| Literature DB >> 29489754 |
Susanne N Gröbner1,2,3, Barbara C Worst1,2,3,4, Joachim Weischenfeldt5,6, Ivo Buchhalter7, Kortine Kleinheinz7, Vasilisa A Rudneva5,8, Pascal D Johann1,2,3,4, Gnana Prakash Balasubramanian1,2,9, Maia Segura-Wang5, Sebastian Brabetz1,2,3, Sebastian Bender1,2, Barbara Hutter3,7,9, Dominik Sturm1,2,3,4, Elke Pfaff1,2,3,4, Daniel Hübschmann4,9,10, Gideon Zipprich7, Michael Heinold7,10, Jürgen Eils7, Christian Lawerenz7, Serap Erkek1,2,3,5, Sander Lambo1,2,3, Sebastian Waszak5, Claudia Blattmann3,11, Arndt Borkhardt3,12, Michaela Kuhlen3,12, Angelika Eggert3,13, Simone Fulda3,14, Manfred Gessler15, Jenny Wegert15, Roland Kappler3,16, Daniel Baumhoer17, Stefan Burdach3,18, Renate Kirschner-Schwabe3,13, Udo Kontny3,19, Andreas E Kulozik1,3,4, Dietmar Lohmann3,20, Simone Hettmer21, Cornelia Eckert3,13, Stefan Bielack11, Michaela Nathrath3,18,22, Charlotte Niemeyer3,21, Günther H Richter3,18, Johannes Schulte3,13, Reiner Siebert23, Frank Westermann3,24, Jan J Molenaar25, Gilles Vassal26, Hendrik Witt1,2,3,4, Birgit Burkhardt27, Christian P Kratz28, Olaf Witt1,3,4,29, Cornelis M van Tilburg1,3,30, Christof M Kramm31, Gudrun Fleischhack3,32, Uta Dirksen32, Stefan Rutkowski33, Michael Frühwald34, Katja von Hoff33, Stephan Wolf35, Thomas Klingebiel3,36, Ewa Koscielniak11, Pablo Landgraf37, Jan Koster38, Adam C Resnick39, Jinghui Zhang40, Yanling Liu40, Xin Zhou40, Angela J Waanders41, Danny A Zwijnenburg38, Pichai Raman39, Benedikt Brors3,7,8, Ursula D Weber3,42, Paul A Northcott2,3,8, Kristian W Pajtler1,2,3,4, Marcel Kool1,2,3, Rosario M Piro3,42,43,44, Jan O Korbel5, Matthias Schlesner7,45, Roland Eils7,10, David T W Jones1,2,3, Peter Lichter3,42, Lukas Chavez1,2,3, Marc Zapatka42,43, Stefan M Pfister1,2,3,4.
Abstract
Pan-cancer analyses that examine commonalities and differences among various cancer types have emerged as a powerful way to obtain novel insights into cancer biology. Here we present a comprehensive analysis of genetic alterations in a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer. Using a standardized workflow, we identified marked differences in terms of mutation frequency and significantly mutated genes in comparison to previously analysed adult cancers. Genetic alterations in 149 putative cancer driver genes separate the tumours into two classes: small mutation and structural/copy-number variant (correlating with germline variants). Structural variants, hyperdiploidy, and chromothripsis are linked to TP53 mutation status and mutational signatures. Our data suggest that 7-8% of the children in this cohort carry an unambiguous predisposing germline variant and that nearly 50% of paediatric neoplasms harbour a potentially druggable event, which is highly relevant for the design of future clinical trials.Entities:
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Year: 2018 PMID： 29489754 DOI： 10.1038/nature25480
Source DB: PubMed Journal: Nature ISSN： 0028-0836 Impact factor: 49.962