Clinical and Molecular Features in Medulloblastomas Subtypes in Children in a Cohort in Taiwan
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Patient Cohort
2.2. Retrieve of Clinical Data
2.3. Gene Expression Profiles by RNA-Seq
2.4. Applying RNA-Seq to Identify Mutations
2.5. Immune Cell Deconvolution
2.6. DNA Methylation Array Profiling
2.7. Applying DNA Methylation Profiles to Identify Copy Number Variations
2.8. Similarity Network Fusion (SNF) Analysis for WNT and SHH Subtype Clustering
2.9. Random Forest (RF) for Non-WNT/Non-SHH Subtype Clustering
2.10. Survival Analysis
3. Results
3.1. Subsection of Molecular Subgroups Were Identified by Integrative Gene Expression and DNA Methylation Profiles
3.2. Characteristics in Subtypes of WNT MBs
3.3. Characteristics in Subtypes of SHH MBs
3.4. Characteristics in Subtypes of Non-WNT/Non-SHH MBs
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Louis, D.N.; Perry, A.; Reifenberger, G.; von Deimling, A.; Figarella-Branger, D.; Cavenee, W.K.; Ohgaki, H.; Wiestler, O.D.; Kleihues, P.; Ellison, D.W. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A summary. Acta Neuropathol. 2016, 131, 803–820. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cavalli, F.M.G.; Remke, M.; Rampasek, L.; Peacock, J.; Shih, D.J.H.; Luu, B.; Garzia, L.; Torchia, J.; Nor, C.; Morrissy, A.S.; et al. Intertumoral Heterogeneity within Medulloblastoma Subgroups. Cancer Cell 2017, 31, 737–754.e6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Northcott, P.A.; Buchhalter, I.; Morrissy, A.S.; Hovestadt, V.; Weischenfeldt, J.; Ehrenberger, T.; Grobner, S.; Segura-Wang, M.; Zichner, T.; Rudneva, V.A.; et al. The whole-genome landscape of medulloblastoma subtypes. Nature 2017, 547, 311–317. [Google Scholar] [CrossRef] [Green Version]
- Louis, D.N.; Perry, A.; Wesseling, P.; Brat, D.J.; Cree, I.A.; Figarella-Branger, D.; Hawkins, C.; Ng, H.K.; Pfister, S.M.; Reifenberger, G.; et al. The 2021 WHO Classification of Tumors of the Central Nervous System: A summary. Neuro-Oncology 2021, 23, 1231–1251. [Google Scholar] [CrossRef] [PubMed]
- Kumar, R.; Liu, A.P.Y.; Northcott, P.A. Medulloblastoma genomics in the modern molecular era. Brain Pathol. 2020, 30, 679–690. [Google Scholar] [CrossRef] [PubMed]
- Hovestadt, V.; Ayrault, O.; Swartling, F.J.; Robinson, G.W.; Pfister, S.M.; Northcott, P.A. Medulloblastomics revisited: Biological and clinical insights from thousands of patients. Nat. Rev. Cancer 2020, 20, 42–56. [Google Scholar] [CrossRef]
- Wang, B.; Mezlini, A.M.; Demir, F.; Fiume, M.; Tu, Z.; Brudno, M.; Haibe-Kains, B.; Goldenberg, A. Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 2014, 11, 333–337. [Google Scholar] [CrossRef]
- Wu, K.S.; Ho, D.M.; Jou, S.T.; Yu, A.L.; Tran, H.M.; Liang, M.L.; Chen, H.H.; Lee, Y.Y.; Chen, Y.W.; Lin, S.C.; et al. Molecular-Clinical Correlation in Pediatric Medulloblastoma: A Cohort Series Study of 52 Cases in Taiwan. Cancers 2020, 12, 653. [Google Scholar] [CrossRef] [Green Version]
- Chang, C.H.; Housepian, E.M.; Herbert, C., Jr. An operative staging system and a megavoltage radiotherapeutic technic for cerebellar medulloblastomas. Radiology 1969, 93, 1351–1359. [Google Scholar] [CrossRef]
- Bray, N.L.; Pimentel, H.; Melsted, P.; Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 2016, 34, 525–527. [Google Scholar] [CrossRef]
- Soneson, C.; Love, M.I.; Robinson, M.D. Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences. F1000Research 2015, 4, 1521. [Google Scholar] [CrossRef] [PubMed]
- Curtin, N.J. DNA repair dysregulation from cancer driver to therapeutic target. Nat. Rev. Cancer 2012, 12, 801–817. [Google Scholar] [CrossRef] [PubMed]
- Wei, S.; Li, C.; Yin, Z.; Wen, J.; Meng, H.; Xue, L.; Wang, J. Histone methylation in DNA repair and clinical practice: New findings during the past 5-years. J. Cancer 2018, 9, 2072–2081. [Google Scholar] [CrossRef]
- Zhang, J.; Walsh, M.F.; Wu, G.; Edmonson, M.N.; Gruber, T.A.; Easton, J.; Hedges, D.; Ma, X.; Zhou, X.; Yergeau, D.A.; et al. Germline Mutations in Predisposition Genes in Pediatric Cancer. N. Engl. J. Med. 2015, 373, 2336–2346. [Google Scholar] [CrossRef] [Green Version]
- Waszak, S.M.; Northcott, P.A.; Buchhalter, I.; Robinson, G.W.; Sutter, C.; Groebner, S.; Grund, K.B.; Brugieres, L.; Jones, D.T.W.; Pajtler, K.W.; et al. Spectrum and prevalence of genetic predisposition in medulloblastoma: A retrospective genetic study and prospective validation in a clinical trial cohort. Lancet Oncol. 2018, 19, 785–798. [Google Scholar] [CrossRef]
- Zhu, G.; Rankin, S.L.; Larson, J.D.; Zhu, X.; Chow, L.M.; Qu, C.; Zhang, J.; Ellison, D.W.; Baker, S.J. PTEN Signaling in the Postnatal Perivascular Progenitor Niche Drives Medulloblastoma Formation. Cancer Res. 2017, 77, 123–133. [Google Scholar] [CrossRef] [Green Version]
- Kim, D.; Langmead, B.; Salzberg, S.L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 2015, 12, 357–360. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, K.; Li, M.; Hakonarson, H. ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010, 38, e164. [Google Scholar] [CrossRef]
- Tate, J.G.; Bamford, S.; Jubb, H.C.; Sondka, Z.; Beare, D.M.; Bindal, N.; Boutselakis, H.; Cole, C.G.; Creatore, C.; Dawson, E.; et al. COSMIC: The Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 2019, 47, D941–D947. [Google Scholar] [CrossRef] [Green Version]
- Robinson, J.T.; Thorvaldsdottir, H.; Wenger, A.M.; Zehir, A.; Mesirov, J.P. Variant Review with the Integrative Genomics Viewer. Cancer Res. 2017, 77, e31–e34. [Google Scholar] [CrossRef]
- Wu, K.S.; Jian, T.Y.; Sung, S.Y.; Hsieh, C.L.; Huang, M.H.; Fang, C.L.; Wong, T.T.; Lin, Y.L. Enrichment of Tumor-Infiltrating B Cells in Group 4 Medulloblastoma in Children. Int. J. Mol. Sci. 2022, 23, 5287. [Google Scholar] [CrossRef] [PubMed]
- Aran, D.; Hu, Z.; Butte, A.J. Xcell: Digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017, 18, 220. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Aryee, M.J.; Jaffe, A.E.; Corrada-Bravo, H.; Ladd-Acosta, C.; Feinberg, A.P.; Hansen, K.D.; Irizarry, R.A. Minfi: A flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 2014, 30, 1363–1369. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tian, Y.; Morris, T.J.; Webster, A.P.; Yang, Z.; Beck, S.; Feber, A.; Teschendorff, A.E. ChAMP: Updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics 2017, 33, 3982–3984. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hovestadt, V.; Remke, M.; Kool, M.; Pietsch, T.; Northcott, P.A.; Fischer, R.; Cavalli, F.M.; Ramaswamy, V.; Zapatka, M.; Reifenberger, G.; et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays. Acta Neuropathol. 2013, 125, 913–916. [Google Scholar] [CrossRef] [Green Version]
- Sturm, D.; Witt, H.; Hovestadt, V.; Khuong-Quang, D.A.; Jones, D.T.; Konermann, C.; Pfaff, E.; Tonjes, M.; Sill, M.; Bender, S.; et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 2012, 22, 425–437. [Google Scholar] [CrossRef] [Green Version]
- Capper, D.; Jones, D.T.W.; Sill, M.; Hovestadt, V.; Schrimpf, D.; Sturm, D.; Koelsche, C.; Sahm, F.; Chavez, L.; Reuss, D.E.; et al. DNA methylation-based classification of central nervous system tumours. Nature 2018, 555, 469–474. [Google Scholar] [CrossRef] [Green Version]
- Shih, D.J.; Northcott, P.A.; Remke, M.; Korshunov, A.; Ramaswamy, V.; Kool, M.; Luu, B.; Yao, Y.; Wang, X.; Dubuc, A.M.; et al. Cytogenetic prognostication within medulloblastoma subgroups. J. Clin. Oncol. 2014, 32, 886–896. [Google Scholar] [CrossRef] [Green Version]
- O’Connor, T.; Heikenwalder, M. CCL2 in the Tumor Microenvironment. Adv. Exp. Med. Biol. 2021, 1302, 1–14. [Google Scholar] [CrossRef]
- Taylor, M.D.; Northcott, P.A.; Korshunov, A.; Remke, M.; Cho, Y.J.; Clifford, S.C.; Eberhart, C.G.; Parsons, D.W.; Rutkowski, S.; Gajjar, A.; et al. Molecular subgroups of medulloblastoma: The current consensus. Acta Neuropathol. 2012, 123, 465–472. [Google Scholar] [CrossRef]
- Northcott, P.A.; Korshunov, A.; Witt, H.; Hielscher, T.; Eberhart, C.G.; Mack, S.; Bouffet, E.; Clifford, S.C.; Hawkins, C.E.; French, P.; et al. Medulloblastoma comprises four distinct molecular variants. J. Clin. Oncol. 2011, 29, 1408–1414. [Google Scholar] [CrossRef] [PubMed]
- Skowron, P.; Ramaswamy, V.; Taylor, M.D. Genetic and molecular alterations across medulloblastoma subgroups. J. Mol. Med. 2015, 93, 1075–1084. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ramaswamy, V.; Taylor, M.D. Medulloblastoma: From Myth to Molecular. J. Clin. Oncol. 2017, 35, 2355–2363. [Google Scholar] [CrossRef] [PubMed]
- Robinson, G.W.; Rudneva, V.A.; Buchhalter, I.; Billups, C.A.; Waszak, S.M.; Smith, K.S.; Bowers, D.C.; Bendel, A.; Fisher, P.G.; Partap, S.; et al. Risk-adapted therapy for young children with medulloblastoma (SJYC07): Therapeutic and molecular outcomes from a multicentre, phase 2 trial. Lancet Oncol. 2018, 19, 768–784. [Google Scholar] [CrossRef]
- Lafay-Cousin, L.; Bouffet, E.; Strother, D.; Rudneva, V.; Hawkins, C.; Eberhart, C.; Horbinski, C.; Heier, L.; Souweidane, M.; Williams-Hughes, C.; et al. Phase II Study of Nonmetastatic Desmoplastic Medulloblastoma in Children Younger Than 4 Years of Age: A Report of the Children’s Oncology Group (ACNS1221). J. Clin. Oncol. 2020, 38, 223–231. [Google Scholar] [CrossRef]
- Heinlein, C.; Krepulat, F.; Lohler, J.; Speidel, D.; Deppert, W.; Tolstonog, G.V. Mutant p53(R270H) gain of function phenotype in a mouse model for oncogene-induced mammary carcinogenesis. Int. J. Cancer 2008, 122, 1701–1709. [Google Scholar] [CrossRef]
- Olive, K.P.; Tuveson, D.A.; Ruhe, Z.C.; Yin, B.; Willis, N.A.; Bronson, R.T.; Crowley, D.; Jacks, T. Mutant p53 gain of function in two mouse models of Li-Fraumeni syndrome. Cell 2004, 119, 847–860. [Google Scholar] [CrossRef] [Green Version]
- Korshunov, A.; Remke, M.; Kool, M.; Hielscher, T.; Northcott, P.A.; Williamson, D.; Pfaff, E.; Witt, H.; Jones, D.T.; Ryzhova, M.; et al. Biological and clinical heterogeneity of MYCN-amplified medulloblastoma. Acta Neuropathol. 2012, 123, 515–527. [Google Scholar] [CrossRef]
- Zhukova, N.; Ramaswamy, V.; Remke, M.; Pfaff, E.; Shih, D.J.; Martin, D.C.; Castelo-Branco, P.; Baskin, B.; Ray, P.N.; Bouffet, E.; et al. Subgroup-specific prognostic implications of TP53 mutation in medulloblastoma. J. Clin. Oncol. 2013, 31, 2927–2935. [Google Scholar] [CrossRef] [Green Version]
- Beauchamp, E.M.; Ringer, L.; Bulut, G.; Sajwan, K.P.; Hall, M.D.; Lee, Y.C.; Peaceman, D.; Ozdemirli, M.; Rodriguez, O.; Macdonald, T.J.; et al. Arsenic trioxide inhibits human cancer cell growth and tumor development in mice by blocking Hedgehog/GLI pathway. J. Clin. Investig. 2011, 121, 148–160. [Google Scholar] [CrossRef]
- Berman, D.M.; Karhadkar, S.S.; Hallahan, A.R.; Pritchard, J.I.; Eberhart, C.G.; Watkins, D.N.; Chen, J.K.; Cooper, M.K.; Taipale, J.; Olson, J.M.; et al. Medulloblastoma growth inhibition by hedgehog pathway blockade. Science 2002, 297, 1559–1561. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Katoh, Y.; Katoh, M. Hedgehog target genes: Mechanisms of carcinogenesis induced by aberrant hedgehog signaling activation. Curr. Mol. Med. 2009, 9, 873–886. [Google Scholar] [CrossRef] [PubMed]
- Low, J.A.; de Sauvage, F.J. Clinical experience with Hedgehog pathway inhibitors. J. Clin. Oncol. 2010, 28, 5321–5326. [Google Scholar] [CrossRef] [PubMed]
- Bockmayr, M.; Mohme, M.; Klauschen, F.; Winkler, B.; Budczies, J.; Rutkowski, S.; Schuller, U. Subgroup-specific immune and stromal microenvironment in medulloblastoma. Oncoimmunology 2018, 7, e1462430. [Google Scholar] [CrossRef] [Green Version]
- Dehne, N.; Mora, J.; Namgaladze, D.; Weigert, A.; Brune, B. Cancer cell and macrophage cross-talk in the tumor microenvironment. Curr. Opin. Pharmacol. 2017, 35, 12–19. [Google Scholar] [CrossRef]
- Maximov, V.; Chen, Z.; Wei, Y.; Robinson, M.H.; Herting, C.J.; Shanmugam, N.S.; Rudneva, V.A.; Goldsmith, K.C.; MacDonald, T.J.; Northcott, P.A.; et al. Tumour-associated macrophages exhibit anti-tumoural properties in Sonic Hedgehog medulloblastoma. Nat. Commun. 2019, 10, 2410. [Google Scholar] [CrossRef] [Green Version]
- Margol, A.S.; Robison, N.J.; Gnanachandran, J.; Hung, L.T.; Kennedy, R.J.; Vali, M.; Dhall, G.; Finlay, J.L.; Erdreich-Epstein, A.; Krieger, M.D.; et al. Tumor-associated macrophages in SHH subgroup of medulloblastomas. Clin. Cancer Res. 2015, 21, 1457–1465. [Google Scholar] [CrossRef] [Green Version]
- Zhang, J.; Yuan, X.; Wang, Y.; Liu, J.; Li, Z.; Li, S.; Liu, Y.; Gong, X.; Sun, Y.; Wu, W.; et al. Tumor-Associated Macrophages Correlate With Prognosis in Medulloblastoma. Front. Oncol. 2022, 12, 893132. [Google Scholar] [CrossRef]
- Lee, C.; Lee, J.; Choi, S.A.; Kim, S.K.; Wang, K.C.; Park, S.H.; Kim, S.H.; Lee, J.Y.; Phi, J.H. M1 macrophage recruitment correlates with worse outcome in SHH Medulloblastomas. BMC Cancer 2018, 18, 535. [Google Scholar] [CrossRef] [Green Version]
- Sharma, T.; Schwalbe, E.C.; Williamson, D.; Sill, M.; Hovestadt, V.; Mynarek, M.; Rutkowski, S.; Robinson, G.W.; Gajjar, A.; Cavalli, F.; et al. Second-generation molecular subgrouping of medulloblastoma: An international meta-analysis of Group 3 and Group 4 subtypes. Acta Neuropathol. 2019, 138, 309–326. [Google Scholar] [CrossRef]
- Northcott, P.A.; Shih, D.J.; Peacock, J.; Garzia, L.; Morrissy, A.S.; Zichner, T.; Stutz, A.M.; Korshunov, A.; Reimand, J.; Schumacher, S.E.; et al. Subgroup-specific structural variation across 1000 medulloblastoma genomes. Nature 2012, 488, 49–56. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pham, C.D.; Flores, C.; Yang, C.; Pinheiro, E.M.; Yearley, J.H.; Sayour, E.J.; Pei, Y.; Moore, C.; McLendon, R.E.; Huang, J.; et al. Differential Immune Microenvironments and Response to Immune Checkpoint Blockade among Molecular Subtypes of Murine Medulloblastoma. Clin. Cancer Res. 2016, 22, 582–595. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kennis, B.A.; Michel, K.A.; Brugmann, W.B.; Laureano, A.; Tao, R.H.; Somanchi, S.S.; Einstein, S.A.; Bravo-Alegria, J.B.; Maegawa, S.; Wahba, A.; et al. Monitoring of intracerebellarly-administered natural killer cells with fluorine-19 MRI. J. Neurooncol. 2019, 142, 395–407. [Google Scholar] [CrossRef] [PubMed]
- Liang, K.H.; Chang, C.C.; Wu, K.S.; Yu, A.L.; Sung, S.Y.; Lee, Y.Y.; Liang, M.L.; Chen, H.H.; Fen, J.J.; Chao, M.E.; et al. Notch signaling and natural killer cell infiltration in tumor tissues underlie medulloblastoma prognosis. Sci. Rep. 2021, 11, 23282. [Google Scholar] [CrossRef]
- Hara, A.; Koyama-Nasu, R.; Takami, M.; Toyoda, T.; Aoki, T.; Ihara, F.; Kobayashi, M.; Hirono, S.; Matsutani, T.; Nakayama, T.; et al. CD1d expression in glioblastoma is a promising target for NKT cell-based cancer immunotherapy. Cancer Immunol. Immunother. 2021, 70, 1239–1254. [Google Scholar] [CrossRef]
- Liu, D.; Song, L.; Brawley, V.S.; Robison, N.; Wei, J.; Gao, X.; Tian, G.; Margol, A.; Ahmed, N.; Asgharzadeh, S.; et al. Medulloblastoma expresses CD1d and can be targeted for immunotherapy with NKT cells. Clin. Immunol. 2013, 149, 55–64. [Google Scholar] [CrossRef] [Green Version]
- Powell, A.B.; Yadavilli, S.; Saunders, D.; Van Pelt, S.; Chorvinsky, E.; Burga, R.A.; Albihani, S.; Hanley, P.J.; Xu, Z.; Pei, Y.; et al. Medulloblastoma rendered susceptible to NK-cell attack by TGFbeta neutralization. J. Transl. Med. 2019, 17, 321. [Google Scholar] [CrossRef]
- Santhana Kumar, K.; Neve, A.; Guerreiro Stucklin, A.S.; Kuzan-Fischer, C.M.; Rushing, E.J.; Taylor, M.D.; Tripolitsioti, D.; Behrmann, L.; Kirschenbaum, D.; Grotzer, M.A.; et al. TGF-beta Determines the Pro-migratory Potential of bFGF Signaling in Medulloblastoma. Cell Rep. 2018, 23, 3798–3812.e8. [Google Scholar] [CrossRef]
- Gate, D.; Danielpour, M.; Rodriguez, J., Jr.; Kim, G.B.; Levy, R.; Bannykh, S.; Breunig, J.J.; Kaech, S.M.; Flavell, R.A.; Town, T. T-cell TGF-beta signaling abrogation restricts medulloblastoma progression. Proc. Natl. Acad. Sci. USA 2014, 111, E3458–E3466. [Google Scholar] [CrossRef] [Green Version]
- Aref, D.; Moffatt, C.J.; Agnihotri, S.; Ramaswamy, V.; Dubuc, A.M.; Northcott, P.A.; Taylor, M.D.; Perry, A.; Olson, J.M.; Eberhart, C.G.; et al. Canonical TGF-beta pathway activity is a predictor of SHH-driven medulloblastoma survival and delineates putative precursors in cerebellar development. Brain Pathol. 2013, 23, 178–191. [Google Scholar] [CrossRef]
- Parker, M.; Mohankumar, K.M.; Punchihewa, C.; Weinlich, R.; Dalton, J.D.; Li, Y.; Lee, R.; Tatevossian, R.G.; Phoenix, T.N.; Thiruvenkatam, R.; et al. C11orf95-RELA fusions drive oncogenic NF-kappaB signalling in ependymoma. Nature 2014, 506, 451–455. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nobusawa, S.; Hirato, J.; Sugai, T.; Okura, N.; Yamazaki, T.; Yamada, S.; Ikota, H.; Nakazato, Y.; Yokoo, H. Atypical Teratoid/Rhabdoid Tumor (AT/RT) Arising From Ependymoma: A Type of AT/RT Secondarily Developing From Other Primary Central Nervous System Tumors. J. Neuropathol. Exp. Neurol. 2016, 75, 167–174. [Google Scholar] [CrossRef] [Green Version]
- Polivka, J.; Svajdler, M.; Priban, V.; Mracek, J.; Kasik, P.; Martinek, P.; Ptakova, N.; Sharif Bagheri, M.; Shetti, D.; Pesta, M.; et al. Oncogenic Fusions in Gliomas: An Institutional Experience. Anticancer Res. 2022, 42, 1933–1939. [Google Scholar] [CrossRef] [PubMed]
- Zeng, J.; Xi, S.Y.; Wang, F.; Liao, H.D.; Yang, Y.Z.; Hu, W.M. L1CAM High Expression Associates with Poor Prognosis in Glioma but Does Not Correlate with C11orf95-RELA Fusion. Biomed. Res. Int. 2020, 2020, 1353284. [Google Scholar] [CrossRef]
- Pajtler, K.W.; Witt, H.; Sill, M.; Jones, D.T.; Hovestadt, V.; Kratochwil, F.; Wani, K.; Tatevossian, R.; Punchihewa, C.; Johann, P.; et al. Molecular Classification of Ependymal Tumors across All CNS Compartments, Histopathological Grades, and Age Groups. Cancer Cell 2015, 27, 728–743. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ozawa, T.; Kaneko, S.; Szulzewsky, F.; Qiao, Z.; Takadera, M.; Narita, Y.; Kondo, T.; Holland, E.C.; Hamamoto, R.; Ichimura, K. C11orf95-RELA fusion drives aberrant gene expression through the unique epigenetic regulation for ependymoma formation. Acta Neuropathol. Commun. 2021, 9, 36. [Google Scholar] [CrossRef]
Subgroup | WNT | SHH | Non-WNT/non-SHH | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Subtype | α | β | α | β | γ | II | III | IV | V | VI | VII | VIII |
Frequency (%) | 87.5 | 12.5 | 29.2 | 37.5 | 33.3 | 11.4 | 8.6 | 11.4 | 2.9 | 14.3 | 37.1 | 14.3 |
Median age (years) | 8.4 | 3.1 | 6.5 | 4.1 | 1.4 | 5.5 | 4.3 | 3.0 | 15.4 | 6.6 | 8.3 | 10.1 |
Male/female ratio | 0.2 | 1.3 | 0.8 | 1 | 0.3 | 3 | 4 | 1.6 | 0.7 | |||
Metastasis (%) | 0 | 0 | 14.3 | 33.3 | 0 | 50.0 | 33.3 | 50.0 | 0 | 40.0 | 38.5 | 20.0 |
Pathology variant (%) | ||||||||||||
Classic | 57.1 | 100 | 28.6 | 77.8 | 37.5 | 25.0 | 33.3 | 75.0 | 0 | 60.0 | 23.1 | 80.0 |
DNMB | 14.3 | 0 | 42.9 | 22.2 | 50.0 | 0 | 0 | 0 | 0 | 0 | 7.7 | 0 |
MBEN | 0 | 0 | 0 | 0 | 12.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
LCA | 28.6 | 0 | 28.6 | 0 | 0 | 75.0 | 33.3 | 25.0 | 100 | 40.0 | 69.2 | 20.0 |
MMMB | 0 | 0 | 0 | 0 | 0 | 0 | 33.3 | 0 | 0 | 0 | 0 | 0 |
5-year OS (%) | 100 | 100 | 85.7 | 77.8 | 62.5 | 0 | 100 | 100 | 50.0 | 71.8 | 100 | |
Molecular features | CTNNB1 and DDX3X mutation, monosomy 6 | MYCN amplification, PTEN deletion, TP53 mutation | MYCN, GLI2 amplification, SHH pathway mutation | MYC amplification, homologous recombination mutation | MYCN, CDK6 amplification | MYCN, GLI2 amplification, i17q | ||||||
Immune features | M2 macrophages infiltration | NK, NKT infiltration |
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Wu, K.-S.; Sung, S.-Y.; Huang, M.-H.; Lin, Y.-L.; Chang, C.-C.; Fang, C.-L.; Wong, T.-T.; Chen, H.-H.; Tsai, M.-L. Clinical and Molecular Features in Medulloblastomas Subtypes in Children in a Cohort in Taiwan. Cancers 2022, 14, 5419. https://doi.org/10.3390/cancers14215419
Wu K-S, Sung S-Y, Huang M-H, Lin Y-L, Chang C-C, Fang C-L, Wong T-T, Chen H-H, Tsai M-L. Clinical and Molecular Features in Medulloblastomas Subtypes in Children in a Cohort in Taiwan. Cancers. 2022; 14(21):5419. https://doi.org/10.3390/cancers14215419
Chicago/Turabian StyleWu, Kuo-Sheng, Shian-Ying Sung, Man-Hsu Huang, Yu-Ling Lin, Che-Chang Chang, Chia-Lang Fang, Tai-Tong Wong, Hsin-Hung Chen, and Min-Lan Tsai. 2022. "Clinical and Molecular Features in Medulloblastomas Subtypes in Children in a Cohort in Taiwan" Cancers 14, no. 21: 5419. https://doi.org/10.3390/cancers14215419
APA StyleWu, K. -S., Sung, S. -Y., Huang, M. -H., Lin, Y. -L., Chang, C. -C., Fang, C. -L., Wong, T. -T., Chen, H. -H., & Tsai, M. -L. (2022). Clinical and Molecular Features in Medulloblastomas Subtypes in Children in a Cohort in Taiwan. Cancers, 14(21), 5419. https://doi.org/10.3390/cancers14215419