A map of human cancer signaling
2007; Springer Nature; Volume: 3; Issue: 1 Linguagem: Inglês
10.1038/msb4100200
ISSN1744-4292
AutoresQinghua Cui, Yun Ma, María Jaramillo, Hamza Bari, Arif Awan, Song Yang, Simo Zhang, Lixue Liu, Lu Meng, Maureen D. O'Connor‐McCourt, Enrico O. Purisima, Edwin Wang,
Tópico(s)Gene expression and cancer classification
ResumoArticle18 December 2007Open Access A map of human cancer signaling Qinghua Cui Qinghua Cui Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Search for more papers by this author Yun Ma Yun Ma Department of Biology, Tianjin Normal University, Tianjin, China Search for more papers by this author Maria Jaramillo Maria Jaramillo Receptor, Signaling and Proteomics Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Search for more papers by this author Hamza Bari Hamza Bari Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Search for more papers by this author Arif Awan Arif Awan Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Search for more papers by this author Song Yang Song Yang School of Chemical Engineering, Tianjin University, Tianjin, China Search for more papers by this author Simo Zhang Simo Zhang Department of Biology, Tianjin Normal University, Tianjin, China Search for more papers by this author Lixue Liu Lixue Liu Department of Biology, Tianjin Normal University, Tianjin, China Search for more papers by this author Meng Lu Meng Lu Department of Biology, Tianjin Normal University, Tianjin, China Search for more papers by this author Maureen O'Connor-McCourt Maureen O'Connor-McCourt Receptor, Signaling and Proteomics Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Search for more papers by this author Enrico O Purisima Enrico O Purisima Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Center for Bioinformatics, McGill University, Montreal, QC, Canada Search for more papers by this author Edwin Wang Corresponding Author Edwin Wang Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Center for Bioinformatics, McGill University, Montreal, QC, Canada Search for more papers by this author Qinghua Cui Qinghua Cui Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Search for more papers by this author Yun Ma Yun Ma Department of Biology, Tianjin Normal University, Tianjin, China Search for more papers by this author Maria Jaramillo Maria Jaramillo Receptor, Signaling and Proteomics Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Search for more papers by this author Hamza Bari Hamza Bari Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Search for more papers by this author Arif Awan Arif Awan Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Search for more papers by this author Song Yang Song Yang School of Chemical Engineering, Tianjin University, Tianjin, China Search for more papers by this author Simo Zhang Simo Zhang Department of Biology, Tianjin Normal University, Tianjin, China Search for more papers by this author Lixue Liu Lixue Liu Department of Biology, Tianjin Normal University, Tianjin, China Search for more papers by this author Meng Lu Meng Lu Department of Biology, Tianjin Normal University, Tianjin, China Search for more papers by this author Maureen O'Connor-McCourt Maureen O'Connor-McCourt Receptor, Signaling and Proteomics Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Search for more papers by this author Enrico O Purisima Enrico O Purisima Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Center for Bioinformatics, McGill University, Montreal, QC, Canada Search for more papers by this author Edwin Wang Corresponding Author Edwin Wang Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada Center for Bioinformatics, McGill University, Montreal, QC, Canada Search for more papers by this author Author Information Qinghua Cui1, Yun Ma2, Maria Jaramillo3, Hamza Bari1, Arif Awan1, Song Yang4, Simo Zhang2, Lixue Liu2, Meng Lu2, Maureen O'Connor-McCourt3, Enrico O Purisima1,5 and Edwin Wang 1,5 1Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada 2Department of Biology, Tianjin Normal University, Tianjin, China 3Receptor, Signaling and Proteomics Group, Biotechnology Research Institute, National Research Council Canada, Montreal, QC, Canada 4School of Chemical Engineering, Tianjin University, Tianjin, China 5Center for Bioinformatics, McGill University, Montreal, QC, Canada *Corresponding author. Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, 6100 Royalmount, Montreal, QC, Canada H4P 2R2. Tel.: +1 514 496 0914; Fax: +1 514 496 0943; [email protected] Molecular Systems Biology (2007)3:152https://doi.org/10.1038/msb4100200 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info We conducted a comprehensive analysis of a manually curated human signaling network containing 1634 nodes and 5089 signaling regulatory relations by integrating cancer-associated genetically and epigenetically altered genes. We find that cancer mutated genes are enriched in positive signaling regulatory loops, whereas the cancer-associated methylated genes are enriched in negative signaling regulatory loops. We further characterized an overall picture of the cancer-signaling architectural and functional organization. From the network, we extracted an oncogene-signaling map, which contains 326 nodes, 892 links and the interconnections of mutated and methylated genes. The map can be decomposed into 12 topological regions or oncogene-signaling blocks, including a few 'oncogene-signaling-dependent blocks' in which frequently used oncogene-signaling events are enriched. One such block, in which the genes are highly mutated and methylated, appears in most tumors and thus plays a central role in cancer signaling. Functional collaborations between two oncogene-signaling-dependent blocks occur in most tumors, although breast and lung tumors exhibit more complex collaborative patterns between multiple blocks than other cancer types. Benchmarking two data sets derived from systematic screening of mutations in tumors further reinforced our findings that, although the mutations are tremendously diverse and complex at the gene level, clear patterns of oncogene-signaling collaborations emerge recurrently at the network level. Finally, the mutated genes in the network could be used to discover novel cancer-associated genes and biomarkers. Synopsis Cancer is largely a genetic disease that is caused by acquiring genomic alterations in cells. It is proposed that a malignant tumor arises from a single cell, which undergoes a series of evolutionary processes of genetic or epigenetic changes and selections so that a cell within the population can acquire additional selective advantages, resulting in progressive clonal expansion (Nowell, 1976). Enormous efforts have been made over the past few decades to identify gene mutations that are causally implicated in human cancer. Recently, a whole-genome or large-scale efforts toward the identification of genetic and epigenetic changes in tumor samples have been conducted (Stephens et al, 2005; Sjoblom et al, 2006; Greenman et al, 2007; Ohm et al, 2007; Schlesinger et al, 2007; Thomas et al, 2007; Widschwendter et al, 2007). These studies showed that a substantial fraction of the cancer-associated mutated and methylated genes is involved in cell signaling. Although a wide variety of genetic and epigenetic alterations contribute to the signaling of tumorigenesis, it has been challenging to gain a global view of where and how they affect the signaling alterations to generate tumors on the entire signaling network. To address this question, we performed an integrative analysis of a human signaling network incorporating the cancer mutated and methylated genes. We uncovered an overall picture of the network architecture to determine the sites at which oncogenic stimuli occur and the oncogenic regulatory mechanisms underlying the mutated and methylated genes. Genetic mutations preferentially occur in the proteins (signaling hubs) that receive and send more signals but not in the proteins (neutral hubs) that simply have more physical interactions with others. However, methylated genes have no such preference. Furthermore, we showed that genetic mutations are enriched in positive regulatory loops, whereas methylated genes are enriched in negative regulatory loops. These results suggest that genetic and methylated alterations have different regulatory mechanisms in tumorigenesis. Signaling information propagates through a series of built-in regulatory motifs to contribute to cellular phenotypic functions (Ma'ayan et al, 2005). The transition from a normal cellular state into an oncogenic state is often driven by prolonged activation of downstream proteins, which are regulated by upstream proteins or regulatory motifs. In cancer cells, constitutive activation of the oncogene signaling is necessary. The enrichment of genetic mutations in positive regulatory loops suggests that the mutants in the motifs must have gain of function or increase their biochemical activities compared with the wild-type genes to constitutively activate the downstream proteins. Indeed, a recent survey showed that 14 out of the 15 PI3K mutants in tumors have gain of function (Gymnopoulos et al, 2007). A gain-of-function mutant in a positive regulatory loop offers the amplification of weak input stimuli and serves as information storage to extend the duration of activation of the affected downstream proteins. This might allow the downstream signaling cascades to persistently hold and transfer information leading to tumor phenotypes. One the other hand, methylation is a known mechanism of inducing loss of function of genes (Ohm et al, 2007; Widschwendter et al, 2007). Negative regulatory loops suppress positive signals and play an important role in maintaining homeostasis and restraining the cellular-state transitions (Ma'ayan et al, 2005). A loss of function by gene methylation in a negative regulatory loop could inhibit the negative-feedback mechanism, thereby releasing the restrained activation signals and promoting the oncogenic state transition. Both the gain-of-function mutated genes in positive regulatory loops and the loss-of-function methylated genes in negative regulatory loops could break this delicate balance, thus promoting state transitions and tumor phenotypes. Extensive efforts have been made to illustrate cancer signaling during the past few decades. However, it has been a struggle to get clues of how the oncogene signaling is structurally and functionally organized. To answer these questions, we extracted an oncogene-signaling map from the network, which contains 326 nodes, 892 links and the interconnections of mutated and methylated genes (Figure 3). We further systematically identified the 'oncogene-signaling-dependent events' (the phenomenon by which certain cancer cells become dependent on certain signaling cascades for growth or survival), which are frequently used in many tumors. Within the map, the oncogene-signaling-dependent events form three highly connected regions that resemble oncogene-signaling superhighways that are frequently used in tumorigenesis (Figure 3). Two of the regions consist of genes that are heavily methylated in cancer stem cells. This map provides a blueprint of the oncogene signaling in cancer cells and can be used to generate testable hypotheses for a given mutation in a particular cancer sample. To get insights into how the map is functionally organized, we first divided the map into 12 oncogene-signaling blocks based on the connectivity of the map nodes. We then queried the 592 tumor samples, in which each sample contains at least two mutations of the network genes, using the 12 oncogene-signaling blocks. Interestingly, we found that two oncogene-signaling blocks are enriched in gene mutations and tend to collaborate in most tumor types (Figure 4A). These two blocks are called p53 (composed of p53, p14, Rb, BRAC1 and BRAC2 etc.) and Ras (Ras, PI3K and EGFR etc.) blocks. In all the tumor types analyzed, at least 2 signaling gene mutations, one from the p53 block and the other from another block, are necessary for tumorigenesis and further support the notion that both the prevention of cell death (p53 block) and the promotion of cell proliferation (Ras or other blocks) are necessary to generate most tumors. The same analysis was extended to six representative cancer types. Breast and lung cancers have more complex oncogene-signaling block collaborative patterns than other four cancer types that have similar oncogene-signaling block collaborative patterns found in the 592 samples. We further benchmarked the gene mutation data from the systematic sequencing of tumor samples using the oncogene-signaling map as a framework. We also obtained a oncogene-signaling block collaborative pattern similar to that found in the 592 tumor samples, when using the mutation data of the NCI-60 cancer cell line, in which 24 known cancer genes were screened for mutations (Figure 4B). For the data derived from the genome-wide mutation screening, colon cancer showed a simple collaborative pattern of the oncogene-signaling blocks, whereas breast tumors showed complex patterns (Figure 4C and D). These findings imply that, although the mutations seem tremendously diverse and complex at the gene level, clear patterns emerge recurrently at the network level in most tumors. This work uncovered novel features of human cancer signaling that help in understanding the underlying mechanisms of tumorigenesis. Furthermore, it provides a conceptual and technical framework for incorporating tumor genome sequencing outputs to get more insights into the cancer-signaling mechanisms that will lead in identifying the key genes for biomarkers and drug development. Introduction Cells use sophisticated communication between proteins in order to initiate and maintain basic cellular functions such as growth, survival, proliferation and development. Traditionally, cell signaling is described via linear diagrams and signaling pathways. As many more 'cross-talks' between signaling pathways have been identified (Natarajan et al, 2006), a network view of cell signaling emerged: the signaling proteins rarely operate in isolation through linear pathways, but rather through a large and complex network. As cell signaling is crucial to affect cell responses such as growth and survival, alterations of cellular signaling events, such as those that arise by mutations, can result in tumor development. Indeed, cancer is largely a genetic disease that is caused by acquiring genomic alterations in somatic cells. Alterations to the genes that encode key signaling proteins, such as RAS and PI3K, are commonly observed in many types of cancers. During tumor progression, it is proposed that a malignant tumor arises from a single cell, which undergoes a series of evolutionary processes of genetic or epigenetic changes and selections so that a cell within the population can acquire additional selective advantages for cellular growth or survival, resulting in progressive clonal expansion (Nowell, 1976). Genetic mutations of the signaling proteins might overactivate key cell-signaling properties such as cell proliferation or survival and then give rise to the cell with selective advantages for uncontrolled cellular growth and promoting tumor progression. In addition, mutations may also inhibit the function of tumor-suppressor proteins, resulting in a relief from normal constraints on growth. Furthermore, epigenetic alterations by promoter methylation, resulting in transcriptional repression of genes controlling tumor malignancy, is another important mechanism for the loss of gene function that can provide a selective advantage to tumor cells. Enormous efforts have been made over the past few decades to identify mutated genes that are causally implicated in human cancer. Furthermore, a genome-wide or large-scale sequencing of tumor samples across many kinds of cancers represents a largely unbiased overview of the spectrum of mutations in human cancers (Stephens et al, 2005; Sjoblom et al, 2006; Greenman et al, 2007; Thomas et al, 2007). Most of these efforts have been made by the Cancer Genome Project (CGP, http://www.sanger.ac.uk/genetics/CGP/), which aims to identify cancer-mutated genes using genome-wide mutation-detection approaches. Similarly, genome-wide identification of epigenetic changes in cancer cells has been conducted recently (Ohm et al, 2007; Schlesinger et al, 2007; Widschwendter et al, 2007). These studies showed that a substantial fraction of the cancer-associated mutated and methylated genes is involved in cell signaling, which is in agreement with the previous finding that the most common domain encoded by cancer genes is the protein kinase domain (Futreal et al, 2004). Although there is a wealth of knowledge regarding molecular signaling in cancer, the complexity of human cancer prevents us from gaining an overall picture of the mechanisms by which these genetic and epigenetic events affect cancer cell signaling and tumor progression. Where are the oncogenic stimuli embedded in the network architecture? What are the principles by which genetic and epigenetic alterations trigger oncogene-signaling events? Given that so many genes have genetic and epigenetic aberrations in cancer signaling, what is the architecture of cancer signaling? Do any tumor-driven signaling events represent 'oncogenic dependence' (the phenomenon by which certain cancer cells become dependent on certain signaling cascades for growth or survival)? Who are the central players in oncogene signaling? Are there any signaling partnerships generally used to generate tumor phenotypes? To answer these questions, we conducted a comprehensive analysis of cancer mutated and methylated genes on a human signaling network, focusing on network structural aspects and quantitative analysis of gene mutations on the network. Results and discussion The architecture and the relationships among the proteins of a signaling network are important for determining the sites at which oncogenic stimuli occur and through which oncogenic stimuli are transduced. Extensive signaling studies during the past decades have yielded an enormous amount of information regarding regulation of signaling proteins for more than 200 signaling pathways, most of which have been assembled and collected in public databases in diagrams. We manually curated the data on signaling proteins and their relations (activation and inhibitory and physical interactions) from the BioCarta database and the Cancer Cell Map database (see Materials and methods). We merged the curated data with another literature-mined signaling network that contains ∼500 proteins (Ma'ayan et al, 2005). As a result, we have built a human signaling network containing 1634 nodes and 5089 links. Integrative network analyses have provided numerous biological insights (Wuchty et al, 2003; Han et al, 2004; Ihmels et al, 2004; Luscombe et al, 2004; Kharchenko et al, 2005; Wang and Purisima, 2005; Cui et al, 2006). Thus, the integration of the data on mutated and methylated cancer-associated genes onto the network will help us to identify critical sites involved in tumorigenesis and increase our understanding of the underlying mechanisms in cancer signaling. To integrate mutated and methylated genes onto the network, we first collected the cancer mutated genes from the Catalogue Of Somatic Mutations In Cancer (COSMIC) database, which collects the cancer mutated genes through literature curation and large-scale sequencing of tumor samples in the CGP. We then combined these data with the cancer mutated genes derived from other genome-wide and high-throughput sequencing of tumor samples (Stephens et al, 2005; Sjoblom et al, 2006; Greenman et al, 2007; Thomas et al, 2007). The merged gene set represents a mixture of the past directed approach and current systematic screening of cancer mutations. The cancer-associated methylated genes were taken from the genome-wide identification of the DNA methylated genes in cancer stem cells (Ohm et al, 2007; Schlesinger et al, 2007; Widschwendter et al, 2007). Finally, 227 cancer mutated genes and 93 DNA methylated genes were mapped onto the network. Among the 227 cancer mutated genes, 218 (96%) and 55 (24%) genes were derived from large-scale gene sequencing of tumors and literature curation, respectively (see Materials and methods, Figure 1A). In general, cancer genes can be divided into two groups: positive regulators (oncogenes) that promote cancer cell proliferation and the negative regulators (tumor suppressors) that restrain it. By comparing the mutated genes with the known tumor suppressors, we found that only 6.6% (15 genes) of the mutated genes are known tumor suppressors and that the majority of the mutated genes are oncogenes (Supplementary Figure 1). On the other hand, methylated genes are mainly found to encode tumor suppressors in cancer cells (Supplementary Figure 1) (Ohm et al, 2007; Widschwendter et al, 2007). Figure 1.Illustration of the sources of cancer mutated network genes and oncogenic signal transduction events. (A) Most of the cancer mutated network genes were discovered by large-scale sequencing of tumor samples, whereas a small fraction of them was found in literature. (B) Oncogenic signal transduction events and oncogene-signaling-dependent events. (a) Signaling divergent unit. The line in red represents an oncogenic signal transduction event. (b) Signaling convergent unit. The line in red represents an oncogene-signaling-dependent event. In this case, both genes have high mutation frequency (⩾0.02), suggesting that the signaling event between the two genes is frequently used in tumorigenesis. Nodes in red represent mutated genes, whereas numbers represent mutation frequencies. Signs + and − represent activating and inhibitory links, respectively. Download figure Download PowerPoint Cancer mutated genes are enriched in signaling hubs but not in neutral hubs Genes that, when mutated or silenced, result in tumorigenesis often lead to the aberrant activation of certain downstream signaling nodes resulting in dysregulated growth, survival and/or differentiation. The architecture of a signaling network is important for determining the site at which a genetic defect is involved in cancer. To discover where the critical tumor signaling stimuli occur on the network, we explored the network characteristics of the mutated and methylated genes. The signaling network is presented as a graph, in which nodes represent proteins. Directed links are operationally defined to represent effector actions such as activation or inhibition, whereas undirected links represent protein physical interactions that are not characterized as either activating or inhibitory. For example, scaffold proteins do not directly activate or inhibit other proteins but provide regional organization for activation or inhibition between other proteins through protein interactions. In this case, undirected links are used to represent the interactions between scaffold proteins and others. On the other hand, adaptor proteins are able to activate or inhibit other proteins through direct protein interactions. In this situation, directed links are used to represent these relations. There are two kinds of directed links. An incoming link represents a signal from another node. The sum of the number of incoming links of a node is called the indegree of that node. An outgoing link represents a signal to another node. The sum of the number of outgoing links of a node is called the outdegree of that node. We call incoming and outgoing links as signal links, whereas the physical links are neutral links. We first examined the characteristics of the nodes that represent mutated genes on the network. We compared the average indegree of the mutated genes with that of the nodes in the whole network. We found that the average indegree of the mutated nodes is significantly higher than that of the network nodes (P<1.1 × 10−6, Wilcoxon test, Supplementary Figure 2). A similar result was obtained for the average outdegree of the mutated nodes (P<6.0 × 10−14, Wilcoxon test, Supplementary Figure 2). In contrast, there is no difference of the average neutral degrees between the mutated nodes and other nodes in the network. To refine these results further, we calculated the correlations between the indegree, outdegree and neutral degree of the network nodes. We found a significant correlation between the indegree and the outdegree of the network nodes (R=0.41, P<2.2 × 10−16, Spearman's correlation), but no correlation between the indegree and neutral degree of the nodes (R=−0.02, P=0.54, Spearman's correlation). Taken together, these results suggest that cancer mutations most likely occur in signaling proteins that are acting as signaling hubs (i.e., RAS) actively sending or receiving signals rather than in nodes that are simply involved in passive physical interactions with other proteins. As these hubs are focal nodes that are shared by, and/or are central in, many signaling pathways, alterations of these nodes, or signaling hubs, are predicted to affect more signaling events, resulting in cancer or other diseases. In previous studies, we found that cancer-associated genes are enriched in hubs (Awan et al, 2007). However, these results indicate that cancer-associated genes are enriched in signaling hubs but not neutral hubs. We also investigated the relations between the node degree and the methylated genes in the network. Methylated gene nodes do not appear to differ significantly from the network nodes with regard to their indegree, outdegree and neutral degree, respectively (P=0.32, P=0.16, P=0.09, Supplementary Figure 2). These results suggest that cancer mutated genes and methylation-silenced genes have different regulatory mechanisms in oncogene signaling. Activating and inhibitory signals enhance and alleviate oncogene-signaling flows, respectively Signaling flow branching represents the splitting of one signal at a source node (Figure 1B), whereas signaling flow convergence represents the consolidation of the signals at a target node from two source nodes (Figure 1B). Both types of the signaling flows are the basic elements of the network architectural organization. In the network, when the upstream and downstream nodes of a particular signal transduction event get altered either genetically or epigenetically, we considered the transduction event (link) to be most likely selected and used in cancer signaling and defined it as an oncogenic signal transduction event (Figure 1B). If a particular oncogenic signal transduction event is frequently found in many tumor samples, we infer that the tumor cells are 'dependent' on this highly used signaling event and call it 'oncogene-signaling-dependent event' (Figure 1B). To investigate how cancer signaling is distributed on these signal transduction routes, we extracted all the branching and convergent signaling flow units that contain at least one oncogenic signal transduction event and conducted a quantitative analysis by overlaying the gene mutation frequency onto these units. The mutation frequency of a gene was defined as the number of tumor samples that contain that mutated gene divided by the total number of the tumor samples that are used to screen the mutations for that gene. The mutation frequency of each mutated gene was obtained by using the COSMIC database, which contains the data on more than 200 000 tumor samples screened for cancer gene mutations. For the signaling branching units, we divided the signaling flows into two groups: activating and inhibitory group (Figure 1B) and compared the gene mutation frequencies of the upstream nodes with those of the downstream nodes in each group. Interestingly, in the activating group, the upstream nodes often have lower mutation frequencies than those of the downstream nodes. In contrast, in the inhibitory group, the upstream nodes often have higher mutation frequencies than those of the downstream nodes (Table I). Statistical tests confirmed that these observations are statistically significant (Table I). Similar results were obtained for the signaling convergent units as well (Figure 1B, Table I). These results suggest that the oncogene-signaling event triggered by mutations is preferentially associated with activating downstream signaling paths or conduits. Conversely, oncogene-signaling event triggered by mutations are less likely to be associated with downstream inhibitory signaling paths. Table 1. Effects of the positive and negative signals on the oncogene-signaling flows Signaling branching type Signaling convergence type Increasing Decreasing Increasing Decreasing Activating group 676 551 1032 418 Inhibitory group 46 140 93 96 Odds ratio 3.7 2.5 P-value 3.7 × 10−15 2.5
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