DatasetsGBM). 365 of the 368 samples we analyzed had an existing G-CIMP classification in the TCGA package. Out of the 365 samples, only four samples were found to have contradicting labels, i.e. they are labeled as G-CIMP positive in TCGA and as G-CIMP negative in our list (We labeled the larger cluster (338 samples) as G-CIMP negative and the smaller cluster (30 samples) as G-CIMP positive . TCGA reour list . Accordiour list . Thus, wThere are many samples in the TCGA repository where mRNA expression data is available but more comprehensive genome-wide methylation data (27 k or 450 k) is not available. We hypothesized that the G-CIMP calls of these GBM samples could be predicted based on gene expression data. To test this hypothesis, we used the gene expression data set for samples with available G-CIMP calls (based on methylation) as the training data. After training five prediction models, we predicted the G-CIMP status of the subset of TCGA samples that had available expression data but not methylation data. The difference between these five models was the use of either the top 10, 25, 50, 100 or 200 differentially expressed probe sets between the G-CIMP positive and negative groups, respectively.We obtained expression profiles of 403 GBMs and ten normal brain samples using the Affymetrix U133A platform from the TCGA data repository. We performed a principal component analysis and observed that three of the GBM samples cluster with normal brain samples . These stwo-fold cross validation. Briefly, we divided the sample set with known G-CIMP status into two groups and labeled one of these groups as the training set and the other group as the test set. We developed a prediction model based on the expression data of the training set and applied this model on the test set (see The G-CIMP status of 218 out of 400 GBM samples was available from methylation-based classifications (last section). Before predicting the G-CIMP status of the remaining samples, we used samples with available G-CIMP data to measure the prediction performance on expression data by applying a method called  set see . Then, w set see .After validating the performance of our prediction algorithm, we used it to predict the G-CIMP status of the subset of TCGA samples that did not have methylation data. Following our prediction, there were 30 G-CIMP positive and 370 G-CIMP negative samples in the TCGA expression data set.contamination filtering (see www.affymetrix.com). We removed this batch effect by using an ANOVA based batch effect removal algorithm  using the top 10, 25, 50, 100, and 200 differentially expressed probe sets between G-CIMP positive and negative samples. We repeated the two-fold cross-validation within the TCGA expression data set. The two-fold cross-validation in the previous section would not suffice since (i) earlier cross-validation was within only 218 GBM samples, and (ii) the gene expression data set had been manipulated substantially with the batch effect removal. The cross-validation results achieved 99.75% accuracy for all models.We applied all five models on NOB samples to predict their G-CIMP status. Five models agreed on 10 samples as G-CIMP positive and 159 samples as G-CIMP negative. There was no consensus for the remaining five samples, which were labeled as Non-Consistent (NC).It has been previously demonstrated that there are significant age and survival differences between patients with G-CIMP positive and G-CIMP negative GBMs The calculated median survival for TCGA G-CIMP positive GBMs in our analysis was 840.5 days whereas the median survival for TCGA G-CIMP negative GBMs was 327 days . Consistent with these data, the calculated median survival for the predicted G-CIMP positive NOB samples was 1121 days whereas median survival for predicted G-CIMP negative NOB samples was 470 days  . These rWe used the Illumina Human Methylation 450 K platform to generate methylation data on a subset of the NOB samples to validate our predictions. We selected nine G-CIMP positive samples, seven G-CIMP negative samples, all five NC samples, and one normal brain sample for the methylation assays see .http://epigenome.usc.edu). In this combined data set, 1438 out of 1509 methylation sites passed through our detection filter.We combined our methylation data set with the TCGA methylation data set methylation data from four normal brain samples provided by USC Epigenome Center . We observed that normal samples clustered tightly in the combined data set suggesting that the batch effect between data sets was minimal . After dWe noticed that the mis-predicted sample and a majority of the NC samples fell between the two G-CIMP methylation clusters . In the Of interest, we observed many NCs when we tried to classify non-tumor samples into G-CIMP subtypes. Thus, it appears that our classification system has difficulty in characterizing non-GBM pathology. This probably reflects the fact that G-CIMP biology is unique to GBMs and cannot be applied to other tumor types and/or normal tissue.In order to evaluate the methylation status and perform functional enrichment analysis for the genes in our prediction models we first analyzed the genes in prediction models in terms of methylation difference between G-CIMP positive and G-CIMP negative subtypes. Next, we analyzed the genes in the prediction models in terms of methylation-expression correlation. Finally, we uploaded the genes in our prediction models to Ingenuity Pathway Analysis (IPA) to inquire whether these genes show enrichment for specific biological processes.top10. If it is not in the first prediction model but in the second prediction model, which has twenty-five probe sets, it is in the second category, which is denoted by top11–25. Similarly, we created three more categories denoted by top26–50, top51–100 and top101–200. If a gene is not included in any of the five prediction models we used, it is assigned to the last category, which is denoted by not-in-lists.Our prediction models used probe sets, which represent mRNA expression. By contrast, methylation data is based on the methylation ratio of a site on a given gene. We combined these two data types at the gene level. We converted the probe sets to gene names and generated six categories based on the existence of genes in our prediction models as follows. If a gene is included in our first prediction model with ten probe sets, it is assigned in the first category, which is denoted by After defining the categories, we compared the methylation status for the genes in each category. First, we calculated the median methylation for each methylation site, for G-CIMP positive group and G-CIMP negative group. Accordingly, we used the Beta-values in TCGA methylation data set we had created . Then, we measured the absolute median methylation difference by subtracting these numbers and taking the absolute value. Next, we compared the genes in six categories in terms of absolute median methylation difference. We observed that genes we used in prediction models indicated higher methylation difference compared to genes that are not included .We next compared the methylation-expression correlations among the six categories by computing the methylation-expression Pearson correlation for each methylation site using Beta-values and TCGA Level-3 expression data (Agilent Platform). When we compare the methylation-expression correlations for the genes in the six categories, we observed a strong negative correlation for the genes in our prediction models . These rWe next used IPA to assess the functional enrichment for the genes in our prediction models. We identified the genes that are included in the prediction model with 200 probe sets and uploaded these genes to IPA along with the gene names and expression fold changes between G-CIMP positive and G-CIMP negative samples using TCGA expression data set. IPA reported only one significant function in its functional enrichment module, that being an increase in ‘proliferation of cells’ associated genes in G-CIMP negative GBMs. This result is consistent with the poor survival rate of G-CIMP negative GBM patients. In the transcription factor activity module, IPA identified KDM5B (lysine (K)-specific demethylase 5B) as the most significantly altered transcription factor. According to IPA report, KDM5B is activated in G-CIMP positive samples. It is interesting to observe a demethylase with increased activity in G-CIMP subtype considering the altered methylation profile in this subtype Although the literature is replete with studies finding correlations between patient clinical outcome  and specific genotypes , the majority of such studies have treated GBM as a homogeneous disease. This approach might have led to false interpretations if different genomic subtypes of GBM have different clinical outcomes. For instance, it is possible that some of these “survival-specific” genes in GBM could be enriched for in G-CIMP tumors. Similarly, a disproportionate number of patients with G-CIMP positive or negative tumors could influence the outcome of a clinical trial and be falsely interpreted as a therapeutic effect.To measure the effect of G-CIMP status on survival, we ran a Cox-regression for all genes with and without the G-CIMP samples. We found 200 and 3 survival-correlated probe sets in TCGA (n = 391) and the NOB data (n = 134), respectively with a FDR<0.05 (Benjamini-Hochberg step-up multiple test correction Using less stringent criteria (p<0.05) to measure the G-CIMP effect on survival, we found by a Cox-regression analysis 3258 probe sets related to survival for all samples with survival information (n = 391) in the TCGA data set. By contrast, there were only 986 probe sets related to survival when G-CIMP positive samples were excluded . Similarly, using NOB data we found 2377 probe sets related to survival for all samples (n = 134) but only 1142 probe sets for G-CIMP negative samples .These results show that more than 84% of the survival-related probes sets in the initial TCGA dataset are changed when G-CIMP positive samples are removed (60% in the NOB dataset). Moreover the number of survival-related probe sets identified is reduced by 70% after removing G-CIMP samples in the TCGA data set and by 52% in the NOB data set. Although reducing the sample size will lower the statistical power to identify survival related probe sets, the less than 8% reduction in sample size is unlikely to be responsible for the large decrease in survival related probe sets when we look only at G-CIMP negative tumors. In order to roughly measure the affect of sample size on the number of survival related probe sets, we randomly deleted ∼10% (36/361) of G-CIMP negative samples and repeated the analyses. In the new data set, 39% of probe sets have been changed (381 out of 986 probe sets) and the number of probe sets has been reduced by only 10% (986 to 881 probe sets).In this study, we have established a method to classify GBM samples into G-CIMP subtypes based on gene expression data. As expected, we have observed a high concordance between the TCGA G-CIMP groups and predicted NOB G-CIMP groups in terms of age and survival. We have also validated the methylation differences between NOB G-CIMP positive and negative samples by performing in vitro methylation assays. These results demonstrate that the G-CIMP subtypes are distinct and can be reproducibly validated on a totally independent data set. An alternative approach to identify G-CIMP positive GBMs would be to check for IDH1 mutations. However, a small but significant subset of G-CIMP positive GBMs 5/23 in  do not cWe used the TCGA Affymetrix U133A data set as training data because the NOB expression data was based on the Affymetrix U133PLUS2.0 platform which uses a similar technology to U133A. TCGA has expression data on multiple platforms such as the Agilent 244 K G4502A and Affymetrix Human Exon 1.0 platforms. Investigators interested in adopting our method for G-CIMP classification and have expression data from any of the array platforms used by TCGA can simply apply our methods by using the corresponding TCGA data set as training data.Since multidimensional clustering algorithms are able to produce subclasses even on random data, it is important to validate clusters on an external dataset. There are well-known differences between microarray data produced in different settings  large enough, then sampling the same population allows one to assume they represent the same entity. In this case, we can apply batch effect removal algorithms that scale the data sets and make them statistically comparable. However, if data sets are not large enough or they are not derived from identical populations, then the representation assumption fails and the results will be flawed. In this instance, a GBM data set may not be combined with a general brain tumor data set although one population is the subset of the other. Similarly we cannot combine two GBM data sets that contain tumors from patients with disproportionate distributions of age or sex unless we show that there is no molecular difference between these different clinical groups.Batch effects can be removed via two general mechanisms. One of these involves running identical specimens  on both data sets. Both data sets could then be scaled to make these identical specimens equivalent on the combined data set. Another method involves combining data sets based on a representation assumption. If two data sets are In this vein, the establishment of gene classifiers is also based on the assumption that the original data on which the classifiers were developed and the new data on which the classifiers are to be used, represent the same entity. If data sets are not large enough or they represent different populations this operation would be flawed and might lead to false interpretations. For example, if a gene signature, which separates different subsets of GBM is applied to a general brain tumor data set, results are likely to be unreliable. We saw this in our analysis where our G-CIMP classifiers gave us indeterminate results when we used them to attempt to classify non-GBM gliomas and normal brain tissue.One issue that would potentially negatively affect the accuracy of our analysis concerns the origin of the tumor samples. The TCGA samples are generally selected from untreated primary GBMs while the NOB tumor samples are mostly previously treated and recurrent GBMs. Interestingly, our analyses demonstrate that this difference did not affect our ability to identify the G-CIMP subtypes. In fact, the similarity between TCGA and NOB data in terms of age and survival profiles of the derived G-CIMP positive and G-CIMP negative groups were excellent.Unlike the situation with gene expression data, we did not observe any separation or batch effect between the two un-normalized methylation data sets but rather found that the non-tumor samples from both data sets clustered tightly. This positive outcome suggests that Infinium methylation data sets from different labs are comparable without data manipulation. This is a major advantage that will allow increasing statistical power for future analyses by creating larger data sets by combining multiple smaller data sets.Identifying and assigning patient-specific tumors to stable genetic sub-types in heterogeneous diseases like GBM will be an important step towards personalized medicine. Alternative therapeutic regimes can be developed for different subtypes, and should target subtype specific vulnerabilities. Establishing the stability and reproducibility  of these subtypes is a critical step in achieving this goal. Our study demonstrates that G-CIMP methylation subtypes are stable enough to be independently validated through gene expression array data despite strong batch effects. Moreover we demonstrate that the prognostic significance of the G-CIMP subtype is very consistent, even when evaluated across two totally independent large cohorts of patients. This result suggests that identification of the G-CIMP will have significant clinical relevance for clinical trial design stratification, patient prognosis and potentially treatment in the future.Figure S13 TCGA GBM samples are clustered with normal brain samples and filtered out from TCGA expression data set.(DOC)Click here for additional data file.Figure S227 NOB GBM samples are clustered with normal brain samples and filtered out from NOB expression data set.(DOC)Click here for additional data file.Figure S3Davies-Boudin clustering performance score for clustering on TCGA methylation data for various number of clusters. Clustering separation is better when this score is lower.(DOC)Click here for additional data file.Figure S41438 methylation sites. Four samples show disagreement between TCGA and our G-CIMP calls.(DOC)Click here for additional data file.Figure S5PCA plot of TCGA GBM and normal brain samples based on gene expression data.(DOC)Click here for additional data file.Figure S6 PCA plots of GBM and normal brain samples in TCGA and NOB gene expression data sets before batch effect removal. 506 probe sets with largest variation are shown (C) Sample histogram of GBM and normal brain samples in TCGA and NOB gene expression data sets. We have used common 22,277 probe sets between Affymetrix U133 Plus 2.0 and Affymetrix U133A platforms.(DOC)Click here for additional data file.Figure S7Hierarchical clustering of GBM and normal brain samples in TCGA and NOB gene expression data sets before batch effect removal. We used high variation 506 probe sets of combined data set for hierarchical clustering.(DOC)Click here for additional data file.Figure S8 PCA plots of GBM and normal brain samples in TCGA and NOB gene expression data sets after batch effect removal. 742 probe sets with largest variation are shown (C) Sample histogram of GBM and normal brain samples in TCGA and NOB gene expression data sets. We have used common 22,277 probe sets between Affymetrix U133 Plus 2.0 and Affymetrix U133A platforms.(DOC)Click here for additional data file.Figure S9Methylation sites in different categories are represented with respect to absolute median methylation difference between G-CIMP positive and G-CIMP negative subtypes.(DOC)Click here for additional data file.Figure S10Methylation sites in different categories are represented with respect to methylation-expression correlations (Pearson).(DOC)Click here for additional data file.Table S1Prediction models with 10 probe sets.(DOCX)Click here for additional data file.Table S2Prediction models with 25 probe sets.(DOCX)Click here for additional data file.Table S3Prediction models with 50 probe sets.(DOCX)Click here for additional data file.Table S4Prediction models with 100 probe sets.(DOCX)Click here for additional data file.Table S5Prediction models with 200 probe sets.(DOCX)Click here for additional data file.Table S6Prediction accuracy of the G-CIMP prediction method based on two-fold cross validation on the samples with known G-CIMP calls.(DOCX)Click here for additional data file.


In previous geographical genomics studies of the impact of lifestyle on gene expression inferred from microarray analysis of peripheral blood samples, we described the complex influences of culture, ethnicity, and gender in Morocco, and of pregnancy in Brisbane. Here we describe the use of nanofluidic Fluidigm quantitative RT-PCR arrays targeted at a set of 96 transcripts that are broadly informative of the major axes of immune gene expression, to explore the population structure of transcription in Fiji. As in Morocco, major differences are seen between the peripheral blood transcriptomes of rural villagers and residents of the capital city, Suva. The effect is much greater in Indian villages than in Melanesian highlanders and appears to be similar with respect to the nature of at least two axes of variation. Gender differences are much smaller than ethnicity or lifestyle effects. Body mass index is shown to associate with one of the axes as it does in Atlanta and Brisbane, establishing a link between the epidemiological transition of human metabolic disease, and gene expression profiles. Globally, a major shift in public health burden known as the epidemiological transition has seen a shift from infectious disease to a Western profile of chronic metabolic diseases in most developing countries over the past three decades . The remainder live in rural villages around the coastal plains, in the mountainous central regions, or on small islands. We have not included genotypic profiling in this study, but note that here is no reason to expect genetic divergence between urban and rural populations of either ethnicity, since the former are drawn essentially at random from the large pool of diversity present in Melanesians or the Indian settlers.The Fiji Islands lie in the Melanesian region of the South Pacific Ocean . Under informed consent, approximately 10 ml peripheral blood samples were collected into LeukoLOCK filters, which size fractionate red blood cells and platelets, and retain leukocytes that are then immediately preserved in RNAlater solution  which is the traditional Fijian drink grown by these villagers. The main source of water is tanks and springs, and Koro, Nasavu, and Nauria lack electricity with a few houses in each village having diesel generators for night use.Seventy-three samples were obtained from the rural Fijian villages of Naga, Koro, Nasavu, and Nauria which are are located in the highland district on the north side of Viti Levu Island. Naga and Koro, in the Nadarivatu region, are respectively 92 and 100 km from Tavua township and have total populations of 150 and 143 adults and children. Nasavu and Nauria are 65 and 80 km from the township of Rakiraki and have populations of 98 and 150 respectively. All four villages are mainly self sustaining with home grown fruits, vegetables, and root crops. The men are farmers who plant root crops and “kava”  and Indian (n = 26) Fijians residing in the capital city of Suva. All men and the majority of the women work in industries in the main city. The urban population generally consumes processed foods and purchase their fruit and vegetables from city markets.Fifty-four urban samples were a mixture of Melanesian . RNA quantity was checked using a GE NanoVue Spectrophotometer. The quality of the total RNA was determined using an RNA 6000 Nano LabChip kit in conjunction with an Agilent 2100 Bioanalyzer which contrasts the ratios of the mRNA and the 18S and 28S ribosomal RNA peaks as a measure of RNA degradation or DNA contamination. Samples that had RIN (RNA Integrity Number) greater than three were retained for potential expression profiling, though many of these were subsequently removed. Since repeated measures of RIN are often off by as much as two “points,” we classified the samples into RIN categories – BAD samples  at Expression Analysis  using RNA extracted from the LeukoLock filters according to manufacturer’s recommendations. Raw probe-level data, namely average bead intensity for each probe from the Illumina Bead Studio, was log2 transformed, and 14,343 probes that we routinely find expressed in peripheral blood  were retained for analysis, with expression of other transcripts deemed statistically indistinguishable from background.Quantitative RT-PCR was performed using Fluidigm 96 × 96 nanofluidic arrays . Variance component analysis was performed on the first five principal components to evaluate the contributions of gender, lifestyle, and ethnicity on transcript abundance. Analysis of variance was performed at the probe-level, testing for influences of Lifestyle , Ethnicity (Melanesian versus Indian), or the combined Type . Standard linear regression on age and BMI was also performed.Axes of variation  were extracted in clusters of up to 12 transcripts representing each of seven axes, by generating the first principal component of the correlation of the standardized probe abundance (or Ct count) for each axis, with imputation of these PC1 scores if any of the transcript measures were missing data in the Fluidigm experiments. The derivation of blood informative axes of variation is described in detail in . Briefly, Chaussabel et al.  identifiAnalysis of variance was then used to contrast the distribution of the axes scores according to Lifestyle and Ethnicity, or linear regression was performed to test the association with anthropomorphic measures . Raw and transformed data for all experiments can be obtained from the authors’ website as Table S1 in Supplementary Material, and experimental design files are in Table S2 in Supplementary Material. Axes comparison for BMI was done with the axes derived from transcriptome data of peripheral blood samples from our CHDWB  study in Atlanta  were analyzed for 14,343 probes. The correlation heat map Figure . This suIn order to extend this analysis to a larger sample including rural Indians, we next performed targeted gene expression profiling of 96 transcripts. Recent work in our lab contrasting multiple cohorts indicates that at least nine major axes of gene expression variation account for in excess of half the variance of all peripheral blood transcripts measured on microarrays. These axes are thought to reflect particular aspects of blood function from innate immune activation to inflammation and anti-viral response, and can be captured as the first principal component of just 10 transcripts that are most strongly associated with the axes . We reasoned that coregulation of these BITs might also be detectable with qRT-PCR arrays, and here tested the first seven axes using two overlapping sets of BITs listed in Table S3 in Supplementary Material. Additional probes were included representing cell surface markers and intracellular inflammatory signaling pathways.p < 0.05, as random permutation of labels with respect to expression profiles established that no more than 3% of the variation is explained by chance at this level.Before describing the Axis associations, we first present the variance components analysis of the first five principal components of gene expression measured on the Fluidigm arrays. In experiment 1, there were 106 individuals with high quality profiles, and 94 probes with signal above background in most individuals. PC1 through PC5 explain 61.7% of the variance in Ct counts, and Lifestyle  explained 16.8% of this variation while Ethnicity (Melanesian versus Indian) just 9.3%  or inflammation/neutrophil signaling , implying a generally lower immune response. The comparisons involving rural Fijians and the urban population groups . Based on recent analyses of several large microarray datasets, we chose two sets of approximately 70 PCR amplicons that represent seven major axes of variation in the form of BIT, and document how these BIT repeatedly capture differences among population groups on the islands of Fiji.The correlation between measures of gene expression using the targeted Fluidigm arrays and whole transcriptome microarrays in this study was somewhat less than that observed in pilot analyses of our Atlanta cohort. Three reasons are likely to have contributed: the small sample size of just 41 individuals leads to imprecise estimates with both technologies; the Fiji data was affected by relatively low RNA quality (RIN < 7) for half of the samples ; and the BIT probes have not been optimized for capturing the same information as the bead arrays, so some may perform less well or capture different splice forms.In fact, the correspondence of alternate probes for the same gene on microarrays is often as divergent as the difference between the less strongly correlated Fluidigm and Illumina probes reported here (unpublished observations). For this reason, we repeated the analyses with a second set of probes that resulted from refinement of the definition of the Axes after completing the first experiment, and found both the results to be in agreement with each other.The major biological finding of this study is that there is a highly significant impact of lifestyle and ethnicity on gene expression in the peripheral blood of Fijians. Specifically, rural Indian villagers on the island of Vanua Levu are markedly distinct from their urban counterparts in Suva. The rural highland Melanesians only showed modest differentiation from their urban counterparts, who are not distinguishable from the urban Indians with regards their gene expression. These results recapitulate our findings in southern Morocco  have not detected any correlation between Axis 2 score and erythrocyte (or platelet) numbers, and genes that are known to be enriched in these cell types do not predict the Axis 2 score, so it is not simply a molecular reporter of red blood cell number. It seems less likely that genetic variation producing this Axis causes variation in BMI, than that elevated body fat influences gene expression in the axis. Nevertheless, people with similar body weights also differ with respect to their position along the axis, as can be seen in Figure The other axes that distinguish rural Indians, Axes 5 and 6, appear to be related to inflammation/innate immune responsiveness and development of immunity respectively. Axis 5 is for example heavily enriched for genes annotated to TLR signaling. A similar study (Freedman et al., The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.http://www.frontiersin.org/Applied_Genetic_Epidemiology/10.3389/fgene.2012.00243/abstractThe Supplementary Material for this article can be found online at Supplementary Table S1Raw and transformed illumina and fluidigm data.Click here for additional data file.Supplementary Table S2Experimental design files.Click here for additional data file.Supplementary Table S3List of probes used to measure axes of variation.Click here for additional data file.


When using cDNA microarrays, normalization to correct labeling bias is a common preliminary step before further data analysis is applied, its objective being to reduce the variation between arrays. To date, assessment of the effectiveness of normalization has mainly been confined to the ability to detect differentially expressed genes. Since a major use of microarrays is the expression-based phenotype classification, it is important to evaluate microarray normalization procedures relative to classification. Using a model-based approach, we model the systemic-error process to generate synthetic gene-expression values with known ground truth. These synthetic expression values are subjected to typical normalization methods and passed through a set of classification rules, the objective being to carry out a systematic study of the effect of normalization on classification. Three normalization methods are considered: offset, linear regression, and Lowess regression. Seven classification rules are considered: 3-nearest neighbor, linear support vector machine, linear discriminant analysis, regular histogram, Gaussian kernel, perceptron, and multiple perceptron with majority voting. The results of the first three are presented in the paper, with the full results being given on a complementary website. The conclusion from the different experiment models considered in the study is that normalization can have a significant benefit for classification under difficult experimental conditions, with linear and Lowess regression slightly outperforming the offset method.


ZBTB32 emerges as the most consistent and differentially expressed gene in ABC-DLBCL while other transcription factors such as ARID3A, BATF, and TCF4 are also amongst the 24 genes associated with this class in all datasets. Analysis of enrichment of 12323 gene signatures against meta-profiles and all data sets individually confirms consistent associations with signatures of molecular pathways, chromosomal cytobands, and transcription factor binding sites. We provide DAC as an open access Windows application, and the accompanying meta-analyses as a resource.Cell of origin classification of diffuse large B-cell lymphoma (DLBCL) identifies subsets with biological and clinical significance. Despite the established nature of the classification existing studies display variability in classifier implementation, and a comparative analysis across multiple data sets is lacking. Here we describe the validation of a cell of origin classifier for DLBCL, based on balanced voting between 4 machine-learning tools: the DLBCL automatic classifier (DAC). This shows superior survival separation for assigned Activated B-cell (ABC) and Germinal Center B-cell (GCB) DLBCL classes relative to a range of other classifiers. DAC is effective on data derived from multiple microarray platforms and formalin fixed paraffin embedded samples and is parsimonious, using 20 classifier genes. We use DAC to perform a comparative analysis of gene expression in 10 data sets (2030 cases). We generate ranked meta-profiles of genes showing consistent class-association using ≥6 data sets as a cut-off: ABC (414 genes) and GCB (415 genes). The transcription factor  Diffuse large B-cell lymphomas (DLBCL), the commonest human lymphoma type, can be separated into distinct categories based on gene expression signature, and relationship to normal stages of B-cell differentiation nd stage using 100 genes to differentiate Burkitt Lymphoma from each subgroup of DLBCL), and the application to patients treated with chemotherapy regimes supplemented by anti-CD20 monoclonal antibody therapy by Lenz et al. Since the inception of the cell of origin classification In routine practice most diagnostic material is formalin-fixed and paraffin embedded (FFPE), which may not yield comparable gene expression data to that obtained from fresh material. While approaches have been developed to circumvent this issue, immunohistochemical surrogates fail to recapitulate the success of the gene expression based classifier on a consistent basis The segregation of DLBCL into cell of origin classes has led to profound insight into disease biology Here we describe a systematic analysis of tools for implementation of the cell of origin classification of DLBCL. We establish a platform robust classifier based on balanced voting between four machine-learning tools. This is effective on FFPE material, and provides improved survival separation for ABC and GCB classes for the majority of data sets analyzed. We make this tool available as open source software. The development of this tool allows the first comparative analysis of gene expression across 10 DLBCL data sets encompassing 2030 cases uniformly classified with the same implementation. We define meta-profiles of genes consistently associated with ABC- and GCB class, assess consistent molecular signature enrichments and provide these data as a resource.http://llmpp.nih.gov/DLBCLpredictor/) A formalin-fixed paraffin embedded (FFPE) data set was produced by the Haematological Malignancy Diagnostic Service  and details of preparation, epidemiology and outcome data are described in detail elsewhere Each dataset was quantile normalized using the R Limma package MedianMerge), and (2) the approach used by Monti et al. - taking the average value for probes with a Pearson correlation >0.2, while taking the maximum value for those with a correlation < = 0.2 (MaxAvgMerge) For all data sets the probes for each of the classifier genes were merged using 2 methods: (1) the median value across the probes for each gene  were trained using the Weka package (Weka version 3.6.5) The Wright et al. data set consists of expression data for 240 patients annotated as one of three classes: ABC , GCB  or Type-III . The trained Weka classifiers output predictions for each sample analyzed consisting of p-values for each of the 3 classes, the class with the largest p-value giving the predicted class. The LPS classifiers assign everything with a confidence greater than a p-value threshold (0.8/0.9) to ABC/GCB and everything below the threshold to Type-III/Unclassified. The predicted classes were used to rank the classifiers (see Classifier Ranking).The 6 best individual Weka classifiers  were combined using the Weka Vote scheme with the average of probabilities combination rule. Each classifier was removed in turn to generate all possible 5-tool classifiers (5N). This process was iterated with the best meta-classifier at each N progressing to the next level . As with the individual classifiers these were analyzed by comparing the survival of their ABC/GCB assigned cases.The data sets GSE10846 (split into CHOP/R-CHOP), GSE4475 and GSE19246 were used to generate additional lists of classifier genes from 3 sources using the published classifications : (1) the 20 Wright genes were ranked using the Weka CfsSubsetEval method (search method: GreedyStepwise). (2) the 185 genes from Dave et al. present on the 3 platforms were ranked using CfsSubsetEval (search method: GreedyStepwise) Using the classes assigned by the best classifier (LMT_J48_RF100_SMO) a linear model was fitted to the gene expression data using the R Limma package Enrichment of genes against gene-lists was assessed using a hypergeometric test, where the draw is the significantly differentially expressed genes, the successes are the signature genes and the population is the genes present on the platform. To avoid any bias the genes used for training the machine-learning tool were removed from the signatures before assessment. For each assessment Z-scores were generated by comparing against random distributions.7 samples). The Z-scores were then averaged between the two directions of analysis (data set 1 vs data set 2 and data set 2 vs data set 1) and also between ABC/GCB.The ABC/GCB classification of the 11 DLBCL datasets (GSE10846 split in 2) was assessed by comparing the overlap between the up-regulated genes in ABC/GCB for each dataset against all others. The genes that were significantly up-regulated  in ABC/GCB were defined in each data set, creating a set of gene lists linked to either ABC or GCB class. Enrichment analysis (described above) was then carried out for differentially expressed genes from each dataset individually against the set of gene lists defined for all other data sets for each class  (Z-scores from random distributions of 10http://Lymphochip.nih.gov/signaturedb/(SignatureDB), http://www.broadinstitute.org/gsea/msigdb/index.jsp MSigDB V3.1 (MSigDB C1–C6), http://compbio.dfci.harvard.edu/genesigdb/Gene Signature Datbase V4 (GeneSigDB) and 3 individual papers A data set of 12,323 gene signatures was created by merging signatures downloaded from Signature enrichment analysis was carried out for the significantly differentially expressed genes . The contrast of ABC and GCB classes yields two lists, those up-regulated in ABC or GCB, which were analyzed separately. Enrichment of the signatures was assessed as described above.Heat-maps were generated using the MEV program from the TM4 package of microarray tools http://www.python.org/) while the GUI was generated using the WxPython (http://www.wxpython.org/) toolkit.A graphical user interface (GUI) driven implementation of the DAC was created to simplify the classification process for users. The code behind the GUI was written in Python  and then if there are multiple probes for a gene these are merged by taking their median value. Finally, Z-scores are generated for each gene across the samples The automatic classifier allows a background file  to be used for classification of individual samples. The program finds the genes shared between the two files, though ideally the two files should be from the exact same platform. The file to be classified (1 or more samples) is split into individual samples and each of these is separately appended to the background file, followed by quantile normalization and generation of Z-scores. Once this process finishes for all individual samples the resulting Z-scores are merged and used to generate an ARFF file, which is then processed as above.http://www.bioinformatics.leeds.ac.uk/~bgy7mc/DAC/.The classifier and manual is available from: Our goal was to establish an implementation of the cell of origin classifier that was robust against variation in microarray platform and fresh or FFPE sample type. The cell of origin classifier is distinguished by two linked characteristics: (1) the ability to define two primary classes of DLBCL, ABC and GCB, with significant differences in outcome when treated with the combination chemotherapy regimen CHOP, , and prednisolone), alone or including rituximab anti-CD20 monoclonal antibody therapy (R-CHOP), and (2) the fact that these classes are linked to extended patterns of gene expression reflecting underlying molecular pathogenesis. Our assessment of classifier performance was therefore based first on the ability to define ABC and GCB classes with differences in outcome, using overall survival, and second on the demonstration that the defined classes of DLBCL across multiple data sets showed similar overall patterns of gene expression and appropriate segregation of non-classifier genes and molecular signatures.In addition to the data set generated on a custom Lymphochip spotted cDNA microarray, on which the original formulation of the cell of origin classification by Wright et al. was based We used two data sets  for classifier development. At each stage classifiers were ranked by the survival separation between the assigned ABC/GCB classes (see Classifier ranking in methods). We proceeded through the following steps : (1) we Twenty of the 27 genes, described by Wright et al. ).Machine-learning tools can be combined to provide the potential advantage of balanced voting between classifiers generated from individual tools. We again used survival separation between assigned ABC and GCB classes as a metric to test the performance of balanced voting between classifiers. To assess such meta-classifiers we combined the best 6 individual machine-learning tools  LMT, SM.To arrive at a single classifier we considered both the rank of all classifiers assessed across the data sets and the percentage of cases assigned to ABC or GCB class, since improved segregation of outcome between ABC and GCB cases could come at the expense of fewer cases assigned to one or other of these classes, and increased assignment to Type-III/unclassified. Only three classifiers, LMT_J48_RF100_SMO, RF100_SMO, and SMO, were consistently ranked in the top 25% of all classifiers tested in every data set (8 out of 31 or better). Amongst these LMT_J48_RF100_SMO gave the lowest average assignment to the Type-III/unclassified subset (17%), and was therefore selected for further analysis . This baA notable feature amongst published applications of the cell of origin classification is the variable number of genes used for classifier implementation (15 to 183) For this analysis we used the GSE10846 data set, which was generated on the Affymetrix Human Genome U133 Plus 2.0 and contained a more comprehensive representation of genes than the Wright et al. data generated on the Lymphochip platform. We trained on the R-CHOP treated component and tested on the CHOP treated component. Increasing the number of classifier genes did not improve outcome separation for either LMT_J48_RF100_SMO or LPS classifiers . We alsoWe next tested the performance of the LMT_J48_RF100_SMO meta-classifier on two additional data sets, GSE4475 and Monti et al., for which outcome data was available In contrast to GSE4475, the Monti et al. data set proved difficult to separate into classes with significant differences in survival when using probe selection and normalization parameters effective on other data sets. However using the same parameters for probe selection and normalization as reported by Monti et al. We conclude that a cell of origin classifier using balanced voting of LMT_J48_RF100_SMO machine-learning tools, using 20 classifier genes is applicable to data sets generated on multiple platform types, separating DLBCL into ABC and GCB classes with significant survival differences. Training on the Wright et al. data set provides better performance on the FFPE data generated on the Illumina platform, and performs well on data sets derived from fresh material generated on Affymetrix platforms. We therefore selected the combination of balanced voting of LMT_J48_RF100_SMO machine-learning tools, 20 classifier genes and training on the Wright et al. data for downstream analysis.The comparison of survival separation for ABC and GCB classes by different classifiers did not assess the effects of classifier choice on a case-by-case basis. To assess this we examined the classification choice for each case, for every classifier tested in all 3 data sets used for selecting classifiers. The resulting maps of classification choice illustrate an important point . A signiThe ABC and GCB classes assigned by balanced voting between LMT_J48_RF100_SMO were characterized by more significant survival separation than published classification choices for most data sets. Assessing individual case-by-case class assignments, this was not attributable to an increase in the percentage of Type-III class. Across both components of the GSE10846 data set there was little difference in the number of Type-III cases assigned: R-CHOP - 15% LMT_J48_RF100_SMO vs 14% GEO-published, CHOP –15% LMT_J48_RF100_SMO vs 17% GEO-published. The improvement in outcome separation was thus due to the selection of cases for inclusion in the ABC or GCB groups. In regard to this, LMT_J48_RF100_SMO assigned more cases to the GCB category (52% vs 46% and 51% vs 42% for R-CHOP and CHOP subsets) and fewer cases to the ABC category (33% vs 40% and 34% vs 41% for R-CHOP and CHOP subsets). Since the assignments made by LMT_J48_RF100_SMO were associated with more significant separation in survival between ABC and GCB classes, and this is the primary clinical characteristic of the cell of origin classification The concept of a “molecular gray zone” was inherent in the original formulation of the cell of origin classifier where cases that fall below a defined confidence threshold for either ABC or GCB class were assigned to the Type-III/unclassified category The balanced voting classifier provides a confidence for assignment to ABC, GCB or Type-III/unclassified and cases are assigned to the class with highest confidence, without a hard threshold. To assess whether classifier confidence assigned by the balanced voting classifier had clinical significance we examined the outcome of ABC and GCB cases by classifier confidence across all 4 data sets using hard confidence thresholds in 0.1 steps incrementing from 0.5. The results demonstrated that classifier confidence was generally associated with increasing differences in survival separation, although not in a linear fashion, since in some instances the Hazard Ratio of GCB vs ABC class was lowest for a 0.7 rather than 0.8 threshold. This was potentially attributable to the small number of highest confidence cases in any individual data set. Overall, high confidence ABC-DLBCL had a poor outcome  or individual samples, given a background data-set from the same platform. We refer to this application as the “Diffuse Large B-cell Lymphoma Automatic Classifier” (DAC), and use this designation for the remainder of this manuscript. This application offers other groups the opportunity to directly compare their own classifications to those generated by DAC in this study. The classifier, user guide, and example data can be downloaded from: To verify that DAC represented a fully transferrable implementation we evaluated additional DLBCL data sets imported from GEO . Retaini7 random gene lists of equivalent size were used to establish the expected distribution against which the observed overlaps were assessed. This analysis showed that differentially expressed genes associated with ABC and GCB class defined by DAC were very similar for each data set. Two FFPE derived data sets, GSE32918 and GSE31312, are included in this analysis We identified differentially expressed genes between ABC and GCB class defined by DAC for each data set. We refer to these differentially expressed genes, as “class-associated”. We performed a pairwise comparison of the resulting lists of class-associated genes for all data set combinations, using a hypergeometric test . For eacWe next proceeded to a more refined comparative analysis of gene expression, assessing consistent associations of individual genes with the primary cell of origin classes, ABC and GCB DLBCL. We compared the lists of class-associated genes for all 11 data sets, maintaining the sub-division of GSE10846 by CHOP/R-CHOP treatment, ranking genes first by the number of data sets in which the genes were class-associated and second by the median normalized fold change . A thresMYBL1, a GCB classifier gene, was represented in 10 data sets, in all 10 data sets it was strongly GCB class-associated, and ranked 21st in the meta-profile. TBC1D27, an ABC classifier gene, was represented in 8 data sets; in all of these it was strongly ABC class-associated and ranked 103rd in the ABC meta-profile. The third excluded classifier gene IGHM was also represented on 10 data sets, and while class-associated in 7 of these data sets and hence ranked 172nd, it was the single most strongly ABC-DLBCL associated gene when considering median normalized fold-change alone. Thus the appropriate class-segregation of these excluded Wright classifier genes, also confirms the internal consistency of classification with DAC.Excluded Wright classifier genes  and S7 pThe meta-profiles are informed by consistency of expression across multiple different data sets. This provides an important variable for assessing the likely significance of a gene in the lymphoma class. Notably of the 414 genes in the ABC-DLBCL meta-profile only 24 genes were class-associated in 11/11 data sets. Microarray platforms differ in the selection of probes and hence the ability to assess individual genes. When taking differential representation of genes into account, 42 genes were ABC class-associated in all data sets in which probes for the gene were present on the platform used (100% class-association). Equally for GCB-DLBCL 20 genes were class-associated in 11/11 data sets, with 35 being 100% class-associated when accounting for gene representation on platforms used.ZBTB32, KCNA3 and CYB5R2) in the ABC-DLBCL meta-profile were not primary classifier genes. In contrast for GCB-DLBCL the classifier genes MME (CD10) and LMO2 topped the ranking. It was also notable that the most consistent ABC-DLBCL meta-profile genes included the transcriptional regulators BATF, TCF4, ARID3A, and CREB3L2 in addition to ZBTB32 and the primary classifier genes IRF4 and FOXP1.A notable feature was that the top three genes . Furthermore if this condition were met, and since the meta-profiles were uniquely informed by consistency of gene expression across multiple data sets, then analysis of gene signature databases would additionally form the basis for identifying novel associations relevant to disease biology. The databases MSigDB, GeneSigDB, as well as the SignatureDB of the Lymphoma Leukemia Molecular Profiling Project, provide an extensive compendium of gene signatures/sets related to particular cell states, pathways, or gene features We also considered signature enrichment for differentially expressed genes for each class in each data set independently. This approach assesses the consistency of signature enrichment across data sets , and emprd , MYD88_Ngo_etal ranked 72nd, NFkB_Up_HBL1 ranked 85th, and NFkB_Up_bothOCILy3andLy10 ranked 113thSeveral oncogenic pathways have been established for ABC-DLBCL CCND2, IRF4 and PIM1) the ABC meta-profile genes contributing to the enrichment of these three NFkB signatures were: ADAM8, BATF, CCL22, CCR7, CD44, CFLAR, ELL2, EPHB1, GPR183, HCK, IL10, IL12A, LITAF, LYN, MARCKS, MIR155HG, NOSIP, PLAGL1, PLK3, RAB7L1, RNF183, SLC38A5, SMARCA2, SNX18, STAT3, TCEB3. Since these signatures were generated from ABC-DLBCL cell lines there is potential circularity in detecting enrichment in the ABC subset. However independent evidence for the specific role of NFkB target genes in the ABC-DLBCL meta-profile was also provided by the enrichment of gene sets linked to promoters with evolutionarily conserved transcription factor motifs contained in the C3 component of MSigDB BMF) contributing to this enrichment only two overlapped with those linked to the experimental NFkB target lists (BATF and IL12A). The enrichment of the NFkB motif gene set in the ABC meta-profile contrasted with the GCB meta-profile in which the most significantly enriched MSigDB_C3 gene sets were FOXO motif variants as well as the NFAT motif . Notably the NFkB and NFAT gene sets were not enriched in the reciprocal meta-profiles indicating enrichment specific to DLBCL class. Since NFkB and NFAT are two of the major transcription factor targets downstream of surface receptor signaling in B-cells, these results support the concept that differential usage of these pathways is a central feature separating ABC- and GCB-DLBCL.Other signatures of NFkB activity have been defined in distinct cellular contexts. Our analysis allowed a ranking of the relative enrichment of these signatures across both the meta-profiles and individual data sets. Of 46 signatures related to NFkB, 8 were enriched in the ABC but not GCB meta-profiles and of these 3  were enriched in 11/11 individual data sets  and S9 ; and.92E-49) . Indeed Recurrent cytogenetic abnormalities characterise DLBCL and show association with cell of origin class and outcome Despite the established nature of the cell of origin classification a comparative analysis of gene expression across multiple data sets classified with the same algorithm has not been published. To allow such an analysis we have developed a robust classifier based on 20 genes, described in the original Wright classifier implementation An issue of primary importance in the evaluation of a test is the performance against a “gold standard” and the choice of metric that is used to assess performance. The most significant clinical feature of the cell of origin classification is the ability to separate DLBCL into two major subgroups ABC and GCB with different survival An important observation emerging from this study is the existence of an extended molecular gray zone, representing cases whose classification is sensitive to the type of classifier implementation used. A substantial group of cases in each data set was equivalently classified by most classifier implementations and thus had a consistent class. In contrast the differences in outcome separation observed were attributable to cases that had more marginal expression of classifier genes and moved been class in a fashion dependent on classifier implementation. While a “molecular gray zone” was inherent in the cell of origin classification, and in its original form encompassed the Type-III or unclassifiable cases BATF, an NFkB target gene that has recently been identified as a key partner of IRF4 BATF expression in ABC-DLBCL may be explained in a similar fashion since it features on multiple NFkB signature lists, including the MSigDB signature V$NFKB_Q6_01. Indeed BATF is induced transiently in activated B-cells during CD40 driven plasma cell differentiation SPIB, leading to functional addiction to this transcription factor The development of meta-profiles representing the most consistent differentially expressed genes between ABC- and GCB-DLBCL is significant since these gene lists are uniquely informed by the consistency of differential gene expression between multiple data sets. Indeed limited numbers of genes were detected as ABC- or GCB-associated in all data sets, and these genes are likely to be enriched for core regulators. Amongst the transcription factors most consistently linked to the ABC-subset is ZBTB32, KCNA3 and CYB5R2 show a more consistent class-association than any of the primary classifier genes. ZBTB32 encodes a transcription factor, also known as Repressor of GATA (ROG), with functions in T-cell activation and differentiation ZBTB32 has recently been identified as a repressor of transcription rapidly induced during murine B-cell differentiation, which can co-operate with BLIMP1 in silencing CIITACIITA may thus contribute to immune-evasion in ABC-DLBCL KCNA3  encodes a potassium channel, which is expressed in effector memory T-cells and memory B-cells KCNA3 identifies this as an intriguing candidate for a novel therapeutic approach in ABC-DLBCL.Another feature of the ABC-DLBCL meta-profile was the fact that three genes, BCL2BCL2 is complicated by the fact that BCL2 translocations are a common feature of GCB-DLBCL, but nonetheless BCL2 was ABC class-associated. However, the most consistently differentially expressed gene from chr18q21 in ABC-DLBCL was TCF4. This gene encodes a transcription factor also known as E2-2, which can drive SPIB expression and co-operate with SPIB to regulate the transcriptional program of plasmacytoid dendritic cells th of all signatures in the ABC meta-profile . Thus in addition to the deregulation of BCL2, amplification of chr18q21 is likely to contribute to the consistent expression of TCF4, and hence to establishing the transcriptional network of ABC-DLBCL.Enrichments of gene signatures derived from previous studies In conclusion, the generation of the robust classifier algorithm, DAC, provides a tool with which to consistently classify DLBCL cases regardless of microarray platform type. It has potential applications in the research and clinical setting, since it is designed, and is currently being used, to allow real-time assessment of individual incident cases. Currently real-time classification of DLBCL cases into molecular classes does not affect primary clinical management decisions, but in future this may change. The analysis we have performed highlights the issues surrounding the effect that classifier choice can have on class assignment, and argues for a robust analysis of classifier algorithm in such settings. The development of this classifier has allowed the generation of a useful resource in which the consistency of class-associated gene expression provides a method for identifying associations of relevance to disease biology, and in particular highlights transcription factors operating in ABC-DLBCL.Figure S1Consistent classification and classification confidence. This accompanies A) GSE10846 CHOP, (B) GSE10846 R-CHOP, (C) GSE32918 data set are shown along with published classes in GEO and those assigned by the LPS classifier . Part (A) reproduces the data shown in th (shown in red) for survival separation. On the far right the published class assignments linked in GEO to the data set  and classes assigned by the LPS classifier using either a 0.8 or 0.9 p-value threshold classes are shown (dark gray bars respectively).(PDF)Click here for additional data file.Table S1Dataset details. This table provides details of the datasets used, showing the GEO accession number where applicable, the title of the associated publication, data set size, array type (Chip Number), presence of associated survival information (Yes/No), and the stages of the evaluation for which the data set was used.(XLSX)Click here for additional data file.Table S2Classifier genes. This table lists the original Wright et al. classifier genes and their cell of origin class association. Genes used in classifier development are shown in bold, asterisks identify Lymphochip features for which an official gene symbol was not identified. The GeneSelections work sheet shows the extended classifier gene sets which were generated and analysed Click here for additional data file.Table S3Cross-validation and details of classifier evaluation. In the upper panel of this table the results for 10-fold cross-validation are provided for individual machine learning tools on the Wright et al. data. In the lower panels the results are shown for all individual classifiers, and meta-classifiers on data sets GSE32918, and GSE10846 divided into CHOP and R-CHOP components. For each component of the table classifiers are ordered by their average rank for survival separation (ABC vs GCB). Shown are the classifier identity, the Hazard Ratio (GCB vs ABC as baseline), 95% confidence interval of the hazard ratio, p-value and log rank.(XLSX)Click here for additional data file.Table S4Overall classifier ranking and Type-III/unclassified assignments by top 3 classifiers. The upper panel shows a summary of the performance of individual and meta-classifiers. Classifiers are order by their average rank for survival separation between ABC and GCB classes across the datasets GSE32918, and GSE10846 divided into CHOP and R-CHOP components. In the lower panel the top three classifiers LMT_J48_RF100_SMO, RF100_SMO, and SMO are compared for assignments to each class GCB, TypeIII, and ABC as % of cases, and ranked by Average % TypeIII (lowest to highest).(XLSX)Click here for additional data file.Table S5Detailed class assignments for all data sets. This file includes tables for all data sets used in this manuscript. It provides the available details for each sample (as indicated) such as GEO_Number, Original order, GEO_Class, Age, Sex and meta-data, followed by the classes assigned for each sample for every classifier tested including the selected class, the confidence of selected class, and the scores assigned for other classes. For data sets used in classifier training and development the results for all tested classifiers, and all combinations of classifier genes and training data sets are provided. For data sets used in later stages only classes assigned by DAC (LMT_J48_RF100_SMO) trained on Wright et al. data are shown.(XLSX)Click here for additional data file.Table S6Confidence, threshold and survival. Shown are the results observed with DAC (LMT_J48_RF100_SMO) using either the highest confidence for class-assignment or a hard threshold of confidence starting from p-value of 0.5 and incrementing in 0.1 steps. Shown are the Dataset identifier, the threshold used , the resulting % Type-III/unclassified (%Unclassified), and the associated Hazard Ratios, and 95% Confidence Intervals of the Hazard Ratios for GCB vs ABC as baseline.(XLSX)Click here for additional data file.Table S7ABC/GCB DLBCL Meta-profiles. Shown are the complete lists of genes differentially expressed between ABC and GCB classes across all data sets. The table is divided into ABC and GCB class-associated genes. Shown are the Official Gene Symbol (Gene), the Median normalised fold change (MedianNormFC), the number of data sets (files) in which a gene was differentially expressed (Numfiles), the % of data sets in which the gene was differentially expressed (%GenePresent), followed by the Fold change in expression for each data set individually (GSE10846 divided into CHOP and R-CHOP components). A threshold of ≥6 data sets (NumFiles) was used for inclusion in meta-profiles.(XLSX)Click here for additional data file.Table S8Meta-profile signature enrichment analysis. The table is divided into two parts showing results for the ABC and GCB DLBCL meta-profiles separately. The first worksheet shows results for the ABC class (upABC_GCB), the second the results for the GCB class (ABC_upGCB). Shown are the enrichments observed for all signatures evaluated using the GeneSigDB, MSigDB and SignatureDB compendia as well as selected literature sources. For each signature the following are shown: the Gene Signature designation (Gene Signature), source of the signature (Source), number of genes overlapping between meta-profile and gene signature (Overlapping), number of genes in the signature (GeneSigSize), the average number of overlapping genes observed in 106 random samplings (randomAvg), the standard deviation of observed for 106 random samplings (randomAvg), the percentage of the gene signature that overlaps (%Overlap), whether a gene signature is enriched (Enriched) shown as 1 if probability of enrichment is <probability of depletion, the standard score for the observed overlap (ZScore), the probability of the overlap given the GeneSigSize (probablity), the Benjamini and Hochberg correction of probability scores (FDR), the enrichment or depletion of signatures and reciprocal signature pairs in ABC and GCB (Annotation), the genes contributing to overlap (EnrichedGenes), genes contributing to enrichment separated with ‘|’ . For the “Annotation” column in each case the first symbol refers to the signature status in ABC (“+” = enriched (+ve Zscore), “−” = depleted (-ve Zscore), “*” = not significantly enriched or depleted) and the second symbol to the status in GCB. Where the signature is part of a signature pair  and down (_DN) regulated genes for a given condition) these are separated by a “/”. These symbols can be used to search for signatures and signature pairs with coherent regulation.(XLSX)Click here for additional data file.Table S9Signature enrichments for individual data sets. The table is divided into two parts including results for all tested signatures for each data set individually in the comparison of ABC vs GCB DLBCL class. The first worksheet shows results for the ABC class (upABC_GCB), the second the results for the GCB class (ABC_upGCB). This emphasises the consistency of signature enrichment rather than the consistant differential expression of individual genes. For each signature the following are shown: the Gene Signature designation (Gene Signature), Source of the signature (Source), the number of data sets in which the signature was enriched/depleted (Number_of_Samples), the average Z score of the observed enrichment (avgZscore), the standard deviation of the Z scores of enrichment (sdZscore), the average probability (avgProb), the standard deviation of probabilities of enrichment (sdProb), whether there is consistent enrichment or depletion across data sets in which enrichment/depletion is observed (ZscoresAllSameSign), annotation of the signature and any associated reciprocal signature/signature pairs for each data set (shown using the data set identifier). For the latter in each case the first symbol refers to the signature status in ABC (“+” = enriched (+ve Zscore), “−” = depleted (-ve Zscore), “*” = not significantly enriched or depleted) and the second symbol to the status in GCB. Where the signature is part of a signature pair  and down (_DN) regulated genes for a given condition) these are separated by a “/”. These symbols can be used to search for signatures and signature pairs with coherent regulation.(XLSX)Click here for additional data file.