Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding “angiogenesis signature” was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials. Advanced epithelial ovarian cancer is notable for initial sensitivity to platinum- and taxane-based chemotherapy http://cancergenome.nih.gov) consortium released a set of 500 gene expression profiles from 500 serous ovarian cancer tumor samples that they used to infer the existence of multiple subtypes Gene expression profiling data represents the largest source of genomic data that might be of use in identifying clinically-relevant subtypes in ovarian cancer, and multiple studies have explored its use for finding predictive biomarkers and clinically-relevant subtypes in ovarian cancer Our goal was to identify robust molecular subtypes of high-grade serous ovarian cancer and sets of functionally defined classification genes that might give insight into potential therapies. We began with a collection of 129 clinically-annotated formalin-fixed, paraffin-embedded (FFPE) FIGO stage III and stage IV high grade serous ovarian samples previously used to construct a tissue microarray Having collected and normalized the gene expression data, we ran the rISIS class discovery algorithm Approval was obtained from the Dana-Farber/Harvard Cancer Center Institutional Review Board (IRB) to review all pathology reports between January 1999 and December 2005 in the Brigham and Women's Hospital Department of Pathology database that included the diagnosis of “ovarian cancer” and collect clinical data associated with those patients. Eligible patients had a diagnosis of late stage  high grade papillary serous ovarian carcinoma, pathology blocks available for generation of a high-density tissue microarray (HTMA) RNA was extracted from FFPE blocks originally used for TMA construction. H&E slides were reviewed by a dedicated gynecologic pathologist (MSH), and three 0.8 mm tissue cores were taken from the corresponding FFPE samples at locations adjacent to the original TMA cores; selected areas for sampling were based on having low levels of infiltrating, necrotic, or other contaminating non-tumor tissue. Messenger RNA was extracted using the Qiagen RNeasy FFPE kit with; RNA quality and integrity was assessed using profiles on the Agilent BioAnalyzer and 129 samples passing this basic QC analysis were analyzed using a prototype Illumina DASL BeadArray containing approximately 12,000 selected mRNAs (ArrayExpress Array Design Accession A-MEXP-931) To identify ovarian cancer subtypes, the 129 tumor samples were divided into a training set (n = 82) and a model selection set (n = 47). Genes on the microarray were filtered to select the 1000 most highly variable in their expression levels across samples but which also had low variability among the twelve sets of duplicates. We used the rISIS class discovery algorithm with these 1000 genes to identify distinct partitions of the 82 training samples and to select the 100 genes that provided the most significant statistical support for the partition. We then tested our initial candidate set of partitions against the 47 model selection test set of samples and retained four that retained statistical significance (classification stability >95% during bootstrap) in the model selection set.MicroRNA expression data from 743 known human miRNAs profiled in the same 129 tumor samples were tested using PAM  Gene set enrichment analysis (GSEA) of GO biological processes was applied to identify biological themes associated with the candidate subtypes (FDR<10%).The validity of the rISIS subtypes was further validated in ten independent datasets . Data frGene expression-based stratification of cancer into transcriptionally distinct subtypes has proven to be extremely powerful in separating patients with unique clinical characteristics, and in shedding light on the genes and mechanisms responsible for driving subtype distinctions. However no robust molecular classification in ovarian has been found, despite a large number of available expression datasets. This may be due to the fact that many studies include multiple histological subtypes, reducing their power to effectively identify new molecular phenotypes To overcome the limitations of these previous studies, we generated a large gene expression data set from tumors consisting only of high grade, late stage serous carcinomas and used these for subtype discovery. We focused on high grade serous tumors as they represent, by far, the most common histologic subtype of ovarian cancer and the one most responsive to chemotherapy. We deviated from the widely-used strategy of clustering the patients based on the global similarity of their gene expression profiles as described in Tothill et al. We used the unsupervised class discovery algorithm ISIS In the analysis presented here we implemented a robust version of the ISIS algorithm, rISIS, that includes an additional bootstrap step to identify only those partitions of the samples in the test set that are not dependent on the composition of the initial training sample set. To do this rISIS chooses random subsets of the training samples, searches for subtype partitions and trains classifiers for each of these, and monitors the consistency of the predictions on the independent test samples. Only predicted partitions were consistent on the independent test samples are kept and used for further analysis.Using rISIS we found four independent, robust binary partitions, or “Splits,” in our ovarian cancer gene expression profiling data (S1–S4), each supported by expression of a defined set of 100 genes (modules) as shown in The expression profiles for the 743 known microRNAs represented on the Illumina DASL platform Because the microRNA data provides the greatest independent support for Split 1, we chose to focus the remainder of our analysis on this subtype assignment. The consensus set microRNAs significant in classifying the samples as S1g0 or S1g1 in all cross-validation classifiers are included in We then performed Gene Set Enrichment Analysis using Gene Ontology (GO) Biological Process terms to functionally characterize the gene module responsible for the Split 1 classification. GSEA significant processes included angiogenesis and extracellular matrix proteins, inducing the GO categories “vascular development”  and “regulation of cell adhesion” , both of which had relatively higher expression levels in S1g1, the smaller of the two classes comprising Split 1. The larger sample group, S1g0, showed relatively higher expression for genes involved in “single stranded DNA binding”  and “structure specific DNA binding” . Because of the significance of angiogenesis in defining the groups, we refer to S1g1 as the “angiogenic” and S1g0 as the “non-angiongenic” subtypes.We tested our angiogenic subtype classification for reproducibility and association with clinical variables in our original dataset and ten previously published gene expression datasets collected on a number of diverse microarray platforms . We normWithin our patient set, those with angiogenic subtype tumors had a hazard ratio (HR) = 1.3  relative to the non-angiogenic subtype. We saw similar, and highly significant differences for the published high-grade, late stage, serous tumors  and the entire published ovarian dataset . Additional plots, including classification of the ten individual published datasets can be found in We then tested the association of the angiogenic subtype classification with the clinical information, including stage, grade, optimal debulking during surgery, and age, available for the compendium of datasets . In our However, in the validation set of 1,606 ovarian cancer patients we found significant association with grade , C2 (n = 50), C3 (n = 28), C4 (n = 46), C5 (n = 36), C6 (n = 8), and NC for a set of “unassigned” cases (n = 34), were reported to correspond to different clinical and pathological features. The most significant of these was C1 which Tothill found to have significantly poor prognosis and which they described as characterized by a “reactive stroma signature.”We then compared our angiogenic subtype classification to the expression-based molecular subtypes assigned by Tothill and colleagues When we classified the Tothill samples using our angiogenic subtype classification, we observed a highly significant association with Tothill's clusters  recently defined from the gene expression dataset generated by the TCGA consortium . These fThere have been many published studies that have attempted to find robust, clinically-relevant molecular subtypes in ovarian cancer, but there has been no clear consensus as to what subtypes exist. One reason for this may be that most analyses have used methods that are sensitive to subtle variations in the data, resulting in putative subtypes that cannot be generalized to independent validation datasets In the analysis presented here, we focused on a single histological type—high-grade serous ovarian cancer. We chose this because it is by far the most common histologic subtype of ovarian cancer and the one that is most responsive to chemotherapy. Moreover the vast majority of published gene expression data, including that from the TCGA, is from patients with high-grade serious cancer. Although there are other recognized ovarian histological subtypes, including mucinous, clear cell and endometrioid cancers, we did not have sufficient numbers of these to search for subtypes or to robustly validate their existence in independent datasets.For subtype discovery, we used rISIS, a robust clustering method that combines class discovery and feature selection by searching for binary groupings of the samples that are strongly supported by statistically significant differences in gene expression The corresponding two subtypes, defined by expression of genes associated with angiogenesis has been shown to be robust and reproducible in independently published gene expression datasets including 1,090 high grade, late stage, serious  ovarian tumors, with statistically significant differences in overall survival. The angiogenic subtype was found to be fairly concordant with Tothill's C1 “reactive stroma” cluster This identification of an angiogenesis-driven subtype is of potential clinical and translational importance since several anti-angiogenic agents  are being added to chemotherapy both in newly diagnosed patients and those with recurrent cancer. and inhibitors of DNA repair (Olaparib) Bevacizumab is a humanized monoclonal antibody that recognizes circulating vascular endothelial growth factor (VEGF) and which has documented anti-cancer activity patients with recurrent ovarian cancer In Gynecologic Oncology Group study 218, bevacizumab was added to upfront chemotherapy for newly diagnosed patients with advanced ovarian cancer, and the study design was comprised of 3 groups: carboplatin and paclitaxel given IV with placebo during chemotherapy and maintenance, carboplatin/paclitaxel/bevacizumab with placebo maintenance, and carboplatin/paclitaxel/bevacizumab followed by 12 months of bevacizumab maintenance. A statistically significant improvement of 4 months was observed in the carboplatin/paclitaxel/bevacizumab plus bevacizumab maintence arm compared to carboplatin/paclitaxel alone ICON7 also demonstrated a statistically significant improvement in PFS with the addition of bevacizumab to upfront chemotherapy Given the important toxicities of these agents such as gastrointestinal bowel perforations, thromboembolic events such as strokes and pulmonary emboli, and hypertension, as well as their financial cost, a classification signature that could identify and pre-select patients whose tumors would most likely benefit from receipt of anti-angiogenic agents would be of tremendous clinical importance. A retrospective assessment of gene expression profiles of patients in the treatment arm of one of these clinical trials would help establish the validity of this signature for predicting relevant response.There may be additional translational applications of our angiogenic/non-angiogenic subtype assignments. The classification gene set also was enriched for genes associated with the extracellular matrix and a number of agents that interfere with the ECM, including notch inhibitors and integrin modulators, are currently being tested in ovarian cancer. Again, a retrospective analysis of gene expression data from patients in the treatment arms of these trials could help to establish a clinical application for the subtypes we identified.Although much remains to be done, we have developed a new approach to robust subtype discovery, demonstrated its application in disease in which subtype identification has proven challenging, and validated that our subtype assignments are robust in a large independent dataset—and more strongly predictive of outcome than any previously reported signature in ovarian cancer. Given the overlap between the genes that drive the subtype classification, and the clinical trials underway in ovarian cancer, we believe our classification has great potential to help influence treatment and improve outcomes for patients.Figure S1Identification of the most likely number of Gaussians to model the subtype scores in the training set  and in the validation set for the 1,090 patients having high grade (≥3), late stage (≥3), serous ovarian tumors  and for all the 1,606 patients . Panel A displays the distribution of the subtype scores and the mixture of two Gaussians, what is the most likely model given the data as estimated by the Bayesian Information Criterion (BIC) in panel B. As can be seen in panels C and E, the distribution of the subtype scores still exhibits a bimodal pattern despite the heterogeneity of the validation datasets ; this is confirmed by the estimation of the BIC in the validation (panels D and F) where a mixture of two or three Gaussians are the most likely models given the data.(DOCX)Click here for additional data file.Figure S2Subtype identification for high grade, late stage, serous ovarian tumors in each of the ten independent validation sets separately: from left to right, a short description of the dataset is given along with the number of tumors in the angiogenic and non-angiogenic subtypes, the bimodal distribution of the rescaled subtype scores, and the survival curves of patients having an angiogenic of non-angiogenic subtype.(DOCX)Click here for additional data file.Figure S3Subtype identification for all tumors  in the training set and in each of the ten independent validation sets separately: from left to right, a short description of the dataset is given along with the number of tumors in the angiogenic and non-angiogenic subtypes, the bimodal distribution of the rescaled subtype scores, and the survival curves of patients having an angiogenic of non-angiogenic subtype.(DOCX)Click here for additional data file.Table S1Association between the angiogenic subtype classifications and (A) the putative subtype clusters in Tothill's dataset, and (B) the proposed subtypes in the ovarian TCGA dataset.(DOCX)Click here for additional data file.Text S1Angiogenic mRNA and microRNA gene expression signatures predict a novel subgroup of serous ovarian cancer.(DOCX)Click here for additional data file.File S1An Excel workbook containing the list of genes in the modules that define each of the four binary partitions found using rISIS, with each represented in separate tab.(XLSX)Click here for additional data file.File S2An Excel spreadsheet containing the consensus set microRNAs significant for classifying the samples as S1g0 or S1g1 in all cross-validation classifiers.(XLSX)Click here for additional data file.


Age is a powerful predictor of survival in glioblastoma multiforme (GBM) yet the biological basis for the difference in clinical outcome is mostly unknown. Discovering genes and pathways that would explain age-specific survival difference could generate opportunities for novel therapeutics for GBM. Here we have integrated gene expression, exon expression, microRNA expression, copy number alteration, SNP, whole exome sequence, and DNA methylation data sets of a cohort of GBM patients in The Cancer Genome Atlas (TCGA) project to discover age-specific signatures at the transcriptional, genetic, and epigenetic levels and validated our findings on the REMBRANDT data set. We found major age-specific signatures at all levels including age-specific hypermethylation in polycomb group protein target genes and the upregulation of angiogenesis-related genes in older GBMs. These age-specific differences in GBM, which are independent of molecular subtypes, may in part explain the preferential effects of anti-angiogenic agents in older GBM and pave the way to a better understanding of the unique biology and clinical behavior of older versus younger GBMs. Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor Advanced age has been identified as an independent significant prognostic factor for survival in glioblastoma clinical trials since the 1970s . An analThe reasons why older age is such a negative prognostic factor remain unclear. Retrospective data and randomized controlled trials do not suggest that older patients receive less than optimal treatment and/or tolerate treatment less well than younger patients thereby suggesting a potential difference in the biology of GBMs in older patients. Thus, it would be valuable to discover age-specific signatures in GBM biology that might explain this survival difference and allow clinicians to develop age-specific therapeutic clinical trials for GBM.et al. discovered a glioma-CpG island methylator phenotype (G-CIMP) in GBMs Noushmehr, In this study, we computationally analyzed gene expression, exon expression, microRNA expression, DNA methylation, copy number alteration, somatic mutation derived from whole exome sequence, and SNP data sets of the TCGA GBM samples to discover age-specific signatures at the transcriptional, genetic, and epigenetic levels. In order to avoid the confounding variable of the G-CIMP status of the tumor, we trained a model to predict G-CIMP status of GBM samples based on gene and exon expression profiles in order to exclude G-CIMP positive patients in our analyses.young group and patients ≥70 years old to the old group. The number of available samples in the young and old groups changes depending on the data set  to increase the power of the analysis.For two-sample tests, we defined a “young” and “old” group. We hypothesized that if there is an old and young biology then samples with “intermediate” ages might represent a mix of these biologies. Thus, we did not include samples with “intermediate” ages in our old and young groups. In order to define the age boundaries for the old and young groups, we examined the histogram of survival for different age groups  and numbdata set , but oveWe obtained G-CIMP calls of samples that have methylation data from G-CIMP positive patients are significantly younger than G-CIMP negative patients and there is a significant difference between G-CIMP positive and negative patients at the transcriptional, genetic, and epigenetic levels ranked-based linear regression to find age-specific differentially expressed microRNAs and differentially methylated genes (DMGs). We used a ranked-based linear regression on microRNA expression and DNA methylation data, since these data were not normally distributed. We used the samr v1.28 package in R We used data sets from Affymetrix U133A, Affymetrix Human Exon 1.0, and Agilent 244 K G4502A platforms in TCGA for differential gene expression analysis . We usedcomparison function to find differentially altered regions between old and young groups . The comparison function compares the frequency of alteration in both groups and finds areas where there is significant difference in frequency We used segmented copy number/SNP data sets in Agilent HG CGH 244 K, Affymetrix Human SNP 6.0, and Illumina 550 K Infinium HumanHap550 platforms in TCGA . The samWe used somatic mutations derived from whole exome sequences in TCGA . We performed Fisher’s exact test to find genes that are significantly mutated in old or young GBMs.coxph function in R We applied Cox multivariate analysis on variables namely age, molecular subtypes derived in p≤0.05. We also used DAVID www.ingenuity.com) on age-specific angiogenesis related genes to display the interactions among these genes.We applied the gene set enrichment analysis (GSEA) algorithm In order to compute enriched motifs in the promoters of the age-specific DEGs, we used PScan To validate age-specific DEGs, we applied 10-fold cross-validation on TCGA U133A data set of old and young patients. For each fold, we used the support vector machine (SVM) algorithm to build a model based on training data  and used this model to predict the old/young status of the remaining 10% of samples. For comparison, we also used the same algorithm to build a model based on the molecular subtype-specific DEGs of training data. We used Partek® Genomics SuiteTM version 6.5 and tried different parameter choices for the SVM algorithm. To compute DEGs, we used one-way ANOVA.http://caintegrator-info.nci.nih.gov/rembrandt). We predicted the G-CIMP status of these samples as described above. There were 153 G-CIMP negative samples . Due to the small sample size, we were unable to compute statistically significant age-specific DEGs on this data set. We, therefore, filtered in the age-specific genes at the transcriptional level derived from both TCGA Agilent 244 K G4502A and Affymetrix U133A data sets (hereafter the TCGA age signature) in the REMBRANDT data set to create a filtered data set. We clustered the filtered data set via hierarchical clustering to see if old and young samples would be separated by TCGA age signature. We also clustered the unfiltered REMBRANDT data set  and compared both results. As a more quantitative approach, we also built an ANOVA model on filtered and unfiltered REMBRANDT data sets to compute how much of the variation could be explained by the age group . We built a 3-way ANOVA by using gender, age group, and sample source institute as categorical factors. We used Partek® Genomics Suite™ version 6.6 for clustering and building ANOVA model.To validate the age-specific signatures derived from the TCGA data set, we obtained gene expression profiles of GBM samples from the REMBRANDT database  have also age-specific differential expression in normal brain tissues based on a data set described in We computed age-specific DEGs by using both t-test and linear regression. DEGs found by linear regression mostly contain DEGs found by t-test in all three platforms suggesting that the use of all samples gives more power to the analysis . Using lWe applied ranked-based linear regression and found 19 differentially expressed microRNAs (FDR<0.05) . All these microRNAs are downregulated in older GBMs. We used the mirWalk database We found 389 age-specific DMGs by using ranked-based linear regression . Ninety-Each copy number/SNP data set was analyzed in Nexus independently. Analyzing the SNP 6 data set, we detected differential deletions only on chromosome 10 for 722 genes. We observed that the old group had a higher frequency of deletion than the young group. We found 321 differentially amplified genes on chromosome 7 with a higher frequency in the old group than the young group and one gene on chromosome 1q (CFHR3) with a higher frequency in the young group than the old group. The high frequency of chromosome 10 deletion and chromosome 7 amplification in the old group, and high frequency of chromosome 1q amplification in the young group have also been reported in a study that compared a cohort of pediatric GBMs with adult GBMs We compared the list of DAGs on SNP 6 and the list of DMGs, and found three genes that are both heterozygously deleted and hypermethylated in the old group . Among these genes, HHEX is downregulated in older GBMs. HHEX is known to downregulate VEGF and VEGF receptors We also compared the list of DAGs on SNP 6 and the list of DEGs found on both Agilent G4502A and Affymetrix U133A data sets. There are 21 genes that are deleted and downregulated, and 7 genes that are amplified and upregulated in the old group .We also analyzed somatic mutations derived from whole exome sequence data. In general, there are more mutations in the old group . There aWe analyzed the promoter regions of differentially expressed genes appear in Affymetrix U133A and Agilent G4502A data sets. We have found several motifs statistically enriched in the promoter regions of these genes including HIF-1A and MYC FDR≤0.05 .We ran a GSEA on the old and young groups to discover upregulated gene sets for each group . GSEA anWe also ran DAVID on upregulated genes that appear in the Affymetrix U133A and Agilent G4502A data sets. We found enrichment in several GO terms such as “response to hypoxia” ; “vasculogenesis”  .We applied 10-fold cross-validation and built a model based on age-specific DEGs on training data and used this model to predict the old/young status of the remaining test samples. The model achieved over 77% prediction accuracy. For comparison, we also used the same algorithm to build a model based on the molecular subtype-specific DEGs of training data. This model predicted about 64% of prediction accuracy.We obtained the gene expression profiles of G-CIMP negative GBM samples from the REMBRANDT database to validate our findings. We created a filtered REMBRANDT data set by filtering in the TCGA age signature . We clustered both filtered -A and unWe also checked whether TCGA DEGs have the same direction of regulation (up or down) in REMBRANDT data set. We applied age-specific linear regression to compute p-value for TCGA DEGs in REMBRANDT data set and created a gene list for both FDR<0.05 and unadjusted p-value<0.05 thresholds. There were 55 and 148 genes in these gene lists, respectively, which had 100% consistency with respect to the directionality of regulation in TCGA and REMBRANDT data sets.Age has consistently been shown to be one of the most powerful prognostic factors for survival in patients with malignant gliomas with younger patients generally living much longer than older patients. The negative effects of age seen in a number of systemic cancers have often been ascribed to the physiological stress of metastatic cancer in the setting of concurrent medical problems leading to an increased rate of medical related deaths There have been a relatively large number of studies over the last decade demonstrating that GBM is a heterogeneous tumor with the most recent studies suggesting that there are at least four major molecular subtypes of GBM based on gene expression profiling We observed a relative small number of DAGs that consistently differentiated old versus young GBMs. Specifically we found that chromosome 10 deletion and chromosome 7 amplification was found commonly in the old group whereas there was a relatively high frequency of chromosome 1q amplification in the young group, observations that have been previously reported in a study that compared a cohort of pediatric GBMs with adult GBMs In contrast to the relatively few consistent genomic changes between each group of tumors, we did observed a large age-specific signature at the transcriptional level. We observed a major overlap between the DEGs found in Affymetrix U133A and Agilent G4502A platforms, although not surprisingly, each platform had unique DEGs as described in We applied 10-fold cross validation on TCGA U133A data set to validate age-specific DEGs. We applied the SVM algorithm to build a model based on age-specific DEGs of training samples and applied this model to predict old/young status of the test samples. This model achieved over 77% prediction accuracy whereas a model based on molecular subtype-specific DEGs only achieved 64% accuracy.We also validated age-specific signatures at the transcriptional level on an external data set. We obtained G-CIMP negative old and young GBM samples from the REMBRANDT database and clustered these samples based on the TCGA age signature genes and all genes. The clustering results showed that the TCGA age signature could separate the old and young REMBRANDT samples as well as it separates the old and young TCGA samples. We also showed that when the TCGA age signature was selected in the REMBRANDT data set, the majority of the variation could be explained by age group . The age group, however, could not explain the variation on the entire REMBRANDT data set. We also showed that the upregulated (downregulated) TCGA DEGs are also upregulated (downregulated) in REMBRANDT data set. These findings indicate that the TCGA age-specific signature at the transcriptional level is also age-specific in REMBRANDT data set.We also observed a large age-specific signature at the epigenetic level. In particular, we found that about 98% of the DMGs between the old and young group are hypermethylated in the old group. There are several studies that show that aging increases methylation of DNA including cancer-related genes The primary focus of this study was to identify the significant genomic, genetic and epigenetic signatures between young and old GBMs for hypothesis generation and future study and although the annotation and the biological significance of these changes are well beyond the scope of this manuscript, there is one striking observation worth noting. We found a significant number of genes involved in the hypoxic response and in angiogenesis deregulated in older GBMs compared to younger GBMs. In particular, we found a remarkable number of genes involved in the regulation of the proangiogenic protein, VEGF, deregulated in the older GBMs. This is consistent with older pathology-based studies that have demonstrated VEGF to be more highly expressed in older GBMs than in younger GBMs. Examples of the genes we found deregulated at the expression level or through transcriptional factor motif activation in older GBM that contribute to VEGF expression include HIF, HHEX, EGR1, CTCF, HTATIP2, lox, and DLK1.To better elucidate the potential angiogenesis-related signaling aberrations found in older GBMs, we entered into the IPA network analysis a number of the genes deregulated in older GBMs at either the transcriptional level or at the TF motif enrichment level that have been associated with angiogenesis in the literature. These genes included EGR1 Thus, it appears that a key biological difference between older GBMs compared to younger GBMs is the central role of VEGF and angiogenesis signaling. Although we cannot know for certain that the more prominent angiogenic profile of older GBMs is responsible in part for the shorter survival of older patients with these tumors, there is an extensive literature linking greater angiogenic potential with decreased survival in GBMs In conclusion, we have demonstrated through computational analyses of high-throughput genomic data from hundreds of tumors that there are substantial and consistent biological differences between GBMs found in older patients compared to those found in younger patients. Although the ultimate biological meaning and clinical significance of many of these findings await experimental validation, it appears clear that the pro-angiogenic phenotype of older GBMs compared to younger GBMs has biological, clinical, and therapeutic significance. This finding demonstrates how computational analysis of high-throughput data of a human tumor can help explain long standing clinical observations and point the way to more rationale therapeutics targeted to a specific biological process in selected patients.Figure S1Histogram of survival days of samples for each age bin.(TIFF)Click here for additional data file.Figure S2Histogram of number of samples for each age bin.(TIFF)Click here for additional data file.Figure S3The comparison of copy number/SNP samples obtained from different institutes in the TCGA project. MSKCC: Agilent Human Genome CGH Microarray 244A , BI: Affymetrix Genome-Wide Human SNP Array 6.0 (Broad Institute of MIT and Harvard), HAIB: Illumina 550 K Infinium HumanHap550 SNP Chip (HudsonAlpha Institute for Biotechnology.(TIFF)Click here for additional data file.Table S1Survival analysis of GBM in randomized phase trials.(XLS)Click here for additional data file.Table S2Sample IDs for each data type used in this study.(XLSX)Click here for additional data file.Table S3Cox multivariate analysis results for survival. P-values≤0.05 are bold faced.(XLSX)Click here for additional data file.Table S4List of differentially expressed genes found by standard linear regression (FDR≤0.05).(XLSX)Click here for additional data file.Table S5Experimentally validated targets of the differentially expressed miRs between the old and young GBMs.(XLSX)Click here for additional data file.Table S6List of differentially methylated genes found by ranked-based regression (FDR≤0.05).(XLSX)Click here for additional data file.Table S7List of differentially methylated genes that are polycomb group protein target genes.(XLSX)Click here for additional data file.Table S8List of genes that undergo hypermethylation only in GBMs.(XLSX)Click here for additional data file.Table S9List of differentially methylated genes that have been reported to undergo hypermethylation in cancer in Pubmeth database.(XLSX)Click here for additional data file.Table S10List of genes that are differentially expressed (Agilent and Affymetrix U133A platforms) and methylated with respect to age.(XLSX)Click here for additional data file.Table S11List of differentially altered genes between Old and Young on different data sets.(XLSX)Click here for additional data file.Table S12Pearson and Spearman Rank Correlation between HHEX and VEGFA expression on Affymetrix U133A, Agilent G4502A, and Affymetrix Human Exon platforms. For all correlations, p-values are significant (p≤0.05)(XLSX)Click here for additional data file.Table S13List of genes that are (a) deleted and downregulated (b) amplified and upregulated in the old group. DAGs are computed from SNP 6 platform. DEGs are the intersection of genes found in Affymetrix U133A and Agilent G4502A platforms.(XLSX)Click here for additional data file.Table S14List of enriched motifs in the promoter regions of the differentially expressed genes appear in both Affymetrix U133A and Agilent G4502A platforms. FDR(BH): Benjamini-Hochberg FDR value(XLSX)Click here for additional data file.Table S15GSEA results on old vs. young genes. NES: normalized enrichment score. Gene sets with negative (positive) enrichment scores are active in young (old) group.(XLSX)Click here for additional data file.Table S16DAVID analysis results on age-specific upregulated genes in old that appear in Affymetrix U133A and Agilent G4502A data sets. Fisher's exact test p-value<0.05(XLS)Click here for additional data file.


Permanent Atrial fibrillation (pmAF) has largely remained incurable since the existing information for explaining precise mechanisms underlying pmAF is not sufficient. Microarray analysis offers a broader and unbiased approach to identify and predict new biological features of pmAF. By considering the unbalanced sample numbers in most microarray data of case - control, we designed an asymmetric principal component analysis algorithm and applied it to re - analyze differential gene expression data of pmAF patients and control samples for predicting new biological features. Finally, we identified 51 differentially expressed genes using the proposed method, in which 42 differentially expressed genes are new findings compared with two related works on the same data and the existing studies. The enrichment analysis illustrated the reliability of identified differentially expressed genes. Moreover, we predicted three new pmAF – related signaling pathways using the identified differentially expressed genes via the KO-Based Annotation System. Our analysis and the existing studies supported that the predicted signaling pathways may promote the pmAF progression. The results above are worthy to do further experimental studies. This work provides some new insights into molecular features of pmAF. It has also the potentially important implications for improved understanding of the molecular mechanisms of pmAF. Atrial fibrillation (AF) is an extremely common cardiac rhythm disorder in population Over the last decade, several studies have characterized the molecular basis of remodeling on a more global scale In this work, we designed an asymmetric principal component analysis (APCA) algorithm by considering the unbalanced sample numbers in most microarray data of case – control. From the data used by Barth et al. The gene expression data used in this work were obtained from the public functional genomics data repository of the National Institute of Health . The data with record #GSE2240 consists of samples from 30 patients, in which 10 patients had pmAF, whereas 20 patients had no history of AF and were in SR. The data are related to two Affymetrix platforms U133A and U133B. According to our statistics, 20995 of the 22283 genes in U133A have been annotated and the percentage of annotated genes is 94.2%. Only 14586 of the 22283 genes in U133B have been annotated and the annotated percentage is less than 65%. This may result in wrong interpretation to the final results. Therefore, our study focus on the data in U133A, which is a gene expression data set with 22283 rows (probes) and 30 columns (samples).The existing researches have shown that the PCA-based method on this data set succeed in discriminating patients from controls and in offering a mechanistic view relating AF condition to both cardiac muscle organization and inflammatory processes X with m rows (probes) and l columns (samples) and there is m>>l. The novel APCA – based method consists of four phases. In the phase one, we project X into a low dimension matrix Xs. The steps are as follows.Assume that a gene expression dataset is represented by S = XTX, where T denotes the transpose. Obviously, the size of S is l×l.Construct S for obtaining the eigenvector matrix SEIV with size l×l, where one column is one eigenvector.Apply PCA to L = XEIVSEIV. The LEIV is the eigenvector matrix with size m×l corresponding to the nonzero eigenvalues of XXT.Compute X into SX through computing LXs = EIVTX, which is of size l×l.Project the X. In phase two, we construct a new data matrix, shown as below.All principal components (PCs) and classification on Xs will be exactly the same with that on n control samples and d disease samples in the original data matrix X, and n + d = l. Xs is divided into two block matrices, that is, SX = . Thus, SnX is of size l×n and SdXl×d.Suppose that there are SnX, SdX and SX, SnM, SdM and SM whose elements are the mean values over rows and whose sizes are all l×1.Calculate the mean vectors of SnX, SdX and SX to yield SnY, SdY and SY.Centralize the data matrices above by subtracting the means from SnC = (1/n)SnYSnTY , SdC = (1/d)SdYSdTY and SC = (1/l)[n(SnM-SM)(SnM-SM)T+d(SdM-SM)(SdM-SM)T] whose size are all l×l.Calculate the covariance matrices by C = aSnC + (1-a) SdC +bSC, where the parameter b is a large value such as 20 and the parameter a is in the range of 0<a<1. They are determined according to the optimal classification result of patients/control samples based on the first two PCs.Construct a new matrix C using the PCA algorithm a and b, the second PC is used as the discriminant PC, which will be used to identify differentially expressed genes.In the third phase, we extract the principal components from θ), are identified as the DEGs. As we all know, the selection of the threshold is very important. Many biologically relevant DEGs could not be identified if the threshold is too high, while lowering the identification threshold will increase the number of potential DEGs, including many false ones. Based on the reasons above, we select about half of the numerical range of the score S as the threshold.The discriminant PC is a vector with dimension of 22283×1, in which the value in each row represents the score of corresponding gene. In final phase, we identify the DEGs according to the scores of the genes. Those genes, whose scores  are higher than a given threshold  are respectively indicated by The Affymetrix IDs of all the identified DEGs are entered into the KO-Based Annotation System (KOBAS) In order to illustrate the reasonableness of threshold in our method as well as the reliability of the results, we assess the discrimination powers of identified DEG expression levels to classify normal and pmAF samples by calculating their receiver operating characteristic (ROC) curves and the areas under the curve (AUC). In ROC calculation, we respectively define pmAF and normal patients as positive and negative samples. Therefore, the true positive rate indicates how many correct positive results occur among all pmAF samples available during the test. False positive rate, on the other hand, represents how many incorrect positive results occur among all normal samples available during the test.It is well known that pmAF is a kind of polygenic and multifactorial disease and so its occurrence and progress are associated with the combinational work of multiple genes. Thus, we calculate the ROCs and the AUCs of combinations among 51 identified DEGs. In order to find the combination rules of these DEGs, we analyze the connection relationships among them using correlation technique. Using the gene expression values of 51 DEGs across 10 samples in pmAF group of U133A, we calculate the correlation coefficient (CC) between each DEG pair. Under CC≥0.9 m = 22283, l = 29, n = 19 and d = 10.In U133A data set, there are respectively 22283 probes and 30 samples, which include 20 control samples and 10 disease samples. The independent number of control samples is 19 since the gene expression values of two samples in the control group are completely same. Thus, the total independent sample number is 29. The dimension of data matrix in our experiment are b be an integer of 20 and then uniformly sampled a over the interval . Our simulation showed that two kinds of samples can be correctly classified by the first two PCs when taking a = 0.3 and b = 20 in the APCA algorithm, as shown in a = 0.3 and b = 20 when our method was applied to the U133A data set.We let b (here b = 20), our algorithm ensures that the first PC is caused by the two class means and it represents the commonality between gene expression profiles on the genome-wide scale. Hence, all minor PCs are caused by the within-class variations of the both classes, which mainly concentrate on the second PC as shown in The results in the θ is calculated as 5. We identified 51 DEGs (corresponding to the 63 Affymetrix IDs) that have the scores  higher than the threshold. Their symbols, Affymetrix IDs (ID_REF), titles and scores are shown in We obtained each gene's score using the second PC in The DEGs in P = 0) (P = 0). 10 of 29 individual DEGs were found to have an AUC of more than 0.7 (P = 0). Similarly, two AUCs for the combinations of DEGs respectively involved in focal adhesion and dilated cardiomyopathy pathways are 0.826 (P = 0.004) and 0.774 (P = 0.017). The combination of other 15 individual DEGs was also found to have an AUC of more than 0.7 .The connection relationships among 51 DEGs correspondent to pmAF patients and the calculation of corresponding ROCs and AUCs are respectively shown in P = 0) . In 8 DEthan 0.7 . Among tAs have discussed above, different combinations among 51 DEG according to their connection relationships in pmAF samples showed great discrimination power of classifying normal and pmAF samples. This demonstrated that the identified DEGs are reliable and the threshold of identifying DEGs is reasonable.The results for the enrichment analysis of biological process and cellular component on Gene Ontology (GO) and the diseases on Genetic Association Database (GAD) are respectively shown in In order to further illustrate the reliability of identified DEGs, we established the association between the AF-related etiological factors and all the identified DEGs. We firstly connected the factors and the “terms” according to the biological meaning of each term and then established the relationships between the identified DEGs and the etiological factors via the terms in the enrichment analysis results. The 51 DEGs and their association with the AF - related etiological factors are shown in There are respectively 5, 4, and 3 DEGs in the PPAR, focal adhesion and dilated cardiomyopathy signaling pathways . Our preIn addition, some existing researches indirectly supported our prediction. For the PPAR signaling pathway, a = 0.3 and so the larger weight ((1-a) = 0.7 comparing to 0.345 (10/29) of PCA) is used for the class of pmAF. Thus, information of the class of pmAF is emphasized in APCA (0.7>0.5) while it is deemphasized in PCA (0.345<0.5). Furthermore, with b = 20 (it is significantly larger than b = 1 in PCA), APCA forces the largest PC to capture the difference of the class means and hence clearly separates the information about the difference of the class means from the information about the within-class variations into different principal components. PCA with b = 1 makes these two different types of information mixed in various PCs. Thus, the first two PCs of APCA have higher discriminating power of classifying normal and pmAF samples than that of PCA since APCA considers the unbalanced sample numbers.The study of Censi, et al. Numerous feature selection methods have been applied to the identification of DEGs on microarray, including Fold change, Welch t-statistic, SAM , etc. By PCA, Censi, et al. identified 50 pmAF - related DEGs from the same data set Our enrichment analysis about biological process and cellular component on GO for 50 DEGs also shows the majority of them  are individually related to the etiological factors inducing AF. Using 50 DEGs extracted by Censi, et al., we do not find any a gene is included in the statistically enriched GAD terms of disease on GAD (we have 22 DEGs), and only one statistically enriched pathway named focal adhesion is found on KOBAS, in which genes JUN, PIK3R1, TNC and THBS4 are involved. This illustrates that the correlation in biological functions among our 51 DEGs is higher than that of 50 DEGs. Therefore, there are more genes and combinational works of multiple genes in our 51 DEGs to be associated with occurrence and progress of pmAF. APCA is a more appropriate method to microarray data that have unbalanced samples.Finally, it is worthy explaining that we do not analyze the U133B data set because too many genes were not annotated on this chip, which may result in wrong interpretation to the final results. The pathophysiology of pmAF is extremely complex. In our future work, we shall validate the suggested pmAF-related DEGs in experiments and integrate multiple types of data  to build functional networks promoting pmAF for more comprehensive understanding of pmAF pathophysiology.This work proposes a novel method to identify the DEGs from microarray data with unbalanced sample numbers. 51 DEGs associated with pmAF are identified, in which 42 DEGs are different from the existing related results. The PPAR, focal adhesions and dilated cardiomyopathy signaling pathways are predicted to be associated with pmAF based on all of the identified DEGs. This work provides some new insights into biological features of pmAF and has also the potentially important implications for improved understanding of the molecular mechanisms of pmAF.Figure S1The connection network among 51 identified DEGs. The No. of each DEG is same with that in (TIF)Click here for additional data file.Table S1The AUCs of 51 DEGs individually.(DOC)Click here for additional data file.Table S2The AUCs of combination among multiple genes.(DOC)Click here for additional data file.Table S3The statistically enriched GO terms of biological processes.(XLS)Click here for additional data file.Table S4The statistically enriched GO terms of cellular component.(XLS)Click here for additional data file.Table S5The statistically enriched GAD terms of disease.(XLS)Click here for additional data file.Table S6The association between the identified DEGs and the etiological factors inducing pmAF.(DOC)Click here for additional data file.


We introduce Iterative Feature Removal (IFR) as an unbiased approach for selecting features with diagnostic capacity from large data sets. The algorithm is based on recently developed tools in machine learning that are driven by sparse feature selection goals. When applied to genomic data, our method is designed to identify genes that can provide deeper insight into complex interactions while remaining directly connected to diagnostic utility. We contrast this approach with the search for a minimal best set of discriminative genes, which can provide only an incomplete picture of the biological complexity.Microarray data sets typically contain far more features (genes) than samples. For this type of data, we demonstrate that there are many equivalently-predictive subsets of genes. We iteratively train a classifier using features identified via a sparse support vector machine. At each iteration, we remove all the features that were previously selected. We found that we could iterate many times before a sustained drop in accuracy occurs, with each iteration removing approximately 30 genes from consideration. The classification accuracy on test data remains essentially flat even as hundreds of top-genes are removed.sets of genes that are highly predictive, even when comprised of genes that individually are not. Through automated and manual analysis of the selected genes, we demonstrate that the selected features expose relevant pathways that other approaches would have missed.Our method identifies Our results challenge the paradigm of using feature selection techniques to design parsimonious classifiers from microarray and similar high-dimensional, small-sample-size data sets. The fact that there are many subsets of genes that work equally well to classify the data provides a strong counter-result to the notion that there is a small number of “top genes” that should be used to build classifiers. In our results, the best classifiers were formed using genes with limited univariate power, thus illustrating that deeper mining of features using multivariate techniques is important. The sequencing of complete genomes has accelerated biomedical research by promising a greater degree of understanding of the biological processes encoded. The role of computational biology has become increasingly important because there is a significant need for progressing beyond statistics and data clustering. New analysis methods are needed that afford the ability to use more of the generated data, thus revealing more insights into biological processes. Mathematical optimization algorithms, and related machine learning techniques arising in computer science, have emerged as having significant potential for knowledge discovery in biological data sets arising in genomics, metabolomics and proteomics investigations. These methods are attractive given they are capable of identifying multivariate interactions associated with biological complexity, e.g., co-expressing genes in single or related pathways. Further, the gene selection procedure is data driven, thus providing an unbiased approach to knowledge discovery.parsimonious models – that is, models which attain high accuracy while minimizing the number of features – is now standard operating procedure in machine learning. Currently, many machine learning feature selection methods applied to microarray data explicitly eliminate genes which are redundant, in terms of discriminative or predictive value, with an existing set[The emphasis on the search for ting set-8. A recMicroarray data typically includes highly correlated gene expression levels associated with a  biological process. Yet feature selection methods tend to eliminate genes which fail to provide additional discriminatory power to the model. To the machine learning expert, this yields the desired parsimonious model. To the biologist, the suppression of potentially biologically-relevant information can impede process discovery.We hypothesize that genes selected as most important to a parsimonious model are in fact only a small subset of a large set of highly discriminative, and thus informative, genes. To test this hypothesis, we employ a sparse Support Vector Machine to build an accurate model using only a small fraction of the features in the data. We then repeat this process, but at each iteration we remove all the genes selected by previous iterations from the data. At each iteration, the model must be built without the “most important” genes identified previously. We call this process Iterative Feature Removal (IFR).If our hypothesis holds, there should be several iterations where the accuracy remains high, even as the top genes are iteratively removed from consideration. Eventually, the process will have removed all the relevant genes, and the accuracy should fall to random performance. Our results support this hypothesis. On the evaluated data sets, IFR demonstrates that at least 20 iterations are required before we see a significant loss in discriminatory power, providing hundreds of genes for further analysis.For this study, we examine four microarray data sets from the literature, as summarized in Tablea.We use two subsets of the influenza data, labeled as “Influenza 14-16” and “Influenza 11-14” in the table. The first uses only those samples from time intervals 14 through 16, the second from intervals 11 through 14. The 14-16 data are used in, and areb. The BCell lymphoma data differs from the others in the following ways. First, it is smaller, both in terms of the number of samples and the number of features. Secondly, there are missing values in the data. We replace missing values with the average expressions for the same gene over all samples. Thirdly, there is no defined distinction between training and testing samples. We randomly withheld 25% of the samples for testing .The other three data sets, lung cancer, prostate cancer, and BCell lymphoma, were downloaded from the Kent Ridge Biomedical Data Set RepositoryWe refer the reader to the original sources, cited in Tablethe same accuracy.Our Iterative Feature Removal (IFR) process works by repeatedly building a predictive model on training data using a classifier that assigns non-zero weights to only a minimal subset of non-redundant features. In other words, if a gene does not improve the classification accuracy, it is not included in this minimal set. At each subsequent iteration, we remove all features that were selected in previous iterations, limiting the model construction to the remaining features. Thus, we are effectively removing highly discriminatory genes from the data, and forming a new model without these genes. We observe that for a surprisingly large number of iterations, the resulting model is essentially as highly discriminatory. For example, we can remove the best 500 genes whose subsets had accuracies of over 90% on the influenza testing data, and still discover new genes that can classify with essentially The genes in each set are selected based on their predictive power as a group and we observe that they do not necessarily classify well individually, i.e., they may not provide univariate separation of the data. In contrast, they provide a multivariate model for classifying the data that captures potentially complex biological relationships amongst the variables.Given the small number of samples in a high-dimensional space, model over-fitting is a concern and must be carefully monitored. At each iteration, we gauge the quality of the model by testing its accuracy on data that was not included in the training. A description of the sparse SVM used for IFR and other methodological details are provided in the Methods section. Results in Additional fileFigureDue to the small number of samples, test accuracy from one iteration to the next can vary substantially. This is due, in part, to the fact that the genes are being selected by an optimization criterion on the training data and, as such, this selection is ignorant of the test data. It also suggests that, in general, one should avoid using a hard threshold for stopping the iterative removal process.c. This effect is less pronounced in the lung cancer data where there are more test samples. When the test accuracy is viewed using a rolling average (red line), the trend becomes clearer.The interpretation above is complicated by the fact that when fitting a separating hyperplane to a small number of points in a high dimensional space, there can be many equivalently-optimal partitions. There is thus some element of luck in model selection when validated against withheld dataThe training fit (dashed green line) is perfect through the first 200 or so iterations, even as test accuracy varies from trial-to-trial. When the training accuracy is 100% and the test accuracy is low, we infer that the model is over-fit, meaning that the model does not generalize beyond the training data. Genes selected by those iterations showing model over-fitting are not considered strongly discriminative in general, and they are considered to be unrelated to the biology that distinguishes, e.g., symptomatic and asymptomatic subjects in the influenza data.In the lung cancer data, there is a sharp increase in test accuracy near the end of the iterations. This behavior is explained by the uneven distribution of test samples. Approximately 89% of the test data are from adenocarcinoma (ADCA) tissue samples. After all informative genes have been removed using the IFR process, the training samples can not be accurately fit to the data (green dashed line), even as the test accuracy spikes. In this case, a hyperplane has been constructed such that essentially all points are being classified as ADCA, which results in an artificially high test accuracy.optimal set is removed. Given that genes express in pathways, and can be highly correlated, it is perhaps not surprising that even 20-40 sets can yield highly accurate predictions. While these genes are redundant from the perspective of machine learning, they are essential to the process of biological discovery. Since all the retained sets are predictive, we assert that they play a role in characterizing the differences between the classes in the data.We have demonstrated that a significant number of feature subsets selected via IFR have essentially equivalent predictive power and hence relate to potentially discriminative biological processes. Our hypothesis suggests that, in many microarray data sets, there is substantial additional biological information contained in the features that remain after the first To evaluate how the resulting larger set of informative genes relates to discriminatory biological pathways, we follow a two-pronged approach, one relying on manual analysis, the other automated. The first approach uses manual expert analysis of the features selected using IFR. The disadvantage of this approach is that it is labor-intensive and thus difficult to apply to cross-validate results. The automated approach relies on employing the Gene Ontology (GO), queried via the GATHER system, to idenenriching the feature selection.There is some similarity between this knowledge-driven analysis and feature enrichment strategies, such as Gene Set Enrichment Analysis (GSEA). GSEA usWe used the influenza 14-16 data for our manual analysis because this allows for direct comparison to other published results.We examined the 40 most predictive iterations from our IFR process for association with known pathways activated by influenza infection. This number of iterations should not be viewed as a hard cutoff and should be driven to some extent at least by the additional biological information being gained. We made our selection by stopping at the iteration before the consistent decline in test accuracy begins, and where the variance in test results remains relatively low.As described below, we accumulated related genes across these predictive sets and organized them by pathway. As long as the predictive accuracy of a set is high, the actual iteration that the gene was selected should not be viewed as a strong indicator of its biological importance.TableFigureα, β, γ, and λ) themselves are not in this subset, nor did we find them to be in our most predictive iterations. That may be in part due to the paracrine, rather than endocrine, nature of these proteins, and that the samples were blood samples, rather than samples directly from the site of infection and viral replication such as the respiratory epithelium. We found many interferon pathway members OAS1-3, OAS-L, PSME1/2, ISG20, IFI35, IFI6, IFI30, IFI16, IFIT2 and IFITM2, for example, all increased in active infection. Interleukin 15 (IL-15) and the IL-15 receptor α chain, which are induced by the interferons[Several markers of the Interferon pathway were found in our analysis Table and the α and IL-1 β also played a role. These two proinflammatory cytokines are, like the interferons, well known for their roles in antiviral responses and, in contrast to the interferons, have endocrine effects, notably causing fever[α induced proteins TNFAIP6, TNFAIP1, and TNFAIP3 as well as TNF super family 10 (TNFSF10) all increased. Additionally, TNF receptor super family 10B (TNFRSF10B) is the receptor for TRAIL/TNFSF10 but was decreased in symptomatic subjects. Three other TNFRSF members were found to be highly predictive in this analysis, TNFRSF4 (OX40 receptor), TNFRSF9 (CD137/4-1BB), and TNFRSF14 (HVEM), although the expression levels between symptomatic and asymptomatic subjects were only slightly different. All four TNFRSF members are critical for T cell activation and maturation[Using the 40 most predictive iterations from IFR to look for more biologically relevant pathways activated by infection, we noted two other major cytokine pathways, in particular TNF ng fever. In addituration.β, IL-1 receptor type I, IL-1 receptor accessory protein, and IL-1RL2 were also found to be modulated by infection. IL-1 receptor type I and IL-1 receptor accessory protein decreased in symptomatic subjects, which is somewhat paradoxical as these form the receptor complex for IL-1 β. Many B cell genes all decreased upon active infection including IgM heavy chain, IgD heavy chain, CD9, CD19, CD22, CD24, CD72, CD79A, and CD200. This may be due to activation and sequestration of B cells in lymphoid tissues in response to the infection. Similarly, CD8A  and Ly9 and KIR2DL3  also decreased, suggesting that these cells were also being activated and sequestered in tissues at the site of infection.IL-1 Chemokines and their receptors are also important small signaling molecules. CXCL10, which activates chemotaxis in effector T and B increased after infection, but the neutrophil-specific chemokine CXCL6 decreased. CCR1 increased with illness, and is a receptor for many of the CCL chemokines. CCR6, the receptor for CCL20, decreased upon illness. CCRL2 (chemerin receptor) also increased. The fraCaspases are involved in apoptotic death of cells. Caspases CASP4, 5, and 7 all increased. Several Programmed Cell Death genes, PDCD1LG2, PCDHGA11, and PCDHA3, also increased in symptomatic subjects. TLR5 increased in symptomatic subjects, unexpected as its most well-known ligand is flagellin. Complement component C2 decreased, but the complement receptors CR2, C3AR1 both increased. Chen et al. had alsoSeveral genes involved in antigen presentation to T lymphocytes were also found to be highly predictive. Chen et al., found TAP1 upregulated in symptomatic subjects, and our analysis also found TAP2. TAP1/2 form a transporter complex to carry peptides into the endoplasmic reticulum for loading into Class I Major Histocompatibility Complex (MHC) proteins. MHC Class I protein/Human Leukocyte Antigen (HLA)-B was also upregulated in symptomatic subjects, along with the related protein HLA-E, which is an activator of NK cells. In contrast, in the Class II chains HLA-DQA1, HLA-DQB1, HLA-DPA1 and HLA-DOB1 were higher in asymptomatic subjects.In addition to what has already been described, our hypothesis further suggests that better diagnostic tools can potentially be designed by performing a deeper analysis of the discriminative features selected via IFR. If true, then certain combinations of features from the discriminative pathways should be able to yield classification accuracies that exceed any single iteration because we can improve the signal-to-noise ratio by first selecting discriminative genes and then combining the information from complementary pathways.As stated in, a necesTo test whether this is true, we combine pairs of pathways and measure the classification accuracy using the influenza data. We generated a Support Vector Machine classifier from each pair of pathways listed in TableIn searching for discriminative pathways using IFR, we found that we could construct a perfect classifier using the genes identified with B Cell Maturation and Activation (BCell) combined with those identified as relating to Cell Adhesion Molecules (CAM). The classifiers trained from these two pathways make different errors. The BCell classifier misses samples: , while the CAM classifier misses samples: . The only overlap between the two sets of errors is sample 11, but when the two pathways are combined to train a single classifier, no errors are made.We computed the t-test scores for all genes in the influenza data set. Ranked according to t-test, the 17 genes comprising the combination of the BCell and CAM classifiers are shown in TableFigureThe weakness in the manual analysis presented herein, including the results of the pathway and pathway pair classifiers, is a lack of cross-validation due to the limits posed by human annotation. We address this limitation by automating our pathway analysis using queries to the well-known Gene Ontology database. These results are presented next.As we did in the previous section, we first present the pathways associated with each iteration of IFR, but this time the pathway annotations are derived from GO using the GATHER web query service. We present the biological significance of features selected beyond the first optimal subset in the lung cancer data. As a reminder, this data consists of two classes of tumors, and so the selected features are those that can be used to differentiate between the two, not to discern whether a tissue sample is cancerous. Following that, we present automated pathway and pathway pair classification using four microarray data sets.The GO annotations associated with the features selected at each of the first thirty iterations are shown in FigureThe annotations in FigureBy considering a larger pool of correlated genes, a biologist might discover important information that otherwise could be masked by feature selection methods optimized for removing predictive redundancy.As we did with the manual analysis, we can use our automated approach to discover discriminative pathways and pathway pairs. Our protocol was to use 50-trial random subset cross-validation on the training partition to identify features associated with the top models. After selecting the top-performing models from the validation stage, we then tested on the withheld data. More details on this procedure can be found in the Methods section.We also performed the same cross-validation without using multiple IFR iterations, instead using the sparse SVM (SSVM) to select the optimal set of features to classify the data. This is equivalent to using only the first iteration of IFR. The three methods  differ in how genes are selected, but a common classifier engine is used to compare performance. Once the features are identified, validation and test accuracies are computed using a standard (non-sparse) SVM built from the training data using the selected features, and evaluated on the validation or test data. Thus, performance differs due to the features, not the classifier.Finally, since there can be several models from the validation trials with near equivalent performance, we also report on the average test accuracy of the top five models selected from the validation results.TableIn summary, the pathway, pathway-pair, and SSVM feature selections were found to perform comparably on the withheld test data for the lung cancer, prostate cancer, and BCell lymphoma data sets. There was, however, a statistically significant improvement using the pathway-pair features in the influenza data. Significance was measured using Welch’s two-sample t-test between methods, evaluated on the test accuracies returned by the top five models from each. P-values for all comparisons can be found in Additional fileThe benefit of using IFR is that several predictive pathways can be identified with performance similar to that of the optimal model, but with the advantage of providing more insight into the biology of the discriminating factors.In the lung cancer results, there is some evidence that pathway-pair feature selection performs better than SSVM , but the p-value is too high to be conclusive. Since all methods perform well, ceiling effects may inhibit the ability to differentiate amongst them.In the influenza 11-14 results, there was strong evidence that the pathway-pair features yield better classifiers than those selected via SSVM . Additionally, there was some evidence that even individual pathway features perform better than SSVM , and that genes selected via pathway-pair analysis outperform those of individual pathways . The results of the automated analysis and the manual analysis when applied to the influenza data appear to be consistent. One aspect of the influenza data that is different from the other three is that the training and validation data is of a slightly different pathology (H3N2) than the test data (H1N1).The results from the prostate data show no statistically-significant differences among the three methods, even though the validation distribution for SSVM has a higher mean accuracy and narrower variance. The features selected by the top performing pathway, pathway-pair, and SSVM model yielded classifiers exhibiting the same accuracy when tested against the withheld test data, correctly classifying 27 of 34 test samples (79.4%). The average test accuracies from the top five models of each method show no statistically-significant difference. With SSVM, the accuracy of the cross-validation result appears to overestimate the performance of the classifiers when applied to withheld data. This phenomenon often indicates that there is a substantial difference in the test data from the training, and that the model is over-fit to the training data. From the description of the data as provided by the Kent Ridge Bio-medical Data Set repository, we note that the test data was compiled from an independent experiment from a different lab with different collection methods. In this case, the pathway-oriented analysis was found to provide more consistent performance  than the parsimonious features selected via SSVM. A similar trend was noted for the influenza data.There is no statistically-significant performance differences amongst the methods applied to the BCell lymphoma data. The top model from each method, when applied to the withheld test data, correctly classified 10 of 12 samples (83.3%). The BCell lymphoma data only has 12 testing samples, so we observe much higher variance in classification accuracy, where a single misclassification drops the score by over eight percentage points. We note that for all three methods, the top test performance was within one standard deviation from the cross-validated mean. The BCell lymphoma data differs from the rest in that it is smaller, both in terms of samples and features, and it has missing values, which were imputed using averages.l1 regularization on a linear classifier, resulting in a model built using only a small number of features (those with non-zero weights).Saeys et al. provides a review of feature selection techniques common in bioinformatics, categorredundant genes. In contrast, we iteratively remove the best features, i.e., most highly discriminatory, in order to demonstrate that there exist many subsets of highly discriminative genes. We assemble all sets of discriminatory genes produced by the iterative removal to form one large set of biologically important genes. Our perspective is that while genes may be redundant in terms of a model’s optimization criteria, they are not necessarily biologically redundant, and may be key to developing a full understanding of the interacting biological processes.A Support Vector Machine (SVM) is a popular machine learning method that maximizes the width of the margin surrounding a hyperplane that separates classes of data. SVMs haRedundancy has previously been explored in the context of gene expression data. Yu et al. provide formal definitions of “strongly relevant,” “weakly relevant,” and “irrelevant” features. In the Selecting genes by ranking according to a univariate measure is a common practice among biologists. Significance Analysis of Microarrays (SAM) is a scoWhat is the goal of feature selection on data sets consisting of a small number of samples of microarray data? One goal might be the development of a diagnostic tool. Another goal may be to use the feature selection as an aid in further discovery and learning about a particular biological process.Regarding the first goal, developing a classifier using a data set with a small number of samples can lead to overly optimistic results. While the classifier may be able to yield high accuracy on the test data, little can be said of how well it could perform as a diagnostic tool in the general population. Imagine developing a decision tree for classifying a notional influenza data set. With only one question: “does the subject have a fever?”, the influenza data could be perfectly partitioned into symptomatic and asymptomatic patients. Some small set of genes could be identified that essentially answer this question. While the selected set of genes may be a perfect classifier on the data set, it would be useless as a diagnostic tool in the clinic, lacking any specificity as to the cause of the fever (flu or otherwise). To restate: identifying a set of features that perfectly separates test data is not the same as identifying a set of features that will diagnose a disease in general. The former only approximates the latter when enough variety is present in the training data.Lacking a large sample size, using feature selection methods to reduce the decision to a small set of features exacerbates this issue. Training a classifier on a larger set of genes is more likely to lead to the development of a model with greater specificity than training a classifier using a minimum set of genes found to be discriminative in the training data. Consider again the example above, where the classifier learns to partition the data based on the presence of a fever. If instead the classifier were asked to fit a much larger set of genes, then more factors would be involved in the prediction. Instead of generating a false positive for every patient exhibiting a fever but not having the flu, the more complex model would generate false positives only for patients having a larger number of factors similar to the symptomatic training set.Regarding the second goal, predictive redundancy is not the same as biological redundancy. For example, when considering the host response to a pathogen, there may be several sets of genes which are equally predictive on the test data. One set might relate to the innate immune response (“Process A”), and another set might relate to an acquired immune response (“Process B”). Either set may work for partitioning the test data into symptomatic/asymptomatic subjects. A feature selection technique that suppresses redundant features might eliminate all genes related to Process B without loss of predictive accuracy. Yet when the goal is to select features to generate insight into the biology, the elimination of genes associated with Process B is a problem, not a benefit.The broader impact of the work presented in this paper is to challenge the paradigm that seeks to identify a small number of discriminative features for microarray data, or other high-dimensional biological data with relatively few samples. Classifiers built in this way are unlikely to be useful as diagnostic tools unless they are trained using a very large number of samples that better approximates the target population for the tool. Feature selection to aid in understanding a biological process based on gene expression data is most useful when a maximally-sized subset of informative genes can be presented to the researcher.Iterative Feature Removal provides a process for the unbiased selection of discriminative sets of features from a high-dimensional data set. The procedure results in a deeper mining of relevant information by iteratively suppressing top weighted features in order to unmask the less-obvious features that are nearly equally discriminative.We recognize that others have previously observed the phenomenon where top genes can be removed from a microarray data set and a subsequent classifier built to similar accuracy using the remaining set . HoweveWhen applied to microarray data, IFR identified sets of genes that were highly predictive even when the sets were composed of genes that, taken individually, appear non-discriminatory. Through automated and manual expert analysis of the selected genes, we demonstrated that the selected features exposed biologically-relevant pathways that other approaches would have missed.The pathway analysis provides an alternative way of grouping the genes, as opposed to by ranking, and also potentially provides more insight to the biologist. By showing that strong classifiers can be built without consideration to the iteration number or any other ranking (other than the fact that we selected the gene via IFR), we prove that strong discriminative power can reside in lower ranking genes.The model for the classifier is a separating hyperplanex is a vector of gene expression levels. The learning task is to use labeled observations to determine the parameters w and b such that unlabeled observations can be characterized, e.g., as associated with symptomatic or asymptomatic subjects.where Extending the work of Cortes and Vapnik, MangasaS+ and S- denote the sets of positive and negative class samples, respectively. The width of the margin separating the two classes is 2/∥w∥, which is maximized when ∥w∥ is minimized. Slack variables, ξi, measure the degree of misclassification of samples xi. The parameter, C, determines the penalty assigned to the total error from misclassified samples, resulting in a trade-off between the width of the margin and the error penalty.where p-norm of a vector is defined asp = 2, we have the standard SVM. For p = 1, sparsity is induced in the weight vector w. In other words, a separating hyperplane is found where many components of w are essentially zero. We refer to the solution of Equation (2) with p = 1 as a sparse support vector machine (SSVM). The sparsity inducing properties of the 1-norm are now well-known; see[The own; see and refexi co-expresses with another gene xj then the model will attempt to set either wi or wj to zero. Both genes may have high discriminatory value, but they may not both be needed in a single model since they contain similar information. We view one of these genes as a proxy for the other.In our gene selection problem, we may interpret sparsity as the elimination of genes that are not essential for classification. This is modeled mathematically by setting the corresponding weight component to zero. For example, if gene At each iteration of the IFR process, the SSVM determines a minimal set of genes capable of optimally discriminating the training data using the remaining features. For reasons described above, in general we do not expect genes in the same pathway to be found in the same IFR iteration. Indeed, we see that genes in the same pathway tend to be spread over many sets as they are iteratively removed.p = 2 SVM model which is not parsimonious.When we have a given set of predictive genes and seek to build a new SVM model, such as when building the pathway-specific classifiers discussed earlier, we no longer use the SSVM since we are not implementing feature selection. In this case we use the For each data set, the source microarray data is normalized to have zero mean and unit deviation for each feature. For the influenza data, the H3N2 and H1N1 data are normalized independently, following what was done in,10. IndeC was not sensitive within an order of magnitude, and selected C = 1.0. We employed a tolerance value of 0.001 for the convergence of the optimization function. These choices also performed well for the other data sets, so we employed the same parameters for all.We employed cross-validation on the H3N2 data to select the parameters used by the SVM classifier. We found that the choice of With SSVM, the weights were determined to be zero by looking for at least a factor of ten drop in the ratio of the weights ordered by magnitude. As shown in FigureThe following provides more detail on how we identified the pathways and pathway pairs in the cross-validated automated analysis, as reported in TableFor each data set, we run 50 trials of IFR on a random 75/25 percent splits of the training/validation data. For each trial, we take the first 30 iterations (20 for BCell lymphoma) to identify the pathways represented by the selected genes using queries to the Gene Ontology. We build a classifier by selecting the genes (without enrichment) representing each pathway and compute the validation accuracy. From one trial to the next, we tend to find the same discriminatory pathways represented, but there is some variation to the representative set of genes. We compute the mean accuracy of a pathway by averaging the pathway classification results over all trials, using zero percent accuracy for any trial where a pathway was not represented. Using these averages over 50 trials, notwithstanding the fact that there is some variation trial-to-trial in which genes represent a pathway, we select the top scoring in terms of mean validation accuracy.Using the training/validation trials to select a top pathway (or top 5), we then test how well that pathway classifies the withheld test data. We do this by using the union of all the discovered genes relating to that pathway over the 50 trials, creating a new model on the training data, and reporting accuracy on the test.A similar procedure is followed for pathway pairs. From the same 50 trials we already have the individual pathways. For each trial, we examine the combinations of those pathways that exceed a validation accuracy threshold. The threshold is used to prevent analyzing a combination where one pathway is non-discriminative. This threshold saves computation time, but does not change the results. For each combination, we build a new model on the training data using the union of the genes from the two pathways, and record the validation accuracy of the combination. As we did with the individual pathways, we compute the average validation accuracy of a pathway pair over the 50 trials, using zero percent accuracy for any trial where a pathway pair was not represented. We select the best pathway pairs using the mean validation scores, generate new models on the training data using the union of the genes found by each pair over the trials, and test on the withheld data.ahttp://people.ee.duke.edu/~lcarin/reproduce.htmlbhttp://levis.tongji.edu.cn/gzli/data/mirror-kentridge.htmlc This is not to suggest that random models will classify this data with any accuracy – they don’t.The authors declare that they have no competing interests.SO and KW provided most of the implementation and analysis presented in this article, with an equivalent technical contribution on IFR. SO developed the pathway classifiers and related analysis, and provided most of the writing for the manuscript as a whole. MK, GH, CO, and MS had the original conceptualization of the iterative removal process, and were involved in subsequent discussions and manuscript editing. MK developed the SSVM implementation used as the sparse classifier for the main IFR results. RS and AS provided the biological expertise for interpreting the nature and relevance of the results. AS performed the manual expert analysis of the selected influenza features and authored the relevant discussion. RS provided editorial support. All authors read and approved the final manuscript.Supplemental Material. Additional material and analysis is available in the supplemental.pdf file.Click here for fileIFR_SSVM_H3N2_genes. This file is the comprehensive list of genes selected at each iteration when using IFR with SSVM on the influenza data.Click here for fileIFR_SSVM_H3N2_acc. This file provides the accuracy of each iteration of IFR on influenza data using SSVM.Click here for fileIFR_SSVM_Lung_genes. This file is the comprehensive list of genes selected at each iteration when using IFR with the SSVM on the lung cancer data.Click here for fileIFR_SSVM_Lung_acc. This file provides the accuracy of each iteration of IFR on lung cancer data using SSVM.Click here for fileIFR_LR_H3N2. This file is the comprehensive list of genes selected at each iteration when using IFR with sparse Logistic Regression on the influenza data.Click here for fileIFR_LR_Lung. This file is the comprehensive list of genes selected at each iteration when using IFR with sparse Logistic Regression on the lung cancer data.Click here for file