GENERAL SCIENTIFIC SESSION III: OUR FUTURE IS NOW!: Chapter 11
Intertumoral and Intratumoral Heterogeneity as a Barrier for Effective Treatment of Medulloblastoma
Wang, Xin BHSc; Ramaswamy, Vijay MD; Remke, Marc MD; Mack, Stephen C. BSc; Dubuc, Adrian M. BSc; Northcott, Paul A. PhD; Taylor, Michael D. MD, PhD, FRCS(C)
Brain tumors remain the leading cause of cancer-related mortality in childhood.1,2 Medulloblastoma is the most common malignant pediatric brain tumor, representing up to 20% of newly diagnosed central nervous system malignancies.3,4 Current therapy consists of surgical resection, craniospinal irradiation with a posterior fossa boost, and adjuvant chemotherapy.5 Although overall survival in these patients has reached 70% to 80%, a high degree of treatment-induced morbidity remains.6-8 Furthermore, patients presenting with metastatic dissemination or those who later relapse still experience significant mortality.9 The past decade has seen an ever-growing interest in the molecular characterization of medulloblastoma, with the recent delineation of 4 core subgroups of medulloblastomas, each with distinct molecular, demographic and clinical features.10-19 The identification of these subgroups underscores the significant intertumoral heterogeneity that exists between patients and highlights the need to treat medulloblastoma subgroups as 4 distinct diseases.
Following the advent of next-generation sequencing technology, 4 recent publications further highlight the critical importance of examining medulloblastoma in a subgroup-specific manner.20-23 These sequencing efforts presented the first comprehensive examination of the mutational landscape of medulloblastoma across a combined cohort of > 310 patients. Their findings offered insights into the cellular origin of the disease and the hijacked developmental pathways that contribute to the initiation, maintenance, and progression of medulloblastoma.
Furthermore, in an effort to dissect the heterogeneity of medulloblastoma, an international collaboration was established, the Medulloblastoma Advanced Genomics International Consortium (MAGIC), comprising > 50 centers from around the globe. In its seminal work, the MAGIC consortium was able to analyze 1087 primary medulloblastoma samples at the copy number level to identify putative somatic driver events. This collective effort has led to the identification of novel actionable targets that can be pursued in future clinical trials and presented the first recurrent gene fusion in medulloblastoma.24
Besides the intertumoral heterogeneity appreciated in these large series of medulloblastomas, evidence suggests additional complexity within a given patient’s tumor. Intratumoral heterogeneity arises from the clonal architecture of the tumor and results in a bicompartmental disease: the primary tumor and the metastatic disease.25 This review highlights recent efforts to understand the intertumoral and intratumoral heterogeneity in medulloblastoma. Furthermore, this review highlights one of the most pressing challenges in treating medulloblastoma, which is leptomeningeal metastases arising from divergent clonal selection from the primary compartment. Thus, this review aims to discuss the remaining challenges and to offer clinical insights into future trial design and drug development.
CURRENT PATIENT RISK STRATIFICATION BASED ON METASTATIC STATUS AND HISTOLOGY AND THE NEED FOR FURTHER CLINICALLY RELEVANT SUBCLASSIFICATION
Patients presenting with medulloblastoma are currently stratified into different treatment arms based on clinicopathological variables, including extent of surgical resection, age at diagnosis, and metastatic status.26-28 Patients with significant residual disease, presenting at < 3 years of age, and having dissemination beyond the primary site are classified as having high-risk disease; the remainder are considered to be at standard risk. After treatment consisting of maximal safe resection, craniospinal irradiation, and adjuvant chemotherapy, cure rates have peaked at 85% for average-risk patients and around 70% for high-risk patients.1,5,29 Regrettably, significant clinical sequelae remain, including neurocognitive, endocrine, and psychosocial deficits, as well as an increased risk of future malignancies.6-8
Histological classifications have been used to inform clinical outcomes. According to the World Health Organization Classification of Tumors of the Central Nervous System, 5 major histological variants exist: classic, desmoplastic, large-cell, anaplastic, and medulloblastoma with extensive nodularity.30 Of these variants, classic histology is the most prevalent, whereas desmoplastic/medulloblastoma with extensive nodularity histology has the best survival outcomes.31,32 There are several shortcomings with respect to histological classification. Primarily, discrepancies between pathologists in the determination of anaplasia provide a significant challenge when guiding clinical decisions or generalizing the results of clinical trials to the community. Furthermore, although > 70% of medulloblastomas exhibit classic histology, response to current treatment strategies is highly variable, indicating remarkable biological heterogeneity within this group of patients. On the basis of these limited prognostic criteria, some patients may be overtreated and result in unnecessary morbidity. Several molecular genetic markers have been proposed to refine clinically based stratification, including MYC, ERBB2, CTNNB1, and TP53.33 Several chromosomal aberrations such as monosomy 6, isochromosome 17q, and loss of 17p raise further possibilities for clinical diagnostics.34-36 β-Catenin nuclear positivity and MYC amplifications are currently being used to assist in defining standard-risk and high-risk patients, respectively, in addition to clinical features. However, the inclusion of robust molecular markers and cytogenetic aberrations in current clinical staging remains sporadic, and their prognostic effects remain controversial.
This prognostic variability has motivated the development of molecularly based strategies in stratifying patients according to their transcriptomes. Patients with similar gene expression profiles exhibit similar biological behavior, providing a rational scheme for the design of targeted therapies. This paradigm shift in the approach taken to classify patients has given rise to the identification of 4 distinct subgroups of medulloblastoma.
DELINEATION OF 4 SUBGROUPS OF MEDULLOBLASTOMA
Integrated genomic profiling of large numbers of primary tumor samples was first popularized through the subclassification of lung, breast, and glial malignancies, among others.37-41 More recently, this strategy was adopted by the medulloblastoma community. In 2002, Pomeroy et al42 successfully demonstrated that desmoplastic medulloblastoma was distinct from classic medulloblastoma and that the atypical rhabdoid tumor is a distinct biological entity. Furthermore, using a gene signature, this model was more accurate in outcome prediction than conventional metastatic status. Subsequently, multiple groups around the world have published on this topic, with studies spanning several medium (n = 46) to large (n = 194) cohorts of patient samples.10-15,17,23,43-47 These studies concluded that medulloblastoma does not represent 1 disease but rather comprises at least 4 distinct molecular subgroups (Table 1).
A recent global consensus was reached validating the existence of 4 core medulloblastoma subgroups. The first 2 subgroups are called WNT and SHH on the basis of their aberrant pathway activation. The other 2 subgroups are less characterized and hence are generically called groups 3 and 4. Since their discovery, these findings have transformed our collective view of medulloblastoma and are already beginning to revolutionize how this disease is studied both at the laboratory bench and at the hospital bedside.15,48,49 A meta-analysis of 550 medulloblastomas from 7 independent studies using gene expression approaches concluded that independently of current stratification features, subgrouping is overwhelmingly robust in terms of prognostication.43,44
The WNT subgroup, the smallest of the 4 subgroups, is characterized by the activation of WNT signaling and confers the best prognosis. WNT-driven tumors frequently have loss of chromosome 6, harbor β-catenin activating mutations, and exhibit classic histology.33,50,51 The SHH subgroup is characterized by aberrations in the Sonic Hedgehog signaling pathway, with mutations converging on signaling intermediates, including PTCH1 and SUFU.28,52,53 These patients exhibit intermediate prognosis with a cytogenetic profile including predominantly loss of chromosome 9q and, in much lower frequencies, 17p loss, 3q gain, and 10q loss.43 Interestingly, these patients exhibit a bimodal age distribution predominantly affecting infant and adult patients but infrequently children. Unsupervised clustering from 4 independent transcriptomic data sets demonstrated that adult SHH patients segregate distinctly with infant SHH patients.28 These traits are maintained at the molecular and cytogenetic levels. For example, homeobox family genes are upregulated in adult SHH tumors, whereas neuronal development genes such as ID2, ZIC5, and ZIC2 are overexpressed in infant cases.28,43 In terms of cytogenetics, pediatric patients have a preponderance of MYCN amplification and chromosome 10q deletion compared with adult cases. Further molecular characterization of SHH medulloblastoma is warranted and will likely reveal additional heterogeneity.
Group 3 remains the subgroup with the worst prognosis, with an overall 5-year survival of < 50%. Frequently of the large-cell/anaplastic histology, 50% of group 3 tumors are metastatic at presentation, and many exhibit MYC amplification.13,54 Group 4 disease consists of classic medulloblastoma patients in that it targets mainly boys with classic histology, with very frequent isochromosome 17q in the tumor, and exhibits an intermediate survival of about 75% with multimodal treatment12,13,43 (additional cytogenetic aberrations summarized in Table 1).
It is clear now that in addition to histology and clinical features, molecular subgroups will be a mainstay in risk stratification. Further research using more sensitive technologies may reveal additional heterogeneity and subtypes within the subgroups. Functional validation of these subgroups is ongoing, and already recent studies have shown distinct cells of origin among the different subgroups.46,55 This body of literature also underscores the heterogeneity and the need for subgroup-specific therapy. The fact that these subgroups have unique clinical features and underlying biology strongly suggests that these patients need to be treated differently. Smoothened inhibitors, which target the Sonic Hedgehog pathway, are currently in phase II clinical trials for recurrent medulloblastoma and will likely be considered in the future for upfront targeted therapy of SHH subgroup patients.56,57 WNT subgroup patients, for example, may benefit from therapy de-escalation to reduce treatment-induced morbidity, whereas group 3 patients need to be prioritized for novel therapeutic interventions.44
RECENT GENOMIC ADVANCES REVEAL NOVEL SUBGROUP-SPECIFIC MOLECULAR ALTERATIONS
The availability of next-generation sequencing and integrated genomics has recently provided significant insight into medulloblastoma pathogenesis and further given credence to subgroup-specific classification of the disease.20-22,58 These studies have also validated previously identified mutations while unveiling previously unappreciated novel driver genes.23,59-61 An examination of somatic copy number aberrations across the largest assembly of medulloblastoma samples in the world was performed and revealed several novel actionable targets in a subgroup-specific manner (Table 2).24
WNT Subgroup Medulloblastoma
WNT subgroup medulloblastoma remains quite genomically bland with an absence of any focal recurrent somatic copy number aberrations. The most common somatic mutation is CTNNB1, which confirms the importance of β-catenin and downstream WNT signaling.43,62 Other recurrent somatic mutations found within this subgroup include TP53; the DEAD-box RNA helicase DDX3X, involved in cellular growth and division; and chromatin-modifier SMARCA4. The developmental origin of WNT tumors has recently been suggested to be the progenitor cells of the lower rhombic lip. A mouse model harboring activated Ctnnb1 in the Blbp-expressing radial glial cells in a Trp53 heterozygote background with activated PI3K signaling (Blbp-cre; Ctnnb1+/lox(Ex3); Trp53+/flx; Pik3caE545K) developed highly penetrant WNT medulloblastoma.46
SHH Subgroup Medulloblastoma
Activation of Sonic Hedgehog signaling has long been known to play a pathogenic role in medulloblastoma. Somatic mutations targeting the SHH receptor PTCH1 are found only in this subgroup. Other somatically mutated genes include TP53 and MLL2 in 14% and 12% of patients, respectively.44 SHH medulloblastoma also exhibits frequent somatic copy number aberrations, especially targeting genes involved in the PI3K signaling cascade.24 This finding is especially notable in that inhibitors of the PI3K pathway are widely available and may be used in combination with SHH inhibitors to treat this disease variant.56,63,64 Several mouse models of this subgroup exist, targeting both the cerebellar granule neuron precursors and the neural stem cells located in the subventricular zone.65-67 Specifically, inactivation of patched in either the Atoh1 (marks cerebellar granule neuron precursor cells) or GFAP (marks neural stem cells) compartment leads to medulloblastoma development.55,68,69
Group 3 Medulloblastoma
Because this molecular subgroup of medulloblastoma is associated with the worst prognosis, a great deal of attention has been focused on understanding the pathogenesis of group 3 medulloblastomas. This subgroup frequently harbors genomic instability and high-level amplification targeting the proto-oncogene MYC.43 Somatic mutations in this subgroup appear to converge on deregulation of the epigenome, mainly SMARCA4 and MLL2, validating previous work done using traditional Sanger-based sequencing method.23 More interesting is the identification of a novel PVT1-MYC fusion, the first recurrent gene fusion identified in medulloblastoma.24 This discovery offers new insights into the complex function of MYC in driving tumorigenesis. Furthermore, the finding that > 20% of group 3 tumors have aberrant transforming growth factor-β signaling provides a new opportunity for therapeutic intervention in these aggressive tumors. Two recent publications presented the first mouse models of group 3 medulloblastoma using orthotopic transplantation.70-72 In both cerebellar granule neuron precursor and neural stem cell populations, the activation of myc with concomitant p53 inactivation was able to generate the first group 3 preclinical mouse models.
Group 4 Medulloblastoma
Group 4 disease is characterized by amplification of MYCN (in addition to the SHH subgroup) and isochromosome 17q. Prior gene expression studies have reported that this particular subgroup exhibits a neuronal expression signature.12,73 Recurrent somatic mutations, similar to group 3, seem to converge on epigenetic aberrations, particularly histone modifiers.74-77 This feature can be identified by frequent KDM6A, MLL3, and ZMYM3 mutations. These genes may play a role in maintaining cells in an undifferentiated state, paralleling their role in normal stem cell function. Further research is needed to elucidate this pathogenic mechanism. One of the recurrently altered genes identified through the MAGIC cohort constitutes a tandem duplication of the SNCAIP gene, which is mutated in a subset of Parkinson disease patients.24,78 The biological relevance of this amplification needs to be functionally characterized further. One of the remaining hurdles in this subgroup is the lack of an animal model. Recent evidence suggests that an MYCN-driven mouse model independent of SHH signaling resembles a non-SHH/WNT model, although its specific subgroup affiliation remains to be definitively defined.71,79
These findings, identified with modern genomic technologies, uncover an astoundingly small number of somatic mutations in medulloblastoma, on the order of 10 to 12, compared with solid tumors in adulthood.44,80 This suggests that group 3 and 4 medulloblastoma may be a copy number--driven disease with the pathogenic process converging on a few key molecular processes, or it may be regulated through an epigenetic mechanism.74,76,81,82 Over the coming years, with the establishment of preclinical models recapitulating subgroup-specific disease, these findings will herald a new wave of targeted therapeutics.
MEDULLOBLASTOMA METASTASES ARE BIOLOGICALLY DISTINCT FROM THEIR MATCHED PRIMARY TUMOR
Recent evidence in adult glioblastomas highlights the intratumoral heterogeneity whereby closely intermingled cells can exhibit disparate driver genes.83-86 These findings support the notion that within an individual patient, there lie subclonal compartments with unique biological behaviors.25 This observation is particularly troubling because therapies aimed at eradicating the bulk of the tumor may in fact spare resistant clones that subsequently seed relapses.
Although previous publications and current clinical management of human medulloblastoma assume that the primary tumor and its matched metastases respond to therapy in a similar manner, this notion may be jarringly false. In fact, a recent publication by Wu et al90 offers support for a bicompartmental model whereby the primary disease and the matched metastatic disease are inherently different. Failure to study the leptomeningeal disease as an important and separate entity may result in the ineffectiveness of targeted therapies.
Functional genomics studies using Sleeping Beauty insertional mutagenesis technology have been successfully applied to the study of solid tumor initiation and progression.87-89 Recently, this system has been used to model a highly metastatic subset of medulloblastoma.90,91 Comparing the primary and matched tumors revealed that although different metastases are genetically very similar to each other, they deviate markedly from the primary tumor. Certain genetic events in the metastatic lesions were not present in the matched primary tumor, whereas other genetic events were completely restricted to the primary tumor. This finding aligns with the hypothesis that metastases arise from a restricted subclone of the primary tumor that has been selected in the metastatic tumor niche. This complex pattern of genetic variance may explain the existence of therapy-resistant clones, underscoring the difficulty in treating patients with metastatic disease. A silver lining is that many of the genes found to be enriched in the metastatic compartment target the PI3K pathway, implicating PI3K signaling as a potential driver for medulloblastoma dissemination and an avenue for therapeutic intervention.
These observations add another degree of complexity as a result of intratumoral heterogeneity, which raises additional concerns regarding the management of the disease. Treatment decisions will need to be made while accounting for this added complexity, and molecular targeting will need to focus on multiple compartments, perhaps relying on combinatorial therapy. For example, biopsies of the metastatic disease, which are currently not done, may be required to characterize the metastatic lesion and to direct treatment to this particular compartment. Finally, in subgroups with a high incidence of metastatic dissemination such as group 3, safeguards must be put in place during the upfront treatment phase to limit the survival of resistant clones.
FUTURE CONSIDERATIONS IN TRIAL DESIGN AND TARGETED THERAPIES
With the first wave of medulloblastoma genomics studies coming to a conclusion, a wealth of new insight into its pathogenesis has been gained. The delineation of the 4 molecular subgroups is already beginning to affect the way this disease is studied. To facilitate inclusion of subgroup stratification in clinical trials, efforts have already been made on using inexpensive subgroup assignment with immunohistochemistry (DKK1 for WNT, SFRP1 for SHH, NPR3 for group 3; KCNA1 for group 4) or RNA-based digital expression counts from fresh-frozen or paraffin sections using as little as 100 ng RNA.13,54 The upcoming challenges will be to interrogate each aberration and to functionally validate their biological contributions in subgroup-specific preclinical models. Within the sea of passenger mutations are a select few driver genes; it will be up to the research community to pinpoint which events are crucial to the initiation and maintenance of medulloblastoma.
The integration of all the medulloblastoma genomic data will be necessary to increase our understanding of the disease.92,93 Combining data from the medulloblastoma genome, transcriptome, epigenome, and, in the future, proteome will form the comprehensive data set needed to develop targeted therapies. These future studies will no doubt reveal increased depth and further substructure within subgroups.94 It is thus pivotal that large, multicenter, collaborative efforts such as MAGIC continue to exist and expand, using collective resources and reason to push forward a new age of molecular innovations to improve patient care.
For related video content, please access the Supplemental Digital Content: http://www.youtube.com/watch?v=QVTdLlH98dQ
Dr Taylor holds a Canadian Institutes of Health Research Clinician-Scientist Phase II Award, was a Sontag Foundation Distinguished Scholar, and is supported by the Garron Family Chair in Childhood Cancer Research. Dr Taylor is supported by grants from the National Institutes of Health (R01CA148699, and R01CA159859), the Pediatric Brain Tumor Foundation, the Canadian Cancer Society, the Terry Fox Research Institute, and Brainchild. X. Wang is supported by the Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research. The authors have no personal financial or institutional interest in any of the drugs, materials, or devices described in this article.
1. Crawford JR, MacDonald TJ, Packer RJ. Medulloblastoma in childhood: new biological advances. Lancet Neurol. 2007;6(12):1073–1085.
2. Pui CH, Gajjar AJ, Kane JR, Qaddoumi IA, Pappo AS. Challenging issues in pediatric oncology. Nat Rev Clin Oncol. 2011;8(9):540–549.
3. Rutkowski S, Von Hoff K, Emser A, et al.. Survival and prognostic factors of early childhood medulloblastoma: an international meta-analysis. J Clin Oncol. 2010;28(33):4961–4968.
4. Dolecek TA, Propp JM, Stroup NE, Kruchko C. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005-2009. Neuro Oncol. 2012;14(suppl 5):v1–v49.
5. Gajjar A, Chintagumpala M, Ashley D, et al.. Risk-adapted craniospinal radiotherapy followed by high-dose chemotherapy and stem-cell rescue in children with newly diagnosed medulloblastoma (St Jude Medulloblastoma-96): long-term results from a prospective, multicentre trial. Lancet Oncol. 2006;7(10):813–820.
6. Hudson MM, Mertens AC, Yasui Y, et al.. Health status of adult long-term survivors of childhood cancer: a report from the Childhood Cancer Survivor Study. JAMA. 2003;290(12):1583–1592.
7. Spiegler BJ, Bouffet E, Greenberg ML, Rutka JT, Mabbott DJ. Change in neurocognitive functioning after treatment with cranial radiation in childhood. JAMA. 2004;22(4):706–713.
8. Zeltzer LK, Recklitis C, Buchbinder D, et al.. Psychological status in childhood cancer survivors: a report from the Childhood Cancer Survivor Study. J Clin Oncol. 2009;27(14):2396–2404.
9. Packer RJ, Vezina G. Management of and prognosis with medulloblastoma: therapy at a crossroads. Arch Neurol. 2008;65(11):1419–1424.
10. Thompson MC, Fuller C, Hogg TL, et al.. Genomics identifies medulloblastoma subgroups that are enriched for specific genetic alterations. J Clin Oncol. 2006;24(12):1924–1931.
11. Kool M, Koster J, Bunt J, et al.. Integrated genomics identifies five medulloblastoma subtypes with distinct genetic profiles, pathway signatures and clinicopathological features. PLoS One. 2008;3(8):e3088.
12. Cho YJ, Tsherniak A, Tamayo P, et al.. Integrative genomic analysis of medulloblastoma identifies a molecular subgroup that drives poor clinical outcome. J Clin Oncol. 2011;29(11):1424–1430.
13. Northcott PA, Korshunov A, Witt H, et al.. Medulloblastoma comprises four distinct molecular variants. J Clin Oncol. 2011;29(11):1408–1414.
14. Remke M, Hielscher T, Korshunov A, et al.. FSTL5 is a marker of poor prognosis in non-WNT/non-SHH medulloblastoma. J Clin Oncol. 2011;29(29):3852–3861.
15. Taylor MD, Northcott PA, Korshunov A, et al.. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol. 2012;123(4):465–472.
16. Northcott PA, Rutka JT, Taylor MD. Genomics of medulloblastoma: from Giemsa-banding to next-generation sequencing in 20 years. Neurosurg Focus. 2010;28(1):E6.
17. Northcott PA, Dubuc AM, Pfister S, Taylor MD. Molecular subgroups of medulloblastoma. Expert Rev Neurother. 2012;12(7):871–884.
18. Dubuc AM, Northcott PA, Mack S, et al.. The genetics of pediatric brain tumors. Curr Neurol Neurosci Rep. 2010;10(3):215–223.
19. Rausch T, Jones DT, Zapatka M, et al.. Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations. Cell. 2012;148(1-2):59–71.
20. Robinson G, Parker M, Kranenburg TA, et al.. Novel mutations target distinct subgroups of medulloblastoma. Nature. 2012;488(7409):43–48.
21. Pugh TJ, Weeraratne SD, Archer TC, et al.. Medulloblastoma exome sequencing uncovers subtype-specific somatic mutations. Nature. 2012;488(7409):106–110.
22. Jones DT, Jäger N, Kool M, et al.. Dissecting the genomic complexity underlying medulloblastoma. Nature. 2012;488(7409):100–105.
23. Parsons DW, Li M, Zhang X, et al.. The genetic landscape of the childhood cancer medulloblastoma. Science. 2011;331(6016):435–439.
24. Northcott PA, Shih DJ, Peacock J, et al.. Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature. 2012;488(7409):49–56.
25. Scheel C, Onder T, Karnoub A, Weinberg RA. Adaptation versus selection: the origins of metastatic behavior. Cancer Res. 2007;67(24):11476–11479; discussion 11479-11480.
26. Gajjar A, Hernan R, Kocak M, et al.. Clinical, histopathologic, and molecular markers of prognosis: toward a new disease risk stratification system for medulloblastoma. J Clin Oncol. 2004;22(6):984–993.
27. Packer RJ, Rood BR, MacDonald TJ. Medulloblastoma: present concepts of stratification into risk groups. Pediatr Neurosurg. 2003;39(2):60–67.
28. Northcott PA, Hielscher T, Dubuc A, et al.. Pediatric and adult sonic hedgehog medulloblastomas are clinically and molecularly distinct. Acta Neuropathol. 2011;122(2):231–240.
29. Packer RJ, Gajjar A, Vezina G, et al.. Phase III study of craniospinal radiation therapy followed by adjuvant chemotherapy for newly diagnosed average-risk medulloblastoma. J Clin Oncol. 2006;24(25):4202–4208.
30. Louis DN, Ohgaki H, Wiestler OD, et al.. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 2007;114(2):97–109.
31. Rutkowski S, Bode U, Deinlein F, et al.. Treatment of early childhood medulloblastoma by postoperative chemotherapy alone. N Engl J Med. 2005;352(10):978–986.
32. Grill J, Dufour C, Kalifa C. High-dose chemotherapy in children with newly-diagnosed medulloblastoma. Lancet Oncol. 2006;7(10):787–789.
33. Ellison DW, Dalton J, Kocak M, et al.. Medulloblastoma: clinicopathological correlates of SHH, WNT, and non-SHH/WNT molecular subgroups. Acta Neuropathol. 2011;121(3):381–396.
34. Cogen PH, Daneshvar L, Metzger AK, et al.. Involvement of multiple chromosome 17p loci in medulloblastoma tumorigenesis. Am J Hum Genet. 1992;50(3):584–589.
35. Bayani J, Zielenska M, Marrano P, et al.. Molecular cytogenetic analysis of medulloblastomas and supratentorial primitive neuroectodermal tumors by using conventional banding, comparative genomic hybridization, and spectral karyotyping. J Neurosurg. 2000;93(3):437–448.
36. Zhang B, Chambers KJ, Leprince D, Faller DV, Wang S. Requirement for chromatin-remodeling complex in novel tumor suppressor HIC1-mediated transcriptional repression and growth control. Oncogene. 2009;28(5):651–661.
37. Bhattacharjee A, Richards WG, Staunton J, et al.. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A. 2001;98(24):13790–13795.
38. Gatza ML, Lucas JE, Barry WT, et al.. A pathway-based classification of human breast cancer. Proc Natl Acad Sci U S A. 2010;107(15):6994–6999.
39. Verhaak RG, Hoadley KA, Purdom E, et al.. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17(1):98–110.
40. Noushmehr H, Weisenberger DJ, Diefes K, et al.. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell. 2010;17(5):510–522.
41. Witt H, Mack SC, Ryzhova M, et al.. Delineation of two clinically and molecularly distinct subgroups of posterior fossa ependymoma. Cancer Cell. 2011;20(2):143–157.
42. Pomeroy SL, Tamayo P, Gaasenbeek M, et al.. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature. 2002;415(6870):436–442.
43. Kool M, Korshunov A, Remke M, et al.. Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, group 3, and group 4 medulloblastomas. Acta Neuropathol. 2012;123(4):473–484.
44. Northcott PA, Jones DT, Kool M, et al.. Medulloblastomics: the end of the beginning. Nat Rev Cancer. 2012;12(12):818–834.
45. Remke M, Hielscher T, Northcott PA, et al.. Adult medulloblastoma comprises three major molecular variants. J Clin Oncol. 2011;29(19):2717–2723.
46. Gibson P, Tong Y, Robinson G, et al.. Subtypes of medulloblastoma have distinct developmental origins. Nature. 2010;468(7327):1095–1099.
47. Dubuc AM, Morrissy AS, Kloosterhof NK, et al.. Subgroup-specific alternative splicing in medulloblastoma. Acta Neuropathol. 2012;123(4):485–499.
48. Ramaswamy V, Northcott PA, Taylor MD. FISH and chips: the recipe for improved prognostication and outcomes for children with medulloblastoma. Cancer Genet. 2011;204(11):577–588.
49. Wang X, Venugopal C, Manoranjan B, et al.. Sonic hedgehog regulates Bmi1 in human medulloblastoma brain tumor-initiating cells. Oncogene. 2011;31(2):187–199.
50. Clifford SC, Lusher ME, Lindsey JC, et al.. Wnt/wingless pathway activation and chromosome 6 loss characterize a distinct molecular sub-group of medulloblastomas associated with a favorable prognosis. Cell Cycle. 2006;5(22):2666–2670.
51. Lindsey JC, Hill RM, Megahed H, et al.. TP53 mutations in favorable-risk Wnt/wingless-subtype medulloblastomas. J Clin Oncol. 2011;29(12):e344–e346.
52. Taylor MD, Liu L, Raffel C, et al.. Mutations in SUFU predispose to medulloblastoma. Nat Genet. 2002;31(3):306–310.
53. Slade I, Murray A, Hanks S, et al.. Heterogeneity of familial medulloblastoma and contribution of germline PTCH1 and SUFU mutations to sporadic medulloblastoma. Fam Cancer. 2011;10(2):337–342.
54. Northcott PA, Shih DJ, Remke M, et al.. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathol. 2012;123(4):615–626.
55. Yang ZJ, Ellis T, Markant SL, et al.. Medulloblastoma can be initiated by deletion of patched in lineage-restricted progenitors or stem cells. Cancer Cell. 2008;14(2):135–145.
56. Rudin CM, Hann CL, Laterra J, et al.. Treatment of medulloblastoma with hedgehog pathway inhibitor GDC-0449. N Engl J Med. 2009;361(12):1173–1178.
57. Yauch RL, Dijkgraaf GJ, Alicke B, et al.. Smoothened mutation confers resistance to a Hedgehog pathway inhibitor in medulloblastoma. Science. 2009;326(5952):572–574.
58. Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet. 2010;11(10):685–696.
59. Dubuc AM, Northcott PA, Kenney AM, Taylor MD. Calculating a cure for cancer: managing medulloblastoma MATH1-ematically. Expert Rev Neurother. 2010;10(10):1489–1492.
60. Bertrand KC, Mack SC, Northcott PA, et al.. PCDH10 is a candidate tumour suppressor gene in medulloblastoma. Childs Nerv Syst. 2011;27(8):1243–1249.
61. Alimova I, Venkataraman S, Harris P, et al.. Targeting the enhancer of zeste homologue 2 in medulloblastoma. Int J Cancer. 2012;131(8):1800–1809.
62. Pei Y, Brun SN, Markant SL, et al.. WNT signaling increases proliferation and impairs differentiation of stem cells in the developing cerebellum. Development. 2012;139(10):1724–1733.
63. Dijkgraaf GJ, Alicke B, Weinmann L, et al.. Small molecule inhibition of GDC-0449 refractory smoothened mutants and downstream mechanisms of drug resistance. Cancer Res. 2011;71(2):435–444.
64. Buonamici S, Williams J, Morrissey M, et al.. Interfering with resistance to smoothened antagonists by inhibition of the PI3K pathway in medulloblastoma. Sci Transl Med. 2010;2(51):51ra70.
65. Hallahan AR, Pritchard JI, Hansen S, et al.. The SmoA1 mouse model reveals that notch signaling is critical for the growth and survival of sonic hedgehog-induced medulloblastomas. Cancer Res. 2004;64(21):7794–7800.
66. Grammel D, Warmuth-Metz M, Von Bueren AO, et al.. Sonic hedgehog-associated medulloblastoma arising from the cochlear nuclei of the brainstem. Acta Neuropathol. 2012;123(4):601–614.
67. Oliver TG, Read TA, Kessler JD, et al.. Loss of patched and disruption of granule cell development in a pre-neoplastic stage of medulloblastoma. Development. 2005;132(10):2425–2439.
68. Schüller U, Heine VM, Mao J, et al.. Acquisition of granule neuron precursor identity is a critical determinant of progenitor cell competence to form Shh-induced medulloblastoma. Cancer Cell. 2008;14(2):123–134.
69. Northcott PA, Fernandez- LA, Hagan JP, et al.. The miR-17/92 polycistron is up-regulated in sonic hedgehog-driven medulloblastomas and induced by N-myc in sonic hedgehog-treated cerebellar neural precursors. Cancer Res. 2009;69(8):3249–3255.
70. Pei Y, Moore CE, Wang J, et al.. An animal model of MYC-driven medulloblastoma. Cancer Cell. 2012;21(2):155–167.
71. Swartling FJ, Savov V, Persson AI, et al.. Distinct neural stem cell populations give rise to disparate brain tumors in response to N-MYC. Cancer Cell. 2012;21(5):601–613.
72. Dang CV. MYC on the path to cancer. Cell. 2012;149(1):22–35.
73. Korshunov A, Remke M, Kool M, et al.. Biological and clinical heterogeneity of MYCN-amplified medulloblastoma. Acta Neuropathol. 2012;123(4):515–527.
74. Unterberger A, Dubuc AM, Taylor MD. Genome-wide methylation analysis. Cancer Epigenetics. Dumitrescu RG, Verma M, eds. New York, NY: Humana Press. Volume 863; 2012, pp 303-317. ISBN: 978-1-61779-612-8.
75. Baylin SB, Jones PA. A decade of exploring the cancer epigenome: biological and translational implications. Nat Rev Cancer. 2011;11(10):726–734.
76. Northcott PA, Nakahara Y, Wu X, et al.. Multiple recurrent genetic events converge on control of histone lysine methylation in medulloblastoma. Nat Genet. 2009;41(4):465–472.
77. Dubuc AM, Remke M, Korshunov A, et al.. Aberrant patterns of H3K4 and H3K27 histone lysine methylation occur across subgroups in medulloblastoma. Acta Neuropathol. 2012;125(3):373–384.
78. Eyal A, Engelender S. Synphilin isoforms and the search for a cellular model of Lewy body formation in Parkinson’s disease. Cell Cycle. 2006;5(18):2082–2086.
79. Wu X, Northcott PA, Croul S, Taylor MD. Mouse models of medulloblastoma. Chin J Cancer. 2011;30(7):442–449.
80. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70.
81. Kongkham PN, Northcott PA, Ra YS, et al.. An epigenetic genome-wide screen identifies SPINT2 as a novel tumor suppressor gene in pediatric medulloblastoma. Cancer Res. 2008;68(23):9945–9953.
82. Nakahara Y, Northcott PA, Li M, et al.. Genetic and epigenetic inactivation of Kruppel-like factor 4 in medulloblastoma. Neoplasia. 2010;12(1):20–27.
83. Snuderl M, Fazlollahi L, Le LP, et al.. Mosaic amplification of multiple receptor tyrosine kinase genes in glioblastoma. Cancer Cell. 2011;20(6):810–817.
84. Misale S, Yaeger R, Hobor S, et al.. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature. 2012;486(7404):532–536.
85. Diaz LA, Williams RT, Wu J, et al.. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature. 2012;486(7404):537–540.
86. Szerlip NJ, Pedraza A, Chakravarty D, et al.. Intratumoral heterogeneity of receptor tyrosine kinases EGFR and PDGFRA amplification in glioblastoma defines subpopulations with distinct growth factor response. Proc Natl Acad Sci U S A. 2012;109(8):3041–3046.
87. Dupuy AJ, Akagi K, Largaespada DA, Copeland NG, Jenkins NA. Mammalian mutagenesis using a highly mobile somatic Sleeping Beauty transposon system. Nature. 2005;436(7048):221–226.
88. Collier LS, Carlson CM, Ravimohan S, Dupuy AJ, Largaespada DA. Cancer gene discovery in solid tumours using transposon-based somatic mutagenesis in the mouse. Nature. 2005;436(7048):272–276.
89. Mann KM, Ward JM, Yew CC, et al.. Sleeping Beauty mutagenesis reveals cooperating mutations and pathways in pancreatic adenocarcinoma. Proc Natl Acad Sci U S A. 2012;109(16):5934–5941.
90. Wu X, Northcott PA, Dubuc A, et al.. Clonal selection drives genetic divergence of metastatic medulloblastoma. Nature. 2012;482(7386):529–533.
91. Mumert M, Dubuc A, Wu X, et al.. Functional genomics identifies drivers of medulloblastoma dissemination. Cancer Res. 2012;72(19):4944–4953.
92. Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487(7407):330–337.
93. Cancer Genome Atlas Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455(7216):1061–1068.
94. Sengupta R, Dubuc A, Ward S, et al.. CXCR4 activation defines a new subgroup of sonic hedgehog-driven medulloblastoma. Cancer Res. 2012;72(1):122–132.
Copyright © by the Congress of Neurological Surgeons