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. 2017 Jul 31:8:918.
doi: 10.3389/fimmu.2017.00918. eCollection 2017.

Multitype Network-Guided Target Controllability in Phenotypically Characterized Osteosarcoma: Role of Tumor Microenvironment

Affiliations

Multitype Network-Guided Target Controllability in Phenotypically Characterized Osteosarcoma: Role of Tumor Microenvironment

Ankush Sharma et al. Front Immunol. .

Abstract

This study highlights the relevance of network-guided controllability analysis as a precision oncology tool. Target controllability through networks is potentially relevant to cancer research for the identification of therapeutic targets. With reference to a recent study on multiple phenotypes from 22 osteosarcoma (OS) cell lines characterized both in vitro and in vivo, we found that a variety of critical proteins in OS regulation circuits were in part phenotype specific and in part shared. To generalize our inference approach and match cancer phenotypic heterogeneity, we employed multitype networks and identified targets in correspondence with protein sub-complexes. Therefore, we established the relevance for diagnostic and therapeutic purposes of inspecting interactive targets, namely those enriched by significant connectivity patterns in protein sub-complexes. Emerging targets appeared with reference to the OS microenvironment, and relatively to small leucine-rich proteoglycan members and D-type cyclins, among other collagen, laminin, and keratin proteins. These described were evidences shared across all phenotypes; instead, specific evidences were provided by critical proteins including IGFBP7 and PDGFRA in the invasive phenotype, and FGFR3 and THBS1 in the colony forming phenotype.

Keywords: multitype networks; osteosarcoma cell lines; protein network tomography; target controllability; tumor microenvironment.

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Figures

Figure 1
Figure 1
Computational and analytical flowchart. Differentially expressed gene (DEG) profiles are reproduced from each osteosarcoma (OS) cell line and comprehensive comparative analyses are derived. Venn diagrams show DEGs and DE miRNAs for the different phenotypes here considered: tumorigenic, invasive, colony forming, and proliferation. Different types of networks are employed: gene co-expression, miRNA-target, and protein–protein interaction networks, including drugs. These are then functionally annotated, including pathways and protein complexes. Deciphering cancer regulation networks suggests the application of control concepts. These are hard to implement, but this challenge may be transformed into a sequence of tasks solved with the help of accurately selected fractions of nodes and corresponding links describing critical features. This goal corresponds to setting a target control problem, whose solution requires the search for a minimum number of driver nodes. In real cancer networks, it is natural to expect that only approximate solutions may hold. Through the identification of targets in cancer networks, we can establish the cancer relevance of functional controllability.
Figure 2
Figure 2
DE miRNA-TF co-regulatory dynamics in Cp (inset: C-MYC sub-network). Cp state: the overexpressed hsa-miR-545 can induce cell apoptosis and cell cycle arrest by targeting CCND1 and CDK4. Hsa-miR-7 is involved in major cancer pathways. The over-expressed MYC is involved with highly over-expressed hsa-miR-449a and hsa-miR-622, and with down-expressed hsa-miR-516a-5p and hsa-miR-375. Also, the over-expressed hsa-miR-224 interacts with SP7 and hsa-miR-199a-5p interacts with MAFB [role in producing osteoblasts and osteoclasts, and in their differentiation]. The down-expressed hsa-miR-492 interacts with TF Pod1 (TCF21), a tumor suppressor frequently silencing through epigenetic mechanisms. Other states present further aspects of interest (see Figure S3 in Supplementary Material): Tp state: the down-expressed hsa-miR181a-2 shows deregulation in human cancers, and the down-expressed hsa-mir181a is pro-apoptotic and suppresses invasion and proliferation in OS (38). The over-expressed miR142-3p suppresses tumor growth, invasion, migration, and proliferation in OS cells. A hub appears between TFs and the DEG NFIX interacting with 50 partners, including DE hsa-miR-375, hsa-miR-149, hsa-miR-324-5p (down-expressed) and hsa-miR-765, miR, hsa-miR-423-5p, hsa-miR149, hsa-mir361-5p (over-expressed). Note hsa-miR-375 also regulates hub DEGs (NPPB, PHLDA1, EMP1, and IGFBP) functional in cancer processes. Ip state: the down-expressed has-mir-363, suppressing invasion, migration, and OS cell growth through direct targeting of MAP2K4 (39) and the over-expressed miR-193a are correlated with PLAU, which modulates signaling in DNA damage, Notch, NF-κB, Myc/Max. Pp state: Hsa-miR-152 is over-expressed here and in osteoblasts. Both hsa-miR376c and hsa-miR-377 showed high down-expression, potentially suggesting a role in OS proliferation. Inverse correlation in Hsa-miR-376c and its target TGFA is observed in OS tissues and cell lines. Decrease in TGFA and its downstream signaling molecule’s expression due to over-expressed mir-376c is relevant in cellular proliferation and invasion in OS (40). Increased expression of hsa-miR-377 with target CDK6 is already known to reduce cell proliferation and inhibit invasion in MG63 cell (41). No major TFs were DE in these cell lines.
Figure 3
Figure 3
Composite targets in Ip. (A) Network configuration. (B) Identified sub-complexes. (C) TOP eigenvector component values corresponding to interacting seed proteins for the top-5 eigenvalues. The first eigenvector values refer to principal eigenvalues. Red dots denote critical nodes in first order PPIN networks and violet circles denote proteins participating in complexes. Notes: miR-140-5p regulates a critical node, ALDH1A1 and is classified as a critical link; critical nodes KRT8, COL4A2, COL4A1, and PLAT interact with other non-critical nodes.
Figure 3
Figure 3
Composite targets in Ip. (A) Network configuration. (B) Identified sub-complexes. (C) TOP eigenvector component values corresponding to interacting seed proteins for the top-5 eigenvalues. The first eigenvector values refer to principal eigenvalues. Red dots denote critical nodes in first order PPIN networks and violet circles denote proteins participating in complexes. Notes: miR-140-5p regulates a critical node, ALDH1A1 and is classified as a critical link; critical nodes KRT8, COL4A2, COL4A1, and PLAT interact with other non-critical nodes.
Figure 4
Figure 4
Composite targets in Cp. (A) Network configuration. (B) Identified sub-complexes. (C) TOP Eigenvector component values corresponding to interacting seed proteins for the top-5 eigenvalues. The first eigenvector values depend on principal eigenvalues. Red dots denote critical nodes in first order PPIN networks and violet circles denotes proteins participating in complexes. Notes: miRNAs constituted the majority of critical interactions along with critical nodes DLC1, ACTG2, and FARP1 showing interaction with other non-critical nodes, and critical node KRT8 interacts with another DE critical node ACTG2. FARP1 showed missense mutation and involvement in pathways related to RhoA regulation.
Figure 4
Figure 4
Composite targets in Cp. (A) Network configuration. (B) Identified sub-complexes. (C) TOP Eigenvector component values corresponding to interacting seed proteins for the top-5 eigenvalues. The first eigenvector values depend on principal eigenvalues. Red dots denote critical nodes in first order PPIN networks and violet circles denotes proteins participating in complexes. Notes: miRNAs constituted the majority of critical interactions along with critical nodes DLC1, ACTG2, and FARP1 showing interaction with other non-critical nodes, and critical node KRT8 interacts with another DE critical node ACTG2. FARP1 showed missense mutation and involvement in pathways related to RhoA regulation.
Figure 5
Figure 5
Node classification. (A) Gene–gene co-expression networks. (B) Gene–miRNA targets. (C) Protein–protein interaction (PPI)-miRNA target network.
Figure 6
Figure 6
Controllability analysis. Top panel: gene–gene co-expression networks, miRNA-gene target networks and protein–protein interaction (PPI)-miRNA interaction networks showing occurrence of critical, ordinary, and redundant nodes. Mid panel: Critical nodes in multi-layered networks mapped to first order networks. Bottom panel: critical nodes computed in PPI first order networks and number of critical nodes in protein complexes that are manually curated and experimentally validated in CORUM database. Further statistics on classification of nodes in various networks is provided in Figure 5.
Figure 7
Figure 7
Drug repositioning networks for (A) Ip and (B) Cp. The R/Bioconductor package rDGIdb is used, as an R wrapper to query the drug–gene interaction database (DGIdb). As a result, PDGFRA has interactors, such as imatinib, dasatinib, sunitinib, sorafenib, pazopanib, and nilotinib, none specific and all inhibiting different kinases [i.e., imatinib also KIT and AB1 (with dasatinib used for imatinib resistance), sunitinib also VEGF and FLT3 (like crenolanib too), sorafenib also RAF etc], which might reveal advantageous. Two other networks in Figure S6 in Supplementary Material. In Tp, critical nodes CTSB and PLAU widely interact with drugs. MAOA interacts with antidepressant drugs, associated with decrease in bone mineral density and increasing risk of fracture. The Pp network proteins participating in complexes showed limited interactions with drugs, Pyridoxal Phosphate interacts with KYNU (collagen) relevant to osteosarcoma. The list of interactions is available in Data S6 in Supplementary Material.

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