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The Intrinsic and Extrinsic Implications of RANKL/RANK Signaling in Osteosarcoma: From Tumor Initiation to Lung Metastases

The Intrinsic and Extrinsic Implications of RANKL/RANK Signaling in Osteosarcoma: From Tumor Initiation to Lung Metastases

* Correspondence: dominique.heymann@univ-nantes.fr (D.H.); frederic.lezot@univ-nantes.fr (F.L.); Tel.: +33-(0)2-4067-9841 (D.H.); +33-(0)2-4041-2846 (F.L.); Fax: +33-(0)2-4041-2860 (F.L.) Received: 26 September 2018; Accepted: 19 October 2018; Published: 24 October 2018    Abstract: Background: Osteosarcoma is the most frequent form of malignant pediatric bone tumor. Despite the current therapeutic arsenal, patient life-expectancy remains low if metastases are detected at the time of diagnosis, justifying research into better knowledge at all stages of osteosarcoma ontogenesis and identification of new therapeutic targets. Receptor Activator of Nuclear factor κB (RANK)expression has been reported in osteosarcoma cells, raising the question of Receptor Activator of Nuclear factor κB Ligand (RANKL)/RANK signaling implications in these tumor cells (intrinsic), in addition to previously reported implications through osteoclast activation in the tumor microenvironment (extrinsic). Methods: Based on in vitro and in vivo experimentations using human and mouse osteosarcoma cell lines, the consequences on the main cellular processes of RANK expression in osteosarcoma cells were analyzed. Results: The results revealed that RANK expression had no impact on cell proliferation and tumor growth, but stimulated cellular differentiation and, in an immune-compromised environment, increased the number of lung metastases. The analysis of RANKL, RANK and osteoprotegerin (OPG) expressions in biopsies of a cohort of patients revealed that while RANK expression in osteosarcoma cells was not significantly different between patients with or without metastases at the time of diagnosis, the OPG/RANK ratio decreased significantly. Conclusion: Altogether, these results are in favor of RANKL-RANK signaling inhibition as an adjuvant for the treatment of osteosarcoma.
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RANKL/RANK/OPG: new therapeutic targets in bone tumours and associated osteolysis.

RANKL/RANK/OPG: new therapeutic targets in bone tumours and associated osteolysis.

bones as shown by radiological analyses (Figure 3c), than controls treated with empty adenovirus (Figure 3b; Figure 3a : tumour controls). In this case, RANK-Fc inhibits the tumour induced-osteolysis but is not sufficient to decrease the tumour burden. In a therapeutic approach, RANK-Fc may be associated with anti-tumour drugs to stop both the tumour proliferation and the tumour-associated osteolysis. Overall, these studies demonstrate the effectiveness of RANK-Fc in inhibiting bone resorption in different models of malignant osteolytic pathologies and the upside of using RANK-Fc, which cannot interfere with TRAIL- mediated cancer cell apoptosis. Another approach lies in using novel OPG-like peptidomimetics that restore bone loss in vivo by facilitating a defective RANKL-RANK receptor complex, thus modulating RANK-RANKL signalling pathways and altering the biological functions of RANKL-RANK receptor complex [67]. Therefore, these OPG derived small molecules can be used to develop more useful therapeutic agents in bone diseases.
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RANKL/RANK/OPG: Key Therapeutic Target in Bone Oncology.

RANKL/RANK/OPG: Key Therapeutic Target in Bone Oncology.

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r]

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RANK-RANKL signalling in cancer: RANK-RANKL and cancer

RANK-RANKL signalling in cancer: RANK-RANKL and cancer

Initially considered to be a pro-metastatic factor, our vision of RANKL changed when the factor was linked to mammary gland development [ 119 ]. RANKL deficiency leads to a defect in the formation of the lobo-alveolar structures required for lactation [ 120 , 121 ]. In addition, RANKL is able to promote the survival and proliferation of epithelial cells simultaneously with the up- regulated expression of RANK during mammary gland develop- ment [ 119 – 121 ]. Disturbance in this coordinated mechanism can lead to the formation of pre-neoplasias and subsequently to that of tumour foci, as revealed by Gonzalez-Suarez et al. [ 122 ]. These authors established a mouse mammary tumour virus – RANK transgenic mice overexpressing the protein in mammary glands – and reported a high incidence of pre-neoplasia foci (multifocal ductal hyperplasias, multifocal and focally extensive mammary intraepithelial neoplasias), as well as the development of adeno- carcinoma lesions in these transgenic mice compared with the wild-type mice. Confirming the involvement of RANKL in the initial oncogenic process, administration of RANK-Fc decreased both mammary tumorigenesis and the development of lung meta- stases in MMTV-neu transgenic mice, a spontaneous mammary tumour model [ 122 ]. In a complementary work, this team demon- strated that the RANKL/RANK axis was pro-active in epithelial mesenchymal transition (EMT), promoted cell migration simul- taneously with neo-vascularization, and that their expression was significantly associated with metastatic tumours [ 123 ]. Overall, their data revealed that RANK/RANKL signalling promotes the initial stage in breast cancer development by inducing stemness and EMT in mammary epithelial cells. A similar process has been confirmed in head and neck squamous carcinoma [ 124 ], and in endometrial cancer [ 125 ], and RANKL expression has been as- sociated with the EMT and appears to be a new marker for EMT in prostate cancer cells [ 83 ].
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Receptor activator of nuclear factor-kappa B ligand (RANKL) stimulates bone-associated tumors through functional RANK expressed on bone-associated cancer cells?

Receptor activator of nuclear factor-kappa B ligand (RANKL) stimulates bone-associated tumors through functional RANK expressed on bone-associated cancer cells?

Summary. Primary and secondary bone tumors clearly deteriorate quality of life and the activity of daily living of patients. These undesirable diseases become a major social and economic burden. As both primary and secondary bone tumors develop in the unique bone tissue, it is therefore necessary to understand bone cell biology in tumor bone environment. Recent findings of the Receptor Activator of Nuclear Factor-κB ligand (RANKL)/RANK/osteoprotegerin (OPG) molecular triad, the key regulators of bone remodeling, opened new era of bone research. Although RANK is an essential receptor for osteoclast formation, activation and survival, functional RANK expression has been recently identified on several bone-associated tumor cells. When RANK is expressed on secondary bone tumor cells, it is implicated in tumor cell migration, whereas this is not the case for primary bone tumors. In any case, RANK is not involved in RANK-positive cell proliferation or death. In two models of bone metastases secondary to melanoma or prostate carcinoma, in vivo neutralization of RANKL by OPG resulted in complete protection from paralysis, due to metastases of vertebral body, and a marked reduction in tumor burden in bones, but not in other organs. OPG also decreased tumor formation and tumor burden in a mouse model of primary bone tumor, osteosarcoma. In all these models, tumor cells express RANK. These data revealed that local differentiation factors, such as RANKL, play an important role in cell migration in a metastatic tissue-specific manner. These findings substantiate the novel direct role of
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RANK/RANKL/OPG Signalization Implication in Periodontitis: New Evidence from a RANK Transgenic Mouse Model

RANK/RANKL/OPG Signalization Implication in Periodontitis: New Evidence from a RANK Transgenic Mouse Model

An increased RANKL/OPG ratio has been reported in periodontal tissues under pathological conditions, such as Pd ( Mogi et al., 2004 ). RANKL levels in periodontal fibroblasts are induced either by mechanical forces or bacterial challenge in periodontitis ( Bostanci et al., 2007 ), whereas OPG levels decrease under similar conditions ( César-Neto et al., 2007 ). The OPG null mutant mouse, which exhibits alveolar bone loss ( Koide et al., 2013 ) and early onset root resorption ( Liu et al., 2016 ), was recently reported as a model of Pd using the dental ligature procedure ( Mizuno et al., 2015 ). This study validated the importance of the RANKL/RANK/OPG signaling pathway in the physiopathology of Pd.
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Genetic deletion of muscle RANK or selective inhibition of RANKL is not as effective as full-length OPG-fc in mitigating muscular dystrophy

Genetic deletion of muscle RANK or selective inhibition of RANKL is not as effective as full-length OPG-fc in mitigating muscular dystrophy

Although there is a strong association between osteoporosis and skeletal muscle atrophy/dysfunction, the functional relevance of a particular biological pathway that regulates synchronously bone and skeletal muscle physiopathology is still elusive. Receptor-activator of nuclear factor κB (RANK), its ligand RANKL and the soluble decoy receptor osteoprotegerin (OPG) are the key regulators of osteoclast differentiation and bone remodelling. We thus hypothesized that RANK/RANKL/OPG, which is a key pathway for bone regulation, is involved in Duchenne muscular dystrophy (DMD) physiopathology. Our results show that muscle-specific RANK deletion (mdx-RANK mko ) in dystrophin deficient mdx mice improves significantly specific force [54% gain in force] of EDL muscles with no protective effect against eccentric contraction-induced muscle dysfunction. In contrast, full-length OPG-Fc injections restore the force of dystrophic EDL muscles [162% gain in force], protect against eccentric contraction-induced muscle dysfunction ex vivo and significantly improve functional performance on downhill treadmill and post-exercise physical activity. Since OPG serves a soluble receptor for RANKL and as a decoy receptor for TRAIL, mdx mice were injected with anti-RANKL and anti-TRAIL antibodies to decipher the dual function of OPG. Injections of anti-RANKL and/or anti-TRAIL increase significantly the force of dystrophic EDL muscle [45% and 17% gains in force, respectively]. In agreement, truncated OPG-Fc that contains only RANKL domains produces similar gains, in terms of force production, than anti-RANKL treatments. To corroborate that full-length OPG-Fc also acts independently of RANK/RANKL pathway, dystrophin/RANK double-deficient mice were treated with full-length OPG-Fc for 10 days. Dystrophic EDL muscles exhibited a significant gain in force relative to untreated dystrophin/RANK double-deficient mice, indicating that the effect of full-length OPG-Fc is in part independent of the RANKL/RANK interaction. The sarco/endoplasmic reticulum Ca 2+ ATPase (SERCA) activity is significantly depressed in dysfunctional and dystrophic muscles and full-length OPG-Fc treatment increased SERCA activity and SERCA-2a expression. These findings demonstrate the superiority of full-length OPG-Fc treatment relative to truncated OPG-Fc, anti-RANKL, anti-TRAIL or muscle RANK deletion in improving dystrophic muscle function, (Continued on next page)
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RANKL directly induces bone morphogenetic protein-2 expression in RANK-expressing POS-1 osteosarcoma cells.

RANKL directly induces bone morphogenetic protein-2 expression in RANK-expressing POS-1 osteosarcoma cells.

cells participates in the development of osteolytic lesions observed in our POS-1 model. The OPG/RANK/RANKL triad is an important therapeutic axis in pathologies involving a dysregulation in bone re- modelling, including tumor-associated osteolysis (4). A number of studies provide evidence for the direct production of RANKL by tumor cells themselves, as reported in multiple myeloma (25), prostate cancer (26), carcinoma cell lines (6) or human neuroblastoma (27). RANKL can then bind to its cognate receptor, RANK, at the surface of osteoclast precursors acting directly on osteoclast differentiation and activation. In the present POS-1 osteosarcoma model, POS-1 cells express RANK, not RANKL. RANK is known to be predominantly present at the surface of osteoclasts and some immune cells (28), but its expression has also been revealed in marrow stromal cells and osteoblasts and was strongly up-regulated when activated by T-cell conditioned medium (21). Another publication from Miyamoto et al reported that human osteosarcoma-derived cell lines expressed both RANK and RANKL mRNAs but the functionality of the receptor, RANK, was not investigated (20). However, the presence of a functional receptor, RANK, at the surface of cancer cells is in agreement with the results of Tometsko et al who recently reported the direct effects of RANKL on RANK-expressing human breast cancer cells, MDA-MB-231, and prostate PC3 (29). They demonstrated that RANKL treatment of both MDA-231 and PC3 cells led to the activation of signal trans- duction pathways (p38 MAPK, p42/44 MAPK, NF- κ B) and upregulated the expression of 194 mRNA as assessed by micro-array. From these data, we can hypothesize that, while neither cell line (MDA-231, PC3 and POS-1) expresses RANKL in vitro, it is probable that the locally increased RANKL within the bone microenvironment could activate tumor cell-expressed RANK in a paracrine manner.
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Symmetric tensors and symmetric tensor rank

Symmetric tensors and symmetric tensor rank

It is known that the lower bound is often accurate but the upper bound is not tight [16]. Furthermore, exact results are known in the case of binary quantics (n = 2) and ternary cubics (k = 3) [22, 16, 47, 35]. 7.1. Alexander-Hirschowitz Theorem. It was not until the work [1] of Alexan- der and Hirschowitz in 1995 that the generic symmetric rank problem was completely settled. Nevertheless, the relevance of their result has remained largely unknown in the applied and computational mathematics communities. One reason is that the connection between our problem and the interpolating polynomials discussed in [1] is not at all well-known in the aforementioned circles. So for the convenience of our readers, we will state the result of Alexander and Hirschowitz in the context of the symmetric outer product decomposition below.
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Regulators of rank one quadratic twists

Regulators of rank one quadratic twists

the subset of d ∈ F (T ) such that L 0 (E d , 1) = 0, which is really surprising compared to the even-rank case. The numerical data seems to support this fact. On the other hand, extensive numerical computations by Watkins [Wat] seem to indicate otherwise. Indeed we want to emphasize that one has always to be careful with deducing too strong of statements from numerical investigations.

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Computing symmetric rank for symmetric tensors

Computing symmetric rank for symmetric tensors

(Brachat et al., 2009), (Comas, Seiguer, 2001), (Landsberg, Teitler, 2009) and (Sylvester, 1886)); σ 2 (X n,d ), σ 3 (X n,d ) (any n,d, see Section 4); σ r (X 2,4 ), for r ≤ 5. In the first three cases we also give an algorithm to compute the symmetric rank. Some of these results were known or partially known, with different approaches and different algorithms, e.g in (Landsberg, Teitler, 2009) bounds on the symmetric rank are given for tensors in σ 3 (X n,d ), while the possible values of the symmetric rank on σ 3 (X 2,3 ) can be found in

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Rank optimality for the Burer-Monteiro factorization

Rank optimality for the Burer-Monteiro factorization

When solving large scale semidefinite programs that admit a low-rank solution, a very efficient heuristic is the Burer-Monteiro factorization: Instead of optimizing over the full matrix, one optimizes over its low-rank factors. This strongly reduces the number of variables to optimize, but destroys the convexity of the problem, thus possibly introducing spurious second-order critical points which can prevent local optimization algorithms from finding the solution. Boumal, Voroninski, and Bandeira [2018] have recently shown that, when the size of the factors is of the order of the square root of the number of linear constraints, this does not happen: For almost any cost matrix, second-order critical points are global solutions. In this article, we show that this result is essentially tight: For smaller values of the size, second-order critical points are not generically optimal, even when considering only semidefinite programs with a rank 1 solution.
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Split rank of two-row cuts

Split rank of two-row cuts

Most inequalities used in commercial softwares are split cuts Question : what is the split rank of the 2 row-inequalities ? In how many rounds of split cuts only can we generate the inequalities ? The Cook-Kannan-Schrijver has infinite rank and we prove that the other triangles have finite rank.

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Non-iterative low-multilinear-rank tensor approximation with application to decomposition in rank-(1,L,L) terms

Non-iterative low-multilinear-rank tensor approximation with application to decomposition in rank-(1,L,L) terms

goulart@gipsa-lab.fr , pierre.comon@gipsa-lab.fr ). This work is supported by the European Research Council under the European Programme FP7/2007-2013, Grant AdG-2013-320594 “DECODA.” 1 Though (R 1 , . . . , R N ) is the mrank of ˆ X rather than its rank, we employ the usual terminology

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Differentiability properties of Rank-Linear Utilities

Differentiability properties of Rank-Linear Utilities

When L(t, x) = f 0 (1 − t)U (x), with f a convex distortion satisfying f (0) = 0, f (1) = 1 and U a concave utility index then V is a rank-dependent utility (RDU), in the linear case U (x) = x then V is a Yaari utility. In the case of a Yaari utility, V is the support function of the core of the distortion of the underlying probability by f , hence differentiablity properties of V are tightly linked to the geometry of the core. The differentiability properties of RDU functionals have been studied in [1] using a characterization of the core of convex distortions of a probability. For a more general L, the previous approach is not adapted and different arguments have to be developed to compute the superdifferential of V and the set of random variables where V is Gˆateaux-differentiable.
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On the cactus rank of cubic forms

On the cactus rank of cubic forms

sr(F ) = min{lengthΓ | Γ ⊂ P(T 1 ) smoothable, dimΓ = 0, I Γ ⊂ F ⊥ } and the rank r(F ) is defined as r(F ) = min{lengthΓ | Γ ⊂ P(T 1 ) smooth, dimΓ = 0, I Γ ⊂ F ⊥ }. Clearly cr(F ) ≤ sr(F ) ≤ r(F ). A separate notion of border rank, br(F ), often considered, is not defined by apolarity. The border rank is rather the minimal r, such that F is the limit of polynomials of rank r. Thus br(F ) ≤ sr(F ). These notions of rank coincide with the no- tions of length of annihilating schemes in Iarrobino and Kanev book [Iarrobino, Kanev 1999, Definition 5.66]: Thus cactus rank coincides with the scheme length, cr(F ) = lsch(F ), and smoothable rank coincides with the smoothable scheme length, sr(F ) = lschsm(F ), while border rank coincides with length br(F ) = l(F ). In addition they consider the differential length ldiff(F ), the maximum of the dimensions of the space of k-th order partials of F as k varies between 0 and degF . This length is the maximal rank of a catalecticant or Hankel matrix at F , and is always a lower bound for the cactus rank: ldiff(F ) ≤ cr(F ).
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On Drinfeld modular forms of higher rank

On Drinfeld modular forms of higher rank

In the first section, we sketch the background on Drinfeld modules/mod- ular forms and introduce notation.. It doesn’t contain any new material.[r]

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Split rank of triangle and quadrilateral inequalities

Split rank of triangle and quadrilateral inequalities

As before, let α be the goal inequality such that L α is triangle of type T 2B . Disjunction Sequence 7.2. 1. Initialization step: First consider the two-variable problem P ((r 1 , r 3 ), f ). By definition the triangle C := f w 1 w 3 does not contain any integer point in its interior. Therefore φ(C) is a valid inequality for conv(P ((r 1 , r 3 ), f )). By Proposition 5.1, there also exists  > 0 such that, denoting u [2] := f +r 2 , we obtain that β [2] := φ(4(w 1 w 3 u [2] )) is a valid inequality for P (R, f ). By Proposition 5.1 and Proposition 6.1, we also know that this inequality has a split rank at most two. Let q [2] be the intersection point of (4(w 1 w 3 u [2] )) with the line {x ∈ R 2 | x 2 = 1}. We then directly proceed to step 2 in the inductive process.
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PAC-Bayesian estimation of low-rank matrices

PAC-Bayesian estimation of low-rank matrices

The first two parts of the thesis are dedicated to two practical problems of estimation of low-rank matrices: the matrix completion problem and quan- tum state tomography, where the objective is to estimate the so-called density matrix, that is often assumed to be low-rank by physicists. For matrix com- pletion, we show that a quasi-Bayesian estimator satisfies an optimal oracle inequality, and thus reaches the minimax-optimal rates (up to log terms). The strong point of our results is that it holds without any assumption on the sampling distribution - this is the first result without such an assumption up to our knowledge. For the quantum state tomography problem, we build a pseudo-Bayesian estimator. Note that in most previous works, the definition of a prior probability distribution was only tackled in the case of the 1 qubit problem (the smallest possible instance of the problem, where the matrix to be estimated is 2 × 2). Inspired by the prior used for matrix completion, we propose a prior distribution that can be used to estimate density matrices of any dimension. We show that our pseudo-Bayesian estimator reaches the best up-to-date known rate of convergence while its numerical performance was tested on simulated and real data sets.
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A Framework for Web Page Rank Prediction

A Framework for Web Page Rank Prediction

n(n 2 −1) . 4.2 RSim Quality Measure The observed similarity measures do not cover sufficiently the fine grained re- quirements arising, comparing top-k rankings in the Web search context. So we need a new similarity metric taking into consideration: a)The absolute difference between the predicted and actual position for each Webpage as large difference indicates a less accurate prediction and b)The actual ranking position of a Web page, because failing to predict a highly ranked Webpage is more important than a low-ranked. Based on these observations, we introduce a new measure, named RSim. Every inaccurate prediction made incurs a certain penalty depending on the two noted factors. If prediction is 100% accurate (same predicted and actual rank), the penalty is equal to zero. Let B i be the predicted rank position for
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