Eva Hermans
Esther Hulleman- 1Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands
- 2Departments of Pediatric Oncology/Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
In recent years, molecular profiling has led to the discovery of an increasing number of brain tumor subtypes, and associated therapeutic targets. These molecular features have been incorporated in the 2016 new World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS), which now distinguishes tumor subgroups not only histologically, but also based on molecular characteristics. Despite an improved diagnosis of (pediatric) tumors in the CNS however, the survival of children with malignant brain tumors still is far worse than for those suffering from other types of malignancies. Therefore, new treatments need to be developed, based on subgroup-specific genetic aberrations. Here, we provide an overview of the currently available orthotopic xenograft models for pediatric brain tumor subtypes as defined by the 2016 WHO classification, to facilitate the choice of appropriate animal models for the preclinical testing of novel treatment strategies, and to provide insight into the current gaps and challenges.
Introduction
Whilst over the past few decades there has been an improvement in the survival of patients in multiple domains within pediatric oncology, the prognosis for the majority of children with malignant brain tumors remains grim (1). Their poor survival can be attributed to a lack of efficacious therapies, and a limited understanding of the underlying genetic and biochemical abnormalities associated with this group of diseases, which has hindered the development of more effective and patient-specific treatment. In the past years, a number of recurrent mutations have been identified that allow for the identification of tumor subgroups with distinct biological characteristics (2, 3). Importantly, these molecular features have been incorporated into the new (2016) World Health Organization (WHO) classification, which now distinguishes tumor subgroups not only histologically, but also based on molecular characteristics (4). The new classification has improved the diagnosis of pediatric brain tumors, but this knowledge has not yet led to a better prognosis for pediatric brain tumor patients. In order to increase survival rates whilst decreasing treatment-related side-effects, new targeted treatments must be developed which feature subgroup-specific clinical trials, and are conducted based on the distinct underlying genetic aberrations. However, with an increasing number of tumor subgroups and consequently a decreasing number of eligible patients, it will become ever more important to test novel treatment strategies in preclinical research before proceeding to clinical trials. Representative cell lines and animal models will therefore have to be developed, representing the broad spectrum of pediatric brain tumors. To facilitate the choice of the appropriate preclinical animal model, and emphasize the need for new models that are still lacking, we here provide an overview of the currently available orthotopic xenograft models for pediatric brain tumors, divided by specific subtypes as defined by the 2016 WHO classification (4). Although multiple types of animal models are currently available for the investigation of new treatments for pediatric brain tumors in vivo, we will focus on patient-derived xenografts (PDXs) rather than Genetically Engineered Mouse Models (GEMMs) within which tumor-specific genetic aberrations are introduced. PDXs have been shown to have an increased reliability when reproducing the heterogeneity of the human disease, which may better reflect the therapy response in patients than GEMMs (5, 6). In addition, we will focus on the models that have been established by xenografting fresh patient-derived material rather than established human cancer cell lines that have adapted to growth under artificial culture conditions, and are generally considered less relevant for clinical translation due to a more homogeneous, undifferentiated histology (7–9). Finally, we will only consider intracranial/orthotopic models, as these models retain the tumor-host microenvironment which may play a role in tumor response (10), and tumor growth (11). Moreover, such orthotopic models closely mimic human metastasis and allow to study drug delivery past the blood-brain barrier (5, 7, 12, 13).
PDX Models
Currently available pediatric brain tumor PDXs are established by xenografting fresh tissue, freshly isolated cell suspensions, or shortly cultured neurospheres in immunosuppressed rats (14), or immunodeficient mice (7, 15–17). Various immunocompromised mouse strains are available, with different rates of engraftment, lifespan, and sensitivity for chemotherapy or radiation (5, 9, 18). Not all strains have been fully characterized, and it is therefore essential to understand these differences when choosing the most appropriate animal model. BALB/c mice, for example, are particularly sensitive to the effects of radiation due to an unknown autosomal recessive genetic locus (19). Therefore, immunodeficient mice on a BALB/c genetic background should not be used for studies involving radiotherapy. Similarly, SCID (severe combined immunodeficient) animals are very sensitive to γ-irradiation, as they harbor a mutation in the Prkdc gene, which is involved in the repair of double strand DNA breaks (20). In contrast, other strains—such as Rag1-deficient (recombination activating gene 1) mice—have been reported to survive radiation doses up to 8.5 Gray, and are considered radioresistant (21). Working with mice on defined genetic backgrounds is therefore advisable for irradiation studies. The same holds true for experiments aimed at testing therapy response when DNA damaging agents are used. The response to cisplatin, doxorubicin, 5-fluoroacil, and oxaliplatin was shown to depend on PRKDC function (22), and should therefore not be tested in SCID mice. For more targeted compounds no clear guidelines exist for the choice of mouse strain, although some differences have been reported on drug sensitivity depending on drug transporters and metabolism (23). In those cases, the choice of the most appropriate PDX model should be based on the molecular subtype of the tumor.
Aside from different responses to therapy, there are also significant differences in tumor engraftment between various strains. Generally, it is believed that the level of immunodeficiency correlates with the tumor take rate (8, 9); as such, the more immunocompromised mouse strains, NOD/SCID/IL2γ-receptor null (NSG) and NOS/Rag/IL2γ-receptor null (NRG), would be most suitable strains for the implantation of primary cancerous cells, stem cells or tissue (9, 19, 24). It has been reported that these models support more robust post-engraftment tumor growth compared to double-mutant mice (25, 26), whilst maintaining the characteristics of the original primary patient tumor (27). However, studies confirming this view have only been performed with specific PDX models for hematological forms of cancer or using subcutaneous injections of tumor cells, and no convincing assessment regarding the preferred mouse strain for pediatric brain tumors has been carried out (24, 28–30).
One major limitation of the use of immunocompromised mice is that the interaction between the tumor and the immune microenvironment is partially or completely lost to ensure tumor engraftment is successful (5, 9). Consequently, the current PDX models cannot be used to study the (tumor) immune microenvironment, or to test novel immunotherapeutic treatment strategies (9). One solution to this problem has been found in the use of humanized-xenograft models (5, 9, 12, 18), in which the peripheral blood or bone marrow of the patient is co-engrafted with the tumor material into mouse strains lacking mouse natural killer cell activity (for example NSG or NRG mice) (9). Although this is a promising strategy for the testing of immunotherapy in the future, no humanized-xenograft models for pediatric brain tumors have yet been described.
Besides the choice of animal strain, other factors may influence the success rate of tumor engraftment. For instance, patient tissue can be collected either at time of diagnosis (biopsy), as part of treatment (surgical resection), or post-mortem. The moment of tissue collection may affect the characteristics of the PDX model, as treatment can change the molecular features of the tumor (31). As such, PDX models established from samples that are retrieved before treatment may be more suitable to test new therapies that can be implemented in the initial treatment schedules, while PDX models from autopsy samples, representing the late stage of disease, may be more appropriate to study resistance mechanisms and treatment effects (32).
In addition, various methods are used for the processing of the tumor cells before injection. Although occasionally whole tumor pieces have been used for implantation (33, 34), the most used method to establish pediatric brain tumor PDX models, is the preparation of cell suspensions either by dissociation of neurospheres or directly from surgical specimen (Table A1). Alternatively, tumor cells can be enriched for brain tumor-initiating cells (BTICs) by sorting for CD133+ cells (35), grown as an adherent layer (31, 36–44), transplanted in the thalamus or subcutaneously to expand the tumor cells (32, 40, 45, 46), or injected intracranially after serial transplantation (16, 35, 40, 46–55).
Although subcutaneous propagation has been shown to retain tumor characteristics and to decrease the time required for the PDX model procedure (7), no significant differences appear to exist between the direct- and indirect xenografting of tumor cells. In a head to head comparison of tumor models, generated by the injection of tumor cells derived directly from the patient and implantation of cultured cells, no variance was observed in tumorigenicity or histopathology of the xenograft (32). The authors did however find a discrepancy in survival time, with xenograft models obtained from cells in culture living longer (see Table A1), correlating to a greater degree with patient survival. This discrepancy between the direct- and indirect method could originate from inequivalent numbers of injected tumor cells, or the presence of stroma and microenvironment in direct implantation.
Besides a better correlation with patient survival, indirect xenografting, encompassing a cell culture step before intracranial implantation, additionally allows for the introduction of the Firefly luciferase gene by lentiviral transduction, facilitating non-invasive monitoring of tumor growth by bioluminescent imaging (BLI) in preclinical therapeutic studies (56). Although a temporary culture step as an adherent monolayer may be needed for effective transduction (57), cells are generally grown as neurospheres, since spheroid cultures have been shown to have a greater degree of genetic stability compared to cells grown in attachment (58). Independent of the culture conditions or method of implantation, PDXs should always be compared to the original tumor to validate the models. Preferably this is done both histologically and by molecular analyses, e.g., by confirmation of copy number variations/tumor-specific mutations or DNA methylation profiling. Such validation is extremely important, as some studies even suggest that the presence of stroma cells in post-mortem tissue may generate murine tumors rather than human xenografts (59, 60).
The large variety of available methods and mouse strains indicates that, until recently, no clear consensus existed in the field regarding the best model set-up. However, in the past decade multiple consortia have been founded, such as the Pediatric Preclinical Testing Consortium, the Childhood Solid Tumor Network, the Children's Oncology Group (COG), and the European EurOPDX resource, that collect and validate PDX models to increase the reproducibility of PDX studies (16). Although currently only few pediatric PDX models are included in the abovementioned databases, these initiatives emphasize the importance of a validated set-up. Furthermore, in order to assure the quality of newly established PDX models, a PDX models Minimal Information standard (PDX-MI) has been developed that defines the minimal information regarding the clinical characteristics and the procedures of implantation in a host mouse strain (31). For all these models it will be important to validate to which extent the xenograft tumor diverges from the donor tumor, both molecularly and histologically (8). However, the provision of such data, as well as peruse of the clinical patient information, might be challenging due to patient privacy or data inaccessibility (31).
Future Perspectives
Whilst the number of available orthotopic xenograft models for pediatric brain tumor research is growing, some tumor types are still underrepresented. Models for craniopharyngioma, germinoma, embryonal tumors with multilayered rosettes (ETMR), pineoblastoma, diffuse astrocytoma, oligodendroglioma, and cancers belonging to the “other astrocytic tumors/gliomas” are scarce, and no models have currently been described for e.g., choroid plexus tumors. This paucity may be attributed to a minimal research interest into certain tumor types, the limited availability of tumor material, or a low tumor take-rate (17). Failure of tumor engraftment often occurs with the less aggressively growing (low-grade) tumors, such as pilocytic astrocytoma (61). For some of these tumor types, the use of more invading cells from a metastatic site (62), or samples from recurrent tumors might be an interesting alternative, as more aggressive tumor cells are thought to have a higher take rate in vivo (18). Care however needs to be taken to assure the practical use of such models, as recurrences and metastatic clones may differ from the primary tumor at diagnosis. Alternatively, more effective tumor-specific protocols may have to be developed. So far, only few comparative studies have been performed to determine the most optimal protocols per tumor type, with regard to sample size, sample processing, and mouse strain (17). In addition, the choice of animal model and experimental set-up may vary, depending on the research question; for low-grade tumors, for example, studies may be aimed at diminishing treatment-related side-effects, while survival studies will be more relevant for tumor subtypes with a poor prognosis.
Whilst appropriate PDX models for some tumor types are still missing, other pediatric brain tumor types seem to be more strongly represented. This especially holds true for models of glioblastoma, diffuse midline glioma, ependymoma, and medulloblastoma. Preclinical research in these fields is expanding, partly due to the raised interest in these tumor types, and to the increased availability of tumor material. For example, the development of autopsy protocols and the reintroduction of surgical biopsies for diffuse midline gliomas (63) has boosted preclinical research for these tumors, leading to the development of several animal models (16). Yet, more PDX models may be required for these tumor types as well, to cover different subgroups, stages, and heterogeneity of the disease. Full tumor dynamics may be captured by the collection of paired tumor samples at the time of diagnosis and at autopsy, while intratumoral heterogeneity may be covered by the sampling of multiple lesions from the same tumor in rapid autopsy protocols (64). Additional PDX models comprising the complete spectrum of the disease are needed to confirm the reproducibility of preclinical results, and to ensure clinical relevance of laboratory findings.
Despite the presence of a relatively high number of pediatric glioma models, PDXs covering IDH1 mutations are lacking. Moreover, many described PDX models for pediatric glioma have not been molecularly characterized (16, 35, 38, 48, 65), even though mutation analysis could classify them as belonging to specific biological subgroups (66). The same holds true for ependymoma (14, 38, 45, 46, 67) and, to a lesser extent, medulloblastoma models (38, 42–44, 68). For other tumor types, such as pineoblastoma, or germ cell tumors no molecular subgroups have yet been identified. Proper model validation and characterization of the available PDXs will be essential to test new therapies, especially when targeted therapy is applied. Many of the currently available PDX models without molecular designation have been established in the early 2000s, and these models may still be useful, provided that molecular profiling is performed. This might be an option for tumor types for which less PDXs are currently available, such as the atypical teratoid rhabdoid tumors (AT/RTs), a relatively rare, but highly aggressive pediatric brain tumor with a poor survival (69), which would benefit from preclinical in vivo studies to ameliorate prognosis and diminish long-term sequelae. One should however keep in mind that validation of those models by comparing the molecular features of the PDX with the original tumor will often not be possible. In such cases, models may be validated by comparing RNAseq-, whole genome sequencing-, and DNA methylation profiles with cohorts of patient data to ensure their representability of the human disease.
In order to translate preclinical findings to the clinic, the proper choice of animal model and experimental set-up will be paramount. Improved PDX models may be used for personalized medicine purposes, where the predictive value of therapy for a certain patient is determined based on a personal panel of mouse tumors. However, such a personalized approach is currently hampered by the time that is needed to develop these models, costs, and the variable rate of engraftment. Alternatively, multiple tumor-specific animal models may be used to conduct so-called Mouse Clinical Trials (MCTs). MCTs use small numbers of mice per treatment arm across a large number of PDX models, resembling human clinical trials more closely than preclinical trials in which large numbers of a specific PDX model are used (70). MCTs will help researchers to understand the correlation of specific genetic factors to therapy response, and may allow to predict patient response, as well as correct patient stratification. For this reason, additional, fully characterized models need to be developed with a special focus on the poorly represented subtypes. These models may be used to determine the best therapeutic regimes for each tumor subtype to implement in standard protocols.
In summary, although progress has been made in the development of orthotopic xenograft models for pediatric brain tumors, there is a clear imbalance in the number of PDX models for different tumor types, and a high variability in methodology and animal strains used. Combined efforts of neurosurgeons, pathologists, pediatric oncologists and preclinical researchers will be needed to develop additional animal models for the design of effective therapeutic strategies.
Author Contributions
EHe wrote the first draft of the manuscript, while EHu revised the manuscript. Both authors contributed to the conception, design, and approved the submitted version.
Funding
EHu was funded by the Semmy Foundation (Stichting Semmy) and Children Cancer-free Foundation (KiKa).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We thank Joshua Goulding for critically reading the manuscript.
References
1. Udaka YT, Packer RJ. Pediatric brain tumors. Neurol Clin. (2018) 36:533–56. doi: 10.1016/j.ncl.2018.04.009
2. Kumar R, Liu APY, Orr BA, Northcott PA, Robinson GW. Advances in the classification of pediatric brain tumors through DNA methylation profiling: from research tool to frontline diagnostic. Cancer. (2018)124:4168–80. doi: 10.1002/cncr.31583
3. Rutkowski S, Modena P, Williamson D, Kerl K, Nysom K, Pizer B, et al. Biological material collection to advance translational research and treatment of children with CNS tumors: position paper from the SIOPe brain tumor group. Lancet Oncol. (2018)19:e419–e28. doi: 10.1016/S1470-2045(18)30364-4
4. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization Classification of tumors of the central nervous system: a summary. Acta Neuropathol. (2016) 131:803–20. doi: 10.1007/s00401-016-1545-1
5. Wainwright DA, Horbinski CM, Hashizume R, James CD. Therapeutic hypothesis testing with rodent brain tumor models. Neurotherapeutics. (2017)14:385–92. doi: 10.1007/s13311-017-0523-1
6. Lee HW, Lee K, Kim DG, Yang HK, Nam DH. Facilitating tailored therapeutic strategies for glioblastoma through an orthotopic patient-derived xenograft platform. Histol Histopathol. (2016) 31:269–83. doi: 10.14670/HH-11-695
7. Rubio-Viqueira B, Hidalgo M. Direct in vivo xenograft tumor model for predicting chemotherapeutic drug response in cancer patients. Clin Pharmacol Ther. (2009) 85:217–21. doi: 10.1038/clpt.2008.200
8. Tentler JJ, Tan AC, Weekes CD, Jimeno A, Leong S, PittsTM, et al. Patient-derived tumour xenografts as models for oncology drug development. Nat Rev Clin Oncol. (2012) 9:338–50. doi: 10.1038/nrclinonc.2012.61
9. Zarzosa P, Navarro N, Giralt I, Molist C, Almazán-Moga A, Vidal I, et al. Patient-derived xenografts for childhood solid tumors: a valuable tool to test new drugs and personalize treatments. Clin Transl Oncol. (2017)19:44–50. doi: 10.1007/s12094-016-1557-2
10. Phoenix TN, Patmore DM, Boop S, Boulos N, Jacus MO, Patel, et al. Medulloblastoma genotype dictates blood brain barrier phenotype. Cancer Cell. (2017) 29:508–22. doi: 10.1016/j.ccell.2016.03.002
11. Venkatesh HS, Johung TB, Caretti V, Noll A, Tang Y, Nagaraja S, et al. Neuronal activity promotes glioma growth through neuroligin-3 secretion. Cell. (2015)161:803–16. doi: 10.1016/j.cell.2015.04.012
12. Byrne AT, Alférez DG, Amant F, Annibali D, Arribas J, Biankin AV, et al. Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nat Rev Cancer. (2017) 17:254–68. doi: 10.1038/nrc.2016.140
13. Bibby MC. Orthotopic models of cancer for preclinical drug evaluation: advantages and disadvantages. Eur J Cancer. (2004) 40:852–7. doi: 10.1016/j.ejca.2003.11.021
14. Pavon LF, Sibov TT, Caminada de Toledo SR, Mara de Oliveira D, Cabral FR, Gabriel de Souza J, et al. Establishment of primary cell culture and an intracranial xenograft model of pediatric ependymoma: a prospect for therapy development and understanding of tumor biology. Oncotarget. (2018) 9:21731–43. doi: 10.18632/oncotarget.24932
15. Morton CL, Houghton PJ. Establishment of human tumor xenografts in immunodeficient mice. Nat Protoc. (2007) 2:247–50. doi: 10.1038/nprot.2007.25
16. Shu Q, Wong KK, Su JM, Adesina AM, Yu LT, Tsang YT, et al. Direct orthotopic transplantation of fresh surgical specimen preserves CD133+ tumor cells in clinically relevant mouse models of medulloblastoma and glioma. Stem Cells. (2008) 26:1414–24. doi: 10.1634/stemcells.2007-1009
17. Tsoli M, Shen H, Mayoh C, Franshaw L, Ehteda A, Upton D, et al. International experience in the development of patient-derived xenograft models of diffuse intrinsic pontine glioma. J Neurooncol. (2019)141:265. doi: 10.1007/s11060-018-03060-4
18. Hidalgo M, Amant F, Biankin AV, Budinská E, Byrne AT, Caldas C, et al. Patient derived xenograft models: an emerging platform for translational cancer research Europe PMC funders group (PDX model). Cancer Discov. (2014) 4:998–1013. doi: 10.1158/2159-8290.CD-14-0001
19. Grahn D. Acute radiation response of mice from a cross between radiosensitive and radioresistant strains. Genetics. (1958) 43:835–43.
20. Biedermann KA, Sun JR, Giaccia AJ, Tosto LM, Brown JM. Scid mutation in mice confers hypersensitivity to ionizing radiation and a deficiency in DNA double-strand break repair. Proc Natl Acad Sci USA. (1991) 88:1394–7. doi: 10.1073/pnas.88.4.1394
21. Shultz LD, Lang PA, Christianson SW, Gott B, Lyons B, Umeda S, et al. NOD/LtSz-Rag1null mice: an immunodeficient and radioresistant model for engraftment of human hematolymphoid cells, HIV infection, and adoptive transfer of NOD mouse diabetogenic T cells. J Immunol. (2000) 164:2496–507. doi: 10.4049/jimmunol.164.5.2496
22. Sun G, Yang L, Dong C, Ma B, Shan M, Ma B. PRKDC regulates chemosensitivity and is a potential prognostic and predictive marker of response to adjuvant chemotherapy in breast cancer patients. Oncol Rep. (2017) 37:3536–42. doi: 10.3892/or.2017.5634
23. Mosedale M. Mouse population-based approaches to investigate adverse drug reactions. Drug Metab Dispos. (2018) 46:1787–95. doi: 10.1124/dmd.118.082834
24. Lai Y, Wei X, Lin S, Qin L, Cheng L, Li P. Current status and perspectives of patient-derived xenograft models in cancer research. J Hematol Oncol. (2017)10:1–14. doi: 10.1186/s13045-017-0470-7
25. Ito R, Takahashi T, Katano I, Ito M. Current advances in humanized mouse models. Cell Mol Immunol. (2012) 9:208–14. doi: 10.1038/cmi.2012.2
26. Nomura T, Tamaoki N, Takakura A, Suemizu H. Basic concept of development and practical application of animal models for human diseases. Curr Top Microbiol Immunol. (2008) 324:1–24. doi: 10.1007/978-3-540-75647-7_1
27. Simpson-Abelson MR, Sonnenberg GF, Takita H, Yokota SJ, Conway TF Jr, Kelleher RJ Jr, et al. Long-term engraftment and expansion of tumor-derived memory T cells following the implantation of non-disrupted pieces of human lung tumor into NOD-scid IL2Rgamma (null) mice. J Immunol. (2008) 180:7009–18. doi: 10.4049/jimmunol.180.10.7009
28. Ye W, Jiang Z, Li GX, Xiao Y, Lin S, Lai Y, et al. Quantitative evaluation of the immunodeficiency of a mouse strain by tumor engraftments. J Hematol Oncol. (2015) 8:1–13. doi: 10.1186/s13045-015-0156-y
29. Machida K, Suemizu H, Kawai K, Ishikawa, Sawade R, Ohnishi Y, et al. Higher susceptibility of NOG mice to xenotransplanted tumors. J Toxicol Sci. (2009) 34:123–7. doi: 10.2131/jts.34.123
30. Agliano A, Martin-Padura I, Mancuso P, Marighetti P, Rabascio C, Pruneri G, et al. Human acute leukemia cells injected in NOD-LtSz/scid-IL/2Rγ null mice generate a faster and more efficient disease compared to other NOD-scid/related strains. Int J Cancer. (2008) 123:2222–7. doi: 10.1002/ijc.23772
31. Meehan TF, Conte N, Goldstein T, Inghirami G, Murakami MA, Brabetz S, et al. PDX-MI: Minimal information for patient-derived tumor xenograft models. Cancer Res. (2017) 77:e62–6. doi: 10.1158/0008-5472.CAN-17-0582
32. Plessier A, Le Dret L, Varlet P, Beccaria K, Lacombe J, Mériaux S, et al. New in vivo avatars of diffuse intrinsic pontine gliomas (DIPG) from stereotactic biopsies performed at diagnosis. Oncotarget. (2017) 8: 52543–59. doi: 10.18632/oncotarget.15002
33. Stache C, Hölsken A, Schlaffer SM, Hess A, Metzler M, Frey B, et al. Insights into the infiltrative behavior of adamantinomatous craniopharyngioma in a new xenotransplant mouse model. Brain Pathol. (2015) 25:1–10. doi: 10.1111/bpa.12148
34. Hölsken A, Schwartz M, Gillmann C, Pfister C, Uder M, Doerfler A, et al. Characterization of the murine orthotopic adamantinomatous craniopharyngioma PDX model by MRI in correlation with histology. PLoS ONE. (2018) 13:e0197895. doi: 10.1371/journal.pone.0197895
35. Baxter PA, Lin Q, Mao H, Kogiso M, Zhao X, Liu Z, et al. Silencing BMI1 eliminates tumor formation of pediatric glioma CD133+ cells not by affecting known targets but by down-regulating a novel set of core genes. Acta Neuropathol Commun. (2014) 2:1–14. doi: 10.1186/s40478-014-0160-4
36. Larsson S, Wenger A, Dosa S, Sabel M, Kling T, Carén H. Cell line-based xenograft mouse model of paediatric glioma stem cells mirrors the clinical course of the patient. Carcinogenesis. (2018) 39:1304–9. doi: 10.1093/carcin/bgy091
37. Wenger A, Larsson S, Danielsson A, Elbæk KJ, Kettunen P, Tisell M, et al. Stem cell cultures derived from pediatric brain tumors accurately model the originating tumors. Oncotarget. (2017) 8:18626–39. doi: 10.18632/oncotarget.14826
38. Hussein D, Punjaruk W, Storer LC, Shaw L, Ottoman R, Peet A, et al. Pediatric brain tumor cancer stem cells: cell cycle dynamics, DNA repair, and etoposide extrusion. Neuro Oncol. (2011) 13:70–83. doi: 10.1093/neuonc/noq144
39. Mueller S, Hashizume R, Yang X, Kolkowitz I, Olow AK, Phillips J, et al. Targeting wee1 for the treatment of pediatric high-grade gliomas. Neuro Oncol. (2014) 16:352–60. doi: 10.1093/neuonc/not220
40. Xu C, Liu X, Geng Y, Bai Q, Pan C, Sun Y, et al. Patient-derived DIPG cells preserve stem-like characteristics and generate orthotopic tumors. Oncotarget. (2017) 8:76644–55. doi: 10.18632/oncotarget.19656
41. Barszczyk M, Buczkowicz P, Castelo-Branco P, Mack SC, Ramaswami V, Mangerei J, et al. Telomerase inhibition abolishes the tumorigenicity of pediatric ependymoma tumor-initiating cells. Acta Neuropathol. (2014) 128:863–77. doi: 10.1007/s00401-014-1327-6
42. Giangaspero F, Pession A, Trerè D, Badiali M, Galassi E, Ceccarelli C, et al. Establishment of a human medulloblastoma cell line (BO-101) demonstrating skeletal muscle differentiation. Tumori. (1991) 77:196–205. doi: 10.1177/030089169107700303
43. Xu J, Erdreich-Epstein A, Gonzalez-Gomez I, Melendez EY, Smbatyan G, Moats RA, et al. Novel cell lines established from pediatric brain tumors. J Neurooncol. (2012) 107:269–80. doi: 10.1007/s11060-011-0756-5
44. Vachon P, Girard C, Théorêt Y. Effects of basic fibrobalstic growth factor on the growth of human medulloblastoma xenografts. J Neurooncol. (2004) 67:139–46. doi: 10.1023/B:NEON.0000021824.41701.e5
45. Servidei T, Meco D, Trivieri N, Patriarca V, Vellone VG, Zannoni GF, et al. Effects of epidermal growth factor receptor blockade on ependymoma stem cells in vitro and in orthotopic mouse models. Int J Cancer. (2012) 131:E791–803. doi: 10.1002/ijc.27377
46. Guan S, Shen R, Lafortune T, Tiao N, Houghton P, Yung WK, et al. Establishment and characterization of clinically relevant models of ependymoma: a true challenge for targeted therapy. Neuro Oncol. (2011) 13:748–58. doi: 10.1093/neuonc/nor037
47. Grasso CS, Tang Y, Truffaux N, Berlow NE, Liu L, Debily MA, et al. Functionally-defined therapeutic targets in diffuse intrinsic pontine glioma: a report of the children's oncology group DIPG preclinical consortium. Nat Med. (2015) 21:555–9. doi: 10.1158/1535-7163.TARG-15-LB-B06
48. Liu Z, Zhao X, Mao H, Baxter PA, Huang Y, Yu L, et al. Intravenous injection of oncolytic picornavirus SVV-001 prolongs animal survival in a panel of primary tumor-based orthotopic xenograft mouse models of pediatric glioma. Neuro Oncol. (2013) 15:1173–85. doi: 10.1093/neuonc/not065
49. Zhao X, Liu Z, Yu L, Zhang Y, Baxter P, Voicu H, et al. Global gene expression profiling confirms the molecular fidelity of primary tumor-based orthotopic xenograft mouse models of medulloblastoma. Neuro Oncol. (2012) 14:574–83. doi: 10.1093/neuonc/nos061
50. Yu L, Baxter P, Voicu H, Gurusiddappa S, Zhao Y, Adesina A, et al. A clinically relevant orthotopic xenograft model of ependymoma that maintains the genomic signature of the primary tumor and preserves cancer stem cells in vivo. Neuro Oncol. (2010) 12:580–94. doi: 10.1093/neuonc/nop056
51. Girard E, Ditzler S, Lee D, Richards A, Yagle K, Park J, et al. Efficacy of cabazitaxel in mouse models of pediatric brain tumors. Neuro Oncol. (2015) 17:107–15. doi: 10.1093/neuonc/nou163
52. Kool M, Jones DT, Jäger N, Northcott PA, Pugh TJ, Hovestadt V, et al. Genome sequencing of SHH medulloblastoma predicts genotype-related response to smoothened inhibition. Cancer Cell. (2014) 25:393–405. doi: 10.1016/j.ccr.2014.02.004
53. Pei Y, Liu KW, Wang J, Garancher A, Tao R, Esparza LA, et al. HDAC and PI3K antagonists cooperate to inhibit growth of MYC-driven medulloblastoma. Cancer Cell. (2016) 29:311–23. doi: 10.1016/j.ccell.2016.02.011
54. Morfouace M, Shelat A, Jacus M, Freeman BB 3rd, Turner D, Robinson S, et al. Pemetrexed and gemcitabine as combination therapy for the treatment of group3 medulloblastoma. Cancer Cell. (2014) 25:516–29. doi: 10.1016/j.ccr.2014.02.009
55. Rubens JA, Wang SZ, Price A, Weingart MF, Allen SJ, Orr BA, et al. The TORC1/2 inhibitor TAK228 sensitizes atypical teratoid rhabdoid tumors to cisplatin-induced cytotoxicity. Neurooncol. (2017) 19:1361–71. doi: 10.1093/neuonc/nox067
56. Rehemtulla A, Stegman LD, Cardozo SJ, Gupta S, Hall DE, Contag CH, et al. Rapid and quantitative assessment of cancer treatment response using in vivo bioluminescence imaging. Neoplasia. (2000) 2:491–5. doi: 10.1038/sj.neo.7900121
57. Meel MH, Metselaar DS, Waranecki P, Kaspers GJL, Hulleman E. An efficient method for the transduction of primary pediatric glioma neurospheres. MethodsX. (2018) 5:173–83. doi: 10.1016/j.mex.2018.02.006
58. De Witt Hamer PC, van Tilborg AA, Eijk PP, Sminia P, Troost D, van Noorden CJ, et al. The genomic profile of human malignant glioma is altered early in primary cell culture and preserved in spheroids. Oncogene. (2008) 27:2091–6. doi: 10.1038/sj.onc.1210850
59. Caretti V, Sewing AC, Lagerweij T, Schellen P, Bugiani M, Jansen MH, et al. Human pontine glioma cells can induce murine tumors. Acta Neuropathol. (2014) 127:897–909. doi: 10.1007/s00401-014-1272-4
60. Strand A, Cole B, Leary S, Olson J. Brain tumor patient derived orthotopic xenografts induce tumors of mouse origin. PCLN-04 in: Abstracts from the 18th International Symposium on Pediatric Neuro-Oncology (ISPNO 2018) June 30 – July 3, 2018 Hyatt Regency Hotel Denver, Colorado, USA. Neuro-Oncol. (2018) 20(suppl_2):i27–213. doi: 10.1093/neuonc/noy059.573
61. Selt F, Hohloch J, Hielscher T, Sahm F, Capper D, Korshunov A, et al. Establishment and application of a novel patient-derived KIAA1549:BRAF-driven pediatric pilocytic astrocytoma model for preclinical drug testing. Oncotarget. (2017) 8:11460–79. doi: 10.18632/oncotarget.14004
62. Lindsay H, Huang Y, Du Y, Braun FK, Teo WY, Kogiso M, et al. Preservation of KIT genotype in a novel pair of patient-derived orthotopic xenograft mouse models of metastatic pediatric CNS germinoma. J Neurooncol. (2016) 128:47–56. doi: 10.1007/s11060-016-2098-9
63. Puget S, Beccaria K, Blauwblomme T, Roujeau T, James S, Grill J, et al. Biopsy in a series of 130 pediatric diffuse intrinsic pontine gliomas. Childs Nerv Syst. (2015) 31:1773–80. doi: 10.1007/s00381-015-2832-1
64. Bugiani M, Veldhuijzen van Zanten SEM, Caretti V, Schellen P, Aronica E, Noske DP, et al. Deceptive morphologic and epigenetic heterogeneity in diffuse intrinsic pontine glioma. Oncotarget. (2017) 8:60447–52. doi: 10.18632/oncotarget.19726
65. Gholamin S, Mitra SS, Feroze AH, Liu J, Kahn SA, Zhang M, et al. Disrupting the CD47-SIRPα anti-phagocytic axis by a humanized anti-CD47 antibody is an efficacious treatment for malignant pediatric brain tumors. Sci Transl Med. (2017) 9:1–14. doi: 10.1126/scitranslmed.aaf2968
66. Sturm D, Witt H, Hovestadt V, Khuong-Quang DA, Jones DT, Konemann C, et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell. (2012) 22:425–37. doi: 10.1016/j.ccr.2012.08.024
67. McLendon RE, Fung KM, Bentley RC, Ahmed Rasheed BK, Trojanowski JQ, Bigner SH, et al. Production and characterization of two ependymoma xenografts. J Neuropathol Exp Neurol. (1996) 55:540–8. doi: 10.1097/00005072-199605000-00007
68. Zhao X, Zhao YJ, Lin Q, Yu L, Liu Z, Lindsay H, et al. Cytogenetic landscape of paired neurospheres and traditional monolayer cultures in pediatric malignant brain tumors. Neuro Oncol. (2015) 17:965–77. doi: 10.1093/neuonc/nou337
69. Ginn KF, Gajjar A. Atypical teratoid rhabdoid tumor: current therapy and future directions. Front Oncol. (2012) 2:114. doi: 10.3389/fonc.2012.00114
70. Guo S, Jiang X, Mao B, Li QX. The design, analysis and application of mouse clinical trials in oncology drug development. BMC Cancer. (2019) 19:718. doi: 10.1186/s12885-019-5907-7
71. Anderson JL, Muraleedharan R, Oatman N, Klotter A, Sengupta S, Waclaw RR, et al. The transcription factor Olig2 is important for the biology of diffuse intrinsic pontine gliomas. Neuro Oncol. (2017) 19:880–1. doi: 10.1093/neuonc/now299
72. Brabetz S, Leary SES, Grobner SN, Nakamoto MW, Seker-Cin H, Girard EJ, et al. A biobank of patient-derived pediatric brain tumor models. Nat Med. (2018) 24:1652–761. doi: 10.1038/s41591-018-0207-3
73. Jansen MH, Lagerweij T, Sewing AC, Vugts DJ, van Vuurden DG, Molthoff CF, et al. Bevacizumab targeting diffuse intrinsic pontine glioma: results of 89Zr-Bevacizumab PET imaging in brain tumor models. Mol Cancer Ther. (2016) 15:2166–74. doi: 10.1158/1535-7163.MCT-15-0558
74. Kogiso M, Qi L, Braun FK, Injac SG, Zhang L, Du Y, et al. Concurrent inhibition of neurosphere and monolayer cells of pediatric glioblastoma by Aurora A inhibitor MLN8237 predicted survival extension in PDOX models. Clin Cancer Res. (2018) 24:2159–70. doi: 10.1158/1078-0432.CCR-17-2256
75. Taylor IC, Hütt-Cabezas M, Brandt WD, Kambhampati M, Nazarian J, Chang HT, et al. Disrupting NOTCH slows diffuse intrinsic pontine glioma growth, enhances radiation sensitivity, and shows combinatorial efficacy with bromodomain inhibition. J Neuropathol Exp Neurol. (2015) 74:778–90. doi: 10.1097/NEN.0000000000000216
76. Mount CW, Majzner RG, Sundaresh S, Arnold EP, Kadapakkam M, Haile S, et al. Potent antitumor efficacy of anti-GD2 CAR T cells in H3-K27M+ diffuse midline gliomas. Nat Med. (2018) 24:572–9. doi: 10.1038/s41591-018-0006-x
77. Hashizume R, Andor N, Ihara Y, Lerner R, Gan H, Chen X, et al. Pharmacologic inhibition of histone demethylation as a therapy for pediatric brainstem glioma. Nat Med. (2014) 20:1394–6. doi: 10.1038/nm.3716
78. Monje M, Mitra SS, Freret ME, Raveh TB, Kim J, Masek M. Hedgehog-responsive candidate cell of origin for diffuse intrinsic pontine glioma. Proc Natl Acad Sci USA. (2011) 108:4453–8. doi: 10.1073/pnas.1101657108
79. Nagaraja S, Vitanza NA, Woo PJ, Taylor KR, Liu F, Zhang L, et al. Transcriptional dependencies in diffuse intrinsic pontine glioma. Cancer Cell. (2017) 31:635–52. doi: 10.1016/j.ccell.2017.03.011
80. Venkatesh HS, Tam LT, Woo PJ, Lennon J, Nagaraja S, Gillespie SM, et al. Targeting neuronal activity-regulated neuroligin-3 dependency in high-grade glioma. Nature. (2017) 549:533–7. doi: 10.1038/nature24014
81. Meel MH, de Gooijer MC, Guillén Navarro M, Waranecki P, Breur M, Buil LCM, et al. MELK inhibition in diffuse intrinsic pontine glioma. Clin Cancer Res. (2018) 24:5645–57. doi: 10.1158/1078-0432.CCR-18-0924
82. Kogiso M, Qi L, Lindsay H, Huang Y, Zhao X, Liu Z, et al. Xenotransplantation of pediatric low grade gliomas confirms the enrichment of BRAF V600E mutation and preservation of CDKN2A deletion in a novel orthotopic xenograft mouse model of progressive pleomorphic xanthoastrocytoma. Oncotarget. (2017) 8:87455–71. doi: 10.18632/oncotarget.20713
83. Xu J, Margol AS, Shukla A, Ren X, Finlay JL, Krieger MD, et al. Disseminated medulloblastoma in a child with germline BRCA2 6174delT mutation and without Fanconi anemia. Front Oncol. (2015) 5:191. doi: 10.3389/fonc.2015.00191
84. Milde T, Lodrini M, Savelyeva L, Korshunov A, Kool M, Brueckner LM, et al. HD-MB03 is a novel group 3 medulloblastoma model demonstrating sensitivity to histone deacetylase inhibitor treatment. J Neurooncol. (2012) 110:335–48. doi: 10.1007/s11060-012-0978-1
85. Yu L, Baxter PA, Zhao X, Liu Z, Wadhwa L, Zhang Y, et al. A single intravenous injection of oncolytic picornavirus SVV-001 eliminates medulloblastomas in primary tumor-based orthotopic xenograft mouse models. Neuro Oncol. (2011) 13:14–27. doi: 10.1093/neuonc/noq148
86. Dietl S, Schwinn S, Dietl S, Riedel S, Deinlein F, Rutkowski S, et al. MB3W1 is an orthotopic xenograft model for anaplastic medulloblastoma displaying cancer stem cell- and Group 3-properties. BMC Cancer. (2016) 16:1–13. doi: 10.1186/s12885-016-2170-z
87. Sandén E, Dyberg C, Krona C, Gallo-Oller G, Olsen TK, Enríquez Pérez J, et al. Establishment and characterization of an orthotopic patient-derived group 3 medulloblastomamodel for preclinical drug evaluation. Sci Rep. (2017) 7:46366. doi: 10.1038/srep46366
88. Spence T, Perotti C, Sin-Chan P, Picard D, Wu W, Singh A, et al. A novel C19MC amplified cell line links Lin28/let-7 to mTOR signaling in embryonal tumor with multilayered rosettes. Neuro Oncol. (2013) 16:62–71. doi: 10.1093/neuonc/not162
89. Liu Z, Zhao X, Wang Y, Mao H, Huang Y, Kogiso M, et al. A patient tumor-derived orthotopic xenograft mouse model replicating the group 3 supratentorial primitive neuroectodermal tumor in children. Neuro Oncol. (2014) 16:787–99. doi: 10.1093/neuonc/not244
90. Schmidt C, Schubert NA, Brabetz S, Mack N, Schwalm B, Chan JA, et al. Preclinical drug screen reveals topotecan, actinomycin D, and volasertib as potential new therapeutic candidates for ETMR brain tumor patients. Neuro Oncol. (2017) 19:1607–17. doi: 10.1093/neuonc/nox093
Appendix
Table A1. Overview of available orthotopic xenograft models per tumor entity, based on the 2016 WHO classification of tumors of the central nervous system.
Keywords: PDX, pediatric, orthotopic, xenograft, WHO classification
Citation: Hermans E and Hulleman E (2020) Patient-Derived Orthotopic Xenograft Models of Pediatric Brain Tumors: In a Mature Phase or Still in Its Infancy? Front. Oncol. 9:1418. doi: 10.3389/fonc.2019.01418
Received: 24 April 2019; Accepted: 28 November 2019;
Published: 08 January 2020.
Edited by:
David D. Eisenstat, University of Alberta, CanadaReviewed by:
Anat Erdreich-Epstein, Children's Hospital of Los Angeles, United StatesLukas Chavez, University of California, San Diego, United States
Copyright © 2020 Hermans and Hulleman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Esther Hulleman, ZS5odWxsZW1hbiYjeDAwMDQwO3ByaW5zZXNtYXhpbWFjZW50cnVtLm5s