Germline Elongator mutations in Sonic Hedgehog medulloblastoma.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 0410462 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-4687 (Electronic) Linking ISSN: 00280836 NLM ISO Abbreviation: Nature Subsets: MEDLINE

Publication: Basingstoke : Nature Publishing Group
Original Publication: London, Macmillan Journals ltd.

Germ-Line Mutation*; Cerebellar Neoplasms/*metabolism ; Medulloblastoma/*metabolism ; Transcriptional Elongation Factors/*metabolism; Cerebellar Neoplasms/genetics ; Cerebellar Neoplasms/pathology ; Child ; Female ; Humans ; Male ; Medulloblastoma/genetics ; Pedigree ; RNA, Transfer/metabolism ; Transcriptional Elongation Factors/genetics

Cancer genomics has revealed many genes and core molecular processes that contribute to human malignancies, but the genetic and molecular bases of many rare cancers remains unclear. Genetic predisposition accounts for 5 to 10% of cancer diagnoses in children ^1,2 , and genetic events that cooperate with known somatic driver events are poorly understood. Pathogenic germline variants in established cancer predisposition genes have been recently identified in 5% of patients with the malignant brain tumour medulloblastoma ³ . Here, by analysing all protein-coding genes, we identify and replicate rare germline loss-of-function variants across ELP1 in 14% of paediatric patients with the medulloblastoma subgroup Sonic Hedgehog (MB SHH ) . ELP1 was the most common medulloblastoma predisposition gene and increased the prevalence of genetic predisposition to 40% among paediatric patients with MB SHH . Parent-offspring and pedigree analyses identified two families with a history of paediatric medulloblastoma. ELP1-associated medulloblastomas were restricted to the molecular SHHα subtype ⁴ and characterized by universal biallelic inactivation of ELP1 owing to somatic loss of chromosome arm 9q. Most ELP1-associated medulloblastomas also exhibited somatic alterations in PTCH1, which suggests that germline ELP1 loss-of-function variants predispose individuals to tumour development in combination with constitutive activation of SHH signalling. ELP1 is the largest subunit of the evolutionarily conserved Elongator complex, which catalyses translational elongation through tRNA modifications at the wobble (U 34 ) position ^5,6 . Tumours from patients with ELP1-associated MB SHH were characterized by a destabilized Elongator complex, loss of Elongator-dependent tRNA modifications, codon-dependent translational reprogramming, and induction of the unfolded protein response, consistent with loss of protein homeostasis due to Elongator deficiency in model systems ^7-9 . Thus, genetic predisposition to proteome instability may be a determinant in the pathogenesis of paediatric brain cancers. These results support investigation of the role of protein homeostasis in other cancer types and potential for therapeutic interference.

Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018). (PMID: 2948975410.1038/nature25480)
Zhang, J. et al. Germline Mutations in Predisposition Genes in Pediatric Cancer. N. Engl. J. Med. 373, 2336–2346 (2015). (PMID: 26580448473411910.1056/NEJMoa1508054)
Waszak, S. M. et al. Spectrum and prevalence of genetic predisposition in medulloblastoma: a retrospective genetic study and prospective validation in a clinical trial cohort. Lancet Oncol. 19, 785–798 (2018). (PMID: 29753700598424810.1016/S1470-2045(18)30242-0)
Cavalli, F. M. G. et al. Intertumoral heterogeneity within medulloblastoma subgroups. Cancer Cell 31, 737–754 (2017). (PMID: 28609654616305310.1016/j.ccell.2017.05.005)
Hawer, H. et al. Roles of elongator dependent tRNA Modification pathways in neurodegeneration and Cancer. Genes 10, E19 (2018). (PMID: 3059791410.3390/genes10010019)
Johansson, M. J. O., Xu, F. & Byström, A. S. Elongator-a tRNA modifying complex that promotes efficient translational decoding. Biochim. Biophys. Acta. Gene Regul. Mech. 1861, 401–408 (2018). (PMID: 2917001010.1016/j.bbagrm.2017.11.006)
Goffena, J. et al. Elongator and codon bias regulate protein levels in mammalian peripheral neurons. Nat. Commun. 9, 889 (2018). (PMID: 29497044583279110.1038/s41467-018-03221-z)
Laguesse, S. et al. A dynamic unfolded protein response contributes to the control of cortical neurogenesis. Dev. Cell 35, 553–567 (2015). (PMID: 2665129210.1016/j.devcel.2015.11.005)
Nedialkova, D. D. & Leidel, S. A. Optimization of codon translation rates via tRNA modifications maintains proteome integrity. Cell 161, 1606–1618 (2015). (PMID: 26052047450380710.1016/j.cell.2015.05.022)
Rahman, N. Realizing the promise of cancer predisposition genes. Nature 505, 302–308 (2014). (PMID: 24429628497551110.1038/nature12981)
Aydin, D. et al. Mobile phone use and brain tumors in children and adolescents: a multicenter case-control study. J. Natl. Cancer Inst. 103, 1264–1276 (2011). (PMID: 2179566510.1093/jnci/djr244)
Karczewski, K. J. et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. Preprint at https://www.bioRxiv.org/content/10.1101/531210v3 (2019).
The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
Kool, M. et al. Genome sequencing of SHH medulloblastoma predicts genotype-related response to smoothened inhibition. Cancer Cell 25, 393–405 (2014). (PMID: 24651015449305310.1016/j.ccr.2014.02.004)
Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017). (PMID: 28726821590570010.1038/nature22973)
Schwalbe, E. C. et al. Novel molecular subgroups for clinical classification and outcome prediction in childhood medulloblastoma: a cohort study. Lancet Oncol. 18, 958–971 (2017). (PMID: 28545823548969810.1016/S1470-2045(17)30243-7)
Robinson, G. W. et al. Risk-adapted therapy for young children with medulloblastoma (SJYC07): therapeutic and molecular outcomes from a multicentre, phase 2 trial. Lancet Oncol. 19, 768–784 (2018). (PMID: 29778738607820610.1016/S1470-2045(18)30204-3)
Dauden, M. I. et al. Architecture of the yeast Elongator complex. EMBO Rep. 18, 264–279 (2017). (PMID: 2797437810.15252/embr.201643353)
Setiaputra, D. T. et al. Molecular architecture of the yeast Elongator complex reveals an unexpected asymmetric subunit arrangement. EMBO Rep. 18, 280–291 (2017). (PMID: 2787220510.15252/embr.201642548)
Rubin, B. Y. & Anderson, S. L. IKBKAP/ELP1 gene mutations: mechanisms of familial dysautonomia and gene-targeting therapies. Appl. Clin. Genet. 10, 95–103 (2017). (PMID: 29290691573598310.2147/TACG.S129638)
Yoshida, M. et al. Rectifier of aberrant mRNA splicing recovers tRNA modification in familial dysautonomia. Proc. Natl Acad. Sci. USA 112, 2764–2769 (2015). (PMID: 2567548610.1073/pnas.14155251124352824)
Gold-von Simson, G., Romanos-Sirakis, E., Maayan, C. & Axelrod, F. B. Neoplasia in familial dysautonomia: a 20-year review in a young patient population. J. Pediatr. 155, 934–936 (2009). (PMID: 1991443310.1016/j.jpeds.2009.04.055)
Shvartsbeyn, M., Rapkiewicz, A., Axelrod, F. & Kaufmann, H. Increased incidence of tumors with the IKBKAP gene mutation? A case report and review of the literature. World J. Oncol. 2, 41–44 (2011). (PMID: 291472245649887)
Hetz, C. & Saxena, S. ER stress and the unfolded protein response in neurodegeneration. Nat. Rev. Neurol. 13, 477–491 (2017). (PMID: 2873104010.1038/nrneurol.2017.99)
Forget, A. et al. Aberrant ERBB4–SRC signaling as a hallmark of group 4 medulloblastoma revealed by integrative phosphoproteomic profiling. Cancer Cell 34, 379–395 (2018). (PMID: 3020504310.1016/j.ccell.2018.08.002)
Argelaguet, R. et al. Multi-omics factor analysis—a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018). (PMID: 29925568601076710.15252/msb.20178124)
Creppe, C. et al. Elongator controls the migration and differentiation of cortical neurons through acetylation of α-tubulin. Cell 136, 551–564 (2009). (PMID: 1918533710.1016/j.cell.2008.11.043)
Huang, B., Johansson, M. J. & Byström, A. S. An early step in wobble uridine tRNA modification requires the Elongator complex. RNA 11, 424–436 (2005). (PMID: 15769872137073210.1261/rna.7247705)
Murphy, F. V. IV, Ramakrishnan, V., Malkiewicz, A. & Agris, P. F. The role of modifications in codon discrimination by tRNA ^Lys UUU. Nat. Struct. Mol. Biol. 11, 1186–1191 (2004). (PMID: 1555805210.1038/nsmb861)
The GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013). (PMID: 401006910.1038/ng.2653)
Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014). (PMID: 24695229410518810.1038/nature13185)
Carter, R. A. et al. A single-cell transcriptional atlas of the developing murine cerebellum. Curr. Biol. 28, 2910–2920 (2018). (PMID: 3022050110.1016/j.cub.2018.07.062)
Begemann, M. et al. Germline GPR161 mutations predispose to pediatric medulloblastoma. J. Clin. Oncol. 38, 43–50 (2019). (PMID: 3160964910.1200/JCO.19.005776943973)
Tan, A., Abecasis, G. R. & Kang, H. M. Unified representation of genetic variants. Bioinformatics 31, 2202–2204 (2015). (PMID: 25701572448184210.1093/bioinformatics/btv112)
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016). (PMID: 27268795489382510.1186/s13059-016-0974-4)
Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012). (PMID: 22962449343680510.1093/bioinformatics/bts378)
Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011). (PMID: 21737059313581110.1016/j.ajhg.2011.05.029)
Mallick, S. et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature 538, 201–206 (2016). (PMID: 27654912516155710.1038/nature18964)
Waszak, S. M. et al. Population variation and genetic control of modular chromatin architecture in humans. Cell 162, 1039–1050 (2015). (PMID: 2630012410.1016/j.cell.2015.08.001)
Ainsworth, H. F., Shin, S. Y. & Cordell, H. J. A comparison of methods for inferring causal relationships between genotype and phenotype using additional biological measurements. Genet. Epidemiol. 41, 577–586 (2017). (PMID: 28691305565574810.1002/gepi.22061)
Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018). (PMID: 29539639609321810.1038/nature26000)
Schubert, O. T. et al. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat. Protoc. 10, 426–441 (2015). (PMID: 2567520810.1038/nprot.2015.015)
Poullet, P., Carpentier, S. & Barillot, E. myProMS, a web server for management and validation of mass spectrometry-based proteomic data. Proteomics 7, 2553–2556 (2007). (PMID: 1761030510.1002/pmic.200600784)
Röst, H. L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219–223 (2014). (PMID: 2472777010.1038/nbt.2841)
Shao, W. et al. Comparative analysis of mRNA and protein degradation in prostate tissues indicates high stability of proteins. Nat. Commun. 10, 2524 (2019). (PMID: 31175306655581810.1038/s41467-019-10513-5)
Choi, M. et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30, 2524–2526 (2014). (PMID: 2479493110.1093/bioinformatics/btu305)
Aken, B. L. et al. The Ensembl gene annotation system. Database (Oxford) 2016, baw093 (2016). (PMID: 10.1093/database/baw093)
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). (PMID: 10.1093/bioinformatics/bts63523104886)
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). (PMID: 25516281430204910.1186/s13059-014-0550-8)
Sergushichev, A. An algorithm for fast preranked gene set enrichment. Preprint at https://www.bioRxiv.org/content/10.1101/060012v1 (2016).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005). (PMID: 161995171239896)
Doerks, T., Copley, R. R., Schultz, J., Ponting, C. P. & Bork, P. Systematic identification of novel protein domain families associated with nuclear functions. Genome Res. 12, 47–56 (2002). (PMID: 1177983015526510.1101/gr.203201)
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016). (PMID: 27535533501820710.1038/nature19057)
Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.11–11.10.33 (2013).
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010). (PMID: 20601685293820110.1093/nar/gkq603)
Cardoso-Moreira, M. et al. Gene expression across mammalian organ development. Nature 571, 505–509 (2019). (PMID: 31243369665835210.1038/s41586-019-1338-5)
Su, D. et al. Quantitative analysis of ribonucleoside modifications in tRNA by HPLC-coupled mass spectrometry. Nat. Protoc. 9, 828–841 (2014). (PMID: 24625781431353710.1038/nprot.2014.047)
Machnicka, M. A. et al. MODOMICS: a database of RNA modification pathways—2013 update. Nucleic Acids Res. 41, D262–D267 (2013). (PMID: 2311848410.1093/nar/gks1007)

001 International WHO_ World Health Organization; R01 CA232143 United States CA NCI NIH HHS

0 (Elp1 protein, human)
0 (Transcriptional Elongation Factors)
9014-25-9 (RNA, Transfer)

Date Created: 20200417 Date Completed: 20200508 Latest Revision: 20240324

20240324

PMC7430762

10.1038/s41586-020-2164-5

32296180