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Enregistrement W2799574682 · doi:10.1016/s1470-2045(18)30242-0

Spectrum and prevalence of genetic predisposition in medulloblastoma: a retrospective genetic study and prospective validation in a clinical trial cohort

2018· article· en· W2799574682 sur OpenAlexafffundabout
Sebastian M. Waszak, Paul A. Northcott, Ivo Buchhalter, Giles Robinson, Christian Sutter, Susanne N. Groebner, Kerstin Grund, Laurence Brugières, David Jones, Kristian W. Pajtler, A. Sorana Morrissy, Marcel Kool, Dominik Sturm, Lukas Chávez, Aurélie Ernst, Sebastian Brabetz, M. Hain, Thomas Zichner, Maia Segura‐Wang, Joachim Weischenfeldt, Tobias Rausch, Balca R. Mardin, Xin Zhou, Cristina Baciu, Christian Lawerenz, Jennifer A. Chan, Pascale Varlet, Léa Guerrini‐Rousseau, Daniel W. Fults, Wiesława Grajkowska, Péter Hauser, Nada Jabado, Young‐Shin Ra, Karel Zitterbart, Suyash Shringarpure, Francisco M. De La Vega, Carlos D. Bustamante, Ho‐Keung Ng, Arie Perry, Tobey J. MacDonald, Pablo Hernáiz Driever, Anne Bendel, Daniel C. Bowers, Geoffrey McCowage, Murali Chintagumpala, Richard J. Cohn, Tim Hassall, Gudrun Fleischhack, Tone Eggen, Finn Wesenberg, Maria Feychting, Birgitta Lannering, Joachim Schüz, Christoffer Johansen, Tina Veje Andersen, Martin Röösli, Claudia E. Kuehni, Michael A. Grotzer, Kristina Kjærheim, Camelia Maria Monoranu, Tenley C. Archer, Elizabeth S. Duke, Scott L. Pomeroy, Shelagh Redmond, Stephan Frank, David Sumerauer, Wolfram Scheurlen, Marina Ryzhova, Till Milde, Christian P. Kratz, David Samuel, Jinghui Zhang, David A. Solomon, Marco A. Marra, Roland Eils, Claus R. Bartram, Katja von Hoff, Stefan Rutkowski, Vijay Ramaswamy, Richard J. Gilbertson, Andrey Korshunov, Michael D. Taylor, Peter Lichter, David Malkin, Amar Gajjar, Jan O. Korbel, Stefan M. Pfister

Notice bibliographique

RevueThe Lancet Oncology · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueGlioma Diagnosis and Treatment
Établissements canadiensCanada's Michael Smith Genome Sciences CentreBC Cancer AgencyUniversity of CalgaryMcGill UniversityUniversity of TorontoToronto General HospitalUniversity Health NetworkHospital for Sick Children
Organismes subventionnairesNational Heart, Lung, and Blood InstituteStrategic Research CouncilCanadian Institutes of Health ResearchBrain Tumour ResearchNational Cancer InstituteFondation de l'Hôpital de Montréal pour enfantsBC Cancer FoundationLékařská fakulta, Masarykova univerzitaEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentMasarykova UniverzitaNationales Centrum für Tumorerkrankungen HeidelbergGöteborgs UniversitetUniversitetet i OsloKarolinska InstitutetTerry Fox Research InstituteCancerfondenUniversität ZürichBundesministerium für Bildung und ForschungNorges ForskningsrådSemmelweis EgyetemBundesamt für GesundheitPediatric Brain Tumor FoundationDeutsche KrebshilfeSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungBroad InstituteForskningsrådet om Hälsa, Arbetsliv och VälfärdUniversidad Nacional Autónoma de MéxicoPediatric Oncology Group of OntarioFH FoundationInstituto Mexicano del Seguro SocialColorado State UniversityBarncancerfondenOntario Institute for Cancer ResearchUniversity of MiamiEuropean Molecular Biology OrganizationUniversity of BernMinisterstvo Zdravotnictví Ceské RepublikyRockefeller UniversityMassachusetts Institute of TechnologyUniversität BaselNational Institutes of HealthChildren's Hospital FoundationJohn D. and Catherine T. MacArthur FoundationGenome CanadaGarron Family Cancer CentreEuropean CommissionSontag FoundationHospital for Sick ChildrenGenome British ColumbiaStrategiske ForskningsrådCancer Research UKWorld Health OrganizationDeutsche KinderkrebsstiftungUniversity of ChicagoUniversity of CaliforniaVetenskapsrådetEuropean Research CouncilV Foundation for Cancer ResearchGovernment of OntarioAlexander and Margaret Stewart TrustDeutsches Krebsforschungszentrum
Mots-clésMedulloblastomaGenetic predispositionRetrospective cohort studyMedicineProspective cohort studyCohortClinical trialOncologyPediatricsInternal medicinePathology

Résumé

récupéré en direct d'OpenAlex

Background Medulloblastoma is associated with rare hereditary cancer predisposition syndromes; however, consensus medulloblastoma predisposition genes have not been defined and screening guidelines for genetic counselling and testing for paediatric patients are not available. We aimed to assess and define these genes to provide evidence for future screening guidelines. Methods In this international, multicentre study, we analysed patients with medulloblastoma from retrospective cohorts (International Cancer Genome Consortium [ICGC] PedBrain, Medulloblastoma Advanced Genomics International Consortium [MAGIC], and the CEFALO series) and from prospective cohorts from four clinical studies (SJMB03, SJMB12, SJYC07, and I-HIT-MED). Whole-genome sequences and exome sequences from blood and tumour samples were analysed for rare damaging germline mutations in cancer predisposition genes. DNA methylation profiling was done to determine consensus molecular subgroups: WNT (MB WNT ), SHH (MB SHH ), group 3 (MB Group3 ), and group 4 (MB Group4 ). Medulloblastoma predisposition genes were predicted on the basis of rare variant burden tests against controls without a cancer diagnosis from the Exome Aggregation Consortium (ExAC). Previously defined somatic mutational signatures were used to further classify medulloblastoma genomes into two groups, a clock-like group (signatures 1 and 5) and a homologous recombination repair deficiency-like group (signatures 3 and 8), and chromothripsis was investigated using previously established criteria. Progression-free survival and overall survival were modelled for patients with a genetic predisposition to medulloblastoma. Findings We included a total of 1022 patients with medulloblastoma from the retrospective cohorts (n=673) and the four prospective studies (n=349), from whom blood samples (n=1022) and tumour samples (n=800) were analysed for germline mutations in 110 cancer predisposition genes. In our rare variant burden analysis, we compared these against 53 105 sequenced controls from ExAC and identified APC, BRCA2, PALB2, PTCH1, SUFU , and TP53 as consensus medulloblastoma predisposition genes according to our rare variant burden analysis and estimated that germline mutations accounted for 6% of medulloblastoma diagnoses in the retrospective cohort. The prevalence of genetic predispositions differed between molecular subgroups in the retrospective cohort and was highest for patients in the MB SHH subgroup (20% in the retrospective cohort). These estimates were replicated in the prospective clinical cohort (germline mutations accounted for 5% of medulloblastoma diagnoses, with the highest prevalence [14%] in the MB SHH subgroup). Patients with germline APC mutations developed MB WNT and accounted for most (five [71%] of seven) cases of MB WNT that had no somatic CTNNB1 exon 3 mutations. Patients with germline mutations in SUFU and PTCH1 mostly developed infant MB SHH . Germline TP53 mutations presented only in childhood patients in the MB SHH subgroup and explained more than half (eight [57%] of 14) of all chromothripsis events in this subgroup. Germline mutations in PALB2 and BRCA2 were observed across the MB SHH , MB Group3 , and MB Group4 molecular subgroups and were associated with mutational signatures typical of homologous recombination repair deficiency. In patients with a genetic predisposition to medulloblastoma, 5-year progression-free survival was 52% (95% CI 40–69) and 5-year overall survival was 65% (95% CI 52–81); these survival estimates differed significantly across patients with germline mutations in different medulloblastoma predisposition genes. Interpretation Genetic counselling and testing should be used as a standard-of-care procedure in patients with MB WNT and MB SHH because these patients have the highest prevalence of damaging germline mutations in known cancer predisposition genes. We propose criteria for routine genetic screening for patients with medulloblastoma based on clinical and molecular tumour characteristics. Funding German Cancer Aid; German Federal Ministry of Education and Research; German Childhood Cancer Foundation (Deutsche Kinderkrebsstiftung); European Research Council; National Institutes of Health; Canadian Institutes for Health Research; German Cancer Research Center; St Jude Comprehensive Cancer Center; American Lebanese Syrian Associated Charities; Swiss National Science Foundation; European Molecular Biology Organization; Cancer Research UK; Hertie Foundation; Alexander and Margaret Stewart Trust; V Foundation for Cancer Research; Sontag Foundation; Musicians Against Childhood Cancer; BC Cancer Foundation; Swedish Council for Health, Working Life and Welfare; Swedish Research Council; Swedish Cancer Society; the Swedish Radiation Protection Authority; Danish Strategic Research Council; Swiss Federal Office of Public Health; Swiss Research Foundation on Mobile Communication; Masaryk University; Ministry of Health of the Czech Republic; Research Council of Norway; Genome Canada; Genome BC; Terry Fox Research Institute; Ontario Institute for Cancer Research; Pediatric Oncology Group of Ontario; The Family of Kathleen Lorette and the Clark H Smith Brain Tumour Centre; Montreal Children's Hospital Foundation; The Hospital for Sick Children: Sonia and Arthur Labatt Brain Tumour Research Centre, Chief of Research Fund, Cancer Genetics Program, Garron Family Cancer Centre, MDT's Garron Family Endowment; BC Childhood Cancer Parents Association; Cure Search Foundation; Pediatric Brain Tumor Foundation; Brainchild; and the Government of Ontario.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,030

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,009
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,036
Tête enseignante GPT0,377
Écart entre enseignants0,341 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations394
Publié2018
Routes d'admission3
Résumé présentoui

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