TITLE

An approach for the identification of targets specific to bone metastasis using cancer genes interactome and gene ontology analysis

AUTHORS

Shikha Vashisht and Ganesh Bagler*

AFFILIATIONS

Biotechnology Division, CSIR-IHBT, Institute of Himalayan Bioresource Technology, Council of Scientific and Industrial Research, Palampur, 176061 (H.P.), India.

*Author for correspondence: Phone Number: +919816931177; Fax: +91-1894-230433; Email: [email protected], [email protected].

ABSTRACT

Metastasis is one of the most enigmatic aspects of cancer pathogenesis and is a major cause of cancer-associated mortality. Secondary bone cancer (SBC) is a complex disease caused by metastasis of tumor cells from their primary site and is characterized by intricate interplay of molecular interactions. Identification of targets for multifactorial diseases such as SBC, the most frequent complication of breast and prostate cancers, is a challenge. Towards achieving our aim of identification of targets specific to SBC, we constructed a ‗Cancer Genes Network‘, a representative protein interactome of cancer genes. Using graph theoretical methods, we obtained a set of key genes that are relevant for generic mechanisms of cancers and have a role in biological essentiality. We also compiled a curated dataset of 391 SBC genes from published literature which serves as a basis of ontological correlates of secondary bone cancer. Building on these results, we implement a strategy based on generic cancer genes, SBC genes and gene ontology enrichment method, to obtain a set of targets that are specific to bone metastasis. Through this study, we present an approach for probing one of the major complications in cancers, namely, metastasis. The results on genes that play generic roles in cancer phenotype, obtained by network analysis of ‗Cancer Genes Network‘, have broader implications in understanding the role of molecular regulators in mechanisms of cancers. Specifically, our study provides a set of potential targets that are of ontological and regulatory relevance to secondary bone cancer.

1

INTRODUCTION

Cancer is a disease of multiple systems and components that interact at both molecular and cellular levels leading to initiation, progression and spread of the disease [1,2]. The changing interactions of these systems in a dynamic environment underscore the inherent complexity of the disease. Until recently, cancer has been studied with a reductionist approach focusing on a specific mutation or a pathway. Lately there has been a tremendous increase in systems-level study of cancer and the use of integrative approaches to understand mechanisms of cancers [3,4] and their metastases [5,6].

Metastasis is one of the most enigmatic hallmarks of cancers characterized by complex molecular interactions [1,7]. It is responsible for as much as 90% of cancer-associated mortality, yet remains the most poorly understood component of cancer pathogenesis [7,8]. Tumor metastasis is a multistage process during which malignant cells spread from the primary tumor to discontiguous organs [7]. Metastatic dissemination involves a sequence of steps involving invasion, intravasation, extravasation, survival, evasion of host defense and adaptation to the foreign microenvironment [7,8].

Secondary bone cancer (SBC) is a complex disease involving interplay of osteolytic and osetoblastic mechanisms [9] (Figure 1). Bone metastases are the most frequent complication of breast and prostate cancers with a very high propensity of metastasizing to bone causing bone pain, fracture, hypercalcemia and paralysis [10–13]. Breast and prostate carcinomas are often known to take years to develop metastatic colonies (in a limited number of sites) suggesting that in these cancers, cells employ distinct adaptive programs to laboriously cobble together complex shifts in gene-expression programs [14]. Many molecules and associated pathways are reported to be involved in metastasis of cancer cells from breast cancer [13,15–22] and those from prostate cancer [10,18,23–30].

Cancers are characterized by hallmark processes and shared mechanisms involved in expression of disease phenotype. It is a challenge to identify such genes involved in generic cancer mechanisms. Identification of such ‗generic cancer genes‘ may help us focus on ‗disease specific cancer genes‘ of potential therapeutic value. Due to complexity and subtle mechanisms involved in metastasis, it is difficult to identify their control mechanisms. Therefore it is important to have methods for identification of genes and regulatory mechanisms that are key to a complex pathogenic state such as secondary bone cancer. Complex network models of interactomes, along with graph-theoretical analysis and overrepresentation studies, present us a useful strategy for probing molecules that are central to SBC mechanisms and hence potential therapeutic targets.

Cellular functions reflect the state of the cell as a function of an intricate web of interactions among large number of genes, metabolites, proteins and RNA molecules. A disease phenotype reflects various pathobiological processes that interact in a complex network and is rarely a consequence of abnormality in a single effector gene product [3]. Understanding diseases in the context of organizing principles of the architecture of biological networks allows us to address some fundamental properties of genes that are involved in disease. Study of disease protein interactomes offer a better understanding of disease-specific genes and processes involved and may offer better targets for drug development. Molecular interaction networks are characterized by the presence of a few highly connected nodes, often called hubs, suggesting a special role of these promiscuous interactors. Hubs of protein interactomes are more likely to be essential for the survival [31] and also reported to be important for cellular growth rate [32]. Proteins with high

2 betweenness [33,34] are reported to have much higher tendency to be essential genes [35,36]. Cancer proteins are reported to be more central in the protein interactome and are, on an average, involved in twice as many interactions as those of non-cancer proteins [37].

The Gene Ontology [38] project provides an ontology of defined terms representing gene product properties. The ontology covers three domains: cellular component, parts of a cell or its extracellular environment; molecular function, elemental activities of a gene product at the molecular level and biological process, sets of molecular events with a defined beginning and end, pertinent to the functioning of integrated living units. GO enrichment methods provide a way to extract biological insight from a set of genes using the power of gene sets [39]. Enrichment analysis involves identification of GO terms that are significantly overrepresented in a given set of genes using statistical models such as hypergeometric and chi squared distributions [40]. A large repertoire of tools has been developed in recent years for enrichment analysis [41,42]. Methods of network analysis and enrichment studies have been effectively used to identify targets of diseases such as chronic fatigue syndrome [43], major depressive disorder [44], glioblastoma [45], colorectal carcinogenesis [46] and primary immunodeficiency disease [47].

In this study, we aimed to identify secondary bone cancer specific genes. Towards this goal, we used a composite strategy (Figure 2) involving identification of cancer genes specifying generic cancer mechanisms, compilation of genes implicated in metastasis to bone and identification of genes annotated with GO terms specific to secondary bone cancer, to obtain disease specific targets. While network analysis provides a systems perspective of complex molecular mechanisms and helps to identify its central components (functional elements), gene enrichment method enables identification of characteristic ontological features of the gene sets. We first constructed a representative protein interactome of all cancer genes and obtained hubs that are involved in generic cancer mechanisms. Further, we compiled a set of experimentally verified genes from the literature that is involved in metastasis of primary breast and prostate cancer into bone, the dominant cause of secondary bone cancer. Using a combination of protein interactome analysis and gene ontology enrichment studies, we obtained a set of genes (targets) specific to SBC mechanisms. Our study provides an approach to identify targets specific to a complex disease phenotype (bone metastasis) by combining systems-level interactome analysis and ontological studies.

RESULTS

CGN as a representative interactome of cancer mechanisms

We intended to construct an interactome that represents mechanisms involved in processes contributing to cancers. For this purpose we used genes listed in CancerGenes database [48], a compilation of cancer genes that are causally implicated in oncogenesis. We obtained 3164 cancer genes from CancerGenes database, which were used to construct an interactome. These genes were mapped on Human Protein Reference Database [49], a database of curated proteomic information pertaining to human proteins, to construct the Cancer Genes Network (CGN). CGN thus represents an intricate network of cancer proteins. CGN comprises 11602 interactions among 2665 proteins. Figure 3 depicts the CGN, a representative protein interactome of molecular agents and their regulatory interactions, involved in disease phenotype of cancers. Based on earlier reports [3], we hypothesize that proteins that are key to the structural integrity and interaction dynamics of CGN would correspond to proteins involved in regulatory mechanisms generic to cancers.

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Topologically central genes of CGN correlate to generic cancer mechanisms

Molecular interaction networks have been reported to have a scale-free nature marked by the presence of few hubs that are critical for the networks [50]. Such hubs of protein interactomes are reported to be more essential for the survival [31] and also important for cellular growth rate [32]. Proteins with high betweenness are reported to have much higher tendency to be essential genes [35,36]. Cancer proteins are reported to be more central in the protein interactome and are, on an average, involved in twice as many interactions as non-cancer proteins [37]. As reported for other biological molecular networks [50,51], we find that CGN has a scale-free nature indicating presence of exceptionally promiscuously interacting hub nodes and those mediating a large number of interactions (Figure 4) [52].

We computed seven parameters that reflect topological features (degree [53] and neighborhood connectivity [54]), network flow (betweenness [33,34], stress [33,34] and average shortest path length [55]) and local clustering (clustering coefficient [53,55,56] and topological coefficient [57]). The ‗key‘ proteins, that are topologically and dynamically central to CGN, were identified by network analysis. For this purpose ‗degree‘, ‗stress‘ and the normalized counterpart of the latter, ‗betweenness‘, were used for identification of central proteins of CGN. We find that the selected parameters show very high mutual positive correlations (Figure 5). The best-ranked proteins from each of these parameters were compiled, for various thresholds (Top25—Top200), to identify proteins designated as ‗hubs‘ (Set-A in Figure 2 and Supplementary Information Table S1.1).

We find that the ‗hubs‘ (Figure 3; Top75 threshold), thus identified, include genes involved in regulation of processes known to be generically present in most cancers [1]. Proteins known to be involved in growth signals, thus leading to self-sufficiency in cancer cells, such as TGFBR1, EGFR, IGFR1R, GRB2, are in the ‗hubs‘ of CGN [2]. The hubs also include RB1, BCL2, AKT1, CDK2, CASP8 which are involved in mechanisms of apoptosis, to which cells are known to acquire resistance in all types of cancers [1,58]. The TP53 tumor suppressor protein, known to be involved in most commonly occurring loss of proapoptotic regulation and affecting the apoptotic effector cascade [59,60], is also present in the CGN hubs. One of the hub proteins, SMAD4, is known to be involved in differentiation, apoptosis and cell cycle [61]. MYC, known to upregulate cyclins and downregulate CDKN1A (P21), is also one of the hubs of CGN.

Further, we checked how well the identified hubs correlate with KEGG-PIC [62], a collection of genes from generic pathways involved in cancers (Supplementary Information Table S1.2). Figure 6 shows the overlap of hub genes (Top25 to Top200) with KEGG-PIC genes. We find that indeed the Top25 hubs, comprising the most promiscuously interacting proteins and mediating a large number of interactions in cancer genes interactome, have a 74% overlap with KEGG-PIC. The precision of identification of generic cancer proteins drops as the definition of ‗hubs‘ is relaxed further (57% for Top50 and 52% for Top75). As a negative control, for the ability of network-metrics- based ranking to identify generic proteins, we use genes ranked worst according to the selected parameters. We find that these genes have a very poor overlap with KEGG-PIC genes, even when compared to that expected from corresponding random samplings (Figure 6). We treat Top25, Top50 and Top75 CGN hubs as representatives of generic cancer genes and use them, further, to identify SBC-specific targets.

GO enrichment of CGN hub genes

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We performed GO enrichment analysis of CGN ‗hub‘ genes identified. We expect the ‗hubs‘ to represent generic cancer processes which are shared by most, and perhaps, all types of human cancers. The molecular machinery regulating proliferation, differentiation and death of all mammalian cells is highly similar [1]. The genetic transformation of normal body cells results in defects of regulatory circuits that govern normal cell proliferation and homeostasis, and collectively dictate malignant growth.

Cancer cells show self-sufficiency of growth signals involving alteration of transcellular or intracellular mechanisms [2]. The growth signaling pathways are suspected to suffer deregulation in all human tumors [2]. This was reflected in the following significantly overrepresented GO terms of CGN hubs: cellular response to growth hormone stimulus (GO:0071378), regulation of epidermal growth factor receptor signaling pathway (GO:0042058), transforming growth factor beta receptor signaling pathway (GO:0007179), signal complex assembly (GO:0007172).

In contrast to normal cells, which respond to antigrowth signals, cancerous cells are insensitive to antigrowth signals due to disruption of pathways related to cell cycle clock. We find that our list of significantly enriched GO terms for CGN hubs had many terms associated with cell cycle processes: mitotic cell cycle G1/S transition checkpoint (GO:0031575), regulation of G1/S transition of mitotic cell cycle (GO:2000045), DNA damage response, signal transduction by p53 class mediator (GO:0030330), regulation of stress-activated MAPK cascade (GO:0007173).

In human carcinogenesis, all types of cancers are known to acquire resistance to apoptosis [58]. The overrepresented GO terms of CGN hubs underline this observation: release of cytochrome c from mitochondria (GO:0001836), activation of pro-apoptotic gene products (GO:0008633), cell- type specific apoptotic process (GO:0097285), cellular component disassembly involved in apoptotic process (GO:0006921), positive regulation of anti-apoptosis (GO:0045768).

In addition, we find following significantly enriched ‗molecular function‘ terms: transforming growth factor beta receptor, pathway-specific cytoplasmic mediator activity (GO:0030618), receptor signaling protein tyrosine kinase activity (GO:0004716), ephrin receptor binding (GO:0046875), MAP kinase activity (GO:0004707), beta-catenin binding (GO:0008013), cytokine receptor binding (GO:0005126), androgen receptor binding (GO:0050681).

All the GO-terms mentioned above broadly support generic cancer processes. The p-values for these ‗significantly enriched GO terms of CGN Top75 hubs‘ are in the range of 10e-4 and 10e-13. Figure 7 depicts these overrepresented GO terms and statistics of their prevalence.

Topologically central genes of CGN correlate with biological essentiality

We further checked the biological relevance of the central genes of CGN. We divided CGN into two sets, essential and non-essential, using Mammalian Phenotype Ontologies [63] to classify genes as essential when they caused embryonic, perinatal, postnatal or neonatal lethality in mouse models [64] using phenotypic data from Mouse Genome Informatics (MGI) [65]. The percentage of essential genes were computed in the ‗hubs‘ and their corresponding non-hubs for varying hub definitions. Figure 8 shows the statistics for percentage of hub and non-hub genes annotated as essential. Among the seven parameters computed, the hubs identified using degree, betweenness and stress have a significant percentage of essential genes (between 82% to 92%) (Figure 8A). Incidentally, none of the remaining four network parameters show good correlation with biological essentiality. The non-hubs, as expected from the random samplings statistics, have around 50% of

5 essential genes associated, regardless of the metric used for ranking (Figure 8B). For hubs comprising of Top75 genes, the chosen parameters have a very high proportion of essential genes (between 88% and 89%), compared to the rest of the parameters. Figure 3 depicts the central nature of Top75 hub genes of CGN and highlights the essential and non-essential genes among them. This emphasizes the relevance of the network metrics chosen in encoding biological relevance. This is consistent with earlier reports about hubs being more essential [64] and network centrality of cancer genes [37].

SBCGs and their characteristic GO terms

Knowing that metastasis into bone is primarily caused by spread from primary prostate and breast cancers [10–13], we aimed at compiling genes linked with these processes. We compiled a list of 391 Secondary Bone Cancer (SBC) susceptible genes from literature reporting experimental studies (Set-B in Figure 2 and Supplementary Information Table S1.3). For identifying the relevance of these genes in the context of metastasis into bone, we performed an overrepresentation analysis using CGN as universe. Gene ontology enrichment studies of SBCGs are expected to reflect GO categories of biological processes and molecular functions that are relevant for metastasis into bone, consistent with the known aspects of regulatory mechanisms of bone metastasis (Figure 1).

We identified 93 significantly enriched GO terms of SBCGs for biological processes, molecular functions and cellular components. We identify CGN genes annotated with either of these 93 GO terms to construct ‗Enriched Genes‘ set (Set-C in Figure 2). We find that out of the 93 significantly enriched GO terms, 31 are associated with metastasis or bone processes. Out of these 31 GO terms, 21 are most relevant for metastasis mechanisms and 10 for bone-related processes (Figure 9).

The following GO terms, that were significantly enriched, are related to bone processes: osteoblast differentiation (GO:0001649), regulation of bone remodeling (GO:0046850), endochondral ossification (GO:0001958), replacement ossification (GO:0036075), bone mineralization (GO:0030282), ossification (GO:0001503), cartilage development (GO:0051216), response to vitamin D (GO:0033280), collagen metabolic process (GO:0032963), collagen fibril organization (GO:0030199).

The following biological processes GO terms, that were significantly enriched, are related to mechanisms of metastasis: leukocyte migration (GO:0050900), cell-cell adhesion (GO:0016337), angiogenesis (GO:0001525), positive regulation of cell adhesion (GO:0045785), negative regulation of cell adhesion (GO:0007162), positive regulation of leukocyte migration (GO:0002687), regulation of leukocyte chemotaxis (GO:0002688), positive regulation of leukocyte chemotaxis (GO:0002690), positive regulation of catenin import into nucleus (GO:0035413).

Following molecular functions GO terms that were significantly enriched, are related to mechanisms of metastasis: laminin binding (GO:0043236), fibronectin binding (GO:0001968), fibroblast growth factor receptor binding (GO:0005104), platelet-derived growth factor binding (GO:0048407), extracellular matrix binding (GO:0050840), collagen binding (GO:0005518), integrin binding (GO:0005178), glycosaminoglycan binding (GO:0005539), cytokine activity (GO:0005125), carbohydrate binding (GO:0030246).

Predicted SBC-specific targets

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For predicting SBC-specific targets of potential therapeutic value, we logically juxtaposed the three sets of genes obtained: CGN hub genes (Top75), SBCGs and the cancer genes annotated with GO terms overrepresented for SBCGs (Figure 2 and Figure 10). On the basis of specificity for secondary bone cancer, as depicted in Figure 10A, the Venn diagram corresponding to these gene sets was divided into seven distinct regions: Set-a, Set-b, Set-c, Set-ab, Set-bc, Set-ac and Set- abc. Overall, the sets belonging to central genes of CGN (Set-a, Set-ab, Set-ac and Set-abc) contain genes that are involved in generic cancer mechanisms (Figure 6) and are also essential (Figure 8). Hence, while obtaining SBC-specific targets, we don‘t consider these gene sets. Out of the rest of three sets, Set-b comprises genes that are already reported to be associated with SBC mechanisms, hence may not reveal novel genes. We focus on Set-c and Set-bc, corresponding to non-generic cancer genes annotated with SBC-specific GO terms and those reported to be having a role in SBC mechanisms, respectively, for the search of novel SBC-specific targets.

Set-bc and Set-c contain 53 and 134 genes, respectively. These lists were refined to select only those genes that are annotated with GO terms most relevant for SBC (Figure 9 and Supplementary Information Table S1.4), to obtain 28 and 60 genes from Set-bc and Set-c, respectively. We find that, in these sets, we were still left with KEGG-PIC genes known to be involved in generic cancer pathways. The list of targets was refined further to obtain a total of 72 targets that are non-generic to cancers and relevant for secondary bone cancer.

Out of these 72 genes, 14 genes are annotated with GO terms relevant to bone processes, 51 with metastasis and 7 with both (Figure 3). We predict these seven proteins, with ontological relevance to bone processes as well as metastasis, to be most potent targets and key regulators of metastasis into bone. The non-generic SBC-specific targets, with relevance to metastasis and bone mechanisms, identified are: SPP1 (Secreted Phospoprotein 1), CD44 (Cluster of Differentiation 44), CTGF (Connective Tissue Growth Factor), TNXB (Tenascin X), BMP1 (Bone Morphogenetic Protein 1), BMPR1A (Bone Morphogenetic Protein Receptor, Type IA) and VWF (Von Willebrand Factor).

We find that the SBC-specific targets identified are indeed relevant for the metastasis into bone and involved in regulation of SBC mechanisms (Supplementary Information Table S1.5). SPP1 is reported to interact with CD44 receptor and is thought to exert pro-metastatic effects leading to tumor progression by regulating the cell signaling events [66,67]. CD44 is also reported to enhance integrin-mediated adhesion and transendothelial migration of breast cancer cells [68]. CTGF expression has been shown to be associated with tumor development and progession. The overexpression of CTGF in breast cancer cells may promote their metastasis to bone [69]. BMPR1A, belonging to the family of transmembrane serine/threonine kinases, is reported to be necessary for extracellular matrix deposition by osteoblasts, while not essential for osteoblast formation or proliferation [70]. BMP1, which does not belong to TGF superfamily unlike other BMPs, is known to induce bone and cartilage development. TNXB is an extracellular matrix glycoprotein expressed in connective tissues including skin, joints and muscles and in known to have role in cell-matrix and cell-cell adhesion. It has been reported that TNX deficiency leads to the invasion and metastasis of tumor cells by facilitating increase in the activity of MMPs which results in the degradation of laminin. The over-expression of TNX could be of potential therapeutic benefit in reducing tumor progression [71]. VWF is a large multimeric glycoprotein present in blood plasma and is produced constitutively in endothelium, megakaryocytes and subendothelial connective tissue. It has been reported that in osteosarcoma tumors the expression of VWF gets deregulated, potentially leading to metastasis [72].

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DISCUSSION

Towards our goal of identifying key targets specific for mechanisms of metastases of primary breast and prostate cancers to bone, we used network analysis and gene ontology enrichment studies. The motivation behind using a combination of network analysis and gene enrichment methods is that, while the prior provides a systems perspective of complex molecular mechanisms and helps to identify its central components (functional elements), the latter enables identification of characteristic ontological features of the gene sets. Secondary Bone Cancer (SBC) is a complex disease triggered from the primary form of cancers, most commonly through breast cancer and prostate cancer. Many pathways and molecular regulators involved in the metastasis of primary prostate cancer and breast cancer are well known. Starting from SBC-related proteins and interactome of proteins involved in cancer mechanisms, using network analysis and overrepresentation studies, we identify targets that are specific to SBC.

The final list of seven targets was identified using generic cancer genes obtained with Top75 hubs. We repeated this task with Top25 and Top50 lists, which have better overlap with KEGG-PIC genes (74% and 57%, respectively), as compared to that of Top75 hubs (52%) (Figure 7). Using the procedure described, we identified SBC-specific targets starting from Top25 and Top50 CGN hubs. Figure 11 illustrates the results of these experiments obtained with better overlap with generic cancer genes (KEGG-PIC). Using these thresholds we obtained 31 and 60 hubs, respectively, which were filtered from the potential target set. This resulted in increase in number of potential targets in Set-c to 141 and 139, respectively, compared to those obtained with Top75 hubs (134). There was no change in the number of genes (53) obtained in Set-bc. Even with these criteria that correspond better with generic genes, the number of targets (that are annotated with GO terms relevant to metastasis and that of bone processes) does not increase, indicating the soundness of criteria used for identification of SBC-specific targets.

Network analysis has been shown to be a very useful and potent tool in understanding the disease phenotype and probing for therapeutic targets [3]. The identification of network parameters relevant to the question(s) being asked is an important task. We chose degree, betweenness and stress that are known to be important in the topology of the disease interactome and dynamic interplay of proteins involved [3]. These parameters were also found to have very good correlation with ‗essential genes‘ (Figure 8). The significant terms emerging from overrepresentation analysis of CGN too support our choice of parameters (Figure 7).

Gene Ontology (GO) defines a set of functional terms related by parenthood relationships forming a directed acyclic graph. It produces sets of explicitly defined, structured vocabularies that describe biological processes, molecular functions and cellular components of gene products [38]. GO classification is expected to become an increasingly powerful tool for data analysis and functional predictions as the ontologies and annotations continue to evolve [40]. It is characterized by high quality manual curation, consistent annotation standards across species, and has the advantage of lesser bias as compared to domain specific classification schemes due to its comprehensive nature [40]. GO enrichment methods, which provide means of identification of significantly overrepresented GO terms, could be effectively used to get biological insights from a given set of genes [40]. One of the critical points in GO enrichment analysis is the selection of background set for getting the correct results. In this study, we use a pool of cancer genes (CGN) as universe, which serves the purpose of a meaningful background set of cancer mechanisms.

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Cancer is a complex genetic disease characterized by intricate regulations among a diverse set of cancer genes. Hence, it is useful to have a detailed map of interactions that could help in probing hallmark regulatory modules and perhaps cancer-specific motifs. Here, we used CancerGenes Database (CGDb) [48] as a premise of cancer genes to construct a representative cancer interactome. This could potentially be improved by compiling a more exhaustive list of cancer genes. Though, we believe that the ultimate set of generic cancer genes, central to the topology of the cancer genes network, would not alter much, it may enrich the data and enable a more meaningful analysis of subtle regulatory features of cancer phenotype. It would be interesting to see whether and how the modules of CGN reflect the hallmarks of cancers. Also, while a few of the network centrality metrics for cancer genes may enumerate biological essentiality, it would be interesting to explore a larger parameter space to search for topological correlates of essentiality. We use KEGG-PIC genes as a prototype of generic cancer mechanisms. There is scope for improvement of this data by inclusion of generic genes through curation from landmark studies [1,2,73,74]. We believe that better metrics that embody structure and information dynamics over the cancer genes interactome could be developed that are more successful in elucidating the regulatory features characterizing molecular circuitries of cancers.

In view of the complex and subtly intertwined regulatory mechanisms of cancers, we modeled it as a network of protein interactions and aimed to identify generic cancer genes that specify hallmark features of cancers. We find that, indeed hubs of the cancer genes interactome that are central to the structural integrity and dynamical interplay of proteins, correlate with genes from generic pathways of cancers. We believe that the methodology presented here could be useful in obtaining cancer-specific targets of potential therapeutic value.

MATERIALS AND METHODS

Cancer Genes Network (CGN)

We used HPRD (Human Protein Reference Database) [49], one of the most comprehensive resources of human protein-protein interactions (PPIs), to construct a reference human protein interactome. HPRD is a manually curated human protein-protein interaction resource containing 36617 unique human PPIs and 9427 associated proteins (Release 9: April 13, 2010). It is one of the best resources of human PPIs, containing the largest number of binary non-redundant human PPIs, largest number of genes annotated with at least one interactor, and largest citations of PPIs curated [75].

The data of cancer genes involved in carcinogenesis were compiled from CancerGenes database [48] (as on May 2012). CancerGenes database is a compilation of cancer gene lists annotated by experts with information from key publicly available databases. Cancer associated genes are collected from various sources such as Cancer Map Pathways, Sanger Cancer Gene Census, Sanger Catalogue of Somatic Mutations in Cancer, reviews on cancer, Entrez queries and prostate cancer list. This data of 3164 cancer genes was used to compile a protein interactome representing molecular mechanisms of cancers. The interactions associated with proteins corresponding to these genes were collected from HPRD [49]. This network was called CGN (Cancer Genes Network), in which nodes represent cancer genes and edges represent experimentally validated interactions between a pair of genes i and j. Thus CGN is an intricate network of cancer proteins, comprising of 11602 interactions among 2665 proteins. It contains a giant cluster of 2376 proteins interlinked via 11590 interactions and the rest fragmented into minor clusters and isolated nodes (Figure 3). To adjudge the scale-free nature of degree and

9 betweenness distributions of CGN, we tested the power-law hypothesis and estimated the parameters for these distributions with the technique based on maximum likelihood methods and the Kolmogorov-Smirnov statistic (Figure 4) [52].

Secondary Bone Cancer Genes (SBCGs)

We curated and compiled 391 genes involved in ‗metastasis to bone from primary prostate and breast cancer‘ (167 and 230 genes, respectively), through literature mining (Set-B in Figure 2). These genes were called SBCGs (Secondary Bone Cancer Genes). We used following keywords to search for SBCGs: ―secondary bone cancer genes from primary prostate cancer‖, ―secondary bone cancer genes from primary breast cancer‖ (Pubmed) and ―genes involved in metastasis of prostate cancer to bone‖, ―genes involved in metastasis of breast cancer to bone‖ (Google Scholar). Supplementary Information Table S1.3 provides the details of SBCGs compiled from the literature.

Gene Ontology analysis

Gene Ontology (GO) enrichment or overrepresentation analysis allows one to identify characteristic biological attributes in a given gene set. It is based on the hypothesis that functionally related genes should accumulate in the corresponding GO category. We used GOrilla [41], a tool to identify enriched GO terms, to obtain biological attributes characterizing CGN hub genes as well as SBCGs. It uses the hypergeometric distribution to identify enriched GO terms in a given set of genes. We performed GO enrichment using ‗two unranked lists of genes‘ mode, with an ‗unranked target set‘ in the background of an ‗unranked source set‘. We identify ‗significantly enriched GO terms‘, for Biological Process, Molecular Function and Cellular Components, with p- value cut-off of 0.001 and those having at most 100 genes associated with, in the source data (B≤100). The latter criterion is used to weed out terms those are too generic. The CGN Top75 hub genes (target) were enriched against all genes present in CGN (source). Similarly, the SBCGs (target) were enriched against CGN (source). These GO enrichment experiments help us obtain biological attributes that characterize the CGN hubs and those characterizing SBCGs, respectively, in the background of ‗cancer genes‘ universe.

We performed the GO enrichment studies at three different p-values (0.01, 0.001 and 0.0001) for, both, SBCGs and CGN Top75 gene-set. We found that the significantly enriched GO terms obtained for BP, MF and CC were similar for p-values 0.01 and 0.001. For stricter p-value of 0.0001, we found that many of the GO terms relevant for secondary bone cancer and with generic role in cancer mechanisms, respectively, were lost. In the case of SBCGs, 14 BP and 4 MF disease-specific GO terms were lost at p-value 0.0001, including following key GO terms: angiogenesis (GO:0001525), regulation of bone remodelling (GO:0046850), bone mineralization (GO:0030282), collagen fibril organisation (GO:0030199), integrin binding (GO:0005178), laminin binding (GO:0043236), platelet derived growth factor binding (GO:0048407) and regulation of chemotaxis (GO:0050920). In the case of CGN Top75 gene-set analysis, 20 BP and 2 MF GO terms were lost, including following GO terms important for generic cancer mechanisms, at p-value 0.0001: cellular component disassembly involved in apoptotic process (GO:0006921), positive regulation of MAPK cascade (GO:0043410), positive regulation of cell cycle process (GO:0090068), regulation of G1/S transition of mitotic cell cycle (GO:2000045), mitotic cell cycle G1/S transition checkpoint (GO:0031575), growth hormone receptor signaling pathway (GO:0060396), response to fibroblast growth factor stimulus (GO:0071774) and transforming growth factor beta receptor signaling pathway (GO:0007179).

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The value of ‗B‘ (number of genes from the source set associated with a given GO term) was chosen to increase the ‗signal‘ (specific GO terms) and to reduce ‗noise‘ (non-specific GO terms). High values of ‗B‘ increase noise by populating the enrichment results with non-specific GO terms; whereas small values of B reduce the signal by rejecting specific and relevant GO terms. We performed multiple experiments at different thresholds of B-values. (i) For B≤50, we missed 6 BP and 3 MF GO terms specific to SBC including the following: positive regulation of cell adhesion, angiogenesis (GO:0001525), cell-cell adhesion (GO:0016337), cytokine activity (GO:0005125), glycosaminoglycan binding (GO:0005539) and carbohydrate binding (GO:0030246). Similarly, in the case of ACGN Top75 gene-set, 12 BP and 3 MF relevant GO terms were lost including DNA damage response, signal transduction by p53 class mediator (GO:0030330), regulation of DNA replication (GO:0006275), positive regulation of cell cycle process (GO:0090068), positive regulation of MAPK cascade (GO:0043410), Ras protein signal transduction (GO:0007265) and response to UV (GO:0009411). (ii) For 50100, almost all the terms obtained were non-specific (noise). Hence, we chose B≤100 to maximize the relevant GO terms and to reduce the non- specific GO terms in the enrichment results.

Protein interactome analysis

We performed network analysis of CGN to compute various graph-theoretical metrics, using NetworkAnalyzer plugin of Cytoscape [76]. We computed seven network centrality parameters based on network connectivity (degree [53] and neighborhood connectivity [54]), network flow (betweenness [33,34], stress [33,34], average shortest path length [55]) and local clustering (clustering coefficient [53,55,56] and topological coefficient [57]).

Degree [53] corresponds to the number of nodes adjacent to a given node v, where adjacent means directly connected. The degree distribution P(k) of a network is then defined to be the fraction of nodes in the network with degree k. Thus if there are N nodes in total in a network and nk of them have degree k, we have P(k) = nk/N.

‗Neighbourhood connectivity‘ [54] of a node 푣 is defined as the average connectivity of all neighbors of 푣. For a node 푣 with 푘푣 number of neighbors, the neighborhood connectivity is defined as:

푘 푛푐 푣 ≡ 푤∈푊 푤 푘푣 Where W is the set of neighbors of 푣, and 푘푤 is the degree of each of the neighboring node.

‗Stress‘ [33,34] and (its normalized counterpart) ‗betweenness‘ [33,34] enumerate number of shortest paths from all pairs of vertices passing through the node of interest. In a graph G = (V, E) with N nodes, stress (푠푡푟(푣)) is defined as the total number of shortest paths passing through a node 푣:

푠푡푟(푣) ≡ 휎푠푡 푣 푠≠푣∈푁 푡≠푣∈푁

Where, 휎푠푡 푣 is the number of shortest paths from s to t that pass through vertex 푣. Betweenness is normalized (with the total number of shortest paths in graph G, 푠≠푣≠푡∈푉 휎푠푡 ) value of stress. 푠≠푡 The higher the value of stress/betweenness, the higher is the relevance of the protein as a critical 11 mediator of regulatory molecules and/or functional modules.

The average shortest path length [55] of a vertex 푣 in graph G, corresponds to the average of all the shortest paths between 푣 and the rest of the vertices. The average shortest path length of vertex 푣 is defined as: 푑푖푠푡 푣, 푤 푎푠푝 푣 ≡ 푁 − 1 푤≠푣휖푉 Where V is the set of nodes in G, dist(푣,w) is the shortest path from 푣 to 푤, and N is the number of nodes in G.

Clustering Coefficient 퐶푣 [53,55,56] of a node 푣 is defined as: 2푒푣 퐶푣 ≡ 푘푣 (푘푣 − 1)

Where 푘푣 is the number of neighbors of 푣 and 푒푣 is the number of connected pairs of nodes between all neighbors of 푣.

Topological Coefficient 푡푐(푣) [57] of a node 푣 is defined as, 퐽 푣, 푤 푡푐 푣 ≡ 푤≠푣휖푉 푘푣

Where V is the set of nodes in G and J(푣,w) is the number of neighbors shared between the nodes 푣 to 푤, plus one if there is a direct link between 푣 and 푤.

We used degree, betweenness and stress metrics for identification of hubs of CGN. These metrics have been reported to be useful in identification of hubs of biological relevance [31,35,36] and those relevant to cancer [37].

Identification of hub nodes and their controls

First, we ranked the genes of CGN for each of the chosen parameters (degree, betweenness and stress). Then, we compiled eight ‗hub gene-sets‘ (called Top25, Top50, so on till Top200), containing genes with ranks above the cut-off threshold, for each of the three parameters (Supplementary Information Table S1.1). Each of these hub gene-sets contains hub genes identified by either of the three parameters. Thus defined, the size of a hub gene-set may be up to three times the hub cut-off threshold, depending upon the similarity between the hubs identified by each of the parameters. Top25, Top50 and Top75 hub gene-sets of CGN contain 31, 60 and 92 hub genes, respectively. Figure 3 depicts the CGN hub genes identified using Top75 hubs criterion. As a negative control for these hub gene-sets, we identified the corresponding genes (from the bottom of ranked lists), with lowest ranking for the chosen parameters. As random controls, we randomly sample a corresponding number of genes from CGN (1000 instances each).

Compilation of genes involved in generic cancer mechanisms (KEGG-PIC)

Cancer cell mechanisms could be envisaged as an elaborate integrated circuit of intracellular signaling networks [1,2]. This map of molecular mechanisms could be represented as a combination of circuits and subcircuits with considerable cross talk among them [1]. We compiled a set of (328) representative genes known to be implicated in generic cancer mechanisms from 12 cancer pathways/circuits (Supplementary Information Table S1.2). ‗Pathways in Cancer‘ (PIC) (hsa05200) from KEGG PATHWAY database, are representative of generic cancer circuits. Here, we call this ‗generic cancer genes‘ set KEGG-PIC [62]. KEGG-PIC comprise the following KEGG pathways: colorectal cancer (hsa05210), pancreatic cancer (hsa05212), thyroid cancer (hsa05216), acute myeloid leukemia (hsa05221), chronic myeloid leukemia (hsa05220), basal cell carcinoma (hsa05217), melanoma (hsa05218), renal cell carcinoma (hsa05211), bladder cancer (hsa05219), prostate cancer (hsa05215), endometrial cancer (hsa05213), small cell lung cancer (hsa05222), non-small cell lung cancer (hsa05223) and glioma (hsa05214).

Mouse phenotype data

For inferring the biological significance of the network parameters, we divided CGN into two sets, essential and non-essential, using the phenotypic information of the corresponding mouse ortholog [47,77]. It is assumed that human orthologs of mouse genes could be mapped onto each other for their function and biological essentiality. We considered the classes of embryonic, perinatal, neonatal or postnatal lethality in mouse models as lethal phenotypes, and the rest of the phenotypes as non-lethal ones. The human orthologs of murine genes were considered as essential, when the murine gene was annotated with one of the following phenotypes [63]: neonatal lethality (MP:0002058), embryonic lethality (MP:0002080), perinatal lethality (MP:0002081), postnatal lethality (MP:0002082), lethality-postnatal (MP:0005373), lethality- embryonic/perinatal (MP:0005374), embryonic lethality before implantation (MP:0006204), embryonic lethality before somite formation (MP:0006205) or embryonic lethality before turning of embryo (MP:0006206). The human-mouse orthology and mouse phenotype data was obtained from Mouse Genome Informatics [65] (May 2012). Out of a total 2665 cancer genes of CGN, 1315 (49.34%) were essential genes and the rest 1350 (50.66%) were non-essential. For each hub definition, from the hub-genes identified using each of the seven network metric, we compute the percentage of hubs that are essential (Figure 8A). We also found the percentage of essential genes in the corresponding non-hubs (Figure 8B).

Venn diagram

We logically juxtaposed the hubs of cancer genes network, curated bone cancer metastasis genes and cancer genes annotated with characteristic SBC GO terms to identify SBC-specific targets (Figure 2). Figure 10A illustrates this process highlighting the hubs of CGN (shaded area) that correspond to generic cancer genes and subsets potentially containing genes specific to SBC (hatched area). Figure 10B depicts the data when Top75 hubs of CGN are taken into consideration. For this data, there are 92 hubs of which 21 (Set-ab and Set-abc) happen to be common with SBCGs and 9 of those (Set-ac and Set-abc) are common to ‗enriched genes‘. Among the ‗secondary bone cancer enriched cancer genes‘ 55 are common to SBGCs (Set-bc and Set-abc). We find that, for this data, there are 2 genes (Set-abc) that happen to be CGN hubs that are common to SBCGs and are also annotated with characteristic SBC GO terms. The logical juxtaposition results for the data of Top25 and Top50 hubs of CGN are depicted in Figure 11A and Figure 11B, respectively.

Prediction of SBC-specific candidate genes

We propose that the genes that are specific to secondary bone cancer mechanisms would be annotated with characteristic GO terms that are obtained from overrepresentation analysis of a literature curated list of genes implicated in metastasis to bone. From the ‗secondary bone cancer

13 enriched cancer genes‘, that serve as a ‗source set of targets‘ we filtered the CGN hubs (Set-ac and Set-abc; shaded area) as they are generic to cancers (Figure 6) and found to be correlating with essential genes (Figure 8). Towards our aim of identifying SBC-specific targets, we focused on genes in Set-c and Set-bc (hatched area in Figure 10). We identified SBC-specific targets by refining these sets of genes to obtain genes that are annotated with any of the GO terms representing ‗bone processes‘ as well as that of ‗metastasis‘ (Figure 9) (Supplementary Information Table S1.4).

SUPPORTING INFORMATION Supplementary Material S1.

In the supplementary information we present the details of hubs of CGN; the KEGG-PIC genes that serve as a reference set of generic cancer genes; secondary bone cancer genes that were curated and compiled; characteristic GO terms that were used to obtain SBC-specific targets and relevance of SBC-specific targets identified from experimentally validated studies. The supplementary information contains 5 tables, out of which Table S1.3 has 46 references, Table S1.4 has 23 references and Table S1.5 has 8 references.

ACKNOWLEDGEMENTS

We acknowledge the computational infrastructure provided by Institute of Himalayan Bioresource Technology (CSIR-IHBT), a constituent national laboratory of Council of Scientific and Industrial Research, India. The authors thank Dr. Paramvir Singh Ahuja for the encouragement and support. Authors thank Vinay Randhawa for technical help in manuscript preparation. The CSIR-IHBT communication number for this article is 2245.

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FIGURES:

Figure 1. Regulatory mechanisms underlying metastasis to bone reflecting complex interplay of molecules. Bone metastasis results from imbalance of normal bone remodeling process involving osteolytic (leading to bone destruction) and osteoblastic (leading to aberrant bone formation) mechanisms. Breast cancer metastases are usually osteolytic, whereas prostate cancer metastases are usually osteoblastic. Osteolytic metastasis: Osteolytic metastasis of tumor cells involves a ―vicious cycle‖ between tumor cells and the skeleton. The vicious cycle is propagated by four contributors: tumor cells, bone-forming osteoblasts, bone resorbing osteoclasts and stored factors within bone matrix. Osteoclast formation and activity are regulated by the osteoblast, adding complexity to the vicious cycle. Tumor cells release certain factors including IL-1, IL-6, IL-8, IL-11, PTHrP and TNF that stimulate osteoclastic bone resorption. These factors enhance the expression of RANKL over OPG by osteoblasts, tipping the balance toward osteoclast activation thus causing bone resorption. This bone lysis stimulates the release of BMPs, TGFβ, IGFs and FGFs for stimulating the growth of metastatic cancer cells to bone. Osteoblastic metastasis: Factors released by osetoblastic cells, such as ET-1, Wnt, ERBB3, VEGF play an important role in osteoblastic metastasis by increasing cancer cell proliferation and enhance the effect of other growth factors including PDGF, FGFs, IGF- 1. Osteoblast differentiation is also increased by BMPs through the activation of certain transcription factors. Urokinase Plasminogen Activator (uPA), a protease, also acts as mediator for osteoblastic bone metastasis by cleaving osteoclast-mediated bone resorption factors responsible for regulation of osteoclast differentiation; thereby blocking the bone resorption.

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Figure 2. Strategy implemented for the identification of targets specific to secondary bone cancer. The strategy that was implemented in this work comprised three tasks, leading to three corresponding gene-sets that were used for obtaining SBC-specific targets of potential therapeutic value. (a) A compilation of cancer genes from CancerGenes database was used to construct a representative cancer genes interactome (Cancer Genes Network; CGN) by mapping them on to a reference human protein interactome (Human Protein Reference Database; HPRD). Using methods of network analysis, proteins that are central to CGN and interaction dynamics were obtained. These set of genes (SET-A; shaded area) was found to be correlating well with genes implicated in generic cancer mechanisms (Figure 6) as well as those annotated as essential using mouse phenotype data (Figure 8). The CGN, comprising of 11602 interactions among 2665 proteins, also serves as a reference set (universe) for gene enrichment studies; (b) A set of genes (Secondary Bone Cancer Genes; SBCGs) that are implicated in metastasis to bone from primary breast and prostate cancer, the most prevalent causes of bone metastasis, was compiled from literature. This set (SET-B) serves as a basis of genes and ontological correlates of secondary bone cancer that characterize the disease phenotype; (c) Significantly enriched GO terms that characterize SBCGs were obtained by overrepresentation analysis against the ‗cancer genes‘ universe. SET-C, a subset of CGN, was obtained by segregating cancer genes that were annotated with these SBC-specific ontological terms. Part of SET-C (hatched area; Set-c and Set- bc in Figure 10A) serves as a ‗source set of target cancer genes‘ that, both, carry ontological essence of SBCGs and are not involved in generic cancer mechanisms. SBC-specific targets (Figure 3 and Figure 10B), that are annotated with key GO terms (Figure 9) reflecting role in, both, bone processes and metastasis mechanisms, were further obtained from the source set. 21

Figure 3. CGN, a representative protein interactome of cancer genes, Top75 hub genes and SBC-specific targets. Cancer Genes Network (CGN) is a protein interactome of cancer genes embodying molecular mechanisms of cancers. Each node represents a cancer protein and an edge between two nodes represents a protein-protein interaction. The giant cluster comprises 89% of all cancer genes. The hubs of CGN, cancer genes central to the structural stability and information dynamics, are depicted in shades of red. The hubs encode genes involved in generic cancer mechanisms and those classified as essential using phenotypic data from Mouse Genome Informatics. 88% of these hubs are essential (‗dark red‘) and the rest non-essential (‗light red‘). SBC-specific targets, with ontological role in bone and metastasis processes, are highlighted in ‗green‘.

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Figure 4. Scale-free nature of degree and betweenness distributions of CGN. The distributions of (A) degree and (B) stress (as well as its normalized counterpart ‗betweenness‘) show a scale-free nature. Dotted lines show the power law fit with an exponent of -2.85 and -2.13, respectively. This indicates presence of promiscuously interacting proteins with high degrees and central mediators that play role in information dynamics across the network, respectively.

Figure 5. Heatmap of topological metrics computed for Cancer Genes Network. The heatmap of pair-wise correlations among seven parameters that enumerate topological, dynamical and local clustering features of the network: betweenness, stress, degree, neighborhood connectivity (neigh_conn), clustering coefficient (clust_coeff), average shortest path length (avg_short_paths) and topological coefficient (topo_coeff). The heatmap highlights three parameters with very high mutual positive correlations (r=0.8989, 0.9930 and 0.9210): degree, betweenness and stress. The upper triangle of the heatmap depicts pair-wise correlations as pie charts. The lower triangle depicts positive and negative correlations in shades of blue and red, respectively; the darker the color the stronger the correlation. Positive and negative correlations are also depicted with right- and left-handed diagonal lines. 23

Figure 6. Hubs of CGN correlate with generic cancer genes (KEGG-PIC). The hubs of CGN, identified using chosen network centrality parameters, correlate with the set of generic cancer genes (KEGG-PIC). For hub definitions varying between Top25—Top200, the CGN hubs (top ranked cancer genes) show good overlap with KEGG-PIC genes (filled circles). The correspondence of network centrality and generic role in cancers, expectedly, drops as the strictness of criterion used for identifying hubs is loosened. For the corresponding random samples, the overlap with KEGG-PIC is as expected (stars; error bars indicate standard errors from 1000 samples). The cancer genes with worst centrality rankings (open circles), show almost no overlap with generic cancer genes; worse than that expected from random samplings. Top75, Top50 and Top25 hubs, with 52%, 57% and 74% overlap with generic cancer genes, respectively, were further used for identification of SBC-specific cancer genes.

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Figure 7. Significantly enriched GO terms for hubs of CGN reflecting their role in generic cancer mechanisms. The overrepresentation studies of Top75 CGN hubs reflect their role in generic cancer processes and functions. Among the significantly enriched GO terms, 17 represent self-proliferation circuits (light-gray), 20 represent cytostasis and differentiation circuits (dark gray) and 4 represent viability circuits (black).

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Figure 8. The percentage of essential genes in the hubs and the corresponding non-hubs of CGN. A. For hub definitions varying between Top25 to Top200, the hubs identified using degree (1, ‗red‘), betweenness (2, ‘green‘), and stress (3, ‗blue‘) have significantly high percentage (82%—92%) of essential genes, classified using phenotypic data for Mouse Genome Informatics. Among the rest of the four parameters neighborhood connectivity (4) and topological coefficient (5) show neither significant nor consistent correlation with essential genes. Due to the nature of ‗clustering coefficient‘ and ‗average shortest path length‘ parameters, the data could not be binned at the same intervals. For clustering coefficient, percentage of essential genes among the nodes having up to Top200 rankings is in the range of 27% and 62%. For average shortest path length, the percentage of essential genes for nodes having ranking up to Top200 is 26%, worse than expected from random sampling. B. In the corresponding non-hubs, the percentage of essential genes is as expected from random sampling, regardless of the centrality measure or cut-off used for hub definition.

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Figure 9. Overrepresented GO terms of processes relevant and necessary for execution of bone metastasis. From the GO enrichment studies of SBCGs, curated from literature, 21 GO terms relevant for metastasis (‗light gray‘) and 10 GO terms relevant for bone (‗dark gray‘) processes were identified.

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Figure 10. Venn diagrams depicting the strategy used for the identification of SBC-specific targets. A. Venn diagram legend representing generic cancer genes identified by network analysis (CGN hub genes; gray area) and gene subsets used as a source of target genes that are specific to bone metastasis (hatched area). In search of SBC-specific targets, the generic cancer genes were rejected and the potential source of target genes was refined to obtain the final targets. B. Venn diagram depicting components of gene-sets identified using Top75 hubs. The source set of target genes (53+134) was refined to obtain seven targets.

Figure 11. Venn diagram of gene sets obtained with Top25 and Top50 CGN hubs. The Venn diagrams with generic gene-set (gray area) and source set of target genes (hatched area) for (A) Top25 and (B) Top50 CGN hubs.

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SUPPLEMENTARY INFORMATION S1

An approach for the identification of targets specific to bone metastasis using cancer genes interactome and gene ontology analysis

Shikha Vashisht and Ganesh Bagler*

Biotechnology Division, CSIR-IHBT, Institute of Himalayan Bioresource Technology, Council of Scientific and Industrial Research, Palampur, 176061 (H.P.), India

SUMMARY

In the supplementary information we present the details of hubs of CGN; the KEGG-PIC genes that serve as a reference set of generic cancer genes; secondary bone cancer genes that were curated and compiled; characteristic GO terms that were used to obtain SBC-specific targets and relevance of SBC-specific targets identified from experimentally validated studies. The supplementary information contains 5 tables, out of which Table S1.3 has 46 references, Table S1.4 has 23 references and Table S1.5 has 8 references.

CONTENTS

Table S1.1. Hub genes (Top25—Top200) of Cancer Genes Network.

Table S1.2. Details of KEGG-PIC genes (328) from KEGG PATHWAY Database.

Table S1.3. SBCGs (391) compiled for metastasis of primary breast and prostate cancer to bone.

Table S1.4. Significantly enriched GO terms, characteristic to metastasis to bone, identified from enrichment analysis of SBCGs.

Table S1.5. Relevance of SBC targets.

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Table S1.1. Hub genes (Top25—Top200) of Cancer Genes Network.

Top25 Top50 Top75 Top100 Top125 Top150 Top175 Top200 hubs (31) hubs (60) hubs (92) hubs (125) hubs (158) hubs (185) hubs (213) hubs (247) ABL1 ABL1 ABL1 ABL1 ABL1 ABL1 ABL1 ABL1 AKT1 AKT1 ACTB ACTB ACTB ACTB ACTB ACTB AR AR AKT1 AKT1 AKT1 AKT1 ACVR1 ACVR1 BRCA1 BCL2 APP APP APP APP AKT1 AKT1 CASP3 BRCA1 AR AR AR AR APP APP CREBBP CASP3 BCL2 BCL2 AXIN1 ATM AR AR CSNK2A1 CAV1 BRCA1 BRCA1 BCL2 AXIN1 ATM ATF2 CTNNB1 CBL CASP3 CASP3 BRCA1 BAT3 AXIN1 ATM EGFR CDK2 CASP8 CASP8 BTK BCL2 BAT3 AXIN1 EP300 CREBBP CAV1 CAV1 CALR BCR BCAR1 BAD ESR1 CSNK2A1 CBL CBL CASP3 BRCA1 BCL2 BAT3 FYN CTNNB1 CDK2 CDK2 CASP8 BTK BCR BCAR1 GRB2 DLG4 CHUK CDKN1A CAV1 CADM1 BIRC2 BCL2 HDAC1 EGFR CREBBP CDKN1B CBL CALR BRCA1 BCR JUN EP300 CRK CEBPB CCNB1 CASP3 BRCA2 BIRC2 MAPK1 ESR1 CSNK2A1 CHUK CCND1 CASP8 BTK BMPR1B MAPK3 FLNA CTNNB1 CREBBP CDC42 CAV1 CADM1 BRCA1 PRKACA FYN DLG4 CRK CDK2 CBL CALR BRCA2 PRKCA GRB2 EEF1A1 CSNK2A1 CDK9 CCNB1 CASP3 BTK RAF1 GSK3B EGFR CSNK2A2 CDKN1A CCND1 CASP8 BTRC RB1 HDAC1 EP300 CTNNB1 CDKN1B CDC42 CAV1 CADM1 RELA HSP90AA1 ESR1 DLG4 CEBPB CDK2 CBL CALR SMAD2 INSR EWSR1 DYNLL1 CHUK CDK4 CCNB1 CASP3 SMAD3 JAK2 FLNA EEF1A1 CREBBP CDK9 CCND1 CASP8 SMAD4 JUN FYN EGFR CRK CDKN1A CDC42 CAV1 SRC LCK GNAI2 EP300 CRKL CDKN1B CDK2 CBL STAT3 LYN GRB2 EPHB2 CSNK2A1 CEBPB CDK4 CCNB1 TGFBR1 MAPK1 GSK3B ERBB2 CSNK2A2 CHUK CDK9 CCND1 TP53 MAPK14 HDAC1 ESR1 CTNNB1 CREB1 CDKN1A CD44 YWHAG MAPK3 HRAS EWSR1 DAXX CREBBP CDKN1B CDC42 YWHAZ NFKB1 HSP90AA1 FLNA DLG4 CRK CEBPB CDH1 NR3C1 IGF1R FOS DYNLL1 CRKL CHUK CDK2 PAK1 INSR FYN EEF1A1 CSK CREB1 CDK4 PCNA IRS1 GNAI2 EGFR CSNK2A1 CREBBP CDK9 PLCG1 JAK1 GRB2 EP300 CSNK2A2 CRK CDKN1A PRKACA JAK2 GSK3B EPB41L3 CTNNB1 CRKL CDKN1B PRKCA JUN HDAC1 EPHB2 DAXX CSK CDKN2A PRKCD KAT5 HDAC2 ERBB2 DLG4 CSNK2A1 CEBPB PTK2 KIT HIF1A ESR1 DYNLL1 CSNK2A2 CHD3 PTPN11 LCK HMGB1 EWSR1 E2F1 CTNNB1 CHUK RAF1 LRP1 HRAS FLNA EEF1A1 DAXX CREB1 RB1 LYN HSP90AA1 FN1 EGFR DLG4 CREBBP RELA MAPK1 HTT FOS EP300 DYNLL1 CRK

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SMAD2 MAPK14 IGF1R FYN EPB41L3 E2F1 CRKL SMAD3 MAPK3 IKBKB GNAI2 EPHB2 EEF1A1 CSK SMAD4 MAPK8 IKBKG GNB2L1 ERBB2 EGFR CSNK2A1 SP1 MDM2 INSR GRB2 ESR1 EP300 CSNK2A2 SRC MYC IRS1 GSK3B ESR2 EPB41L3 CSNK2B STAT1 NCK1 JAK1 HCK EWSR1 EPHB2 CTNNB1 STAT3 NFKB1 JAK2 HDAC1 FLNA EPOR DAXX TGFBR1 NFKBIA JUN HDAC2 FN1 ERBB2 DLG4 TP53 NOTCH1 KAT5 HDAC3 FOS ESR1 DOK1 TRAF2 NR3C1 KIT HIF1A FYN ESR2 DVL1 TRAF6 PAK1 LCK HIPK2 GNAI2 ETS1 DYNLL1 UBB PCNA LRP1 HMGB1 GNB2L1 EWSR1 E2F1 UBE2I PLCG1 LYN HRAS GRB2 FGFR1 EEF1A1 VIM PML MAPK1 HSP90AA1 GSK3B FLNA EGFR YWHAB PRKACA MAPK14 HSPA1A HCK FN1 EP300 YWHAG PRKCA MAPK3 HTT HDAC1 FOS EPB41 YWHAZ PRKCD MAPK8 IGF1R HDAC2 FYN EPB41L3 PRKCZ MDM2 IKBKB HDAC3 GNA13 EPHB2 PTK2 MYC IKBKG HIF1A GNAI2 EPOR PTK2B NCK1 INSR HIPK2 GNB2L1 ERBB2 PTPN11 NCOA1 IRS1 HMGB1 GRB2 ERBB4 PTPN6 NCOR2 JAK1 HRAS GSK3B ESR1 RAC1 NFKB1 JAK2 HSP90AA1 HCK ESR2 RAF1 NFKBIA JUN HSPA1A HDAC1 ETS1 RASA1 NFKBIB KAT2B HTT HDAC2 EWSR1 RB1 NOTCH1 KAT5 IGF1R HDAC3 FADD RELA NR3C1 KIT IKBKB HIF1A FGFR1 SMAD1 PAK1 LCK IKBKG HIPK2 FLNA SMAD2 PCNA LRP1 INSR HMGB1 FN1 SMAD3 PDGFRB LYN IRS1 HRAS FOS SMAD4 PIN1 MAP3K7 ITGB1 HSP90AA1 FYN SP1 PLCG1 MAPK1 JAK1 HSPA1A GNA13 SRC PML MAPK14 JAK2 HTT GNAI2 STAT1 POLR2A MAPK3 JUN IGF1R GNB2L1 STAT3 PRKACA MAPK8 KAT2B IGF2 GRB2 SUMO4 PRKCA MBP KAT5 IKBKB GSK3B SYK PRKCD MDM2 KDR IKBKG HCK TGFBR1 PRKCZ MET KIT INSR HDAC1 TP53 PTCH1 MYC LCK IRS1 HDAC2 TRAF2 PTEN MYOD1 LRP1 ITGB1 HDAC3 TRAF6 PTK2 NCK1 LYN ITGB4 HGS UBB PTK2B NCOA1 MAP3K14 JAK1 HIF1A UBE2I PTPN11 NCOA3 MAP3K7 JAK2 HIPK2 VAV1 PTPN6 NCOR1 MAPK1 JUN HMGB1 VIM RAC1 NCOR2 MAPK14 KAT2B HRAS YWHAB RAD51 NFKB1 MAPK3 KAT5 HSP90AA 1 YWHAG RAF1 NFKBIA MAPK8 KDR HSPA1A 31

YWHAZ RASA1 NFKBIB MAPT KHDRBS1 HTT ZBTB16 RB1 NOTCH1 MBP KIT IGF1R RELA NR3C1 MDM2 KPNB1 IGF2 RXRA PAK1 MET KRT18 IKBKB SIN3A PCNA MYC LCK IKBKG SMAD1 PDGFRB MYOD1 LRP1 IL6ST SMAD2 PIAS1 NCK1 LRP6 INSR SMAD3 PIN1 NCOA1 LYN IRAK1 SMAD4 PLCG1 NCOA3 MAP3K14 IRS1 SMURF2 PML NCOR1 MAP3K5 IRS2 SP1 POLR2A NCOR2 MAP3K7 ITGB1 SRC PPP2CA NDRG1 MAPK1 ITGB3 STAT1 PRKACA NFKB1 MAPK14 ITGB4 STAT3 PRKAR2A NFKBIA MAPK3 JAK1 STAT5A PRKCA NFKBIB MAPK8 JAK2 SUMO1 PRKCD NOTCH1 MAPK9 JUN SUMO4 PRKCZ NR3C1 MAPT KAT2B SVIL PTCH1 PAK1 MBP KAT5 SYK PTEN PCNA MDM2 KDR TBP PTK2 PDGFRB MET KHDRBS1 TGFBR1 PTK2B PIAS1 MMP2 KIT TNFRSF1A PTPN11 PIN1 MYC KPNB1 TP53 PTPN6 PLCG1 MYOD1 KRT18 TRAF2 PTPRC PLSCR1 NCK1 LCK TRAF6 RAC1 PML NCOA1 LRP1 UBB RAD51 POLR2A NCOA2 LRP6 UBE2I RAF1 PPP2CA NCOA3 LYN VAV1 RASA1 PRKAA1 NCOR1 MAP2K1 VIM RB1 PRKACA NCOR2 MAP3K14 XRCC6 RELA PRKAR2A NDRG1 MAP3K5 YWHAB RET PRKCA NFKB1 MAP3K7 YWHAG RHOA PRKCD NFKBIA MAPK1 YWHAH RXRA PRKCZ NFKBIB MAPK14 YWHAZ SIN3A PRKDC NOTCH1 MAPK3 ZBTB16 SMAD1 PSEN1 NR3C1 MAPK8 SMAD2 PTCH1 PAK1 MAPK9 SMAD3 PTEN PCNA MAPT SMAD4 PTK2 PDGFRB MBP SMURF2 PTK2B PDPK1 MDM2 SP1 PTPN1 PIAS1 MET SRC PTPN11 PIN1 MITF STAT1 PTPN6 PLCG1 MLLT4 STAT3 PTPRC PLSCR1 MMP2 STAT5A RAC1 PML MMP9 STAT5B RAD51 POLR2A MPP3 SUMO1 RAF1 PPP2CA MYC SUMO4 RARA PPP2R1B MYOD1 SVIL RASA1 PRKAA1 NCK1 32

SYK RB1 PRKACA NCOA1 TBP RELA PRKAR2A NCOA2 TGFBR1 RET PRKCA NCOA3 TNFRSF1A RHOA PRKCD NCOA6 TP53 RXRA PRKCZ NCOR1 TRAF2 SIN3A PRKDC NCOR2 TRAF6 SKP1 PSEN1 NDRG1 TUBB SMAD1 PTCH1 NFKB1 UBB SMAD2 PTEN NFKBIA UBE2I SMAD3 PTK2 NFKBIB VAV1 SMAD4 PTK2B NGFR VIM SMURF2 PTPN1 NOTCH1 XRCC6 SOCS1 PTPN11 NR3C1 YWHAB SOS1 PTPN6 PAK1 YWHAE SP1 PTPRC PARP1 YWHAG SRC RAC1 PCNA YWHAH STAT1 RAD51 PDGFRB YWHAQ STAT3 RAF1 PDPK1 YWHAZ STAT5A RARA PIAS1 ZBTB16 STAT5B RASA1 PIK3R2 SUMO1 RB1 PIN1 SUMO4 RELA PLCG1 SVIL RET PLSCR1 SYK RHOA PML TBP RUNX1 POLR2A TGFBR1 RXRA PPP2CA TNFRSF1A SIN3A PPP2R1B TP53 SKIL PRKAA1 TP73 SKP1 PRKACA TRAF2 SMAD1 PRKAR2A TRAF6 SMAD2 PRKCA TSC2 SMAD3 PRKCD TUBB SMAD4 PRKCZ UBB SMAD7 PRKDC UBE2I SMARCA4 PSEN1 VAV1 SMURF1 PTCH1 VIM SMURF2 PTEN XPO1 SNCA PTK2 XRCC6 SOCS1 PTK2B YWHAB SOS1 PTPN1 YWHAE SP1 PTPN11 YWHAG SRC PTPN6 YWHAH STAT1 PTPRC YWHAQ STAT3 RAC1 YWHAZ STAT5A RAD51 ZAP70 STAT5B RAF1 ZBTB16 SUMO1 RARA SUMO4 RASA1 33

SVIL RB1 SYK RBL1 TBP RELA TCF3 RELB TGFBR1 RET TNFRSF1 RHOA A TP53 RIPK1 TP73 RUNX1 TRAF2 RXRA TRAF6 SIN3A TSC2 SKIL TUBB SKP1 UBB SKP2 UBE2I SMAD1 VAV1 SMAD2 VHL SMAD3 VIM SMAD4 XPO1 SMAD7 XRCC6 SMARCA 4 YWHAB SMURF1 YWHAE SMURF2 YWHAG SNCA YWHAH SOCS1 YWHAQ SOS1 YWHAZ SP1 ZAP70 SRC ZBTB16 STAT1 STAT3 STAT5A STAT5B SUMO1 SUMO4 SVIL SYK TBP TCF3 TERT TGFBR1 TGFBR2 TNFRSF1 A TP53 TP73 TRAF2 TRAF6 TSC2 TUBB 34

UBB UBE2I VAV1 VHL VIM XPO1 XRCC6 YWHAB YWHAE YWHAG YWHAH YWHAQ YWHAZ ZAP70 ZBTB16

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Table S1.2. Details of KEGG-PIC genes (328) from KEGG PATHWAY Database.

HGNC Gene Gene Symbol Full Name of the Gene ID 1630 DCC deleted in colorectal carcinoma 836 CASP3 caspase 3, apoptosis-related cysteine peptidase (EC:3.4.22.56)

842 CASP9 caspase 9, apoptosis-related cysteine peptidase (EC:3.4.22.62)

26060 APPL1 adaptor protein, phosphotyrosine interaction, PH domain and leucine zipper containing 1 999 CDH1 cadherin 1, type 1, E-cadherin (epithelial) 1499 CTNNB1 catenin (cadherin-associated protein), beta 1, 88kDa 1496 CTNNA2 catenin (cadherin-associated protein), alpha 2 1495 CTNNA1 catenin (cadherin-associated protein), alpha 1, 102kDa 29119 CTNNA3 catenin (cadherin-associated protein), alpha 3 8312 AXIN1 axin 1 8313 AXIN2 axin 2 10297 APC2 adenomatosis polyposis coli 2 324 APC adenomatous polyposis coli 2932 GSK3B glycogen synthase kinase 3 beta (EC:2.7.11.1 2.7.11.26) 6932 TCF7 transcription factor 7 (T-cell specific, HMG-box) 83439 TCF7L1 transcription factor 7-like 1 (T-cell specific, HMG-box) 6934 TCF7L2 transcription factor 7-like 2 (T-cell specific, HMG-box) 51176 LEF1 lymphoid enhancer-binding factor 1 332 BIRC5 baculoviral IAP repeat containing 5 4609 MYC v-myc myelocytomatosis viral oncogene homolog (avian) 595 CCND1 cyclin D1 7471 WNT1 wingless-type MMTV integration site family, member 1 7482 WNT2B wingless-type MMTV integration site family, member 2B 7472 WNT2 wingless-type MMTV integration site family member 2 7473 WNT3 wingless-type MMTV integration site family, member 3 89780 WNT3A wingless-type MMTV integration site family, member 3A 54361 WNT4 wingless-type MMTV integration site family, member 4 7474 WNT5A wingless-type MMTV integration site family, member 5A 81029 WNT5B wingless-type MMTV integration site family, member 5B 7475 WNT6 wingless-type MMTV integration site family, member 6 7477 WNT7B wingless-type MMTV integration site family, member 7B 7476 WNT7A wingless-type MMTV integration site family, member 7A 7479 WNT8B wingless-type MMTV integration site family, member 8B 7478 WNT8A wingless-type MMTV integration site family, member 8A 7483 WNT9A wingless-type MMTV integration site family, member 9A 7484 WNT9B wingless-type MMTV integration site family, member 9B 7480 WNT10B wingless-type MMTV integration site family, member 10B 80326 WNT10A wingless-type MMTV integration site family, member 10A 7481 WNT11 wingless-type MMTV integration site family, member 11

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51384 WNT16 wingless-type MMTV integration site family, member 16 8321 FZD1 frizzled family receptor 1 8324 FZD7 frizzled family receptor 7 2535 FZD2 frizzled family receptor 2 7976 FZD3 frizzled family receptor 3 8322 FZD4 frizzled family receptor 4 8325 FZD8 frizzled family receptor 8 7855 FZD5 frizzled family receptor 5 8323 FZD6 frizzled family receptor 6 8326 FZD9 frizzled family receptor 9 11211 FZD10 frizzled family receptor 10 1856 DVL2 dishevelled, dsh homolog 2 (Drosophila) 1857 DVL3 dishevelled, dsh homolog 3 (Drosophila) 1855 DVL1 dishevelled, dsh homolog 1 (Drosophila) 1284 COL4A2 collagen, type IV, alpha 2 1286 COL4A4 collagen, type IV, alpha 4 1288 COL4A6 collagen, type IV, alpha 6 1287 COL4A5 collagen, type IV, alpha 5 1282 COL4A1 collagen, type IV, alpha 1 284217 LAMA1 laminin, alpha 1 3908 LAMA2 laminin, alpha 2 3911 LAMA5 laminin, alpha 5 3909 LAMA3 laminin, alpha 3 3910 LAMA4 laminin, alpha 4 3912 LAMB1 laminin, beta 1 3913 LAMB2 laminin, beta 2 (laminin S) 3914 LAMB3 laminin, beta 3 22798 LAMB4 laminin, beta 4 3915 LAMC1 laminin, gamma 1 (formerly LAMB2) 3918 LAMC2 laminin, gamma 2 10319 LAMC3 laminin, gamma 3 2335 FN1 fibronectin 1 3673 ITGA2 integrin, alpha 2 (CD49B, alpha 2 subunit of VLA-2 receptor) 3674 ITGA2B integrin, alpha 2b (platelet glycoprotein IIb of IIb/IIIa complex, antigen CD41) 3675 ITGA3 integrin, alpha 3 (antigen CD49C, alpha 3 subunit of VLA-3 receptor)

3655 ITGA6 integrin, alpha 6 3685 ITGAV integrin, alpha V (vitronectin receptor, alpha polypeptide, antigen CD51) 3688 ITGB1 integrin, beta 1 (fibronectin receptor, beta polypeptide, antigen CD29 includes MDF2, MSK12) 5747 PTK2 PTK2 protein tyrosine kinase 2 (EC:2.7.10.2) 5293 PIK3CD phosphoinositide-3-kinase, catalytic, delta polypeptide (EC:2.7.1.153)

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5291 PIK3CB phosphoinositide-3-kinase, catalytic, beta polypeptide (EC:2.7.1.153)

5294 PIK3CG phosphoinositide-3-kinase, catalytic, gamma polypeptide (EC:2.7.11.1 2.7.1.153) 5290 PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide (EC:2.7.11.1 2.7.1.153) 8503 PIK3R3 phosphoinositide-3-kinase, regulatory subunit 3 (gamma) 23533 PIK3R5 phosphoinositide-3-kinase, regulatory subunit 5 5296 PIK3R2 phosphoinositide-3-kinase, regulatory subunit 2 (beta) 5295 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 5728 PTEN phosphatase and tensin homolog (EC:3.1.3.67 3.1.3.16 3.1.3.48)

4824 NKX3-1 NK3 homeobox 1 207 AKT1 v-akt murine thymoma viral oncogene homolog 1 (EC:2.7.11.1)

208 AKT2 v-akt murine thymoma viral oncogene homolog 2 (EC:2.7.11.1)

10000 AKT3 v-akt murine thymoma viral oncogene homolog 3 (protein kinase B, gamma) (EC:2.7.11.1) 1147 CHUK conserved helix-loop-helix ubiquitous kinase (EC:2.7.11.10) 3551 IKBKB inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta (EC:2.7.11.10) 8517 IKBKG inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase gamma 4792 NFKBIA nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha 4790 NFKB1 nuclear factor of kappa light polypeptide gene enhancer in B-cells 1

4791 NFKB2 nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100) 5970 RELA v-rel reticuloendotheliosis viral oncogene homolog A (avian) 5743 PTGS2 prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase) (EC:1.14.99.1) 4843 NOS2 nitric oxide synthase 2, inducible (EC:1.14.13.39) 596 BCL2 B-cell CLL/lymphoma 2 330 BIRC3 baculoviral IAP repeat containing 3 331 XIAP X-linked inhibitor of apoptosis 329 BIRC2 baculoviral IAP repeat containing 2 598 BCL2L1 BCL2-like 1 7185 TRAF1 TNF receptor-associated factor 1 7186 TRAF2 TNF receptor-associated factor 2 7187 TRAF3 TNF receptor-associated factor 3 9618 TRAF4 TNF receptor-associated factor 4 7188 TRAF5 TNF receptor-associated factor 5 7189 TRAF6 TNF receptor-associated factor 6, E3 ubiquitin protein ligase 2475 MTOR mechanistic target of rapamycin (serine/threonine kinase) (EC:2.7.11.1) 38

572 BAD BCL2-associated agonist of cell death 2308 FOXO1 forkhead box O1 4193 MDM2 Mdm2, p53 E3 ubiquitin protein ligase homolog (mouse) (EC:6.3.2.19)

7157 TP53 tumor protein p53 1027 CDKN1B cyclin-dependent kinase inhibitor 1B (p27, Kip1) 1026 CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1) 613 BCR breakpoint cluster region (EC:2.7.11.1) 25 ABL1 c-abl oncogene 1, non-receptor tyrosine kinase (EC:2.7.10.2) 1399 CRKL v-crk sarcoma virus CT10 oncogene homolog (avian)-like 1398 CRK v-crk sarcoma virus CT10 oncogene homolog (avian) 23624 CBLC Cbl proto-oncogene, E3 ubiquitin protein ligase C (EC:6.3.2.19)

868 CBLB Cbl proto-oncogene, E3 ubiquitin protein ligase B (EC:6.3.2.19)

867 CBL Cbl proto-oncogene, E3 ubiquitin protein ligase (EC:6.3.2.19)

6776 STAT5A signal transducer and activator of transcription 5A 6777 STAT5B signal transducer and activator of transcription 5B 3716 JAK1 Janus kinase 1 (EC:2.7.10.2) 6774 STAT3 signal transducer and activator of transcription 3 (acute-phase response factor) 6772 STAT1 signal transducer and activator of transcription 1, 91kDa 7423 VEGFB vascular endothelial growth factor B 5228 PGF placental growth factor 7422 VEGFA vascular endothelial growth factor A 7424 VEGFC vascular endothelial growth factor C 2277 FIGF c-fos induced growth factor (vascular endothelial growth factor D)

7039 TGFA transforming growth factor, alpha 1950 EGF epidermal growth factor 1956 EGFR epidermal growth factor receptor (EC:2.7.10.1) 2064 ERBB2 v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) (EC:2.7.10.1)

5154 PDGFA platelet-derived growth factor alpha polypeptide 5155 PDGFB platelet-derived growth factor beta polypeptide 5156 PDGFRA platelet-derived growth factor receptor, alpha polypeptide (EC:2.7.10.1) 5159 PDGFRB platelet-derived growth factor receptor, beta polypeptide (EC:2.7.10.1)

3479 IGF1 insulin-like growth factor 1 (somatomedin C) 3480 IGF1R insulin-like growth factor 1 receptor (EC:2.7.10.1) 4254 KITLG KIT ligand 3815 KIT v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (EC:2.7.10.1)

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2323 FLT3LG fms-related tyrosine kinase 3 ligand 2322 FLT3 fms-related tyrosine kinase 3 (EC:2.7.10.1) 3082 HGF hepatocyte growth factor (hepapoietin A 4233 MET met proto-oncogene (hepatocyte growth factor receptor) (EC:2.7.10.1)

2258 FGF13 fibroblast growth factor 13 2253 FGF8 fibroblast growth factor 8 (androgen-induced) 8817 FGF18 fibroblast growth factor 18 2257 FGF12 fibroblast growth factor 12 2248 FGF3 fibroblast growth factor 3 2251 FGF6 fibroblast growth factor 6 2256 FGF11 fibroblast growth factor 11 8823 FGF16 fibroblast growth factor 16 2252 FGF7 fibroblast growth factor 7 2259 FGF14 fibroblast growth factor 14 8822 FGF17 fibroblast growth factor 17 9965 FGF19 fibroblast growth factor 19 2254 FGF9 fibroblast growth factor 9 (glia-activating factor) 2250 FGF5 fibroblast growth factor 5 2249 FGF4 fibroblast growth factor 4 8074 FGF23 fibroblast growth factor 23 27006 FGF22 fibroblast growth factor 22 26281 FGF20 fibroblast growth factor 20 2255 FGF10 fibroblast growth factor 10 2247 FGF2 fibroblast growth factor 2 (basic) 26291 FGF21 fibroblast growth factor 21 2246 FGF1 fibroblast growth factor 1 (acidic) 2260 FGFR1 fibroblast growth factor receptor 1 (EC:2.7.10.1) 2263 FGFR2 fibroblast growth factor receptor 2 (EC:2.7.10.1) 2261 FGFR3 fibroblast growth factor receptor 3 (EC:2.7.10.1) 2885 GRB2 growth factor receptor-bound protein 2 6654 SOS1 son of sevenless homolog 1 (Drosophila) 6655 SOS2 son of sevenless homolog 2 (Drosophila) 3265 HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog 3845 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog 4893 NRAS neuroblastoma RAS viral (v-ras) oncogene homolog 369 ARAF v-raf murine sarcoma 3611 viral oncogene homolog (EC:2.7.11.1)

673 BRAF v-raf murine sarcoma viral oncogene homolog B1 (EC:2.7.11.1) 5894 RAF1 v-raf-1 murine leukemia viral oncogene homolog 1 (EC:2.7.11.1)

5604 MAP2K1 mitogen-activated protein kinase kinase 1 (EC:2.7.12.2) 5605 MAP2K2 mitogen-activated protein kinase kinase 2 (EC:2.7.12.2) 5594 MAPK1 mitogen-activated protein kinase 1 (EC:2.7.11.24) 5595 MAPK3 mitogen-activated protein kinase 3 (EC:2.7.11.24) 3725 JUN jun proto-oncogene

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2353 FOS FBJ murine osteosarcoma viral oncogene homolog 4312 MMP1 matrix metallopeptidase 1 (interstitial collagenase) (EC:3.4.24.7)

4313 MMP2 matrix metallopeptidase 2 (gelatinase A, 72kDa gelatinase, 72kDa type IV collagenase) (EC:3.4.24.24) 4318 MMP9 matrix metallopeptidase 9 (gelatinase B, 92kDa gelatinase, 92kDa type IV collagenase) (EC:3.4.24.35) 3576 IL8 interleukin 8 1019 CDK4 cyclin-dependent kinase 4 (EC:2.7.11.22) 5979 RET ret proto-oncogene (EC:2.7.10.1) 8030 CCDC6 coiled-coil domain containing 6 8031 NCOA4 nuclear receptor coactivator 4 4914 NTRK1 neurotrophic tyrosine kinase, receptor, type 1 (EC:2.7.10.1) 7170 TPM3 tropomyosin 3 7175 TPR translocated promoter region (to activated MET oncogene) 10342 TFG TRK-fused gene 11186 RASSF1 Ras association (RalGDS/AF-6) domain family member 1 83593 RASSF5 Ras association (RalGDS/AF-6) domain family member 5 6789 STK4 serine/threonine kinase 4 (EC:2.7.11.1) 1613 DAPK3 death-associated protein kinase 3 (EC:2.7.11.1) 23604 DAPK2 death-associated protein kinase 2 (EC:2.7.11.1) 1612 DAPK1 death-associated protein kinase 1 (EC:2.7.11.1) 5335 PLCG1 phospholipase C, gamma 1 (EC:3.1.4.11) 5336 PLCG2 phospholipase C, gamma 2 (phosphatidylinositol-specific) (EC:3.1.4.11) 5579 PRKCB protein kinase C, beta (EC:2.7.11.13) 5578 PRKCA protein kinase C, alpha (EC:2.7.11.13) 5582 PRKCG protein kinase C, gamma (EC:2.7.11.13) 5900 RALGDS ral guanine nucleotide dissociation stimulator 5898 RALA v-ral simian leukemia viral oncogene homolog A (ras related) 5899 RALB v-ral simian leukemia viral oncogene homolog B (ras related 10928 RALBP1 ralA binding protein 1 998 CDC42 cell division cycle 42 (GTP binding protein, 25kDa) 5879 RAC1 ras-related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1) 5880 RAC2 ras-related C3 botulinum toxin substrate 2 (rho family, small GTP binding protein Rac2) 5881 RAC3 ras-related C3 botulinum toxin substrate 3 (rho family, small GTP binding protein Rac3) 387 RHOA ras homolog family member A 5602 MAPK10 mitogen-activated protein kinase 10 (EC:2.7.11.24) 5601 MAPK9 mitogen-activated protein kinase 9 (EC:2.7.11.24) 5599 MAPK8 mitogen-activated protein kinase 8 (EC:2.7.11.24) 7849 PAX8 paired box 8 5468 PPARG peroxisome proliferator-activated receptor gamma 6256 RXRA retinoid X receptor, alpha

41

6257 RXRB retinoid X receptor, beta 6258 RXRG retinoid X receptor, gamma 5915 RARB retinoic acid receptor, beta 5467 PPARD peroxisome proliferator-activated receptor delta 3728 JUP junction plakoglobin 7704 ZBTB16 zinc finger and BTB domain containing 16 5371 PML promyelocytic leukemia 5914 RARA retinoic acid receptor, alpha 861 RUNX1 runt-related transcription factor 1 862 RUNX1T1 runt-related transcription factor 1 6688 SPI1 spleen focus forming virus (SFFV) proviral integration oncogene spi1

1050 CEBPA CCAAT/enhancer binding protein (C/EBP), alpha 1438 CSF2RA colony stimulating factor 2 receptor, alpha, low-affinity (granulocyte- macrophage) 1441 CSF3R colony stimulating factor 3 receptor (granulocyte) 1436 CSF1R colony stimulating factor 1 receptor (EC:2.7.10.1) 3569 IL6 interleukin 6 (interferon, beta 2) 1029 CDKN2A cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4)

1871 E2F3 E2F transcription factor 3 1869 E2F1 E2F transcription factor 1 1870 E2F2 E2F transcription factor 2 4149 MAX MYC associated factor X 8554 PIAS1 protein inhibitor of activated STAT, 1 51588 PIAS4 protein inhibitor of activated STAT, 4 10401 PIAS3 protein inhibitor of activated STAT, 3 9063 PIAS2 protein inhibitor of activated STAT, 2 1030 CDKN2B cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4) 1021 CDK6 cyclin-dependent kinase 6 (EC:2.7.11.22) 1163 CKS1B CDC28 protein kinase regulatory subunit 1B 6502 SKP2 S-phase kinase-associated protein 2, E3 ubiquitin protein ligase 1017 CDK2 cyclin-dependent kinase 2 (EC:2.7.11.22) 9134 CCNE2 cyclin E2 898 CCNE1 cyclin E1 5925 RB1 retinoblastoma 1 4286 MITF microphthalmia-associated transcription factor 7040 TGFB1 transforming growth factor, beta 1 7042 TGFB2 transforming growth factor, beta 2 7043 TGFB3 transforming growth factor, beta 3 7046 TGFBR1 transforming growth factor, beta receptor 1 (EC:2.7.11.30) 7048 TGFBR2 transforming growth factor, beta receptor II (70/80kDa) (EC:2.7.11.30)

4087 SMAD2 SMAD family member 2 4088 SMAD3 SMAD family member 3 4089 SMAD4 SMAD family member 4 42

2122 MECOM MDS1 and EVI1 complex locus 1488 CTBP2 C-terminal binding protein 2 1487 CTBP1 C-terminal binding protein 1 3066 HDAC2 histone deacetylase 2 (EC:3.5.1.98) 3065 HDAC1 histone deacetylase 1 (EC:3.5.1.98) 4292 MLH1 mutL homolog 1, colon cancer, nonpolyposis type 2 (E. coli) 4436 MSH2 mutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli) 4437 MSH3 mutS homolog 3 (E. coli) 2956 MSH6 mutS homolog 6 (E. coli) 581 BAX BCL2-associated X protein 675 BRCA2 breast cancer 2, early onset 5888 RAD51 RAD51 homolog (S. cerevisiae) 356 FASLG Fas ligand (TNF superfamily, member 6) 355 FAS Fas (TNF receptor superfamily, member 6) 8772 FADD Fas (TNFRSF6)-associated via death domain 841 CASP8 caspase 8, apoptosis-related cysteine peptidase (EC:3.4.22.61)

637 BID BH3 interacting domain death agonist 54205 CYCS cytochrome c, somatic 7428 VHL von Hippel-Lindau tumor suppressor, E3 ubiquitin protein ligase

6921 TCEB1 transcription elongation factor B (SIII), polypeptide 1 (15kDa, elongin C) 6923 TCEB2 transcription elongation factor B (SIII), polypeptide 2 (18kDa, elongin B) 9978 RBX1 ring-box 1, E3 ubiquitin protein ligase 8453 CUL2 cullin 2 112399 EGLN3 egl nine homolog 3 (C. elegans) (EC:1.14.11.29) 112398 EGLN2 egl nine homolog 2 (C. elegans) (EC:1.14.11.29) 54583 EGLN1 egl nine homolog 1 (C. elegans) (EC:1.14.11.29) 2271 FH fumarate hydratase (EC:4.2.1.2) 3091 HIF1A hypoxia inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) 2034 EPAS1 endothelial PAS domain protein 1 405 ARNT aryl hydrocarbon receptor nuclear translocator 9915 ARNT2 aryl-hydrocarbon receptor nuclear translocator 2 1387 CREBBP CREB binding protein (EC:2.3.1.48) 2033 EP300 E1A binding protein p300 (EC:2.3.1.48) 6513 SLC2A1 solute carrier family 2 (facilitated glucose transporter), member 1

6469 SHH sonic hedgehog 5727 PTCH1 patched 1 6608 SMO smoothened, frizzled family receptor 27148 STK36 serine/threonine kinase 36 (EC:2.7.11.1) 51684 SUFU suppressor of fused homolog (Drosophila) 2737 GLI3 GLI family zinc finger 3

43

2736 GLI2 GLI family zinc finger 2 2735 GLI1 GLI family zinc finger 1 650 BMP2 bone morphogenetic protein 2 652 BMP4 bone morphogenetic protein 4 64399 HHIP hedgehog interacting protein 8643 PTCH2 patched 2 367 AR androgen receptor 3320 HSP90AA1 heat shock protein 90kDa alpha (cytosolic), class A member 1

3326 HSP90AB1 heat shock protein 90kDa alpha (cytosolic), class B member 1

7184 HSP90B1 heat shock protein 90kDa beta (Grp94), member 1 354 KLK3 kallikrein-related peptidase 3 (EC:3.4.21.77) 112401 BIRC8 baculoviral IAP repeat containing 8 2113 ETS1 v-ets erythroblastosis virus E26 oncogene homolog 1 (avian) 2950 GSTP1 glutathione S-transferase pi 1 (EC:2.5.1.18) 5337 PLD1 phospholipase D1, phosphatidylcholine-specific (EC:3.1.4.4) 79444 BIRC7 baculoviral IAP repeat containing 7 8900 CCNA1 cyclin A1

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Table S1.3. SBCGs (391) compiled for metastasis of primary breast and prostate cancer to bone.

SBCGs compiled for metastasis of primary breast cancer to bone Gene Symbol Reference LRRC15, MMP13, MMP2, BCAM, COL10A1, AEBP1, IGFBP4, [1] ESR1, AEBP1, ASPN, ATP6V0C, BCAM, COMP, CRIP1, DPT, IGFBP4, LRRC15, LRP1, MAB21L2, MMP13, MMP2, PDLIM7, PTOV1, PCDHGC3, RFNG, STK32B, TBC1D10B, TCIRG1, ZNF444 COX2, PGE2, TNFSF11, TNFRSF11A, FGF7, PTX3, NID2, [2] RAP1GDS1, NPNT, COL6A3, IFIT3, IGRC, FMOD, IGIT1B, EGR1, PTGS2, CXCL10, VCAM1, FGF7, PTN, CD1D, LAMB1, CCL5, SFRP2, CXCL1 RASSF1A, MGMT, RAR-β2, APC [3] IBSP, SPP1, FGF13, COX7B2, SPARC, PRG1, GJA1, SOCS2, [4] EDN1, IL11, CYR61, IGFBP4, IL8, PLAU, FST, MADH3, PTGS2, EXTL2, CTGF, CBFB, CDH11, S100A4, TIMP3, NOG, TGFA, THBS1, IGFBP3, PLAT, TIMP2, FN1, CCNB1, LRPAP1, MMP1, COL5A1, COL8A1, CALB2, CYR61, CTGF, WISP, BMP2, IL11, FST, CXCR4, ADAMTS1, MMP1, CX43, DUSP1, NOG, COX2, CBFB, GJA1, THBS1 SERPINB5 [5] ABCB1, ESR1, RUNDC3B, RAP2A [6] CSFIR [7] CAV1 [8] TGFB1, ATF3, MMP13, CCNA1 [9] COL1A1, VIM, S100A4, ACTA2, PDGFRB, NG2, CXCL1, CXCL5, [10] PTGS2 HISTIH2AC, POMZP3, PON2, TMC5, SCNN1A, C10orf116, [11] NAP1L3, GPRD5C, CTGF, FHL1, DUSP1, DLC1, SOCS2, IL11, ADAMTS1, CYP1B1, SAA2, SAA1, PPL, IL11, SPANXB1, FST, SERPINA1, HLA-DRA, HLA-DPA1, ABCC3, CSGALNACT1, FKBP11, CXCR4, PRG1, SES7-1, SOX4, KHDRBS3, FGF5, C14orf1139, PTX7, MCAM, TGFBI, S100A2, MMP1, FGF5, RGC32 TFF1, TFF3, AGR2, NAT1, CRIP1, TNRC9, SCUBE2, TNRC9, [12] CYP2B6, RND1, DLALT1, KIF5C, PLA2GHB, UNG2, HMGCS2, SLC1A1, CEACAM6, TSPAN-1, REPS2, HPX, PDE4DIP, TOM1L1, SCGB2A2, ANXA9, BCAS1, TIMP4, CYP2B6, C9orf116, MSMB, FGFR3, FGFBP1, BAG1, FOXO3A, KRT16, MALL, KCNG1, IGLC2, KLK8, KRT6B, SNAI1, TMSNB, NRTN, EPHB3, ENO1, SOD2, IGHG1, KLK5, SOS, RARRES1, TTYH1, CD24, MGC27165, SERPINB5, UCHL1, ROPN1, LOC56901, ITGA6, TUBB, UBE4B, IGHM, COL2A1, IMPA2, DRE1, KLK7, TAZ, USP34, C6orf4, ALAD, ARHGDIA, ARS2, CLCA2, ELMO2, LRRC31, HTATSF1, HTR2B, MLPH, SLC2A8, TFPI2, BRCA1, BRCA2 DIP2B, MCM6, PLEK2 , FOXI1 , SYT13 , PRDM6 , LONP2, [13] ZNF185, ENTPD4, PPP2R2C, LANCL2, SFTPD, TSGA10, B3GALT2, SEZ6L, KCND2, VSTM2, FANCL, FDPS , VRK2 HIF1A, VEGF, DUSP1, CXCR4 [14] IL1B, COL1A1, COL1A2, CTGF, IL10, IL8, SPP1, VCAM1, PGE2, [15] EDN1, BMP6, IL6, PDGFRA, PTHLH, TGFB, LAMC2, IGFBP3, PLAU, TNF, TNFSF11, TNFRSF11A, TNFRSF11B, CXCR4,

45

CXCL12 CXCR4, CXCL12, HER2 [16] PASAT1, PHGDH, PSPH, LTBP1, ATF3, CCNA1, MMP13 [17] ADAM9, ADAMTS1, AREG, EGF, EGFR, ERBB2, HBEGF, [18] MMP1, PTHLH, TNFRSF11A, TNFRSF11B CTNNB1, DKK1, PLAU, PLAUR, SERPINB2, SERPINE1, WNT3A [19] BMP4, FGF1, FGF17, FGF4, FGF6, FGF8, IGF1, IL11, IL1B, IL6, [20] IL8 CTTN, CXCL12, CXCR4, IL11, IL6, [21] ADAMTS1, EGF, EGFR, MMP1, TNFRSF11A, TNFRSF11B [22] HIF1A [23]

AKT1, AKT3, BMP2, CLEC11A, CXCL12, CXCR4, JAG1, NOV, [24] PDGFA, PGF, PRG2, SPP1, SRC, TGFB1, TGFB3, TNFSF10, VEGFC CTGF, NOV, WISP2, WISP3 [25] CSF1, IL8, PTHLH, TNFRSF11A, TNFRSF11B [26] SBCGs compiled for metastasis of primary prostate cancer to bone APX1, ANTXR1, CDC23, CDC37, CETN3, CENPE, CCNC, [27] CCNG1, CDK4, CDKN2C, CDKN3, CDKL1, DDB1, E2F1, FEN1, GTPBP4, KAT7, MCM7, MAD2L1,MSH6, NBN, PCNA, EIF2AK2, RAN, RPA2, RPA3, RRM2, SSBP1, SKP1, ADD1, DST, CDH1, CTNNAL1, CYB5R3, DSC1, DSG2, ADAM9, FER, TNC, ITGB1, MFGE8, MCAM, ACVRL1, BGN, BST1, IBSP, CDH11, COL1A2, COL6A1, COL6A2, COL7A1, COL11A1, COL16A1, CSF1, DCN, FSTL1, INHA, MGP, SHH, VCAN, COL17A1, ITGB4, PLEC, DSC2, DSP, PKP1, PKP2, PKP3, PKP4, ASRGL1, BMP2, VDR, RUNX2, BGLAP, SPARC, SPP1, TNFRSF11B, TNFSF11, DRG1, PTHRP, EDN1, TNFRSF11A, TP53, HPN, TFF3, BGLAP, ERBB3, PSMD9, CDKN1A, MYCN, AR, HIF1A, MAGEA1, VEGF, KLK3, GDF15, BMP6, ERBB2, LTBP1, MMP9, MMP1, MMP3, PLAU, HRAS, NRAS, KRT1, PTRF, MDM2, RB1 HPN [28] TFF3 [29] BGLAP [30] ERBB3 [31] PSMD9, CDKN1A [27] ERBB2, EGFR [32] PALB, TGFB1, EGFR, BMP [33] BIN1, MYC, ABL1 [34] MAGEA1 [35] MMP1, MMP2, MMP13, DRG1, PTEN, NME1, CD82, KISS1, [36] BRMS1, MAP2K4, TP53 IGFBP3, PTH1R, TNFSF11, MMP, MDM2, RB1, TNFRSF11A [37] RB1 [38] CDH11 [39] LYN, SRC [40] ADAMTS1, AREG, EGF, EGFR, HBEGF, PTHLH, TGFΑ, TNF, [18] TNFRSF11A, TNFRSF11B, VEGFA VEGFA, NR1I2 [41] AKT1, PTHLH, TNFRSF11A, TNFRSF11B [42] BMP4, CSF1, DKK1, IL8, NOG, PTHLH, SFRP1, SFRP2, [43] TGFB1, TNFRSF11A, TNFRSF11B RAP1GAP, TERF2IP, VTNR [44]

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CXCL12, CXCR4, MAPK1, MAPK3, [45] FGF1, FGF17, FGF4, FGF6, FGF8, FGFR3, [46]

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Table S1.4. Significantly enriched GO terms, characteristic to metastasis to bone, identified from enrichment analysis of SBCGs.

GO ID GO terms Reference Bone related processes GO:0001958 endochondral ossification [1] GO:0036075 replacement ossification [1] GO:0030282 bone mineralization [1] GO:0030199 collagen fibril organization [2] GO:0032963 collagen metabolic process [2] GO:0033280 response to vitamin D [3] GO:0046850 regulation of bone remodeling [4] GO:0001649 osteoblast differentiation [1,4] GO:0001503 ossification [1] GO:0051216 cartilage development [1] Metastasis related processes GO:0002690 positive regulation of leukocyte chemotaxis [5] GO:0002688 regulation of leukocyte chemotaxis [5] GO:0002687 positive regulation of leukocyte migration [5] GO:0050920 regulation of chemotaxis [5] GO:0007162 negative regulation of cell adhesion [6] GO:0002685 regulation of leukocyte migration [7] GO:0050900 leukocyte migration [7] GO:0016337 cell-cell adhesion [6,8] GO:0001525 angiogenesis [9,10] GO:0050679 positive regulation of epithelial cell proliferation [3] GO:0045785 positive regulation of cell adhesion [6] GO:0043236 laminin binding [11] GO:0001968 fibronectin binding [12,13] GO:0005104 fibroblast growth factor receptor binding [14] GO:0048407 platelet-derived growth factor binding [15,16] GO:0005518 collagen binding [2,17] GO:0005178 integrin binding [12,18] GO:0005539 glycosaminoglycan binding [19] GO:0005125 cytokine activity [20,21] GO:0030246 carbohydrate binding [22] GO:0035413 positive regulation of catenin import into nucleus [23]

References (for Supplementary Table S1.4)

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3. Sprenger CC, Peterson a, Lance R, Ware JL, Drivdahl RH, et al. (2001) Regulation of proliferation of prostate epithelial cells by 1,25-dihydroxyvitamin D3 is accompanied by an increase in insulin-like growth factor binding protein-3. The Journal of endocrinology 170: 609–618.

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4. Andersen TL, Sondergaard TE, Skorzynska KE, Dagnaes-Hansen F, Plesner TL, et al. (2009) A physical mechanism for coupling bone resorption and formation in adult human bone. The American journal of pathology 174: 239–247. doi:10.2353/ajpath.2009.080627.

5. Moore M a (2001) The role of chemoattraction in cancer metastases. BioEssays 23: 674– 676. doi:10.1002/bies.1095.

6. Hirohashi S, Kanai Y (2003) Cell adhesion system and human cancer morphogenesis. Cancer science 94: 575–581.

7. JONES BM (1976) MECHANISMS OF LEUCOCYTE MIGRATION INHIBITION BY BREAST TUMOUR CELL FRACTIONS. Br J Cancer 34: 14–19.

8. Okegawa T, Pong R-C, Li Y, Hsieh J-T (2004) The role of cell adhesion molecule in cancer progression and its application in cancer therapy. Acta biochimica Polonica 51: 445–457. doi:035001445.

9. Liotta LA, Steeg PS, Stetler-Stevenson WG (1991) Cancer metastasis and angiogenesis: An imbalance of positive and negative regulation. Cell 64: 327–336. doi:10.1016/0092- 8674(91)90642-C.

10. Carmeliet P, Jain RK (2000) Angiogenesis in cancer and other diseases. Nature 407: 249– 257.

11. Terranova VP, Llotta LA, Russo RG, Liotta LA, Martin GR (1982) Role of Laminin in the Attachment and Metastasis of Murine Tumor Cells Role of Laminin in the Attachment and Metastasis of Murine Tumor Cells. Cancer research 42: 2265–2269.

12. Akiyama SK, Olden K, Yamada KM (1995) Fibronectin and integrins in invasion and metastasis. Cancer and Metastasis Reviews 14: 173–189. doi:10.1007/BF00690290.

13. Malik G, Knowles LM, Dhir R, Xu S, Yang S, et al. (2010) Plasma fibronectin promotes lung metastasis by contributions to fibrin clots and tumor cell invasion. Cancer research 70: 4327–4334. doi:10.1158/0008-5472.CAN-09-3312.

14. Kwabi-Addo B, Ozen M, Ittmann M (2004) The role of fibroblast growth factors and their receptors in prostate cancer. Endocrine-related cancer 11: 709–724. doi:10.1677/erc.1.00535.

15. Yu J, Ustach C, Kim H-RC (2003) Platelet-derived growth factor signaling and human cancer. Journal of biochemistry and molecular biology 36: 49–59.

16. Russell MR, Liu Q, Lei H, Kazlauskas A, Fatatis A (2010) The alpha-receptor for platelet- derived growth factor confers bone-metastatic potential to prostate cancer cells by ligand- and dimerization-independent mechanisms. Cancer research 70: 4195–4203. doi:10.1158/0008- 5472.CAN-09-4712.

17. Viguet-Carrin S, Garnero P, Delmas PD (2006) The role of collagen in bone strength. Osteoporosis international 17: 319–336. doi:10.1007/s00198-005-2035-9.

18. Vogelmann R, Kreuser ED, Adler G, Lutz MP (1999) Integrin alpha6beta1 role in metastatic behavior of human pancreatic carcinoma cells. International journal of cancer 80: 791–795.

19. Yip GW, Smollich M, Götte M (2006) Therapeutic value of glycosaminoglycans in cancer. Molecular cancer therapeutics 5: 2139–2148. doi:10.1158/1535-7163.MCT-06-0082.

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20. Koizumi K, Hojo S, Akashi T, Yasumoto K, Saiki I (2007) Chemokine receptors in cancer metastasis and cancer cell-derived chemokines in host immune response. Cancer science 98: 1652–1658. doi:10.1111/j.1349-7006.2007.00606.x.

21. Cheng X, Hung M-C (2009) Regulation of breast cancer metastasis by atypical chemokine receptors. Clinical cancer research 15: 2951–2953. doi:10.1158/1078-0432.CCR-09-0141.

22. Kannagi R, Izawa M, Koike T, Miyazaki K, Kimura N (2004) Carbohydrate-mediated cell adhesion in cancer metastasis and angiogenesis. Cancer science 95: 377–384.

23. Kau TR, Way JC, Silver PA (2004) Nuclear transport and cancer: from mechanism to intervention. Nature reviews Cancer 4: 106–117. doi:10.1038/nrc1274.

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Table S1.5. Relevance of SBC Targets.

SBC Specific Reference Targets

TNXB [1]

SPP1 [2]

CTGF [3–5]

BMP1 [6]

BMPR1A [6]

CD44 [7]

VWF [8]

References (for Supplementary Table S1.5)

1. Matsumoto K, Takayama N, Ohnishi J, Ohnishi E, Shirayoshi Y, et al. (2001) Tumour invasion and metastasis are promoted in mice deficient in tenascin-X. Genes to cells 6: 1101– 1111.

2. Rangaswami H, Bulbule A, Kundu GC (2006) Osteopontin: role in cell signaling and cancer progression. Trends in cell biology 16: 79–87. doi:10.1016/j.tcb.2005.12.005.

3. Chen P-S, Wang M-Y, Wu S-N, Su J-L, Hong C-C, et al. (2007) CTGF enhances the motility of breast cancer cells via an integrin-alphavbeta3-ERK1/2-dependent S100A4-upregulated pathway. Journal of cell science 120: 2053–2065. doi:10.1242/jcs.03460.

4. Lau LF, Lam SC (1999) MINIREVIEW The CCN Family of Angiogenic Regulators : The Integrin Connection. Experimental cell research 248: 44–57.

5. Kang Y, Siegel PM, Shu W, Drobnjak M, Kakonen SM, et al. (2003) A multigenic program mediating breast cancer metastasis to bone. Cancer cell 3: 537–549.

6. Mishina Y, Starbuck MW, Gentile M a, Fukuda T, Kasparcova V, et al. (2004) Bone morphogenetic protein type IA receptor signaling regulates postnatal osteoblast function and bone remodeling. The Journal of biological chemistry 279: 27560–27566. doi:10.1074/jbc.M404222200.

7. Wang H-S, Hung Y, Su C-H, Peng S-T, Guo Y-J, et al. (2005) CD44 cross-linking induces integrin-mediated adhesion and transendothelial migration in breast cancer cell line by up- regulation of LFA-1 (alpha L beta2) and VLA-4 (alpha4beta1). Experimental cell research 304: 116–126. doi:10.1016/j.yexcr.2004.10.015.

8. Eppert K, Wunder JS, Aneliunas V, Kandel R, Andrulis IL (2005) von Willebrand factor expression in osteosarcoma metastasis. Modern Pathology 18: 388–397. doi:10.1038/modpathol.3800265.

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