Abstract
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 children1,2, 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 medulloblastoma3. 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 (MBSHH). ELP1 was the most common medulloblastoma predisposition gene and increased the prevalence of genetic predisposition to 40% among paediatric patients with MBSHH. Parent–offspring and pedigree analyses identified two families with a history of paediatric medulloblastoma. ELP1-associated medulloblastomas were restricted to the molecular SHHα subtype4 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 (U34) position5,6. Tumours from patients with ELP1-associated MBSHH 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 systems7,8,9. 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.
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Data availability
Germline and tumour DNA sequencing, RNA sequencing, and DNA methylation array datasets have been deposited to the European Genome-phenome Archive (EGA) with accession number EGAS00001004126. Proteomic datasets have been deposited to the Proteomics Identifications Database (PRIDE) with accession number PXD016832. Molecular datasets can be freely explored using the St Jude PeCan Data Portal (https://pecan.stjude.cloud/proteinpaint/study/MB-ELP1). Source Data for Figs. 1, 3 and 4 are provided with the paper. All other data are available from the corresponding authors upon reasonable request.
Code availability
All custom code used to generate the data in this study is available upon reasonable request.
References
Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018).
Zhang, J. et al. Germline Mutations in Predisposition Genes in Pediatric Cancer. N. Engl. J. Med. 373, 2336–2346 (2015).
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).
Cavalli, F. M. G. et al. Intertumoral heterogeneity within medulloblastoma subgroups. Cancer Cell 31, 737–754 (2017).
Hawer, H. et al. Roles of elongator dependent tRNA Modification pathways in neurodegeneration and Cancer. Genes 10, E19 (2018).
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).
Goffena, J. et al. Elongator and codon bias regulate protein levels in mammalian peripheral neurons. Nat. Commun. 9, 889 (2018).
Laguesse, S. et al. A dynamic unfolded protein response contributes to the control of cortical neurogenesis. Dev. Cell 35, 553–567 (2015).
Nedialkova, D. D. & Leidel, S. A. Optimization of codon translation rates via tRNA modifications maintains proteome integrity. Cell 161, 1606–1618 (2015).
Rahman, N. Realizing the promise of cancer predisposition genes. Nature 505, 302–308 (2014).
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).
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).
Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).
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).
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).
Dauden, M. I. et al. Architecture of the yeast Elongator complex. EMBO Rep. 18, 264–279 (2017).
Setiaputra, D. T. et al. Molecular architecture of the yeast Elongator complex reveals an unexpected asymmetric subunit arrangement. EMBO Rep. 18, 280–291 (2017).
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).
Yoshida, M. et al. Rectifier of aberrant mRNA splicing recovers tRNA modification in familial dysautonomia. Proc. Natl Acad. Sci. USA 112, 2764–2769 (2015).
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).
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).
Hetz, C. & Saxena, S. ER stress and the unfolded protein response in neurodegeneration. Nat. Rev. Neurol. 13, 477–491 (2017).
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).
Argelaguet, R. et al. Multi-omics factor analysis—a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).
Creppe, C. et al. Elongator controls the migration and differentiation of cortical neurons through acetylation of α-tubulin. Cell 136, 551–564 (2009).
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).
Murphy, F. V. IV, Ramakrishnan, V., Malkiewicz, A. & Agris, P. F. The role of modifications in codon discrimination by tRNALysUUU. Nat. Struct. Mol. Biol. 11, 1186–1191 (2004).
The GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).
Carter, R. A. et al. A single-cell transcriptional atlas of the developing murine cerebellum. Curr. Biol. 28, 2910–2920 (2018).
Begemann, M. et al. Germline GPR161 mutations predispose to pediatric medulloblastoma. J. Clin. Oncol. 38, 43–50 (2019).
Tan, A., Abecasis, G. R. & Kang, H. M. Unified representation of genetic variants. Bioinformatics 31, 2202–2204 (2015).
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).
Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).
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).
Mallick, S. et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature 538, 201–206 (2016).
Waszak, S. M. et al. Population variation and genetic control of modular chromatin architecture in humans. Cell 162, 1039–1050 (2015).
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).
Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).
Schubert, O. T. et al. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat. Protoc. 10, 426–441 (2015).
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).
Röst, H. L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219–223 (2014).
Shao, W. et al. Comparative analysis of mRNA and protein degradation in prostate tissues indicates high stability of proteins. Nat. Commun. 10, 2524 (2019).
Choi, M. et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30, 2524–2526 (2014).
Aken, B. L. et al. The Ensembl gene annotation system. Database (Oxford) 2016, baw093 (2016).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
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).
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).
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).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
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).
Cardoso-Moreira, M. et al. Gene expression across mammalian organ development. Nature 571, 505–509 (2019).
Su, D. et al. Quantitative analysis of ribonucleoside modifications in tRNA by HPLC-coupled mass spectrometry. Nat. Protoc. 9, 828–841 (2014).
Machnicka, M. A. et al. MODOMICS: a database of RNA modification pathways—2013 update. Nucleic Acids Res. 41, D262–D267 (2013).
Acknowledgements
This project was supported by the PedBrain Tumor Project contributing to the International Cancer Genome Consortium (ICGC), funded by the German Cancer Aid (109252), the German Federal Ministry of Education and Research (BMBF) (01KU1201A, 01KU1201C), and through BMBF grants BioTop (01EK1502A, 01EK1502B), ICGC-DE-Mining (01KU1505F), MedSys (0315416C) and NGFNplus (01GS0883). J.O.K. was supported by a European Research Council Starting Grant (336045) and acknowledges EurocanPlatform (260791) funding from the European Commission. S.M.W. was supported by a Swiss National Science Foundation Early Postdoc Mobility Fellowship (P2ELP3_155365) and an EMBO Long-Term Fellowship (ALTF 755-2014). A.K. is supported by the Helmholtz Association Research Grant (Germany). M.R. is supported by the RSF Research Grant no. 18-45-06012. We acknowledge the EMBL IT facilities for supporting the genomic analyses. P.A.N. is a Pew-Stewart Scholar for Cancer Research (Margaret and Alexander Stewart Trust) and recipient of The Sontag Foundation Distinguished Scientist Award. P.A.N. was also supported by the National Cancer Institute (R01CA232143-01), American Association for Cancer Research (NextGen Grant for Transformative Cancer Research), The Brain Tumour Charity (Quest for Cures and Clinical Biomarkers), the American Lebanese Syrian Associated Charities (ALSAC), and St Jude. From St Jude, we explicitly acknowledge the Hartwell Center, the Biorepository, members of the Department of Computational Biology, Clinical Genomics, the Diagnostic Biomarkers Shared Resource in the Department of Pathology, and the Center for In Vivo Imaging and Therapeutics. Where authors are identified as personnel of IARC/WHO, the authors alone are responsible for the views expressed in this Article, and they do not necessarily represent the decisions, policy, or views of IARC/WHO.
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Contributions
Study conception: P.A.N., S.M.P. Study design: S.M.W., G.W.R., J.O.K., P.A.N., S.M.P. Sample collection, processing and patient data generation: G.W.R., J.G.-L., J.H., E.I., N.J., T.R., M. Kool, D.S., D.T.W.J., A.V., R.G.T., G.N., B. Lombard, D.L., J.N., M. Rusch, D.C.B., A.B., S. Partap, M.C., J.C., N.G.G., A.S., C.D., S.R., T.E., F.W., K.K., M.F., B. Lannering., J.S., C.J., T.V.A., M. Röösli, C.E.K., M.G., M. Remke, S. Puget, K.W.P., T.M., O.W., M. Ryzhova, A.K., B.A.O., D.W.E., L.B., A.G., O.A., P.A.N., S.M.P. Germline call-set and burden analysis: S.M.W., J.O.K. Pedigree analysis: K.V.H., K.E.N., G.W.R., L.B. Molecular classification: K.S.S., T.S. Somatic genome analysis: S.M.W., K.S.S., I.B., J.O.K. Transcriptome analysis: S.M.W., B.L.G. Proteome analysis: S.M.W., B.L.G., A.F., O.A., J.O.K. tRNA modification analysis: M. Kojic, B.J.W. Data deposition: I.B., J.K. Manuscript preparation (with feedback from all authors): S.M.W., G.W.R., B.L.G., K.S.S., J.O.K., P.A.N., S.M.P. Study supervision and funding: P.L., A.G., O.A., J.O.K., P.A.N., S.M.P.
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Extended data figures and tables
Extended Data Fig. 1 Case–control germline LOF variant burden analysis.
a–d, Case–control germline rare LOF variant association analysis in paediatric medulloblastoma subgroups versus paediatric controls (CEFALO) using burden tests (implemented in SKAT). P values were corrected for multiple testing using Bonferroni correction. e–h, Case–control germline rare LOF variant association analysis in paediatric medulloblastoma subgroups versus adult controls (gnomAD) using burden tests (two-sided Fisher’s Exact tests). P values were corrected for multiple testing using Bonferroni correction. i, Case–control germline LOF burden analysis in paediatric medulloblastoma versus adult controls (gnomAD). j–l, Case–control germline LOF burden analysis in infant (j), childhood (k) and adult (l) MBSHH versus adult controls (gnomAD).
Extended Data Fig. 2 Congenital radioulnar synostosis.
Right-arm X-rays of an unaffected child (control) and the ELP1-associated MBSHH patient (SJMBWES339).
Extended Data Fig. 3 Molecular MBSHH subtypes.
a, DNA methylation-based UMAP plot of MBSHH with inference of MBSHH subtypes (n = 262). b, Co-occurring and mutually exclusive somatic gene alterations in ELP1-associated medulloblastoma subtype SHHα. c, Co-occurring and mutually exclusive somatic chromosomal aberrations in ELP1-associated medulloblastoma subtype SHHα. d, Recurrent somatic copy-number alterations in ELP1-associated medulloblastoma subtype SHHα.
Extended Data Fig. 4 Inference of somatic evolution in ELP1-associated MBSHH.
a, Possible genetic models explaining the relationship between the germline status of ELP1 and two somatic mutational events (PTCH1 mutation and loss of chromosome arm 9q) in MBSHH. b, Posterior probabilities derived from Bayesian network analysis of all possible genetic models shown in a and data for 230 patients with MBSHH.
Extended Data Fig. 5 Molecular features of ELP1-associated MB.
a, Expression of ELP1 stratified by consensus medulloblastoma subgroup (n = 208 patients). P value was calculated using likelihood ratio tests. b, Expression of ELP1 stratified by molecular MBSHH subtype (n = 90 patients). P value was calculated using likelihood ratio tests. c, Expression of ELP1 stratified by germline ELP1 mutation status (n = 90 patients). P value was calculated using likelihood ratio tests. d, Differential gene expression in mutant (n = 10) and wild-type (n = 9) ELP1 SHHα. P values were derived from models that use negative binomial test statistics and were adjusted for multiple testing based on FDR correction. e, Functional gene enrichment in ELP1-associated SHHα. f, GSEA-based enrichment of UPR pathways in MBSHH proteomes. **P < 0.01, ns (not significant), P > 0.05, two-sided Mann–Whitney U-test. g, GSEA-based enrichment of the Elongator complex in MBSHH proteomes. ***P < 0.001, two-sided Mann–Whitney U-test. All box plots are as defined in Fig. 1g.
Extended Data Fig. 6 Unsupervised multi-omics factor integration analysis of MBSHH.
a, Overview of input samples and data types. b, Summary of variance in latent factors (LF1–LF4) across data types. c, Somatic and germline gene alterations that contribute to latent factor 1 (LF1). d, Association between germline ELP1 mutation status and LF1 score (n = 16 patients). P value was calculated using a two-sided Mann–Whitney U-test. Box plots are as defined in Fig. 1g. e, Functional enrichment of LF1-ranked proteins and mRNAs.
Extended Data Fig. 7 Quantification of tRNA modifications in ELP1-associated MBSHH.
a, Quantification of mcm5U nucleosides in mutant and wild-type ELP1 MBSHH PDX (n = 4 biologically independent samples for each). b, Quantification of m1A nucleosides in mutant and wild-type ELP1 MBSHH PDX (n = 4 biologically independent samples for each). c, Quantification of m7G nucleosides in mutant and wild-type ELP1 MBSHH PDX (n = 4 biologically independent samples for each). All data are mean and s.e.m. n.s., not significant (P > 0.05). *P < 0.05, two-sided Welch t-test.
Extended Data Fig. 8 Spatio-temporal ELP1 expression in human and mouse.
a, Expression of ELP1 in adult human tissues (n = 9–653 donors per tissue). Violin plots depict kernel density estimates and represent the density distribution. b, Expression of ELP1 during human brain development (n = 3–12 donors per tissue and time point). Box plots are as in Fig. 1g. c, Expression of ELP1 during human organ development. Shaded areas define 90% confidence intervals (n = 18–58 donors per tissue). d, Expression of Elp1 during mouse cerebellum development (n = 27 mice). Data are mean and s.e.m. expression of cells with non-zero ELP1 expression.
Supplementary information
Supplementary Table
Supplementary Table 1: Sample overview.
Supplementary Table
Supplementary Table 2: Germline ELP1 loss-of-function mutations in MBSHH patients.
Supplementary Table
Supplementary Table 3: Familial transmission of germline ELP1 LoF variants in parent-offspring trios.
Supplementary Table
Supplementary Table 4: ELP1-associated transcriptome (nmut=10 and nwt=80) and proteome (nmut=6 and nwt=9). P values were calculated using negative binomial tests and empirical Bayes statistics and adjusted for multiple testing using FDR correction.
Supplementary Table
Supplementary Table 5: Dynamic mass spectrometer parameters for ribonucleotides.
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Waszak, S., Robinson, G., Gudenas, B.L. et al. Germline Elongator mutations in Sonic Hedgehog medulloblastoma. Nature 580, 396–401 (2020). https://doi.org/10.1038/s41586-020-2164-5
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DOI: https://doi.org/10.1038/s41586-020-2164-5
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