Preclinical models for neuroblastoma: Advances and challenges

Journal Pre-proof Preclinical models for neuroblastoma: advances and challenges J.C. Nolan, T. Frawley, J. Tighe, H. Soh, C. Curtin, O. Piskareva PII:

S0304-3835(20)30023-9

DOI:

https://doi.org/10.1016/j.canlet.2020.01.015

Reference:

CAN 114657

To appear in:

Cancer Letters

Received Date: 13 September 2019 Revised Date:

14 January 2020

Accepted Date: 15 January 2020

Please cite this article as: J.C. Nolan, T Frawley, J Tighe, H Soh, C. Curtin, O. Piskareva, Preclinical models for neuroblastoma: advances and challenges, Cancer Letters, https://doi.org/10.1016/ j.canlet.2020.01.015. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 The Author(s). Published by Elsevier B.V.

Abstract Neuroblastoma is a paediatric cancer of the sympathetic nervous system and the most common solid tumour of infancy, contributing to 15% of paediatric oncology deaths. Current therapies are not effective in the long-term treatment of almost 80% of patients with this clinically aggressive disease. The primary challenge in the identification and validation of new agents for paediatric drug development is the accurate representation of tumour biology and diversity. In addition to this limitation, the low incidence of neuroblastoma makes the recruitment of eligible patients for early phase clinical trials highly challenging and highlights the need for robust preclinical testing to ensure that the best treatments are selected. The research field requires new preclinical models, technologies, and concepts to tackle these problems. Tissue engineering offers attractive tools to assist in the development of three-dimensional (3D) cell models using various biomaterials and manufacturing approaches that recreate the geometry, mechanics, heterogeneity, metabolic gradients, and cell communication of the native tumour microenvironment. In this review, we discuss current experimental models and assess their abilities to reflect the structural organisation and physiological conditions of the human body, in addition to current and new techniques to recapitulate the tumour niche using tissue-engineered platforms. Finally, we will discuss the possible use of novel 3D in vitro culture systems to address open questions in neuroblastoma biology.

Title: Preclinical models for neuroblastoma: advances and challenges

Authors: Nolan, J.C.1–3, Frawley, T1–3, Tighe, J1, Soh, H1, Curtin, C.4–6, Piskareva, O.1–3,5,6* 1. Cancer Bio-Engineering Group, Department of Anatomy and Regenerative Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland 2. School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland 3. National Children’s Research Centre, Our Lady’s Children’s Hospital, Crumlin, Dublin, Ireland 4. Tissue Engineering Research Group, Department of Anatomy and Regenerative Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland 5. Trinity Centre for Bioengineering, Trinity College Dublin, Dublin, Ireland 6. Advanced Materials and Bioengineering Research Centre (AMBER), RCSI and TCD, Dublin, Ireland

*Corresponding author: Olga Piskareva Corresponding author address: Olga Piskareva, PhD NCRC funded Principal Investigator and StAR Research Lecturer Department of Anatomy and Regenerative Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland 123 St Stephen Green Dublin 2, Ireland Office: +353 1 402 2123 Fax: +353 1 402 2453 Email: [email protected]

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Abstract Neuroblastoma is a paediatric cancer of the sympathetic nervous system and the most common solid tumour of infancy, contributing to 15% of paediatric oncology deaths. Current therapies are not effective in the long-term treatment of almost 80% of patients with this clinically aggressive disease. The primary challenge in the identification and validation of new agents for paediatric drug development is the accurate representation of tumour biology and diversity. In addition to this limitation, the low incidence of neuroblastoma makes the recruitment of eligible patients for early phase clinical trials highly challenging and highlights the need for robust preclinical testing to ensure that the best treatments are selected. The research field requires new preclinical models, technologies, and concepts to tackle these problems. Tissue engineering offers attractive tools to assist in the development of three-dimensional (3D) cell models using various biomaterials and manufacturing approaches that recreate the geometry, mechanics, heterogeneity, metabolic gradients, and cell communication of the native tumour microenvironment. In this review, we discuss current experimental models and assess their abilities to reflect the structural organisation and physiological conditions of the human body, in addition to current and new techniques to recapitulate the tumour niche using tissue-engineered platforms. Finally, we will discuss the possible use of novel 3D in vitro culture systems to address open questions in neuroblastoma biology. Keywords: neuroblastoma, tissue-engineering, 2D cell models, 3D cell models, scaffolds, ECM, tumour microenvironment

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Manuscript

1. Introduction Neuroblastoma is a paediatric cancer of the sympathetic nervous system and the most common solid tumour in infants [1]. In children under the age of 15, the incidence rate of neuroblastoma is 1.2 cases per 100,000 and accounts for approximately 15% of all paediatric oncology deaths [2]. The clinical behaviour and outcomes of neuroblastoma are highly heterogeneous, ranging from total regression to the development of multi-foci- and multi-drug-resistant disease [1,3]. Almost 80% of patients with the clinically aggressive disease do not show sustained responses to recent advances in anticancer therapy, thus highlighting the shortcomings in our understanding of neuroblastoma biology and our ability to represent this disease in preclinical models accurately [4,5]. Neuroblastoma survivors are at risk of severe or lifethreatening illnesses due to late treatment toxicities, including the development of secondary cancers, further emphasising the need for comprehensive and reliable preclinical drug screening [6]. A significant barrier in drug development for both paediatric and adult cancers is the inconsistency between in vitro and in vivo testing results, leading to only 1 in 10 drugs that enter clinical trials’ being approved by the FDA [7]. Current neuroblastoma studies primarily employ two-dimensional (2D) cell culture systems and zebrafish and murine models. However, 2D cell culture cannot reflect the three-dimensional (3D) architecture and complexity of human tumours since they lack cell-cell and cell-matrix communication, metabolic gradients, and cell polarity. Therefore, the existing knowledge of neuroblastoma biology at the tissue level is insufficient to make confident predictions about patient responses to new agents that target either widely expressed cellular components (e.g., topoisomerases and microtubules) or broadly active signalling pathways (e.g., MAP kinase and AKT). These models also do not reflect the paediatric context of neuroblastoma, immature immune system, differences in drug metabolism to adult cancers, and continuing developmental changes. Furthermore, thorough evaluation of new agents in paediatric

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patients is limited by the number of children eligible for early phase clinical trials [5,8]. All these limitations directly impact the extent of knowledge and available resources for researchers to identify potential therapeutic targets and prioritise new agents in the clinical development pipeline. These restrictive barriers highlight opportunities for researchers to build upon the improvements made in adult cancer studies. By exploiting advances in tissue engineering and biomaterial development, we can reconstruct a physiologically relevant tumour microenvironment in 3D in vitro, using appropriately sourced and propagated human cells which retain the fundamental genetic and epigenetic landscape. Tumours function at seven main levels of structural organisation within the human body: chemical, molecular, cellular, tissue, organ, organ system, and organismal levels (Figure 1) [9]. Each level displays interconnected mechanisms and regulatory feedback loops that affect disease progression and response to treatment. As the complexity of structural organisation increases, the reconstruction of a reliable physiologically relevant cellular environment becomes increasingly challenging but vital for the development of reliable experimental systems and preclinical models. This review provides an outline of current and developing experimental models and evaluates their ability to reflect the structural organisation and physiological conditions of a tumour in a human patient. Finally, we will discuss the possible use of novel 3D in vitro culture systems to address many of the open questions in neuroblastoma biology.

2. Tumour microenvironment

Tumours can be classified as organs based on their multi-tissue organisation and their continual response to different environments and stimuli [9]. The native tumour is comprised of cellular, structural, and molecular components collectively known as the tumour microenvironment (TME), which supports a multitude of signalling pathways. The TME hosts a heterogeneous cellular population of cancerous and noncancerous cells such as fibroblasts, stromal cells, and immune cells, which migrate from the bloodstream and neighbouring tissues. TME is defined by secreted factors and extracellular matrix (ECM) proteins that form essential 3D physical scaffolding. Both cellular and molecular 4

components shape TME architecture and maintain its homeostasis at the tissue and organ levels. The involvement of tumour microenvironment (TME) in disease progression, patient prognosis, and response to treatment is widely recognised [reviewed elsewhere [10,11]]. Differentiating

the

relative

contributions

of

these

structural,

molecular,

and

microenvironmental processes in disease progression is essential to our understanding of tumour function at the tissue level. This challenge can be addressed by the reconstruction of a TME in vitro, where cellular and molecular components of the system can be interchangeable and finely controlled using tissue-engineered strategies and platforms.

2.1.

Cell composition

The neuroblastoma TME is primarily composed of neuroblastic and Schwann cells in different proportions. Both cell types have the same progenitor neural crest stem cells and are present at varying degrees of differentiation in neuroblastoma. Cross-talk between these neuroblastoma cell types has been demonstrated previously to drive therapy resistance and the proliferation of Schwann cells through the release of the Neuregulin 1 (NRG1) growth factor from neuroblastic SH-SY5Y cells [12]. The grade of a tumour and its stromal cell differentiation are histopathological indicators used to classify neoplasms into three subtypes—undifferentiated, poorly differentiated, and differentiating—and act as prognostic markers for disease outcome [13]. The primary and metastatic neuroblastoma microenvironment contains different types of noncancerous cells, depending on the hosting tissue niche. The bone and bone marrow niches are rich with osteoblasts and mesenchymal stem cells, respectively. Both cell types chemo-attract neuroblastoma cells via paracrine signalling, involving several pathways acting in parallel. They secrete Stromal Cell-Derived Factor-1 (SDF-1), which binds to chemokine receptors type 4 and 7 (CXCR4 and CXCR7) on the neuroblastoma cell surface both in vitro and in vivo, stimulating neuroblastoma cell migration to these niches [14–16]. In primary neuroblastomas, CXCR4 expression correlates strongly with metastasis to bone and bone marrow and poor patient outcome [17]. Insulin-like growth factors (IGF) -1 and -2 produced by neuroblastoma cells bind and activate receptors (IGF-1R) on the surface of preosteoclasts [18]. Interestingly, neuroblastoma cells also express IGF-1R on their surface, with high levels of IGF-IR expression triggering osteoclast differentiation and the formation 5

of osteolytic lesions in the bone microenvironment [19]. Other reported mechanisms of in vitro osteoclast activation and bone degradation are through the mesenchymal stem cellsecreted IL-6, triggered by neuroblastoma cells [20] or the pre-osteoclast-secreted RANK ligand (RANKL) [21].

2.2.

Extracellular matrix composition

Extracellular matrix (ECM) provides essential structural support and biochemical interactions for cells within the TME. The ECM is created and maintained by TME cell secretion of various ECM macromolecules, such as proteoglycans, polysaccharides, glycosaminoglycans, glycoproteins, and fibrous proteins such as collagen and fibronectin [22]. These macromolecules form a scaffold and define the physical properties of tissues, determining their structure and influencing cell growth, survival, and proliferation [23]. A recent multiparametric analysis of neuroblastoma TME found ECM components, including reticulin, collagen fibres, and glycosaminoglycans were correlated with neuroblast, Schwann, and lymphocyte cell behaviour, respectively. This study emphasised the need to incorporate quantitative measures of TME and ECM characteristics into histopathologic analyses to improve the Neuroblastoma Pathology Classification value [24]. Neuroblastoma cells sense mechanical forces of ECM and alter their gene expression accordingly, as demonstrated by neurite outgrowth and suppressed neuroblastoma cell proliferation in vitro in response to an increasing ECM stiffness [25]. Of note, the mechanical properties of the ECM also modulate the expression of the critical oncogenic transcription factor in neuroblastoma, N-Myc [25].

3. Tissue - engineering strategies to model neuroblastoma

3.1.

Traditional 2D cell cultures

The progression and properties of cancer have been traditionally investigated in twodimensional in vitro models and subsequently verified in vivo using animal models. Approximately 70% of biomedical researchers still heavily depend on 2D cell models for initial investigations [26]. The same approach is used in the development of new drugs as 2D 6

cell culture techniques are widespread due to being easy to use, high in productivity, and relatively low in cost. For neuroblastoma research, a broad panel of cell lines has been developed and extensively characterised [27,28]. Despite the many advantages offered by these neuroblastoma cell line models, the cells are grown in a monolayer and deprived of the complex interactive environment and 3D architecture present in a tumour, resulting in altered signalling pathways, cell behaviour, growth, metabolism, and response to stimuli compared to cells grown in vivo [29].

3.2.

3D in vitro tissue-engineering

Many 3D tissue-engineered platforms initially developed for tissue regeneration are now available for cancer research applications (reviewed in [30,31]). In this chapter, we describe only models that have already been explored in neuroblastoma (Figure 2). Regardless of the 3D platform tested, the source of neuroblastoma cells included commercially available and extensively published neuroblastoma cell lines [27].

3.2.1. Multicellular tumour spheroids The multicellular tumour spheroid (MCS) model is currently one of the most popular and well-characterised scaffold-free 3D cell culture platforms [31]. First reported in the early 1970s by Sutherland et al., the MCS model has been increasingly utilised for drug discovery and development due to its ability to mimic in vivo tumour diffusional gradients and the transfer of nutrients, oxygen, and waste [32]. MCSs are generated either through the aggregation and compaction of multiple cells in suspension or by establishing cell masses from a single cell via consecutive cell doublings. Different approaches to the fabrication of matrix-free MCSs include forced flotation on non-adhesive surfaces, the hanging drop method, bioreactor rotational culture, and the force-driven method, which are comprehensively reviewed in [33]. Spheroid models offer many advantages over 2D cultures and enable high reproducibility, a well-defined cell colony architecture, and adaptability to multi-cell-type co-culturing, which more closely reflects the in vivo tissue organisation [34,35]. The formation of features observed in tissues, such as desmosomes and dermal junctions by cells growing in spheroids, reveals the enhanced physiological relevance and complexity of

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this model over traditional monolayer cultures [36]. Despite the advantages of the MCS model, it has not yet been widely adopted in neuroblastoma research for the in vitro reconstruction of the neuroblastoma primary and metastatic niche. The studies outlined in this section utilised this MCS platform to carry out neuroblastoma stem cell research [37– 40], drug screening, and the development of radiobiology and radioimmunotherapy techniques [41–47]. As with MCSs of adult cancers [35], neuroblastoma spheroids displayed increased expression of epithelial-to-mesenchymal markers, drug resistance proteins, and survival response regulators, coupled with more aggressive phenotypes than cells grown in conventional 2D culture models [39,40]. Spheroids mimic the 3D architecture and behaviour of cells, displaying a central area of necrotic cells surrounded by differentiating cells and by outer proliferating cells. The degree of compaction and density of cell masses grown in this model also influence drug perfusion and sensitivity [31,35,45]. All studies (Table 1) found that neuroblastoma MCS growth patterns and cytotoxic sensitivity closely resembled that of the in vivo solid tumour mass and, as such, these models can be recommended for novel and repurposing drug screening. This is illustrated by greater resistance to increasing concentrations of 15-deoxy-PGJ2 and doxorubicin by the SK-N-SH and SH-SY5Y spheroids compared to cells grown in 2D [46]. Additionally, 10 out of 12 neuroblastoma cell lines tested demonstrated increased resistance to cisplatin and dFdC treatment in 3D vs. 2D [42]. Notwithstanding the many advantages offered by the MCS model, one important limitation to be considered is the inability of some cell types to form MCSs or adapt their life cycle [31].

3.2.2. Scaffold-based platforms Further advances of the MCS model incorporate the use of synthetic matrices; poly(ethylene glycol) (PEG), poly(lactic-co-glycolic acid) (PLGA), polyacrylamide (PAAG), poly(ethylene oxide) (PEO), poly(ɛ-carpolactone) and natural matrices; collagen, hydroxyapatite, chitosan, fibronectin, gelatine, alginate, agarose and cellulose for the generation, and in vitro testing of spheroids. These supportive polymers provide increased complexity and bio-mimicking potential by providing cell-matrix interactions, not present in matrix-free MCSs. This model facilitates the fine-tuning of MCS microenvironment components and incorporation of various bioactive molecules to reconstitute the in vivo ECM with high precision and accuracy, allowing the detailed analysis of their role in structure and function at the 8

tissue/organ level. Using this carefully defined bottom-up approach to design in vitro models for neuroblastoma research, scaffold systems provide the added advantage of increased biological complexity with a high level of adaptability.

3.2.2.1.

Hydrogels

This class of matrices consists of networks of crosslinked hydrophilic polymers that swell in water while maintaining their 3D structures. Hydrogels are relatively easy to manipulate to replicate biomechanical properties of soft tissues, and they allow for the incorporation of cell adhesion ligands. In cancer research, 3D collagen hydrogels have become popular for studying the plasticity of invasion mechanisms and tissue morphogenesis, as they recapitulate the in vivo metastatic niche [48,49]. This 3D platform was recently adopted to study neuroblastoma cell morphology and invasion [50–52]. Neuroblastoma MCSs embedded into collagen hydrogels displayed different morphology depending on MYCN status [52]. A higher proportion of the cell population harbouring MYCN amplification displayed a rounded shape, while more cells in non-MYCN-amplified cell lines had an elongated shape. In the same study, Rac GTPase inhibition changed the cell morphology of non-MYCN-amplified cell lines and affected cell invasion potential. The advances in affordable 3D printing technology have increased the incorporation of these hydrogels into the modelling of cancer, with 3D printed chitosangelatin thermosensitive hydrogel being used to build neuroblastoma-cell-laden physical scaffolds [53]. Several studies have adopted the hydrogel matrix for co-culturing different cell types, in varying proportions, to investigate cell-cell interactions present in vivo. In the study by Yeung et al. [54], the 3D neuroblastoma environment was engineered by the comicroencapsulation of neuroblastoma cells with mesenchymal stem cells in liquid collagen drops/microspheres. In this model, mesenchymal stem cells served as a supportive stromal niche, while collagen provided physical 3D scaffolding. This combination stimulated neuroblastoma cell proliferation, demonstrating irregular tumour outgrowth, epithelialmesenchymal structures, tumour invasion, and vascular spaces — features common to in vivo tumours. Another study demonstrated the formation of Homer-Wright-like rosettes by co-culturing mesenchymal stem cells, human umbilical vein endothelial cells, and 9

neuroblastoma SH-SY5Y cells embedded in collagen type I hydrogel matrices, recreating the cellular organisation and interactions seen in clinical neuroblastoma tumours [50]. 3.2.2.2.

Porous Scaffolds

In contrast to hydrogels, scaffolds provide a porous 3D structural matrix to support cell proliferation, migration, differentiation and deposition of ECM in response to stimuli. Threedimensional, scaffold-based cell systems have become a relatively recent focus in the neuroblastoma research field to mimic neuroblastoma microenvironment by exploring bacterial nanocellulose [55], calcium phosphate [56] and collagen-based 3D scaffolds [57]. Neuroblastoma SH-SY5Y cells demonstrated growth and differentiation in bacterial nanocellulose (BNC) pellicles scaffolds covered with Collagen I in vitro [55]. This BNC matrix did not reflect a true 3D porous scaffolding but instead consisted of a flat substrate with an uneven brush-like surface providing an additional dimension for cells to attach, grow, colonise the environment, and build cell-to-cell contacts. The model has the potential for further advancement to provide a fully 3D environment through the fabrication of nanocellulose fibres in 3D moulds and the use of porogens to form pores and channels in the matrix. Our group recently modelled a minimal neuroblastoma niche using two types of porous collagen-based 3D scaffolds that contained either glycosaminoglycan (Coll-GAG) or nanohydroxyapatite (Coll-nHA) colonised with chemotherapeutic (cisplatin)–sensitive Kelly and resistant KellyCis83 neuroblastoma cell lines with distinct genotypes and biological phenotypes (Figure 3) [57,58]. Both glycosaminoglycan and nanohydroxyapatite are constituents of bone tissue, making them more representative of the common metastatic sites of neuroblastoma: bone marrow (70.5%) and bone (55.7%) [24,59]. The scaffolds were fabricated using freeze-drying techniques initially developed for bone tissue engineering applications and extensively characterised in relation to their physical properties and biological relevance, including pore size, height, and porosity [60,61]. Both neuroblastoma cell lines actively proliferated and migrated within the scaffolds, forming tumour colonies of different size and density. As discussed in Section 3.2.1, these features may explain a 100fold increased resistance to cisplatin treatment when compared to 2D cultures, demonstrating the chemosensitivity reflective of orthotopic xenograft in vivo models [57].

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Of note, this 3D neuroblastoma model was also able to differentiate cell response to the ectopic overexpression of miRNA in 2D and 3D in vitro, illustrating the influence of the local architecture on the stimulation of different signalling pathways. By growing cancer cells on these 3D scaffolds and allowing the formation of a ‘tumour mass,’ the limitations of using 2D cultured cells can be overcome by re-establishing minimal communication networks and drug diffusional/cellular gradients observed within in vivo tumours. The reviewed 3D in vitro models highlight the available opportunities and new approaches to study neuroblastoma pathogenesis and develop new therapeutics. These models allow step-by-step dissection of the molecular, cellular, and mechanical cues that underlie neuroblastoma biology at the tissue level. These tissue-engineered platforms represent a more physiologically relevant in vitro scenario at the tissue level by incorporating other cell types, such as endothelial, mesenchymal stem cells, fibroblasts, or cells of the immune system in varying proportions. These advances should significantly reduce the gap between in vitro preclinical analyses and more complex in vivo models. The major drawback of 3D in vitro cancer models includes variations in the ability to simulate in vivo tissue conditions at the organ level; they lack other tissue properties such as a developed vasculature, perfusion, and other cell-cell interactions. These models represent short-term or static conditions, in contrast to the continually progressing in vivo system [31].

3.3.

Therapeutic screening

The main challenge in the preclinical screening of drug efficacy and determination of drug sensitivity is the representation of a tumour-specific molecular profile, architecture, and patient diversity when the number of paediatric patients eligible for early phase clinical trials is limited. High-throughput screening (HTS) addresses this challenge, enabling systemslevel studies of cancer cell behaviour in a biomimetic 3D microenvironment. These platforms can provide a wealth of information on a wide range of cellular responses through simultaneous assessment of morphological changes, cell viability and cytotoxicity, gene expression analysis, and apoptosis assays in an automated/semi-automated fashion, transforming HTS into High Content Screening (HCS). Comparative studies of the drug response of epithelial ovarian, lung, breast, and prostate cancer cells as a traditional 11

monolayer culture versus MCSs demonstrated enhanced chemo-resistance in 3D HTS platforms [35,41,62]. In a similar way, twelve neuroblastoma cell lines grown as 3D MCS were tested for cisplatin and gemcitabine (or dFdC) cytotoxicity coupled with gene expression assays [42]. The study facilitated the simultaneous assessment of cell sensitivity to the tested drugs, their synergism and contribution of the copper uptake and efflux transporters, hCTR1, ATP7A, and ATP7, respectively. Sidarovich et al. utilised this HCS approach further to test a library of 349 small molecules using five neuroblastoma cell lines [45]. Verification of toxicity data in murine models using only FDA-approved compounds identified the multiple tyrosine kinase inhibitor, Ponatinib, as a candidate for repurposing in neuroblastoma therapy. High-content drug screening may be exploited in clinical settings by assessing the sensitivity of neuroblastoma cells derived from patient biopsies to current drug therapy, leading to a personalised approach in treatment protocol design. Currently, the main challenges with these models’ application in clinical settings are the demand for skilled labour and the high costs. Additive biomanufacturing, including 3D bioprinting, also offers a cost-effective and user-friendly approach to generating complex 3D in vitro models for cancer research and drug screening with high precision, reproducibility, and control over microarchitecture, which in turn allows for the replication of the natural form and function of tumour tissue [50,53,63].

4. In vivo models To advance our understanding of tumour biology, pathogenesis, and drug response at the tissue, organ, and organismal levels, adequate experimental models with physiological relevance to humans are essential. To date, the majority of neuroblastoma model systems are based on mice or zebrafish or, to a lesser extent, chick chorioallantoic membrane (CAM). The in vivo models commonly used for neuroblastoma research and drug efficacy studies are syngeneic, transgenic, xenograft, and humanized animal models (outlined in table 2 and reviewed in [64]). While each animal model provides insights into specific questions about disease progression, they have individual inadequacies in reproducing the physiological conditions present in neuroblastoma patients.

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The first syngeneic neuroblastoma murine model, C1300 NB, arose from spontaneously occurring spinal cord tumours in strain A mice. Subsequently, the clone, TBJ-NB, was developed from C1300 NB, which displayed rapid growth and metastatic capabilities. While these syngeneic models have an intact murine immune system, are highly reproducible, easy to handle, and have a relatively low cost, they do not recreate the biology or genetic alternations of human tumours. Furthermore, these models of neuroblastoma do not arise during embryogenesis, unlike human neuroblastoma [64,65]. Transgenic murine models overcome some of the disadvantages associated with syngeneic models. TH-MYCN and LSL-MYCN;Dbh-iCre are transgenic neuroblastoma murine models engineered to overexpress MYCN in neural crest cells [66]. These models closely recreate human neuroblastoma with respect to tumour biology and histology, genetic aberrations and gene expression, and localisation of the tumour. However, they are more expensive and labour intensive than syngeneic models, have murine tumour cells, and have ubiquitous and supraphysiological levels of MYCN expression [64–66]. Xenograft murine models became available following the development of athymic (nu/nu) and severe combined immunodeficiency (SCID) mice, facilitating the transplantation of cell lines into these mice. Neuroblastoma cell lines can be injected subcutaneously, intraperitoneally, or intravenously into these mice and subsequently form ectopic tumour masses, reproducing the genetic complexity of human tumours. However, even with this advancement, these models inadequately reflect the disease in humans, and they have, in many cases, poorly predicted human clinical outcomes [65,67]. Xenografts are established from genetically homogeneous cancer cell lines and lack any interaction with human stroma. Tumour development progresses quickly, uninhibited by an inflammatory response, and does not possess the features of multistage cancer progression. Patient-derived xenograft (PDX) models incorporate human patient tumour fragments via subcutaneous or orthotopic injection into immunocompromised mice, thereby retaining features of human neuroblastoma, including tumour histopathology, TME architecture, mutational, and proteomic profiles. While the subcutaneous PDX model allows for convenient monitoring of tumour growth, it also restricts metastatic capacity, which is significantly higher in orthotopic models but more challenging to visualise [68–70]. PDX 13

models are limited by the absence of an intact immune system in addition to high costs and demand for labour, skill, and time. Due to the importance of the immune system and tumour interactions, and the increase in available immunotherapies for cancer, there have been considerable efforts to humanize the immune systems of mice. Different approaches have been established from transplantation of human peripheral blood mononuclear cells (immunoavatar murine models) to transplantation of CD34+ human hematopoietic stem and progenitor cells (hemato-lymphoid humanized mice) into immunocompromised mice, followed by xenograft or PDX transplantation. These humanized models facilitate interactions between the xenograft and a human immune system; however, they are expensive and highly sophisticated, and the influence of a remnant murine immune system on the xenograft/human immune system interaction remains unclear [65]. Anti-GD2 immunotherapy is now the standard of care for high-risk neuroblastoma patients post myeloablative therapy, and its use in combination with natural killer cell infusions and chimeric antigen receptor T-cells is currently being investigated [3,71]. The murine models reviewed each has different applications in immunotherapy preclinical research. Syngeneic and transgenic models have been used extensively as a proof of concept in testing immune checkpoint blockers, adoptive cell therapy, tumour vaccines, and synergies between different combinations of these therapies [65]. Xenograft or PDX murine models are also suitable for investigating adoptive cell therapy, oncolytic viruses, and cytokine therapy; however, they are not applicable for investigating immune checkpoint blockers or cancer vaccines [72]. Humanized murine models enable the screening and study of immune checkpoint blockers, adoptive cell therapy, oncolytic viruses, cytokine therapy, and combinational immunotherapies [65]. In recent years, the use of zebrafish in modelling neuroblastoma has grown noticeably. These models can be developed through xenograft or PDX transplantation or by genetic engineering. The most recent zebrafish neuroblastoma model was developed by coinjection of dβh:EGFP and dβh:MYCN, resulting in tumour histology comparable to human neuroblastoma [73]. Zebrafish facilitate the high-throughput screening of drugs as they are easy to handle, inexpensive, breed prolifically, and develop quickly. The optical transparency 14

of zebrafish larvae is convenient for imaging and live-tracking of cell growth in vivo [74]. However, they have a considerably less complex organ system than murine models. The disparity between zebrafish physiological temperature (28/29⁰C) and human body temperature (37⁰C) is also likely to distort a broad range of cellular processes from proliferation to enzymatic and metabolic activity [75]. The CAM model of neuroblastoma is less frequently used than mouse and zebrafish models; however, it is gradually utilised for investigation of tumour differentiation, proliferation, invasion, and migration [76–78]. Recent CAM model studies demonstrated recapitulation of the effects of differentiation agents on neuroblastoma, and the ability to identify promising drugs for preclinical analysis. As with zebrafish models, the CAM model is markedly less expensive and demanding than similar studies in murine models. CAMs enable the detailed study of a range of mechanisms in neuroblastoma without prohibitive costs, and supporting its utility as a cost-effective model is compliant with the principles of the 3Rs by reducing and replacing animals [79]. All these models are fundamental for screening anticancer compounds and for extrapolating the biology and characteristics of neuroblastoma and other diseases. However, as we have outlined, each model has significant shortcomings. None of the discussed models can completely replicate the complexity of human cancer due to the interspecies variations in organismal organisation, tissue architecture and composition, immune systems, life cycles, and cytogenetic alterations [65,67,80]. By tailoring our selection of models to address the specific research questions at hand and by exploiting these in vivo and in vitro approaches in tandem, we can consolidate our understanding of neuroblastoma pathogenesis. There is a high demand for improvement in the design and complexity of ‘bottom-up’ in vitro models of neuroblastoma, which would permit the scale-down of high-cost/highvariability ‘top-down’ in vivo models that currently predominate preclinical investigations. Therefore, this approach will allow for in vitro determination of the best use of downstream in vivo models and reduce the number of animals used, associated costs, and the level of attrition [81].

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5. Conclusion and future directions Cancer progression and metastasis are multistage processes involving alterations at the molecular, cellular, tissue, and organ levels operating in a feedback and feed-forward mechanism. This paradigm has traditionally been addressed through the use of complex in vivo murine models, following initial investigation in 2D cell cultures. While these in vivo models provide a physiologically representative platform for investigating tumours at the organ and organismal levels, they also have important shortcomings, including murine tumour cells, stromal cells, and immune systems.

These limitations and the growing

understanding of the importance of the TME, ECM, and cell composition in disease progression have driven the advancement of 3D in vitro cancer models over the past 50 years. This highlights the opportunity for a major improvement in the characterisation of the neuroblastoma TME and the development of a well-defined ‘bottom-up’ well disease model. The research discussed in this review has already demonstrated significant progress in testing drugs in physiologically relevant and technically reproducible 3D tissue-engineered model systems. These 3D systems will accelerate the drug discovery pipeline for tailored therapies and reduce the attrition rate of the drug development process. These models can also be used to advance the testing of new drug chemistry, formulations, and delivery technologies, including nanoparticle delivery systems. Importantly, 3D cell technology directly affects the number of animals in preclinical research addressing the principles of the 3Rs by reducing and replacing animals. While still in the early stages of development, these 3D in vitro cell models can help in dissecting neuroblastoma biology at the tissue level. Exploiting advances in tissueengineering to deconstruct and reconstruct the tumour microenvironment layer by layer in a controllable fashion can lead to a better understanding of the individual steps contributing to neuroblastoma pathogenesis, enabling the study of this disease in multiscale and systems-level platforms. In the future, incorporation of patient-derived neuroblastoma cells in these 3-D platforms would enable testing of sensitivity to current and novel drugs and combination therapies leading to a more efficient and ‘personalised’ design of reinitiation treatments. In the long run, it will reduce the exposure of paediatric patients to additional rounds of chemotherapy. 16

These advances will facilitate the prediction and identification of drugs with strong potential, which is essential to improving the quality of life and ultimately increasing the survival of children with neuroblastoma.

Acknowledgements This project is supported by Neuroblastoma UK Project Grant (O.P.), the National Children’s Research Centre Project Grant (O.P.), The Fulbright-HRB Health Impact Scholar Award (O.P.), Science Foundation Ireland (O.P.), Wellcome Trust Vacation Summer Studentship (J.T.), StAR International Summer Studentship (H.S); EPS-IRC PhD Fellowship (T.F.).

Conflict of interest The

authors

declare

that

they

17

have

no

conflict

of

interest.

References [1]

A.M. Davidoff, Neuroblastoma., Semin. Pediatr. Surg. 21 (2012) 2–14. doi:10.1053/j.sempedsurg.2011.10.009.

[2]

G. Gatta, L. Botta, S. Rossi, T. Aareleid, M. Bielska-Lasota, J. Clavel, N. Dimitrova, Z. Jakab, P. Kaatsch, B. Lacour, S. Mallone, R. Marcos-Gragera, P. Minicozzi, M.J. Sánchez-Pérez, M. Sant, M. Santaquilani, C. Stiller, A. Tavilla, A. Trama, O. Visser, R. Peris-Bonet, Childhood cancer survival in Europe 1999-2007: Results of EUROCARE-5a population-based study, Lancet Oncol. 15 (2014) 35–47. doi:10.1016/S14702045(13)70548-5.

[3]

V.P. Tolbert, K.K. Matthay, Neuroblastoma: clinical and biological approach to risk stratification and treatment, Cell Tissue Res. 372 (2018) 195–209. doi:10.1007/s00441-018-2821-2.

[4]

M.A. Applebaum, A. V. Desai, J.L. Glade Bender, S.L. Cohn, Emerging and investigational therapies for neuroblastoma, Expert Opin. Orphan Drugs. 5 (2017) 355–368. doi:10.1080/21678707.2017.1304212.

[5]

P.C. Adamson, Challenges in drug development for children, Clin. Adv. Hematol. Oncol. 15 (2017) 26–29.

[6]

F. Peinemann, E.C. van Dalen, D.A. Tushabe, F. Berthold, Retinoic acid post consolidation therapy for high-risk neuroblastoma patients treated with autologous hematopoietic stem cell transplantation., Cochrane Database Syst. Rev. 1 (2015) CD010685. doi:10.1002/14651858.CD010685.pub2.

[7]

M. Hay, D.W. Thomas, J.L. Craighead, C. Economides, J. Rosenthal, Clinical development success rates for investigational drugs, Nat. Biotechnol. 32 (2014) 40– 51. doi:10.1038/nbt.2786.

[8]

K.I. Kaitin, Developing New Medicines for Pediatric Oncology: Assessing Needs and Overcoming Challenges, Clin. Ther. 39 (2017) 236–237. doi:10.1016/j.clinthera.2017.01.014.

[9]

M. Egeblad, E.S. Nakasone, Z. Werb, Tumors as organs: Complex tissues that interface 18

with the entire organism, Dev. Cell. 18 (2010) 884–901. doi:10.1016/j.devcel.2010.05.012. [10]

L. Borriello, R.C. Seeger, S. Asgharzadeh, Y.A. DeClerck, More than the genes, the tumor microenvironment in neuroblastoma, Cancer Lett. (2015). doi:10.1016/j.canlet.2015.11.017.

[11]

K.J. Cheung, A.J. Ewald, A collective route to metastasis: Seeding by tumor cell clusters., Science. 352 (2016) 167–9. doi:10.1126/science.aaf6546.

[12]

K.W. Pajtler, E. Mahlow, A. Odersky, S. Lindner, H. Stephan, I. Bendix, A. Eggert, A. Schramm, J.H. Schulte, Neuroblastoma in dialog with its stroma: NTRK1 is a regulator of cellular cross-talk with Schwann cells, Oncotarget. 5 (2014) 11180–11192. doi:10.18632/oncotarget.2611.

[13]

H. Shimada, S. Umehara, Y. Monobe, Y. Hachitanda, A. Nakagawa, S. Goto, R.B. Gerbing, D.O. Stram, J.N. Lukens, K.K. Matthay, International Neuroblastoma Pathology Classification for prognostic evaluation of patients with peripheral neuroblastic tumors: A report from the Children’s Cancer Group, Cancer. 92 (2001) 2451–2461. doi:10.1002/1097-0142(20011101)92:9<2451::AID-CNCR1595>3.0.CO;2S.

[14]

M. Ma, J.Y. Ye, R. Deng, C.M. Dee, G.C.F. Chan, Mesenchymal stromal cells may enhance metastasis of neuroblastoma via SDF-1/CXCR4 and SDF-1/CXCR7 signaling, Cancer Lett. 312 (2011) 1–10. doi:10.1016/j.canlet.2011.06.028.

[15]

A. Mühlethaler-Mottet, J. Liberman, K. Ascenção, M. Flahaut, K.B. Bourloud, P. Yan, N. Jauquier, N. Gross, J.M. Joseph, The CXCR4/CXCR7/CXCL12 axis is involved in a secondary but complex control of neuroblastoma metastatic cell homing, PLoS One. 10 (2015). doi:10.1371/journal.pone.0125616.

[16]

V. Shankar, H. Hori, K. Kihira, Q. Lei, H. Toyoda, S. Iwamoto, Y. Komada, Mesenchymal stromal cell secretome Up-regulates 47 kDa CXCR4 expression, and induce invasiveness in neuroblastoma cell lines, PLoS One. 10 (2015) 1–21. doi:10.1371/journal.pone.0120069.

[17]

H. V. Russell, J. Hicks, M.F. Okcu, J.G. Nuchtern, CXCR4 expression in neuroblastoma 19

primary tumors is associated with clinical presentation of bone and bone marrow metastases, J. Pediatr. Surg. 39 (2004) 1506–1511. doi:10.1016/j.jpedsurg.2004.06.019. [18]

S. Avnet, M. Salerno, G. Quacquaruccio, D. Granchi, A. Giunti, N. Baldini, IGF2 derived from SH-SY5Y neuroblastoma cells induces the osteoclastogenesis of human monocytic precursors, Exp. Cell Res. 317 (2011) 2147–2158. doi:10.1016/j.yexcr.2011.05.030.

[19]

C.M. Van Golen, T.S. Schwab, B. Kim, M.E. Soules, S.S. Oh, K. Fung, K.L. Van Golen, E.L. Feldman, Insulin-Like Growth Factor-I Receptor Expression Regulates Neuroblastoma Metastasis to Bone Neuroblastoma Metastasis to Bone, Cancer Res. 66 (2006) 6570– 6578. doi:10.1158/0008-5472.CAN-05-1448.

[20]

Y. Sohara, H. Shimada, C. Minkin, A. Erdreich-Epstein, J.A. Nolta, Y.A. DeClerck, Bone marrow mesenchymal stem cells provide an alternate pathway of osteoclast activation and bone destruction by cancer cells, Cancer Res. 65 (2005) 1129–1135. doi:10.1158/0008-5472.CAN-04-2853.

[21]

D. Granchi, I. Amato, L. Battistelli, S. Avnet, S. Capaccioli, L. Papucci, M. Donnini, A. Pellacani, M.L. Brandi, A. Giunti, N. Baldini, In vitro blockade of receptor activator of nuclear factor-??B ligand prevents osteoclastogenesis induced by neuroblastoma cells, Int. J. Cancer. 111 (2004) 829–838. doi:10.1002/ijc.20308.

[22]

A. Naba, K.R. Clauser, H. Ding, C.A. Whittaker, S.A. Carr, R.O. Hynes, The extracellular matrix: Tools and insights for the “omics” era, Matrix Biol. 49 (2016) 10–24. doi:10.1016/j.matbio.2015.06.003.

[23]

A. Naba, O.M.T. Pearce, A. Del Rosario, D. Ma, H. Ding, V. Rajeeve, P.R. Cutillas, F.R. Balkwill, R.O. Hynes, Characterization of the Extracellular Matrix of Normal and Diseased Tissues Using Proteomics, J. Proteome Res. 16 (2017) 3083–3091. doi:10.1021/acs.jproteome.7b00191.

[24]

I. Tadeo, M. Piqueras, D. Montaner, E. Villamón, A.P. Berbegall, A. Cañete, S. Navarro, R. Noguera, Quantitative modeling of clinical, cellular, and extracellular matrix variables suggest prognostic indicators in cancer: A model in neuroblastoma, Pediatr. 20

Res. 75 (2014) 302–314. doi:10.1038/pr.2013.217. [25]

W.A. Lam, L. Cao, V. Umesh, A.J. Keung, S. Sen, S. Kumar, Extracellular matrix rigidity modulates neuroblastoma cell differentiation and N-myc expression, Mol. Cancer. 9 (2010). doi:10.1186/1476-4598-9-35.

[26]

D.W. Hutmacher, Biomaterials offer cancer research the third dimension, Nat. Mater. 9 (2010) 90–93. doi:10.1038/nmat2619.

[27]

C.C.J. Thiele, Neuroblastoma Cell Lines, 1998. https://www.yumpu.com/en/document/view/2288090/neuroblastoma-cell-linesnational-cancer-institute.

[28]

J.L. Harenza, M.A. DIamond, R.N. Adams, M.M. Song, H.L. Davidson, L.S. Hart, M.H. Dent, P. Fortina, C.P. Reynolds, J.M. Maris, Transcriptomic profiling of 39 commonlyused neuroblastoma cell lines, Sci. Data. 4 (2017) 1–9. doi:10.1038/sdata.2017.33.

[29]

W. Asghar, R. El Assal, H. Shafiee, S. Pitteri, R. Paulmurugan, U. Demirci, Engineering cancer microenvironments for in vitro 3-D tumor models, Mater. Today. 18 (2015) 539–553. doi:10.1016/j.mattod.2015.05.002.

[30]

D. Loessner, B.M. Holzapfel, J.A. Clements, Engineered microenvironments provide new insights into ovarian and prostate cancer progression and drug responses, Adv. Drug Deliv. Rev. 79 (2014) 193–213. doi:10.1016/j.addr.2014.06.001.

[31]

M.E. Katt, A.L. Placone, A.D. Wong, Z.S. Xu, P.C. Searson, In Vitro Tumor Models: Advantages, Disadvantages, Variables, and Selecting the Right Platform, Front. Bioeng. Biotechnol. 4 (2016). doi:10.3389/fbioe.2016.00012.

[32]

R.M. Sutherland, W.R. Inch, J.A. McCredie, J. Kruuv, A multi-component radiation survival curve using an in vitro tumour model, Int. J. Radiat. Biol. 18 (1970) 491–495. doi:10.1080/09553007014551401.

[33]

X. Cui, Y. Hartanto, H. Zhang, Advances in multicellular spheroids formation, J. R. Soc. Interface. 14 (2017). doi:10.1098/rsif.2016.0877.

[34]

M. Zanoni, F. Piccinini, C. Arienti, A. Zamagni, S. Santi, R. Polico, A. Bevilacqua, A. Tesei, 3D tumor spheroid models for in vitro therapeutic screening: A systematic 21

approach to enhance the biological relevance of data obtained, Sci. Rep. 6 (2016). doi:10.1038/srep19103. [35]

K. Stock, M.F. Estrada, S. Vidic, K. Gjerde, A. Rudisch, V.E. Santo, M. Barbier, S. Blom, S.C. Arundkar, I. Selvam, A. Osswald, Y. Stein, S. Gruenewald, C. Brito, W. van Weerden, V. Rotter, E. Boghaert, M. Oren, W. Sommergruber, Y. Chong, R. de Hoogt, R. Graeser, Capturing tumor complexity in vitro: Comparative analysis of 2D and 3D tumor models for drug discovery., Sci. Rep. 6 (2016) 28951. doi:10.1038/srep28951.

[36]

A. Tesei, A. Sarnelli, C. Arienti, E. Menghi, L. Medri, E. Gabucci, S. Pignatta, M. Falconi, R. Silvestrini, W. Zoli, V. D’Errico, A. Romeo, E. Parisi, R. Polico, In vitro irradiation system for radiobiological experiments, Radiat. Oncol. 8 (2013). doi:10.1186/1748717X-8-257.

[37]

B.T. Craig, E.J. Rellinger, A.L. Alvarez, H.L. Dusek, J. Qiao, D.H. Chung, Induced differentiation inhibits sphere formation in neuroblastoma, Biochem. Biophys. Res. Commun. 477 (2016) 255–259. doi:10.1016/j.bbrc.2016.06.053.

[38]

C.M. Fife, S.M. Sagnella, W.S. Teo, S.T. Po’uha, F.L. Byrne, Y.Y.C. Yeap, D.C.H. Ng, T.P. Davis, J.A. McCarroll, M. Kavallaris, Stathmin mediates neuroblastoma metastasis in a tubulin-independent manner via RhoA/ROCK signaling and enhanced transendothelial migration, Oncogene. (2016) 1–11. doi:10.1038/onc.2016.220.

[39]

V. Pandian, S. Ramraj, F.H. Khan, T. Azim, N. Aravindan, Metastatic neuroblastoma cancer stem cells exhibit flexible plasticity and adaptive stemness signaling., Stem Cell Res. Ther. 6 (2015) 2. doi:10.1186/s13287-015-0002-8.

[40]

M. Nardella, L. Guglielmi, C. Musa, I. Iannetti, G. Maresca, D. Amendola, M. Porru, E. Carico, G. Sessa, R. Camerlingo, C. Dominici, F. Megiorni, M. Milan, C. Bearzi, R. Rizzi, G. Pirozzi, C. Leonetti, B. Bucci, D. Mercanti, A. Felsani, I.D. Agnano, Down-regulation of the Lamin A / C in neuroblastoma triggers the expansion of tumor initiating cells, Oncotarget. 6 (2015) 32821–32840. doi:10.18632/oncotarget.5104.

[41]

N. Baek, O.W. Seo, M. Kim, J. Hulme, S.S.A. An, Monitoring the effects of doxorubicin on 3D-spheroid tumor cells in real-time., Onco. Targets. Ther. 9 (2016) 7207–7218. doi:10.2147/OTT.S112566. 22

[42]

O.G. Besançon, G.A.M. Tytgat, R. Meinsma, R. Leen, J. Hoebink, G. V. Kalayda, U. Jaehde, H.N. Caron, A.B.P. van Kuilenburg, Synergistic interaction between cisplatin and gemcitabine in neuroblastoma cell lines and multicellular tumor spheroids, Cancer Lett. 319 (2012) 23–30. doi:10.1016/j.canlet.2011.12.016.

[43]

M. Eichler, H.G. Jahnke, D. Krinke, A. Müller, S. Schmidt, R. Azendorf, A.A. Robitzki, A novel 96-well multielectrode array based impedimetric monitoring platform for comparative drug efficacy analysis on 2D and 3D brain tumor cultures, Biosens. Bioelectron. 67 (2015) 582–589. doi:10.1016/j.bios.2014.09.049.

[44]

R. Cuperus, G. a M. Tytgat, R. Leen, P. Brites, J. Bras, H.N. Caron, A.B.P. Van Kuilenburg, Pleiotropic effects of fenretinide in neuroblastoma cell lines and multicellular tumor spheroids, Int. J. Oncol. 32 (2008) 1011–1019.

[45]

V. Sidarovich, M. De Mariano, S. Aveic, M. Pancher, V. Adami, P. Gatto, S. Pizzini, L. Pasini, M. Croce, F. Parodi, F. Cimmino, M. Avitabile, L. Emionite, M. Cilli, S. Ferrini, A. Pagano, M. Capasso, A. Quattrone, G.P. Tonini, L. Luca, A high-content screening of anti-cancer compounds suggests the multiple tyrosine kinase inhibitor ponatinib for repurposing in neuroblastoma therapy, Mol. Cancer Ther. (2018) 1405–1416. doi:10.1158/1535-7163.MCT-17-0841.

[46]

D.I. Wallace, A. Dunham, P.X. Chen, M. Chen, M. Huynh, E. Rheingold, O. Prosper, A model for spheroid versus monolayer response of SK-N-SH neuroblastoma cells to treatment with 15-Deoxy-PGJ2, Comput. Math. Methods Med. 2016 (2016). doi:10.1155/2016/3628124.

[47]

Y.T. Kuo, Y.L. Liu, B.O. Adebayo, P.H. Shih, W.H. Lee, L.S. Wang, Y.F. Liao, W.M. Hsu, C.T. Yeh, C.M. Lin, JARID1B expression plays a critical role in chemoresistance and stem cell-like phenotype of neuroblastoma cells, PLoS One. 10 (2015). doi:10.1371/journal.pone.0125343.

[48]

E.R. Shamir, A.J. Ewald, Three-dimensional organotypic culture: experimental models of mammalian biology and disease, Nat. Rev. Mol. Cell Biol. 15 (2014) 647–64. doi:10.1038/nrm3873.

[49]

K. Wolf, S. Alexander, V. Schacht, L.M. Coussens, U.H. von Andrian, J. van Rheenen, E. 23

Deryugina, P. Friedl, Collagen-based cell migration models in vitro and in vivo, Semin. Cell Dev. Biol. 20 (2009) 931–941. doi:10.1016/j.semcdb.2009.08.005. [50]

D.F.D. Campos, A.B. Marquez, C. O’seanain, H. Fischer, A. Blaeser, M. Vogt, D. Corallo, S. Aveic, Exploring cancer cell behavior in vitro in three-dimensional multicellular bioprintable collagen-based hydrogels, Cancers (Basel). 11 (2019). doi:10.3390/cancers11020180.

[51]

A. Marrella, A. Dondero, M. Aiello, B. Casu, D. Olive, S. Regis, C. Bottino, D. Pende, R. Meazza, G. Caluori, R. Castriconi, S. Scaglione, Cell-Laden Hydrogel as a ClinicalRelevant 3D Model for Analyzing Neuroblastoma Growth, Immunophenotype, and Susceptibility to Therapies, Front. Immunol. 10 (2019) 1–15. doi:10.3389/fimmu.2019.01876.

[52]

C.B. Mitchell, G.M. O’Neill, Rac GTPase regulation of 3D invasion in neuroblastomas lacking MYCN amplification, Cell Adhes. Migr. 11 (2017) 68–79. doi:10.1080/19336918.2016.1183868.

[53]

K.D. Roehm, S. V Madihally, Bioprinted chitosan-gelatin thermosensitive hydrogels using an inexpensive 3D printer Bioprinted chitosan-gelatin thermosensitive hydrogels using an inexpensive 3D printer, Biofabrication. 10 (2018) 15002. doi:10.1088/1758-5090/aa96dd.

[54]

P. Yeung, H.S. Sin, S. Chan, G.C.F. Chan, B.P. Chan, Microencapsulation of neuroblastoma cells and mesenchymal stromal cells in collagen microspheres: A 3D model for cancer cell niche study, PLoS One. 10 (2015) 1–22. doi:10.1371/journal.pone.0144139.

[55]

M. Innala, I. Riebe, V. Kuzmenko, J. Sundberg, P. Gatenholm, E. Hanse, S. Johannesson, 3D Culturing and differentiation of SH-SY5Y neuroblastoma cells on bacterial nanocellulose scaffolds, Artif. Cells, Nanomedicine Biotechnol. 42 (2014) 302–308. doi:10.3109/21691401.2013.821410.

[56]

S. Aveic, R. Davtalab, M. Vogt, M. Weber, P. Buttler, G.P. Tonini, H. Fischer, Calcium phosphate scaffolds with defined interconnecting channel structure provide a mimetic 3D niche for bone marrow metastasized tumor cell growth, Acta Biomater. 24

88 (2019) 527–539. doi:10.1016/j.actbio.2019.02.030. [57]

C. Curtin, J.C.C. Nolan, R. Conlon, L. Deneweth, C. Gallagher, Y.J.J. Tan, B.L.L. Cavanagh, A.Z.Z. Asraf, H. Harvey, S. Miller-Delaney, J. Shohet, I. Bray, F.J.J. O’Brien, R.L.L. Stallings, O. Piskareva, A physiologically relevant 3D collagen-based scaffold– neuroblastoma cell system exhibits chemosensitivity similar to orthotopic xenograft models, Acta Biomater. 70 (2018) 84–97. doi:10.1016/j.actbio.2018.02.004.

[58]

O. Piskareva, H. Harvey, J. Nolan, R. Conlon, L. Alcock, P. Buckley, P. Dowling, F. O’Sullivan, I. Bray, R.L.R.L. Stallings, The development of cisplatin resistance in neuroblastoma is accompanied by epithelial to mesenchymal transition in vitro, Cancer Lett. 364 (2015) 142–155. doi:10.1016/j.canlet.2015.05.004.

[59]

S.G. DuBois, Y. Kalika, J.N. Lukens, G.M. Brodeur, R.C. Seeger, J.B. Atkinson, G.M. Haase, C.T. Black, C. Perez, H. Shimada, R. Gerbing, D.O. Stram, K.K. Matthay, Metastatic sites in stage IV and IVS neuroblastoma correlate with age, tumor biology, and survival., J. Pediatr. Hematol. Oncol. 21 (1999) 181–189. doi:10.1097/00043426199905000-00005.

[60]

F.G. Lyons, A.A. Al-Munajjed, S.M. Kieran, M.E. Toner, C.M. Murphy, G.P. Duffy, F.J. O’Brien, The healing of bony defects by cell-free collagen-based scaffolds compared to stem cell-seeded tissue engineered constructs, Biomaterials. 31 (2010) 9232–9243. doi:10.1016/j.biomaterials.2010.08.056.

[61]

G.M. Cunniffe, G.R. Dickson, S. Partap, K.T. Stanton, F.J. O’Brien, Development and characterisation of a collagen nano-hydroxyapatite composite scaffold for bone tissue engineering, J. Mater. Sci. Mater. Med. 21 (2010) 2293–2298. doi:10.1007/s10856009-3964-1.

[62]

D. Loessner, K.S. Stok, M.P. Lutolf, D.W. Hutmacher, J.A. Clements, S.C. Rizzi, Bioengineered 3D platform to explore cell-ECM interactions and drug resistance of epithelial ovarian cancer cells, Biomaterials. 31 (2010) 8494–8506. doi:10.1016/j.biomaterials.2010.07.064.

[63]

N.A. Sears, D.R. Seshadri, P.S. Dhavalikar, E. Cosgriff-Hernandez, A Review of ThreeDimensional Printing in Tissue Engineering, Tissue Eng. Part B Rev. 22 (2016) 298– 25

310. doi:10.1089/ten.teb.2015.0464. [64]

G. Seitz, S. Armeanu-Ebinger, S. Warmann, J. Fuchs, Animal models of extracranial pediatric solid tumors (Review), Oncol. Lett. 4 (2012) 859–864. doi:10.3892/ol.2012.852.

[65]

M.F. Sanmamed, C. Chester, I. Melero, H. Kohrt, Defining the optimal murine models to investigate immune checkpoint blockers and their combination with other immunotherapies, Ann. Oncol. 27 (2016) 1190–1198. doi:10.1093/annonc/mdw041.

[66]

K. Althoff, A. Schramm, MYCN-mediated murine cancer models, Aging (Albany. NY). 9 (2017) 1084–1085. doi:10.18632/aging.101222.

[67]

I.W.Y. Mak, N. Evaniew, M. Ghert, Lost in translation: Animal models and clinical trials in cancer treatment, Am. J. Transl. Res. 6 (2014) 114–118.

[68]

E. Rosfjord, J. Lucas, G. Li, H.P. Gerber, Advances in patient-derived tumor xenografts: From target identification to predicting clinical response rates in oncology, Biochem. Pharmacol. 91 (2014) 135–143. doi:10.1016/j.bcp.2014.06.008.

[69]

R.M. Hoffman, Patient-derived orthotopic xenografts: Better mimic of metastasis than subcutaneous xenografts, Nat. Rev. Cancer. 15 (2015) 451–452. doi:10.1038/nrc3972.

[70]

N. Braekeveldt, D. Bexell, Patient-derived xenografts as preclinical neuroblastoma models, Cell Tissue Res. 372 (2018) 233–243. doi:10.1007/s00441-017-2687-8.

[71]

F. Morandi, F. Frassoni, M. Ponzoni, C. Brignole, Novel immunotherapeutic approaches for neuroblastoma and malignant melanoma, J. Immunol. Res. 2018 (2018). doi:10.1155/2018/8097398.

[72]

Q. Chen, J. Wang, W.N. Liu, Y. Zhao, Cancer Immunotherapies and Humanized Mouse Drug Testing Platforms, Transl. Oncol. 12 (2019) 987–995. doi:10.1016/j.tranon.2019.04.020.

[73]

T. Tao, S.B. Sondalle, H. Shi, S. Zhu, A.R. Perez-Atayde, J. Peng, S.J. Baserga, A.T. Look, The pre-rRNA processing factor DEF is rate limiting for the pathogenesis of MYCNdriven neuroblastoma, Oncogene. 36 (2017) 3852–3867. doi:10.1038/onc.2016.527. 26

[74]

D. Corallo, S. Candiani, M. Ori, S. Aveic, G.P. Tonini, The zebrafish as a model for studying neuroblastoma, Cancer Cell Int. 16 (2016). doi:10.1186/s12935-016-0360-z.

[75]

S. Kirchberger, C. Sturtzel, S. Pascoal, M. Distel, Quo natas, Danio?—Recent Progress in Modeling Cancer in Zebrafish, Front. Oncol. 7 (2017). doi:10.3389/fonc.2017.00186.

[76]

R. Carter, D. Mullassery, V. See, S. Theocharatos, B. Pizer, P.D. Losty, E. Jesudason, D.J. Moss, Exploitation of chick embryo environments to reprogram MYCN-amplified neuroblastoma cells to a benign phenotype, lacking detectable MYCN expression, Oncogenesis. 1 (2012). doi:10.1038/oncsis.2012.24.

[77]

R. Swadi, G. Mather, B.L. Pizer, P.D. Losty, V. See, D. Moss, Optimising the chick chorioallantoic membrane xenograft model of neuroblastoma for drug delivery, BMC Cancer. 18 (2018). doi:10.1186/s12885-017-3978-x.

[78]

D. Ribatti, R. Tamma, The chick embryo chorioallantoic membrane as an in vivo experimental model to study human neuroblastoma, J. Cell. Physiol. (2018). doi:10.1002/jcp.26773.

[79]

B.K. Russell WMS, The principles of humane experimental technique., UFAW, London. (1992) Available from: http://altweb. jhsph.edu/.

[80]

C.P. Day, G. Merlino, T. Van Dyke, Preclinical Mouse Cancer Models: A Maze of Opportunities and Challenges, Cell. 163 (2015) 39–53. doi:10.1016/j.cell.2015.08.068.

[81]

A. Villasante, G. Vunjak-Novakovic, Tissue-engineered models of human tumors for cancer research, Expert Opin. Drug Discov. 10 (2015) 257–268. doi:10.1517/17460441.2015.1009442.

[82]

J. Suebsoonthron, T. Jaroonwitchawan, M. Yamabhai, P. Noisa, Inhibition of WNT signaling reduces differentiation and induces sensitivity to doxorubicin in human malignant neuroblastoma SH-SY5Y cells, Anticancer. Drugs. 28 (2017) 469–479. doi:10.1097/CAD.0000000000000478.

[83]

J. Namkaew, T. Jaroonwitchawan, N. Rujanapun, J. Saelee, P. Noisa, Combined effects of curcumin and doxorubicin on cell death and cell migration of SH-SY5Y human

27

neuroblastoma cells, Vitr. Cell. Dev. Biol. - Anim. (2018). doi:10.1007/s11626-0180288-9. [84]

M.M. Ziegler, H. Ishizu, E. Nagabuchi, N. Takada, G. Arya, A comparative review of the immunobiology of murine neuroblastoma and human neuroblastoma, Cancer. 79 (1997) 1757–1766. doi:10.1002/(SICI)1097-0142(19970501)79:9<1757::AIDCNCR18>3.0.CO;2-X.

[85]

H.N. Lode, T. Dreier, R. Xiang, N.M. Varki, a S. Kang, R. a Reisfeld, Gene therapy with a single chain interleukin 12 fusion protein induces T cell-dependent protective immunity in a syngeneic model of murine neuroblastoma., Proc. Natl. Acad. Sci. U. S. A. 95 (1998) 2475–2480. doi:10.1073/pnas.95.5.2475.

[86]

W.A. Weiss, K. Aldape, G. Mohapatra, B.G. Feuerstein, J.M. Bishop, Targeted expression of MYCN causes neuroblastoma in transgenic mice, EMBO J. 16 (1997) 2985–2995. doi:10.1093/emboj/16.11.2985.

[87]

K. Althoff, A. Beckers, E. Bell, M. Nortmeyer, T. Thor, A. Sprüssel, S. Lindner, K. De Preter, A. Florin, L.C. Heukamp, L. Klein-Hitpass, K. Astrahantseff, C. Kumps, F. Speleman, A. Eggert, F. Westermann, A. Schramm, J.H. Schulte, A Cre-conditional MYCN-driven neuroblastoma mouse model as an improved tool for preclinical studies, Oncogene. 34 (2015) 3357–3368. doi:10.1038/onc.2014.269.

[88]

L. Helson, S.K. Das, S.I. Hajdu, Human neuroblastoma in nude mice, Cancer Res. 35 (1975) 2594–2599. http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L6 125050%5Cnhttp://sfx.library.uu.nl/utrecht?sid=EMBASE&issn=00085472&id=doi:&a title=Human+neuroblastoma+in+nude+mice&stitle=CANCER+RES.&title=Cancer+Rese arch&volume=35&issue=9&spage.

[89]

W. Turner, J. Chatten, L. Lampson, Human neuroblastoma cell growth in xenogeneic hosts: comparison of T cell-deficient and NK-deficient hosts, and subcutaneous or intravenous injection routes, J. Neuro-Oncologyoncology. 8 (1990) 121–32.

[90]

S. Zhu, A. Thomas Look, Neuroblastoma and its zebrafish model, in: Adv. Exp. Med. Biol., 2016: pp. 451–478. doi:10.1007/978-3-319-30654-4_20. 28

[91]

G.M. Cunniffe, C.M. Curtin, E.M. Thompson, G.R. Dickson, F.J. O’Brien, ContentDependent Osteogenic Response of Nanohydroxyapatite: An in Vitro and in Vivo Assessment within Collagen-Based Scaffolds, ACS Appl. Mater. Interfaces. 8 (2016) 23477–23488. doi:10.1021/acsami.6b06596.

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Figure and Table Legends Figure 1. Representation of tumour functions important for tissue-engineering. Figure 2. Arsenal of available tissue engineering models to study neuroblastoma. Representative images of neuroblastoma cells cultured in 2D and 3D in vitro and in vivo (left to right). Neuroblastoma cell line SK-N-AS was cultured in traditional 2D, fixed on day 4, and stained immune-fluorescence (Dapi, nuclei; Red – MYH9, Green – PLEC). SK-N-AS cells were grown as spheroids and imaged with live light phase microscopy on day 14. Kelly cells were embedded in Matrigel hydrogels, fixed, and stained with DAPI (blue, nuclei) and Phalloidin (Red, Actin). Kelly cells were plated onto collagen-based scaffolds, fixed, and imaged with DAPI. The same cell line Kelly was engrafted into mice, and tumours were collected, fixed, and stained with H&E as described in [57].

Figure 3. Experimental strategies using scaffold-based engineered cancer models. Scaffold manufacturing involves several steps that allows to manipulate with scaffold composition and mechanical properties. Cell growth and response to stimuli can be carried out in different formats, e.g., 24 well formats. A wide variety of biological assays and imaging techniques can be used to characterise cell growth on scaffolds. Some images were adapted from Curtin et al. (2018). An illustration of the manipulation of mechanical properties of collagen-based scaffolds by incorporation of nanohydroxyapatite (nHA) particles and crosslinking with EDAC. (A) The nHA addition in the S-500 scaffolds (5:1 nHA/collagen) significantly increased its mechanical strength compared to the collagen only and S-100 scaffolds (1:1 nHA/collagen), as determined by Young’s modulus. EDAC crosslinking treatment further enhanced the mechanical strength following. *p < 0.05 (n = 8). (B) SEM images of collagen, S-100, and S-500 cell-free scaffolds, demonstrating the preservation of interconnected porosity in all scaffolds (scale bar = 100 μm). Image adapted from [91]. Table 1. Application of 3D in vitro models for drug screening. Table 2. Neuroblastoma in vivo models.

30

Table 1: Application of 3D in vitro models for drug screening. Drug treatment

Ref In

3-D model

Agents

Cell line(s)

In

vitro vivo

SH-SY5Y FISK, NASS, SY5Y, SK-N-SH, GI-M-EN, SHEP2,

Cisplatin

x

[41]

SJNB8, SJNB10, SK-N-BE, NGP, SJNB6,

Cisplatin, Gemcitabine

x

[42]

x

[43]

x x x

[44] [45] [46]

IMR32.k1 Multicellular spheroids (MCSs)

Doxorubicin, Etoposide, and

SHSY5Y, T98G, U138MG

Vincristine IMR32, SJ8, SJNB10, FISK, NASS, SH-SY5Y IMR-32, SK-N-AS, CHP-212, CHP-134, SK-NSK-N-SH SH-SY5Y

Fenretinide (Vitamin A analogue) Ponatinib, Axitinib 15-PGJ2 (PPARγ ligand) XAV939 (small molecule inhibitor of

x

x

[82,83]

x x x

[52] [51]

TANK1 and 2), Doxorubicin, Collagen encapsulated MCSs Alginate Hydrogels Porous Collagen-based scaffolds

SHEP, SK-N-AS, SK-NSH, BE2C, IMR32, HTLA-230, SH-SY5Y KellyLuc, KellyCis83Luc

EHT 1864 (inhibitor of Rac activity) Imatinib mesylate Cisplatin

1

x

[57]

Table 2: Neuroblastoma in vivo models Human In vivo model

Strengths

Limitations

Ref

Cost relevance

• Reproducible and easy to handle

• Genetically different to human neuroblastoma

Syngeneic

• Histologically resembles human neuroblastoma

• Low cellular heterogeneity

murine model

• Intact murine immune system

• No human immune system targets

• High metastatic capacity

• Murine tumour cells

• Histologically resembles human neuroblastoma

• Labour intensive and time-consuming

Transgenic

• Orthotopic tumour

• Ubiquitous MYCN overexpression may alter

murine model

• Genetic aberrations resemble human disease

Human xenograft murine model

the development of the tumour environment.

• Intact murine immune system

• No human immune system targets

• High metastatic capacity

• Murine tumour cells

• Reproducible and easy to handle

• Lack of human tumour microenvironment

• Histologically resembles human neuroblastoma

€

Low

[84,85]

€€

Medium

[86,87]

€

Low

[88,89]

complexity

• Reproduces human tumour genetic complexity

• Ectopic tumour

• Human tumour cells

• Lack of immune system • Low metastatic capacity with subcutaneous or intraperitoneal injection

1

• Histologically resembles human neuroblastoma

• Labour intensive and time-consuming

Patient-

• Reproduces human tumour genetic complexity

• Lack of immune system

derived

• Reproduces human tumour microenvironment

• Low metastatic capacity with subcutaneous

xenograft murine model

complexity

€€€

High

€€-

Medium—

€€€

High

€

Low

[70]

transplantation

• Orthotopic transplanted tumour retains high metastatic capabilities • Varying degrees of human tumour – human immune

Immune murine model

system interactions • Suitable for screening and studying mechanisms of

• Highly labour intensive and time consuming • Low rates of successful human immune system transplantation

[65,72]

action of checkpoint blockers • Reproducible and easy to handle

• No human immune system targets

• Easy manipulation of the genome

• Require specific tools and reagents

Zebrafish

• Optical transparency

• Not suitable for screening of water-insoluble

model

• High reproductive rate and throughput • Less ethical issues compared to murine models

drugs • Temperature requirement is different to mammalian tumour cells

2

[74,90]

Chick chorioallantoic membrane model

• Reproducible and easy to handle

• Lack of immune system

• Real-time visualization of the experiment

• Only suitable for short term therapeutic study

• High throughput

• Drug metabolism different from humans

• Less ethical issues compared to murine models

• Pre-existing and newly formed blood vessels indistinguishable

3

€

Low

[77,78]

Cellular

Molecular

The smallest unit of living matter

Subatomic particles, atoms, molecules, organelles

Phenotype Signalling Pathways

Cell-to-cell communications Morphogenesis Metabolism 3D architecture Extra Cellular Matrix Tissue Mechanics A group of structurally and functionally similar cells and their intercellular material

Epigenetics Gene mutations RNA, Protein modifications Gene expression modifications

Immune system components Paracrine/endocrine signalling Gradient of pH, nutrients Vasculature Organ Innervation Stroma An anatomically distinct structure of the body composed of two or more tissue types

2D Cell Culture

3D Cell Models Spheroids

Hydrogels

Scaffolds

In Vivo

100 µm

Biological Complexity

Molecular/Cellular

Molecular/Cellular

Molecular/Cellular/ Tissue

Molecular/Cellular/ Tissue

Molecular/Cellular/ Tissue/Organ

Substrate

Glass/plastic

Suspension/matrix

Polymer hydrogel

Porous polymer scaffold

Ortho-/heterothopic

Cell type

Homogenous

Homo-/heterogenous

Homo-/heterogenous

Homo-/heterogenous

Homo-/heterogenous

TME

No

Limited

Yes

Yes

Chimeric: animal/human

Cost

€

€

€€

€€

€€€

Technical complexity

Low

Intermediate

Intermediate

Intermediate

High

Comparability to human drug sensitivity

Low

High

High

High

High

Increasing system complexity: from molecular to organ level Microscopic Investigation

Collagen slurry

Scaffolds

Scaffold sheet

Cell plating & growth

+/- Additives

******

Scale of culturing

Cross-linking e.g. DHT, EDAC/NHS

6 well12 well24 well48 well96 well384 well-

Freeze - dry

Microscopy

Collagen slurry 0.5% (w/v)

+ nHA 1:1

+ nHA 1:5

Histology

Biomarker secretion

Characterisation of cell growth and response to stimuli

Cell growth & viability

Highlights • •

•

•

Tumour microenvironment plays a key role in neuroblastoma progression and prognosis. There is a substantial need for improvement in the design and complexity of ‘bottom-up’ in vitro models of neuroblastoma to allow for the scale down of high cost/high variability ‘topdown’ in vivo models which currently predominate preclinical investigations. The tissue-engineering toolbox offers robust protocols and concepts as well as various biomaterials to deconstruct layer by layer and then reconstruct the tumour microenvironment in a controllable fashion allowing researchers to address their research questions. 3D tissue-engineered model system will accelerate the drug discovery pipeline for tailored therapies and reduce the attrition rate of the drug development process addressing the principles of 3Rs.

Olga Piskareva, Ph.D. StAR Research Lecturer Department of Anatomy and Regenerative Medicine Royal College of Surgeons in Ireland Dublin, Ireland T: +353 1 4022123 E:[email protected]

07 September 2019

Dear Editor, On behalf of my co-authors, I would like to submit the manuscript “Translating tissue engineering concepts into neuroblastoma pre-clinical research models” by Nolan, J.C., Frawley, T., Tighe, J., Soh, H., Curtin, C., Piskareva, O. for your consideration for publication in Cancer Letters. This work is not under consideration for publication elsewhere nor has it been published in whole or in part elsewhere. All the authors were fully involved in the study and preparation of the manuscript and agree on its submission to Cancer Letters. None of the authors declare any conflicts of interest. Sincerely yours,

Olga Piskareva, Ph.D.

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