Identifying perturbed pathways in a given condition is crucial in understanding biological phenomena. In addition to identifying perturbed pathways individually, pathway analysis should consider interactions among pathways. Currently available pathway interaction prediction methods are based on the existence of overlapping genes between pathways, protein-protein interaction (PPI) or functional similarities. However, these approaches just consider the pathways as a set of genes, thus they do not take account of topological features. In addition, most of the existing approaches do not handle the explicit gene expression quantity information that is routinely measured by RNA-sequecing.α. Our approach successfully identified the pathways reported in the original papers. For the pathways that are not directly mentioned in the original papers, we were able to find evidences of pathway interactions by the literature search. Our method outperformed two existing approaches, overlapping gene-based approach (OGB) and protein-protein interaction-based approach (PB), in experiments with the three data sets.To overcome these technical issues, we developed a new pathway interaction network construction method using PPI, closeness centrality and shortest paths. We tested our approach on three different high-throughput RNA-seq data sets: pregnant mice data to reveal the role of serotonin on beta cell mass, bone-metastatic breast cancer data and autoimmune thyroiditis data to study the role of IFN- http://biohealth.snu.ac.kr/software/PINTnet/.Our results show that PINTnet successfully identified condition-specific perturbed pathways and the interactions between the pathways. We believe that our method will be very useful in characterizing biological mechanisms at the pathway level. PINTnet is available at  Identifying perturbed pathways in a given condition is crucial in understanding biological phenomena. Over-representation analysis (ORA) , gene seThere are several approaches to find pathway interactions and construct a pathway interaction network. The first and simplest one is to consider the shared components such as genes or proteins between pathways. It assumes that shared genes may mediate interactions and predicts such interactions by testing the significance of the overlapping genes between pathways using hypergeometric test such as Fisher’s exact test . The secThough the previously mentioned methods suggested an insight on how to predict interactions between pathways, the methods are based mainly on testing differential gene expression. None of these methods use the explicit quantity of gene expression. Therefore, the methods are not able to identify the subtle but important changes in gene expression. Moreover, many of the methods do not take into account the topological features, treating the pathways just as a set of genes. Recently, a path-based approach was studied  but it dpathway interaction network construction method (PINTnet). The summary of our method are: The interactions between pathways are represented by the subnetworks that are constructed considering two topological features: closeness centrality and shortest path.Shortest paths on the subnetworks are computed based on an assumption that pathway interactions occur by a series of spontaneous reactions among genes belonging to the pathways.The explicit quantity of gene expression is used to measure the activation status of pathway interactions.The flow of the changes in expression is weighted. Higher weight is given to any edge between genes when the edge connects differentially expressed genes (DEGs).Here, we propose a new In this section, we describe the process of how PINTnet measures the activation status of the interactions and constructs the pathway interaction network including the preprocess steps in detail. The overview of the method is depicted in Fig. We collected protein-protein interaction data from STRING (ver.9.1)  and pathDefining edges between two pathways is the key issue in constructing a pathway interaction network. Below are the steps for defining edges.We constructed a subnetwork for every possible pair of pathways. To do so, we used two criteria for the pathways to be paired: whether the two pathways have at least one overlapping gene and whether the two pathways have at least one gene connected to the overlapping genes via PPI. We defined every pair of two pathways as a possible pair only when the two pathways satisfied the both criteria. Then, for every possible pair, a subnetwork was constructed using PPI involving the two pathways.For each subnetwork generated above, we calculated closeness centrality of all the genes within. The centrality was to evaluate the degree of a node to be central in a given network, by taking a reciprocal of an average shortest path length to all the nodes within a network from the source. The shorter the average shortest path length of a node, the closer to 1 the closeness centrality of the corresponding node is, otherwise, closer to zero. In this way, genes reflect the topological importance of themselves concerning all possible neighbor nodes within a given subnetwork.A and B, let the genes in A as Agenes={a1,a2,…,am} and the genes in B as Bgenes={b1,b2,…,bn} where m is the number of genes in A and n is the number of genes in B. The shortest paths were computed for every pair of genes ai and bj where 1≤i≤m, 1≤j≤n and both ai and bj are the direct neighbor genes to the overlapping genes. The shortest paths must pass through any overlapping gene of the two pathways.After calculating the closeness centrality, we pruned the genes that are not direct neighbors to overlapping genes in subnetworks. Then, we computed the shortest paths. Given two pathways Finally, we weaved the shortest paths and constructed shortest path-weaved subnetworks (SSN). We conjectured that the pathway interaction occurs by the rapid and spontaneous flow of biological signal or interaction through topologically important genes. This concept is realized in our method by computing shortest path in the weighted subnetworks in terms of closeness centrality. The SSN thus is the network that connects the topologically important genes using the shortest paths. The overview of these steps is depicted in Fig. Measuring the activation status of biological systems or networks is technically difficult. For example, we may want to compute the average expression level of all genes in a network as the activation status of the network. However, this computation completely ignores topological features. A recent work  demonstrk is the index of SSN constructed from each pathway pair, l is the index of an edge, vl1 and vl2 are two genes connected by the edge l, ck(v) is the closeness centrality of a gene v in SSNk and x(v) is the expression level of a gene v. ek,l is the condition-specific edge centrality of edge l in SSNk. After measuring the co-expression score for every edge in SSNk, PINTnet takes the average of the summation of the scores as the activation score (AS) and that is: where N is the total number of edges in SSNk and l is the index of an edge. PINTnet then calculates the ratio of where Computing DEGs is a simple but effective approach for detecting perturbed pathways and even signaling impacts in the pathways in a given condition. However, DEGs are widely interspersed and are not connected in the networks or pathways. To utilize DEG information, we applied the ratio of DEGs that are connected by edges as a weight. A higher interaction score is assigned for more DEG connections. In this step, PINTnet simply calculates the fold change of expression level of each gene to define DEGs and the default threshold is log20.5 as used in other studies that used RNA-seq data –21. The k is the index of SSN, ISk is the interaction score of SSNk, SSNk of the case data, SSNk of the control data, U is the number of connected up-regulated DEGs of the case data compared to the control data and D is the number of connected down-regulated DEGs of the case data compared to the control data. When PINTnet calculates the fold change, the cutoff value of 1.0 for the expression level is used to prevent noise such as extremely high fold change due to the comparison between small numbers. The cutoff value was set based on other studies [where  studies , 23. In  studies . We trieAfter measuring the activation status of all pairs of pathways and obtaining the interaction score, PINTnet converts the interaction score using the sigmoid function . It is tAfter the interaction score is converted, a pathway interaction network is constructed with the edges between pathways when the interaction score of edges satisfies the cutoff value. We empirically determined the cutoff value by testing PINTnet on various data from other biological researches.To evaluate the performance of PINTnet, we used three different high-throughput RNA-seq datasets in Gene Expression Omnibus (GEO). Cytoscape was used to visualize the networks . The tesDataset1 is the data that measured the gene expression levels of pregnant mice to reveal how serotonin regulates pancreatic beta cell mass during pregnancy [Tph1 and Tph2 were the genes most markedly induced during pregnancy. These two genes encode two isoforms of tryptophan hydroxylase, the rate-limiting enzyme in the synthesis of serotonin, 5-HT. The authors also reported that beta cells share a common gene expression program and the ability to synthesize, store and secrete serotonin with serotonergic neurons.regnancy . The autDataset2 is the data generated by a study investigating how ABL kinases promote breast cancer osteolytic metastasis [tastasis . Bone isDataset3 is from a study that used thyroiditis as a model to reveal how IFN- α plays a pivotal role in auto immunity [α in the thyroid and performed RNA-seq analysis. The transgenic mice showed upregulation of pathways such as antigen presentation pathway, interferon signaling, complement system, apoptosis, pattern recognition receptors and RAR activation.immunity . The autβ-cells. A lack of serotonin in β-cells can lead to reduced insulin secretion [It is well known that nutrient requirements by the fetus incur change in the maternal metabolism during pregnancy. Nutrient flow to the fetus is maintained by increasing insulin resistance. The resistance may cause maternal hyperglycemia but the glucose level is maintained by the expansion of beta cells driven by prolactin and placental lactogen –32. Failecretion . Also, iecretion –37 and iecretion .ABL1 and ABL2 belong to and it is known that Ras signaling pathway activation is implicated in breast cancer invasion and growth [β signaling pathway was reported to be important for the development of osteolytic bone metastases by numerous studies [Ras signaling pathway is the pathway which d growth . Thus thd growth . MAPK sid growth . In addid growth , 43. Wntd growth . Upreguld growth , 46. TGF studies . Proteog studies . A high  studies . Hippo s studies . In addi studies –59.It is known that Toll-like receptor signaling pathway plays an important role in autoimmunity including thyroid autoimmunity , 61. AlsTo compare the performance of PINTnet to other approaches, we implemented the overlapping gene-based approach (OGB) and the PPI-based approach (PB) and ran three methods including ours on the three test datasets in the previous sections.p-value of 0.05 or less. The significant edges were used to construct a pathway network.This method is a two-step approach. In the first step, the activation status of each pathway was calculated using Fisher’s exact test with a contingency table dealing with two parameters: one is whether a gene is a DEG or not and the other is whether a gene belongs to the pathway or not. Then, in the second step, the significance of edges among pathways was evaluated using Fisher’s exact test. The significance of pathways and the edges among the pathways were determined at a p-values for every possible pair of pathways to find significantly interacting pathway pairs by shuffling the original PPI network 1,000 times, counting the number of round when the number of shuffled PPI edges between pathways was bigger than or equal to that of the original PPI edges and dividing the number by the number of round, in this case, 1,000. Then we adjusted the p-values using Bonferroni correction and took the edges with the p-value less than 0.05 as the significantly interacting edges. Connecting the edges, we constructed a template network and calculated active PPIs on the network using the datasets.For PPI-based approach, we implemented the simple version of the approach since no executable code is available. We implemented the approach based on the hypothesis that the more interactions may guarantee the higher probability of interaction. To do this, we calculated the empirical dataset1. It seemed almost impossible to determine which pathways and interactions between the pathways were important in the given conditions. Thus we did not include this approach for the performance comparison.Running the approach on the test datasets, we observed that too many nodes and edges were connected even though multiple testing correction was performed using the Bonferroni correction. For example, there were 271 nodes and 12,264 edges for We compared the performance of the approaches based on the biological evidences found by the literature search and organized in Evaluation criteria section. The following criteria were used for the quantitative measure of the performance. The first criterion was the interactions between the pathways. We calculated the percentage of the number of the evidence-supported edges between the evidence-supported pathways against the total number of edges in the network. It was to measure how successfully the approaches connected the correct edges supported by evidence. The second criterion was the degree of pathways. We calculated the ratio of the average degree of the evidence-supported pathways against that of all pathways in the network. It was to measure how the approaches placed the important pathways as hubs in the central position of the network. The last criterion was to see how successfully the approaches rescued the correct pathways. We calculated the percentage that how many evidence-supported pathways were rescued. We confirmed that PINTnet surpasses other methods from the results described in the following paragraphs and the comparison results are shown in Table dataset1 with the cutoff value of 0.95, there were 60 pathways and 92 edges between the pathways. The network is shown in Fig. In the pathway interaction network constructed using our method on dataset2 with the cutoff value of 0.99. The network is shown in Fig. β signaling pathway (hsa04350), PI3K-Akt signaling pathway (hsa04151) and proteoglycans in cancer (hsa05205). The result implies that the pathways implicated in bone metastasis from breast cancer interact each other and the interactions among the pathways along with the paths may give the insight of how bone metastatic breast cancer is caused by pathways interaction. However, TGF- β signaling pathway was not rescued by OGB and only two edges were detected: MAPK signaling pathway and proteoglycans in cancer; Ras signaling pathway and PI3K-Akt signaling pathway.There were 66 pathways and 122 edges between the pathways in the pathway interaction network constructed using our method on dataset3 with the cutoff value 0f 0.99 included 102 pathways and 149 edges between the pathways. The network is shown in Fig. α belongs to and autoimmune thyroid disease is the overall context of the original paper. Moreover, the findings reported in [The pathway interaction network constructed using our method on orted in , 61 suppp-value yielded by the tests or use the p-value itself to calculate the secondary score for the determination. In addition, even though several approaches incorporated PPI to infer the interactions between pathways, the approaches have a limitation that PPI was treated merely as a set of individually represented genes without considering any relation between the genes. To address these issues, we applied the concept of closeness centrality and shortest paths to define the edges in the pathway interaction network. We assumed that the interaction between two pathways will occur when the biological signals rapidly flow through the topologically important genes. Based on the assumption, we constructed shortest path-weaved subnetworks to represent the edges and calculated interaction score using explicit gene expression quantity on the subnetworks.Currently available approaches for constructing pathway network were designed to handle microarray data so the approaches mostly rely on the statistical tests. The approaches determine the significance of the interaction by the The scoring scheme of PINTnet is a ranking method. It constructs the pathway interaction network using pairs of pathways of which the score is higher than the cutoff value. The results on the test datasets suggest that PINTnet successfully reproduced the results of the original papers and, therefore, is useful in analyzing the perturbed pathways and their interactions in a given condition.Like existing methods, PINTnet is based on the identification of overlapping genes between two pathways. We assumed that the overlapping genes function as a bridge between two pathways. Based on the assumption, we considered the situation that at least one overlapping gene exists as one of the rules to define the edge in the pathway interaction network. This criterion, though reasonable and popular, may be too stringent. For example, when two pathways are well connected by direct edges in PPI but do not share any genes, it is not clear whether the two pathways interact or not. Therefore, the pairs of truly interacting pathways might be ruled out. We will further work on the matter to overcome the limitation.dataset1, PINTnet successfully detected the pathways related to the changes occurring during pregnancy. Also we observed that the pathways were connected by the edges supported by the literatures. For dataset2, we also identified that the pathways related to bone-metastatic breast cancer were rescued in the pathway interaction network and the edges between the pathways implied the interactions participating in the induction of the phenotype. For dataset3, the pathways reported by the original paper were included as nodes in the pathway interaction network and there was a connected edge between Toll-like receptor signaling pathway and autoimmune thyroid disease. We expect PINTnet to be a useful tool for pathway interaction network analysis. PINTnet is available at http://biohealth.snu.ac.kr/software/PINTnet/.In this work, we developed a new pathway interaction network construction method, PINTnet. Running PINTnet on the three datasets to test the performance, we observed that it successfully rescued the findings reported in the original papers. In the result of


In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user interface (GUI), (2) computationally efficient core program and (3) convenient network visualization capability. The CyNetSVM app has been used to analyze breast cancer data to identify network genes associated with breast cancer recurrence. The biological function of these network genes is enriched in signaling pathways associated with breast cancer progression, showing the effectiveness of CyNetSVM for cancer biomarker identification. The CyNetSVM package is available at Cytoscape App Store and  Genes usually work collaboratively as modules, networks or pathways, and different modules can interact with each other to take effect . The natIn this paper, we present a Cytoscape  app, cali.e. protein-protein interaction (PPI) data and gene expression data), the core program first pre-processes the data through standardization and then identifies the networks from the processed data. Once the core program completes, the gene network is created, and the node color is set based on the log2 fold change between the two phenotypes. Along with the network, CyNetSVM also reports the sensitivity, specificity, ROC curve and AUC values for the classification.An overview of the CyNetSVM package is shown in NetSVM  is a comThe CyNetSVM package has been implemented in Java as a Cytoscape app for network biomarker identification. A screenshot of the CyNetSVM app is shown in When running the CyNetSVM app, the GUI will pass all the input data and options to the core program. The class diagram of the GUI component is shown in https://www.osgi.org), CyNetSVM has been packaged as a bundle in Cytoscape. Since Cytoscape uses the OSGi architecture (We first compared CyNetSVM with NetSVM (implemented in MATLAB) and conventional SVM using simulation data to prove the correctness of our implementation and demonstrate the improvement of performance with network information incorporated. The simulation data were generated on a breast cancer-related network with 584 genes and 2280 nodes following the same strategy used in . For eacet al. data) [https://david-d.ncifcrf.gov/) on the identified genes. The genes in the network are significantly enriched in breast cancer-related pathways such as FOXO signaling pathway [To demonstrate the effectiveness of CyNetSVM for real biomedical applications, the CyNetSVM app was used to analyze a breast cancer gene expression dataset (Loi l. data) . The saml. data)  (9673 nol. data) , 11 on bl. data)  function pathway , MAPK si pathway , Ras sig pathway , TGF-Bet pathway , Estroge pathway , Wnt sig pathway  and ErbB pathway . The detet al. data and the discovery data are quite different, with only seven genes  in common. However, enriched signaling pathways are consistent  and network visualization capability of Cytoscape, the CyNetSVM app can be applied to large-scale real biomedical data to effectively identify biomarkers and conveniently visualize biomarker networks.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.


Cholangiocarcinoma (CCA) is an aggressive malignancy associated with unfavorable prognosis, and it’s difficult to diagnose and no effective treatments are available. Long non-coding RNAs (lncRNAs) play important roles in tumorigenesis and metastasis. Intact lncRNAs from exosomes have sparked much interest as potential biomarker for the non-invasive analysis of disease. Here, via exosome sequencing on lncRNAs, GO analysis, KEGG pathway and co-expression analysis, receiver operating characteristic curve and survival analyses, we found that, compared with control group, lncRNAs of ENST00000588480.1 and ENST00000517758.1 showed significantly increased expressions in CCA group. Moreover, area under the curve (AUC) was increased to 0.709 when combined the two lncRNAs, they had a sensitivity and specificity of 82.9% and 58.9% respectively. Further, the higher levels of the two lncRNAs showed a significantly increasing trend with the advancement of cancer TNM stages, and prognosticated a poor survival. In addition, KEGG pathway analysis showed that the most significant difference term was “p53 signaling pathway” . The altered lncRNAs and their target genes were included to reconstruct a co-expression network. These altered lncRNAs were mainly related to cellular processes, environmental information processing and organismal systems, etc. Collectively, our findings provided the potential roles of lncRNAs of ENST00000588480.1 and ENST00000517758.1 in CCA, and implicated these lncRNAs as potential diagnostic and therapeutic targets for CCA. Cholangiocarcinoma (CCA) is an incurable and lethal cancer due to its silent clinical characters, difficulties in early diagnosis, and limited therapeutic approaches. Biopsy and cytology brush biopsy examination is the current gold standard for diagnosis, but it is very difficult to obtain the cells, and the sensitivity is only about 20% . SurgeryTumor-derived exosomes are emerging as a new type of cancer biomarker . ExosomeAs a subclass of ncRNAs that are present in exosomes, long-ncRNAs (lncRNAs), which are > 200 nucleotides (nt) in length, regulate gene expression at the epigenetic, transcriptional, and posttranscriptional levels, play important roles in diverse biological processes, including cell proliferation, invasion, differentiation, apoptosis, metastasis and immune response . A recenExosomes isolated from human bile were imaged by using Transmission electron microscope, TEM. We noted the presence of 30-150 nM of vesicles, consistent with previously reported features of exosomes Figure . Then weA total of 64, 530 transcripts were detected in the current sequencing study, including 30632 transcripts identified as lncRNA. Comparing the mean value of Reads Per Kilobase Million (RPKM) of the CCA group with that of the normal group, 54 transcripts were selected from the 30632 lncRNAs according to the criteria described in the materials and methods section (19 up-regulated and 35 down-regulated). The top 15 up-regulated and 15 down-regulated lncRNAs were listed in Table p-value were listed in Table p-value were shown in Table A total of 4002 terms in the GO-biological process were involved in current sequencing data. The top 10 biological process terms with the highest p-value were p53 signaling pathway, apoptosis, lysosome, metabolic pathways, FOXO signaling pathway, mucin type O-Glycan biosynthesis, amino sugar and nucleotide sugar metabolism, endocytosis, sulfur metabolism, tryptophan metabolism. Further details were shown in Table KEGG pathway analysis indicated that 240 pathways were involved in the current sequencing data, in which many pathways were related to cellular processes, environmental information processing, genetic information processing, human diseases as well as organismal systems Figure . The mosWe then evaluated the co-expression network of lncRNAs and their target genes based on the criterion of combined score > 0.9. In this co-expression network, the co-expression of lncRNA-gene and gene-gene was showed by lines of different colors Figure . The lncWe detected exosomes of differentially expressed lncRNAs between bile specimens of CCA and the control patients. Significant different expression lncRNAs: ENST00000588480.1 and ENST00000517758.1 were selected for further detection using real-time RT-PCR in bile samples of CCA (n=35) and the control patients (n=56) Figure . These rA gene KEGG pathway and Go analysis was performed according to the meterial and methods section. 479 target genes were concerned to form a gene list, while 17 of them were enriched in 3 pathways: “P53 signaling pathway”, “apoptosis” and ENST00000588480.1 and ENST00000517758.1. A chart was drown to explore the associated genes and pathways of the three lncRNAs for further mechanism study Figure . The majExosomes, endosome-derived membrane microvesicles, contain specific RNA transcripts that are thought to be involved in cell-cell communication . ExosomeLncRNAs were detected in exosomes of body fluid, and they were enriched in exosomes than cells , and of GO and KEEG pathway analyses showed that many important functional pathways were closely related to the altered genes. Further analysis showed that the pathways involved were mainly related to cellular processes, environmental information processing as well as genetic information processing. These results emphasized the important role of micro-environmental information processing on the cellular function. Constant with the previous studies supported that lncRNAs in the tumor inflammation  or hypoxhttp://kobas.cbi.pku.edu.cn/index.php. ENST00000588480.1 enriches the Hedgehog signaling pathway, and may have the function of ‘lipid biosynthetic process’, ‘Wnt signaling pathway’, “binding of glycoprotein, ribosomal protein and phosphoprotein”, “peptide kinase and phosphotransferase activity”. ENST00000517758.1 enriches “p53 signaling pathway”, “Pathways in cancer” and “Endocytosis” etc. and may have the function of “Metal ion binding”, “regulation of leukocyte and T cell apoptotic process”, “cholesterol metabolism” and “transmembrane transporter activity”. These informations may improve the current understanding of the close association between the two lncRNAs and CCA, but the underlying mechanism has not been deciphered. Additional prospective and multicentre studies with a considerable pool of patients are needed to determine whether this signature is applicable for CCA.Because the two lncRNAs were differently expressed not only between CCA and contol bile samples, but also between well- and poor prognosis patients, we speculated that these lncRNAs may play critical roles in both tumourigenesis and the progression of CCA, and that both of the two lncRNAs may be potential therapeutic targets in CCA. To preliminarily reveal the potential functions of the lncRNAs in CCA, the mRNAs co-expressed with each of the three lncRNAs were used to conduct KEGG and GO analysis in KOBAS, the website is Bile samples of CCA n = 35,  and biliPlease see the All the biological specimens were analysed retrospectively. Data were presented as the min to max. Statistical analyses were performed using Graph pad 6.0 and/or IBM SPSS Statistics 17.0 software, differences were considered statistically significant when the p < 0.05. The difference between CCA and control group was analyzed using a Mann-Whitney test. Then CCA patients were divided into high and low groups according to the median of the lncRNA expression, survival curves were estimated using the Kaplan-Meier method, and differences in survival distributions were evaluated by the log-rank test.


Reactome functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data such as gene expression profiles or somatic mutation catalogues from tumor cells. To support the continued brisk growth in the size and complexity of Reactome, we have implemented a graph database, improved performance of data analysis tools, and designed new data structures and strategies to boost diagram viewer performance. To make our website more accessible to human users, we have improved pathway display and navigation by implementing interactive Enhanced High Level Diagrams (EHLDs) with an associated icon library, and subpathway highlighting and zooming, in a simplified and reorganized web site with adaptive design. To encourage re-use of our content, we have enabled export of pathway diagrams as ‘PowerPoint’ files.The Reactome Knowledgebase ( Homo sapiens, and applying a single consistent data model across all of these domains of biology. Processes are systematically described in molecular detail to generate an ordered network of molecular transformations, resulting in an extended version of a classic metabolic map , supporting the annotation of 24 704 specific forms of proteins distinguished by co- and post-translational modifications and subcellular localizations. These function with 1768 small molecules as substrates, catalysts, and regulators in 11 302 reactions annotated on the basis of data from 27 526 literature references. These tallies include 1334 mutant variants and their post-translationally modified forms derived from 285 gene products, used to annotate 906 disease-specific reactions, tagged with 294 Disease Ontology terms (Reactome (version 62—September 2017) has entries for 10 719 human genes, 53% of the 20 338 predicted human protein-coding genes  that combine an iconography familiar from textbooks and review articles with web functionality, to represent superpathways, and have added features to our pathway diagrams to improve their legibility. A redesigned web site with adaptive technology is accessible from tablets and mobile devices, and supports more intuitive navigation.Here, we provide brief descriptions of these changes and their contributions to the usability of the Reactome data resources.Relational databases work well to model and store complex pathway information and their engines have been optimized to provide efficient execution of global SQL queries that aggregate large amounts of data without requiring traversal operations. Reactome data, however, contain many relationships and thus many join tables, so queries generally require traversal operations resulting in degraded performance and long response times.To preserve our well-established and well-tested tools for data annotation and internal storage while improving performance of our public resource, we continue to maintain the relational Reactome database but have developed a graph database batch importer to migrate content to a Neo4j graph database during each quarterly release. Third parties can directly use Cypher, a declarative, pattern matching query language specifically designed to retrieve data from graph data structures. A fully documented REST based web content service has been built on top of the graph-core to provide third party developers with programmatic access to the graph database. Stress-testing the graph and relational implementations of Reactome indicates approximately a 30-fold improvement in throughput for queries submitted to the graph implementation. Users can download the Reactome graph database and access the data through Cypher queries directly on their computers. In the first year that Reactome provided the graph database, there were 2385 downloads by 912 unique users, 118 of whom downloaded the graph database after each data release.https://reactome.org/dev/graph-database. The API for the ContentService is available at https://reactome.org/ContentService with documentation and tutorials available at: https://reactome.org/dev/content-service. The Java source code is freely available at: https://github.com/reactome in the graph-core, graph-importer and content-service repositories. (See Table The Reactome graph database is freely available at: https://github.com/reactome/)  .A new version of the Pathway Diagram Viewer (version 3) was implemented to provide faster data loading, diagram rendering and element seeking, with the goal of completing most user interactions in less than a second. Improvements include: (i) restructuring of the data format used to send the data from the server to the client from XML to JSON, (ii) using a graph data structure to store the pathway content on the client side, (iii) boosting the client content load strategy, (iv) implementing a multi-layer canvas approach and (v) utilizing a space partitioning data structure to store the elements to be rendered. Conversion from XML to JSON reduced diagram sizes only by about 20% but reduced processing and loading times by at least 65% over the full range of sizes.To improve the quality of the graphics used to represent pathways and to make pathway navigation easier, we have integrated three new features into the Reactome pathway browser: textbook-style EHLDs to represent superpathways such as signaling and immune function, a mechanism to highlight different subpathways with colored overlays in zoomed-out views of detailed pathway diagrams coupled to changes in amount of detail displayed as users zoom into and out of diagrams, and an option to export EHLDs, individual EHLD icons and pathway diagrams in editable, reusable forms .EHLDs Figure  resembleEHLDs were produced by a team of curators and illustrators who revised the Reactome event hierarchy as necessary and developed overview diagrams for the higher levels of the hierarchy. They developed a common style and iconography to enhance the ‘recognition effect’ and facilitate efficient navigation. Of the 120 high-level pathways represented as lists of subpathway names, 48 have been converted to EHLDs; conversion of the remainder should be complete by late in 2018.The SVG format was selected for EHLDs because it allows object-based vector representation for easy editing, resolution-independent zooming, interaction features for richer interfaces, use of data in regular text format as a subset of an eXtensible Markup Language (XML) that can be easily handled by computer programs or text editors, and support by most popular web browsers and compatibility with most popular graphic software packages.EHLDs have a limited number of entities, so the multilayered HTML5 Canvas approach implemented for detailed pathway diagrams (above) was not needed. Instead, for EHLDs, their simple overlay features and a rendering strategy where the flow of information does not depend on the level of zoom, allowed the adoption of an SVG renderer. This allowed developers to implement features such as overlays, zoom and translation by applying a series of filters and transformations.https://github.com/reactome-pwp/diagram). EHLDs are also available in the reusable stand-alone JavaScript diagram viewer (https://reactome.org/dev/diagram/js).The Reactome web interface now has an SVG rendering component that, beyond standard zooming and panning, allows specific regions of an EHLD to be highlighted, recognizes mouse click events on specific regions to allow navigation to subpathways and allows analysis results to be overlaid on it. All of the required technical annotations were included in the SVG files by using the database identifier attribute of each relevant diagram element. Any element with no technical annotation was considered a decoration, so EHLDs can accommodate placeholder icons for subpathways that are still in development. The code is part of the EHLD package available on the Reactome public GitHub repository  to facilitate the creation of uniform diagrams through the use of pre-existing glyphs and to offer these components to the community for reuse.EHLDs, including any analysis result overlay, can be easily downloaded and saved in SVG format. Users can edit the content of the exported files with commercial or open-source graphics applications. EHLDs use a consistent iconography that reuses glyphs when the same entity plays a role in more than one biological process. We have made a library of these graphical elements to provide them in SVG, PNG and EMF formats. The icon library is available at https://reactome.org/icon-info).Researchers can use these icons to create their own illustrations to convey their findings and ideas, whether in a paper or a grant application. Additionally, we encourage the community to contribute new icons to extend this library , a commercial JAVA API for reading, writing and manipulating PowerPoint documents, to store the content in PPTX format.Pathway diagrams have previously been available as static PNG images. To enable users to modify diagrams for their own use, we have developed a strategy to export a pathway diagrams from the Pathway Browser as an interconnected set of objects that adapt to layout changes so that when a glyph is moved in an exported diagram all connected objects follow. Our implementation  generateWe have redesigned our landing page and several key pages linked to it to simplify them and make the navigation to desired tools and documentation more straightforward Figure . The newhttps://youtu.be/-skixrvI4nU.A video that introduces the pathway browser and data analysis tools is available at Both the Reactome Knowledgebase and user needs for data visualization and analysis have grown in size and complexity, and will continue to grow briskly. We have addressed the computational aspects of this growth and change by implementing a Neo4j graph database structure for our production web site, developing a new high-performance in-memory implementation of our core overrepresentation data analysis tool, and implementing a new Pathway Diagram Viewer to support faster data loading, diagram rendering and element seeking. To improve usability we have developed Enhanced High Level Diagrams (EHLDs) that combine an iconography familiar from textbooks and review articles with web functionality, to represent superpathways, and have added features to our pathway diagrams to improve their legibility. A redesigned web site with adaptive technology is accessible from tablets and mobile devices, and supports more intuitive navigation.


As one possible solution to the “missing heritability” problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by next generation sequencing technology. However, while a number of methods for pathway-based rare-variant analysis of multiple phenotypes have been proposed, no method considers a unified model that incorporate multiple pathways.Simulation studies successfully demonstrated advantages of multivariate analysis, compared to univariate analysis, and comparison studies showed the proposed approach to outperform existing methods. Moreover, real data analysis of six type 2 diabetes-related traits, using large-scale whole exome sequencing data, identified significant pathways that were not found by univariate analysis. Furthermore, strong relationships between the identified pathways, and their associated metabolic disorder risk factors, were found via literature search, and one of the identified pathway, was successfully replicated by an analysis with an independent dataset.Herein, we present a powerful, pathway-based approach to investigate associations between multiple pathways and multiple phenotypes. By reflecting the natural hierarchy of biological behavior, and considering correlation between pathways and phenotypes, the proposed method is capable of analyzing multiple phenotypes and multiple pathways simultaneously. In the past decade, genome-wide association studies (GWAS) have played a key role in identifying genetic associations between Single Nucleotide Variants (SNVs) and many complex biological pathologies, including type 2 diabetes (T2D), heart disease, and schizophrenia –3. HowevAmong many proposed solutions to solve the missing heritability problem, many researchers have focused on “rare variants”. Methods for rare variants analysis arose from extending individual variant-level approaches to those at the gene-level , 6, and As the number of publicly available biological resources is increasing, recent methods for analyzing rare variants utilize pathway knowledge as a priori information. Since most biological behaviors manifest from a complex interaction of biological pathways , 11, anaAnother effort to enhance the power of rare variants is to develop multivariate analysis methods. In general, many complex diseases arise from multiply correlated traits. For example, according to American Diabetes Association guidelines, diabetic status is diagnosed based on four traits: fasting glucose, two hours after plasma glucose, random plasma glucose, and HbA1c . In thatThere have now been many applications of multivariate analysis to large-scale datasets. In particular, for variant- and gene-level analysis, many multivariate methods, for common and rare variants, have been proposed , 15. DesPathway-based approach using HierArchical component of collapsed RAre variants Of High-throughput sequencing data), for analyzing multiple phenotypes. Previously, we proposed a component-based hierarchical model for analysis of multiple pathways with a single model  be the matrix of phenotypes for N samples, where yiq is the observation of the ith sample on the qth phenotype, and let X be the matrix of gene-level collapsed variables generated by summing rare variants according to their gene variant-gene mapping. Let gij ∈ {0, 1, 2} be the number of minor alleles for the jth genetic variant of the ith sample. Regarding the elements of X, xikt is a gene-level summary of rare variants which is defined as weighted sum of the ith sample’s rare variants in the tth gene of the kth pathway, denoted by Mkt is an index set that defines which rare variants are mapped onto the tth gene in the kth pathway. Several weighting parameters, ωj, can be used, as previously described in [λG and λP on the genes-pathway and pathways-phenotype, we sought to address potential multicollinearity problems, in both genes and pathways, in the proposed method. Such problems may adversely affect the estimation of weights and coefficients. The proposed model assumes that the phenotype, yiq, arises from the multivariate normal distribution with mean μ and covariance Σ . Then we define the proposed PHARAOH-multi model as.Fi indicate the ith observation’s score of the kth pathway, and its matrix form across Q phenotypes, respectively. Moreover, qth phenotype, and ith sample.Let ribed in . By impokth pathway, on multiple phenotypes, can be determined by testing all coefficients of the pathways simultaneously (H0 : βk1 = … = βkQ = 0).The proposed model seeks to associate pathways and phenotypes. The effect of the Moreover, by its nature, the proposed method can further assess three more associations: 1) the effect of a gene on multiple phenotypes conditioned by a given pathway; (2) the effect of a gene on a phenotype conditioned by the pathway; and (3) the effect of a pathway on a phenotype. Detailed characteristics of the proposed model (PHARAOH-multi), including relationships and coefficients, are shown in Table  the effeB is a matrix of wtk and βkq, subject to the conventional scaling constraint λG and λP are the penalty parameters for each specific gene and pathway, respectively, and ‖wtk‖2 and ‖βkq‖2 are the ridge penalty function.Let 1Nfik2=N . The penRi be a “column-trimmed” matrix of GSCA [Fi ⊗ IK, where ⊗ is Kronecker product, and IK is K × K identity matrix. Maximization of (2) in respect of B and W is equivalent to minimizing the following least-square functions:We previously introduced an iteratively reweighted least square (IRLS) method to minimize an univariate version of (2) under the presence of ridge penalties , which i of GSCA , definedR′R) = NINQ, where Φi is a column-trimmed matrix of B′ ⊗ Xi [vec(·) is a vectorization operator. Then, it can be easily shown that the covariance matrix Σ is not related to the above equations since the PHARAOH-multi model uses multivariate linear model. An estimation of Σ can be done after convergence of B and W, by minimizing the first derivate of (2) with respect to Σ, as:These least-square functions are subject to diag and (4) to zero. This then gives the estimating equation of B and W as:Similarly, t = 1.Let W, which are then represented by W(0).Assign random initial values to Ft)(.Update Wt)( and Bt)(.Update Wt)( − Wt − 1)(| + |Bt)( − Bt − 1) to determine the values of λG and λP. First, we construct a two-dimensional grid of different λG and λP values. Then we compute the deviance of each model with the given λG and λP, for all CV fold values. Finally, λG and λP are selected by their average deviance, which is minimized.Finally, we determine the values of p-values. By permuting the given phenotype, our method first generates null distributions for both pathways and gene coefficients. By computing the quantile of estimated pathway and gene coefficients, from the non-permuted dataset in each empirical null distribution, we can obtain an empirical p-value for any specific pathway and gene.To assess the significance of genes or pathways, resampling methods can be used to test the statistical significance of the estimated effects of all pathways on the phenotype. In the proposed method, we utilize a permutation test to obtain βk1, ..., βkQ. The global effect of a pathway, on all phenotypes, can be evaluated by jointly testing βk1, ..., βkQ. Here, we introduce two different schema for determining a joint p-value for the kth pathway, from multiple phenotypes.The testing of joint effects, between multiple phenotypes, is crucial. As shown in Table p-values (referred as “P_K”). Since there are considerations among the estimated coefficients βk1, ..., βkQ, these correlations should be accounted for combining multiple p-values. Let the p-values from the kth pathway be denoted by Pk1, …, PkQ. The simplest way to combine those p-values is to use Fisher’s method, which is denoted by k, follows the χ2 distribution, with the degrees of freedom, 2Q, under the null hypothesis. An extended version of Fisher’s method, Brown’s method, can combine dependent p-values using a rescaled χ2distribution and covariance of p-values [p-values [ρ to the empirical correlation of estimated coefficients, βk1, ..., βkQ, and derive the statistic for joint effect between the kth pathway and multiple phenotypes, as follows:ck, dk and χ2, with the degree of freedom 2dk for the kth pathway, respectively [Our first approach was to combine the individual p-values . Howeverp-values  introducp-values . Here, wectively .Q coefficients (referred as “P_M”). Here, we define a Wald-type statistic, T, as below, and utilize T for the following permutation testing scheme:Our second approach was to construct a single statistic that combines all Then, the estimated covariance λPI−1⊗Σ^ , or can Since the number of pathways is far less than those of genes or genetic variants, the “multiple testing problem” remains. While Bonferroni correction can be a straightforward approach for adjusting for multiple testing, it may impose an adjustment that is too stringent, especially for correlated results . To overp-values using the Westfall & Young permutation procedure [T(0) be a vector of the statistics calculated using observed, unpermuted phenotypes, and let Tj) in ascending order, and let the rank of the kth pathway, and the kth index, be rk and rk) adjustment, by calculating q-values . Here, wTo evaluate the performance of the proposed method, we conducted simulation studies, under various scenarios. For generating rare variants, we first produced a pool of genetic variants, using SimRare , a rare F′F) = NIK, where H1 is the number of causal genes in the first pathway, and Mt1 is the number of rare variants in the tth gene of the first pathway .This is then subject to diag. In our simulation, the settings q = 1,2, and H1 = 1, 2, 5, 10, were used. For each replicate, all rare variants were collapsed into genes.In the above model (eq. 11), For the simulation and the analysis of the real dataset, a workstation system with two Intel Xeon E5–2620 CPUs, with a combined RAM of 128GiB, were used. Note that the aSPU and MARV analyses were performed using the default settings, except that aSPU was performed without “genetic variant pruning” capability, as we observed that aSPU raises “unrecoverable error” with that capability. For our proposed method and aSPU, the number of permutations was 5000, to prevent possible lower bound limitation.λG and λP, based on three-fold CV for each replicate, using two-dimensional grids of λG and λP, with six different starting points of ridge parameters, ranging from 101 to 106 . In the simulation, we considered the following conditions: number of effective genes in the causal pathway (H1), gene-level effect (wt1), pathway-level effect (βq1), and residual correlation (ρ). In our simulation, H1, wt1, β, and ρ were assumed to be 1, 2, and 5; 0.1 and 0.2; 0.1, 0.15, and 0.2; and 0, 0.25, 0.5, respectively, with evaluation of their exhaustive combinations. Other parameters, Q, K and TK, were fixed to 2, 10, and 10, respectively.For PHARAOH-multi, we selected the tuning parameters p-values for the pathways with no effect, and was less than the significance level, across 1000 replicates of permuted phenotypes. As shown in Fig. ρ = 0. In contrast, the quantile-quantile (Q-Q) plots in Fig. We first compared the type 1 error rates of the proposed method vs. the traditional methods. Here, type 1 error rate was computed as a proportion of It was also worthwhile to assess the gain of power in the multivariate analysis, as compared to univariate analysis. In this respect, our simulation study was conducted to compare the power gain from multivariate methods, and between multivariate and univariate analyses.w and β increase (data not shown).First, we checked whether PHARAOH-multi with multiple phenotypes boosts power compared to PHARAOH with a single phenotype, under the same scenarios of the power simulation. As a result, we observed that the power of PHAROH-multi was at least 2.52 times larger than PHARAOH, and this difference becomes even larger, as q-value of the simulated causal pathway (the first pathway) being less than the significance threshold, e.g., 0.05. Despite the proposed method supporting the Westfall-Young permutation procedure, it was not considered in the simulation study, due to the absence of corresponding adjustments in the compared methods.Second, we assessed the statistical power of PHARAOH-multi and the compared methods, defined as the proportion of the adjusted w and β, with different numbers of causal genes in the causal pathway, and each column represents the same number of causal genes, with different effect settings.Figures p-value aggregation (P_K) showed greater power than the other two approaches, aSPU and MARV. However, this did not hold when 50% of the genes were causal for a specific pathway, with effect sizes of w = 0.1 and β = 0.1. In order to investigate whether or not there are significant differences among powers, we performed paired t-tests between a pair of methods. In Fig. w = 0.1, the p-values were 3 × 10− 7 for comparing powers of P_M and aSPU, and 4.2 × 10− 6 for comparing those of P_M and MARV. In Fig. w = 0.2, the same pairwise comparison for the p-values were 7.3 × 10− 7 and 4.2 × 10− 6, respectively. In overall scenarios, powers of P_M were larger up to 18%p compared to aSPU in H1 = 5, w = 0.2 and β = 0.2, and were larger up to 83%p compared to MARV. Generally, P_K exhibited smaller power than P_M, and showed comparable or slightly smaller power, than aSPU.In most scenario comparisons, the two proposed statistics obtained by PHARAOH-multi (P_M) and β increased, as shown in Fig. w to the power was relatively moderate, compared to β, as shown in corresponding scenarios of Figs. Here, we observed three interesting patterns in the results. First, the proposed P_M and aSPU methods showed lower power, when the proportion of causal genes increase, compared to MARV. Second, the power rapidly increased, as To confirm the above hypothesis, we performed an additional comparison using the same dataset, except that the phenotypes were generated without the constraint. As shown in Fig. Mkt. For simplicity, we split 1000 simulation datasets into two groups: the first group where the number of variants is small and the second where it is large. Then, we compared the power of each method between two groups using t-test. As a result, the p-values were 0.097 for aSPU, 0.684 for MARV, and 0.825 for PHARAOH-multi. Thus, we concluded that Mkt is unlikely to affect the simulation result regardless of the methods.Finally, we investigated whether or not the statistical power changes by To evaluate the practical performance of PHARAOH-multi, we conducted a discovery study using a large-scale sequencing dataset. Many studies suggest that the major underlying risk factors for metabolic disorders include high density lipoprotein (HDL), blood pressure , waist circumference (WAISTC), fasting glucose (FAST_GLU), and triglycerides (TG). In this regard, we conducted a multivariate analysis of metabolism-related traits, using a large-scale sequencing dataset, obtained from the Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Consortium, comparing our proposed (PHARAOH-multi) and other common methods. In detail, we analyzed a dataset consisting of 1086 samples selected from the Korean Association REsource (KARE) study .P < 10− 5, the minor allele frequency was < 5% and the minor allele count was > 2, resulted in 198,761 variants. The final dataset was then mapped to genes, using the human genome-19 (hg19) reference genome coordinates, with 10Kbp flanking regions. The gene range of hg19 reference, was extracted from RefSeq track of UCSC Table Browser, as of October 2014. Finally, the gene-level collapsed variable was generated using Workbench for Integrated Superfast Association study with Related Data (WISARD), with beta-transformation weighting, as suggested in [After removal of samples with any missing observations of the aforementioned six phenotypes, we included 1085 samples for analysis. The quality controls with genotype call rates were < 95%, or for Hardy-Weinberg Equilibrium (HWE) test Next, we compared our multivariate and univariate analysis results, using PHARAOH-multi and PHARAOH. As shown in Figs. q-values for each univariate result. Interestingly, no univariate analysis identified significant pathways, except for SBP with KEGG, which identified three pathways . As shown in Table q-values of univariate analyses for pathways identified by multivariate analysis, were not significant.These comparisons clearly support the one advantage of multivariate analysis that we discussed above: elevation of statistical power. As with multivariate analysis, we calculated p-value trends, despite the result from KEGG being modestly deflated, due to the optimization of lambda. The Q-Q plots of MARV look similar to PHARAOH-multi. In contrast, aSPU showed unacceptably inflated patterns of Q-Q plots, regardless of the pathway databases, which were not used in the simulation study. This could possibly be due to substantial overlap of existing pathway databases.Second, we compared the result of multivariate analyses, using PHARAOH-multi, aSPU and MARV. As shown in Fig. q-value < 0.1. Interestingly, PHARAOH-multi identified glutathione-related pathways in both KEGG and Reactome pathway databases, which supports the result of PHRAOH-multi. As shown in Fig. p-values are very small). As a result, 57.7% (Reactome), 29.5% (Biocarta) and 71.5% (KEGG) of the tested pathways by aSPU were statistically significant . Unfortunately, these pathways are highly false positives. In this respect, we included the results of significant pathways identified by either PHARAOH-multi or MARV.As shown in Table The identified pathways suggested evident relationships with metabolic syndrome. Since the peroxisome pathway elucidates peroxisome biogenesis, which contributes to fatty acid oxidation and biosynthesis of ether lipids, many studies have discussed interrelationship between peroxisomes and metabolic processes , 40. Likp-value < 0.1, the peroxisome pathway in KEGG (p = 0.059) and cell2cell pathway in Biocarta (p = 0.093). As shown in the literature search, the two pathways we replicated have strong relationships with metabolic disorders.We conducted a replication study using exome chip dataset from an independent cohort, using the identified pathways in the discovery study. Despite the insufficiency of detected variants in the exome chip dataset, as a result, we successfully replicated two pathways with Compared to univariate approaches, which analyze each phenotype individually, our real data analysis successfully demonstrated that the multivariate approach could identify pathways commonly associated with specific traits. It is important to construct a systematic analysis that considers the correlation between complex diseases and their underlying biological traits. In addition, our results from two well-established pathway databases were strongly supported by many existing publications, thus demonstrating the advantage of our proposed approach.Compared to existing multivariate analysis methods, PHARAOH-multi features several advantages. Firstly, by constructing a hierarchical structure of genes-pathways-phenotypes, four types of associations  can be estimated simultaneously. Compared to our proposed method, existing methods of multivariate analysis were limited to gene-level analysis, and hence, the combinatorial effect of multiple genes, via biological pathways, was impossible to estimate. In addition, the proposed method considers the correlation between genes, pathways, and phenotypes, by imposing penalty parameters on the estimation procedure.Secondly, PHARAOH-multi provides multiple options for correcting for the multiple testing issue. Although Bonferroni correction is simple, and powerfully controls type 1 error, it is a well-known fact that the Bonferroni correction often results in controls that are too stringent, when the tests are correlated. Under such conditions, application of the Westfall-Young permutation procedure can be an appropriate alternative, since its asymptotic optimality under dependence is known . In thisFor analysis times of both simulation and real datasets, MARV was the fastest among all the methods, while PHARAOH-multi ran slightly faster than aSPU. For example, in the analysis of simulation dataset of 100 genes with 1000 samples, the running times of MARV, PHARAOH-multi, and SPU were 13, 67 and 235 s, respectively. The trends of execution time were consistent regardless of simulation parameters or datasets. However, PHARAOH-multi can be further accelerated with multithreading which is not supported by MARV and aSPU. With multithreading of 8 threads, the analysis time of PHARAOH-multi was reduced to 12 s.At this point, there are a number of subjects we can consider for future research. Our current analysis is limited only to Korean population. In our future study, we apply our method to the whole data of 13,000 WES dataset of T2D-GENES consortium  which coIn this study, we proposed a novel statistical approach for multivariate pathway-based analysis of rare variants, from large-scale sequencing datasets. Analyses of multiple phenotypes have been successful in analyzing various complex diseases, including type-2 diabetes (T2D) or hypertension. In general, curated guidelines suggest diagnosing T2D according to traits observed in the individual. Consequently, incorporating multiple correlated traits, to be investigated for association with specific diseases, via multivariate analysis, elevates the statistical power. In this respect, our simulation study reflects the relationship between diseases and their related traits. Throughout the simulation study, PHARAOH-multi outperformed existing multivariate methods. In addition, our proposed method successfully demonstrated several advantages of multivariate analysis, including significantly improving the detection power of causal pathways, as compared to univariate analysis, while also retaining detection power for the individual phenotype. Moreover, we successfully demonstrated that the proposed method is capable of identifying plausible pathways in the real dataset, by identifying eight pathways in the discovery study, and replicating two pathways in the replication study. We firmly believe that the proposed method will assist researchers in understanding the genetic structures that underlie many complex diseases.


With the onset of next-generation sequencing technologies, we have made great progress in identifying recurrent mutational drivers of cancer. As cancer tissues are now frequently screened for specific sets of mutations, a large amount of samples has become available for analysis. Classification of patients with similar mutation profiles may help identifying subgroups of patients who might benefit from specific types of treatment. However, classification based on somatic mutations is challenging due to the sparseness and heterogeneity of the data.Here we describe a new method to de-sparsify somatic mutation data using biological pathways. We applied this method to 23 cancer types from The Cancer Genome Atlas, including samples from 5805 primary tumours.We show that, for most cancer types, de-sparsified mutation data associate with phenotypic data. We identify poor prognostic subtypes in three cancer types, which are associated with mutations in signal transduction pathways for which targeted treatment options are available. We identify subtype–drug associations for 14 additional subtypes. Finally, we perform a pan-cancer subtyping analysis and identify nine pan-cancer subtypes, which associate with mutations in four overarching sets of biological pathways.This study is an important step toward understanding mutational patterns in cancer. HER2, hormone receptors and tumour grade, and these different subtypes have different prognosis and respond differently to hormone therapy.1 While these subtypes are used to manage patient treatment, even here we know that individual subtypes themselves represent a diversity of smaller groups.Cancer is a heterogeneous disease that can develop in different tissues and cell types. Even within one cancer type, the disease may manifest itself in multiple subtypes, which are usually distinguished based on different histology, molecular profiles or specific mutations, and which may lead to different clinical outcomes. Identifying new cancer subtypes can help classification of patients into groups with similar clinical phenotypes, prognosis or response to treatment. As an example, breast cancer is typically classified into four primary molecular subtypes based on the expression of 3 and microRNA expression levels,2 methylation data,4 copy number alterations and combinations of different ‘omics data types,5 but few studies have subtyped patients based on somatic mutations. Somatic mutations play a large role in cancer development and disease progression, and mutational profiling is used far more commonly than other ‘omics analyses in clinical practice because most clinical guidelines are based on single gene mutations. Consequently, classification based on patterns of mutation could be particularly informative for identification of subgroups of patients who might respond to specific targeted treatment regimens and of those who are unlikely to respond.Since the onset of large-scale genomic experiments, cancer subtypes have been identified in multiple cancers, using mRNATP53—are mutated, as well as so-called “passenger” events that are considered mutational noise yet which may still influence tumour properties. And even within the same cancer type, tumours often exhibit very different mutational patterns, including drivers and passengers—as well as mutations that may fall somewhere in between.However, subtype classification using somatic mutations in cancer is challenging, mainly because the data are very sparse: many tumours only have a handful of mutations in coding regions yet the total number of mutations within a population is typically substantial. Often, frequent cancer drivers—such as 9 Hofree et al.,7 for example, use network propagation to “fill in” the mutational status of neighbouring genes (in protein–protein interaction networks) of mutated drivers, while Le Morvan et al.9 use networks from Pathway Commons to normalise a patient’s mutational profile by adding “missing” or by removing “non-essential” mutations.To classify sparse somatic mutation data into subtypes, published methods generally first de-sparsify the data. Some methods use a gene-gene network as “prior” knowledge to de-sparsify the data.8 as well as in identifying genes associated with patient survival.9 However, gene–gene networks depend on a set of known “prior” interactions, but these priors may or may not be “correct” in the sense that they may not be relevant to the tissue or tumour under study. This reliance on “canonical” networks might overemphasise genes that are connected to mutational drivers through such interactions, as well as overemphasise highly connected genes, even though some studies do correct for this.8Data de-sparsification using gene–gene networks has been helpful in identifying subnetworks involved in cancer,In addition, genes belonging to the same biological pathway do not necessarily have to be closely linked in a gene–gene (protein–protein interaction) network—they do not always interact physically, and their functional interactions may be indirect. However, having multiple mutations in the same biological pathway is likely disruptive to the pathway’s function and likely more so than only having one gene mutated in that pathway. Thus, to classify somatic mutation data into meaningful subtypes, we believe it is important to take all genes in a pathway into account when de-sparsifying the data.11 Hoadley et al.12 used pathway scores to integrate somatic mutations with other ‘omics data types to perform a multi-platform classification of 12 cancer types, while Leiserson et al.8 identified pan-cancer subnetworks across the same 12 types of cancer. While these studies have improved our understanding of the genes and pathways that are recurrently mutated in cancer, data are now available for many more samples and cancer types, increasing the power to detect new mutational patterns and cancer subtypes.Finally, because somatic mutation data are very heterogeneous, pan-cancer studies may help understanding the biological processes that play a role in cancer. The inclusion of multiple cancer types in an analysis both increases the sample size and allows for the discovery of mutation subtypes across cancer types. Large consortia, including The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium, have performed comprehensive pan-cancer analyses of somatic mutations.In this study, we describe SAMBAR, or Subtyping Agglomerated Mutations By Annotation Relations, a method to de-sparsify somatic mutation data by summarising these data into pathway mutation scores. We applied SAMBAR to data from 5805 primary tumours from TCGA, including 23 different cancer types. We used the de-sparsified data to associate mutational patterns with phenotypic data, to identify prognostic subtypes and to identify potential drug targets associated with subtypes in each of the cancer types. In addition, we performed a pan-cancer analysis to identify mutation subtypes across multiple cancers and describe the mutational patterns associated with these subtypes.13 to download clinical data for 23 cancer types from TCGA. We curated these data by combining data from all available Firehose versions  for each cancer type consecutively as to retain all clinical information, with the most up-to-date information for data present across different Firehose versions (We used RTCGAToolboxn = 47) containing mutation information for 6406 samples from 23 cancer types from the TCGA website . We removed silent mutations and only retained genes with hg18/19 annotations . We divided the number of non-silent mutations Nij in a sample i and gene j by the gene’s length Lj, defined as the number of non-overlapping exonic base pairs, calculated on either hg18 or hg19, depending on the annotation the sample was mapped to. We next removed samples that were not obtained from primary tumours. Finally, we merged replicate tumour samples that were derived from the same patient by taking the maximum mutation score for each gene, so that we retained all mutations that were observed in the tumour. The resulting data set contained gene mutation scores for 5992 patients.We downloaded .maf files . For the remaining samples, we divided the mutation scores by these mutation rates, resulting in mutation rate-adjusted scores . We converted this file into a binary matrix M, with information of whether a gene j belongs to a pathway q. We calculated pathway mutation scores (P) by correcting the sum of mutation scores of all genes in a pathway for the number of pathways q’ a gene belongs to and for the number of cancer-associated genes present in that pathway (Eq. n = 79) and pathways without mutations in cancer-associated genes (n = 69), leaving us with 5805 patients and 1066 pathways for our subtyping analysis.We downloaded the file “c2.cp.v5.0.edges.gmt” from MSigDb, which included 1135 canonical pathway gene signatures  only. For each data set and cancer type, we removed columns and rows that did not have any mutations prior to calculating distances between the samples.We used 12/16 distance metrics from the vegdist function in R package veganhttps://www.wordclouds.com/. As input, we included parameters associated  with a top five principal component. We normalised this list to the number of times a variable was available across the 23 cancer types. We adjusted for multiple testing using Benjamini and Hochberg’s method17 across each cancer and principal component to report significant associations in Supplemental Table We calculated distance matrices of individuals within each cancer type using the binomial dissimilarity index on pathway mutation scores. We regressed each of the top five principal coordinates (PCs) from the Principal Component Analysis (PCA) on these distance matrices with each clinical variable that had ≥2 different entries. We generated word clouds using k = 2–4, removing clusters of size <10. We used the log-rank test (p < 0.01) to identify significant differences between overall survival profiles, choosing the lowest k if multiple k’s resulted in significant prognostic subtypes for the same cancer type.For each cancer type, we clustered the pathway mutation scores using hierarchical clustering with binomial distance. We cut the dendrogram at k and subtype sample sizes to correspond to those of the subtypes we had identified. We then estimated the permutation p-value, defined as the fraction of permutations with p-values smaller than that from the log-rank test on the actual prognostic subtypes. We assigned a p-value of ≤1/10,000 to subtypes for which all permutation p-values were higher than that of the original log-rank test. We corrected these p-values for multiple testing using the Benjamini–Hochberg method.17We ran 10,000 sample label permutations for each of the prognostic subtypes, fixing To determine what biological pathways drive the poor prognostic subtypes, we selected pathways that were mutated in >90% of samples in the poor prognostic subtype and in <50% in the subtypes with better prognosis.We scanned each tumour type for 2–4 subtypes, as described above. We selected signatures mutated in >95% of samples belonging to a particular subtype and in <10% of samples belonging to the remaining subtypes. We then selected all genes belonging to these signatures and, for each gene, calculated the fraction of samples with mutations in the subtype of interest and the same fraction in the remaining samples. We selected those genes that were more frequently mutated in the subtype of interest.18 . For each drug, we calculated how many of its known targets overlapped with this list of more frequently mutated, pathway-associated genes. We removed interactions with <2 subtype-specific drug targets. Next, we calculated an “observed” score based on the number of subtype-specific targets divided by the total number of known targets for that drug. We also calculated an “expected” score based on the number of cancer-associated genes present in all 1066 pathways that were also present as drug targets in CMap . We defined significant subtype–drug associations as those interactions that had enrichment scores (the observed/expected ratio) >3.We downloaded drugs and their target genes from Connectivity Map (CMap)k that included the exact same set of patients as identified using a lower k.Finally, we filtered this set of subtype–drug interactions by removing redundant subtypes—subtypes identified using a larger k = 2–1000, filtering out clusters of size <50. We observed several breakpoints which the largest subtype was split into new subtypes. We selected the highest k at such a breakpoint, for which >90% of all samples were assigned to subtypes (k = 169), resulting in 9 pan-cancer subtypes. We used Fisher’s exact test to identify whether these subtypes were enriched  for particular cancer types.We used binomial distance to cluster pathway mutation scores of all 5805 samples. We cut the clustering dendrogram at We defined significantly mutated signatures as those signatures that were mutated in at least 95% of all samples in a pan-cancer subtype. We visualised average mutation scores of these pathways in a heatmap and identified four sets of pathways by row clustering  these data.https://www.wordclouds.com/.To make word clouds for these pathways, we identified the frequency of all 1356 unique words (separated by underscores in MSigDb) occurring in the 1066 pathways. We removed words that occurred <3 times (1051 words). We then selected words belonging to one of the four sets of pathways and calculated their observed frequency by dividing the number of times the word occurred in the set of pathways by the total number of words in that set. Next, we calculated the expected frequency by dividing the number of times the specific word occurred in all pathways by the total number of words in all pathways. We multiplied the observed/expected ratio by 10, rounded the number to an integer and used that number of words as input for 19  and filtered these data for primary tumours  and proteins that were available across all samples (121 proteins). We curated a protein activation signature for our “Set 1” subtype by selecting all available protein products (n = 15) of genes in the “Reactome PI3K/AKT activation” pathway and a protein activation signature for our “Set 2” subtype based on protein products (n = 6) of genes in the “Reactome p53-Dependent G1 DNA Damage Response” pathway. We next calculated protein activation scores by summing up protein abundance levels for each of these signatures. We then performed a t-test between protein activation scores of “Set 1” in patients belonging to “Set 1” subtypes (S1–2) and other patients (S3–9) and between scores of “Set 2” pathways in patients belonging to “Set 2” subtypes  and other patients  to identify significant differences  in protein activation between the pan-cancer subtypes.We validated our pan-cancer subtypes using orthogonal evidence on pathway activation. We downloaded Reverse Phase Protein Array (RPPA) data from TCGA using R package RTCGA.RPPAhttp://www.cancerrxgene.org/downloads, accessed February 21, 2018). This file included non-silent mutations for 1001 cell lines and 19,100 genes. We de-sparsified these data using SAMBAR, as described above. We next downloaded IC50 scores for drugs targeting “Set 1” pathways by selecting the target pathway “PI3K/MTOR signalling” (21 unique drugs) and for drugs targeting “Set 2” pathways by selecting the target pathway “DNA replication” (11 drugs) using CGP’s “Data download” tool https://www.cancerrxgene.org/translation/drug/download#ic50, accessed March 26, 2018.We downloaded variants detected using Whole Exome Sequencing from the Cancer Genome Project (CGP) (file “WES_variants.xlsx” from n = 156), and those that did not (n = 845), and performed a t-test to identify significant differences in response to drugs targeting phosphoinositide-3 kinase/mammalian target of rapamycin (PI3K/MTOR) signalling . We note that, for some of the drugs acting on PI3K/MTOR, replicate measurements were available. For those drugs, we pooled data from the replicates. We repeated this analysis on cell lines that had mutations in all 38 “Set 2” pathways (n = 681) and those that did not (n = 320) and drugs targeting DNA replication. We note that we identified a relatively high number of cell lines with mutations in all “Set 2” pathways compared to the number we identified in primary tumours. We believe this number is high because cell lines have more mutations (median number of mutations in cell lines is 158 compared to 91 in primary tumours). In addition, genes involved in DNA replication pathways are often mutated in cell lines to help immortalisation.We divided the cell lines into two groups: those that had mutations in all 94 “Set 1” pathways . We found that the filtered data were very sparse and difficult to assign to subtypes . We then corrected these scores for the sample’s mutation rate, as our goal was to identify subtypes independent of mutation rate.We hypothesised that summarising the gene mutation scores into biological pathway scores would help to de-sparsify the data, as well as help identifying subgroups of patients who might respond to specific drugs targeting those pathways. We therefore used SAMBAR to de-sparsify the data by calculating mutation scores for 1135 canonical pathways from MSigDb.24 we observed large variations in the number of mutated cancer-associated genes in each sample, ranging from 1 to 1003, with a median of 91 mutated genes per sample . While these numbers do depend on sample size for the gene mutation data  and visualised these in a word cloud . For each cancer, we reported the clinical parameters that were associated with the top five principal components . This means that, while we correct for a sample’s mutation rate, it is not completely filtered out and still dominates some of the clustering in UCEC. However, in our subtyping analyses described below, we further corrected for mutation rate by removing samples that clustered separately  from most other samples.In Fig. p < 0.05) variables  for three cancer types—adrenocortical carcinoma (ACC), LAML and low-grade glioma (LGG). Pathways associated with these prognostic subtypes are listed in Supplemental Table We explored whether we could identify subtypes associated with cancer survival. We used the binomial dissimilarity index to cluster the de-sparsified mutation data of each cancer type. We split the cluster dendrograms in 2–4 groups (see Methods). We identified significant prognostic subtypes , compared to two larger clusters including 23 and 144 patients , indicating that the poor prognostic subtypes were not identified by chance but were detected based on mutations in specific biological pathways . For ACC and LAML, unfortunately, no detailed information on treatment status was available. While the numbers of patients in the identified poor prognosis subtypes are small, and while these subtypes might be associated with certain patient characteristics, we believe that treating these patients with personalised therapy regimens that specifically target the subtype-specific signalling pathways could dramatically improve prognosis in these patients.Because the sample sizes of the poor prognostic subtypes were small in comparison to the subtypes with relatively better prognosis, we wanted to make sure these subtypes were not identified owing to random selection of a small group of patients with poor prognosis. For each of the significant cancer types, we performed 10,000 sample label permutations and found that the original log-rank tests were more significant than those on the permuted null background . We identified 251 subtype–drug interactions for a total of 15/23 cancer types. For each cancer type, we selected interactions with the highest enrichment scores and reported these in Table While somatic mutations have been analysed in the context of actionable drug targets,EGFR was the only gene from the EGFR pathways that overlapped with EGFR drug targets. Since we only considered drug–subtype interactions for subtypes with at least two mutated drug targets, these drugs were not included in our analysis. However, the subtypes with intermediate survival were enriched for lonafarnib—a farnesyltransferase inhibitor—in LAML, and atiprimod—an angiogenesis inhibitor—and JAK inhibitors in LGG. Lonafarnib was also enriched in a large subtype of THCA tumours, while Janus-activated kinase (JAK) inhibitors were enriched in BLCA, HNSC, KIRC and LUSC. In addition to these drugs, we identified multiple receptor tyrosine kinase inhibitors associated with subtypes in BLCA, GMB and HNSC. We also identified a PI3K inhibitor (wortmannin) to be enriched in mutated targets in a UCS subtype, a proteasome inhibitor (bortezomib) in KIRP and OV subtypes and a CREBBP/EP300 inhibitor (SGC-CBP30) in a small subgroup of BRCA and PRAD patients.For the poor prognostic subtype in ACC that we described above, we observed an enrichment of targets of MDM2 inhibitors (idasanutlin and nutlin-3). This was not unexpected, as we found specific mutations in p53 pathways in this subtype. We did not identify any enriched drug targets for the poor prognosis subtypes of LAML and LGG. However, only two Notch inhibitors are available in CMap, and both are listed without any targets. While many EGFR inhibitors are available in CMap, In total, the subtypes that were enriched for mutations in drug targets from CMap account for 12% (689/5805) of all primary tumours from TCGA. This is a substantial number of patients, considering the strict thresholds we used for this analysis. We believe that, as more drug target information becomes available, we may find additional subtype–drug associations that might help identify subgroups of patients who could benefit from targeted treatment of their tumour’s mutational profile. This also suggests that a pathway-based analysis might provide a window into new therapeutic options for cancer patients.n = 5805), and because the number of potential pan-cancer mutation subtypes is unknown, we divided the cluster dendrogram into k subtypes, ranging k from 2 to 1 000, removing clusters of size <50. We selected the largest k for which we observed a “breakpoint” in the sample size of the largest cluster . This may indicate that patients belonging to these subtypes might benefit from  immunotherapy.To identify whether these nine pan-cancer mutation subtypes associated with a high mutation rate in specific biological pathways, we selected pathways with mutations in >95% of all samples in a specific subtype. This resulted in a total of 202 pathways  on average mutation scores in these 202 pathways, we identified four overarching “sets” of pathways that were differentially mutated in the pan-cancer subtypes see Fig. . The firThese four sets of pathways highlight important processes that are highly recurrently mutated in large subgroups of patients across different cancer types. Interestingly, there does not seem to be a dependency between these four sets of highly recurrent processes. Most of the sets are either mutated alone (such as Set 2 in pan-cancer subtypes S4 and S6) or in combination with other sets . An exception to this is Set 1, which does not co-occur with Sets 2 and 4, indicating a possible mutual exclusive relationship between PI3K/Akt/MTOR on the one hand and p53 and certain growth factor receptor pathways on the other. These results could indicate that patients assigned to certain subtypes may benefit from targeting specific pathways in selecting therapies, while others may need combinations of targeted treatment approaches to target multiple processes that are disrupted. Finally, subtypes S3 and S5 do not have any recurrently (>95%) mutated pathways, indicating that treating these patients may require individualised analysis of their unique mutational patterns in deciding on a precision medicine strategy.t-test t-statistic = 2.34, p = 0.0198) and a mean DNA damage response protein activation score of 0.283 in patients belonging to “Set 2” subtypes  compared to 0.158 in other patients . This indicates that the pan-cancer subtypes we had identified based on pathway mutation scores also corresponded to higher protein levels in these pathways.We validated the pan-cancer subtypes we identified in two ways. First, we wanted to make sure that the signalling pathways we had identified as highly mutated in the different pan-cancer subtypes were active. To do this, we integrated these results with RPPA data from TCGA. We calculated pathway activation scores for Akt signalling and DNA damage response pathways, corresponding to the “Set 1” and “Set 2” pathways we had identified in Fig. t-statistic = −3.13, largest effect observed for Pictilisib), indicating that these cell lines were more responsive to PI3K/MTOR inhibition , indicating that these cell lines were less responsive to drugs interfering with DNA replication . We identified significant differences  in response to 5/24 PI3K/MTOR inhibitors. Most of which (4/5) had significantly lower IC50s  independent of cancer type. These subgroups indicate that core pathways are often mutated, independent of the tissue of origin and may indicate specific targets for therapeutic intervention that should be explored.Even though exome sequencing data are now available for large numbers of tumours, identifying mutation subtypes in cancer is still a challenge due to the sparseness and heterogeneity of the data. We developed SAMBAR, a de-sparsification method that summarises somatic mutations in genes into pathway-level mutation scores. We showed that SAMBAR helps identifying mutational patterns associated with clinical phenotypes and prognosis and potential targeted treatment options for cancer-specific subtypes, as well as mutational patterns that are manifested across multiple cancer types.Some of the pathways we identified in the prognostic subtypes, including cell cycle, apoptosis and DNA damage response, are frequently mutated in cancer. However, subgroups of patients may still benefit from specific targeted treatment options that can be found through a pathway-level analysis and through developing methods to interrogate diverse data resources. For example, we identified MDM2 inhibitors as potential targets for treatment of the poor prognosis subtype in ACC by integrating subtype-specific mutations with a drug-targeting database. In addition, several signal transduction pathways for which targeted treatment options are available are specifically mutated in the poor prognosis subtypes we identified. These pathways include Notch signalling in ACC and LAML and EGF receptor family pathways in LGG. Thus, by considering mutations in all of the genes associated with subtype-specific mutation of signalling pathways, we may find additional patients who could benefit from targeted treatment options.The results from this analysis suggest that, rather than only focussing on well-known “driver” genes, we should also include genes associated with particular biological pathways when profiling patients to search for personalised treatment options. By generating patient-specific “pathway mutation profiles”, we may not only identify patients who could benefit from specific targeted therapeutics but we will also obtain a clearer picture of the cellular processes that are altered through mutation in a specific tumour. This expanded pathway- and process-based approach may help identify combination therapies that target multiple pathways that are altered in a patient’s tumour.In our pan-cancer analysis, we identified four overarching types of mutational patterns. The first set included PI3K/Akt/MTOR signalling pathways. This means that, likely, a large number of patients will benefit from targeted inhibition of this signal transduction pathway. In addition, one of the pan-cancer mutational patterns we identified was enriched for several growth factor pathways, including EGF receptor family genes, and FGFR and nerve growth factor signalling. Targeted treatment options are available for each of these pathways. Because these pathways are mutated in a large set of the primary tumours we analysed, we believe that these treatment options are worthy of further investigation and may lead to better treatment options for a large numbers of patients.36 However, therapeutic decisions are typically tied to examining single gene mutations. Our framework to classify cancers based on mutational patterns in biological pathways could help expand precision medicine applications both by identifying groups of patients who may or may not respond to particular therapies and by identifying pathways that might be useful targets for therapeutic intervention.Recently, large hospitals have started to include mutational profiling as a standard procedure to characterise tumours and to assign patients to available targeted treatment options or ongoing clinical trials targeting specific mutations.Finally, while we concentrated on identifying subtypes that include relatively large numbers of patients, the pace at which we are collecting mutational data continues to accelerate. As more data become available, we can fine-tune our subtyping analysis, identifying not only the largest but also smaller groups of patients for which targeted treatment options may be available. Analysis of larger sample sizes will also improve our understanding of the biological pathways that are important in driving cancer. Future research may focus on combining our pathway mutation scores with previously published network propagation methods to further fine-tune classification of pathway mutation profiles.Supplemental materials and methodsSupplemental Figure 1Supplemental Figure 2Supplemental Figure 3Supplemental Figure 4Supplemental Figure 5Supplemental Table 1Supplemental Table 2Supplemental Table 3Supplemental Table 4